calcium in the muskoka river watershed patterns, trends, the...
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Calcium in the Muskoka River Watershed –
Patterns, trends, the potential impact of forest harvesting on
lake Ca levels and steps toward an ecosystem approach to mitigation
A Thesis Submitted to the Committee on Graduate Studies in Partial
Fulfilment of the Requirements for the Degree of Master of Science
in the Faculty of Arts and Science
TRENT UNIVERSITY
Peterborough, Ontario, Canada
© Copyright by Carolyn Roberta Reid, 2015
Environmental and Life Sciences M.Sc. Graduate Program
May 2015
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ABSTRACT
Calcium in the Muskoka River Watershed –
Patterns, trends, the potential impact of forest harvesting on
lake Ca levels and steps toward an ecosystem approach to mitigation
Carolyn R. Reid
Decreasing lake calcium (Ca) concentration, in lakes located in base poor
catchments of the Muskoka River Watershed (MRW) in south-central Ontario, is a well-
established acid-rain driven legacy effect threatening the health and integrity of aquatic
ecosystems that can be compounded by additional Ca removals through forest harvesting.
The objectives of this thesis were to assess patterns and temporal trends in key water
chemistry parameters for a set of lakes in forested catchments in the MRW in south-
central Ontario, to predict the pre-industrial steady state lake Ca concentration and the
potential impact of harvesting on lake Ca levels in lakes located in managed MRW Crown
forests, and to assess potential effects of various mitigation strategies in Ca depleted
managed forests. Mean lake Ca (mg L–1) in 104 lakes across the MRW have decreased
by 30% since the 1980’s with the rate of decrease slowing over time. Mean Lake SO4
(mg L–1), and Mg (mg L–1) concentration also decreased significantly with time (37% and
29%, respectively) again with a declining rate of decrease, while mean lake pH and DOC
increased significantly between the 1980’s and the 1990’s (16% and 12%, respectively)
but exhibited no significant pattern after that. Principal components and GIS spatial
analyses of 75 lakes with data from 2011 or 2012 water seasons suggested that smaller
lakes, at higher elevation in smaller catchments with higher runoff and minimally
impacted by the influence of roads and agriculture are associated with lower Ca
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concentrations and thus are the lakes at risk of amplified Ca depletion from forest
harvesting. Spatial analyses of harvested catchments indicated that, under the proposed
10 year land forest management cut volumes, 38% of 364 lakes in the MRW will fall
below the critical 1 mg L–1 Ca threshold compared with 8% in the absence of future
harvesting. With respect to potential mitigation measures, soil pH and foliar Ca were
indicated by meta-analysis to be more responsive to lime addition studies while soil base
saturation and tree growth appeared more responsive to wood-ash addition. Future
research should address the spatial extent of lakes at risk and identify when critical levels
will be reached under harvesting regimes. Further investigation into the use of Ca-
addition as a tool for managing the cumulative effects of past, present and future stressors
is recommended.
Keywords: calcium, lakes, harvesting, lime, wood-ash, Muskoka River Watershed
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Acknowledgements
I have always believed that in order to “up your game” in anything that you have to play
with someone with much better skills. That axiom can be extended to the enrichment of
all types of abilities and particularly to the development of critical thinking skills.
Accordingly, I thank my supervisor Shaun Watmough for providing me with this
incredible opportunity to enhance my intellectual abilities and for his unwavering
support, guidance and infinite patience during my time at Trent University. Shaun
possesses a rare, unique and enviable intellect and yet remains wholly unaffected by
hubris. His good-humoured guidance has allowed me to steer away from a
preoccupation with minutia (well, for the most part) and on to a logical, well-defined
path. It has been an honour and a privilege to have him as my supervisor.
In addition, I thank Julian Aherne and Colin Whitfield for serving on my advisory
committee. Their questions, constructive comments and helpful insights enabled me to
look at the problems and challenges that arose in the development of my thesis from
new perspectives and with renewed energies. I sincerely appreciate all their input,
support and consideration and for being so patient and so generous with their very
valuable time.
I also thank Steve Munroe, Operations Manager and Barry Davidson, Forest Manager,
both of Westwind Forest Stewardship Inc. and Gord Cumming, Chief Forester for the
Algonquin Forest Authority, for their time and contributions, without which, this thesis
would not have been possible. I admire, and am grateful for, their dedication and
commitment to furthering the knowledge of sustainable forest management, especially
in an environment where multiple stressors are taking their toll on the forest
ecosystem. I was also exceedingly fortunate to have had Samantha Luke’s assistance.
Her GIS “mad skills” were invaluable to the development of this thesis.
Finally, I want to thank my amazingly generous family and friends for their continued
support, understanding and tolerance, and for not judging me when the house was in
complete disarray, Willow needed walking, dinner was late, and although present in
body, I was absent in thought. To my children; Jessica, Nick and Casey and my
grandchildren; Monty and Sully, I love you and miss spending time with you and think
of you every day, life is so much better with you in it. I hope you understand my need
to make a difference.
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Co-Authorship Statement
This thesis is based on two manuscripts which have either been accepted for publication
(Chapter 3) or will be submitted for publication (Chapter 2). Carolyn Reid, who led the
development of the research problem, formulation of the study design, completion of the
data analysis, interpretation of results and writing of manuscripts, will be lead author on
each manuscript. Shaun Watmough, who suggested the initial research problem,
contributed to the formulation of the study design and the interpretation of results and
provided invaluable editorial guidance on the writing, will be co-author on all
manuscripts. For Chapter 2, Samantha Luke will be acknowledged as ministering the
compilation of the spatial analysis datasets.
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Table of Contents
ABSTRACT ...................................................................................................................... ii
Acknowledgements ........................................................................................................... iv
Co-Authorship Statement ................................................................................................... v
List of Figures .................................................................................................................... x
List of Tables ................................................................................................................... xii
1. General Introduction ................................................................................................. 1
1.1 The calcium problem ................................................................................................ 1
1.2 Why is calcium important? ........................................................................................ 3
1.3 Study area – The Muskoka River Watershed ............................................................ 3
Crown land forest management in the MRW ..................................................................... 6
1.4 Impacts of forest harvesting – biomass export driven Ca decline ............................. 7
1.5 Mitigation ................................................................................................................ 11
1.6 Thesis objectives ..................................................................................................... 13
1.7 References ............................................................................................................... 14
2. Calcium levels in lakes of the Muskoka River Watershed - patterns, trends,
predictions and the potential impacts of tree harvesting on critical levels ...................... 21
2.1 Abstract ................................................................................................................... 21
2.2 Introduction and rationale ....................................................................................... 21
2.3 Methods ................................................................................................................... 25
Crown land forest management in the MRW .................................................................. 27
Lake datasets .................................................................................................................... 28
Temporal trends in lake chemistry ................................................................................... 29
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Long-term trends analyses ................................................................................................ 30
Physical and chemical parameter pattern analysis ............................................................ 31
Projected impact of timber harvesting on lake Ca levels – GIS spatial evaluation .......... 32
Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen and
Posch 2001) ...................................................................................................................... 33
Biomass export driven Ca decline ................................................................................... 35
2.4 Results ..................................................................................................................... 37
Lake chemistry ................................................................................................................. 37
Temporal trends in lake chemistry ................................................................................... 38
Mann-Kendall long-term trend analysis ........................................................................... 41
Non-parametric Correlations and Principal Components Analysis .................................. 43
Spatial evaluation used for projected impact of timber harvesting on lake Ca levels ...... 45
Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen and
Posch 2001) and quantifying the potential impact of harvesting on lake Ca levels ......... 46
2.5 Discussion ............................................................................................................... 52
Lake chemistry ................................................................................................................. 52
Temporal trends in lake chemistry ................................................................................... 53
Long-term trend analysis .................................................................................................. 57
Spatial patterns in lake Ca ................................................................................................ 57
Steady-State Water Chemistry (SSWC) model (Henriksen and Posch 2001) ................. 58
Biomass export driven Ca decline .................................................................................... 59
Uncertainty ....................................................................................................................... 63
2.6 Conclusions ............................................................................................................. 65
2.7 References ............................................................................................................... 67
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APPENDIX 2A ................................................................................................................ 80
3. Evaluating the effects of liming and wood-ash treatment on forest ecosystems
through systematic meta-analysis..................................................................................... 85
3.1 Abstract ................................................................................................................... 85
3.2 Introduction ............................................................................................................. 86
3.3 Methods ................................................................................................................... 89
Study Selection ................................................................................................................. 89
Statistical Analysis ........................................................................................................... 91
Quantitative synthesis – Meta-analysis ............................................................................ 91
Meta-analysis and Effect Size Metrics ............................................................................. 91
Data Mining – Recursive Partitioning Decision Tree Analysis ........................................ 91
3.4 Results ..................................................................................................................... 97
Study Selection ................................................................................................................. 97
Average Effect Size .......................................................................................................... 98
Zero Effect Frequency .................................................................................................... 102
Recursive Partitioning of Effect Sizes - Decision Tree Analysis ................................... 102
pH Effect Size Regressive Analysis ............................................................................... 102
Base Saturation ............................................................................................................... 103
Ca Foliar Concentration Effect Size Regressive Analysis .............................................. 103
Tree Growth Effects Size Regressive Analysis .............................................................. 104
Uncertainty ..................................................................................................................... 104
3.5 Discussion ............................................................................................................. 109
Soil pH ........................................................................................................................... 109
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Base Saturation ............................................................................................................... 111
Foliar Calcium Concentration ........................................................................................ 112
Tree Growth ................................................................................................................... 113
C:N Ratio, ECM Root Colonization and Microbial Indices ........................................... 115
3.6 Other issues ........................................................................................................... 118
3.7 Conclusions ........................................................................................................... 119
3.8 References ............................................................................................................. 121
APPENDIX 3A .............................................................................................................. 131
Table 3A.1 References ................................................................................................... 137
4.1 Implications for recovery and an ecosystem approach to mitigation .................... 148
Brief Background ........................................................................................................... 148
4.3 Ecosystem mitigation - benefits to lakes, soils and trees - win/win/win ............... 150
4.4 Knowledge Gaps ................................................................................................... 151
4.5 Directions of future research ................................................................................. 152
Four key objectives that could define the structure of future Ca study. ......................... 152
4.6 Conclusions ........................................................................................................... 154
4.7 References ............................................................................................................. 155
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List of Figures
Photo 1.1: Lake St. Nora (top) and Deer Lake (bottom), southern boreal lakes on the
Precambrian Shield in the Muskoka River Watershed…………………………………...4
Photo 1.2: Selection harvesting area in the French-Severn forest near Dorset, Ontario in
the Muskoka River Watershed. Areas were harvested two years prior to photos.
Harvesting residues (tree tops and branches) are left onsite……………………………...8
Photo 1.3: Seedtree shelterwood harvesting area in the French-Severn forest near Dorset,
Ontario in the Muskoka River Watershed………………………………………………...9
Figure 2.1: The Muskoka River Watershed in south-central Ontario (inset). Crown land,
the study area, comprises approximately 50% of the land surface area….…………...…27
Figure 2.2: Key data inputs in estimating MRW lake Ca (mg L–1) on a catchment basis
under predicted tree harvesting and no harvesting 2009–2020……….………………....38
Figure 2.3: Lake patterns for mean Ca concentrations (mg L–1) for N = 104 MRW lakes
with long-term data. All lakes below the 1:1 line decreased in lake Ca (mg L-1) between
the time periods 1981–1990 and 2003–2012. The mean decrease for Ca was 29.4% ±
12% and the mean decrease for SO4 depicted in the inset graph was 36.7% ± 19%........40
Figure 2.5: Principal components analysis of physical and chemical variables for 75
MRW lakes with data from 2011-2012. Principal component 1 described 24% of the
variation in the included parameters. Note: c-dsg is the mean down slope distance
gradiant, cuca is the catchment area, zmean is the mean lake depth, c-con and c-
deciduous are the catchment % of coniferous and deciduous trees respectively,
CLIDUR is lake chloride, CAU is lake calcium concentration, c-urd and c-prd are
catchment unpaved and pave roads respectively, l_elev is lake elevation. In every case,
Dep is deposition and c_ is catchment…..……………………………………………..44
Figure 2.6: 590 (of more than 2000) lakes with Ca concentration data in Muskoka
River Watershed catchments. Lakes most at risk ≥2 mg L–1 (n=177) of additional Ca
removals are represented by red (0.00 – 1.04 mg L–1) and orange (1.05 – 2.04 mg L–1)
dots. The concentration value of 0 represents levels undetected by the equipment used.
Predicted harvesting cuts (2009–2020) are denoted by dark green…………………....49
Figure 2.4: Boxplot distributions over three time periods based on mean lake values of Ca, SO4, Mg, pH, NO3 and DOC for 104 lakes. Outliers are represented by separate dots. Note: all units are in mg L–1 except for pH ...………............................................40
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Figure 2.7: Enlarged area of catchments for n=364 lakes located in catchments with
harvesting cuts with the blown up area indicating an area of high concentration of small
lakes in small catchments and many with Ca ≤2 mg L–1. This area is impacted by
harvesting cuts from two Forest Management Plans (dark and light yellow)…………..50
Figure 2.8: Current and predicted lake concentration as a function of no harvest (steady
state) and harvest volume removals of 100%, 59% (average reported annual volume
removal level) and 40% (verbally reported removal level) of the FMP for 364 (371-7 as
7 lakes with “0” concentration were not included on the graph) lakes in MRW
catchments with predicted harvesting cuts from 2009–020. The red vertical lines denote
literature suggested critical Ca thresholds of 1 mg L–1 (50 µeq L–1) (Hessen et al. 2000)
and as used in Watmough et al. 2003 and of 1.5 mg L–1 (75 µeq L–1) as indicated by
recent work in MRW lakes (Ashforth and Yan 2008)………………………………….51
Figure 2A.1: MRW lake data sets and methods of analysis…………………………….81
Figure 2A.2: Percent of lakes (N=590) sampled by sampling year. Approximately 15%
of the lakes were sampled in 2011-2012………………………………………………..84
Fig. 3.1: Soil pH mean effect size (pH treatment – pH control) optimal decision tree for
all trials………………………………………………………………………………...105
Fig. 3.2: Base Saturation mean effect size (BS treatment – BS control) optimal decision
tree for all trials………………………………………………………………………..105
Fig. 3.3: Foliar calcium concentration mean effect size ((Treatment/Control) −1) %)
optimal decision tree for all trials……………………………………………………..106
Fig. 3.4: Tree growth (of combined measures) mean effect size ((Treatment/Control) −1)
%) optimal decision tree for all trial………………………………………………….106
Fig. 3.5: Driver column contributions for pH, foliar Ca concentration and tree growth for
decision tree and bootstrap forest models. Arrows indicate the strongest drivers of
positive effect response to liming or wood-ash treatment………………………….…107
Fig. 3.6: Mean tree growth (of combined measures) effect size ((Treatment/Control) −1)
%) of softwood treated with wood-ash by trial duration time. Database trials were not
evenly spread over the duration span of years, therefore they were separated by effect
size responses. The largest mean increase in growth effect size over that of the mean
control effect size occurred 10+ years post-treatment…….………………….............108
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List of Tables
Table 2.1: Mean lake parameter and mean difference in lake parameter and associated
standard error (SE) between three sampling time periods for 104 MRW lakes. Lake Ca
is in bold type…………………………………………………………………………..39
Table 2.2: Mann-Kendall long-term trend analysis for 24 MRW lakes with Ca ≤2 mg
L–1 located in catchments with Forest Management Plan predicted cuts to the year
2020……………………………………………………………………………………42
Table 2.3: Summary of results of a multi-group Mann-Kendall trend test to determine
consistent regional trends across lakes in Ca, Mg, K and SO4 concentrations (mg L–1)
in 24 Precambrian Shield lakes with Ca≤2 (mg L–1). Consistent strong declining
trends across lakes were indicated in Ca and SO4 and a less strong but still significant
declining trend in Mg. No trend across lakes was indicated for K…………………...42
Table 2.4: Non-parametric Spearman’s p correlation analysis between lake Ca (mg L–1)
and chemical and physical parameters for 75 lakes sampled in 2011-2012…………..43
Table 2.5: Lakes ≤2 mg L–1 in catchments with harvesting cuts (N=177) means,
standard deviations and ranges for chemical and physical parameters………………..46
Table 2.6: Most current, SSWC modelled and predicted % of lakes under varying
harvesting scenarios for two critical thresholds of lake Ca for Ca-rich Daphnia species,
50 µeq L–1 (Watmough et al. 2003), equivalent to 1.0 mg L–1 and 75 µeq L–1
(Ashforth and Yan 2008), equivalent to 1.5 mg L–1 for 364 MRW lakes. For simplicity
the percentages have been rounded to whole numbers……………………………….48
Table 2A.1: Equations derived from the SSWC model (Henriksen and Posch, 2001)
and the modified derivation equations used in calculations…………………………...80
Table 2A.2: Summary statistics (mean, max-min) for lake area, catchment area, lake
elevation, runoff and selected deposition and lake chemical variables for all lakes with
Ca data within crown land (590), lakes in catchments with harvesting cuts on crown
land (371) and two subsets of the former and latter lakes used in analysis. Values of 0
were assumed to be undetectable by equipment at the time…………………………...82
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Table 3.1: Summary of average, unweighted effect size, zero effect and sample size per
parameter. In addition to the pooled (combined) results, soil pH, Ca foliar nutrition and
tree growth (of the combined growth measures) were assessed by treatment type…..100
Table 3.2: Mean effect sizes of Ca-addition using the weighted effect size Hedges’ d
and the back-transformed natural log of the response ratio, lnR in comparison to the
unweighted relative values…………………………………………………………...101
Table 3A.1: List of studies included in the respective meta-analysis. Figure and table
columns list all figures and tables containing data from each study (note that many
studies had several distinct trials with variations in trial length, dose, addition type etc.)
………………………………………………………………………………………...131
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1. General Introduction
1.1 The calcium problem
For thousands of years, water quality of lakes on the northern boreal shield was a
function of catchment physical conditions and chemical processes that occurred along
the streams that lead to, and within those lakes (Bormann and Likens 1967). It is
hypothesized that over time, a surplus of calcium (Ca) accumulated in the exchangeable
pools within catchment soils (Watmough and Dillon 2001). If not for anthropogenically
caused aberrant sulphur (S) and nitrogen (N) emissions, this pool of surplus Ca would
have remained relatively constant. Instead, the industrial revolution produced emissions
that caused unnaturally high acidic deposition for decades, leaving a legacy of unwanted
consequences.
Initially, enhanced acid deposition increased Ca concentrations in lakes as Ca
was leached from soil and into surface waters in association with strong (S) acids
(Christ et al. 1999). Leaching occurs when protons (H+) exchange with other cations
(Ca2+, magnesium (Mg2+), potassium (K+) and sodium (Na+) bound to soil exchange
sites, subsequently, these replaced cations then go into solution in soil water and become
mobilized in runoff (Foster et al. 1989). Calcium was depleted this way, from soils in
boreal shield areas with low base cation weathering rates and low atmospheric Ca
deposition, with a documented loss of 40% of exchangeable Ca at some sites over a
sixteen year period (Watmough and Dillon 2001) with a concurrent decrease in lake Ca
of 10-25% (Watmough and Dillon 2002). Preferential depletion of Ca occurred as it was
the most abundant soil exchangeable cation and because magnesium (Mg) and
potassium (K) are more tightly biologically cycled than Ca (Brady and Weil 2008). It
2
has been shown that with increasing soil acidification, increasing amounts of cations are
leached; with the level of leaching loss consistent with the size of the exchangeable base
cation pools (Ca2+>Mg2+>K+>Na+) (Haynes and Swift 1986).
Mineral weathering is the major source of Ca in temperate forest soils (Likens
and Bormann 1995) and although some areas in the Muskoka River Watershed (MRW)
are expected to have localised areas of calcite (Jeffries and Snyder 1983), the soils are
mainly podzols and brunisols (Soil Classification Working Group 1998) and are poorly
developed and dominated by poorly weatherable silicate minerals (plagioclase and
hornblende) (Kirkwood and Nesbitt 1991).
With Ca leaching rates exceeding the replenishment rate from weathering and
atmospheric input Ca soil pools have declined (Watmough et al. 2005). Calcium
weathering would take a long time to replenish boreal shield soil pools, even in the
absence of elevated acidic deposition and additional Ca removals through forest
harvesting. Mychorrhizal fungi have been shown to increase the rate of weathering by
secreting organic acids to access nutrients previously unavailable to plant roots (Van
Breemen et al. 2000). However, quantitative measures and contributions of biological
weathering to nutrient budgets have not been established (van Scholl et al. 2008) and
thus the rate of Ca weathering is assumed to be relatively constant (although, over
longer periods of time than considered here, changes in weathering rates could be
expected with climate changes and loss of weatherable materials).
As emission controls drastically reduced acidic deposition, Ca concentration
levels in most boreal shield lakes concurrently declined (Stoddard et al. 1999;
Watmough et al. 2003) and except in areas of other anthropogenic input (Yao et al.
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2011), they have continued to do so (Watmough and Aherne 2008; Jeziorski 2008).
Contrary to expectations, chemical and biological recovery from acidification in these
boreal shield lakes remains lower than expected (Stoddard et al. 1999; Driscoll et al.
2001; Jeffries et al. 2003) with climate driven biogeochemistry dynamics indicated as
the primary cause (Schindler et al. 1996; Keller et al. 2007; Dillon et al 2007).
1.2 Why is calcium important?
Not only does Ca, as a base cation, act to buffer acidic deposition (Starr et al.
1998), it is also an essential nutrient for biota. Consequences of lowering lake Ca
concentrations below critical levels have included biota near extirpations (Jeziorski et
al. 2008) and taxa shifts (Jeziorski et al. 2014), that have induced food web changes
resulting in the increasing frequency of algal blooms, which in turn can affect water
quality (Korosi et al. 2012). While earlier studies indicated a critical level of 1.0 mg L–1
for Daphnia (Hessen at al. 2000) more recent research has indicated that growth,
reproduction and survival of some daphniids are negatively impacted below a Ca
concentration of 1.5 mg L–1 (Ashforth and Yan 2008; Cairns and Yan 2009).
Additionally, it should be emphasized that Ca is also an essential nutrient for
terrestrial biota. Calcium is required by plants for cell structure, cell division, and other
cellular processes including responses to stress (Cronan and Grigal 1995; McLaughlin
and Wimmer 1999; Horsley et al. 2000) and soil base cation losses from acidified soils
could potentially have negative effects on tree growth, health and vigor in harvested
forests (Horsley et al. 2000; Driscoll et al. 2001; Walmsley et al. 2009).
1.3 Study area – The Muskoka River Watershed
The MRW in south-central Ontario is a watershed that extends along the
4
Photo 1.1: Lake St. Nora (top) and Deer Lake (bottom), southern boreal lakes on the
Precambrian Shield.
5
Precambrian Shield from the Algonquin Highlands in Algonquin Provincial Park to
Georgian Bay, encompassing 763,800 hectares (Muskoka Watershed Council 2014). There
are over 2000 lakes (Photo 1.1 as examples) and innumerable streams, comprising over 15
percent of the surface area (Tran et al. 2009). Close to 80 percent of these southern boreal
lakes are dilute, oligotrophic lakes primarily located on base-poor silicate bedrock overlain
by thin glacial tills often less than 1 metre deep (Dillon et al. 1991; Watmough and Dillon
2004), and are poorly buffered (≤1.5 mg L–1 Ca) while only 6 percent of the lakes are
hardwater and strongly buffered (>3 mg L–1 Ca) (O’Connor et al. 2009).
The effects of acid rain and the impact on lake recovery have been well studied in
the MRW; however, in this region Ca decline can be amplified by forest harvesting,
which results in an additional export of Ca with biomass removal and forest regrowth
(Huntington et al. 2000; Watmough et al. 2003; Phillips and Watmough 2012). In view
of research indicating that some stands in Ca depleted, acid-sensitive ecosystems on the
Shield have more Ca in above-ground biomass than in the exchangeable soil pool
(Watmough and Dillon 2004), a detailed scientific assessment of the potential impact of
predicted harvesting on critical calcium levels in lakes of the MRW is essential.
Crown land forest management in the MRW
Approximately 50 percent of the land in the MRW is Crown land which is the focus
of this research as forest harvesting records are limited or unavailable for private land.
Forest harvesting operations on Crown land in the MRW are under the purvue of two
forest authorities. The Algonquin Forest Authority (AFA) was established in 1974 as a
commercially oriented, self-financing crown corporation responsible for forest
management (with timber rights) in Algonquin Park after a groundswell of concern over
6
the logging practices in the 70’s (Algonquin Forest Authority 2010). Westwind Forest
Stewardship Inc. (WFSI) took over from the MNR in 1998 as a not-for-profit,
community-based forest management company to manage the French/Severn Forest by
providing planning services, support, information, and compliance assistance to about
twenty-five logging contractors (Government of Ontario Environmental Registry 2007).
Both WFSI and AFA have Ontario Ministry of Natural Resources (MNR) Sustainable
Forest Licenses (SFL) specifying how allocation of annual allowable volume cuts
included in the current French/Severn 2009-2019 and Algonquin 2010-2020 Forest
Management Plans (FMPs), is to be divided among dozens of logging contractors. There
is currently no consideration of Ca sustainability in the calculation of allowable annual
harvesting cuts in the FMPs.
Past harvesting practices have shaped the MRW forests of today. Extensive and
successive, clearcut logging of MRW forests began in the mid-1800’s and, as market
needs dictated, harvesting was focussed on the high-grading of a series of single species
starting with white pine (Pinus strobus), then red pine (Pinus resinosa) in the 1800’s
followed by eastern hemlock (Tsuga canadensis), and yellow birch (Betula
alleghaniensis) in the 1930’s –1950’s with increased logging of sugar maple (Acer
saccharum) from the 1950’s to the present (French Severn Forest Authority 2009). As a
consequence, and in combination with no regeneration, the current French/Severn forests
have reduced numbers of naturally occurring species (white pine, hemlock, yellow birch)
and a high percentage of low grade trees (French Severn Forest Authority 2009).
Consequently, tolerant hardwood stands now dominate the French/Severn Forest with
sugar maple as the predominant species (French Severn Forest Authority 2009). The
Algonquin Park forests within the MRW are similarily characterised by deciduous stands
7
dominated by sugar maple and coniferous forests dominated by a mix of spruce, cedar,
hemlock and fir (Strickland 1993).
1.4 Impacts of forest harvesting – biomass export driven Ca decline
There are short-term and long-term effects on forest ecosystems from harvesting
but this research focuses on the long-term effects on Ca budgets. In a natural state, forests
are efficient in recycling, conserving and storing nutrients (Brady and Weil 2008) but
forest harvesting can disrupt the Ca cycle (Palviainen 2005). Without harvesting, Ca in
trees is internally cycled and returned to the soil mainly through litterfall (Likens and
Bormann 1995, Piirainen 2002) litter and fine root decomposition, and to a lesser extent
through canopy exchange (Kimmins et al. 1973). Biomass and associated Ca removal in
forest harvesting can contribute to Ca losses from soil (Olsson et al. 1995) and
consequently lower Ca export to lakes. Whole tree harvesting over several rotations for
example, has been found (or predicted) to deplete Ca levels in soil (Federer et al. 1989;
Vance 1996; Adams et al. 2000; Keller et al. 2001; Watmough et al. 2003) which can
have a negative effect on future rotations (Proe et al. 1996; Walmsley et al. 2009).
Additionally, escalating demand for bio-energy fuel by harvesting slash has led to
concerns over forest sustainability since the impact on Ca removals is analogous to that
of whole tree harvesting, with the consequential negative effect on tree growth
(Rosenberg and Jacobson, 2004; Ågren et al. 2010).
The three silvicultural systems used in the MRW FMPs are: selection (uneven-
aged with about 30% of stand cut every 20-25 year cycle with harvesting residues, tree
tops and branches, left onsite) (Photo 1.2), shelterwood (even-aged stands with mature
trees removed in two or more cuts every 15 years with 70 years after the last cut to
8
allow for natural regeneration with harvesting residues left on site) (Photo 1.3) and clearcut
or whole tree (even-aged stands with mature trees removed in one cut on a 90 year cycle).
There are small variations between the two FMPs in the percentages of methods used
during the 10-year plans, with the French/Severn harvesting percentages at 52/41/7 and
Algonquin at 54/41/5 (selection/shelterwood/clearcut respectively). In clearcut (whole
tree) harvesting, more biomass is removed in one cut causing Ca removals to at least
double, compared with the other methods of harvesting (Kimmins 1973, Walmsley et al.
2009). Although clearcutting accounts for only 5-7% of MRW harvest removals, the high
cost of fossil fuels and concerns over climate change may encourage biofuel harvesting of
residues currently left on the forest floor (from selection and shelterwood harvestin) with
resulting Ca removals similar to whole tree removals.
Preliminary estimations of the impacts of harvesting in the MRW using rudimentary
estimates of biomass removals indicated that lake Ca levels will continue to fall
(Watmough et al. 2003; Watmough and Aherne 2008). Any harvesting volume removal
for the MRW will result in lower Ca concentration in surface water because of the large
amount of Ca held in tree biomass that is permanently removed from the catchment and as
a result lakes in harvested catchments will be more sensitive to acid deposition (Warfvinge
and Sverdrup 1992), potentially causing MRW lake acidification and/or inhibiting lakes
from recovering, with those lakes at already low Ca levels at greatest risk of going below
critical thresholds.
The Steady-State Water Chemistry Model (using current lake chemistry and the F
factor) (Henriksen and Posch 2002) has been used previously to determine the impact of
harvesting on MRW soils and lakes with the benefit that it is the net result of integrated
9
Photo 1.2: Selection harvesting area in the French-Severn forest near Dorset, Ontario in the
Muskoka River Watershed. Areas were harvested two years prior to photos. Harvesting residues
(tree tops and branches) are left onsite.
10
Photo 1.3: Photo collage of a seed tree shelterwood harvesting area in the French-Severn
forest near Dorset, Ontario in the Muskoka River Watershed.
11
soil and water nutrient cycling (Watmough et al. 2003). However, earlier work used
estimates of biomass removals to estimate the long-term steady-state lake Ca under
various harvesting scenarios (Watmough et al. 2003). Incorporating prescribed allowable
annual harvesting cut information as a refinement of this approach should yield more
accurate results. By understanding the predicted changes in soil Ca that can be attributed
to forest harvesting, we can further the understanding of how MRW lake Ca levels will
change, at what volume of harvesting lake Ca levels could be sustained above critical
levels, and if remediation measures are necessary.
1.5 Mitigation
Given already depleted soil Ca pools, low Ca deposition levels and low rates of
weathering of silicate based minerals in the MRW, natural recovery of Ca levels in lakes
is unpromising for those lakes approaching and below critical levels. Ca-addition,
through lime and wood-ash treatment has been used in many jurisdictions for forest soil
amelioration with the goals of reducing soil acidity, increasing Ca concentrations in trees,
and improving tree growth with the most commonly used measures for gauging the
success of these goals being pH, Ca foliar concentration and various tree growth metrics.
Previous reviews of the effects of liming (Derome 1990; Huettl and Zoettl 1993;
Nohrstedt 2001; Formanek and Vranova 2003) and wood-ash addition (Vance 1996;
Demeyer et al. 2001; Aronsson and Ekelund 2004; Pitman 2006) have primarily been
narrative evaluations with one wood-ash meta-analysis (Augusto et al. 2008).
Comprehensive liming experiments aimed at improving forest growth in Finland
and Sweden began during the 1950s and 1960s (Tamm 1974; Derome et al. 1986) and in
the 1980s, lime was used to mitigate soil acidification caused by the effects of acid rain
(Derome et al. 1986; Huettl and Zoettl 1993; Nilsson et al. 2001). Wood-ash has also
12
often been used to address forest nutritional deficiencies caused by acid deposition and
whole-tree harvesting (Cronan and Grigal 1995; Olsson et al. 1996; Demeyer 2001).
With the removal of harvest residues for biofuels garnering much attention in a renewable
energy context, recycling of the ash produced has potential for use as a nutrient input in
forest systems (Pitman 2006).
Interest in the effects of Ca-addition in the remediation of acidified soils has
recently increased in Ontario with as yet unpublished but ongoing wood-ash addition
trials in two locations on the boreal shield. This research has been undertaken by a
collaborative group from the University of Toronto, Laurentian University, Ontario
Ministry of Natural Resources (OMNR) and the Canadian Forest Service. Preliminary
results have indicated increased soil pH and increased availability of Ca with no adverse
effects on microbial biomass, function or community structure at wood-ash addition
levels of 5000–7000 kg ha–1.
As conclusions from Ca-addition studies vary widely and recommendations are
often ambiguous, a more current and more extensive meta-analysis was deemed to be
necessary in a preliminary evaluation of the impacts of Ca-addition to acid-sensitive soils
(which could in turn impact surface water) and on the subsequent effects on tree
productivity.
13
1.6 Thesis objectives
The objectives of this thesis were: to assess patterns (relationships between lake Ca
concentration and water chemistry and landscape variables), and temporal trends in key
water chemical parameters for lake data sets in forested catchments in the Muskoka
River Watershed (MRW) in south-central Ontario, to predict the pre-industrial, pre-
forest harvesting steady-state of lake Ca and the potential impact of harvesting on lake
Ca levels in 364 lakes located in managed MRW Crown forests with harvest volume
removals between 2009–2020, and to assess effects of Ca-addition mitigation strategies
in Ca depleted managed forests.
Lake Ca patterns and trends were investigated through analysis of available long-
term chemical records. For 364 lakes, pre-industrial lake Ca concentrations, representing
no-harvest scenarios, were modeled based on lake chemical data and catchment physical
attributes. Future change in lake Ca for these 364 lakes was calculated based on
prescribed 2009–2020 tree biomass exports on a catchment basis as provided by forest
management authorities. To consider ecosystem mitigation strategies, the relationships
between Ca-addition (wood-ash and lime) and soil and tree indicators were investigated
through extensive meta-analyses and decision tree analyses of over 350 trials in acid-
sensitive ecosystems in eastern North America and parts of Europe.
Timber harvesting is, by its nature, a disturbance to the forest ecosystem and
sustainable forest harvesting strives to minimize the disturbance in both the short-term
and long-term. The overriding rational for this thesis was to provide the most up to date,
accurate information on the impact of forest harvesting on MRW lake Ca concentrations
as biomass removal driven lake Ca decline, in acid-sensitive areas on the boreal shield,
should be a consideration in sustainable forest harvesting.
14
1.7 References
Adams, M., Burger, J., Jenkins, A. and Zelazny, L. 2000. Impact of harvesting and
atmospheric pollution on nutrient deletion of eastern US hardwood forests. For.
Ecol. Manage. 138: 301–319. doi:10.1016/S0378-1127(00)00421-7.
Ågren, A., Buffam, I., Bishop, K. and Laudon, H. 2010. Sensitivity of pH in a boreal
stream network to a potential decrease in base cations caused by forest harvest.
Can. J Fish. Aquat. Sci. 67(7): 1116–1125.
Algonquin Forest Authority. 2010. Algonquin Forest Authority Forest Management
Plan 2010– 2020. Available at the Ontario Ministry of Natural Resources and
Forestry website:
http://www.efmp.lrc.gov.on.ca/eFMP/viewFmuPlan.do?fmu=451&fid=58953&typ
e=CURRENT&pid=58953&sid=6472&pn=FP&ppyf=2010&ppyt=2020&ptyf=20
10&ptyt=2015&phase=P1
Aronsson, K.A., and Ekelund, N.G.A. 2004. Biological effects of wood-ash application to
forest and aquatic ecosystems. J. Environ. Qual. 33: 1595–1605.
Ashforth, D. and Yan, N.D. 2008. The interactive effects of calcium concentration and
temperature on the survival and reproduction of Daphnia pulex at high and low
food concentrations. Limnol. Oceanogr. 53(2): 420–432.
Augusto, L., Bakker, M.R., and Meredieu, C. 2008. Wood-ash applications to temperate
forest ecosystems - potential benefits and drawbacks. Plant Soil, 306: 181–198.
Bormann F.H. and Likens G.E. 1967. Nutrient cycling. Science 155: 424–429
Brady, N. C. and Weil, R. R. 2008. The nature and properties of soils. 14th edition.
Prentice Hall, Harlow. 965 pp.
Cairns, A., and Yan, N.D. 2009. A review of the influence of low ambient calcium
concentrations on freshwater daphniids, gammarids and crayfish. Environ. Rev.
17: 67–79.
Christ, M.J., Driscoll, C.T. and Likens, G.E. (1999). Watershed-and plot-scale tests of
the mobile anion concept. Biogeochemistry 47(3): 335–353.
Cronan, C.S and Grigal, D.F. 1995. Use of calcium/aluminum ratios as indicators of
stress in forest ecosystems. J. Environ. Qual. 24 (2): 209–226.
15
Dillon, P.J., Molot, L.A. and Scheider, W.A. 1991. Phosphorus and nitrogen export
from forested stream catchments in central Ontario. J. Environ. Qual. 20: 857–
864.
Dillon, P.J., Watmough, S.A., Eimers, M.C and Aherne, J. 2007. Long-term changes in
boreal lake and stream chemistry: recovery from acid deposition and the role of
climate. In Acid in the Environment. pp. 59–76. Springer US.
Demeyer, A. 2001. Characteristics of wood-ash and influence on soil properties and
nutrient uptake: an overview. Bioresource Technol. 77: 287–295.
Derome, J. 1990. Effects of forest liming on the nutrient status of podzolic soils in
Finland. Water Air Soil Pollut. 54: 337–350.
Derome, J., Kukkola, M., and Mälkönen, E. 1986. Forest liming on mineral soils, results
of Finnish experiments. National Swedish Environmental Protection Board 3084:
1–10.
Driscoll, C.T., Lawrence, G., Bulger, A.J., Butler, T., Cronan, C.S., Eagar, C., Lambert,
K.F., Likens, G.E., Stoddard, J.L., and Weathers, K.C. 2001. Acidic deposition in
the northeastern United States: sources and inputs, ecosystem effects, and
management strategies. Bioscience 51: 180–198.
Federer, C.A., Hornbeck, J.W., Tritton, L.M., Martin, C.W., Pierce, R.S., and Smith,
C.T. 1989. Long-term depletion of calcium and other nutrients in eastern US
forests. Environ. Manag. 13: 593–601.
French Severn Forest Authority (Westwind Forest Stewardship Inc.) 2009. French
Severn Forest Authority Forest Management Plan 2009-2019. Available at the
Ontario Ministry of Natural Resources and Forestry website:
http://www.efmp.lrc.gov.on.ca/eFMP/viewFmuPlan.do?fmu=360&fid=59006&typ
e=CURRENT&pid=59006&sid=4201&pn=FP&ppyf=2009&ppyt=2019&ptyf=20
09&ptyt=2014&phase=P1
Foster, N.W., Hazlett, P.W., Nicolson, J.A., and Morrison, I.K. 1989. Ion leaching from
a sugar maple forest in response to acidic deposition and nitrification. Water Air
Soil Pollut. 48 (1-2): 251–261.
Formanek, P., and Vranova, V. 2003. A contribution to the effect of liming on forest
soils: review of literature. J. For. Sci. 4: 182–190.
Government of Ontario Environmental Registry. 2007. Forest Management Plan for
the French/Severn Forest for the 10-year period April 1, 2009 to March 31,
16
2019 – Public Inspection of Approved Plan. http://www.ebr.gov.on.ca/ERS-
WEBExternal/displaynoticecontent.do?noticeId=MjkwODc=&statusId=Mjkw
ODc
Haynes, R.J. and Swift, R.S. 1986. Effects of soil acidification and subsequent leaching
on levels of extractable nutrients in a soil. Plant Soil 95(3): 327–336.
Henriksen, A., and Posch, M. 2001. Steady-state models for calculating critical loads of
acidity for surface waters. Water Air Soil Pollut. Focus, 1: 375–398.
Hessen, D.O., and Rukke, N.A. 2000. UV radiation and low calcium as mutual stressors
for Daphnia. Limnol. Oceanogr. 45: 1834–1837.
Horsley, S.B., Long, R.P., Bailey, S.W., Hallett, R.A. and Hall, T.J. 2000. Factors
associated with the decline disease of sugar maple on the Allegheny Plateau. Can.
J. For. Res. 30(9): 1365–1378.
Huettl, R., and Zoettl, H. 1993. Liming as a mitigation tool in Germany's declining
forests - reviewing results from former and recent trials. For. Ecol. Manage. 61:
325–338.
Huntington, T.G., Hooper, R.P., Johnson, C.E., Aulenbach, B.T., Cappellato, R. and
Blum, A.E. 2000. Calcium depletion in a southeastern United States forest
ecosystem. Soil Sci. Soc. Am. J. 64(5): 1845–1858.
Jeffries, D.S. and Snyder, W.R. 1983. Geology and geochemistry of the Muskoka-
Haliburton study area. Data Report DR. 83(2).
Jeffries, D.S., Clair, T.A., Couture, S., Dillon, P.J., Dupont, J., Keller, W., McNicol, D.
K., Turner, M.A., Vet, R., and Weeber, R. 2003. Assessing the recovery of lakes
in southeastern Canada from the effects of acid deposition, Ambio 32: 176–182.
Jeziorski, A., Yan, N.D., Paterson, A.M., DeSellas, A.M., Turner, M.A., Jeffries, D.S.,
Keller, B., Weeber, R.C., McNicol, D.K., Palmer, M.E., McIver, K., Arseneau,
K., Ginn, B.K., Cumming, B.F. and Smol, J.P. 2008. The widespread threat of
calcium decline in fresh waters. Science 322: 1374–1377.
Jeziorski, A., Tanentzap, A.J., Yan, N.D., Paterson, A.M., Palmer, M.E., Korosi, J.B.,
Rusak, J.A., Arts, M.T., Keller, W.B., Ingram, R., Cairns, A. and Smol, J.P.
2014. The jellification of north temperate lakes. Proc. R. Soc. B 282: 20142449.
Keller, W., Dixit, S.S., and Heneberry, J. 2001. Calcium declines in northeastern Ontario
lakes. Can. J. Fish. Aquat. Sci. 58: 2011–2020.
17
Keller, W. 2007. Implications of climate warming for boreal shield lakes: a review and
synthesis. Environ. Rev.15 (NA): 99–112.
Kimmins, J.P. 1973. Some statistical aspects of sampling throughfall precipitation in
nutrient cycling studies in British Columbian coastal forests. Ecology 54: 1008-
1019.
Kirkwood, D.E. and Nesbitt, H.W. 1991. Formation and evolution of soils from an
acidified watershed: Plastic Lake, Ontario, Canada. Geochim. Cosmochim. Act.
55(5): 1295–1308.
Korosi, J.B, Burke, S.M., Thienpont, J.R. and Smol, J.P. 2012. Anomalous rise in algal
production linked to lakewater calcium decline through food web interactions.
Proceed. Roy. Soc. B. 279: 1210–1217.
Likens, G.E. and Bormann, F.H. 1995. Biogeochemistry of a forested ecosystem. 2nd
edition. Springer, New York. pp. 159.
McLaughlin, S.B. and Wimmer, R. 1999. Tansley review no. 104. Calcium physiology
and terrestrial ecosystem. New Phytol. 142(3): 373–417.
Muskoka Watershed Council. 2014. Muskoka watershed report card 2014.
http://www.muskokawatershed.org/stewardshipworks/
Nilsson, S.I., Andersson, S., Valeur, I., Persson, T., Bergholm, J., and Wirén, A. 2001.
Influence of dolomite lime on leaching and storage of C, N and S in a Spodosol
under Norway spruce (Picea abies (L.) Karst.). For. Ecol. Manage. 146: 55–73.
Nohrstedt, H.O. 2001 Response of coniferous forest ecosystems on mineral soils to
nutrient additions: a review of Swedish experiences. Scand. J. For. Res. 16: 555–
573.
O’Connor, E. M., Dillon, P. J., Molot, L. A. and Creed, I. F. 2009. Modeling dissolved
organic carbon mass balances for lakes of the Muskoka River Watershed. Hydrol.
Res. 40: 273-290.
Olsson, B.A. and Staaf, H. 1995. Influence of harvesting intensity of logging residues on
ground vegetation in coniferous forests. J. Appl. Ecol. 32:640–654.
Olsson, B.A., Bengtsson, J., and Lundkvist, H. 1996. Effects of different forest harvest
intensities on the pools of exchangeable cations in coniferous forest soils. For. Ecol.
Manag. 84: 135–147.
18
Palviainen, M. 2005. Logging residues and ground vegetation in nutrient dynamics of a
clear-cut boreal forest. Ph.D. University of Joensuu, pp. 39.
Phillips, T. and Watmough, S. A. 2012. A nutrient budget for a selection harvest:
implications for long-term sustainability. Can. J. For. Res. 42(12): 2064–2077.
Piirainen, S. 2002. Nutrient fluxes through a boreal coniferous forest and the effects of
clear-cutting. Ph.D. Finnish Forest Research Institute, Research Papers 859,
Joensuu. pp.50.
Pitman, R.M. 2006. Wood ash use in forestry–a review of the environmental impacts.
Forestry 79(5): 563–588.
Proe, M.F., Cameron, A.D., Dutch, J. and Christodoulou, X.C. 1996. The effect of whole-
tree harvesting on the growth of second rotation Sitka spruce. Forestry 69(4): 389–
401.
Rosenberg, O., and Jacobson, S. 2004. Effects of repeated slash removal in thinned stands
on soil chemistry and understorey vegetation. Silva Fenn. 38: 133–142.
Schindler, D.W., Bayley S.E., Parker, B.R., Beaty, K.G., Cruikshank, D.R., Fee, E.J. and
Stainton, M.P. 1996. The effects of climatic warming on the properties of boreal
lakes and streams at the Experimental Lakes area, northwestern Ontario. Limnol.
Oceanogr. 41: 1004–1017.
Soil Classification Working Group. 1998. The Canadian System of Soil Classification,
3rd ed. Agriculture and Agri-Food Canada Publication 1646. pp. 187.
Starr, M., Lindroos, A.J., Tarvainen, T. and Tanskanen, H. 1998. Weathering rates in the
Hietajärvi integrated monitoring catchment. Boreal Environ. Res. 3: 275–85.
Stoddard, J.L., Jeffries, D.S., Lukewille, A., Clair, T.A., Dillon, P.J., Driscoll, C.T.,
Forsius, M., Johnannessen, M., Kahl, J.S., Kellogg, J.H., Kemp, A., Mannio, J.,
Monteith, D.T., Murdoch, P.S., Patrick, S., Rebsdorf, A., Skjelkvale, B.L.,
Stainton, M.P., Traaen, T., van Dam, H., Webster, K.E., Wieting, J., and Wilander,
A. 1999. Regional trends in aquatic recovery from acidification in North America
and Europe. Nature 401: 575–578.
Strickland, D. 1993. Trees of Algonquin Provincial Park. The Friends of Algonquin Park.
Whitney, Ontario.
Tamm, C.O. 1974. Kalk problemet för jord, skog och miljövård [Lime problem for
soil, tree stand and environmental care]. Skogs- o. Lantbr. Akad. Tidskr. 113:
37–43.
19
Tran, P. and Brouse, J. 2009. The Watershed Inventory Project Aquatic Ecosystem
Assessment Technical Report. pp. 64.
http://www.muskokawatershed.org/wp-
content/uploads/2011/12/wip_aquatic_technical_report1.pdf
Van Breemen, N., Finlay, R., Lundström, U., Jongmans, A. G., Giesler, R. and Olsson, M.
2000. Mycorrhizal weathering: a true case of mineral plant nutrition?
Biogeochemistry 49(1): 53–67.
van Schöll, L., Kuyper, T. W., Smits, M. M., Landeweert, R., Hoffland, E. and Van
Breemen, N. 2008. Rock-eating mycorrhizas: their role in plant nutrition and
biogeochemical cycles. Plant Soil 303(1-2): 35–47.
Vance, E.D. 1996. Land application of wood-fired and combination boiler ashes: an
overview. J. Environ. Qual. 25: 937–944.
Walmsley, J.D., Jones, D. L., Reynolds, B., Price, M. H. and Healey, J. R. 2009.
Whole tree harvesting can reduce second rotation forest productivity. For.
Ecol. Manage. 257(3): 1104–1111.
Watmough, S.A., and Dillon, P.J. 2001. Base cation losses from a coniferous catchment
in central Ontario, Canada. Water Air Soil Pollut. Focus, 1: 507–524.
Watmough, S.A. and Dillon, P.J. 2002. The impact of acid deposition and forest
harvesting on lakes and their forested catchments in south central Ontario: a
critical loads approach. Hydrol. Earth Syst. Sci. 6(5): 833–848.
Watmough, S.A., and Dillon, P.J. 2003. Base cation and nitrogen budgets for a
mixed hardwood catchment in south-central Ontario. Ecosystems 6: 675–693.
Watmough, S. A. and Dillon, P. J. 2004. Major element fluxes from a coniferous
catchment in central Ontario, 1983–1999. Biogeochemistry 67(3): 369-399.
Watmough, S.A. and Aherne, J. 2008. Estimating calcium weathering rates and
future lake calcium concentrations in the Muskoka-Haliburton region of
Ontario. Can. J. Fish Aquat. Sci. 65: 821–833.
Watmough, S.A., Aherne, J. and Dillon, P.J. 2003. Potential impact of forest
harvesting on lake chemistry in south-central Ontario at current levels of acid
deposition. Can. J. Fish. Aquat. Sci. 60: 1095–1103.
Watmough, S.A., Aherne, J., Alewell, C. et al. 2005. Sulphate, nitrogen and base
cation budgets at 21 forested catchments in Canada, the United States and
Europe. Environ. Monit. Assess. 109: 1–36.
20
Watmough, S.A. and Aherne, J. 2008. Estimating calcium weathering rates and future
lake calcium concentrations in the Muskoka-Haliburton region of Ontario. Can. J.
Fish Aquat. Sci. 65: 821–833.
Warfvinge P. and Sverdrup, H.1992. Calculating critical loads of acid deposition
with PROFILE—a steady-state soil chemistry model. Water Air Soil
Pollut. 63: 119–43.
Yao, H., McConnell, C., Somers, K. M., Yan, N. D., Watmough, S and Scheider, W.
2011. Nearshore human interventios reverse patterns of decline in lake calcium
budgets in central Ontario as demonstrated by mass‐balance analyses. Water
Resour. Res. 47(6).
21
2. Calcium levels in lakes of the Muskoka River Watershed - patterns, trends,
predictions and the potential impacts of tree harvesting on critical levels
2.1 Abstract
Decreasing calcium (Ca) concentration in lakes located in base-poor catchments
of the Muskoka River Watershed in south-central Ontario is an acid-rain driven legacy
effect threatening the health and integrity of aquatic ecosystems, and is a problem that
can be compounded by Ca removals through forest harvesting. Mean lake Ca
concentration (mg L–1) in 104 lakes across the Muskoka River Watershed (MRW)
have decreased by ~ 30% since the 1980's with the rate of decrease slowing over time.
Mean lake SO4 (mg L–1), and Mg (mg L–1) also decreased significantly over time
(37% and 29%), again with a declining rate of decrease pattern, while lake mean pH
and DOC increased significantly between the 1980’s and the 1990’s (16% and 12%),
but exhibited no significant pattern after that. Data gathered in 2011 or 2012 suggested
that smaller lakes, at higher elevation, in smaller catchments with higher runoff that are
minimally impacted by the influence of roads (and associated sodium and/or calcium
chloride road dust suppressants and de-icers) and agriculture (and possible Ca input as
fertilizers), are associated with low Ca concentrations and thus are the lakes most at
risk of amplified Ca depletion below critical levels due to forest harvesting. Using
proposed 10 year land forest management cut volumes on crown land, and assuming
the same levels of continuous removal, 38% of 364 lakes in the MRW will fall below
the critical 1 mg L–1 Ca threshold compared with 8% in the absence of future
harvesting.
2.2 Introduction and rationale
Legacy effects of acid rain are testing the resilience of sensitive ecosystems on
22
the Precambrian Shield and some unprecedented, unforeseen problems have emerged
as a result. Anthropogenically induced increases in sulphur (S) deposition has led to
declines in soil Ca in thin, acid-sensitive, mineral poor soils in eastern North America
(Huntington et al. 2000; Watmough and Dillon 2003; Yanai et al. 2005) and Europe
(Sverdrup and Rosén 1998; Thimonier et al. 2000; Jonard et al. 2012). For decades
sulphate (SO4) has been the primary agent of acid precipitation but, more recently,
there has been increasing concern with possible nitrogen (N) saturation that could
cause an increase in nitrate (NO3-N) leaching, resulting in (among other negative
ecological effects), amplified Ca declines (Aber et al. 1998; Gradowski and Thomas
2008; Pardo 2011). Additionally, rising dissolved organic carbon (DOC) levels may
also offset some of the benefits of declining SO4 deposition (Monteith et al. 2007).
Despite a 60% mean reduction in S deposition and an 18% decrease in N
nationally in Canada from 1990 to 2010 (Environment Canada 2012) existing Ca
depletion in acid-sensitive soils and lakes can be exacerbated by tree harvesting and
harvesting biomass for biofuels (Johnson et al. 1994; Watmough and Dillon 2003,
Watmough et al. 2003, 2005; Agren and Lofgren 2012). Over 60 percent of Ca in
trees can be found in stemwood and bark (Paré et al. 2013), the bulk of which is
exported from a site during timber harvesting. Over time, this can further deplete soil
Ca beyond acidification induced losses (Federer et al.1989; Keller et al. 2001;
Watmough and Dillon 2003). Repeating harvesting cycles on shallow soils (Keller et
al. 2001; Watmough et al. 2003), can intensify losses and in turn can have negative
effects on tree and seedling health and growth and reduce stand productivity (Proe et al.
1996; McLaughlin and Wimmer 1999; Horsley et al. 2000; Walmsley et al. 2009).
23
Studies on the impacts of harvesting on surface water have taken place on
two spatial levels, catchment and stand; however, impacts of harvesting regimes on
surface water at the stand scale should not necessarily be extrapolated as they may be
significantly reduced when assessed at the scale of the watershed (Buttle and Metcalfe
2000). Watershed level results have varied, but many small watershed studies have
indicated changes in hydrology with harvesting (MacDonald et al. 2003; Neary et al.
2009; Zhang and Wei 2014) with the impacts decreasing with increasing watershed size
(Guillemette et al. 2005; Stednick 1996). Other short-term impacts include: increases in
surface water temperature (Prevost et al.1999, Bourque and Pomeroy 2001; Curry et al.
2002) and increases in surface water concentrations of SO4, phosphorus, (P), potassium
(K), magnesium (Mg) and Ca (Nicholson et al. 1982; Jewett et al. 1995; Carignan et al.
2000) and NO3 (Nicholson et al. 1982; Prevost et al.1999; Jerabkova et al. 2011). Over
time, base cation losses from the soil can decrease pH and acid-neutralizing capacity
(ANC) and eventually increase acidity and aluminum (Al) concentrations in surface
water (Hornbeck et al. 1990; Dahlgren and Driscoll 1994; Baldigo 2005).
With more than 2,000 lakes comprising over 15% of its total surface area, the
Muskoka River Watershed (MRW), located east of Georgian Bay on the Shield, has
been the focus of much lake Ca research in Ontario. Although acidic deposition has
decreased drastically since its peak in the 1970’s, low weathering rates and low Ca
deposition are typical of the MRW and, as a result, Ca leaching losses cannot be
readily replenished (Watmough and Aherne 2008). Many lakes in the MRW are
currently below a critical Ca level range of 1.5 – 2 mg L–1 (75 – 100) µ eq L –1),
necessary to sustain keystone aquatic biota (Ashforth and Yan 2008; Tan and Wang
24
2010).
Recent work has indicated that Ca declines in some MRW lakes have driven the
elimination of Ca-rich keystone Daphnia species, especially of the large algae grazer
Daphnia pulex. This species elimination has been linked to recent algal blooms (Korosi
et al. 2012). Decreases in lake Ca concentration in south-central Ontario are also
driving the replacement of Daphnia species by a jelly-clad species (Holopedium) with
much lower Ca requirements an lower lake food web nutrient value (Jeziorski et al.
2014). As lake Ca continues to decline the increasing abundance of Holopedium is
predicted to cause cascading effects in the food chain and threaten water quality in the
affected lakes (Jeziorski et al. 2014).
Recent field assessment in northeastern Ontario have demonstrated greater biomass
and nutrient removals during tree length selection harvesting (the method used in over 60
% of operations in the MRW) than modeled estimates had previously indicated (Hazlett
et al. 2014). Although site conditions and tree species differ from the MRW, the latter
study highlights how potential long-term impacts on soil nutrients may be
underestimated in modeled harvesting scenarios (Hazlett et al. 2014), underlining the
value of an analysis of the impact of applied forest management on Ca depletion. While
the collection of harvest forest residues (branches and foliage) for biofuels is not an
existing practice in Forest Management Plans (FMPs) in the MRW, it has the potential to
more than double the loss of forest stand Ca depending on the method of harvesting
(Paré et al. 2013; Lucas et al. 2014). In view of research indicating that some stands
25
in Ca depleted, acid-sensitive ecosystems on the Shield have more Ca in above
ground biomass than in the soil (Watmough and Dillon 2004), a detailed assessment
of the potential impact of FMP predicted, tree harvesting biomass Ca export on Ca
levels in lakes of the MRW is essential.
The overall aim of this study was to evaluate the long-term impacts of timber
harvesting on lake Ca levels. The objectives of this study were: (i) to identify spatial
patterns between lake Ca and landscape and water chemistry variables, to identify
temporal lake chemistry trends between time periods, and to identify chemical and
physical parameter significant correlations with lake Ca concentration, (ii) to
estimate pre-industrial, unharvested catchment lake Ca using the Steady-State Water
Chemistry (SSWC) model (Henriksen et al. 1992) and, (iii) based on 10 year Forest
Management Plans, to quantify the impact of harvesting on lake Ca levels on a
catchment-scale. The latter took into account: maximum allowable cut volumes,
historical annual cut volumes, rotation time and method of harvesting (((Algonquin
Forest Authority Forest Management Plan 2010–2020 (2010); French Severn Forest
Authority Forest Management Plan 2009–2019 (2009)), tree species specific
biomass equations (Lambert et al. 2005; Ung et al. 2008) and biomass based Ca
concentrations for different tree species components removed (stemwood, bark)
(Paré et al. 2013).
2.3 Methods
Study Area
The MRW, in south-central Ontario, is a watershed that extends along the
26
Precambrian Shield from the Algonquin Highlands in Algonquin Provincial Park to
Georgian Bay, encompassing 763,800 hectares (Muskoka Watershed Report Card
2014) (Fig. 1). The climate is north temperate/south boreal with long-term average
annual precipitation of 990 mm (half of which is evapotranspired with the other half
producing runoff) and a long-term average annual temperature of 5.1 °C (1980–2009)
(Environment Canada 2011).
There are over 2000 lakes and innumerable streams, comprising over 15
percent of the surface area (Tran et al. 2009). Seventy-seven percent of the lakes are
poorly buffered (<1.5 mg L–1 CaCO3) (O’Connor et al. 2009) and are dilute,
oligotrophic lakes primarily located on, base-poor silicate bedrock overlain by thin
glacial tills, often less than 1 metre deep (Dillon et al. 1991; Watmough et al. 2004),
while only 6 percent of the lakes are hardwater and strongly buffered (>3 mg L–1
CaCO3) (O’Connor et al. 2009).
Although some regions are expected to have localised areas of calcite (Jeffries
and Snyder 1983), the soils are mainly podzols and brunisols and are poorly
developed and dominated by poorly weatherable silicate minerals (plagioclase and
hornblende) (Kirkwood and Nesbitt 1991). Small wetlands are pervasive throughout
the MRW and cover 12 % of the total area, while forests comprise 66% of the land
cover with mixed hardwood forests dominating on deeper glacial tills and conifers
prevailing on thin soils (Tran et al. 2009).
Crown land forest management in the MRW
Approximately 50 percent of the land in the MRW is Crown land (Fig. 2.1) and
this study considered only areas of publicly owned Crown land due to the availability of
27
existing harvesting data. The Algonquin Forest Authority (AFA) and Westwind Forest
Stewardship Inc. (WFSI) oversee Crown land harvesting. They have Ontario Ministry
of Natural Resources (MNR) Sustainable Forest Licenses specifying how allocation of
their cutcut volume is to be divided among dozens of logging contractors (Government
of Ontario Environmental Registry 2007).
The three silvicultural systems (a set of harvest, renewal and maintenance
treatments) used in the FMPs are: selection (uneven-aged with about 30% of stand
every 20–25 year cycle, shelterwood (even-aged with mature trees removed in two or
more cuts on a 15 year cycle with a 70 year cycle between the last cut of one cycle and
the first of the next to allow for natural regeneration) and clearcut (even-aged, shade
intolerant stands with mature trees removed in one cut on a 90 year cycle). The average
harvesting yield in Algonquin has increased from 47 m3 ha–1 during 1990–95 to ~65 m3
ha–1 during 2000–2010 and is projected to be 58 m3 ha–1 during 2010–2020 (Algonquin
Forest Authority 2010). For the French/Severn, historical yields have been much lower
due to the condition of the forest such that during the first four years of the current
2009–2019 plan, the yield has averaged approximately 37 m3 ha–1 (WFSI Strategic Plan
2012). From 1999–2009 harvest area and volume managed by WFSI were
approximately 50% of planned due to poor quality, accessibility issues and market
demand (KBM Forestry Consultants 2012). Both forest authorities harvest in large
areas outside the MRW and thus silvicultural analysis data specific to the MRW were
generated with the provided FMPs in order to assess the impact of harvesting on lakes
in the study area.
28
Figure 2.1: The Muskoka River Watershed in south-central Ontario (inset).
Crown land, the study area, comprises approximately 50% of the land surface
area.
Lake datasets
There was no single data set that could be used for all the analyses in this study.
Chemical data for MRW lakes were collected during 1981–2012 through a number
of different research projects and lake surveys including ones from: the Canadian Wildlife
Service (CWS), the Ontario Ministry of Environment and Climate Change (OMOE)
Dorset Environmental Science Centre (DESC), the Algonquin Fisheries Assessment Unit,
the District Municipality of Muskoka and the Canadian Water Network (CWN).
29
The collated datasets varied in sampling points, times, methods and analysis, but provided
at least one Ca observation for: 590 lakes located within the study area, of these, 364
lakes were found in catchments with tree harvesting removals and 177 of the 364 lakes
had their most recent sample of Ca ≤ 2 mg L–1, 104 of the 590 lakes had the data required
that was evaluated for temporal trend analysis and 24 of those had data required for long-
term temporal trend analysis (Figure 2A.2). Finally, 75 lakes had data from 2011–2012
that was evaluated to identify patterns in chemical and physical parameters that were
strongly associated with lake Ca concentration (Figure 2A.2). Integrated lake samples
were given preference over unintegrated samples and average values were calculated if
more than one sample per year was available.
Temporal trends in lake chemistry
Changes in average lake Ca concentrations (mg L–1) were evaluated for 104
MRW lakes with time series data across three separate ten year time periods (1981–1990,
1991–2000 and 2003–2012 with matched pair t-tests (with repetition) in order to establish
any significant patterns in mean Ca over time. All data for each lake and time period were
averaged before analysis. Changes in mean lake concentrations (or values for pH) were
also similarly assessed for SO4, NO3, Mg, Na, pH and dissolved organic carbon (DOC)
trends. Although matched pair t-tests are not sensitive to deviations from normality,
non-parametric analyses of means were also evaluated using the Wilcoxon Signed Rank
test and the Sign test. While comparisons were not temporally consistent across lakes
over the three time periods, Ca depletion occurs relatively slowly and dramatic changes
don't normally occur over one or two years.
30
Long-term trends analyses
Trends analysis of lake data has been recommended as a fundamental way of
evaluating large, complex watersheds with widespread changes (Hirsch 1988; Hirsch et
al. 1991) and identification of similar long-term trends across multiple catchments of the
study area may indicate a regional response to direct or indirect influences (Paterson et
al. 2008). Long-term trends in annual and average concentrations for Ca, Mg, K, Na,
SO4 and Ca/Mg, Ca/Na lake ratios were evaluated for 24 lakes with five or more years
of annual means of the chemical parameters assessed (most of these lakes had 8 plus
years of data) with the trends test Kendall’s Tau. A non-parametric trend test, that
provides higher statistical power when data are not normally distributed, Kendall’s Tau
is robust against data gaps, outliers and serial correlation (Conover 1999). Aggregation
of the data set by using mean lake data or by computing means (where it was
possible) was used to reduce autocorrelation (Helsel and Hirsch 1992).
The Mann-Kendall procedure for difference in averages over time is
mathematically equal to the aforementioned estimator of correlation called Kendall’s
Tau (Conover 1999). The statistical methodology for Mann-Kendall trend analysis
has been previously detailed in Helsel and Hirsch (1992) and Lento et al. (2012) and
the analysis method has been used frequently in assessing long-term trends in water
quality (Clair et al. 1995; Jeffries et al. 1995). As the long-term trends were analysed
with annual mean values no trends in seasonality could be evaluated. To account for
the possibility of Type I errors, Benjamin and Hochberg’s (1995) false discovery rate
(FDR) correction procedure on the p value was used (Yan et al. 2008; Lento et al.
2012).
31
The van Belle and Hughes test (1984) for homogeneity has been used in
evaluation of aquatic trends (Yue et al. 2002; Lento et al. 2012) and was utilized in this
research to indicate if the trends across the 24 lakes with long-term data could be
denoted as regional. Sens slope or rate of decline was not included in this evaluation as
abrupt decreases in parameters following S emission controls were included within the
time-frame. For pattern and trend analysis, all statistical evaluation was performed
with JMP 11 (SAS 2013).
Physical and chemical parameter pattern analysis
Summary statistics were calculated for lake area, catchment area, lake elevation,
runoff and selected deposition and lake chemical variables for all lakes with Ca data
within crown land (590), lakes in catchments with harvesting cuts on crown land (364),
and three subsets of the former and latter lakes used in analysis (Table 2A.2). As many
factors and influences contribute to lake Ca concentration levels, it was determined if
there were any relationships that potentially could reflect major drivers of the lake Ca
concentrations for 75 MRW lakes with the most recent physical and chemical sampling
data from 2011-2012 (Table 2A.2). If there were several sampling points in these lakes,
average values were used since all analyses were catchment (not sub-catchment) based.
Non-parametric correlation analysis was performed with Spearman’s rho between lake
Ca and physical and chemical parameters available from 2011–2012 sampling. Physical
parameters in this data set (n=75) that were not measured were either modelled by
another researcher or highly correlated to the selected parameters (for example % of
catchment paved roads and % of urbanized catchment reflect each other, and %
catchment development and % sparse forest reflected % urbanized catchment).
32
A Principal Components Analysis (PCA) was conducted on this data-set as a
means of multivariate exploration in order to visually group various constituents across
sites. The PCA was performed to derive relationships between average lake Ca
concentration and a number of physical and chemical co-distributed variables. The
significance was tested with Bartletts test. JMP 10 software (SAS 2011) was used for
analysis.
Projected impact of timber harvesting on lake Ca levels – GIS spatial evaluation
Since catchments are delimited spatially by the geomorphological property of
drainage, MRW watershed boundaries were delineated (for lakes of all sizes) from a
hydrographic summary layer from the Natural Resources and Values Information
System (NRVIS 2009), and from a digital elevation model (DEM) file with a 1:50,000
spatial scale and 30 m by 30 m grid size (Geobase, 2013), resulting in the delineation of
993 catchments. Subsequently, raster maps of: land ownership from Ontario structured
data layers (Ontario Ministry of Natural Resources various years), lake chemistry (Ca,
Mg, K, Na, SO4, NO3 and Cl), MRW runoff coefficients interpolated from annual
average runoff measured over 30 years at long-term hydrologic gauging stations
throughout Ontario (Environment Canada HYDAT database 2012), interpolated wet plus
dry atmospheric deposition from eastern Canada averages for 1999–2002 (Environment
Canada), Forest Resource Inventory (FRI) (1:10,000) (Ontario Ministry of Natural
Resources 2004), and the French Severn FMP 2009–2019 (1:10,000) and Algonquin
FMP 2010-2020 (1:10,000), were obtained or developed on a catchment basis. The
amalgamated, 10 year FMPs, containing tree species volume removals by catchment and
associated catchment harvesting regimes raster maps, were used to derive predicted lake
33
chemistry post-harvesting. All GIS analysis was done with ArcGIS 10 and 10.1 (ESRI
2012), and data analysis was made using attribute tables within the ArcMap component.
Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen
and Posch 2001)
Ecosystem sensitivity to acidification is strongly dependent on mineral soil
weathering (chemical and physical breakdown of exposed minerals) rates, with
low weathering rates associated with sensitivity to acidic deposition (Warfvinge and
Sverdrup 1992). The rate of release of base cations (BC), Ca, Mg, K and Na, from
minerals largely determines a soils ability to buffer acidic deposition and is the only
self-repair method for anthropogenically acidified ecosystems (Holmquist 2001).
Atmospheric deposition and weathering of soil minerals represent the primary sources
of nutrient base cations to soils, however BC weathering rates are difficult to estimate
due to soil heterogeneity (Jönsson et al. 1995; Hodson et al. 1997) and some estimates
have been associated with at least 40% uncertainty (Hodson et al. 1997; Holmqvist
2001; Koseva et al. 2010). Consequently, mean annual surface water chemistry data
and mean annual runoff data were used to predict pre-industrial, unharvested
catchment lake Ca concentrations for 364 MRW lakes using the SSWC model
(Henriksen et al. 1992).
The SSWC model has been frequently used in North America (Hindar and
Henriksen 1998; Watmough et al. 2003; Watmough and Aherne 2008) and across
Europe (Posch et al. 2001) to estimate BC weathering rates and pre-industrial lake Ca
concentrations. The model assumes a steady state condition where mass balance
nutrient inputs balance outputs, allowing the use of mean chemistry values. The SSWC
model was used to estimate the base cation (BC) weathering rate from the measured
34
base cation flux and used the F-factor, the fraction of current base cations present in
lakes due to soil acidification, to account for part of the base cation leaching due to ion
exchange soil processes (Henriksen and Posch 2001):
(1) [BC]0 = [BC]t – F([SO4 ]t + [NO3 ]t – [SO4]0 – [NO3 ]0)
where the subscripts 0 and t represent pre-acidification and present concentrations,
respectively, and where [BC]0 is the pre-acidification base cation (BC = Ca + Mg + K +
Na) lake concentration. The Ca concentration existing in lakes at steady state under the
three harvesting scenarios was calculated using eq. 1 though the pre-industrial
concentration was calculated for Ca instead of [BC]0 and the removals exported from
harvesting were subtracted:
(2) [Ca]SS = [Ca]0 – (Ca up /Q)
where Q is the mean annual runoff (m year–1), [Ca]SS is the lake Ca concentration at
steady state under a harvesting scenario and [Ca]0 is the pre-industrial Ca concentration
calculated using eq. 1. Due to lack of a full suite of MRW lake data and the addition of
other anthropogenic base cation inputs to MRW catchments, the assessment was made
with a modified SSWC model (Table 2A.1) as has been done in the past (Watmough et
al. 2003). The unmodified SSWC model inputs for equations are: lake concentrations of
base cations (Ca, Mg, Na, and K), chloride (Cl), sulphate (SO4) and nitrate (NO3).
Chloride is used to make a sea salt correction to base cations and SO4 as the model
assumes that all Cl in runoff comes from sea salts (Henriksen and Posch 2001).
However, this is not true of the MRW lakes as many are subject to road salt. As Na and
Cl concentrations are believed to balance each other out, these inputs were excluded
35
from the equations. As well, K concentrations are very low in these lakes and are
thought to contribute little to the base cations and were excluded along with lake NO3
concentrations which are negligible in the MRW (Watmough and Dillon 2002;
Watmough et al. 2003). The SSWC model additional equations (Table 2A.1) and
assumptions used in this study are extensively detailed in Watmough et al. (2003) and
Watmough and Aherne (2008).
Biomass export driven Ca decline
The impact of harvesting on lake Ca was evaluated by subtracting the Ca
removed from the catchment in harvesting biomass from the SSWC calculated lake Ca
concentration to give the Ca level the lake would have, at steady state, under a specific
harvesting scenario (Watmough et al. 2003). The online calculator provided by Natural
Resources Canada 2014 was used to determine the average biomass in kg of the species-
specific tree stem and bark, as well as the quantity of Ca (g) contained in the biomass
using either diameter at breast height or a combination of diameter at breast height and
height (Lambert et al. 2005; Ung et al. 2008). Branch and foliage biomass and their
associated Ca were not included in the calculations as they were left on site in close to
100% of all harvesting operations (based on silvicultural method used). The quantity of
Ca (g) removed by kg of tree component was converted to Ca (g) removed by volume
(m3) using Canadian tree species live wood density estimates (Gonzalez 1990). The
equations used in these calculations, the methodology, and the corresponding error terms
in estimating species specific mean biomass and the associated mean Ca (g) are detailed
in the associated papers (Lambert et al. 2005; Ung et al. 2008).
The overall calculation was performed on a catchment-by-catchment basis for
36
three harvesting scenarios with differing cut volumes based on the annual allowable cut
(AAC). The annual allowable cut (AAC in m3) is the annual level of harvest permitted
on an area of Crown land over a specified number of years. In addition to the hind-cast
of a steady state lake Ca concentration calculation to determine background levels of pre-
industrial, unharvested catchment lake Ca, three biomass export predictions of lake Ca
were made at 100 %, 59% and 40% of the predicted 10 year annual allowable cut
volumes. The 40% removal level was based on email correspondence with the head
foresters who estimated approximately 40% of AAC in the 2009-2020 FMPs will be
removed mainly due to market constraints, but also to cutovers and unforeseen tree poor
quality issues. The 59% level of harvesting was the weighted average volume removal
level by catchment based on annual cut volumes that have been recently harvested as
indicated by 73% of total planned volume harvested in 2005–2010 by the Algonquin
Forest Authority (Cumming 2010) and 52% of total planned volume harvested in 1999–
2009 under Westwind (KBM Forestry Consultants Inc. 2012). The 40% and 59% levels
assumed the same proportion of species-specific cuts as in the 100% FMP by volume
scenario. For each of these harvesting scenarios, the amount in kg of Ca removed in
biomass was divided by the area of the catchment and then by the tree harvest rotation
length to obtain kg Ca ha–1 yr–1. To obtain the change in Ca in mg L–1, the predicted mg
Ca m–2 yr–1 was then divided by the annual runoff (L m–2 yr–1). This value was then
subtracted from the SSWC steady-state lake Ca concentration to obtain the predicted
impact of harvesting biomass export on lake Ca concentrations.
Using 100% of AAC volume prediction may seem high; however, although
demand for wood is currently dictated by a slow market, future demand could increase
37
as electricity prices continue to soar, making fuelwood more desirable for the consumer
that uses electricity to generate heat, and the collection of slash for biofuels more
economically feasible. The impact of increasing volume removals of trees (and
theoretically even beyond AAC with the ACE) was thus assessed using the 100% AAC
prediction. For this analysis seven lakes with “0” Ca concentration (levels assumed to be
low and undetectable with the equipment used at the time) were not included. Although
theses lakes were not arbitrarily assessed values it was thought reasonable to assume all
seven had their most current levels (as determined by their last sampling year) below 1.0
mg L–1 (50 µeq L–1).
2.4 Results
Lake Chemistry
Summary statistics, calculated for 75 MRW lakes with the most recent physical and
chemical data (sampled 2011–2012), indicated that both mean lake Ca (mg L–1) and Mg
(mg L–1) were slightly higher at 2.81 and 0.83, respectively, compared with 2.43 and 0.66
for the total number of lakes in the study area (n=590) and 2.14 and 0.61 for lakes in
catchments with harvesting cuts (n=364) (Table 2A.2). This lake data set (n=75) also had
both mean lake Na (mg L–1) and Cl (mg L–1) values that were markedly higher at 3.21 and
4.47 respectively compared to 1.04 and 0.80 for lakes in catchments in cuts (n=364)
(Table 2A.2). Mean lake elevation of 324 (m) was lowest for this data set (n=75)
compared to 346 (m) (n=590), 357 (m) (n=364), (and the highest mean elevation of 412
(m) (n=177)), as was mean runoff of 0.028 (L m–2 yr–1) compared to 0.029 (L m–2 yr–1)
(n=364) (Table 2A.2).
38
Figure 2.2 Key data inputs in estimating MRW lake Ca (mg L–1) on a catchment basis under
predicted tree harvesting and no harvesting 2009–2020. The equations that were used in
estimating steady state are detailed in Appendix 2A (Table 2A.1).
Temporal trends in lake chemistry
Both the mean lake SO4 (mg L–1) and Ca (mg L–1) decreased significantly over time
(n=104) (Table 2.1) with the mean lake SO4 concentration decreasing between the first
and last time frame by 2.72 mg L–1, a mean 36.7% decrease and the mean Ca (mg L–1)
decreasing overall by 0.65 mg L–1, a 29.4% mean decrease (Fig. 2.3). The relative
difference in mean lake Ca concentration between the 1980s and 1990s was
approximately four times greater than between the 1990s and the 2000s indicating that
the rate of Ca decline has decreased, as has the rate of decline of SO4 (Fig. 2.4). From
the earliest to the latest time period, lake Mg declined significantly by a mean of
Interpolated Annual Runoff
(L m-2)
39
0.18 mg L–1, a 28.5% mean decrease and also indicated the pattern of a declining rate of
decrease over time (Table 2.1, Fig. 2.4). Lake pH and DOC concentration increased
significantly, between the first and last time span with the former increasing by a mean
of 0.2 pH units, corresponding to a mean increase of 15.7%, and the latter increasing by
0.51 mg L–1, a mean increase of 11.6% (Table 2.1, Fig. 2.4). Both pH and DOC did not
increase significantly between the 1991–2000 and the 2003–2012 time periods. Lake
NO3 data was only available for the two later time periods, decreasing significantly by a
mean of 0.0012 mg L–1, a mean decrease of 9.8% (Table 2.1, Fig. 2.4). There were no
significant differences between any time periods indicated for Na.
Table 2.1: Mean lake parameter and matched pairs t test mean difference in lake parameter
and associated standard error (SE) between three sampling time periods for 104 MRW
lakes. Lake Ca is in bold type.
Time period
Ca (mg L-1) SO4 (mg L-1)Mg (mg L-1)
pH DOC (mg L-1) NO3 (mg L-1)
Matched pairs t test
Mean difference (mg L-1)
SE Upper 95% Lower 95%
P
1981-1990
2.21 6.93 0.63 5.73 4.91 ND
1981-1990 1991-2000
-0.53 -1.88 -0.15 0.18 0.38
0.04 0.16 0.01 0.03 0.11
-0.47 -1.57 -0.13 0.24 0.58
-0.61 -2.20 -0.17 0.13 0.17
0.0001 0.0001 0.0001 0.0001 0.0006
1991-2000
1.68 5.05 0.49 5.91 5.29
0.0122
1991-2000 2003-2012
-0.12 -0.83 -0.04 0.02 0.19
-0.001
0.03 0.11 0.01 0.02 0.11
0.002
-0.09 -0.61 -0.04 0.07 0.40
-0.007
-0.18 -1.06 -0.05 -0.02 -0.02
-0.013
0.0001 0.0001 0.0001
NS NS
0.0001
2003-2012
1.56 4.22 0.45 5.93 5.48
0.011
1981-1990 2003-2012
-0.65 -2.72 -0.19 0.21 0.57
0.03 0.18 0.01 0.03 0.12
-0.60 -2.35 -0.16 0.28 0.80
-0.73 -3.08 -0.21 0.14 0.34
0.0001 0.0001 0.0001 0.0001 0.0001
Note: Non-parametric evaluations of means with the Wilcoxon Signed Rank test yielded the identical significant differences
at the 0.0001 probability (P) level and the Sign test yielded the same decreasing patterns at P of 0.0001. ND means no data
and NS means not significant.
40
Mean lake Ca (mg L–1) 1981-1990
Mean lake SO4 (mg L–1) 1981-1990
Figure 2.3: Lake patterns for mean Ca concentrations (mg L–1) for N= 104 MRW lakes
with long-term data. All lakes below the 1:1 line decreased in lake Ca (mg L–1) between
the time periods 1981–1990 and 2003–2012. The mean decrease for Ca was 29.4% ± 12%
and the mean decrease for SO4 depicted in the inset graph was 36.7% ± 19%.
Figure 2.4: Boxplot distributions over three time periods based on mean lake values of Ca,
SO4, Mg, pH, NO3 and DOC for 104 lakes. Outliers are indicated by separate dots. Note: all
units are in mg L–1 except for pH.
Mea
n l
ake
Ca
(mg L
–1)
20
03
-20
12
Mea
n lak
e S
O4 (
mg L
–1)
2003
-2012
0 1 2 3 4 5 6 7 8 9 10 11
11
10
9
8
7
6
5
4
3
2
1
0
–
NO3
41
Mann-Kendall long-term trend analysis
Consistent with the large data set, Mann-Kendall long-term trend analysis for 24
MRW lakes with low Ca indicated significant decreasing trends in 13 lakes, with a
further four showing non-significant declining trends (Table 2.2). Significant declining
trends in lake Mg (mg L–1) were indicated in 10 lakes, non-significant declining trends in
5 lakes and no trends in 9 lakes (Table 2.2). There were no trends indicated for Na in any
of the lakes and thus Na was omitted from the results table. Significant declining trends
in lake K (mg L–1) were indicated in 6 lakes, non-significant declining trends in 2 lakes
and no trends in 16 lakes (Table 2.2). Significant declining trends in lake SO4 (mg L–1)
were indicated in 9 lakes, non-significant declining trends in 4 lakes and no trends in 11
lakes (Table 2.2). There were no long-term positive trends in any assessed parameters.
Strong significant homogeneity of long-term trends across groups of lakes
(subsets) found in multiple catchments was indicated by the van Belle and Hughes test
(1984) for lake Ca (n=17), SO4 (n=13) and a less strong but still significant trend for lake
Mg (n=15) but not for K (n=8) (Table 2.3). The homogeneity of trends was calculated
for all of the lakes indicating a decrease over time separately and the lakes showing no
trend were excluded from this analysis. The strongly significant long-term patterns over
time across groups of lakes for Ca, Mg and SO4 indicate a common lake response to direct
or indirect influences but do not indicate what those influences are. Conversely, the
heterogeneity across lake trends for K may indicate variability in lake response in these
parameters to direct or indirect influences. Lake Na concentration was not included in
this across lake analysis as there were no long-term lake Na (mg L–1) trends indicated.
42
Table 2.2: Mann-Kendall long-term trend analysis for 24 MRW lakes with Ca ≤2 mg L–1 located in
catchments across the MRW with Forest Management Plan predicted cuts to the year 2020.
MRW Lake
Bertie
Bright
Chub
Corbe
tt
Dace
Dale
Kagh
Little Drummer
Little East End
Lumber
MacKinaw
Martin
Namakootchie
Niger
North
Oak
Pincher
Quiver
Slim
Sunset
Thumb
Tonakela
Weed
Wee
West Otterpaw
Note: Declining trends that were significant at P < 0.05 after false discovery rate corrections utilizing Benjamin and Hochberg’s
(1995) method are printed in bold and with an asterisk (*), respectively. There was no difference between the uncorrected
and corrected significant trends. Unbolded trends had less than 7–9 years of data (yes) and blanks indicate no trends. As there
were no trends in Na (mg L–1) in any lake it was omitted from the table, in addition, there were no increasing trends for any
parameter. Percent change using Sens slope was not included as this trend time-frame included abrupt changes in parameter
concentrations (brought about by emission controls) which may skew slope.
Table 2.3: Summary of results of a multi-group Mann-Kendall trend test to determine consistent
regional trends across lakes in Ca, Mg, K and SO4 concentrations (mg L-1) in 24 Precambrian
Shield lakes with Ca≤2 (mg L-1). Consistent strong declining trends across lakes were indicated in
Ca and SO4 and a less strong but still significant declining trend in Mg. No trend across lakes was
indicated for K.
Number of lakes
Effect χ 2 trend df P
17 Ca Trend 16.49 16 <0.0001 15 Mg Trend 5.97 14 0.0113 8 K Trend 2.68 7 0.0886 13 SO4 Trend 36.23 12 <0.0001
Note: Lakes showing no trend were excluded from this analysis.
Time
span
Ca
declining
Mg
declining
K declining
SO4
declining
1990–2012
1982–2002 yes yes
1988–2012 yes* yes* yes* 1990–2004
1984–2004
1990–2004 yes* yes* 1988–2004 yes* yes* yes* yes* 1988–2004 yes* yes* 1989–2004 yes* yes* yes* yes* 1988–2004 yes* 1988–2002 yes yes
1980–2002 yes* 1984–2004 yes yes yes
1982–2003
1990–2006 yes* yes* yes* 1989–2012 yes* yes* yes* yes* 1990–2004 yes yes
1990–2004 yes
1989–2012 yes* yes* yes* 1982–2002 yes yes yes
1988–2004 yes* yes* yes* 1988–2012 yes* yes* yes* yes*
1984–2004 yes yes
1988–2002 yes* yes* yes* yes*
43
Non-parametric Correlations and Principal Components Analysis
For the 75 lakes with data from 2011-2012, chemical parameters significantly
positively correlated with lake Ca (mg L–1) were lake Cl (mg L–1), lake SO4 (mg L–1)
and Ca deposition (kg ha–1
yr–1
). There were no other chemical deposition parameters
that were significantly correlated with lake Ca. Lake pH was strongly significantly
positively correlated with lake Ca concentration. The physical parameters significantly
positively related with lake Ca (mg L–1) were paved road density, percent of
agriculture in the catchment area, unpaved road density, percent of rock and rubble and
lake area and catchment area and significant negative correlations with lake elevation,
runoff and % of deciduous trees in the catchment area were indicated (Table 2.4).
Table 2.4 Non-parametric Spearman’s p correlation analysis between lake Ca (mg L-1) and
chemical and physical parameters for 75 lakes sampled in 2011–2012.
5.3
Note: The scale of measurement was on a catchment basis for the majority of parameters. Significant correlations at p<0.05 are bolded as are the variables significantly correlated to lake Ca (mg L–1).
Variable by Variable Spearman’s ρ Prob>|ρ|Lake CA (mg L–1) Ca deposition (kg ha–1 yr–1) 0.4698 <0.0001
Lake CA (mg L–1) S deposition wet plus dry (kg ha–1 yr–1) -0.1843 0.1063
Lake CA (mg L–1) N deposition wet plus dry (kg ha–1 yr–1) -0.1158 0.3225
Lake CA (mg L–1) Lake SO4 (mg L–1) 0.4767 <0.0001
Lake CA (mg L–1) Lake Chloride (mg L–1) 0.8775 <0.0001
Lake CA (mg L–1) Runoff (mm yr–1) -0.2777 0.0139
Lake CA (mg L–1) Mean downslope distance gradient -0.1280 0.2640
Lake CA (mg L–1) Catchment area (ha) 0.3653 0.0011
Lake CA (mg L–1) Lake area (ha) 0.3286 0.0033
Lake CA (mg L–1) Lake mean depth (m) 0.1512 0.1863
Lake CA (mg L–1) Lake elevation (m) -0.4781 <0.0001
Lake CA (mg L–1) Catchment unpaved road (m–1) 0.6468 <0.0001
Lake CA (mg L–1) Catchment paved road density (m–1) 0.7332 <0.0001
Lake CA (mg L–1) Catchment percent rock/rubble 0.3460 0.0019
Lake CA (mg L–1) Catchment % agriculture 0.7019 <0.0001
Lake CA (mg L–1) Catchment % dense deciduous forest -0.3151 0.0050
Lake CA (mg L–1) Catchment % dense coniferous forest -0.2167 0.0567
Lake CA (mg L–1) Catchment % bog 0.0082 0.9444
Lake CA (mg L–1) Lake DOC (mg L–1) 0.1287 0.2779
Lake CA (mg L–1) Lake pH 0.7809 <0.0001
44
The corresponding PCA graph allowed for pattern visualization of the parameters
parameters positively clustered with lake Ca concentration, which were lake Cl (mg L–
1), paved roads density, percent of agriculture in the catchment area, unpaved roads
density, lake area and catchment area and pH while closely negatively associated
parameters included lake elevation and lake runoff and % deciduous trees (Fig 2.5).
Principal component 1 accounted for 24% of the variation in the data set and
principal component 2 accounted for 17%.
Figure 2.5: Principal components analysis of physical and chemical variables for 75 MRW lakes
with data from 2011–2012. Principal component 1 described 24% of the variation in the included
parameters while principal component 2 described 17% of the variation. Note: c_dsg is the
mean down slope distance gradient, cuca is the catchment area, zmean is the mean lake depth,
c_con and c_deciduous are the catchment % of coniferous and deciduous trees respectively,
CLIDUR is lake chloride, CAU is lake calcium concentration, c_urd and c_prd are catchment
unpaved and pave roads respectively, l_elev is lake elevation. In every case Dep is deposition
and c_ is catchment.
45
Spatial evaluation used for projected impact of timber harvesting on lake Ca levels
The 590 lakes with data located in crown catchments are widely dispersed across
the MRW however they are most highly concentrated in the far eastern portion. This
distribution most likely reflects that lakes with lower Ca were the ones chosen to be
monitored for effects of acid rain throughout the combined data sets (Fig 2.6). The
time of their sample ranged from 1980-2012 with only ~15% sampled in 2011–2012
(Fig 2A.2).
For the 590 lakes found in crown catchments, the lakes with low Ca
concentrations of ≤2.00 mg L–1 can be visually assessed to be highly concentrated in
the most intensively harvested eastern section (Figs 2.6, 2.7). They are comparatively
lightly interspersed throughout the rest of the MRW (Fig 2.6). There were no lakes
≤1.00 mg L–1 in the far western or central portion of the MRW. A visual assessment of
the very high concentration of small lakes in small catchments in the eastern portion of
the MRW is well represented in the magnified image with many of the points
representing these lake Ca concentrations lying on top of each other (Fig 2.7).
The 364 lakes found in catchments with harvesting cuts are representative of
the 590 sampled with regard to mean distributions of available parameters (Table
A2.2) and hence, are thought to be reasonably representative of unsampled lakes
across the MRW surrounded by crown or private harvested catchments. Attributes of
the 177 lakes with Ca ≤2.00 mg L–1 found in catchments with FMP harvesting cuts
were highly variable. But on average they were smaller lakes ((mean value of 47 ha
(S.D. = 244 ha)), located in smaller catchments ((mean value of 412 ha (S.D.= 650
ha)), found at higher elevations ((mean value of 412 m (S.D.= 97 m)), with lower Ca
total deposition ((mean value of 6.09 kg ha–1 yr–1 (S.D.= 0.30)) and lower S total
46
deposition ((mean value of 9.61 kg ha–1 yr–1 (S.D.= 0.21)), with higher runoff
((mean value of 0.030 L m–2 yr–1 (S.D.= 0.003 L m–2 yr–1)) and with a slightly
higher percentage of the catchment in deciduous trees ((mean value of 64 %
(S.D. = 11 %)) (Table 2.5, Fig 2.7).
Table 2.5: MRW Lakes ≤2.00 mg L–1 in catchments with harvesting cuts (N=177) mean values,
standard deviations and ranges for chemical and physical parameters.
Sampling period 1980-2012 Mean SD Range
Deposition Ca (kg ha–1 yr–1) (1999-2002) 6.09 0.30 6.89 - 5.69
Deposition S (kg ha–1 yr–1) (1999-2002) 9.61 0.21 9.05 - 10.0
Lake Ca (mg L–1) 1.41 0.35 0.50 - 2.04
Lake Mg (mg L–1) 0.44 0.10 0.13 - 0.73
Lake Na (mg L–1) 0.61 0.31 2.35 - 4.61
Lake K (mg L–1) 0.32 0.32 0.05 - 2.42
Lake SO4 (mg L–1) 3.11 1.04 1.38 - 5.87
Lake elevation (m) 412 97.3 290 - 518
Lake area (ha) 46.6 243.8 0.23 - 3149
Catchment area (ha) 412 649.9 29 - 34695
Catchment % coniferous 16.9 5.97 0.00 - 37.8
Catchment % deciduous 64.2 10.9 0.00 - 82.4
Catchment runoff (L m–2) 0.03 0.003 0.023 - 0.034
Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen
and Posch 2001) and quantifying the potential impact of harvesting on lake Ca
levels
A cumulative distribution graph of current and predicted lake concentration as a
function of no harvest (steady state) and three harvest volume removals of 100%, 59%
(average reported annual volume removal level) and 40% (verbally reported removal
level) of the FMP for MRW catchments with predicted harvesting cuts from 2009–2020
was used to present the impact of harvesting on 364 lakes.
Natural variability in the SSWC modeled pre-disturbance lake Ca condition for 364
47
MRW lakes ranged from 0.70 to 4.34 (mg L–1) with a similar wide range for the most
current Ca levels which went from 0.72 to 4.43 (mg L–1). Catchment Ca removal levels at
100% FMP ranged widely with annual removals from 0.06 to 7.55 (kg ha–1) (reflecting the %
of catchment harvested) affecting lake Ca changes with declines ranging from 0.02 to 2.93
(mg L–1). The three harvesting volume Ca removal scenarios denoted as the worst (100%
FMP), the most recent historical weighted mean cut scenario (59%) and the best (40% FMP)
with regard to the future impacts of harvesting on lake Ca, gave lake concentration ranges
from 0.11 to 3.81 mg L–1 (5.50 to 190 µeq L–1), 0.44 to 3.87 mg L–1 (22 to 193 µeq L–1) and
0.54 to 3.91 mg L–1 (27 to 195 µeq L–1) respectively. Based on the most recent measurement,
28% and 5% of 364 lakes were already below critical Ca levels of 1.5 mg L–1 (Ashforth and
Yan 2008; Tan and Wang 2010), and 1.0 mg L–1 (Hessen at al. 2000) respectively at their
time of sampling (Table 2.6, Fig. 2.8). The estimated SSWC lake Ca level represents the no
harvest, pre-industrial lake scenario and a predicted 37% of lakes will fall below the critical
level of 1.5 mg L–1 with 8% falling below 1.0 mg L–1 in the absence of forest harvesting
(Table 2.6, Fig. 2.9). Under a 100% FMP volume removal scenario 69% of lakes are
predicted to fall below this critical level with 38% falling below 1.0 mg L–1 while under a
59% FMP volume removal level, 56% of the lakes would be below 1.5 mg L–1 with 26%
falling below 1.0 mg L–1 (Table 2.6, Fig. 2.9). Under a 40% FMP harvest volume removal
the impact to the lakes was most similar to the no harvest steady state scenario but still much
higher with 50% of the lakes declining below 1.5 mg L–1 and 20% falling below 1 mg L–1.
48
Table 2.6: Most current, SSWC modelled and predicted % of lakes under varying
harvesting scenarios for two critical thresholds of lake Ca for Ca-rich Daphnia species,
50 µeq L–1 (Watmough et al. 2003), equivalent to 1.0 mg L–1 and 75 µeq L–1 (Ashforth
and Yan 2008), equivalent to 1.5 mg L–1 for N=364 MRW lakes. The percentages
have been rounded to whole numbers.
N
Critical
Lake Ca
Level
(µeq L–1)
Most
Current
Lake
Ca
SSWC
No
Harvest
Lake Ca
100% FMP
Harvest
Volume
Lake Ca
59% FMP
Harvest
Volume
Lake Ca
40% FMP
Harvest
Volume
Lake Ca
364 <50 5% 8% 38% 26% 20%
364 <75 28% 37% 69% 56% 50%
49
Lake Ca (mg L–1) for 590 lakes located in crown land catchments in the Muskoka River Watershed
Figure 2.6: 590 (of more than 2000) lakes with Ca concentration data in Muskoka River Watershed catchments. Lakes most at risk ≥2 mg L–1
(n=177) of additional Ca removals are represented by red (0.00 – 1.04 mg L–1) and orange (1.05 – 2.04 mg L–1) dots. The concentration value
of 0 represents levels undetectable by the equipment used. Predicted harvesting cuts (2009–2020) are denoted in the catchments by dark
green while the runoff gradient is based on a distinctly different moss green.
50
Area of heavily concentrated small, shallow lakes in small harvested MRW catchments.
Figure 2.7: Enlarged area of catchments for n=364 lakes located in catchments with harvesting cuts with the blown up area
indicating an area of high concentration of small lakes in small catchments and many with Ca ≤2 mg L–1 as indicated by the larger
red X symbol. This area is impacted by intensive harvesting cuts from two Forest Management Plans (dark and light yellow).
51
Figure 2.8: Current and predicted lake Ca concentration as a function of no harvest (steady state)
and harvest volume removals of 100%, 59% (average reported annual volume removal level) and
40% (verbally reported removal level) of the FMP for 364 lakes in MRW catchments with
predicted harvesting cuts from 2009-2020. The red vertical lines denote literature suggested
critical Ca thresholds of 1.0 mg L–1 (50 µeq L–1) (Hessen et al. 2000) and as used in Watmough et
al. 2003 and of 1.5 mg L–1 (75 µeq L–1) as indicated by recent work in MRW lakes (Ashforth and
Yan 2008).
I.0 mg L–1 I.5 mg L–1
52
2.5 Discussion
Lake chemistry
Wide ranges were indicated for Ca (0.78 – 15.7 mg L–1) over all the collated data
sets however, 96% of the 590 lakes had Ca concentrations <4.5, with 100% of the 364
lakes impacted by harvesting with Ca concentrations <4.5. The similar Ca
concentrations measured in the 590 lakes and the subset of 364 lakes impacted by
harvesting indicates the lakes in catchments with cuts are reasonably representative of
the larger data set. The lowest mean lake Ca (1.41 mg L–1) for those lakes in harvested
catchments most at risk to Ca depletion below critical levels (n=177) was half of that of
the data set of the 75 lakes most recently sampled. These 177 lakes were much smaller
lakes, located at the highest mean elevation in catchments with the highest mean runoff,
the lowest mean SO4 deposition and the lowest mean Ca deposition.
Notably, both mean lake Na and lake Cl mean values were much higher in the
most recently sampled lakes than in all other data sets, with mean lake Na being five
times higher than mean Na for all lakes with data in catchments in cuts and over eleven
times higher than those lakes in catchments with cuts with Ca ≤2 mg L–1. These
elevated Na and Cl concentrations may reflect contamination by drainage from road de-
icing chemicals (Ralston 1971; Kaushal 2005; Langen et al. 2006). Other sources of
high lake mean Cl concentrations could include farm sources of potassium chloride
fertilizer (potash) and animal waste and septic system leakage (Shaw et al. 2004). Calcium
can also be a major component of de-icing chemicals which could also contribute to
higher lake mean Ca concentration (Langen et al. 2006) in the data set from 2011– 2012
if used in the MRW.
53
Temporal trends in lake chemistry
Lake chemistry on the boreal shield has not improved as expected from reduced S
emissions and this has been attributed to some or a combination of possible factors
including: declining base cation concentrations (soil acidification), drought induced
mobilization of SO4, increasing NO3 concentration and increasing DOC levels (Jeffries
et al. 2003). The contribution of these factors is the focus of the evaluation of the
significant patterns and trends indicated.
The 104 MRW lakes with historical data had variable initial (1980s) chemical
characteristics, with lake SO4 ranging from 2.65–10 (mg L–1) and pH from 4.58 – 6.70
and with lake Ca ranging from 1.29 – 5.50 (mg L–1) and lake Mg ranging 0.39 – 1.60
(mg L–1). Over the study period (1980s–2012) SO4 deposition in southern Ontario
markedly decreased from the early 1980’s to the late 1990’s, and nationwide from 1990
to 2010 emissions of SO2 declined by 57 percent while emissions of NO3, decreased by
18 percent (Canada-U.S. Air Quality Agreement: Progress Report 2012). Mean lake SO4
(mg L–1),Ca (mg L–1) and Mg (mg L–1) decreased significantly over time across the
three time periods with a declining rate of decrease, while lake mean pH and DOC
increased significantly overall time but indicated no increase between the two most
recent time frames. Using lake SO4 as the measure of the state of lake recovery from
acidification, the lake mean recovery has slowed substantially, while using lake pH as
that metric, significant lake mean recovery is indicated between the 1980’s and 1990’s
but appears to have stalled after that. Lake mean NO3 data were only available for the
last two time frames, but indicated a significantly declining concentration (10%) which
could be expected with a decline in NO3 emissions from the same time frame (1990-
2010). Declining NO3 trends in streamwater have been previously demonstrated in
54
streams in south central Ontario and were arttributed to declining emissions
(Kothawala et al. 2011).
A declining rate of decrease of lake SO4 has also been reported in similar time
frames for lakes in eastern Canada however there was no trend indicated in lake Ca and
Mg with the explanation that these cations had “bottomed out” (Clair et al. 2011). A
declining rate of decrease of MRW lake SO4 was expected but it has been well
demonstrated that despite the large reduction in S deposition, declines in surface water
SO4 concentrations have been much less than what was anticipated in the central
Ontario region (Dillon et al. 1997; Dillon and Evans 2001; Dillon et al. 2003) indicating
a less than expected recovery response from anthropogenically caused lake acidification.
This less than expected recovery could be due to influences of climate causing export of
S from catchments (Dillon et al. 2003; Eimers et al. 2004).
In this study the mean pH of the 104 MRW lakes increased significantly by
15.7% overall but no significant increases were indicated between the period ending in
2000 and the one ending in 2012. MRW limited lake recovery as indicated by an
increased pH (toward the critical value of 6) in this research has been corroborated
within the earlier time frame in some northeastern Canadian lakes (Clair et al. 1995;
Bouchard et al. 1997; Stoddard et al. 1999), but in other research there has been little to
no change in pH in response to declining S emissions (Houle et al. 1997; Dillon et al.
1997) while Clair (2011) found a similar pattern to this investigation, where pH
significantly increased between 1990–2007, but not after that. The same influence of
climate patterns on S re-oxidation could have negatively impacted increases in pH in the
2000’s as the frequency of droughts and rewetting increased over time. Additionally,
55
increased NO3 leaching or DOC export may limit the increase in lake pH (Aber et al.
1989; Stoddard et al. 1999).
There have been increased DOC concentrations reported in surface waters in
North America coinciding with the large decreases in S emissions but the process
explanations have been varied (Driscoll et al. 2003; Evans et al. 2005). Declining
atmospheric S deposition has been identified as the major driver of DOC increases in
some work (Evans et al. 2006; Monteith et al. 2007) but other drivers have also been
suggested as being more influential. In this evaluation DOC concentration increased
significantly by 12% from the 1980’s to the 2000’s however, no significant changes
occurred between the period ending in 2000 and the one ending in 2012.
DOC trends have been related to changes in rainfall and temperature and the
drought/rewetting process in wetlands (Eimers et al. 2008). Changes in DOC
concentration in lakes also depend in part on changes in precipitation patterns and if
increases in precipitation occur in the future, DOC soil leaching and lake-water DOC
concentrations are expected to increase (Keller 2007). Recent climate evaluation in the
MRW indicated that annual precipitation decreased significantly during 1978–1998 and
then weakly increased during 1999–2013 (Yao et al. 2013). This weak increase and
decreasing declines of atmospheric S could partially explain the no change in DOC in
this research between the later time frames. A warming trend has also been indicated
from yearly mean maximum temperature from 1990–2010 from the Muskoka
Automated Weather Observation System climate station (Hadley et al. 2012).
56
Catchments with differing initial chemical conditions and physical attributes are
not expected to have identical responses to broad-scale regional drivers like declining S
deposition and it is not the purvue of this research to determine which drivers will have
more influence in DOC patterns. However, if predicted increases in precipitation and
temperature do continue, without major changes in acidic deposition, DOC is expected to
increase in the future (Clark et al. 2010).
Total N emissions have significantly increased since the 1950s and remain at
unnaturally high levels (Vitousek et al. 1997; Galloway et al. 2003). With S emission
regulation, the rate of N deposition relative to S deposition has increased since the 1980s
and this has brought attention to whether N deposition is now restraining lake recovery
(Galloway 1998; Galloway et al. 2003; Jeffries et al. 2003). Nitrogen can contribute to
acidification directly as NO3 and as ammonium as both nitrification and uptake of
ammonium produce hydrogen ions (Schindler et al. 1985).
Increased NO3 leaching contributes to soil acidification and depletion of soil Ca
(and other cations) with associated declines in lake Ca over the long-term (Aber et al.
1989; 1998; Gradowski and Thomas 2008; Pardo 2011). In recent years, NO3
deposition, although often assumed to have stayed the same, has significantly decreased
by 18% between 1990 and 2011 (Canada-U.S. Air Quality Agreement: Progress Report
2012). Although there was insufficient data to assess MRW lake NO3 in the 1980’s the
mean lake NO3 decreased significantly by almost 10% between the 1990’s and 2000’s
which may indicate that N soil saturation and associated NO3-N leaching are not major
concerns in these catchments. However, any future increases in N deposition could
become problematic for lakes with NO3 levels that are on the high end of the range.
57
Long-term trend analysis
Although there was variation in trends within the 24 lakes, there were consistent
regional decreasing patterns across 13 lakes in SO4, 17 in Ca and 15 in Mg
concentrations with no significant trends across lakes in K or Na. Similar regional trends
for Ca (Watmough and Aherne 2008; Palmer et al. 2011) and Na (Watmough and
Aherne 2008) have previously been identified in the MRW (Watmough and Aherne
2008). Differences in time-scale analysis, lake set analysis and heterogeneity of
catchment physical and chemical characteristics can account for trend variation but the
long-term decreasing patterns across groups of lakes gives credence to the cautious
extrapolation of these trends to other similar lakes in the MRW.
Spatial patterns in lake Ca
Principle components analysis of the lake data set with the most recent sampling
demonstrated strong relationships between lake Ca concentrations and catchment
chemical and physical properties. In a region where lake Ca concentration is, in general,
declining due primarily to decreasing SO4, lakes with higher Ca were the larger lakes
associated with larger catchments with higher densities of roads, higher percentages of
the catchment utilized for agriculture (with deeper, more fertile soils) and were located at
lower elevation with lower runoff and high lake Cl. Not surprisingly lakes with higher
Ca had higher associated pH. It is conceivable that larger lakes with higher densities of
paved roads have highly developed shorelines which has been shown contribute to
higher lake Ca levels (Yao et al. 2011) and/or received runoff from CaCl de-icers
(Ralsten 1971; Kaushal et al. 2005; Langen et al. 2006). Palmer et al. (2011) also found
increases in chloride were greater in developed lakes that were close to winter-
58
maintained roads. Addition of CaCl or KCl to farm soils could also contribute to higher
lake Ca and/or Cl concentrations (Shaw et al. 2004).
Conversely, smaller lakes in smaller catchments at higher elevation with higher
runoff and with lower densities of roads and a lower percentage of the catchment
utilized for agriculture and a higher percentage of deciduous trees would be associated
with lower Ca concentrations and are the lakes most at risk to amplified Ca depletion
from forest harvesting. MRW forest stands are dominated by deciduous tree species
which have higher Ca demands than coniferous and hence the negative correlation. Lake
elevation has been previously negatively correlated with lake Ca (D’Arcy and Carignan
1997; Ito et al. 2005; Wolniewicz et al. 2011). It has been suggested that lakes at higher
elevation are often headwater lakes with shallower soils and steeper slopes
(Wolniewicz et al. 2011) and as a consequence, have higher and more acidic runoff.
These characteristics can combine to minimize the contact of acid precipitation with
soils and consequently reduce neutralisation (Sullivan et al. 2007). It is reasonable to
consider that these lakes with lower Ca are located in relatively undeveloped areas with
less developed shorelines and not subject to the same anthropogenic additions of Ca
that the lakes with higher Ca are experiencing, as confirmed with a visual assessment
of the spatial analyses map.
Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen
and Posch 2001)
Knowledge of the historical pre-disturbance MRW lake conditions is crucial in
understanding how current lake levels relate to the historical condition however, long-
term monitoring of the lakes in this region only began in the early 1980’s and therefore
59
the historical condition has not been recorded and needed to be modeled. Steady state
modelling was used to estimate the steady state lake Ca level (SSWC pre-industrial Ca)
which represents what lake Ca levels were pre-industrially and where they will fall
in the absence of harvesting. Under this scenario Ca will decline as the soils reach
equilibrium with deposition and then the soils will no longer be acidifying. Future
changes in Ca deposition or Ca weathering rates could influence the SSWC estimates but
Ca deposition has been relatively low and thus small changes are thought to have little
influence while weathering rates are assumed to be stable over long periods of time
(Watmough and Dillon 2003). Weathering rates have been hypothesized to be
biologically accelerated by ectomychorrizal fungi in some nutrient deficient ecosystems
but the extent to which this could mitigate existing nutrient depletion and any resulting
declines in forest productivity has not been determined (Vadeboncoeur et al. 2014).
Biomass export driven Ca decline
Recent studies (Watmough and Phillips 2011; Malcolm et al. 2014; Monteith et al.
2014) have demonstrated that forest practices may contribute to the acidification of
surface waters while others have predicted there would be no impact depending on the
forest initial site conditions (Helliwell et al. 2014). According to the most recent sample
date, the 364 lakes have not yet reached steady-state with respect to the deposition that
they currently receiving. Calcium removal levels are equivalent to decreases in lake Ca
between 0.02 to 2.93 mg L–1 (with an estimated average value of 0.64 mg L-1
) under the
predicted 100% FMP harvest biomass export scenario, reflecting the range of
catchment volume removal levels and the % of catchment harvested.
60
Taking into consideration the forest conditions on a catchment by catchment
basis, under the proposed 10 year (2009–2020) forest management cut volumes, and
assuming these levels of harvesting are continuous, 38% of 364 lakes in the MRW are
predicted to fall below the 50 µeq L–1 (1.0 mg L–1) Ca critical threshold at steady-state
compared with 8% in the absence of future harvesting. This represents a large (~30%)
increase in lakes going under that critical level at steady state under 100% of the
predicted harvesting volume removal level. The identification of Ca critical levels is
uncertain because of non-linear impacts and confounding factors such as climate
change (Desellas et al. 2011).
It has been indicated that in the context of the lakes at most risk, the results for
the cumulative distribution graph indicate lakes with already low levels of Ca would
be most heavily impacted by additional Ca removals through harvesting. This is
intuitive, but the statistics are worth noting. For those lakes, with Ca already ≤2 mg L–
1, the percentage of lakes going below 1.0 mg L–1 at 100% FMP harvesting would be
approximately 75% compared to an unharvested steady state where less than 20% of
these low Ca lakes would go below 1.0 mg L–1.
An earlier SSWC analysis of 1300 acid-sensitive lakes in south-central Ontario,
including lakes in the MRW, predicted less than 1% of lakes would go below 1.0 mg
L–1 in a no harvest scenario with Ca removal levels from stem harvesting putting
23.3% of lakes below 1.0 mg L–1 with whole tree harvesting losses increasing that
percentage of lakes up to 52% (Watmough et al. 2003). A similar assessment of 300
acid-sensitive lakes in north-western Ontario indicated that under a no harvesting
scenario 1% of lakes would go below 1.0 mg L–1 with 17% going below the critical
61
level under stem only harvesting and 30% going below that level after whole tree
harvesting (Watmough and Phillips 2011).
This assessment predicted that 8%, of lakes compared with 1% in Watmough
et al. (2003) would go below 1.0 mg L–1 in the steady state, no harvest scenario. The
large difference can, in part, be explained by the continued decline in Ca
concentrations in catchment soils which is not adequately taken into account in the
SSWC model. The data set in this research extended to 2012, representing over a
decade of information since the data set from Watmough et al. (2003) was analysed.
Watmough et al. (2005) has previously shown that SSWC modeling of data for lakes
that was taken 13 years apart resulted in a mean decrease in pre-industrial
concentration of 15%, accounted for by continued soil Ca depletion. This indicates
that historical Ca concentration has been overestimated in past research.
The current evaluations represent more refined estimates than earlier work in
south-central Ontario for several other reasons. The MRW lakes were acid-sensitive
lakes with Ca<225 μeq L–1 (<4.5 mg L–1) while all three of the earlier assessments
evaluated acid-sensitivity in lakes as being Ca <300 μeqL–1 (<6 mg L–1). The
inclusion of more Ca-rich lakes in the earlier assessments could also account for the
differences in results compared to this study. If there were a large proportion of lakes
between 225–300 μeq L–1 in the earlier studies, this would skew results towards lower
estimates of the percentage of lakes going below critical levels compared to the MRW
database of lakes used here. In addition, the current assessment was based on species-
specific volume and Ca removals by catchment and the actual catchment-specific
combinations of harvest removal methods, and on interpolated regional runoff
assessed by catchment, none of which were incorporated into the earlier SSWC
62
estimations. It has been recommended that site specific hydrology, rather than
interpolated regional runoff values would improve SSWC estimations due to the
complexity and heterogeneity of runoff on a catchment scale (Gibson et al. 2010) but
that refinement in MRW estimates could be limited by practicality and cost.
On the ground, whole tree harvesting accounted for less than 10% of the
predicted removals in MRW catchments. However, the large increase in lakes falling
below the critical level when whole tree harvesting Ca losses were considered in
earlier work in the region (Watmough et al. 2003) and the effects on soil Ca in other
acid sensitive managed forests (Akselsson 2007; Kaarakka 2012) gives an indication
of similar heavy increases that could occur if harvesting for biofuels (with additional
tree biomass in branches and coarse woody debris and associated Ca removals) did
become a major forestry management practice in the MRW in the future.
A forestry management practice of a tree harvest removal level at 40% of the
AAC would be closest to the modeled no harvest, pre-industrial lake Ca concentration
level. This reduced volume removal level takes into account the current market
demand, bypass and unexpected quality issues. On an average catchment basis, for
those lakes most at risk to going below critical thresholds, harvesting above 40% of
allowable volume is indicated to be unsustainable. It should be noted that forty
percent of the 10 year AAC volume is an average value as the predicted area to be cut
varied tremendously, from 4% to 87%, by catchment.
Consequently, it is recommended that when determining sustainable
harvesting volumes the removal levels should included in the FMPs on a catchment
by catchment basis taking into account individual lake Ca concentrations, and the
potential of them declining below critical levels in the long-term.
63
Mitigation efforts on Crown land could investigate the possibility of the
addition of Ca to harvested catchments with lakes at risk of Ca depletion below critical
levels, to ensure current and continued volume removals do not exacerbate an already
tenuous lake health and integrity situation. Recent research has suggested that addition
of nutrients may be crucial to sustaining productivity and future rotations in Ca
depleted forests subject to short harvest, heavy cut rotations in the north-eastern United
States (Vadeboncoeur et al. 2014) and slow-release wood-ash has been suggested as an
economical Ca-addition that could close nutrient cycles (Pitman 2006). Future research
could involve both catchment trials and an examination of the effects of global
experimental lime and wood-ash treatment trials on ecosystem nutrient cycling (as
explored in Chapter 3 of this thesis), on Ca concentrations in lakes and on forest
productivity. Using Ca-addition as a tool for managing the cumulative effects of past,
present and future stressors, could not only benefit the integrity of the soils and lakes
and the productivity of managed forests, but also help ensure long-term sustainability
of both ecosystem and economic health in areas such as the MRW where the economy
is so intrinsically tied to the integrity of the environment and especially to lake water
quality.
Uncertainty
There are many unquantifiable uncertainties associated with the results
(although most have been associated with previous work as well) and thus the
predictions for these lakes should be interpreted with caution. To begin with, there is
inherent uncertainty and unknown propagation of error when combining several lake
64
data sets with differing sampling times, frequencies, procedures, equipment and analyses.
In addition, the likelihood that some of the lake sets were not sampled randomly could be
associated with spatial correlation and associated error. Modeling error also needs to be
accounted for. There is uncertainty in using the SSWC method to determine the impact
of harvesting since, although a steady state is assumed, S deposition can be dynamic
which could result in the F factor changing (Henriksen and Posch 2001), and soil Ca
concentration could fall (Watmough et al. 2005). With regard to the species-specific
biomass estimation and associated nutrient content there is large uncertainty associated
with the latter as high estimates of variability associated with nutrient content have been
observed frequently (Futter et al. 2012; Yanai et al 2012; Paré et al. 2013). However, it
has been suggested that combined species-specific estimates over many cut sites in a
catchment should tend towards a population mean (Paré et al. 2013). The interpolation of
deposition and runoff data introduces another level of unquantifiable uncertainty and the
FMPs are based on FRI data which can be notoriously inaccurate.
As well, data gaps in MRW lakes are widespread with less than one-third of all lakes
having any lake Ca concentration data, and of those that have data, less than 15% of the
lakes included in this research were recently sampled (2011-2012). Consequently, more
widespread sampling of lakes in harvested catchments is necessary to determine which
lakes are at most risk to Ca depletion below critical levels.
Despite these limitations, these predictions have been made with catchment based
forest management information and resources in order to give approximations of the
impact of harvesting Ca removal levels on MRW lake Ca concentrations. As such they
65
such they represent a refinement of previous regional estimations.
2.6 Conclusions
Lake Ca (mg L–1), SO4 (mg L–1), and Mg (mg L–1) in lakes across the MRW are
low and have decreased significantly over time with a declining rate of decrease pattern
while lake pH and DOC increased significantly between the 1980’s and the 1990’s but
exhibited no significant pattern after that. There were consistent long-term regional
decreasing patterns in SO4, Ca and Mg concentrations with no significant long-term
trends indicated in K or Na across 24 lakes. Using lake SO4 as a measure of the state of
lake recovery from acidification and lake Ca and Mg as indications of buffering ability,
significant lake recovery has now slowed substantially, while using lake pH as that
metric, significant lake recovery is indicated between the 1980’s and 1990’s but appears
to have stalled after that. Lake mean NO3 data was limited to the 1990’s time period
and later but indicated a significantly declining concentration which could be expected
with declining NO3 emissions from 1990-2010.
Ca removal levels in catchments that are based on annual planned volume
removals would result in a 30% increase in sampled lakes going below a critical level of
1 mg L–1 over that of modeled steady-state no harvest scenarios. The reported harvested
average level of 40% of planned volume removals would be the best case scenario in
having the least impact on lake Ca concentrations with 20% falling below 1 mg L–1. In
some MRW Crown land catchments, at lower elevations and in lower runoff areas that
are impacted by relatively high densities of roads and/or agricultural practices, the
maintenance of lake Ca levels above critical values may not be an issue. However, for
many lakes that are subject to multiple regional stressors, the decline of Ca, that is
66
essential to both forest and lake health and integrity, is beyond the bounds of natural
recovery. These are the lakes most at risk to supplementary Ca removals through tree
harvesting. For the MRW, this research indicates that these lakes are typically small
lakes with lake Ca concentrations approaching or below known critical Ca
concentrations that are located in relatively small intensively harvested catchments,
located at higher elevations with higher runoff and lower road densities.
This research highlights the need for further investigation into this Ca depletion
issue and possible ecosystem remediation efforts, in order to provide forest
management authorities the best available information. On Crown land, future,
sustainable forest management practices could investigate the possibility of the addition
of Ca to harvested catchments with lakes at risk of Ca depletion below critical levels, to
ensure current and continued volume removals do not exacerbate an already tenuous
lake health and integrity situation. Addressing the harvesting/Ca problem on private
land is an even more complex economic, social and environmental problem that may
well prove to be a daunting challenge for lake associations or other non-profit
associations to tackle and follow the lead of forest authority nutrient management
practices.
67
2.7 References
Aber, J.D., Nadelhoffer, K,J,, Steudler, P., Melillo, J.M. 1989. Nitrogen saturation
in northern forest ecosystems. BioScience 39: 378–386.
Aber, J., Nadelhoffer, K.J. and Melillo, J.M. 1998. Nitrogen saturation in northern forest
ecosystem. BioScience 39: 378–386.
Ågren, A. and Löfgren, S. 2012. pH sensitivity of Swedish forest streams related to
catchment characteristics and geographical location–Implications for forest
bioenergy harvest and ash return. For. Ecol. Manag. 276: 10–23.
Akselsson, C., Westling, O., Sverdrup, H., Holmqvist, J., Thelin, G., Uggla, E. and
Malm, G. 2007. Impact of harvest intensity on long-term base cation budgets in
Swedish forest soils. Water Air Soil Pollut. Focus 7(1): 201–210.
Algonquin Forest Authority. 2010. Algonquin Forest Authority Forest Management Plan
2010–2020. Available at the Ontario Ministry of Natural Resources and Forestry
website:
http://www.efmp.lrc.gov.on.ca/eFMP/viewFmuPlan.do?fmu=451&fid=58953&type
=CURR
ENT&pid=58953&sid=6472&pn=FP&ppyf=2010&ppyt=2020&ptyf=2010&ptyt=2
015&ph ase=P1
Ashforth, D. and Yan, N.D. 2008. The interactive effects of calcium concentration and
temperature on the survival and reproduction of Daphnia pulex at high and low
food concentrations. Limnol. Oceanogr. 53(2): 420-432.
Baldigo, B.P., Murdoch, P.S. and Burns, D.A. 2005. Stream acidification and mortality
of brook trout (Salvelinus fontinalis) in response to timber harvest in three small
watersheds of the Catskill Mountains, New York, USA. Can. J. Fish. Aquat. Sci.
62: 1168–1183.
Benjamin, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: A practical
and powerful approach to multiple testing. Journal of the Royal Statistical Society.
Series B (Methodological) 57(1): 289–300.
Bouchard, A., 1997. Recent lake acidification and recovery trends in Southern Quebec,
Canada. Water, Air, Soil Pollut. 94, 225–245.
Bourque, C.P-A. and Pomeroy, J.H. 2001. Effects of forest harvesting on summer
stream temperatures in New Brunswick, Canada: an intercatchment multiple-year
comparison. Hydrol. Earth Syst. Sci. 5: 599–613.
68
Brakke, D.F., Henriksen, A., and Norton, S.A. 1990. A variable F-factor to explain
changes in base cation concentrations as a function of strong acid deposition. Verh.
Verein. Int. Limnol., 24: 146–149.
Buttle, J.M. and Metcalfe, R.A. 2000. Boreal forest disturbance and streamflow
response, northeastern Ontario. Can. J. Fish. Aquat. Sci., 57(S2): 5–18.
Canada-United States Air Quality Agreement Progress Report. 2012.
http://www.ec.gc.ca/air/default.asp?lang=En&n=8ABC14B4-
1&offset=5&toc=show Carignan, R., D'Arcy, P. and Lamontagne, S. 2000.
Comparative impacts of fire and forest harvesting on water quality in Boreal
Shield lakes. Can. J. Fish. Aquat. Sci. 57(S2): 105–117.
Clair, T.A., Dillon, P.J., Ion, J., Jeffries, D.S., Papineau, M. and Vet, R.J. 1995.
Regional precipitation and surface water chemistry trends in southeastern
Canada (1983–1991). Can. J. Fish. Aquat. Sci. 52: 197–212.
Clair, T. A., Dennis, I. F. and Vet, R. 2011. Water chemistry and dissolved organic carbon
trends in lakes from Canada’s Atlantic Provinces: no recovery from acidification
measured after 25 years of lake monitoring. Can. J. Fish. Aquat. Sci. 68(4): 663–674.
Clark, J.M., Bottrll, S.H., Evans, C.D., Monteith, D.T., Bartlett, R., Rose, R. and
Chapman, P.J. 2010. The importance of the relationship between scale and process
in understanding long-term DOC dynamics. Sci. Tot. Environ. 408(13): 2768–2775.
Conover, W.J. 1999. Practical Nonparametric Statistics. Third Edition, Wiley, pp. 319–
327.
Cumming, G. 2010. Year 10 Annual Report for the Algonquin Park Forest Plan Period
April 1, 2005 to March 31, 60pp.
Curry, R.A., Scruton, D.A. and Clarke, K.D. 2002. The thermal regimes of brook trout
incubation habitats and evidence of changes during forestry operations. Can. J. For.
Res. 32: 1200–1207.
Dahlgren, R.A. and Driscoll, C.T. 1994. The effects of whole-tree clear-cutting on soil
processes at the Hubbard Brook Experimental Forest, New Hampshire, USA. Plant
Soil 158: 239–262.
D’Arcy, P. and Carignan, R. 1997. Influence of catchment topography on water
chemistry in southeastern Quebec Shield lakes. Can. J. Fish. Aquat. Sci. 54: 2215–
27.
Desellas, A.M., Paterson, A.M. and Sweetman, J.N. 2011. Assessing the effects of
69
multiple environmental stressors on zooplankton assemblages in Boreal Shield
lakes since pre-industrial times. J. Limnol. 70(1): 41–56.
Dillon, P.J. and Evans, H.E. 2001. Long-term changes in the chemistry of a soft-
water lake under changing acid deposition rates and climate fluctuations.
Internationale Vereinigung fur Theoretische und Angewandte Limnologie
Verhandlungen 27(5): 2615–2619.
Dillon, P.J., Molot, L.A. and Scheider, W.A. 1991. Phosphorus and nitrogen export
from forested stream catchments in central Ontario. J. Environ. Qual. 20: 857–
864.
Dillon, P.J., Molot, L.A. and Futter, M. 1997. A note on the effects of El Niño-
related drought on the recovery of acidified lakes. Environ. Monit. Assess.
46, 105–112.
Dillon, P.J., Somers, K.M., Findeis, J and Eimers, M.C. 2003. Coherent response of
lakes in Ontario, Canada to reductions in sulphur deposition: the effects of climate
on sulphate concentrations. Hydrol. Earth Syst. Sci. Discussions, 7(4): 583–595.
Driscoll, C.T., Driscoll, K.M., Roy, K.M., Mitchell, M.J. 2003. Chemical response
of lakes in the Adirondack region of New York to declines in acidic deposition.
Environ. Sci. Technol. 37: 2036–2042.
Edwards. P.J. 1986. Conversion factors and constants used in forestry, with emphasis on
water and soil resources. United States Department of Agriculture, Forest Service. pp.
12.
Eimers, M. C., Dillon, P. J. and Watmough, S. A. 2004. Long‐term (18‐year) changes in
sulphate concentrations in two Ontario headwater lakes and their inflows in response
to decreasing deposition and climate variations. Hydrol. Process. 18(14): 2617–2630.
Eimers, M.C., Buttle, J. and Watmough, S. A. 2008. Influence of seasonal changes in
runoff and extreme events on dissolved organic carbon trends in wetland and
upland-draining streams. Can. J. Fish. Aquat. Sci. 65(5): 796-808.
Environment Canada. 2011. Canadian Climate Normals & Averages - Calculation of
the 1981 to 2010 Climate Normals for Canada.
http://climate.weather.gc.ca/climate_normals/normals_documentation_e.html?doc
ID=1981
70
Environment Canada. 2012. Canada-United States Air Quality Agreement Progress
Report. http://www.ec.gc.ca/air/default.asp?lang=En&n=8ABC14B4-
1&offset=5&toc=show
Environment Canada HYDAT database 2012.
http://www.ec.gc.ca/rhcwsc/default.asp?lang=En&n=901
8B5EC-1
ESRI 2012. ArcGIS 10, 10.1 software. 380 New York St., Redlands, California, United
States.
Evans, C.D., Monteith, D.T and Cooper, D.M. 2005. Long-term increases in surface
water dissolved organic carbon: observations, possible causes and environmental
impacts. Environ. Pollut. 137(1): 55–71.
Evans, C.D., Reynolds, B., Jenkins, A., Helliwell, R.C., Curtis, C.J., Goodale, C.L. and
Coull, M.C. 2006. Evidence that soil carbon pool determines susceptibility of semi-
natural ecosystems to elevated nitrogen leaching. Ecosystems 9(3): 453-462.
Federer, C.A., Hornbeck, J.W., Tritton, L.M., Martin, C.W., Pierce, R.S., and Smith,
C.T. 1989. Long-term depletion of calcium and other nutrients in eastern US
forests. Environ. Manag. 13: 593-601.
French Severn Forest Authority (Westwind Forest Stewardship Inc.) 2009. French Severn
Forest Authority Forest Management Plan 2009-2019. Available at the Ontario
Ministry of Natural Resources and Forestry website:
http://www.efmp.lrc.gov.on.ca/eFMP/viewFmuPlan.do?fmu=360&fid=59006&type
=CURR
ENT&pid=59006&sid=4201&pn=FP&ppyf=2009&ppyt=2019&ptyf=2009&ptyt=2
014&ph ase=P1
Futter, M.N., Klaminder, J., Lucas, R.W., Laudon, H. and Köhler, S.J. 2012.
Uncertainty in silicate mineral weathering rate estimates: source partitioning and
policy implications. Environ. Res. Lett. 7(2): 024025.
Galloway, J.N. 1998. The global nitrogen cycle: changes and consequences. Environ.
Pollut. 102:15-24.
Galloway, J.N., Aber, J.D., Erisman, J.W., Seitzinger, S.P., Howarth, R.W., Cowling,
E.B. and Cosby, B.J. 2003. The nitrogen cascade. BioScience 53:341–356.
Gibson, J.J., Birks, S.J., Kumar, S., McEachern, P., and Hazewinkel, R., 2010.
Interannual variations in water yield to lakes in northeastern Alberta: implications
for estimating critical loads of acidity. J. Limnol. 69 (Suppl. 1): 126–134
71
Gonzalez, J.S. 1990. Wood density of Canadian tree species. Northwest Region. Info
Report – NOR-X-315.
Government of Ontario Environmental Registry. 2007. Forest Management Plan for
the French/Severn Forest for the 10-year period April 1, 2009 to March 31, 2019
– Public Inspection of Approved Plan. http://www.ebr.gov.on.ca/ERS-WEB-
External/displaynoticecontent.do?noticeId=MjkwODc=&statusId=MjkwODc=
Gradowksi, T. and Thomas S.C. 2008. Responses of Acer saccharum canopy trees and
saplings to P, K and lime additions under high N deposition. Tree Physiol. 28:73–
185.
Guillemette, F., Plamondon, A.P., Prévost, M. and Lévesque, D. 2005. Rainfall
generated stormflow response to clearcutting a boreal forest: peak flow
comparison with 50 world- wide basin studies. J. Hydrol. 302(1): 137-153.
Hadley, K. 2012. A Multi-proxy investigation of ecological changes due to multiple
anthropogenic stressors in Muskoka-Haliburton, Ontario, Canada.
Hazlett, P.W., Morris, D.M. and Fleming, R.L. 2014. Effects of biomass removals on
site carbon and nutrient retention and jack pine tree growth across a site
productivity gradient in upland boreal forests of Ontario. Soil. Sci. Soc. Am. J.
78: S183–S185.
Helliwell, R.C., Aherne, J., Nisbet, T.R., MacDougall, G., Broadmeadow, S., Sample, J.
and Doughty, R. 2014. Modelling the long-term response of stream water
chemistry to forestry in Galloway, Southwest Scotland. Ecol. Ind. 37: 396–411.
Helsel, D. R., and Hirsch, R.M. 1992. Statistical Methods in Water Resources, Elsevier
Science Publishers, Amsterdam.
Henriksen, A., and Posch, M. 2001. Steady-state models for calculating critical loads of
acidity for surface waters. Water Air Soil Pollut. Focus 1: 375–398.
Henriksen, A., Kamari, J., Posch, M., and Wilander, A. 1992. Critical loads of acidity:
Nordic surface waters. Ambio 21: 356–363.
Hessen, D.O., and Rukke, N.A. 2000. UV radiation and low calcium as mutual stressors
for Daphnia. Limnol. Oceanogr. 45: 1834–1837.
Hirsch, R.M. 1988. Statistical methods and sampling design for estimating step trends in
surface water quality. Water Resour. Res. 24: 493–503.
Hirsch, R.M., Alexander, R.B. and Smith, R.A. 1991. Selection of methods for the
72
detection and estimation of trends in water quality. Water Resour. Res. 27: 803–
813.
Hindar, A. and Henriksen, A. 1998. Mapping of critical loads and critical load
exceedances in the Killarney Provincial Park, Ontario, Canada. NIVA-rapport:
3889.
Hodson, M.E., Langan, S.J. and Wilson, J. 1997. A sensitivity analysis of the PROFILE
model in relation to the calculation of weathering rates. Appl. Geochem. 11: 835-
844.
Holmqvist, J. 2001. Modelling Chemical Weathering in Different Scales. Doctoral
dissertation. Lund University Sweden. pp. 98.
Hornbeck, J.W., Smith, C.T., Martin, Q.W., Tritton, L.M. and Pierce, R.S., 1990.
Effects of intensive harvesting on nutrient capitals of three forest types in New
England. For. Ecol. Manage. 30: 55–64.
Horsley, S. B., Long, R. P., Bailey, S. W., Hallett, R. A. and Hall, T. J. 2000. Factors
associated with the decline disease of sugar maple on the Allegheny Plateau. Can.
J. For. Res. 30(9): 1365–1378.
Houle, D.R. Paquin, C. Camire, R. Ouimet, and Duchesne, L. 1997. Response of the Lake
Clair Watershed (Duchesnay, Quebec) to change in precipitation chemistry (1988-
1994). Can. J. For. Res. 27: 1813-1821.
Huntington, T.G., Hooper, R.P., Johnson, C.E., Aulenbach, B.T., Cappellato, R. and
Blum, A.E. 2000. Calcium depletion in a southeastern United States forest
ecosystem. Soil Sci. Soc. Am. J. 64(5): 1845–1858.
Ito, M., Mitchell, M.J., Driscoll, C.T. and Roy, K.M. 2005. Factors affecting acid
neutralizing capacity in the Adirondack Region of New York: a solute mass balance
approach. Environ. Sci. Technol. 39:4076–81.
Jeffries, D.S. and Snyder, W.R. 1983. Geology and geochemistry of the Muskoka-
Haliburton study area. Data Report DR. 83(2).
Jeffries, D.S., Clair, T.A., Dillon, P.J., Papineau, M. and Stainton, M.P. 1995. Trends in
surface water acidification at ecological monitoring sites in southeastern Canada
(1981–1993). Water, Air, Soil Pollut. 85: 457–462.
Jeffries, D.S., Clair, T.C., Couture, S., Dillon, P.J., Dupont, J., Keller, W., McNicol,
D.K., Turner, M.A., Vet, R., and Weeber, R. 2003. Assessing the recovery of
lakes in southeastern Canada from the effects of acid deposition, Ambio 32:
176–182.
73
Jerabkova, L., Prescott, C.E., Titus, B.D., Hope, G.D. and Walters, M.B. 2011. A
meta- analysis of the effects of clearcut and variable-retention harvesting on
soil nitrogen fluxes in boreal and temperate forests. C. J. For. Res. 41(9): 1852–
1870.
Jewett, K., Daugharty, D., Krause, H.H., and Arp, P.A. 1995. Watershed responses to
clear-cutting: effects on soil solutions and stream water discharge in central New
Brunswick. Can. J. Soil Sci. 75: 475–490.
Jeziorski, A., Tanentzap, A.J., Yan, N.D., Paterson, A.M., Palmer, M.E., Korosi, J.B.,
Rusak, J.A., Arts, M.T., Keller, W.B., Ingram, R., Cairns, A. and Smol, J.P.
2014. The jellification of north temperate lakes. Proc. R. Soc. B 282: 20142449.
Johnson, A.H., Anderson, S.B., and Siccama, T.G., 1994. Acid rain and soils of the
Adirondacks. Changes in pH and available calcium: Can. J. For. Res. 24: 193–198.
Jonard, M., Legout, A., Nicolas, M., Dambrine, E., Nys, C., Ulrich, E. and Ponette, Q.
2012. Deterioration of Norway spruce vitality despite a sharp decline in acid
deposition: an integrated perspective. Global Change Biol. 18(2): 711–725.
Jӧnsson, C., Warfvinge, P. and Sverdrup, H. 1995. Uncertainty in predicting weathering
rate and environmental stress factors with the PROFILE model. Water Air Soil
Pollut. 81:1–23.
KBM Forestry Consultants Inc. 2012. French-Severn Forest – Independent Forest Audit
2006–2011 Queen’s Printer for Ontario, pp 44.
Kaarakka, L.M. 2012. The long-term effects of whole-tree harvest at final felling on soil
properties in a Norway Spruce (Picea Abies (L) Karst.) Stand.
Kaushal, S S., Groffman, P.M., Likens, G.E., Belt, K.T., Stack, W.P., Kelly, V.R, and
Fisher, G.T. 2005. Increased salinization of fresh water in the northeastern United
States. Proc. Nat. Acad. Sci. 102(38): 13517–13520.
Keller, W., Dixit, S.S., and Heneberry, J. 2001. Calcium declines in northeastern Ontario
lakes. Can. J. Fish. Aquat. Sci. 58: 2011–2020.
Keller, W. 2007. Implications of climate warming for Boreal Shield lakes: a review
and synthesis. Environ. Rev. 15(NA): 99–112.
Kirkwood, D.E. and Nesbitt, H.W. 1991. Formation and evolution of soils from an
acidified watershed: Plastic Lake, Ontario, Canada. Geochim. Cosmochim. Act.
55(5): 1295–1308.
74
Korosi, J.B., Burke, S.M., Thienpont, J.R. and Smol, J.P. 2012. Anomalous rise in algal
production linked to lakewater calcium decline through food web interactions.
Proc. R. Soc. B. 279: 1210–1217.
Koseva, I.S., Watmough, S.A. and Aherne, J. 2010. Estimating base cation weathering
rates in Canadian forest soils using a simple texture-based model.
Biogeochemistry 101(1-3): 183–196.
Kothawala, D.N., Watmough, S.A., Futter, M.N., Zhang, L. and Dillon, P.J. 2011.
Stream nitrate responds rapidly to decreasing nitrate deposition. Ecosyst. 14(2):
274–286.
Lambert, M.-C.; Ung, C.-H. and Raulier, F. 2005. Canadian national tree aboveground
biomass equations. Can. J. For. Res. 35: 1996–2018.
Langen, T.A., Twiss, M., Young, T., Janoyan, K., Stager, J.C., Osso Jr., S., and Green, B.
2006. Environmental impact of winter road management at the Cascade Lakes and
Chapel Pond. Final Report Clarkson Center for the Environment. New York State
Department of Transportation.
Lento, J., Dillon, P.J. and Somers, K.M. 2012. Evaluating long-term trends in littoral
benthic macroinvertebrate communities of lakes recovering from acid deposition.
Environ. Monitor. Assess. 184(12): 7175–7187.
Lucas, R.W., Holmström, H. and Lämås, T. 2014. Intensive forest harvesting and pools
of base cations in forest ecosystems: A modeling study using the Heureka decision
support system. For. Ecol. Manage. 325: 26–36.
MacDonald, J.S., MacIsaac, E.A. and Herunter, H.E. 2003. The effect of variable-
retention riparian buffers on water temperatures in small headwater streams in
sub-boreal forest ecosystems of British Columbia, Can. J. For. Res. 33: 1371–
1382.
Malcolm, I.A., Gibbins, C.N., Fryer, R.J., Keay, J., Tetzlaff, D. and Soulsby, C.
2014. The influence of forestry on acidification and recovery: insights from
long-term hydrochemical and invertebrate data. Ecol. Ind. 37: 317–329.
McLaughlin, S.B. and Wimmer, R. 1999. Tansley review no. 104. Calcium
physiology and terrestrial ecosystem. New Phytol. 142(3), 373–417.
McLaughlin, J.W. 2014. Forest Soil Calcium Dynamics and Water Quality:
Implications for Forest Management Planning. Soil Sci. Soc. Am. J. 78(3):
1003–1020.
75
Monteith, D.T., Stoddard, J.L., Evans, C.D., de Wit, H.A., Forsius, M., Høgåsen, T. and
Vesely, J. 2007. Dissolved organic carbon trends resulting from changes in
atmospheric deposition chemistry. Nature 450(7169): 537–540.
Monteith, D.T., Evans, C.D., Henrys, P.A., Simpson, G.L. and Malcolm, I.A. 2014.
Trends in the hydrochemistry of acid-sensitive surface waters in the UK 1988–
2008. Ecol. Ind. 37: 287–303.
Muskoka Watershed Council. 2014. Muskoka Watershed Report Card 2014.
http://www.muskokawatershed.org/stewardshipworks/
Natural Resources Canada. 2014. Estimation of biomass and its nutrients content
online calculator. https://apps-scf-cfs.rncan.gc.ca/calc/en/calculateur-calculator
Natural Resources and Values Information System (NRVIS) 2009. Universal
Transverse Mercator (UTM) zone 17.
https://uwaterloo.ca/library/geospatial/collections/canadian- geospatial-data-
resources/indexes-natural-resources-and-values-information-system
Neary, D.G., Ice, G.G. and Jackson, C.R. 2009. Linkages between forest soils and
water quality and quantity. For. Ecol. Manage. 258(10): 2269–2281.
Nicolson, J.A., Foster, N.W., and Morrison, I.K. 1982. Forest harvesting effects on
water quality and nutrient status in the boreal forest. Proc. Can. Hydrol.Symp.
82. Hydrological Processes of Forested Areas. Ottawa: National Research
Council of Canada: 71-89.
O’Connor, E.M., Dillon, P.J., Molot, L A. and Creed, I.F. 2009. Modeling dissolved
organic carbon mass balances for lakes of the Muskoka River Watershed. Hydrol
Res. 40: 273–290.
Ontario Ministry of Natural Resources. 2004. Forest Resources Inventory Planning
Composite Inventory [shapefile]: Ontario Ministry of Natural Resources,
Peterborough, ON.
Ontario Ministry of Natural Resources (various years). Natural Resource Values
Information System Structured Data Classes [shapefile]: Ontario Ministry of Natural
Resources, Peterborough, ON.
Palmer, M.E., Yan N.D., Paterson A.M., and Girard R.E. 2011. Water quality changes in
south-central Ontario lakes and the role of local factors in regulating lake response to
regional stressors. Can. J. Fish. Aquat. Sci. 68: 1038–1050.
Pardo, L.H., Fenn, M.E., Goodale, C.L., Geiser, L.H., Driscoll, C.T., Allen, E.B. and
76
Dennis, R.L. 2011. Effects of nitrogen deposition and empirical nitrogen critical
loads for ecoregions of the United States. Ecol. App. 21(8): 3049–3082.
Paré, D. Bernier, Y. Lafleur, B. Titus, B.D. Thiffault, E. Maynard, D.G. and Guo, X.
2013. Estimating stand-scale biomass, nutrient contents and associated
uncertainties for tree species of Canadian forests. Can. J. For. Res. 43(7): 599–
608.
Paterson, A.M., Winter, J.G., Nicholls, K.H., Clark, B.J., Ramcharan, C.W., Yan, N.D.,
and Somers, K.M. (2008). Long-term changes in phytoplankton composition in
seven Canadian Shield lakes in response to multiple anthropogenic stressors. Can.
J. Fish. Aquat. Sci. 65(5), 846–861.
Pitman R.M. 2006. Wood ash use in forestry–a review of the environmental impacts.
Forestry 79(5): 563–588.
Posch, M., de Smet, P.A.M., Hettelingh, J.-P., and Downing, R.J. (Editors). 2001.
Modelling and mapping of critical thresholds in Europe: status report 2001.
National Institute for Public Health and the Environment (RIVM), Bilthoven.
Prevost, M., Plamondon, A.P. and Belleau, P. 1999. Effects of drainage of a forested
peatland on water quality and quantity. J. Hydrol. 214: 130–143.
Proe, M.F., Cameron, A.D., Dutch, J. and Christodoulou, X.C. 1996. The effect of
whole-tree harvesting on the growth of second rotation Sitka spruce. Forestry
69(4): 389–401.
Ralston, G. 1971. De-icing salts as a source of water pollution. Ontario Ministry of
the Environment, Toronto.
SAS. 2013. SAS JMP 11 Software. SAS Institute, Inc., Cary, NC.
Schindler, D.W., Turner, M.A. and Hesslein, R.H. 1985. Acidification and
alkalinization of lakes by experimental addition of nitrogen compounds.
Biogeochemistry 1(2): 117–133.
Scheitzer, D.L., Sassaman, R.W. and Schallau, C.H. 1972. Allowable cut effect: some
physical and economic implications. J. For. 70(7): 415–418.
Shaw, B., Klessig, L. and Mechenich, C. 2004. Understanding Lake Data. 20 pp.
Stednick, J.D. 1996. Monitoring the Effects of Timber Harvest on Annual Water
Yield. J. Hydrol. 176(1-4): 79–95.
77
Stoddard, J.L., Jeffries, D.S., Lukewille, A., Clair, T.A., Dillon, P.J., Driscoll, C.T.,
Forsius, M., Johnannessen, M., Kahl, J.S., Kellogg, J.H., Kemp, A., Mannio, J.,
Monteith, D.T., Murdoch, P.S., Patrick, S., Rebsdorf, A., Skjelkvale, B.L.,
Stainton, M.P., Traaen, T., van Dam, H., Webster, K.E., Wieting, J., and
Wilander, A. 1999. Regional trends in aquatic recovery from acidification in
North America and Europe. Nature 401: 575–578.
Sullivan, T.J., Webb, J.R., Sayder, K.U., Herlihy, A.T. and Cosby, B.J. 2007. Spatial
distribution of acid-sensitive and acid-impacted streams in relation to watershed
features in the Southern Appalachian Mountains. Water Air Soil Pollut. 182: 57–
71.
Sverdrup, H. and Rosen, K. 1998. Long-term base cation mass balances for Swedish
forests and the concept of sustainability. For. Ecol. Manage. 110(1): 221–236.
Tan, Q. and Wang, W. 2010. Interspecies differences in calcium content and requirement
in four freshwater cladocerans explained by biokinetic parameters. Limnol.
Oceanog. 55: 1426– 1434.
Thimonier, A., Dupouey, J.L. and Le Tacon, F. 2000. Recent losses of base cations from
soils of Fagus sylvatica L. stands in northeastern France. Ambio 314–321.
Tran, P. and Brouse, J. 2009. The Watershed Inventory Project Aquatic Ecosystem
Assessment Technical Report. pp. 64. http://www.muskokawatershed.org/wp-
content/uploads/2011/12/wip_aquatic_technical_report1.pdf
Ung, C.-H., Bernier, P. and Guo, X.-J. 2008. Canadian national biomass equations: new
parameter estimates that include British Columbia data. Can. J. For. Res. 38: 1123–
2232.
Vadeboncoeur, M.A., Hamburg, S.P., Yanai, R.D. and Blum, J.D. 2014. Rates of
sustainable forest harvest depend on rotation length and weathering of soil
minerals. For. Ecol. Manage. 318: 194–205.
Vitousek, P.M., Aber, J.D., Howarth, R.W., Likens, G.E., Matson, P.A., Schindler, D.
W. and Tilman, D.G. 1997. Human alteration of the global nitrogen cycle:
sources and consequences. Ecolog. App. 7(3): 737–750.
Walmsley, J.D., Jones, D.L., Reynolds, B., Price, M.H. and Healey, J.R. 2009. Whole
tree harvesting can reduce second rotation forest productivity. For. Ecol. Manage.
257(3): 1104–1111.
78
Watmough, S.A. and Dillon, P.J. 2002. The impact of acid deposition and forest
harvesting on lakes and their forested catchments in south central Ontario: a critical
loads approach. Hydrol. Earth Syst. Sci. 6(5): 833–848.
Watmough, S.A., and Dillon, P.J. 2003. Base cation and nitrogen budgets for a mixed
hardwood catchment in south-central Ontario. Ecosystems 6: 675–693.
Wamough, S.A., Aherne, J. and Dillon, P.J. 2003. Potential impact of forest harvesting
on lake chemistry in south-central Ontario at current levels of acid deposition. Can.
J. Fish. Aquat. Sci. 60: 1095–1103.
Watmough, S.A. and Dillon, P.J. 2004. Major element fluxes from a coniferous
catchment in central Ontario, 1983–1999. Biogeochemistry 67(3): 369–399.
Watmough, S.A., Aherne, J., and P.J. Dillon. 2005. Effect of declining base cation
concentrations on freshwater critical load calculations. Environ. Sci. Technol. 39:
3255–3260.
Watmough, S.A. and Aherne, J. 2008. Estimating calcium weathering rates and future
lake calcium concentrations in the Muskoka-Haliburton region of Ontario. Can.
J. Fish Aquat. Sci. 65: 821–833.
Watmough, S.A. and Phillips, T. 2011. Potential impacts on lake chemistry in
northwestern Ontario from using woody biomass for electricity production in the
Atikokan Generating Station. Report. pp. 17.
Warfvinge, P. and Sverdrup, H.1992. Calculating critical loads of acid deposition
with PROFILE-a steady-state soil chemistry model. Water Air Soil Pollut. 63:
119–43.
Westwind Forest Stewardship Inc. (WFSI) Strategic Plan. 2012.
http://www.westwindforest.ca/pdf/Westwind%202012%20Strategic%20Plan%20
-%20Public%20Final%20-%20March%2019%202013.pdf
Wolniewicz, M. B., Aherne, J. and Dillon, P. J. 2011. Acid Sensitivity of Lakes in
Nova Scotia, Canada: Assessment of Lakes at Risk. Ecosystems 14(8): 1249–
1263.
Yanai, R.D., Blum, J.D., Hamburg, S.P., Arthur, M.A., Nezat, C.A. and Siccama, T.G.
2005. New insights into calcium depletion in northeastern forests. J. Forest. 103:
14–20.
Yanai, R.D., Levine, C.R., Green, M.B. and Campbell, J.L. 2012. Quantifying
uncertainty in forest nutrient budgets. J. Forest. 110: 448–456.
79
Yan, N.D., Somers, K.M., Girard, R.E., Paterson, A.M., Keller, W., Ramcharan, C.
W., et al. 2008. Long-term trends in zooplankton of Dorset, Ontario, lakes: The
probable interactive effects of changes in pH, total phosphorus, dissolved organic
carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877.
Yao, H., McConnell, C., Somers, K.M., Yan, N.D., Watmough, S and Scheider, W.
2011. Nearshore human interventios reverse patterns of decline in lake calcium
budgets in central Ontario as demonstrated by mass‐balance analyses. Water
Resour. Res. 47(6): W06521, 1–13.
Yao, H., J.A. Rusak, A.M. Paterson, K.M. Somers, M. Mackay, R. Girard, R. Ingram And C. McConnell. 2013. The interplay of local and regional factors in generating
temporal changes in the ice phenology of Dickie Lake, south-central Ontario,
Canada. J. Inland Wat. 3: 1–14.
Yue, S., Pilon, P. and Cavadias, G. 2002. Power of the Mann–Kendall and Spearman’s
rho tests for detecting monotonic trends in hydrological series. Journal of
Hydrology 259: 254–271.
Zhang, M. and Wei, X. 2014. Alteration of flow regimes caused by large‐scale forest
disturbance: a case study from a large watershed in the interior of British
Columbia, Canada. Ecohydrology 7(2): 544–556.
80
APPENDIX 2A Table 2A.1: Equations derived from the SSWC model (Henriksen and Posch, 2001) and
the modified derivation equations used in calculations.
Note: Where UCA, UMG, UK, UNA and USO4 are the current concentrations of calcium, magnesium,
potassium and in sodium in μeq m-2 yr -1, BC stands for base cations, * means multiplied by. The F-factor
equation represents the fraction of current base cations present in lakes due to soil acidification (Brakke et al.
1990). Conversion factors (ie. 49.9) were used in the modified equations (Edwards 1986).
81
Figure 2A.1: Lake data sets and methods of analyses. 7 lakes had “0” Ca concentration and were excluded from the SSWC and impact of
harvesting on lake Ca calculations (the latter analyses then having n= 364).
MRW Lake Chemical Data Sets
82
Table 2A.2: Summary statistics (mean, max-min) for lake area, catchment area, lake elevation, runoff and selected deposition and lake chemical variables for all lakes with Ca data within crown land (590), lakes in catchments with harvesting cuts on crown land (371) and two subsets of the former and latter lakes used in analysis. Values of 0 were assumed to be undetectable by equipment at the time.
MRW Lakes
(N)
Quantiles
(%)
Lake Area
(ha)
Lake
Elevation
(m)
Catchment
Area
(ha)
Runoff
(L ha-1)
Lake Ca (mg L–1)
Lake Mg
(mg L–1)
Lake K (mg L–1)
Lake Na (mg L–1)
Lake SO4
(mg L–1)
Lake Cl (mg L–1)
Dep Ca (mg L–1)
Dep Mg (mg L–1)
Dep K (mg L–1)
Dep Na (mg L–1)
Dep S (mg L–1)
590
100.0
99.5
97.5
90.0
75.0
50.0
25.0
10.0
2.5
0.5
0.0
Mean
120638
6401.9
659.91
137.69
48.333
17.980
6.8805
2.3867
0.7219
0.3194
0.1399
114.79
543
536
528
480
422
322
245
196
201
193
179
346
68135
68135
34274
12115
3242.1
737.03
232.72
114.09
57.190
32.239
21.590
4289.6
0.035
0.035
0.034
0.032
0.031
0.028
0.026
0.024
0.023
0.022
0.022
0.028
15.7
12.5
5.70
3.67
2.90
2.20
1.57
1.24
0.78
0.00
0.00
2.43
5.27
2.85
1.39
0.99
0.81
0.60
0.46
0.34
0.24
0.00
0.00
0.66
NA NA
12.0
11.6
10.0
8.00
7.20
6.00
4.66
3.50
2.34
0.00
0.00
6.00
NA
7.17
7.16
7.14
6.88
6.70
6.23
5.90
5.77
5.65
5.58
5.45
6.29
0.88
0.88
0.87
0.86
0.85
0.84
0.82
0.81
0.79
0.78
0.78
0.84
0.44
0.44
0.44
0.44
0.43
0.43
0.42
0.41
0.41
0.41
0.41
0.43
1.07
1.06
1.06
1.06
1.05
1.04
1.03
1.02
1.02
1.01
1.01
1.04
10.0
10.0
9.97
9.89
9.80
9.60
9.39
9.10
9.05
8.97
8.93
9.57
371
100.0 120638 543 68135 0.035 4.43 3.53 1.12 14.9 12.0 26.9 7.01 0.88 0.44 1.07 10.0
99.5 7516.4 536 68135 0.035 4.40 3.53 1.95 13.6 11.9 21.0 7.00 0.88 0.44 1.06 10.0
97.5 930.72 528 34644 0.034 3.96 1.31 0.78 4.51 10.0 6.15 6.88 0.87 0.44 1.06 9.98
90.0 164.99 480 14226 0.033 3.40 1.28 0.55 1.65 8.00 1.64 6.81 0.85 0.44 1.06 9.90
75.0 48.674 422 4759.2 0.032 2.72 0.91 0.45 0.95 7.00 0.52 6.46 0.84 0.43 1.05 9.81
subset of
590 located in
50.0
25.0
15.563
5.3589
326
254
1563.8
375.54
0.029
0.027
2.10
1.48
0.75
0.56
0.38
0.29
0.71
0.56
4.86
3.80
0.24
0.00
6.14
5.90
0.83
0.82
0.43
0.43
1.04
1.04
9.68
9.44
catchments with cuts
10.0
2.5
1.6543
0.5892
201
201
175.64
72.751
0.025
0.024
1.1
0.78
0.43
0.00
0.20
0.00
0.45
0.00
2.95
0.00
0.00
0.00
5.80
5.73
0.81
0.79
0.42
0.41
1.03
1.02
9.14
9.05
0.5 0.2270 193 34.321 0.023 0.00 0.00 0.00 0.00 0.00 0.00 5.70 0.79 0.41 1.02 8.97
0.0 0.2258 179 28.589 0.023 0.00 0.00 0.00 0.00 0.00 0.00 5.69 0.78 0.41 1.01 8.97
Mean 157.97 357 5834.2 0.029 2.14 0.61 0.37 1.04 5.26 0.80 6.21 0.83 0.43 1.04 9.61
83
MRW Lakes
(N)
Quantiles
(%)
Lake Area
(ha)
Lake
Elevation
(m)
Catchment
Area (ha)
Runoff
(L ha-1)
Lake Ca (mg L–1)
Lake Mg (mg L–1)
Lake K (mg L–1)
Lake Na (mg L–1)
Lake SO4
(mg L–1)
Lake Cl (mg L–1)
Dep Ca (mg L–1)
Dep Mg (mg L–1)
Dep K (mg L–1)
Dep Na (mg L–1)
Dep S (mg L–1)
177 Ca ≤2
mg L–1
subset of 371
100.0
99.5
97.5
90.0
75.0
50.0
25.0
10.0
2.5
0.5
0.0
Mean
3149.1
3149.1
270.01
65.765
21.378
8.7954
2.8950
0.8494
0.3495
0.2258
0.2258
46.607
518
518
518
495
473
412
374
335
290
290
290
411.6
34695
34695
31131
12116
3351.9
1089.2
284.73
146.61
55.200
28.539
28.539
3665.8
0.034
0.034
0.034
0.033
0.032
0.030
0.028
0.026
0.024
0.023
0.023
0.030
2.00
2.00
2.00
1.86
1.72
1.45
1.22
0.78
0.00
0.00
0.00
1.41
3.53
3.53
0.68
0.57
0.52
0.44
0.35
0.27
0.00
0.00
0.00
0.44
1.12
1212
0.55
0.43
0.38
0.32
0.26
0.20
0.00
0.00
0.00
0.32
2.99
2.99
1.27
0.82
0.69
0.59
0.51
0.40
0.00
0.00
0.00
0.61
8.19
8.19
7.00
5.93
4.99
4.26
3.48
2.61
0.00
0.00
0.00
4.18
3.53
3.53
1.46
0.95
0.40
0.29
0.18
0.00
0.00
0.00
0.00
0.39
6.89
6.89
6.85
6.65
6.22
6.01
5.85
5.79
5.72
5.69
5.69
6.09
0.88
0.88
0.86
0.85
0.84
0.83
0.82
0.81
0.80
0.79
0.79
0.83
0.44
0.44
0.44
0.44
0.44
0.44
0.44
0.42
0.41
0.41
0.41
0.43
1.06
1.06
1.06
1.01
1.05
1.04
1.04
1.03
1.02
1.02
1.02
1.04
10.0
10.0
9.92
9.84
9.80
9.63
9.48
9.38
9.05
9.05
9.05
9.61
75 with
100.0 8598.9 528 24541 0.035 12.2 2.91 1.32 37.5 10.1 51.6 6.91 0.86 0.44 1.06 10.0
99.5 8598.9 528 24541 0.035 12.2 2.91 1.32 37.5 10.1 51.6 6.91 0.86 0.44 1.06 10.0
97.5 3239.8 496 22593 0.034 9.29 2.68 1.24 25.3 7.61 36.3 6.91 0.86 0.44 1.06 9.97
90.0 980.59 454 4213.0 0.033 5.62 1.78 0.75 6.41 5.23 10.8 6.74 0.86 0.44 1.06 9.87
75.0 396.34 365 1689.9 0.030 3.36 0.96 0.62 3.27 4.35 5.94 6.65 0.85 0.43 1.05 9.74
50.0 77.751 320 471.09 0.028 2.28 0.59 0.47 1.22 3.91 1.07 6.41 0.84 0.42 1.04 9.46
2011- 2012 25.0 25.659 248 182.96 0.025 1.46 0.43 0.35 0.76 3.20 0.24 6.30 0.84 0.42 1.03 9.25
data 10.0 13.430 227 66.898 0.024 1.16 0.33 0.25 0.58 2.55 0.16 5.77 0.82 0.41 1.02 9.05
2.5 10.362 200 37.594 0.020 0.88 0.29 0.12 0.46 1.67 0.06 5.71 0.81 0.41 1.02 9.01
0.5 8.9304 194 28.539 0.019 0.78 0.24 0.05 0.21 0.65 0.01 5.70 0.80 0.41 1.02 8.97
0.0 8.9304 194 28.539 0.019 0.78 0.24 0.05 0.21 0.65 0.01 5.70 0.80 0.41 1.02 8.97
Mean 394.37 324 1977.4 0.0277 2.81 0.83 0.50 3.21 3.95 4.47 6.38 0.84 0.42 1.04 9.49
84
Figure 2A.2: Percent of lakes (N=590) sampled by sampling year. Approximately 15% of the lakes were sampled in 2011 or 2012.
85
3. Evaluating the effects of liming and wood-ash treatment on forest ecosystems
through systematic meta-analysis
As published in The Canadian Journal of Forest Research 44: 867–
885 (2014) dx.doi.org/10.1139/cjfr-2013-0488
3.1 Abstract
Liming and wood-ash addition have long been used to attenuate the effects of
acidic deposition on forest soils with the goal of promoting tree growth. Quantitative
meta-analyses of treatment studies from managed forest ecosystems was performed in
order to assess general tendencies of effects of treatment on seven selected measures of
performance thought to reasonably reflect the effects of Ca-addition treatment. Over
350 independent trials from 110 peer-reviewed liming and wood-ash addition studies
were retrieved that were integrated to determine: soil pH, base saturation (BS), tree
foliar Ca concentration, tree growth, ectomychorrhizae root colonization, soil C/N ratio
and microbial indices. The results were quantified through three separate meta-analysis
effect size metrics: unweighted relative values and two weighted metrics, Hedges’d and
lnR. A surprising number of treatment trials (22-85%) reported no significant effect
and soil pH, and foliar Ca appeared more responsive to lime studies compared with
wood-ash, while BS and tree growth appeared more responsive to wood-ash addition.
For six of the seven parameters, estimated mean effect sizes were similar in magnitude
and positive in direction for all three meta-analysis metrics. Regression tree optimal
models explained: 38% of the variation in pH, 47% of the variation in BS, 51% of the
variation in foliar Ca concentration and 26% of the variation in tree growth. The largest
predictors of effect size, within our selected group, were as follows; for pH - soil type;
for base saturation – soil type, trial duration in years and species (hardwood
86
or softwood): for foliar Ca concentration - treatment dose and type and for tree growth -
trial duration, initial soil pH and tree species. This analysis indicates that Ca-additions
are not universally beneficial and provides insight into when Ca-additions to forest soils
are likely to be most effective.
3.2 Introduction
One of the long-term legacies of acid rain has been identified as calcium (Ca)
depletion in soils in Europe and eastern North America (Huntington et al. 1998;
Watmough and Dillon 2003; Jandyl et al. 2004; Yanai et al. 2005). Forest harvesting
can also contribute to Ca losses from soil (Lipas 1985; Olsson et al. 1996); whole tree
harvesting over several rotations for example, has been found (or predicted) to deplete
Ca levels in soil (Nykvist and Rosén 1985; Federer et al. 1989; Vance 1996;
Thimonier 2000; Adams et al. 2000; Keller et al. 2001; Watmough et al. 2003), which
can have a negative effect on tree growth within the second rotation (Proe et al. 1996;
Walmsley et al. 2009). Additionally, an escalating demand for bio-energy fuel by
harvesting slash (woody biomass) has led to concerns over forest sustainability since
the impact on Ca removals is analogous to that of whole tree harvesting, with the
consequential negative effect on tree growth (Rosenberg and Jacobson, 2004; Ågren et
al. 2010). Ca-addition, through lime and wood-ash treatment has been used for forest
soil amelioration with the goals of reducing soil acidity, increasing Ca concentrations
in trees, and improving tree growth with the most commonly used measures for
gauging the success of these goals being pH, Ca foliar concentration and various tree
growth metrics.
The use of lime to reduce the natural acidity of forest soils was first
87
recommended in Germany over 100 years ago (Messmer 1959). Forest liming
experimentation escalated with research revealing that Ca concentration in soil was
strongly connected with forest fertility and productivity (Viro 1951; Lipas 1985).
Comprehensive liming experiments aimed at improving forest growth began in Finland
and Sweden during the 1950s and 1960s (Tamm 1974; Derome et al. 1986) and in the
1980s, lime was used to mitigate soil acidification caused by the effects of acid rain
(Derome et al. 1986; Huettl and Zoettl 1993; Nilsson et al. 2001). Liming treatments
differ in types, mixtures, grain sizes, solubility and doses (Lundström et al. 2003). The
two most commonly used buffering compounds utilized in liming research have been
calcite (calcium carbonate, limestone, CaCO3) with a pH ~12.5 and dolomite
(CaMg(CO3)2), which has a slightly higher buffering capacity (Sverdrup 1985).
However, Ca can also be added as wollastonite (CaSiO3), gypsum (CaSO4), calcium
nitrate (Ca(NO3)2), and calcium chloride (CaCl2).
Wood-ash has also been used to address forest nutritional deficiencies caused by
acid deposition and whole-tree harvesting (Cronan and Grigal 1995; Olsson et al. 1996;
Demeyer 2001). While the removal of harvest residues for biofuels is garnering much
attention in a renewable energy context, recycling of the ash produced has growing
potential for use as a nutrient input in forest systems (Pitman 2006). In wood-ash, Ca is
usually in a CaCO3 form, which explains the liming effect of wood-ash (Steenari and
Lindqvist 1997). Although the neutralizing capacity of wood-ash is classified as high
with pH values ranging from 8.9 to 13.5 (Demeyer et al. 2001), the Ca concentration of
wood-ash used in soil amelioration is highly variable. It ranges from 13.2 to 92.4%
(Vance 1996), is often unknown or unrecorded and is dependent on the source tree used
88
(Someshwar 1996; Pitman 2006), the amount of bark it contains and the soil properties
from where it originated (Campbell 1990). Calcium is the most plentiful element in
wood-ash, followed by potassium (K), magnesium (Mg), aluminum (Al), iron (Fe), and
phosphorus (P), but there may also be potentially toxic elements such as cadmium (Cd)
and lead (Pb) (Park 2004). The percentage of nitrogen (N) is low due to its
volatilization during wood combustion.
Previous reviews of the effects of liming (Derome 1990; Huettl and Zoettl
1993; Nohrstedt 2001; Formanek and Vranova 2003) and wood-ash addition (Vance
1996; Demeyer et al. 2001; Aronsson and Ekelund 2004; Pitman 2006) have primarily
been narrative evaluations with one wood-ash meta-analysis (Augusto et al. 2008). Of
the liming reviews, three were specifically single-country analysis, and only one
included studies in Europe and North America of both lime and wood-ash (Lundström
et al. 2003). A more recent meta-analysis of fertilization studies in northeastern
deciduous forests evaluated limitation of primary production by N, P and Ca however,
the majority of treatments with Ca included fertilization with N and P (Vadeboncouer
2010). Existing review conclusions varied widely and recommendations were often
ambiguous. Potential complications include: different trial durations (Vance 1996;
Aronsson and Ekelund 2004), divergent site soil conditions with regard to acidity
(Derome et al. 1986; Nihlgård et al. 1988), variability in the initial nutritional status of
the treated stands (Huettl and Zoettl 1993), variations in nutrient demands as a result of
stand age (Hamburg et al. 2003; Vadeboncoeur 2010), variations in site including
rainfall and temperature regimes (Arvidsson and Lundkvist 2002; van der Perre et al.
2012), variations in the solubility of the treatment applied (Fyles et al. 1994; Fransman
89
and Nihlgård 1995), high variability in wood-ash with regard to both the Ca content
(Vance 1996; Pitman 2006) its pH (Demeyer et al. 2001), the role of mycorrhizal fungi
in weathering and nutrient uptake (Lundström et al. 2003) and tree species specific
responses to treatment (Dijkstra and Smits, 2002; Long et al. 2011).
The aim of our research was to quantify the results of Ca-addition, via an
integrated analysis of liming and wood-ash studies in managed forests in Europe and
North America. Systematic meta-analysis was used to determine the effects of Ca-
addition through lime or wood-ash treatment on seven selected measures of
performance; soil pH, base saturation (BS), tree foliar Ca concentration, tree growth,
ectomychorrhizae (ECM) root colonization, soil carbon to nitrogen (C/N) ratio and
microbial indices. Unweighted meta-analyses was used in order to maximise the
number of observations in our study and then compared this result with the calculated
effect sizes for two weighted meta-analyses metrics. Classification and regression trees
can help to explain variation or heterogeneity in response (in our case of a treatment
effect) by splitting variables into increasingly uniform groups (Breiman et al. 1984;
Breiman 1996). Thus, after meta-analysis, regression tree analysis was performed, with
the focus on four main parameters, pH, BS, foliar Ca concentration and tree growth, in
an attempt to identify drivers of response heterogeneity and under what conditions Ca-
amendments were most effective.
3.3 Methods
Study Selection
A search of primary research e-resources in the Web of Science produced 1154
articles from the keyword search “liming” and “forest” and 580 articles with the
90
combination of “wood-ash” and “forest” all published between 1986 and 2012
(approximately the last 25 years to August 2012). Experimental field trials were
selected for inclusion in the analysis if they presented at least one relevant
measurement and if the data from both the control and treatment were reported. Where
only graph results were presented the treatment and control values were ascertained by
hand measuring. In this study “lime” or “liming” refers to all such types of Ca-
addition and “wood-ash” refers to all varieties used and “treatment” encompasses both
types of Ca-addition. As our focus was on Ca, trials that included lime or wood-ash
treatment combined with fertilizer (N or P) addition were excluded, as it was thought
to be difficult to ascertain whether an effect (in particular a growth effect) was due to
Ca or instead to N or P addition. The exception to this occurred when the control
received identical N or P fertilization. Whole catchment liming treatments (aerial
spraying that focused on water chemistry, but reported at least one of our soil
parameters) were also included in the database. The trial results for both lime and
wood-ash treatment types were integrated despite being fully aware of their differences
but with the belief that during decision tree analyses it was highly likely that the
treatments would be partitioned out by their Ca dose (which for wood-ash was often
unknown and due to the aforementioned noted high Ca content variation, could not be
reasonably attributed an arbitrary percentage) irrespective of the treatment type. As
many factors and nutrients besides Ca contribute to tree growth, I was also interested
in determining if there were any partitions by treatment type (separations into lime and
wood-ash groups) which could potentially reflect the growth contribution of any
additional nutrients found in wood-ash.
91
Statistical Analysis
Quantitative synthesis - Meta-analysis
Meta-analysis depends upon estimating an effect size (i.e., the magnitude and
direction of the experimental effect) for each independent experiment (Gurevitch et al.
1992, Rosenberg et al. 1997). Meta-analysis have recently been commonly being used
to estimate the effects resulting from similar experimental trials in managed forests and
several different effect size metrics have been used (Augusto et al. 2008;
Vadeboncoeur 2010; Jerabkova et al. 2011; Lucas et al. 2011).
In the face of extensive parameter complexity, a simple approach was chosen.
Seven parameters were selected to quantify via treatment effect size: soil pH, BS, tree
foliar Ca concentration, tree growth (of some considered measure and these various
growth metrics included: mean annual basal area increment, mean annual height
increment, mean annual istem growth increment, mean annual relative growth rate,
mean annual radial increment, above ground net primary productivity and mean
annual dry leaf mass), ectomychorrhizae (ECM) root colonization, soil C/N ratio and
microbial (diversity, richness and abundance) indices. These parameters were chosen
as measures that might reasonably reflect the effect of Ca-addition treatment or have
treatment driven ecological impacts, especially with regard to tree growth.
A meta-analysis approach assumes there is independence among studies
(Hedges et al.1999). To meet this assumption, only measurements from each study
that could be assessed as independent were included in the datasets. Studies with more
than one experimental trial were incorporated into the databases however, when at
least two of the trials were obviously in very similar environmental conditions and
92
used the same treatment, then only the mean values of these trials were included. If
the trials differed by any measured parameter, then the results for each trial presented
within one paper received equal weight with the following noted exceptions. When
several sampling dates were presented in a paper, only the latest date was used unless
different sampling methodologies were followed and when research trials were
presented as a combined average by the author then only that average was included in
analysis. Dunnett’s test was used to test whether any average treatment values
calculated differed significantly from their controls at α = 0.05. Data were analysed
with JMP 10 PRO software (SAS 2011).
Meta-analyses and Effect Size Metrics
Initially an unweighted meta-analyses was performed in order to maximise the
number of observations include in our study and then compared this result with the
calculated effect sizes for two weighted meta-analyses metrics. The majority of
treatment trials included in our database did not present the variance of their results
but it has been demonstrated that a comprehensive meta-analysis can be carried out
even without variance weighting (Gurevitch and Hedges 1999; Augusto et al. 2008)
and therefore the analysis was begun with this inclusive but unweighted approach.
This methodology gives all independent observations equal weight in calculating
overall effect size means and their variance, however, when the main goal of analyses
is to document the direction and magnitude of effects and the heterogeneity between
original studies, a major elimination of studies could reduce the power of the meta-
analysis (Hillebrand and Cardinale 2010). To comprehensively identify causes for
variation in response, outlier trials were not downweighted or excluded from any
93
analyses. Since any level of heterogeneity is acceptable within a meta-analysis,
doing so could exclude important studies that could potentially contribute to the
understanding the processes behind the reported high variation in response to Ca-
addition (Higgins 2008). Heterogeneity, the presence of variation in true effect sizes
underlying the different studies not due to chance (Higgins 2008) was assumed but
later verified in the weighted meta-analyses.
For the unweighted analyses relative values were calculated to normalise a
treatment effect and to attenuate any site effect (Gurevitch and Hedges, 1999).
Relative values were calculated as follows: ((Treatment/Control) −1) % (Gurevitch
and Hedges, 1999; Augusto et al. 2008). For discontinuous, transformed or already
relative variables an absolute relative value, the mean difference, (Treatment −
Control) was used (Augusto et al. 2008). In all cases, no significant treatment effect
(no difference between the effect of the control and the effect of the wood ash or lime
treatment at alpha equal to 0.05) was attributed an effect size value of zero.
To assess the sensitivity of the results of our unweighted meta-analyses, two
other effect- size metrics commonly used in ecological evaluations were calculated;
Hedges’ d, which informs a weighted meta-analysis and lnR, the natural log of the
response ratio (R), both following the methodology of MetaWin 2.0 software
(Rosenberg et al. 2000). Means and confidence intervals for the average effects of our
seven parameters were estimated using a random model and standard, weighted meta-
analytic parametric methods (Gurevitch and Hedges 1999). Subsequently, the calculated
weighted results were compared to those of the initial unweighted meta-analyses to
assess the sensitivity of effect size magnitude and direction and then proceeded to
94
identify causes for heterogeneity in the effect size results. The summary statistics
required for both Hedges’ d and lnR, are the sample size, mean, and standard deviation.
The calculation of the commonly used, weighted meta-analysis effect metrics, Hedges’
d, has been comprehensively described in recent research (Jerabkova et al. 2010) as has
that of lnR (Vadeboncoeur 2010).
In the calculation of both Hedges’ d and lnR, the trials that did not report
standard deviation in our database (or a measure of variance that could be used to
derive standard deviation using the MetaWin statistical conversion calculator), were
assessed an estimated standard deviation using a median coefficient of variation
(Jerabkova et al. 2011). It is acknowledged that the use of such estimated variance,
carries uncertainty along with it. For the meta-analyses using the natural logarithm of
the response ratio (R) as the effect size, the ratio of percentage change in a parameter in
the treatment group to that of the control was calculated (Hedges et al. 1999). All
database values were expressed as factors with control values set at one, which allowed
for the inclusion of zero effect trials (as both the control and treatment would equal
one). Trials with means differing in sign could not be ln transformed and thus those
trials were omitted from the analysis (Rosenberg et al. 2000). To express the grand
mean response ratio and its confidence limits, the results were back transformed and
considered significant at α = 0.05 if the 95% confidence interval (CI) did not overlap a
value of one (Rosenberg et al. 2000). In addition to the uncertainty associated with
assessing estimated variance, it should be acknowledged that eliminating trials with
differing signs could result in increased overall effect sizes in meta-analysis results
using the ln response ratio metric (Rosenberg et al. 2000).
95
However, one advantage to using a log value is that it can reduce the statistical
weight of outliers or aberrant values of effect size (Hedges et al. 1999). Heterogeneity
was pre-supposed in all meta-analyses and accordingly, for both MetaWin effect
metrics, the the random effects meta-analysis model was chosen, which assumes study
results vary because there are real differences (heterogeneity) between studies
(Rosenberg et al. 2000). Heterogeneity was determined by the measure Q total where
the probability (chi square) <0.05 established heterogeneity with the implication that
there was more variation between studies than could be attributed to sampling error
(Rosenberg et al. 2000). To estimate the range of uncertainty for mean effect sizes,
resampling was performed in MetaWin, using 2,999, iterations to calculate bias-
corrected bootstrap 95% confidence limits as recommended. Rosenthal's Method was
used to calculate fail-safe numbers, which are values that indicate the number of non-
significant unpublished studies that would have had to be added to a meta-analysis to
change the summary mean effect results from significant to non-significant (Rosenthal
1979). If this fail-safe number is large in comparison to the number of trials in the
meta-analysis then the effect size magnitude can be regarded as a reliable
approximation of the true effect, even with the some publication bias (Rosenberg et al.
2000). A more intensive discussion of the statistics behind the three meta- analyses
models utilized is beyond the scope of this paper, but has been provided in Hedges and
Olkin (1985), Gueritch et al. (2001) and Rosenberg et al. (2000).
Data Mining - Recursive Partitioning Decision Tree Analysis
Recursive partitioning of datasets has three main attributes that were beneficial
to this analysis. Data mining can handle large datasets, is good for exploring
96
relationships without having a prior model and the results are very interpretable. The
four parameters with sample sizes greater than 50 were partitioned and with significant
positive treatment effects (pH, BS, foliar Ca concentration and tree growth), as it was
felt that the analyses would be plausible at this sample size level (SAS 2011). A
decision tree analysis of the soil C/N ratio was not included as the treatment effect size
was found to be either negligible or not significant. Seven potential key drivers of the
four selected parameters of performance were selected and recorded for each treatment
trial and included both categorical and continuous variables. These predictors
included: time since treatment (years), treatment (wood-ash or lime), dose (≤5,000 kg
ha–1or >5000 kg ha–1of treatment type), initial soil pH (<4.5, 4.5-6, >6), soil type
(organic or mineral), species (hardwood or softwood) and stand age at treatment (0-10,
>10-50, >50 years).
All results were initially entered and analysed as absolute values however during
the decision tree analysis each result was automatically attributed to a specified cluster
(i.e. <4.5, 4.5-6, >6) that was solely determined through recursive partitioning.
Therefore, after regression analysis, I assigned these automatically separated categories
as the relevant driver ranges. “Time since treatment” was the trial length in years and
“treatment” included all forms and methods of dispersion while the “dose” was based
on the treatment in kg per ha and not the Ca content of the treatment, which was highly
variable and often not provided (very few studies involving wood ash actually
determined the Ca concentration). To further clarify, soil type for the pH and BS effect
was determined to be either “organic” or “mineral” by horizon and the few samples
that were classified in an article by the researcher as mixed (i.e. 0-20 cm) were
97
included in the “mineral” category after initial decision tree analysis partitioned them
into the mineral soils group. In contrast, for Ca foliar concentration and tree growth
effects, the distinction between organic and mineral soils was based on the parent soil
characterisation (peaty, organic soil versus mineral soil). When pH-CaCl 2 was used in
a trial instead of pH-H2O, an estimated conversion value was calculated by using an
appropriate regression equation (Van Lierop 1981) which is a more accurate way of
converting data than by adding or subtracting a constant. JMP 10 PRO software (SAS
2011) was used to recursively partition data via the Decision Tree method, which can
incorporate both qualitative and quantitative data. Both the minimum split size and
the K-fold cross validation were set at five (SAS 2011).
Subsequent partitioning evaluations of the datasets by Bootstrap Forest were
made to address any uncertainty associated with the optimal model produced via
Decision Tree analysis. Although both predictive models identify dominant, effect
driving variables, the Decision Tree produces the optimal combination while Bootstrap
Forest utilizes coverage optimization with many decision trees. The minimum splits
per tree and the minimum split size reflected those of the four associated Decision
Trees. For both Decision Tree and Bootstrap Forest, an analysis of the column
contributions would divulge which factors were the major predictors of the effect size
results.
3.4 Results
Study Selection
The study included data from over 350 independent treatment studies found
within 110 peer-reviewed published articles. The studies were largely from temperate
98
climates within Europe and to a much smaller degree, eastern North America and were
published between 1986 and 2012 (Appendix 3A, Table 1). Only three articles had
initial soil pH > 6 and subsequently, that category was excluded from analysis due to
the possibility of large bias occurring as a result of small sample size (Gurevitch and
Hedges 1999). Summary statistics were calculated for soil pH, BS, tree foliar Ca, tree
growth and soil C/N ratio effect sizes with the treatments synthesized (lime and wood-
ash trials combined) and separated for comparison purposes (Table 3.1). Two
parameters had insufficient sample sizes to reasonably split results by treatment and
therefore all results for ECM root colonization and microbial indices were based on
synthesizing the lime and wood-ash treatments within their datasets. Effect sizes were
cautiously calculated for these two latter parameters, as the bias could potentially be
large with small sample sizes.
Average Effect Size
With regard to magnitude and direction, the three effect size metrics utilized
generated the same qualitative results for six of the seven parameters evaluated with
significant, positive, large effects for pH, BS, tree foliar Ca, tree growth and ECM root
colonization and no significant difference in effect between the control and treatment for
the soil C/N ratio (Table 3.1, 3.2). Thus it was demonstrated that the weighted Hedges’d
and lnR effect sizes supported the initial unweighted meta-analysis results with the back
transformed R being the metric most comparable to the unweighted analyses effect size
(Table 3.2). Soil pH and foliar Ca appear more responsive to lime studies compared
with wood-ash while BS and tree growth appear more responsive to wood-ash addition.
Sample sizes for ECM root colonization and microbial indices were too small to
meaningfully evaluate by treatment. In the evaluation of microbial indices effect sizes,
99
R, although very large due to the exclusion of five studies with differing control and
treatment effect signs, was not significant (Table 3.2). The exclusion of 20% of the
microbial indices studies suggests lnR is an inappropriate effect size for this parameter.
An evaluation of the robustness of the effect size results was calculated by Rosenthal’s
method in the Hedges’d and lnR meta-analysis and resulted in large fail-safe numbers
for all parameters (Table 3.2). Hedges’d and lnR, also tested for heterogeneity and
significant heterogeneity was demonstrated in the responses in all seven of our datasets
(Table 3.2).
Zero Effect Frequency
The percentage of treatments (lime and wood-ash integrated) that reported no
significant treatment effect when compared with their control ranged from 22 to 85 %
(Table 3.1, Fig. 3.1). Approximately three-quarters of treatment trials exhibited
positive effects on BS, while two-thirds of trials indicated positive effects on soil pH,
foliar Ca concentration and microbial indices. About one third of trials showed
positive tree growth (of the combined growth measures) or ECM root colonization
effects and only fifteen percent of trials demonstrated any differing effect from control
on the soil C/N ratio. The results by treatment suggest that lime treatment resulted in
more positive effects for pH and foliar Ca concentration.
100
Table 3.1: Summary of mean, unweighted effect size, zero effect and sample size per parameter. In
addition to the pooled (combined) results, soil pH, Ca foliar nutrition and tree growth (of the
combined growth measures) were assessed by treatment type (lime or wood-ash).
*Note: Zero Effect is the percentage of treatments in which effect size was equal to 0 (treatment effect did not
significantly differ from control effect at α = 0.05).
101
Table 3.2: Mean effect sizes of Ca addition using the weighted effect size Hedges' d and the back-
transformed natural log of the response ratio (R) in comparison with the unweighted relative
values (listed in this order for each parameter)
Effect Size
Parameter
Effect
Direction
Hedges’ d
R
(Unweighted
relative value)
Mean
Effect Size
Hedges’ d
R
(Unweighted
relative value)
Bootstrap
Confidence
Interval
Hedges’ d
R
(Unweighted
relative value)
Heterogeneity
chi –square
(p)
Hedges’ d
R
(Unweighted
relative value)
Rosenthal’s
Number
Hedges’ d
R
(Unweighted
relative value)
N
Hedges’ d
R
(Unweighted
relative
value)
Soil
pH-H2O
+
+
(+)
1.28
1.16
(0.68)
1.07 – 1.43
1.14 – 1.19
0.0005
<0.0001
39916
267752
250
250
250
Base
Saturation
+
+
(+)
1.46
1.28
(23.6)
1.06 – 2.34
1.22 – 1.34
<0.0001
<0.0001
15979
149924
79
79
79
Ca Foliar
Nutrition
+
+
(+)
2.19
1.38
(36.5)
1.56 – 2.90
1.26 – 1.51
<0.0001
<0.0001
5652
190369
55
55
55
Tree
Growth
(All
Measures
Combined)
+
+
(+)
0.59
1.20
(30.8)
0.35 – 0.90
1.07 – 1.40
<0.0001
<0.0001
2405
16193
121
121
121
ECM Root
Colonization +
+
(+)
1.28
1.18
(20.5)
0.29 – 2.94
1.03 – 1.43
0.04
0.01
403
371
10
10
10
Soil C/N 0
0
(-)
0.03
0.99
(-0.88)
-0.73 – 0.87
-0.01 – 1.00
0.0004
0.0066
0.00
0.00
67
67
67
Microbial
Indices
+
0
(+)
1.26
1.73
(9.96)
0.35 – 3.42
0.98 – 2.46
0.03
0.04
102982
189752
26
21
26
Note: Significant effects (p < 0.05) are presented in bold type; mean effect size gives the magnitude and direction of the
parameter effect; Bootstrap confidence intervals were bias-corrected; the unweighted relative value is from the initial meta-
analysis; for the effect metric Hedges' d, if the confidence intervals include 0, d is not significant at = 0.05; for the response ratio R, if confidence intervals overlap 1, R is not significant at = 0.05; heterogeneity 2 with p < 0.05 means that effects within
a category were significantly heterogeneous; Rosenthal's method gives the “fail-safe results”, the results of the file-drawer
analysis; N is the number of treatment trials.
102
Recursive Partitioning of Effect Sizes – Decision Tree Analysis
pH Effect Size Regressive Analysis
The optimal decision tree model for soil pH effect size explained 38% of the
variation in results with a RMSE of 0.63 pH units (Fig. 3.2). Soil type (by horizon) was
the largest predictor of effect size with organic soil horizons exhibiting a mean increase
of 1.04 pH units (SD = 0.83) over the control, compared with a 0.36 pH units increase
(SD = 0.60) for mineral soil horizons. Within organic soils, lime had a greater effect than
wood-ash and young stands (<50 years) treated with lime exhibited the greatest mean
effect (1.68 pH units, SD = 0.58). Wood-ash had a greater effect on organic soil pH
when soil conditions were more acidic (pH<4.5). In mineral soils the greatest effect
(0.64 pH units, SD = 0.70) was found in acidic soils (pH<4.5) that received the larger
treatment application (>5000 kg ha-1). At lower application rates (≤ 5000 kg ha-1) the
effect on pH was greatest when measured during the first 4 years post treatment addition.
Base Saturation
The optimal regression tree model for BS effect size explained 47% of the
variation in results with a RMSE of 14.8 (Fig 3.2) with the major predictors of effects
indicated as soil type, time since treatment and tree species (Fig. 3.5). The largest
increase in BS (42.4%, SD = 14.1) was found in organic soils of softwood stands where
the sample period was ≥10 years after treatment while hardwood stands had their largest
increase (33.8%, SD=20.2) at higher doses of treatment. The largest increase in BS in
mineral horizons (33.4%, SD = 19.9) occurred in wood-ash treatments ≥10 years after
application.
103
Ca Foliar Concentration Effect Size Regressive Analysis
The optimal decision tree model for foliar Ca concentration effect size was the
only model split initially by treatment (lime or wood-ash) and explained 51% of the
variation in results (Fig. 3.3) with the major predictors of effects indicated as treatment
dose and treatment type (Fig. 3.5). Limed stands exhibited a mean increase in foliar
Ca concentration effect size of 48.5% (SD = 39.6) over control, compared with just a
13.8% increase (SD = 17.5) for stands treated with wood-ash. Within limed stands,
those with the higher treatment (>5000 kg ha-1) had a greater mean foliar Ca
concentration increase (74.3%, SD = 47.4) than those with lower application rates (≤
5000 kg ha-1) (32.1%, SD = 22.3), and hardwood stands treated with lime at doses
>5000 kg ha-1 exhibited the greatest mean increase in foliar Ca concentration, (92.6 %,
SD = 35.6).
Tree Growth Effect Size Regressive Analysis
The optimal decision tree model for tree growth (of the combined growth
measures) explained only 26% of the variation in results with a RMSE of 69.3 % (Fig.
3.4) with the major predictors of effects indicated as the number of years since treatment,
the initial soil pH and tree species (Fig. 3.5). Tree growth showed the highest variation in
effect for the trials included in this research with 8% of treatments having negative
growth effects, 61% having no effect and the remaining 31% demonstrating positive
growth effects. Both softwood and hardwood stands demonstrated their largest mean
104
increases in growth on soils where the initial soil pH was 4.5-6. However, softwoods on
soils with pH >4.5, where tree growth was measured more than 10 years after treatment
application, had the highest overall mean growth increase over control (116%).
When softwood stands treated with wood-ash over differing trial durations were
analysed the results dramatically illustrated the importance of study length in detecting
and assessing mean growth effects (Fig. 3.6). For sites with initial low soil pH (<4.5),
hardwoods showed a greater growth increase than softwoods, especially at higher
doses (>5000 kg ha–1).
Uncertainty
Two bootstrap forest analyses were run using coverage optimization to account
for any uncertainty in the optimal recursive partitioning results. The major predictors
of effects for both 100 and 10,000 trees were found to be in good agreement with the
decision tree model for each parameter (Fig. 3.5).
1.1 Discussion
The effect of wood-ash and lime addition on the seven selected forest ecosystem
response variables was found to be highly variable with a surprising number of studies
that showed no significant effect compared with the control. If a mean treatment effect
had been obtained that was consistent across a series of studies from the populations
sampled the major focus could be put on its significance (Borenstein et al. 2009).
However, with the high dispersion around the summary effects, our quantitative results
highlight the tendencies of magnitude and direction and the significant variability in
105
105
Fig. 3.1: Soil pH mean effect size (pH treatment – pH control) optimal decision tree for all trials.
Fig. 3.2: Base Saturation mean effect size (BS treatment – BS control) optimal decision tree for all trials.
106
Fig. 3.3: Foliar calcium concentration mean effect size ((Treatment/Control) −1) %) optimal decision tree
for all trials.
Fig. 3.4: Tree growth (of combined measures) mean effect size ((Treatment/Control) −1) %) optimal
decision tree for all trials.
107
Fig. 3.5: Driver column contributions for pH, foliar Ca concentration and tree growth for
decision tree and bootstrap forest models. Arrows indicate the strongest drivers of positive
effect response to liming or wood-ash treatment.
108
Fig. 3.6: Mean tree growth (of combined measures) effect size ((Treatment/Control) −1) %) of
softwood treated with wood-ash by trial duration time. Database trials were not evenly spread
over the duration span of years, therefore they were separated by effect size responses. The
largest mean increase in growth effect size over that of the mean control effect size occurred
10+ years post-treatment.
109
treatment response that has been noted in previous narrative reviews (Vance 1996;
Formanek and Vranova 2003; Aronsson and Ekelund 2004; Pitman 2005). For the
most part, the sensitivity of the direction and magnitude of the results of our unweighted
meta-analyses were supported by the two weighted metrics calculated. This result is
consistent with the expectation that the more heterogeneous the effect sizes of
independent trials in a dataset are, the closer one approaches a meta-analysis statistical
weighting methodology where every study would have equal weight (Borenstein et al.
2009). Despite the high percentages of our sample studies demonstrating no
significantly differing effects from their controls and the large variability in study
significant effects, the comparable magnitude and direction of all four mean effect size
metrics calculated for pH, BS, foliar Ca and tree growth are robust and substantial
indicators of positive treatment outcomes.
Soil pH
The greater pH increase in organic horizons compared with mineral horizons is
supported by the results of a much more limited narrative review of lime and wood-ash
trials in Europe and North America (Lundström et al. 2003). Overall, organic horizons
of forests less than 50 years old treated with lime demonstrated the largest mean pH
increase. The greater positive response of organic horizons compared with mineral
horizons to lime and wood-ash treatments is in part due to the fact Ca is applied to the
upper organic horizons and takes time to reach the mineral soil and 50% of our studie
were five years or less. Recycling of Ca by forests and low leaching rates are thought to
contibute to much of the added Ca being held in organic horizons rather than leaching
110
into lower mineral horizons (Cho et al. 2011; Morrison and Foster 2001) as
demonstrated in an in situ isotopic tracing experiment where initially tracers were
strongly held in the litter layer but over time were very slowly vertically transferred
down through soil horizons (van der Heijden et al. 2013). This recent research
explains the much greater increase in pH in the mineral soil fifteen years post
application, than when sampled five years post (Moore et al. 2012). In the bootstrap
analyses time since treatment was an important factor in the soil pH response which
has been supported by previous narrative reviews (Vance 1996; Aronsson and
Ekelund 2004; Pitman 2006).
Lime treatment resulted in a larger pH response than wood-ash, which is
expected as the Ca concentration of the lime treatment used would be higher than the
same mass of wood-ash. Although it could not be assumed so, treatment type may have
been indicative simply of treatment dose, therefore an optimal decision tree model
combining those two potentially key drivers (Lime: L≤ 5,000 kg ha–1 and L>5,000 kg
ha–1, Wood-ash: WA ≤ 5,000 kg ha–1 and WA>5,000 kg ha–1) was evaluated to
determine the effect. The largest effect size was obtained in the organic horizon of
limed stands (L>5,000 kg ha–1) 50 years old or younger. Even though the models were
quite similar, I decided to keep treatment and treatment dose as separate parameters as
it wasn’t possible to separate out other influences within treatment type such as
solubility, form, composition or method of dispersal from the influence of dose or even
within the treatment dose category itself. In fact, the effects of wood-ash have been
previously shown to be highly influenced by the solubility and form of the treatment
111
and not just the dose (Demeyer 2001; Nohrstedt 1992; Vance 1996; Eriksson 1998:
Eriksson et al. 1998) however this information was scarce in the research articles
within our study and thus could not be reasonably evaluated. The greater increase in
pH in organic horizons of young stands may possibly be due to thicker organic
horizons or to greater Ca demands of older forests resulting in fewer of the H+ ions in
soil being replaced by Ca. For mineral horizons, the largest increase in pH was for trials
with extremely acidic intial site conditons treated with high doses.
Base Saturation
Even though pH and BS are generally correlated (Beery and Wilding 1971) there
was a higher percentage of positive significant effect responses for soil BS compared
with pH. In this dataset, BS% at a given pH varied widely between soils, a result
which can be supported by a similar pH-base saturation anomaly demonstrated in
acidified forest soils of calcareous and noncalcareous parent material (Blaser et al.
2008). It can be inferred from Blaser’s research that the relationship between pH and
BS in treated acidified soils could be heavily influenced, not only by the chemistry of
soils derived from parent materials rich in base cations, but also by the chemistry of
Ca-addition. This result is also consistent with previous research demonstrating
pH(H2O) accounting for 46-70% of the total variability in BS% with a stronger
relationship found in organic soil horizons than mineral ones (Beery and Wilding
1971). This may be due in part to a potentially larger variability in pH measurements
(than BS) and/or a concurrent increase in protons, as a consequence of increased soil
processes such as nitrification. Similar to the bootstrap analyses of pH, the major
influence on BS response was soil type and the second strongest predictor was time
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since treatment (trial duration). With 27% of trials in the dataset being short-term (four
or less years in duration) it can be assumed that the time required for Ca and Mg to
vertically progress to the mineral horizons was insufficient (van der Heijden et al.
2013) and that the short-term lack of effects on base saturation in the mineral horizon
would not be indicative of long-term effects. In contrast to pH, wood-ash trials
produced a larger BS effect response than lime, which may be due to the solubility of
the varying treatments. The most soluble nutrient in wood-ash is K, followed by Ca and
Mg and it has been established that after the application of wood ash to a stand, K
concentrations increase as a result of both the high content and the high solubility of K.
In contrast to wood-ash, after the application of lime the decrease of acidity in soil
layers may take longer because of lime’s lower solubility (Meiwes 1995). Soil BS
response was also influenced by tree species (hardwood or softwood) in agreement
with well-established differing influences of tree species on soil organic matter,
acidification and cation and anion availability on the soil exchange complex
(Hallbäcken and Tamm 1986; Finzi et al. 1998).
Foliar Calcium Concentration
There was a strong increase in foliar Ca concentration in response to treatment,
which is consistent with narrative reviews (Vance, 1996; Demeyer et al. 2001;
Arvidsson and Lundkvist 2002) and with a previous meta-analysis (Augusto et al.
2008). Treatment dose and treatment type (wood-ash or lime) were the strongest
drivers of increased Ca foliar concentration effects. Although the Ca rate of transport
in the xylem upward to the foliage has been demonstrated to be slow compared to other
nutrients (Augusto et al. 2011), regressive partitioning did not identify trial duration
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time as one of the major drivers of increased foliar Ca concentration over the control.
This was a consequence of the relatively few studies in the Ca foliar database with trial
duration times of less than a year. Foliar Ca was much more responsive to lime
treatments compared with wood-ash and consequently the first partition was by
treatment type. Hardwood trees, treated with lime at the higher dose, demonstrated the
largest mean foliar Ca increase and this result may be due in part to the higher Ca
demands of hardwood tree species compared to softwood (Long et al. 2011). As with
the analysis of pH, it cannot be assumed that treatment was solely a reflection of dose
as we cannot separate out the effects of other treatment attributes such as form, method
of application and solubility. For wood-ash and lower lime applications, young stands
showed a very strong increase in foliar Ca over the controls. Young forests have been
shown to be able to effectively mobilize Ca from soil (Hamburg et al 2003). As well,
young stands are able to mobilize more Ca than they can accumulate in biomass, which
may result in increased concentrations of Ca in the soil exchangeable pool (Johnson et al.
1994), which could augment Ca cycling for decades (Martin et al. 1984) which in turn
could lead to both sustained positive Ca foliar and growth effects from treatment (as
compared to control effects).
Tree Growth
Initial soil pH, tree species (hardwood or softwood) and trial duration time, were
established as the strongest influences (in increasing order of importance) on mean tree
growth. Wood-ash amendments resulted in a greater response in tree growth than lime
for softwoods at the more acidic soil initial pH range. It is highly likely that the increased
growth response of wood-ash trials could be the result of the addition of other nutrients
114
lacking in initial soil conditions such as Mg, P or K (Augusto et al. 2008). However, in
our decision tree and bootstrap analysis, regressive partitioning did not identify treatment
type as one of the major drivers of increased growth over the control.
Both softwood and hardwood stands demonstrated their largest mean increases
in growth on soils where the initial soil pH was 4.5-6. It can be postulated that these
less acidic sites had a greater growth response for the reason that they may have not
been as limited in other nutrients affecting growth as the more stressed acidic sites
(pH<4.5). Soil Ca limitation, as the result of chronic acidification originating from
atmospheric deposition, may limit tree growth and for sites affected in this way, liming
and wood-ash Ca-addition can induce pronounced soil changes but a growth effect
response of trees will not occur if Mg, K, N or P are also limiting (Formanek and
Vranova, 2003; Aronsson and Ekelund, 2004; Pitman 2006). Thus, one limitation of
our assessment was the lack of a holistic assessment of initial soil conditions, a direct
result of the lack of detailed information provided in the majority of included studies
concerning the initial soil conditions for all nutrients. Previous studies have shown that
in the long-term there are species-specific responses to Ca-additions. Long et al. (2011)
reported that sugar maple (Acer saccharum Marsh.), growth increased significantly in
response to Ca-addition, whereas American beech (Fagus grandifolia Ehrh.), had no
growth response while black cherry (Prunus serotina Ehrh.) experienced a negative
growth response.
Such species-specific variability in response underscores the complexity behind
tree growth requirements and could potentially explain some of the variability in
effects of hardwoods observed in this dataset. Nontheless, it is probable that the lower
115
mean response in growth, compared with foliar Ca or soil pH, is largely a consequence of
the limited number of longer- term studies available in the literature. The lag time for
considerable tree growth response to wood-ash application highlighted in our quantitative
analysis has been noted as well in narrative evaluations (Vance, 1996: Pitman, 2006).
With our wood-ash dataset it was demonstrated that the short-term effects from treatment
varied widely from the longer time period effects. With 25% of the samples taken less
than 3.5 years after treatment and 50% less than 6 years after treatment, the temporal
variation in the dataset may explain much of the significant heterogeneity in effect size
and a substantial portion of the zero effect results. Specific treatments and site
conditions in our analyses appear to induce optimum growth effects but additional long-
term studies would be advantageous in order to estimate more comprehensive mean
growth effects and to explain more of the variability in the optimal regression and
bootstrap models.
C: N Ratio, ECM Root Colonization and Microbial Indices
Effect sizes were calculated for the C/N ratio, ECM root colonization, and
microbial indices. However, due to either non significant effects, small sample sizes or
a combination of both, a lot of the focus for these three parameters was placed on the
large heterogeneity in effect size results within the datasets.
Carbon to Nitrogen Ratio
The response of the soil C/N ratio to treatment addition was examined as some
studies have suggested the ratio would change (Geissen and Brümmer 1999; Nilsson et
al. 2001; Houle 2002). The soil C/N ratio exhibited little response to lime or wood-ash
addition, however this result does not indicate that independently C and N had no
116
response to treatment, merely that the net effect did not change the response ratio. In
fact, very little is known about the long-term effects of liming and wood-ash treatment
on C and N dynamics, as increases in N may be accompanied by similar increases in C,
with unknown causes (Bauhus et al. 2004). Parn (2004) concluded that even if a
treatment-induced increase in litter decomposition rate existed on site, it would be too
small to tease out from the effects of other factors that influence litter formation and
decomposition.
Ectomycorrhizal Fungi (ECM) Root Colonization
The strong, positive effect results from all three meta-analysis metrics calculated
for ECM root colonization should be viewed cautiously due to the very limited number
of studies found in the literature. However, as ECM fungi has been shown to enhance
plant access to base cations derived from weathering of minerals in soils (Landeweert
et al. 2001) it’s importance in tree growth is crucial. Treatment had a positive effect on
ECM root colonization as indicated by abundance, however, knowledge of the
weathering capacity of mycorrhizae and their responses to lime or wood-ash treatment
is very limited and it has been demonstrated that different species of ECM show large
variation in their uptake capacity of nutrients (Burgess et al. 1993; Wallander, 2000).
Shifts in community composition then, may be more indicative of relevant effect
responses to treatment than the abundance parameter used (Bakker et al. 2000). There
are very few studies on ECM community composition response to treatment but it has
been suggested that shifts in species composition caused by treatments, and, or
harvesting of tree residues for biofuels, could potentially mean a loss of weathering
capacity with impacts on both Ca soil cycling and subsequently on tree nutrition and
117
growth (Mahmood et al. 1999; Kjøller and Clemmensen 2009).
Microbial Diversity, Richness and Abundance Indices
An increase in the pH of lime or wood-ash treated soils generally increases
both microbial biomass and respiration rate (Bååth and Arnebrant, 1994; Fritze et al.
1994). Microbial indices in this synthesis were combined as few treatments were
found for each measure but this may have blurred the results as found in one study
included in this evaluation where abundance decreased with treatment while diversity
increased (Chagnon et al. 2001). The average effect size for microbial indices was
significantly positive for the dataset as calculated by the weighted and unweighted
meta-analyses, with 35% of treatments having no differing effect from the control
but treatment effects ranging widely from -74% to 156%. Of the 26 studies analysed,
only three demonstrated a significantly positive effect of increased microbial biomass
(Kreutzer 1995: Anderson 1998; Zimmerman and Frey 2002). A factor that
constrained our ability to detect significant effects on the back transformed response
ratio for microbial indices was the lower number of observations (N=21 instead of
26), a consequence of the constraints of the lnR analysis.
The majority of studies in our synthesized dataset were short-term, which may
also present a limitation in estimating effect size magnitude as lime applied to the
surface takes some time to reach the mineral soil horizon where microflora biomass
was often sampled. Knowledge of long-term effects of Ca-addition treatment on soil
micoflora was extremely limited in the literature with only one long-term study in our
dataset. This study indicated that microorganism response to treatment was highly
118
dependent on initial site conditions and recommended both benchmarking of existing
conditions before treatment and post treatment monitoring to avoid a loss of microbial
diversity that might ultimately result in a loss of functionality with regard to organic
matter turnover and nutrient cycling (Lorenz et al. 2000).
3.5 Other issues
There are many response parameters that were beyond the focus of this
investigation. In particular, large increases in soil pH have been shown to have
detrimental ecological effects to acidophilic ecosystems, particularly to the soil
bacteria and ectomycorrhizal community structure (Pitman 2006). The effects of Ca-
addition on understory flora have not been extensively studied however, biomass
increases in forbs, changes in plant tissue element concentrations (Pabian et al. 2012)
and shifts in understory species composition have been observed (Falkengren-Grerup
1995) and analyses of the effects on soil fauna have indicated marked shifts both in
species abundance and distribution (Bååth et al. 1980; Abrahamsen 1983) to the point
where liming is considered a disturbance with striking, soil macro-invertebrate,
species-specific responses induced by chemical soil changes (Auclerc et al. 2012;
Moore et al. 2013). An increase in pH, following liming, has been noted to promote
fine root development in the organic soil layers, increasing the danger of frost and
drought damage (Huettl and Zoettl1993; Kreutzer 1995; Formánek and Vranová 2002)
and increasing pH due to liming has increased nitrification, with a resulting risk of
nitrate leaching (Vadeboncoeur 2010 Kreutzer 1995; Huber et al. 2006) and cadmium
biotoxicity may result from the addition of wood-ash made from non- pure wood
residues (Aronsson and Ekelund 2004; Demeyer et al. 2001). It is unknown how the
119
effect sizes and drivers of effects that were demonstrated compare with the effects of
environmental change such as climate warming. Although I attempted to evaluate the
effects of climate through the Koppen-Geiger climate classification system (Kottek et
al. 2006) only two distinct classifications could be identified (Dfb and Dfc) and over
94% of the studies fell within one classification (Dfc) rendering the number of studies
in the second classification too small to meaningfully assess.
3.6 Conclusions
Wood-ash or lime addition is not universally beneficial and there was a large
variation in response of pH, BS, Ca foliar concentration, tree growth,
ectomychorrhizae root colonization, soil C/N ratio and microbial indices, with a
surprising number of Ca-addition treatment trials having no effect measurably different
from that of the control. Approximately three-quarters of treatment trials exhibited
positive effects on BS while two-thirds of trials indicated positive effects on soil pH,
foliar Ca concentration and microbial indices. One third of trials indicated positive tree
growth effects, ECM root colonization effects and only fifteen percent of trials
demonstrated any differing effect from control on the soil C/N ratio. Regression tree
models explained; 38% of the variation in pH, 47% of the variation in BS, 51% of the
variation in foliar Ca concentration and 26% of the variation in tree growth effect
sizes, with the corresponding bootstrap analyses in close agreement. According to
bootstrap analysis of 10,000 trees, the largest predictors of effect size were as follows;
for pH and BS - soil type, for foliar Ca concentration – treatment type and dose, and
for tree growth – trial duration in years, initial soil pH and tree species as denoted by
hardwood or softwood. The key role of temporal variation was highlighted in the
120
decision tree and bootstrap regression analyses for growth, where a long lag time was
demonstrated between when wood-ash was applied and when the largest significant
growth effect response occurred. This research stands as a robust analysis of the
synthesis of treatment effects considered, highlights the critical role of long-term studies
in comprehensively assessing effects and offers suggestions for forest management best
practices that seek to offset Ca losses from continued or enhanced harvesting and acid-
induced leaching.
121
3.7 References
Abrahamsen, G. 1983. Effects of lime and artificial acid rain on the
enchytraeid (Oligochaeta) fauna in coniferous forest. Ecography, 6: 247–254.
doi:10.1111/j.1600- 0587.1983.tb01088.x.
Adams, M., Burger, J., Jenkins, A. and Zelazny, L. 2000. Impact of harvesting
and atmospheric pollution on nutrient deletion of eastern US hardwood forests. For.
Ecol. Manage. 138: 301–319. doi:10.1016/S0378-1127(00)00421-7.
Ågren, A.Å.A., Buffam, I.B.I., Bishop, K.B.K., and Laudon, H.L.H. 2010.
Sensitivity of pH in a boreal stream network to a potential decrease in base cations
caused by forest harvest. Can. J. Fish. Aquat. Sci. 67(7): 1116–1125. doi:10.1139/F10-
052.
Anderson, T.H. 1998. The influence of acid irrigation and liming on the soil
microbial biomass in a Norway spruce (Picea abies (L.) Karst.) stand. Plant Soil, 199:
117–122. doi:10.1023/A:1004224112790.
Aronsson, K.A., and Ekelund, N.G.A. 2004. Biological effects of wood-ash
application to forest and aquatic ecosystems. J. Environ. Qual. 33: 1595–
1605.doi:10.2134/jeq2004.1595.
Arvidsson, H., and Lundkvist, H. 2002. Needle chemistry in young Norway
spruce stands after application of crushed wood-ash. Plant Soil, 238:159–174.
doi:10.1023/A:1014252521538.
Auclerc, A., Nahmani, J., Aran, D., Baldy, V., Callot, H., Gers, C., Iorio, E.,
Lapied, E., Lassauce, A.,Pasquet,A., Spelda, J., Rossi, J.P., and Guérold, F. 2012.
Changes in soil macroinvertebrate communities following liming of acidified forested
catchments in the Vosges Mountains (North-eastern France). Ecol. Eng. 42: 260–269.
doi:10.1016/j.ecoleng.2012.02.024.
Augusto, L., Ranger, J., Ponette, Q., and Rapp, M. 2000. Relationships between
forest tree species, stand production and stand nutrient amount. Ann. For. Sci. 57(4):
313–324. doi:10.1051/forest:2000122.
Augusto, L., Bakker, M.R., and Meredieu, C. 2008. Wood-ash applications to
temperate forest ecosystems - potential benefits and drawbacks. Plant Soil, 306:
181–198. doi:10.1007/s11104-008-9570-z.
122
Augusto, L., Zeller, B., Midwood, A.J., Swanston, C., Dambrine, E., Schneider,
A., and Bosc, A. 2011. Two-year dynamics of foliage labelling in 8-year-old Pinus
pinaster trees with15N, 26 Mg and 42 Ca - simulation of Ca transport in xylem using an
upscaling approach. Ann. For. Sci. 68: 169–178. doi:10.1007/s13595-011-0018-x.
Bååth, E., and Arnebrant, K. 1994. Growth rate and response of bacterial
communities to pH in limed and ash treated forest soils. Soil Biol. Biochem. 26: 995–
1001. doi:10.1016/0038- 0717(94)90114-7.
Bååth, E., Berg, B., Lohm, U., Lundgren, B., Lundkvist, H., Rosswall, T.,
Söderström, B., and Wirén, A. 1980. Effects of experimental acidification and liming
on soil organisms and decomposition in a Scots pine forest. Pedobiologia, 20: 85–100.
Bäckman, J.S., Hermansson, A., Tebbe, C.C., and Lindgren, P.E. 2003. Liming
induces growth of a diverse flora of ammonia-oxidizing bacteria in acid spruce forest
soil as determined by SSCP and DGGE. Soil Biol. Biochem. 35:1337–1347.
doi:10.1016/S0038-0717(03)00213-X.
Bakker, M., Garbaye, J., and Nys, C. 2000. Effect of liming on the
Ectomycorrhizal status of oak. For. Ecol. Manage. 126: 121–131. doi:10.1016/S0378-
1127(99)00097-3.
Bauhus, J., Vor, T., Bartsch, N., and Cowling, A. 2004. The effects of gaps and
liming on forest floor decomposition and soil C and N dynamics in a Fagus sylvatica
forest. Can. J. For. Res. 34(3): 509–518. doi:10.1139/x03-218.
Beery, M., and Wilding, L.P. 1971. The relationship between soil pH and base
saturation percentage for surface and subsoil horizons of selected mollisols, alfisols and
utisols in Ohio. Ohio J. Sci. 71(1): 43–55.
Blaser, P., Graf Pannatier, E., and Walthert, L. 2008. The base saturation in
acidified Swiss forest soils on calcareous and noncalcareous parent material. A pH –
base saturation anomaly. J. Plant Nutr. Soil Sci. 171: 155–162.
doi:10.1002/jpln.200625213.
Borenstein, M., Hedges, L., Higgins, J., and Rothstein, H. 2009. Introduction to
meta-analysis. Wiley, Chichester, UK.
Børja, T., and Nilsen, P. 2009. Long term effect of liming and fertilization on
mychorrhizal colonization and tree growth in old Scots pine (Pinus sylvestris L.)
stands. Plant Soil, 314: 109–119. doi:10.1007/s11104-008-9710-5.
Breiman, L. 1996. Bagging predictors. Mach. Learn. 24(2): 123–140.
doi:10.1007/BF00058655.
123
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. 1984. Classification and
regression trees. Wadsworth, Inc., Monterey, California.
Burgess, T.I., Malajczuk, N., and Grove, T.S. 1993. The ability of 16
ectomycorrhizal fungi to increase growth and phosphorous uptake of Eucalyptus globules
Labill. and E. diversicolor F. Muell. Plant Soil, 153: 155–164. doi:10.1007/BF00012988.
Campbell, A.G. 1990. Recycling and disposing of wood ash. TAPPI J. 73: 141–146.
Chagnon, M., Paré, D., Hébert, C., and Camiré, C. 2001. Effects of experimental
liming on collembolan communities and soil microbial biomass in a southern Quebec
sugar maple (Acer saccharum Marsh.) stand. Appl. Soil Ecol. 17:81–90.
doi:10.1016/S0929-1393(00)00134-7.
Cho, Y., Driscoll, C.T., Johnson, C.E., Blum, J.D., and Fahey, T.J. 2012.
Watershed-level responses to calcium silicate treatment in a northern hardwood forest.
Ecosystems, 15: 416–434. doi:10.1007/s10021-012-9518-2.
Clair, T.A., Aherne, J., Dennis, I.F., Gilliss, M., Couture, S., McNicol, D.,
Weeber, R., Dillon, P.J., Keller, W.B., Jeffries, D.S., Page, S., Timoffee, K., and
Cosby, B.J. 2007. Past and future changes to acidified eastern Canadian lakes: a
geochemical modeling approach. Appl. Geochem. 22: 1189–1195.
doi:10.1016/j.apgeochem.2007.03.010.
Cronan, C. and Grigal, D.1995. Use of calcium/aluminium ratios as indicators
of stress in forest ecosystems. J.Environ.Qual. 24: 209–226.
doi:10.2134/jeq1995.00472425002400020002x.
Demeyer, A. 2001. Characteristics of wood-ash and influence on soil
properties and nutrient uptake: an overview. Bioresource Technol. 77: 287–295.
doi:10.1016/S0960- 8524(00)00043-2.
Derome, J. 1990. Effects of forest liming on the nutrient status of podzolic
soils in Finland. Water Air Soil Pollut. 54: 337–350. doi:10.1007/BF02385229.
Derome, J., Kukkola, M., and Mälkönen, E. 1986. Forest liming on mineral soils,
results of Finnish experiments. National Swedish Environmental Protection Board,
3084: 1–10.
Dijkstra, F.A., and Smits, M.M. 2002. Tree species effects on calcium cycling: the
role of calcium uptake in deep soils. Ecosystems, 5: 385–398. doi:10.1007/s10021-001-
0082-4.
124
Falkengren-Grerup, U., Quists, M.E., and Tyler, G. 1995. Relative importance of
exchangeable and soil solution cation concentrations to the distribution of vascular
plants. Environ. Exp. Bot. 35: 9–15. doi:10.1016/0098-8472(94)00039-8.
Federer, C.A., Hornbeck, J.W., Tritton, L.M., Martin, C.W., Pierce, R.S., and
Smith, T.S. 1989. Long-term depletion of calcium and other nutrients in eastern US
forests. Environ. Manage. 13: 593–601. doi:10.1007/BF01874965.
Finzi, A.C., Canham, C.D., and Van Breemen, N. 1998. Canopy tree soil
interactions within temperate forests: species effects on pH and cations. Ecol.
Appl.8: 447–454. doi:10.1890/1051-0761(1998)008[0447:CTSIWT]2.0.CO;2.
Formanek, P., and Vranova, V. 2003. A contribution to the effect of liming on
forest soils: review of literature. J. For. Sci. 4: 182–190.
Eriksson, H.M. 1998. Short-term effects of granulated wood-ash on forest soil
chemistry in SW and NE Sweden. Scand. J. For. Res. 2: 43–55.
Eriksson, H.M., Nilsson, T., and Nordin, A. 1998. Early effects of lime and
hardened and non-hardened ashes on pH and electrical conductivity of the forest floor,
and relations to some ash and lime qualities. Scand. J. For. Res. 2:56–66.
Ernfors, M., Sikström, U., Nilsson, M., and Klemedtsson, L. 2010. Effects of
wood-ash fertilization on forest floor greenhouse gas emissions and tree growth in
nutrient poor drained peatland forests. Sci. Total Environ. 408: 4580–4590.
doi:10.1016/j.scitotenv.2010.06.024.
Fransman, B., and Nihlgård, B. 1995. Water chemistry in forested catchments
after topsoil treatment with liming agents in southern Sweden. Water Air Soil Pollut.
85: 895–900. doi:10.1007/BF00476943.
Fritze, H., Smolander, A., Levula, T., Kitunen, V., and Mälkönen, E. 1994.
Wood-ash fertilization and fire treatments in a Scots pine forest stand: effects on the
organic layer, microbial biomass and microbial activity. Biol.Fertil.Soils, 17: 57–63.
doi:10.1007/BF00418673.
Fyles, J.W., Côtè, B., Courchesne, F., Hendershot, W.H., and Savoie, S. 1994.
Effects of base cation fertilization on soil and foliage nutrient concentrations, and
litter-fall and throughfall nutrient fluxes in a sugar maple forest.Can. J. For. Res.
24(3): 542–549. doi:10.1139/x94-071.
Geissen, V., and Brümmer, G.W. 1999. Decomposition rates and feeding
activities of soil fauna in decidiuous forest soils in relation to soil chemical parameters
following liming and fertilization. Biol. Fertil. Soils, 29: 335–342.
doi:10.1007/s003740050562.
125
Gurevitch, J., and Hedges, L. 1999. Statistical issues in ecological meta-
analyses. Ecology, 80: 1142–1149. doi:10.1890/0012-
9658(1999)080[1142:SIIEMA]2.0.CO;2.
Gurevitch, J., Morrow, L. L., Wallace, A. and Walsh, J.S.1992. A meta-analysis
of field experiments on competition. American Naturalist 140:539–572.
Gurevitch, J., Curtis, P.S., and Jones, M.H. 2001. Meta-analysis in ecology.
Adv.Ecol. Res. 32: 199–247. doi:10.1016/S0065-2504(01)32013-5.
Haimi, J., Fritze, H., and Moilanen, P. 2000. Responses of soil decomposer
animals to wood-ash fertilisation and burning in a coniferous forest stand. For.Ecol.
Manage. 129: 53–61. doi:10.1016/S0378-1127(99)00158-9.
Hallbäcken, L., and Tamm, C.O. 1986. Changes in soil acidity from 1927 to
1982-1984 in a forest area of south-west Sweden. Scand. J. For. Res. 1: 219–232.
doi:10.1080/02827588609382413.
Hamburg, S., Yanai, R.D., Arthur, M.A., Blum, J.D., and Siccama, T.G. 2003.
Bioticcontrol of calcium cycling in northern hardwood forests: acidic rain and aging
forests. Ecosystems, 6: 399–406. doi:10.1007/s10021-002-0174-9.
Hedges, L.V., and Olkin, I. 1985. Statistical methods for meta-analysis.
Academic Press, New York.
Hedges, L.V., Gurevitch, J., and Curtis, P.S. 1999. The meta-analysis of
response ratios in experimental ecology. Ecology, 80(4): 1150–1156.
doi:10.1890/00129658(1999)080[1150:TMAORR]2.0.CO;2.
Higgins, J.P.T. 2008. Heterogeneity in meta-analysis should be expected and
appropriately quantified. Int. J. Epidemiol. 37:1158–1160. doi:10.1093/ije/dyn204.
Hillebrand, H., and Cardinale, B.J. 2010. A critique for meta-analyses and the
productivity–diversity relationship. Ecology, 91(9): 2545–2549. doi:10.1890/09-
0070.1.
Houle, D., Duchesne, L., Moore, J.D., Lafléche, M.R., and Ouimet, R. 2002. Soil
and tree-ring chemistry response to liming in a sugar maple stand. J. Environ. Qual. 31:
1993–2000. doi:10.2134/jeq2002.1993.
Huang, H., Campbell, A.G., Folk, R., and Mahler, R.L. 1992. Wood-ash as a
liming agent and soil additive for wheat-field studies. Commun. Soil Sci. Plant Anal.
23: 25–33. doi:10.1080/00103629209368567.
126
Huber, C., Baier, R., Göttlein, A., and Weis, W. 2006. Changes in soil, seepage,
water and needle chemistry between 1984 and 2004 after liming an N-saturated Norway
spruce stand at the Höglwald, Germany. For. Ecol. Manage. 233: 11–20.
doi:10.1016/j.foreco.2006.05.058.
Huettl, R., and Zoettl, H. 1993. Liming as a mitigation tool in Germany's declining
forests - reviewing results from former and recent trials. For. Ecol. Manage. 61: 325–
338. doi:10.1016/0378-1127(93)902096.
Huntington, T., Hooper, R., Johnson, C., Aulenbac, B., Cappellato, R., and Blum,
A. 2000.Calcium depletion in a southeastern United States forest ecosystem. Soil Sci.
Soc. Am. J. 64:1845–1858.doi:10.2136/sssaj2000.6451845x.
Jandl, R., Alewell, C., and Prietzel, J. 2004. Calcium loss in Central European
forest soils. Soil Sci. Soc. Am. J. 68(2): 588–595. doi:10.2136/sssaj2004.0588.
Jerabkova, L., Prescott, C.E., Titus, B.D., Hope, G.D., and Walters, M.B. 2011. A
meta-analysis of the effects of clearcut and variable-retention harvesting on soil nitrogen
fluxes in boreal and temperate forests. Can. J. For. Res. 41(9): 1852–1870.
doi:10.1139/x11-087.
Johnson, A.H., Anderson, S.B., and Siccama, T.G. 1994. Acid rain and the soils of
the Adirondacks: Changes in pH and available calcium 1930–1984. Can. J. For. Res.
24(1): 39–45. doi:10.1139/x94-006.
Keller, W., Dixit, S.S., and Heneberry, J. 2001. Calcium declines in northeastern
Ontario lakes. Can. J. Fish. Aquat. Sci. 58(10): 2011–2020. doi:10.1139/f01-142.
Kjøller, R., and Clemmensen, K. 2009. Belowground ectomycorrhizal fungal
communities respond to liming in three southern Swedish coniferous forest stands. For.
Ecol. Manage. 257: 2217–2225. doi:10.1016/j.foreco.2009.02.038.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F. 2006. World map of the
Köppen–Geiger climate classification updated. Meteorol. Z. 15: 259–263. doi:
10.1127/0941- 2948/2006/0130.
Kreutzer, K. 1995. Effects of forest liming on soil processes. Plant Soil, 168/169:
447–470. doi:10.1007/BF00029358.
Landeweert, R., Hoffland, E., Finlay, R.D., Kuyper, T.W., and Van Breemen, N.
2001. Linking plants to rocks: Ectomycorrhizal fungi mobilize nutrients from minerals.
Trends Ecol. Evol. 16: 248–254. doi:10.1016/S0169-5347(01)02122-X.
Lipas, E. 1985. Assessment of site productivity and fertilizer requirement by
means of soil properties. Folia For. 618: 16 [English summary].
127
Long, R.P., Horsley, S.B., and Hall, T.J. 2011. Long-term impact of liming on
growth and vigor of northern hardwoods. Can. J. For. Res. 41(6): 1295–1307.
doi:10.1139/x11-049.
Lorenz, K., Feger, K.H., and Kandeler, E. 2001. The response of soil microbial
biomass and activity of a Norway spruce forest to liming and drought. J.Plant Nutr.
Soil Sci. 164: 9–19. doi:10.1002/1522-2624(200102)164:1<9::AID-
JPLN9>3.0.CO;2-Q.
Lucas, R.W., Klaminder, J., Futter, M.N., Bishop, K.H., Egnell, G., Laudon, H.,
and Hogberg, P. 2011. A meta-analysis of the effects of nitrogen additions on base
cations: implications for plants, soils, and streams. For. Ecol. Manage. 262: 95–104.
doi:10.1016/j.foreco.2011.03.018.
Lundström, U.S., Bain, D.C., Taylor, A.F.S., Van Hees, P.A.W., Geibe, C.E.,
Holmstroem, S.J.M., Melkerud, P.-A., Finlay, R., Jones, D.L., Nyberg, L., Gustafsson,
J.P., Riise, G., and Strand, L.T. 2003. Effects of acidification and its mitigation with lime
and wood-ash on forest soil processes in southern Sweden. A joint multidisciplinary
study. Water Air Soil Pollut. Focus, 3(4): 167–188. doi:10.1023/A:1024131615011.
Mahmood, S., Finlay, R.D., and Erland, S. 1999. Effects of repeated harvesting
of forest residues on the micorrhizal community in a Swedish spruce forest. New
Phytol. 142: 577–585. doi:10.1046/j.1469-8137.1999.00414.x.
Martin, C.W., Noel, D.S., and Federer, C.A. 1984. Effects of forest clearcutting
in New England on stream chemistry. J Environ. Qual. 13: 204–210.
doi:10.2134/jeq1984.00472425001300020006x.
Meiwes, K.J. 1995 Application of lime and wood-ash to decrease acidification of
forest soils. Water Air Soil Pollut. 85: 143–152. doi:10.1007/BF00483696.
Moore, J-D., Ouimet, R., and Duchesne, L. 2012. Soil and sugar maple response
15 years after dolomitic lime application. For. Ecol. Manage. 281: 130–139.
doi:10.1016/j.foreco.2012.06.026.
Moore, J.-D., Ouimet, R., and Bohlen, P.J. 2013. Effects of liming on survival
and reproduction of two potentially invasive earthworm species in a northern forest
podzol. Soil Biol. Biochem. 64: 164–180. doi:10.1016/j.soilbio.2013.04.017.
Morrison, I.K., and Foster, N.W. 2001. Fifteen-year change in forest floor
organic and element content and cycling at the Turkey Lakes Watershed. Ecosystems,
4: 545–554.
Nihlgård, B., Nilsson, S.I., and Popovic, B. 1988. Effects of lime on soil
128
chemistry. In: Liming as a measure to improve soil and tree condition in areas affected
by air pollution. Edited by F. Andersson and T. Persson. Results and Experiences of an
Ongoing Research Programme, 27–39.
Nilsson, S.I., Andersson, S., Valeur, I., Persson, T., Bergholm, J., and Wirén, A.
2001. Influence of dolomite lime on leaching and storage of C, N and S in a Spodosol
under Norway spruce (Picea abies (L.) Karst.). For. Ecol. Manage. 146: 55–73.
Nohrstedt, H.O. 1992. Soil water chemistry as affected by liming and N
fertilization at two Swedish coniferous forest sites. Scand. J. For. Res. 7: 143–153.
Nohrstedt, H.O. 2001 Response of coniferous forest ecosystems on mineral soils
to nutrient additions: a review of Swedish experiences. Scand. J. For. Res. 16: 555–
573.
Nykvist, N., and Rosén, K. 1985. Effect of clear-felling and slash removal on the
acidity of northern coniferous soils. For. Ecol. Manage. 11(3): 157–169.
Olsson, B.A., Bengtsson, J., and Lundkvist, H. 1996. Effects of different forest
harvest intensities on the pools of exchangeable cations in coniferous forest soils. For.
Ecol. Manage. 84: 135–147. doi:10.1016/0378-1127(96)03730-9.
Pabian, S.E., Ermer, N.M., Tzilkowski, W.M., and Brittingham, M.C. 2012.
Effects of liming on forage availability and nutrient content in a forest impacted by acid
rain. PLoS One, 7(6): e39755. doi:10.1371/journal.pone.0039755.
Pärn, H. 2004. The effect of wood-ash application on litter decomposition in a
Scots pine stand. Metsanduslikud Uurim. 41: 35–41.
Pitman, R. 2006. Wood-ash use in forestry — a review of the environmental
impacts.
Forestry, 79: 563–586. doi:10.1093/forestry/cpl041.
Proe, M.F., Cameron, A.D., Dutch, J., and Christodoulou, X.C. 1996. The effect
of whole- tree harvesting on the growth of second rotation Sitka spruce. Forestry, 69(4):
389–401.
Rosenberg, M.S., Adams, D.C., and Gurevitch, J. 1997. Metawin: statistical
software for meta-analysis with resampling tests. Sinauer Associates, Sunderland,
Massachusetts, USA.
Rosenberg, M.S., Adams, D.C., and Gurevitch, J. 2000. MetaWin: statistical
software for meta-analysis.Version 2.0. Sinauer Associates Inc., Sunderland
Massachusetts. pp. 133.
129
Rosenberg, O., and Jacobson, S. 2004. Effects of repeated slash removal in
thinned stands on soil chemistry and understorey vegetation. Silva Fenn. 38: 133–142.
Rosenthal, R. 1979. The “file drawer problem” and tolerance for null results.
Psychological Bull. 86: 638–641. doi:10.1037/0033-2909.86.3.638.
Saarsalmi, A., Tamminen, P., Kukkola, M., and Levula, T. 2011. Effects of
liming on chemical properties of soil, needle nutrients and growth of Scots pine
transplants.For.Ecol.Manage.262:278–285.doi:10.1016/j.foreco.2011.03.033.
SAS Institute, Inc. 2011. SAS software. SAS Institute, Inc., Cary, North Carolina.
Someshwar, A.V. 1996. Wood-ash and combination wood-fired boiler ash
characterization J. Environ. Qual. 25: 962–972.
doi:10.2134/jeq1996.00472425002500050006x.
Steenari, B.M., and Lindqvist, O. 1997. Stabilisation of biofuels ashes for
recycling to forest soil. Biomass Bioenergy, 13: 39–50. doi:10.1016/S0961-
9534(97)00024-X.
Sverdrup, H.U. 1985. Calcite dissolution kinetics and lake neutralization. Ph.D.
thesis, Lund Institute of Technology, Lund, Sweden.
Tamm, C. O. 1974. Kalk problemet för jord, skog och miljövård [Lime problem
for soil, tree stand and environmental care]. Skogs-o. Lantbr. Akad. Tidskr. 113: 37-43.
Thimonier, A., Duponey, J.L., and LeTacon, F. 2000. Recent losses of base
cations from soils of Fagus Sylvatica L. stands in northeastern France. ABIO, 29: 314–
321. doi:10.1579/0044- 7447-29.6.314.
Vadeboncoeur, M.A. 2010. Meta-analysis of fertilization experiments indicates
multiple limiting nutrients in northeastern deciduous forests. Can. J. For. Res. 40(9):
1766–1780.
van der Heijden, G., Legout, A., Midwood, A., Craig, C.-A., Pollier, B., Ranger, J.,
and Dambrine, E. 2013. Mg and Ca root uptake and vertical transfer in soils assessed by
an in situ ecosystem-scale multi-isotopic (26 Mg and 44 Ca) tracing experiment in a
beech stand (Breuil- Chenue, France). Plant Soil, 369(1–2): 33-45. doi:10.1007/s11104-
012-1542-7.
130
Van der Perre, R., Jonard, M., André, F., Nys, C., Legout, A., and Ponette, Q.
2012. Liming effect on radial growth depends on time since application and on climate in
Norway spruce stands. For. Ecol. Manage. 281: 59–67.
doi:10.1016/j.foreco.2012.06.039.
Van Lierop, W. 1981. Conversion of organic soil pH values measured in water,
0.01M CaCl2 or lM KCl. Can. J. Soil Sci. 6l: 577–579.
Vance, E.D. 1996. Land application of wood-fired and combination boiler ashes:
an overview. J. Environ. Qual. 25: 937–944.
doi:10.2134/jeq1996.00472425002500050002x.
Viro, P. 1951. Nutrient status and fertility of forest soil I. Pine stands.
Commun.Inst. For. Fenn. 39(4).
Wallander, H., Fossum, A., Rosengren, U., and Jones, H. 2005. Ectomycorrhizal
fungal biomass in roots and uptake of P from apatite by Pinus sylvestris seedlings
growing in forest soil with and without wood-ash amendment. Mycorrhiza, 15: 143–148.
Walmsley, J. D, Jones, D. L., Reynolds, B., Price, M.H., and Healey, J.R. 2009.
Whole tree harvesting can reduce second rotation forest productivity. For. Ecol. Manage.
257(3): 1104– 1111. doi:10.1016/j.foreco.2008.11.015.
Watmough, S.A., and Dillon, P.J. 2003. Base cation and nitrogen budgets for a
mixed hardwood catchment in south-central Ontario. Ecosystems, 6: 675–693.
Watmough, S.A., Aherne, J., and Dillon, P.J. 2003. Potential impact of forest
harvesting on lake chemistry in south-central Ontario at current levels of acid deposition.
Can. J. Fish. Aquat. Sci. 60(9): 1095–1103. doi:10.1139/f03-093.
Yanai, R.D., Blum, J.D., Hamburg, S.P., Arthur, M.A., Nezat, C.A., and Siccama,
T.G.2005. New insights into calcium depletion in northeastern forests. J. For. 103: 14–20.
Zimmermann, S., and Frey, B. 2002. Soil respiration and microbial properties in
an acid forest soil: effects of wood-ash. Soil Biol. Biochem. 34: 1727–173
131
APPENDIX 3A
Table 3A.1: List of studies included in the respective meta-analysis. Figure and table columns list all
figures and tables containing data from each study (note that many studies had several distinct trials).
Meta-analysis Treatment Figure Table
Soil pH Effect
Reference
lime 1, 5a 1, 2 Anderson & Joergensen 1997
lime 1, 5a 1, 2 Anderson 1998
lime 1, 5a 1, 2 Andersson & Persson 1988
lime 1, 5a 1, 2 Arnold & Van Diest 1993a
wood-ash 1, 5a 1, 2 Arvidsson & Lundkvist 2003
lime 1, 5a 1, 2 Backman et al. 2003
lime 1, 5a 1, 2 Blette & Newton 1996
lime 1, 5a 1, 2 Bona et al. 2008
wood-ash 1, 5a 1, 2 Bramryd and Fransman 1995
wood-ash 1, 5a 1, 2 Brunner et al. 2004
lime 1, 5a 1, 2 Butterbach-Bahl & Pappin 2002
lime 1, 5a 1, 2 Carnol et al. 2002
lime 1, 5a 1, 2 Chagnon et al. 2001
lime 1, 5a 1, 2 Cho et al. 2010
lime 1, 5a 1, 2 Cronan et al. 1992
lime 1, 5a 1, 2 Derome et al. 1986
wood-ash 1, 5a 1, 2 Ernfors et al. 2010
wood-ash 1, 5a 1, 2 Feldkirchner et al. 2003
lime 1, 5a 1, 2 Frank & Stuanes 2003
wood-ash 1, 5a 1, 2 Fritze et al. 1994
lime 1, 5a 1, 2 Fyles et al. 1994
lime 1, 5a 1, 2 Geary & Driscoll 1996
wood-ash 1, 5a 1, 2 Geibe et al. 2003
wood-ash 1, 5a 1, 2 Genenger et al. 2003
lime 1, 5a 1, 2 Giessen & Brummer 1999
lime 1, 5a 1, 2 Gradowski & Thomas 2008
lime 1, 5a 1, 2 Hahn & Marshner 1998
wood-ash 1, 5a 1, 2 Haimi et al. 2000
lime 1, 5a 1, 2 Hallbäcken & Zhang 1998
lime 1, 5a 1, 2 Houle et al. 2002
lime 1, 5a 1, 2 Ingerslev 1997
wood-ash 1, 5a 1, 2 Jacobson et al.2004
lime 1, 5a 1, 2 Juice et al. 2006
lime 1, 5a 1, 2 Kakei & Clifford 2000
lime 1, 5a 1, 2 Kreutzer 1995
lime 1, 5a 1, 2 Lehto & Malkonen 1994
lime 1, 5a 1, 2 Lofgren et al. 2009
132
lime 1, 5a 1, 2 Long et al. 1997
lime 1, 5a 1, 2 Long et al. 2011
lime 1, 5a 1, 2 Lorenz et al. 2000
wood-ash 1, 5a 1, 2 Lugwig et al. 2002
wood-ash 1, 5a 1, 2 Mahmood et al. 2003
wood-ash 1, 5a 1, 2 Maljanaen et al. 2006a
wood-ash 1, 5a 1, 2 Mandre 2005
lime 1, 5a 1, 2 Marschner & Wilcynzki 1991
wood-ash 1, 5a 1, 2 Moilanen et al. 2002
lime 1, 5a 1, 2 Moore et al. 2008
lime 1, 5a 1, 2 Musil & Pavlecik 2002
lime 1, 5a 1, 2 Nilsson et al. 2001
lime 1, 5a 1, 2 Nohrstedt 2002.
Wood-ash 1, 5a 1, 2 Norström et al. 2012
lime 1, 5a 1, 2 Nowotony et al. 1998
wood-ash 1, 5a 1, 2 Österås et al. 2005
wood-ash 1, 5a 1, 2 Ozolincius & Varnagirytė 2005
wood-ash 1, 5a 1, 2 Ozolincius et al. 2006
lime 1, 5a 1, 2 Pabian et al. 2012
wood-ash 1, 5a 1, 2 Parn 2004
lime 1, 5a 1, 2 Persson et al. 1995
wood-ash 1, 5a 1, 2 Pothoff et al. 2008
wood-ash 1, 5a 1, 2 Puttsepp et al. 2006
lime 1, 5a 1, 2 Rineau & Garbaye 2009
wood-ash 1, 5a 1, 2 Ring et al. 2006
lime 1, 5a 1, 2 Rosenberg et al. 2003
wood-ash 1, 5a 1, 2 Rosenberg et al. 2010
wood-ash 1, 5a 1, 2 Saarsalmi et al. 2001
wood-ash 1, 5a 1, 2 Saarsalmi et al. 2004
lime 1, 5a 1, 2 Saarsalmi et al. 2011
wood-ash 1, 5a 1, 2 Saarsalmi et al. 2012
lime 1, 5a 1, 2 Sikström et al. 2001
lime 1, 5a 1, 2 Simmons et al. 1996
lime 1, 5a 1, 2 Smallidge & Leopold 1997
wood-ash 1, 5a 1, 2 Solla-Gullon et al. 2006
lime 1, 5a 1, 2 Sramek et al. 2006
wood-ash 1, 5a 1, 2 Staples & Rees 2001
lime 1, 5a 1, 2 Veerkamp et al. 1997
lime 1, 5a 1, 2 Wanner et al. 1993
wood-ash 1, 5a 1, 2 Zimmerman & Frey 2002
Base Saturation Effect
wood-ash 2, 5b 1, 2 Arvidsson & Lundkvist 2003
lime 2, 5b 1, 2 Bakker 1999
133
lime 2, 5b 1, 2 Blette & Newton 1996
wood-ash 2, 5b 1, 2 Bramryd & Fransman 1995
wood-ash 2, 5b 1, 2 Brunner et al. 2004
lime 2, 5b 1, 2 Cho et al. 2010
lime 2, 5b 1, 2 Côté et al. 1995
lime 2, 5b 1, 2 Derome et al. 1986
lime 2, 5b 1, 2 Frank & Stuanes 2003
wood-ash 2, 5b 1, 2 Fritze et al. 1994
wood-ash 2, 5b 1, 2 Haimi et al. 2000
lime 2, 5b 1, 2 Hallbäcken & Zhang 1998
lime 2, 5b 1, 2 Houle et al. 2002
lime 2, 5b 1, 2 Lofgren et al. 2009
lime 2, 5b 1, 2 Moore et al. 2008
lime 2, 5b 1, 2 Nilsson et al. 2001
wood-ash 2, 5b 1, 2 Norström et al. 2012
wood-ash 2, 5b 1, 2 Pothoff et al. 2008
wood-ash 2, 5b 1, 2 Saarsalmi et al. 2001
wood-ash 2, 5b 1, 2 Saarsalmi et al. 2004
lime 2, 5b 1, 2 Sikström et al. 2001
lime 2, 5b 1, 2 Sramek et al. 2006
Foliar Ca Concentration
lime 3, 5c 1, 2 Arnold & Van Diest 1993b
wood-ash 3, 5c 1, 2 Arvidsson & Lundkvist 2002
lime 3, 5c 1, 2 Bakker 1999
lime 3, 5c 1, 2 Berger et al. 2001
lime 3, 5c 1, 2 Bona et al. 2008
lime 3, 5c 1, 2 Børja & Nilsen 2009
lime 3, 5c 1, 2 Côté et al. 1995
wood-ash 3, 5c 1, 2 Ernfors et al. 2010
lime 3, 5c 1, 2 Fyles et al. 1994
wood-ash 3, 5c 1, 2 Hallenbarter et al. 2002
lime 3, 5c 1, 2 Huber et al. 2006
lime 3, 5c 1, 2 Hugget et al. 2006
lime 3, 5c 1, 2 Kobe et al. 2002
lime 3, 5c 1, 2 Long et al. 1997
lime 3, 5c 1, 2 Long et al. 2011
lime 3, 5c 1, 2 Lugwig et al. 2002
wood-ash 3, 5c 1, 2 Lugwig et al. 2002
wood-ash 3, 5c 1, 2 Moilanen et al. 2002
wood-ash 3, 5c 1, 2 Moilanen et al. 2005
lime 3, 5c 1, 2 Moore & Ouimet 2006
wood-ash 3, 5c 1, 2 Ozolincius et al. 2007
lime 3, 5c 1, 2 Rosberg et al. 2006
134
wood-ash 3, 5c 1, 2 Saarsalmi et al. 2004
lime 3, 5c 1, 2 Saarsalmi et al. 2011
lime 3, 5c 1, 2 Sikström et al. 1997
lime 3, 5c 1, 2 Sikström et al. 2001
lime 3, 5c 1, 2 Smallidge & Leopold 1997
wood-ash 3, 5c 1, 2 Solla-Gullon et al. 2006
lime 3, 5c 1, 2 Sramek et al. 2006
wood-ash 3, 5c 1, 2 Varnagirytė-Kabašinskienė 2008
wood-ash 3, 5c 1, 2 Wang et al. 2007
Tree Growth Effect
lime 4, 5d 1, 2 Andersson & Persson 1988
lime 4, 5d 1, 2 Arnold & Van Diest 1993b
lime 4, 5d 1, 2 Bakker 1999
lime 4, 5d 1, 2 Bona et al. 2008
lime 4, 5d 1, 2 Børja & Nilsen 2009
wood-ash 4, 5d 1, 2 Clemenson-Lindell & Persson 1993
lime 4, 5d 1, 2 Côté et al. 1995
lime 4, 5d 1, 2 Derome et al. 1986
wood-ash 4, 5d 1, 2 Ernfors et al. 2010
wood-ash 4, 5d 1, 2 Feldkirchner et al. 2003
wood-ash 4, 5d 1, 2 Ferm 1992
lime 4, 5d 1, 2 Gebauer et al. 1998
wood-ash 4, 5d 1, 2 Genenger et al. 2003
lime 4, 5d 1, 2 Hahn & Marshner 1998
wood-ash 4, 5d 1, 2 Hallenbarter 2002
lime 4, 5d 1, 2 Hindar et al. 2003
lime 4, 5d 1, 2 Huber et al. 2004
lime 4, 5d 1, 2 Hugget et al. 2006
lime 4, 5d 1, 2 Ingerslev 1997
wood-ash 4, 5d 1, 2 Jacobson 2003
lime 4, 5d 1, 2 Juice et al. 2006
lime 4, 5d 1, 2 Kobe et al. 2002
lime 4, 5d 1, 2 Ljungstrom & Nihlgård 1995
lime 4, 5d 1, 2 Long et al. 1997
lime 4, 5d 1, 2 Long et al. 2011
lime 4, 5d 1, 2 Mandre et al. 2006
wood-ash 4, 5d 1, 2 Moilanen et al. 2002
wood-ash 4, 5d 1, 2 Moilanen et al. 2005
lime 4, 5d 1, 2 Moore & Oiumet 2006
lime 4, 5d 1, 2 Nowotony et al. 1998
wood-ash 4, 5d 1, 2 Ozolincius et al. 2006
wood-ash 4, 5d 1, 2 Parn 2005
lime 4, 5d 1, 2 Persson et al. 1995
135
wood-ash 4, 5d 1, 2 Puttsepp et al. 2006
lime 4, 5d 1, 2 Rosberg et al. 2006
wood-ash 4, 5d 1, 2 Saarsalmi et al. 2004
lime 4, 5d 1, 2 Saarsalmi et al. 2011
lime 4, 5d 1, 2 Safford & Czpowskyj 1986
lime 4, 5d 1, 2 Sikström et al. 1997
lime 4, 5d 1, 2 Sikström et al. 2001
lime 4, 5d 1, 2 Simmons et al. 1996
lime 4, 5d 1, 2 Smallidge & Leopold 1997
wood-ash 4, 5d 1, 2 Solla-Gullon et al. 2006
wood-ash 4, 5d 1, 2 Staples 2001
wood-ash 4, 5d 1, 2 Wang et al. 2007
lime 4, 5d 1, 2 Zaccherio & Finzi 2007
Microbial Indices Effect lime
1, 2
Anderson 1998
lime 1, 2 Carnol et al. 2002 Belgium
lime 1, 2 Chagnon et al. 2001
wood-ash 1, 2 Fritz et al. 1994
lime 1, 2 Geissen and Kampichler 2004
wood-ash 1, 2 Haimi et al. 2000
lime 1, 2 Hermannson et al. 2004
lime 1, 2 Kreutzer et al. 1995
lime 1, 2 Lorenz et al. 2000
wood-ash 1, 2 Zimmerman & Frey 2002
Soil C/N Ratio Effect
wood-ash 1, 2 Arvidsson & Lundkvist 2003
lime 1, 2 Backman 2003
lime 1, 2 Bauhaus 2004
lime 1, 2 Chagnon 2001
wood-ash 1, 2 Ernfors et al. 2010
wood-ash 1, 2 Fritze et al. 1994
lime 1, 2 Gradowski & Thomas 2008
lime 1, 2 Geissen & Brümmer 1999
wood-ash 1, 2 Haimi et al. 2000
lime 1, 2 Houle et al. 2002
wood-ash 1, 2 Jacobson et al. 2004
lime 1, 2 Lorenz 2000
lime 1, 2 Marschner & Wilcynzki 1991
lime 1, 2 Moore et al. 2008
lime 1, 2 Nilsson et al. 2001
wood-ash 1, 2 Parn 2004
lime 1, 2 Persson et al. 1995
wood-ash 1, 2 Rosenberg et al. 2010
136
ECM Root Colonization
wood-ash 1, 2 Saarsalmi et al. 2001
lime 1, 2 Saarsalmi et al. 2011
lime 1, 2 Sramek et al. 2006
lime 1, 2 Børja & Nilsen 2009
wood-ash 1, 2 Hagerberg & Wallander 2002
lime 1, 2 Juice et al. 2006
wood-ash 1, 2 Mahmood et al. 2002
lime 1, 2 Qian & Oberwinkler 1998
wood-ash 1, 2 Wallander et al. 2005
137
Table 3A.1 References
Anderson, T.H. 1998. The influence of acid irrigation and liming on the soil
microbial biomass in a Norway spruce (Picea abies (L.) Karst.) stand, Plant Soil 199:
117–122. doi:10.1023/A:1004224112790.
Anderson, T.H., and Joergensen, R.G. 1997. Relationship between SIR and FE
estimates of microbial biomass C in deciduous forest soils at different pH. Soil Biol.
Biochem. 29: 1033– 1042. doi:10.1016/S0038-0717(97)00011-4.
Andersson, F., and Persson, T. 1988. Liming as a countermeasure against
acidification in a terrestrial environment. Acidification Research in Sweden 7:5–7.
Arnold, G., and Van Diest, A. 1993a. Response of a Scots pine (Pinus sylvestris)
stand to application of phosphorus, potassium, magnesium and lime. Soil solution
composition. Neth. J. Agric. Sci. 41: 267–289.
Arnold, G., and Van Diest, A. 1993b. Response of a Scots pine (Pinus sylvestris)
stand to application of phosphorus, potassium, magnesium and lime. Foliar nutrient
concentrations and stand development. Neth. J. Agric. Sci. 41: 291–307.
Arvidsson, H., and Lundkvist, H. 2002. Needle chemistry in young Norway
spruce stands after application of crushed wood-ash. Plant Soil 238:159–174.
doi:10.1023/A:1014252521538.
Arvidsson, H., and Lundkvist, H. 2003. Effects of crushed wood-ash on soil
chemistry in young Norway spruce stands. For. Ecol. Manage. 176: 121–132.
doi:10.1016/S0378- 1127(02)00278-5.
Bäckman, J.S., Hermansson, A., Tebbe, C.C., and Lindgren, P.E. 2003. Liming
induces growth of a diverse flora of ammonia-oxidizing bacteria in acid spruce forest
soil as determined by SSCP and DGGE. Soil Biol. Biochem. 35: 1337–1347.
doi:10.1016/S0038-0717(03)00213-X.
Bakker, M.R. 1999. Fine-root parameters as indicators of sustainability of forest
ecosystems. For. Ecol. Manage. 122: 7–16. doi:10.1016/S0378-1127(99)00028-6.
Bauhus, J., Vor, T., Bartsch, N., and Cowling, A. 2004. The effects of gaps and
liming on forest floor decomposition and soil C and N dynamics in a Fagus sylvatica
forest. Can. J. For. Res. 34(3): 509–518. doi:10.1139/x03-218.
Berger, T.W., Eager, C., Likens, G.E., and Stingeder, G. 2001. Effects of calcium
and aluminum chloride additions on foliar and throughfall chemistry in sugar maples.
For. Ecol. Manage. 149: 75–90. doi:10.1016/S0378-1127(00)00546-6.
138
Blette, V., and Newton, R. 1996. Effects of watershed liming on the soil chemistry
of Woods Lake, New York. Biogeochemistry 32: 175–194. doi:10.1007/BF02187138.
Bona, K.A., Burgess, M.S., Fyles, J.W., Camire, C., and Dutilleul, P. 2008. Weed
cover in hybrid poplar (Populus) plantations on Quebec forest soils under different lime
treatments. For. Ecol. Manage. 255: 2761–2770. doi:10.1016/j.foreco.2008.01.042.
Børja, T., and Nilsen, P. 2009. Long-term effect of liming and fertilization
onectomycorrhizal colonization and tree growth in old Scots pine (Pinussylvestris L.)
stands. Plant Soil, 314: 109–119. doi:10.1007/s11104-008-9710-5.
Bramryd, T., and Fransman, B. 1995. Silvicultural use of wood-ashes effects on
the nutrient and heavy metal balance in a pine (Pinus sylvestris L.) forest soil. Water
Air Soil Pollut. 85: 1039–1044. doi:10.1007/BF00476967.
Brunner, I., Zimmermann, S., Zingg, A., and Blaser, P. 2004. Wood-ash
recycling affects forest soil and tree fine-root chemistry and reverses soil
acidification. Plant Soil 267: 61–71. doi:10.1007/s11104-005-4291-z.
Butterbach-Bahl, K., and Papen, H. 2002. Four years continuous record of CH 4-
exchange between the atmosphere and untreated and limed soil of an N-saturated
spruce and beech forest ecosystem in Germany. Plant Soil 240: 77–90.
doi:10.1023/A:1015856617553.
Carnol, M., Kowalchuk, G.A., and de Boer, W. 2002. Nitrosomonas europaea-
like bacteria detected as the dominant b-subclass Proteobacteria ammonia oxidisers in
reference and limed acid forest soils. Soil Biol. Biochem. 34: 1047–1050.
doi:10.1016/S0038-0717(02)00039-1.
Chagnon, M., Paré, D., Hébert, C., and Camiré, C. 2001. Effects of experimental
liming on collembolan communities and soil microbial biomass in a southern Quebec
sugar maple (Acer saccharum Marsh.) stand. Appl. Soil Ecol. 17: 81–90.
doi:10.1016/S0929-1393(00)00134-7.
Cho, Y., Driscoll, C., Johnson, C., and Sicamma, T. 2010. Chemical changes in
soil and soil solution after calcium silicate addition to a northern hardwood forest.
Biogeochemistry 100: 3–20. doi:10.1007/s10533-009-9397-6.
Clemensson-Lindell, A., and Persson, H. 1993. Long-term effects of liming on
the fine-root standing crop of Picea abies and Pinus sylvestris in relation to chemical
changes in the soil. Scand. J. For. Res. 8: 384–394. doi:10.1080/02827589309382785.
Côté, B., O'Halloran, I., Hendershot, W.H., and Spankie, H. 1995. Possible
interference of fertilization in the natural recovery of a declining sugar maple stand in
139
southern Quebec. Plant Soil 168–169: 471–480. doi:10.1007/BF00029359.
Cronan, C.S., Lashkman, S., and Patterson, H.H. 1992. Effects of disturbance and
soil amendments on dissolved organic carbon and organic acidity in red pine forest
floors. J. Environ. Qual. 21: 457–463. doi:10.2134/jeq1992.00472425002100030025x.
Derome, J., Kukkola, M., and Mälkönen, E. 1986. Forest liming on mineral soils:
results of Finnish experiments. National Swedish Environmental Protection Board,
Statens Naturvårdsverk. Rapport 3084. pp. 1–10.
Ernfors, M., Sikström, U., Nilsson, M., and Klemedtsson, L. 2010. Effects of
wood-ash fertilization on forest floor greenhouse gas emissions and tree growth in
nutrient poor drained peatland forests. Sci. Total Environ. 408: 4580–4590.
doi:10.1016/j.scitotenv.2010.06.024.
Feldkirchner, D.C., Wang, C.K., Gower, S.T., Kruger, E.L., and Ferris, J. 2003.
Effects of nutrient and paper mill biosolids amendments on the growth and nutrient
status of hardwood forests. For. Ecol. Manage. 177: 95–116. doi:10.1016/S0378-
1127(02)00318-3.
Ferm, A., Hokkanen, A., Moilanen, M., and Issakainen, J. 1992. Effects of wood
bark ash on the growth and nutrition of a Scots pine afforestation in central Finland.
Plant Soil 147: 305–316. doi:10.1007/BF00029082.
Frank, J., and Stuanes, A.O. 2003. Short-term effects of liming and vitality
fertilization on forest soil and nutrient leaching in a Scots pine ecosystem in Norway.
For. Ecol. Manage. 176: 371–386. doi:10.1016/S0378-1127(02)00285-2.
Fritze, H., Smolander, A., Levula, T., Kitunen, V., and Mälkönen, E. 1994.
Wood-ash fertilization and fire treatments in a Scots pine forest stand: effects on the
organic layer, microbial biomass and microbial activity. Biol.Fertil.Soils, 17: 57–63.
doi:10.1007/BF00418673.
Fyles, J.W., Côté, B., Courchesne, F., Hendershot, W.H., and Savoie, S. 1994.
Effects of base cation fertilization on soil and foliage nutrient concentrations, and litter-
fall and throughfall nutrient fluxes in a sugar maple forest. Can. J. For. Res. 24(3):
542–549. doi:10.1139/x94-071.
Geary, R.J., and Driscoll, C.T. 1996. Forest soil solutions: acid/base chemistry
and response to calcite treatment. Biogeochemistry 32: 195–220.
doi:10.1007/BF02187139.
140
Gebauer, G., Hahn, G., Rodenkirchen, H., and Zuleger, M. 1998. Effects of acid
irrigation and liming on nitrate reduction and nitrate content of Picea abies (L.) Karst. and
Oxalis acetosella L. Plant Soil 199: 51–70. doi:10.1023/A:1004263223917.
Geibe, C.E., Holmström, S.J.M., van Hees, P.A.W., and Lundström, U.S. 2003.
Impact of lime and ash applications on soil solution chemistry of an acidified podzolic
soil. Water Air Soil Pollut. Focus, 3: 77–96. doi:10.1023/A:1024123413194.
Geissen, V., and Brümmer, G.W. 1999. Decomposition rates and feeding activities
of soil fauna in decidiuous forest soils in relation to soil chemical parameters following
liming and fertilization. Biol. Fertil. Soils 29: 335–342. doi:10.1007/s003740050562.
Geissen, V., and Kampichler, C. 2004. Limits to the bioindication potential of
Collembola in environmental impact analysis: a case study of forest soil liming and
fertilization. Biol. Fertil. Soils 39: 383–390. doi:10.1007/s00374003-0714-2.
Genenger, M., Zimmermann, S., Hallenbarter, D., Landolt, W., Frossard, E.,
and Brunner, I. 2003. Fine root growth and element concentrations of Norway
spruce as affected by wood-ash and liquid fertilisation. Plant Soil 255: 253–264.
doi:10.1023/A:1026118101339.
Gradowski, T., and Thomas, S.C. 2008. Responses of Acer saccharum canopy trees
and saplings to P, K and lime additions under high N deposition. TreePhysiol. 28: 173–
185. doi:10.1093/treephys/28.2.173.
Hagerberg, D., and Wallander, H. 2002. The impact of forest residue removal and
wood- ash amendment on the growth of the ectomycorrhizal external mycelium. FEMS
Microbiol. Ecol. 39: 139–146. doi:10.1111/j.1574-6941.2002.tb00915.x.
Hahn, G., and Marschner, H. 1998. Effect of acid irrigation and liming on root
growth of Norway spruce. Plant Soil 199: 11–22. doi:10.1023/A:1004254709452.
Haimi, J., Fritze, H., and Moilanen, P. 2000. Responses of soil decomposer
animals to wood-ash fertilisation and burning in a coniferous forest stand. For.Ecol.
Manage. 129: 53–61. doi:10.1016/S0378-1127(99)00158-9.
Hallbäcken, L., and Zhang, L. 1998. Effects of experimental acidification,
nitrogen addition and liming on ground vegetation in a mature stand of Norway
spruce (Picea abies (L.) Karst.) in SE Sweden. For. Ecol. Manage. 108: 201–213.
doi:10.1016/S0378-1127(98)00236-9.
141
Hallenbarter, D., Landolt, W. and Bucher, J. 2002. Effects of wood-ash and liquid
fertilization on the nutritional status and growth of Norway spruce (Picea abies (L.)
Karst). Central Forestry Scientific Journal 121: 240–249. doi:10.1046/j.1439-
0337.2002.02033.x.
Hermansson, A., Backman, J.S.K., Svennson, B.H., and Lingren, P.E. 2004.
Quantification of ammonia-oxidising bacteria in limed and non-limed acidic coniferous
forest soil using real-time PCR. Soil Biol. Biochem. 36: 1935–1941.
doi:10.1016/j.soilbio.2004.05.014.
Hindar, A., Wright, R.F., Nilsen, P., Larsen, T., and Høgberget, R. 2003. Effects
on stream water chemistry and forest vitality after whole-catchment application of
dolomite to a forest ecosystem in southern Norway. For. Ecol. Manage. 180: 509–525.
doi:10.1016/S0378- 1127(02)00647-3.
Houle, D., Duchesne, L., Moore, J.D., Laflèche, M. R., and Ouimet, R. 2002. Soil
and tree-ring chemistry response to liming in a sugar maple stand. J. Environ. Qual. 31:
1993–2000. doi:10.2134/jeq2002.1993.
Huber, C., Kreutzer, K., Röhle, H., and Rothe, A. 2004. Response of artificial acid
irrigation, liming, and N-fertilisation on elemental concentrations in needles, litter
fluxes, volume increment, and crown transparency of an N saturated Norway spruce
stand. For. Ecol. Manage. 200: 3–21. doi:10.1016/j.foreco.2004.05.058.
Huber, C., Baier, R., Göttlein, A., and Weis, W. 2006. Changes in soil, seepage
water and needle chemistry between 1984 and 2004 after liming an N-saturated
Norway spruce stand at the Höglwald, Germany. For. Ecol. Manage. 233: 11–20.
doi:10.1016/j.foreco.2006.05.058.
Hugget, B.A., Schaberg, P.G., Hawley, G.J., and Eagar, C. 2006. Long-term
calcium addition increases growth release, wound closure, and health of sugar maple
(Acer saccharum) trees at the Hubbard Brook Experimental Forest. Can. J. For. Res.
37(9): 1692–1700. doi:10.1139/X07-042.
Ingerslev, M. 1997. Effects of liming and fertilization on growth, soil chemistry
and soil water chemistry in a Norway spruce plantation on a nutrient-poor soil in
Denmark. For. Ecol. Manage. 92:55–66. doi:10.1016/S0378-1127(96)03964-3.
Jacobson, S. 2003. Addition of stabilized wood-ashes to Swedish coniferous
stands on mineral soils - effects on stem growth and needle nutrient concentrations.
Silva Fenn. 37(4): 437–450.
142
Jacobson, S., Högbom, L., Ring, E., and Nohrstedt, H.Ö. 2004. Effects of wood-
ash dose and formulation on soil chemistry at two coniferous forest sites. Water Air Soil
Pollut. 158(1): 113–125. doi:10.1023/B:WATE.0000044834.18338.a0.
Juice, S.M., Fahey, T.J., Siccama, T.G., Driscoll, C.T., Denny, E.G., Eagar, C.,
Cleavitt, N.L., Minocha, R., and Richardson, A.D. 2006. Response of sugar maple to
calcium addition to northern hardwood forest. Ecology, 87: 1267–1280.
doi:10.1890/00129658(2006)87[1267:ROSMTC]2.0.CO;2.
Kakei, M., and Clifford, P.E. 2002. Short-term effects of lime application on soil
properties and fine-root characteristics for a 9-year-old Sitka spruce plantation growing
on a deep peat soil. Forestry, 75: 37–50. doi:10.1093/forestry/75.1.37.
Kobe, R.K., Likens, G.E., and Eager, C. 2002. Tree seedling growth and mortality
responses to manipulations of calcium and aluminum in a northern hardwood forest.
Can. J. For. Res. 32(6): 954–966. doi:10.1139/x02-018.
Kreutzer, K. 1995. Effects of forest liming on soil processes. Plant Soil,
168/169:447–470. doi:10.1007/BF00029358.
Lehto, T., and Mälkönen, E. 1994. Effects of liming and boron fertilization on
mycorrhizas of Picea abies. Plant Soil, 163: 65–68. doi:10.1007/BF00033941.
Ljungstrom, M., and Nihlgård, B. 1995. Effects of lime and phosphate additions
on nutrient status and growth of beech (Fagus sylvatica L.) seedlings. For. Ecol.
Manage. 74: 133–148. doi:10.1016/0378-1127(94)03494-H.
Löfgren, S., Cory, N., Zetterberh, T., Larsson, P.E., and Kronnäs, V. 2009. The
long-term effects of catchment liming and reduced sulphur deposition on forest soils
and runoff chemistry in southwest Sweden. For. Ecol. Manage. 258: 567–578.
doi:10.1016/j.foreco.2009.04.030.
Long, R.P., Horsley, S.B., and Lilja, P.R. 1997. Impact of forest liming on
growth and crown vigor of sugar maple and associated hardwoods. Can. J. For. Res.
27(10): 1560–1573. doi:10.1139/x97-074.
Long, R.P., Horsley, S.B., and Hall, T.J. 2011. Long-term impact of liming on
growth and vigor of northern hardwoods. Can. J. For. Res. 41(6): 1295–1307.
doi:10.1139/x11-049.
Lorenz, K., Feger, K.H., and Kandeler, E. 2001. The response of soil microbial
biomass and activity of a Norway spruce forest to liming and drought. J. Plant Nutr.
Soil Sci. 164: 9–19. doi:10.1002/1522-2624(200102)164:1<9::AID-JPLN9>3.3.CO;2-
H.
143
Ludwig, B., Rumpf, S., Mindrup, M., Meiwes, K.J., and Khanna, P.K. 2002.
Effects of lime and wood-ash on soil-solution chemistry, soil chemistry and nutritional
status of a pine stand in northern Germany. Scand. J. For. Res. 17: 225–237.
doi:10.1080/028275802753742891.
Mahmood, S., Finlay, R.D., Wallander, H., and Erland, S. 2002. Ectomycorrhizal
colonisation of roots and ash granules in a spruce forest treated with granulated wood-
ash. For. Ecol. Manage. 160(1−3): 65–74. doi:10.1016/S03781127(01)00462-5.
Mahmood, S., Finlay, R.D., Fransson, A.M., and Wallander, H. 2003. Effects of
hardened wood-ash on microbial activity, plant growth and nutrient uptake by
ectomycorrhizal spruce seedlings. FEMS Microbiol. Ecol. 43: 121–131.
doi:10.1111/j.1574-6941.2003.tb01051.x.
Maljanen, M., Jokinen, H., Saari, A., Strömmer, R., and Martikainen, P.J. 2006.
Methane and nitrous oxide fluxes and carbon dioxide production in boreal forest
fertilized with wood-ash and nitrogen. Soil Use Manage. 22: 151–157.
doi:10.1111/j.1475-2743.2006.00029.x.
Mandre, M. 2005. Possible responses of Norway spruce (Picea abies L.) to wood-
ash application. For. Stud. 32: 34–47.
Mandre, M., Pärn, H., and Katri, O. 2006. Short-term effects of wood-ash on the
soil and the lignin concentration and growth of Pinus sylvestris L. For. Ecol. Manage.
223: 349–357. doi:10.1016/j.foreco.2005.11.017.
Marschner, B., and Wiliczynski, A.W. 1991. The effect of liming on quantity and
chemical composition of soil organic matter in a pine forest in Berlin, Germany. Plant
Soil, 137: 229–236. doi:10.1007/BF00011201.
Moilanen, M., Silverberg, K., and Hokkanen, T.J. 2002. Effects of wood-ash on
the tree growth, vegetation and substrate quality of a drained mire: a case study. For.
Ecol. Manage. 17: 321–338. doi:10.1016/S0378-1127(01)00789-7.
Moilanen, M., Silfverberg, K., Hökkä, H., and Issakainen, J. 2005. Wood ash as a
fertilizer on drained mires - growth and foliar nutrients of Scots pine. Can. J. For. Res.
35(11): 2734– 2742. doi:10.1139/x05-179.
Moore, J.-D., and Ouimet, R. 2006. Ten-year effect of dolomitic lime on the
nutrition, crown vigor, and growth of sugar maple. Can. J. For. Res. 36(7): 1834–1841.
doi:10.1139/x06- 081.
144
Moore, J.D., Duchesne, L., and Ouimet, R. 2008. Soil properties and maple-beech
regeneration a decade after liming in a northern hardwood stand. For. Ecol. Manage.
255: 3460– 3468. doi:10.1016/j.foreco.2008.02.026.
Musil, I., and Pavlicek, V. 2002. Liming of soils: effectiveness of particle-size
fractions. J. For. Sci. 48: 121–129.
Nilsson, S.I., Andersson, S., Valeur, I., Persson, T., Bergholm, J., and Wirén,
A. 2001. Influence of dolomite lime on leaching and storage of C, N and S in a
spodosol under Norway spruce (Picea abies (L.) Karst.). For. Ecol. Manage. 146:
55–73. doi:10.1016/S0378-1127(00)00452-7.
Nohrstedt, H. 2002. Effects of liming and fertilization (N, PK) on chemistry and
nitrogen turnover in acidic forest soils in SW Sweden. Water Air Soil Pollut. 139: 343–
354. doi:10.1023/A:1015858922200.
Norström, S.A., Bylund, D., Vestin, J.L.K., and Lundström, U.S. 2012.
Initial effects of wood-ash application to soil and soil solution chemistry in a
small, boreal catchment.
Geoderma, 187–188: 85–93. doi:10.1016/j.geoderma.2012.04.011.
Nowotny, I., Dähne, J., Klingelhöfer, D., and Rothe, G.M. 1998. Effect of
artificial soil acidification and liming on growth and nutrient status of mycorrhizal
roots of Norway spruce (Picea abies (L.) Karst.). Plant Soil, 199: 29–40.
doi:10.1023/A:1004265511068.
Österås, A.H., Sunnerdahl, I., and Greger, M. 2005. The impact of wood-ash
and green liquor dregs application on Ca, Cu, Zn and Cd contents in bark and wood
of Norway spruce. Water Air Soil Pollut. 166: 17–29. doi:10.1007/s11270005-7747-
0.
Ozolincius, R., and Varnagiryte, I .2005. Effects of wood-ash application on
heavy metal concentrations in soil, soil solution and vegetation in a Lithuanian Scots
pine stand. Metsanduslikud Uurim. 42: 66–73.
Ozolincius, R., Armolaitis, K., Raguotis, A., Varnagiryte, I., and Zenkovaite, J.
2006. Influence of wood-ash recycling on chemical and biological condition of forest
Arenosols. J. For. Sci. 52: 79–86.
Ozolincius, R., Varnagiryte, I., Armolaitis, K., and Karltun, E. 2007. Effects of
wood as and fertilization on Scots pine crown biomass. Biomass Bioenergy, 31(10):
700–709. doi:10.1016/j.biombioe.2007.06.016.
145
Pabian, S.E., Rummel, S.M., Sharpe, .E., and Brittingham, M.C. 2012. Terrestrial
liming as a restoration technique for acidified forest ecosystems. Int. J. For. Res. 2012:
1–10. doi:10.1155/2012/976809.
Pärn, H. 2004. The effect of wood-ash application on litter decomposition in a
Scots pine stand. Metsanduslikud Uurim. 41: 35–41.
Pärn, H. 2005. Effect of wood ash application on radial and height growth of
young Scots pines (Pinus sylvestris L.). Metsanduslikud Uurim. 42: 48–57.
Persson, T., Rudebeck, A., and Wirén, A. 1995. Pools and fluxes of carbon and
nitrogen in 40-year-old forest liming experiments in southern Sweden. Water Air Soil
Pollut. 85: 901–906. doi:10.1007/BF00476944.
Pothoff, A., Asche, N., Stein, B., Muhs, A., and Beese, F. 2008. Earthworm
communities in temperate beech wood forest soils affected by liming. Eur. J. Soil
Biol. 44: 247–254. doi:10.1016/j.ejsobi.2007.05.004.
Püttsepp, U., Lõhmus, K., Persson, H.A., and Ahlström, K. 2006. Fineroot
distribution and morphology in an acidic Norway spruce (Picea abies (L.) Karst.) stand
in SW Sweden in relation to granulated wood-ash application. For. Ecol. Manage. 221:
291–298. doi:10.1016/j.foreco.2005.10.012.
Qian, X., Köttke, I., and Oberwinkler, F. 1998. Influence of liming and
acidification on the activity of the mycorrhizal communities in a Picea abies (L.) Karst.
stand. Plant Soil, 199: 99–109. doi:10.1023/A:1004243207414.
Rineau, F., and Garbaye, J. 2009. Effects of liming on ectomycorrhizal
community structure in relation to soil horizons and tree hosts. Fungal Ecology,
2(3):103–109. doi:10.1016/j.funeco.2009.01.006.
Ring, E., Jacobson, S., and Nohrstedt, H.-Ö. 2006. Soil-solution chemistry in
aconiferous stand after adding wood-ash and nitrogen. Can. J. For. Res. 36(1): 153–163.
doi:10.1139/x05- 242.
Rosberg, I., Frank, J., and Stuanes, A.O. 2006. Effects of liming and fertilization
on tree growth and nutrition cycling in a Scots pine ecosystem in Norway. For. Ecol.
Manage. 237: 191– 207. doi:10.1016/j.foreco.2006.09.045.
Rosenberg, O., Persson, T., Högbom, L., and Jacobson ,S.2010. Effects of wood-
ash application on potential carbon and nitrogen mineralisation at two forest sites with
different tree species, climate and Nstatus. For. Ecol. Manage. 260: 511–518.
doi:10.1016/j.foreco.2010.05.006.
146
Rosenberg, W., Nierop, K.G.J., Knicker, H., de Jager, P.A., Kreutzer, K., and
Weiss, T. 2003. Liming effects on the chemical composition of the organic
surface layer of a mature Norway spruce stand (Picea abies (L.)Karst.). Soil
Biol. Biochem. 35: 155–165. doi:10.1016/S0038-0717(02)00250-X.
Saarsalmi, A., Mälkönen, E., and Piirainen, S. 2001. Effects of wood-ash
fertilization on forest soil chemical properties. Silva Fenn. 35: 355–368.
Saarsalmi, A., Mälkönen, E., and Kukkola, M. 2004. Effect of wood-ash
fertilization on soil chemical properties and stand nutrient status and growth of some
coniferous stands in Finland. Scand. J. For. Res. 19: 217–233.
doi:10.1080/02827580410024124.
Saarsalmi, A., Tamminen, P., Kukkola, M., and Levula, T. 2011. Effects of
liming on chemical properties of soil, needle nutrients and growth of Scots pine
transplants. For. Ecol. Manage. 262:278–285. doi:10.1016/j.foreco.2011.03.033.
Saarsalmi, A., Smolander, A., Kukkola, M., Moilanen, M., and Saramäki, J.
2012. 30-Year effects of wood-ash and nitrogen fertilization on soil chemical
properties, soil microbial processes and stand growth in a Scots pine stand. For. Ecol.
Manage. 278: 63–70. doi:10.1016/j.foreco.2012.05.006.
Safford, L.O., and Czapowskyj, M.M. 1986. Fertilizer stimulates growth and
mortality in a young Populus–Betula stand: 10-year results. Can. J. For. Res. 16(4): 807–
813. doi:10.1139/x86-143.
Sikström, U. 1997. Effects of low-dose liming and nitrogen fertilization on
stemwood growth and needle properties of Picea abies and Pinus sylvestris. For. Ecol.
Manage. 95: 261–274. doi:10.1016/S0378-1127(97)00025-X. Sikström, U. 2001.
Effects of pre-harvest soil acidification, liming and N fertilization on the survival,
growth and needle concentrations of Picea abies (L.) Karst. seedlings. Plant Soil, 231:
255–266. doi:10.1023/A:1010390931645.
Simmons, J.A., Yavitt, J.B., and Fahey, T.J. 1996. Watershed liming effects on
the forest floor N cycle. Biogeochemistry, 32: 221–244. doi:10.1007/BF02187140.
Smallidge, P.J., and Leopold, D.J. 1997. Effects of watershed liming on Picea
rubens seedling biomass and nutrient element concentration. Water Air Soil Pollut. 95:
193–204.
Solla-Gullón, F., Santalla, M., Rodriguez-Soalleiro, R., and Merino, A. 2006.
Nutritional status and growth of a young Pseudotsuga menziesii plantation in a
temperate region after application of wood-bark ash. For. Ecol. Manage. 237: 312–321.
147
Sramek, V., Materna, J., Novovtny, R., and Fadrhonsova, V. 2006. Effect of
forest liming in the Western Krusnehory Mts. J. For. Sci. 52: 45–51.
Staples, T.E., and van Rees, K.C.J. 2001. Wood/sludge ash effects on white
spruce seedling growth. Can. J. Soil Sci. 81: 85–92. doi:10.4141/S00-014.
Varnagiryte˙-Kabašinskiene˙,I. 2008. Complex study of foliage nutrient status in
ash fertilized Scots pine stands in Lithuania. J. For. Sci. 54: 195–206.
Veerkamp, M.T., DeVries, B.W.L., and Kuyper, T.W. 1997. Shifts in species
composition of lignicolous macromycetes after application of lime in a pine forest.
Mycol. Res. 101: 1251–1256. doi:10.1017/S0953756297004036.
Wallander, H., Fossum, A., Rosengren, U., and Jones, H. 2005. Ectomycorrhizal
fungal biomass in roots and uptake of P from apatite by Pinus sylvestris seedlings
growing in forest soil with and without wood-ash amendment. Mycorrhiza, 15: 143–
148.
Wang, P., Olsson, B.A., and Lundkvist, H. 2007. Effects of wood-ash, vitality
fertilizer and logging residues on needle and root chemistry in a young Norway spruce
stand. Scand. J. For. Res. 22: 136–148. doi:10.1080/02827580701231480.
Wanner, M., Funke, I., and Funke, W. 1994. Effects of liming, fertilization and
acidification on pH, soil moisture, and ATP content of soil from a spruce forest in
Southern Germany. Biol. Fertil. Soils, 17: 297–300. doi:10.1007/BF00383985.
Zaccherio, M., and Finzi, T.G. 2007. Atmospheric deposition may affect
northern hardwood forest composition by altering soil nutrient supply. Ecol. Appl.
17(7): 1929–1941.
Zimmermann, S., and Frey, B. 2002. Soil respiration and microbial properties
in an acid forest soil: effects of wood-ash. Soil Biol. Biochem. 34: 1727–1737.
doi:10.1016/S00380717(02)00160-8.
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4.1 Implications for recovery and an ecosystem approach to mitigation
4.2 Brief Background
Legacy effects have slowed the recovery of acidified waters in areas of eastern
Canada that feature both low base cation weathering rates and low base cation
deposition. Although, S deposition has decreased by approximately 50% since its peak
in 1970s (Jeffries et al. 2003) acid rain has left lingering effects in both soil
(Huntington et al. 2000; Watmough and Dillon 2003) and surface water of acid-
sensitive environments on the boreal shield (Dillon et al. 1992; Watmough et al. 2003;
Houle et al. 2006; Clair et al. 2007; MacDougal et al. 2009). Recovery of acidified
lakes and streams has been influenced by the size of base cation reservoirs present in
acid sensitive soils (Likens et al. 1996; Driscoll et al. 2001; Houle et al. 2006) and it
has been well established that these pools have been preferentially calcium (Ca)
depleted in poorly buffered areas (Johnson et al. 1994; Watmough and Dillon 2003).
In a large number of boreal shield lakes and streams, Ca is now low, is
continuing to decline and in some cases is approaching estimated pre-industrial, steady
state concentration levels. Small, shallow lakes at higher elevation in small catchments
with higher runoff, low base cation weathering rates and that are minimally impacted
by the influence of roads (and associated development, salt and/or road dust
suppressants) and agriculture (and associated development and Ca input with fertilizers)
are the lakes most at risk to amplified Ca depletion from forest harvesting. This risk
increases as the forest percentage and volume harvested in the catchment increases.
The only self-repair method for anthropogenically acidified ecosystems is the soils
ability to buffer acidic deposition which is mainly determined by the rate of release
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of base cations from minerals (Holmquist 2001). Weathering rates are low in most areas of
the boreal shield and as a result, Ca has been preferentially depleted from base cation
reservoirs in acid sensitive soils mainly as an unexpected consequence of the impact of
acid rain (Watmough and Dillon 2003). This depletion of exchangeable soil base cations
and the resulting decline in lake Ca cannot be self-repaired by the ecosystem when sites
are harvested.
Many lakes in the MRW have been identified as lakes at risk of Ca limitation
below critical levels required by biota of 1.5–2 mg L–1 (Ashforth and Yan 2008; Tan and
Wang 2010) and recent work has indicated the elimination of some Ca-rich keystone biota
can contribute to lake algal blooms (Korosi et al. 2012) and jellification (Jeziorski et al.
2014). It is possible that some lakes may have had pre-industrial levels less than these
critical levels. However, what were once sufficient lake Ca levels in a natural, pristine,
steady state are likely no longer adequate to protect against the loss of Ca-sensitive species
when anthropogenic multiple stressors drive Ca lower. To depend on ecosystem self-repair
is to ignore the scientific evidence and seriousness of the Ca problem, as amelioration is
required to protect surface water quality and sustain critical Ca levels of impacted lakes.
Under proposed Crown land forest management cut volumes, ~38% of 364 lakes
in the MRW will fall below 1 mg L–1 which is considered a Ca threshold. Under
harvesting yields of 40% of the total allowable volume cut, ~ 20% will fall below Ca
thresholds compared with just ~8% in the absence of harvesting. Natural recovery of Ca
levels in those lakes approaching critical levels is unpromising given the depleted soil Ca
pools and low rates of weathering of silicate based minerals in the MRW. Anthropogenic
influence has increased Ca levels in some lakes, however, these lakes are the ones most
urbanized and the least likely to be impacted by tree harvesting.
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4.3 Mitigation on an ecosystem basis – benefits to lakes, soils and trees –
win/win/win
In the meta-analysis outlined in Chapter 3, it was demonstrated that over 75%
of Ca-addition to forests resulted in increases in soil base saturation (Reid and
Watmough 2014). Research studies focusing on the in-stream liming of lakes,
streams, and rivers have been limited but ongoing in eastern North America for over
30 years with varying results dependent upon lake and catchment physical and
chemical characteristics, however, most have been able to increase the Ca
concentration of surface waters (Clair and Hindar 2005). However, the majority of
the treatments have been in-stream liming trials of short duration that would require
expensive ongoing applications to maintain the aquatic benefits and that do not
address the underlying issue of soil base cation depletion (Clair and Hindar 2005).
With regard to ecosystem remediation, for most sites, one catchment-wide,
wood-ash application per harvest rotation has been shown to be able to replace any
soil Ca, Mg and K lost after harvesting (Vance 1996). As well, in recent research
and reviews of wood-ash addition, long-lasting positive effects of treatment on tree
growth have been observed after a lag time (Pitman et al. 2006; Saarsalmi et al.
2012; Maljanen et al. 2014; Reid and Watmough 2014).
Although there have been fewer investigations on surface water chemistry
from forest soils treated with Ca-addition, there have been increased
concentrations of Ca in leachate surface waters dependent on the form and dose of
wood-ash applied to the catchment (Williams et al. 1996; Piirainen et al. 2001;
2013; Núñez-Delgado et al. 2011).
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4.4 Knowledge Gaps
There are significant gaps in existing Boreal Shield lake chemical data,
which will require an expansion of fieldwork to address questions about the spatial
and temporal aspects of Ca decline patterns. Additionally, future work is required
to determine the impact of forest harvesting on Ca-poor soils and the subsequent
influence on surface water quality on both spatial and temporal scales. More
research will help improve understanding about the extent of Ca depletion and
could predict how soon critical Ca levels in lakes will be reached; moreover, it will
advance knowledge of the effects of Ca addition treatment on both soil and lake Ca
levels. As Mg is also declining, similar spatial and temporal research could enhance
the knowledge of the status of this nutrient on the Boreal shield. Additionally, the
close monitoring of lake pH should be a priority. Even though there has been some
recovery of lake pH, the continued decline of Ca could potentially drive pH lower.
DOC should also be evaluated regarding the potential to increase lake organic acids.
Ultimately, future work is expected to provide beneficial management practice
recommendations that seek to improve Ca pools in soil and concentrations in surface
water while increasing forest productivity (in effect a win/win/win goal).
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4.5 Directions of future research
Existing soil and water data can be analysed and updated with field
measurements for as many MRW lakes as possible in order to achieve the most accurate
results. Research would involve a combination of fieldwork, data analysis, and dynamic
modeling with the overall goal of aiding forest authorities in achieving the highest
standards of sustainable forest management practices possible with regard to nutrients
essential to both forest productivity and water quality.
Four key objectives that could define the structure of future Ca study.
1. Objective one addresses the question of the spatial component of the problem. To
what extent does the footprint of acid rain reach with regard to decreasing surface water
Ca patterns? The uncertainties and unknowns associated with Ca depletion in soils and
surface water needs to be examined and their impact will be assessed. Uncertainties are
associated with: estimating Ca weathering rates through modeling (have weathering rates
changed since steady state preindustrial times?), the integrity and accuracy of Ca
deposition values (what is the confidence level in existing deposition data?), the impact
of changes in Ca deposition, the effects of climate change on changes in runoff volume,
lake volume and the subsequent impacts on Ca lake concentration, changes in demand
for wood products linked to forecasted harvesting volumes, and the effects on Ca
depletion of harvesting practices taking place on private land (for which little to no data
exist). Monte-Carlo based modeling could be used to determine the probabilities of
different outcomes occurring and to predict the bounds of Ca depletion in lakes. Monte
Carlo methods have proven to be extremely useful for simulating environmental
scenarios involving non-linear processes with significant uncertainty in inputs (Phillips
153
and Watmough 2013).
2. Objective two addresses the question of timing. We know surface water Ca is
declining but when will critical levels be reached? In order to estimate how soon lakes
spatially identified by objective one will reach identified critical Ca thresholds,
dynamic modeling with MAGIC will be utilized for those areas with sufficient data.
MAGIC, the model of acidification of groundwater in catchments (Cosby et al. 1985,
2001), has been used extensively in Europe and eastern North America and has proven
to be a useful model for simulating water acidification responses at the catchment scale
(Cosby et al. 1985; Aherne et al. 2003; Clair et al. 2003; Whitfield et al. 2007). The
model uses site-specific information to estimate “patterns, time scales and magnitudes”
of long-term responses in surface water chemistry to variations in the levels acidic
deposition (Cosby et al. 2001). One of the largest uncertainties in MAGIC modeling is
the estimated weathering rate. However, site-specific, soil surface area measurements
should reduce uncertainty associated with the weathering rate model predictions and is
preferred (Warfvinge and Sverdrup 1992).
3. Objective three addresses the question of mitigation of Ca depletion. Can the
addition of wood-ash to forest soils stop or slow the depletion of Ca levels? Further
work should examine the effects of experimental wood-ash treatment trials in eastern
Canada on ecosystem nutrient cycling and subsequently on Ca concentrations in lakes.
It should also investigate whether the future harvest volume increases could potentially
offset costs of such mitigation.
4. Based on the results of the first three objectives, it would be possible to recommend
beneficial forestry management guidelines to delay reaching critical Ca levels in
154
surface water. These nutrient management practices could, in effect, moderate Ca
depletion and slow the approach of critical levels. These recommendations could help
ensure the long-term sustainability of both ecosystem and economic health in areas
like the Muskoka River Watershed where the economy is intrinsically tied to the
integrity of the environment and especially to the lake water quality.
4.6 Conclusions
Decreasing Ca concentrations in lakes located in base poor catchments is an acid-
rain driven legacy effect threatening the health and integrity of surface water and is a
problem that can be compounded by additional Ca removals through forest harvesting.
For many lakes that are already subject to multiple stressors, the decline of this nutrient
that is essential to both forest and lake health and integrity, is beyond the bounds of
natural recovery. For some larger surface-fed lakes, in catchments impacted by
relatively high densities of roads and/or agriculture, maintenance of Ca levels above
critical values may not be an issue. However, for those small, shallow lakes located in
relatively small harvested catchments with lake Ca concentrations approaching or
below known critical Ca concentrations, future forest management practices that
include the addition of Ca to the harvested forests may be the best choice to maintain
lake quality. The use of Ca-addition in the MRW as an ecosystem tool for managing
the cumulative effects of past, present and future stress from harvesting could not only
benefit the integrity of the lakes and productivity of managed forests but could also
further the protection of an economy so dependent on the quality of its environment.
Further research into the potential application of this mitigation tool as a sustainable
forest management best practice should be pursued without delay.
155
4.7 References
Aherne, J., Dillon, P. J. and Cosby, B. J. 2003. Acidification and recovery of aquatic
ecosystems in south-central Ontario, Canada: regional application of the MAGIC
model. Hydrol. Earth Syst. Sci. 7, 561–573.
Ashforth, D. and Yan, N. D. 2008. The interactive effects of calcium concentration and
temperature on the survival and reproduction of Daphnia pulex at high and low food
concentrations. Limnol. Oceanogr. 53(2): 420–432.
Clair, T. A., Dennis, I. F., and Cosby, B. J. 2003. Probable changes in lake chemistry in
Canada’s Atlantic Provinces under proposed North American emission reductions,
Hydrol. Earth Syst. Sci. 7(4): 574–582.
Clair, T. A., Dennis, I. F., Scruton, D. A. and Gilliss, M. 2007. Freshwater acidification
research in Atlantic Canada: a review of results and predictions for the future.
Environ. Rev. 15: 153–167.
Clair, T.A. and Hindar, A. 2005. Liming for the mitigation of acid rain effects in fresh
waters: A review of recent results. Environ. Rev. 13:91–128.
Cosby, B. J., Hornberger, G. M., Galloway, J. N. and Wright, R. F. 1985. Modeling the
effects of acid deposition: Assessment of a lumped parameter model of soil water
and streamwater chemistry, Water Resour., 21: 51–63.
Cosby, B. J., Ferrier, R. C., Jenkins, A. and Wright, R. F. 2001. Modelling the effects of
acid deposition: refinements, adjustments and inclusion of nitrogen dynamics in the
MAGIC model. Hydrol. Earth Syst. Sci. 5: 499–517.
Dillon, P. J. and La Zerte, B. D. 1992. Response of the Plastic Lake catchment, Ontario, to
reduced sulphur deposition, Environ. Pollut.77: 211–217.
Driscoll, C.T., Lawrence, G.B., Bulger, A.J., Butler, T., Cronan, C.S., Eagar, C., Lambert,
K.F., Likens, G.E., Stoddard, J.L., and Weathers, K.C. 2001. Acidic deposition in
the northeastern United States: sources and inputs, ecosystem effects, and
management strategies. Bioscience 51: 180–198.
Houle, D., Ouimet, R., Couture, S. and Gagnon, C. 2006. Base cation reservoirs in soil
control the buffering capacity of lakes in forested catchments. Can. J. Fish. Aquat.
Sci. 63: 471–474.
Huntington, T.G., Hooper, R.P., Johnson, C.E., Aulenbach, B.T., Cappellato, R., and
Blum, A.E. 2000. Calcium depletion in a southeastern United States forest
156
ecosystem. Soil Sci. Soc. Am. J. 64(5): 1845–1858.
Jeffries, D. S., Clair, T. C., Couture, S., Dillon, P. J., Dupont, J., Keller, W., McNicol, D.
K., Turner, M. A., Vet, R., and Weeber, R. 2003. Assessing the recovery of lakes in
southeastern Canada from the effects of acid deposition, Ambio 32: 176–182.
Johnson, A.H., Anderson, S.B., and Siccama, T.G., 1994. Acid rain and soils of the
Adirondacks. Changes in pH and available calcium: Can. J. For. Res. 24: 193–198.
Jeziorski, A., Tanentzap, A.J., Yan, N.D., Paterson, A.M., Palmer, M.E., Korosi, J.B.,
Rusak, J.A., Arts, M.T., Keller, W.B., Ingram, R., Cairns, A. and Smol, J.P. 2014.
The jellification of north temperate lakes. Proc. R. Soc. B. 282: 20142449.
Korosi, J. B., S. M. Burke, J. R. Thienpont and J. P. Smol, 2012. Anomalous rise in
algal production linked to lakewater calcium decline through food web interactions.
Proc. R. Soc. B. 279: 1210–1217.
Likens, G.E., Driscoll, C.T., and Buso, D.C. 1996. Long-term effects of acid rain:
response and recovery of a forest ecosystem. Science 272: 244–246.
MacDougal, G., Aherne, J. and Watmough, S.A. 2009. Impacts of acid deposition at
Plastic Lake: forecasting chemical recovery using a Bayesian calibration and
uncertainty propagation approach. Hydrol. Res. 40 (2-3): 249–260.
Maljanen, M., Liimatainen, M., Hytönen, J. and Martikainen, P. J. 2014: The effect of
granulated wood-ash fertilization on soil properties and greenhouse gas (GHG)
emissions in boreal peatland forests. Boreal Env. Res. 19: 295–309.
Núñez-Delgado, A., Quiroga-Lago, F., Soto-Gonzaléz, B. 2011. Runoff characteristics
in forest plots before and after wood ash fertilization. Maderas. Ciencia y
tecnologia 13: 267–284.
Piirainen, S., Saarsalmi, A., and Mälkönen, E. 2001. Effects of wood-ash fertilization on
forest soil chemical properties. Silva Fenn. 35: 355–368.
Piirainen, S., Domisch, T., Moilanen, M. and Nieminen, M. 2013. Long-term effects of
ash fertilization on runoff water quality from drained peatland forests. Forest Ecol.
Manage. 287(1): 53–66.
Pitman, R. M. 2006. Wood ash use in forestry–a review of the environmental impacts.
Forestry 79(5): 563–588.
Phillips, T. and Watmough, S.A. 2012. A Nutrient Budget for a Selection Harvest in
Eastern Canada: Implications for long-term sustainability. Can. J. For. Res. 42:
2064–2077.
157
Reid, C. and Watmough S.A. 2014. Evaluating the effects of liming and wood-ash
treatment on forest ecosystems through systematic meta-analysis. Can. J. For.
Res. 44: 867–885.
Saarsalmi, A., Smolander, A., Kukkola, M., Moilanen, M. and Saramäki, J. 2012. 30-
year effects of wood ash and nitrogen fertilization on soil chemical properties, soil
microbial processes and stand growth in as Scots pine stand. Forest Ecol. Manage.
278: 63–70.
Tan, Q. and W. Wang, 2010. Interspecies differences in calcium content and
requirements in four freshwater cladocerans explained by biokinetic parameters.
Limnol. Oceanogr. 55: 1426–1434.
Vance, E.D. 1996. Land application of wood-fired and combination boiler ashes: an
overview. J. Environ. Qual. 25: 937–944.
Watmough, S.A., and Dillon, P.J. 2003. Base cation and nitrogen budgets for a mixed
hardwood catchment in south-central Ontario. Ecosystems 6: 675–693.
Watmough, S.A., Aherne, J., Dillon, P.J. 2003. Potential impact of forest harvesting on
lake chemistry in south-central Ontario at current levels of acid deposition. Can. J.
Fish. Aquat. Sci. 60: 1095–1103.
Warfvinge P, Sverdrup H. 1992. Calculating critical loads of acid deposition with
PROFILE-a steady-state soil chemistry model. Water Air Soil Pollut. 63: 119–
43.
Whitfield, C. Watmough, S.A., Aherne, J., and Dillon, P.J. 2007. Modelling
acidification, recovery and target loads for headwater catchments in Nova Scotia,
Canada. Hydrol. Earth Sys. Sci. Disc. 11(2): 951–963.
Williams, T., Hollis, C. and Smith, B. 1996. Forest soil and water chemistry following
bark boiler bottom ash application. J. Environ. Qual. 25: 955–961.