Variation in post-wildfire regeneration of borealmixedwood forests: underlying factors and implicationsfor natural disturbance-based management

Stefanie M. Gartner • Mike Bokalo • S. Ellen Macdonald •

Ken Stadt

Received: 17 September 2013 / Accepted: 20 December 2013 / Published online: 8 January 2014� Springer Science+Business Media Dordrecht 2014

Abstract To test the direct regeneration hypothesis and support natural disturbance-

based forest management we characterized the structure and composition of boreal

mixedwood forests regenerating after large wildfires and examined the influence of pre-fire

stand composition and post-fire competing vegetation. In stands which had been deciduous

(Populus sp.)-dominated, conifer (white spruce)-dominated, or mixed pre-fire we measured

regeneration stocking (presence in 10 m2 plots), density and height 10–20 years post-burn

in five wildfires in Alberta, Canada. Most plots regenerated to the deciduous or mixed

stocking types; plots in the older fire and in stands that were pure conifer pre-fire had

higher amounts of conifer regeneration. Surprisingly, regeneration in pre-fire ‘pure’ white

spruce stands was most often to pine, although these had not been recorded in the pre-fire

inventory. Pre-fire deciduous stands were the most resilient in that poplar species domi-

nated their post-fire regeneration in terms of stocking, density and height. These stands also

had the highest diversity of regenerating tree species and the most unstocked plots. High

grass cover negatively affected regeneration density of both deciduous and conifer trees.

Our results demonstrate the natural occurrence of regeneration gaps, pre- to post-fire

changes in forest composition, and high variation in post-fire regeneration composition.

These should be taken into consideration when developing goals for post-harvest regen-

eration mimicking natural disturbance.

Electronic supplementary material The online version of this article (doi:10.1007/s11056-013-9404-6)contains supplementary material, which is available to authorized users.

S. M. Gartner (&)Chair of Site Classification and Vegetation Sciences, Faculty of Environment and Natural Resources,University of Freiburg, Tennenbacherstr. 4, 79085 Freiburg, Germanye-mail: stefanie.gaertner@waldbau.uni-freiburg.de

M. Bokalo � S. E. MacdonaldDepartment of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada

K. StadtAlberta Environment and Sustainable Resource Development, 9920-108 St., Edmonton, AB T5K 2M4,Canada

123

New Forests (2014) 45:215–234DOI 10.1007/s11056-013-9404-6

Keywords Direct regeneration hypothesis � Forest resilience � Boreal

mixedwood stand dynamics � Picea glauca � Populus tremuloides � Post-fire

regeneration � Wildfire

Introduction

Wildfire is the predominant natural disturbance in many forests around the world,

including the boreal forest of western Canada (Johnson et al. 1998). Post-fire regeneration

is affected by the characteristics of the fire including fire interactions with the landscape

topography (e.g., Albani et al. 2005), site conditions (e.g., Bridge and Johnson 2000), and

the regeneration ecology of species (e.g., Chen et al. 2009). The direct regeneration

hypothesis predicts that forests will recover to the pre-disturbance composition within a

short time period after disturbance (Yih et al. 1991); some studies in the boreal have

supported this (e.g., Greene and Johnson 1999). Other studies, however, found that this

does not hold true in the boreal because, while broadleaf species and conifers with

serotinous cones regenerate directly after a disturbance this may not be true for non-

serotinous conifers (Lavoie and Sirois 1998; Ilisson and Chen 2009b).

To adopt a natural disturbance-based approach to forest management (Bergeron and

Harvey 1997) we need to understand the range of variation in natural regeneration fol-

lowing wildfire. Pre- to post-fire differences in composition and structure are of interest

because they can tell us about the resilience of different pre-fire cover types, and indicate

the extent to which pre-harvest composition should be considered in planning for regen-

eration. There is a need for data with which to benchmark post-harvest regenerating stands

relative to those establishing after natural disturbance, because data from regeneration

surveys are being used to predict future yields (ASRD 2007). Further, such information can

be used to develop approaches for post-harvest regeneration that more closely resemble the

natural situation and do not require expensive management inputs (Lieffers et al. 2008). As

our landscapes become increasing dominated by managed forests and post-fire stands are

widely subjected to salvage harvesting, we are rapidly losing the opportunity to create the

quantitative benchmarks needed to inform approaches and standards for natural distur-

bance-based management.

Most of the boreal mixedwood stands harvested in western Canada today are of fire

origin and their regeneration has been structured by large wildfires of variable severity

(Burton et al. 2008). We have a good understanding of regeneration processes in boreal

forests (e.g., Simard et al. 1998; Greene et al. 1999, 2004; Johnstone et al. 2004) and a

general understanding of post-fire boreal mixedwood stand dynamics (Andison and

Kimmins 1999; Chen and Popadiouk 2002; Peters et al. 2006; Taylor and Chen 2011;

Bergeron et al. 2014). However, we have a poorer understanding of the relationship

between pre- and post-fire forest composition in the western boreal (but see Greene and

Johnson 1999; Lavoie and Sirois 1998; Bergeron 2000; Chen et al. 2009). Most impor-

tantly, we lack quantitative information on the range of variation in structure and com-

position of post-fire regeneration—particularly for the diversity of boreal mixedwood

forest types in the western Canadian boreal and for stands of intermediate age (past the

early post-fire regeneration stage but\20 years). Such information is urgently needed for

the development of approaches and standards for natural-disturbance based management at

the stand and landscape scale (Andison and Kimmins 1999).

216 New Forests (2014) 45:215–234

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The dominant tree species in the boreal forest are generally well adapted to disturbance

by fire (Greene et al. 1999). The broadleaved species [trembling aspen (Populus tremu-

loides Michx.); balsam poplar (Populus balsamifera L.) and white birch (Betula papyrifera

Marsh.)] have strong vegetative reproduction (Greene et al. 1999). The boreal pine species

[Jack pine (Pinus banksiana Lamb.), lodgepole pine (Pinus contorta Dougl.)] and black

spruce [Picea mariana (Mill)] have long lived aerial seed banks held in serotinous or semi-

serotinous cones. Only white spruce [Picea glauca (Moensch) Voss] and balsam fir [Abies

balsamea (L.) Mill.] do not have these advantages; their post-fire establishment therefore

depends on seed dispersal from surviving individuals, patches or from unburned edges

(Greene et al. 1999). These two species are often described as ‘late-successional’ because

of their ability to grow under lower light conditions and establish under the canopy of the

broadleaf or pine pioneers (Greene et al. 1999). However, white spruce can establish

directly after fire during the small window of opportunity when competition is low and

mineral soil seedbeds are available; white spruce regeneration is particularly abundant

when these ideal conditions coincide with a heavy cone crop year in this masting species

(Peters et al. 2005, 2006). Given the short fire cycle in the boreal forest of western Canada

(\5 % of the area is older than 150 years; Weir et al. 2000), immediate post-fire regen-

eration of white spruce must be an important process explaining the prevalence of mature

white spruce forest on the landscape.

Herein we present data on forest regeneration 10–20 years after large wildfires on

upland sites within the boreal mixedwood natural region in Alberta, Canada. Our objective

was to document the range of variation in composition of regenerating forests and to

examine the influence of pre-fire cover type and post-fire competing vegetation on: post-

fire stocking type, presence of P. glauca regeneration, the density of conifer and deciduous

regeneration and the height of Populus sp. regeneration. We hypothesized that, even if

post-fire relative abundance is not proportional to pre-fire, the pre-fire stand composition

will have an important influence on post-fire composition of these forests.

Materials and methods

Study area

We sampled stands in five wildfires located in the Lower Foothills (three fires) and Central

Mixedwoods (two fires) natural subregions of central and northeastern Alberta (Table 1).

These represent a gradient of climatic conditions across which white spruce-dominated

mixedwood forests are found in Alberta (Natural Regions Committee 2006). The Lower

Foothills is at higher elevation than the Central Mixedwood (650–1,625 m vs.

200–1,050 m. a.s.l.) and is cooler in summer (mean temperatures 12.8 vs. 13.7 �C),

warmer in the winter (mean temperatures -7.8 vs. -11.9 �C) and has higher annual (464

vs. 389 mm) and summer (295 vs. 238 mm) precipitation. Soils on upland sites in both

natural subregions developed on morainal and lacustrine deposits and are predominantly

Luvisols and Brunisols (Beckingham and Archibald 1996; Beckingham et al. 1996). In

both regions, mesic sites have varying dominance by broadleaf (trembling aspen, balsam

poplar, paper birch) and conifer (lodgepole pine, Jack pine, white spruce, balsam fir) trees.

Fires 10–20 years old (1988–1998) were selected with the aid of provincial fire maps

and the provincial fire database (ASRD 2008). This age range was chosen because it is

similar to the age range at which postharvest regeneration performance surveys are con-

ducted in Alberta (nominally 8–14 years although most surveys are done towards the

New Forests (2014) 45:215–234 217

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higher end of this range; ASRD 2003, 2007). We selected fires that burned predominantly

on upland (modal) ecosites with a representative range of pre-fire forest composition. We

selected only early season fires because these are historically dominant in this part of the

boreal (Johnson et al. 1999). Salvaged areas were deemed unsuitable for sampling and

finding fires that had sufficient representative unsalvaged area severely constrained our

selection of fires. We were only able to find five suitable and accessible fires, four at the

younger end of the chosen range (10–13 years) and one older (20 years) (Table 1). Each of

these fires was large and encompassed considerable variation within its boundaries, as is

typical in wildfires in this region.

Stratification and sampling design

Within each fire we selected sample units on mesic upland sites which were generally flat.

Forest inventories for the study region delineate stands from aerial stereo photos

(1:15,000–1:40,000 scale) as areas of similar species composition, height, crown cover,

and landscape position subject to a minimum size of 2 ha (Alberta Forest Service 1985).

We designated sample units as areas [2 ha which were homogenous in pre-fire species

composition, structure, and site conditions. Sampling units were selected using the ‘‘Phase

3’’ forest inventory maps (Alberta Forest Service 1985), which provided information on

pre-fire stand structure and species composition; unfortunately, information on pre-dis-

turbance forest composition is not retained in the more recent Alberta Vegetation Inven-

tory. Suitable sample units were those with a pre-fire composition dominated by white

spruce, aspen, or a mixture of the two species. Thus, the sampled areas were assumed to be

suitable for post-fire regeneration to white spruce and aspen. All areas were checked in the

field before sampling to ensure they had been burned (charred snags, few residual trees

remaining) and had not been salvage harvested. The sample units were stratified, based on

the pre-fire species composition, into categories according to the Alberta Forest Man-

agement Planning Standard (ASRD 2006) as follows: conifer dominated (C), mixedwood

with conifer species leading (CD), mixedwood with deciduous species leading (DC), and

deciduous dominated (D) (Table 2). In all cases the conifer species was white spruce and

the deciduous was aspen or balsam poplar. Here we use the term ‘deciduous’ to refer to

broadleaved species as is the convention in forest management in the region.

Table 1 Characteristics of the fires sampled in the study

Fire Name Firetype

Year ofburn

Ecological subregionb Latitude Longitude Size(ha)

Chip Lake (CL-10)a Surface 1998 Lower Foothills (LF) 53.5519 -115.371 10,886

O0Chiese (OC-20) Surface 1988 Lower Foothills 52.6996 -115.3339 7,646

Virginia Hills(VH-10)

Crown 1998 Lower Foothills 54.7068 -116.5306 163,138

Mariana Lakes(ML-13)

Surface 1995 Central Mixedwood(CM)

54.6622 -110.4803 132,679

Mitsue (MI-10) Crown 1998 Central Mixedwood 55.2102 -114.6228 49,670

a Abbreviations in brackets: fire name, including age in years at time of sampling; 10- and 13-year fireswere considered ‘younger’ while the 20-year fire is referred to as ‘older’b Natural Regions Committee (2006)

218 New Forests (2014) 45:215–234

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Data collection

A systematic grid (30 m spacing) was laid out in each sampling unit and circular plots

(10 m2; 1.78 m radius) were centered on each point. We had planned on sampling at least

30 plots per sampling unit (if the sampling units were at least 2.7 ha) but this was not

always possible due to plots falling in unsuitable locations (rock, water, unburned, salvage

logged). In total we sampled 503 plots across the four pre-fire forest types and the five fires

(Table 2).

At the time we sampled there were several different ground survey protocols for

sampling post-harvest tree regeneration in Alberta. We followed one of the more detailed

protocols, the WAS Regeneration Survey Manual (WAS 2008), with several additional

measurements. In each 10 m2 circular plot, the number of trees taller than 0.3 m was

counted per species. We further categorized the abundance of white spruce smaller than

0.3 m in three classes: none, few (B5) and many ([5). For the tallest conifer and deciduous

tree (over 0.3 m) in each plot we measured the total height, the diameter at stump (0.3 m)

height and recorded the species. Crown closure of tall shrubs (i.e., alder and willow) was

estimated in classes (\5; 5.1–10; and [10 % in 10 % classes). The canopy height of tall

shrubs within the plot was estimated. Additionally, percent cover (\5; 5.1–10; and[10 %

in 10 % classes) was visually estimated for eight layers: mosses and lichens, graminoids,

forbs, shrubs, trees (above 10 m in height; i.e., pre-fire residuals), exposed mineral soil,

exposed rock, and fallen dead wood. We estimated the distance to a potential seed tree in

four categories: \30, 30–100, 100–500 and [500 m. The data were collected in the

summer of 2008.

Data analysis

In Alberta, ‘‘stocking’’ is defined as the presence of an acceptable (meeting regional

quality, height and species criteria) tree in a 10 m2 plot. Our criteria were simply the

presence of deciduous species or conifer species[0.3 m height. We summarized stocking

by time since fire, i.e., 10 years [Chip Lake (CL-10), Mitsue (MI-10), Virginia Hills (VH-

10); 13 years: Mariana Lakes (ML-13); 20 years: O’Chiese (OC-20) and also for fires

grouped as: ‘‘younger’’ (y) (10 and 13 years old: CL-10, MI-10, VH-10, ML-13] versus

‘‘older’’ (o) (OC-20) and by the four pre-fire cover types (Supplement 1). Plots were also

categorized as to their stocking type: conifer (‘C’) plots had at least one conifer taller than

Table 2 Number of sampled plots (503 in total) in the five fires by pre-fire cover typea: ‘pure’ conifer (C),conifer-dominated (CD), deciduous-dominated (DC) and ‘pure’ deciduous (D)

Fire name C CD DC D

Chip Lake (CL-10) 46 42 49 103

O’Chiese (OC-20) 13 18 17 28

Virginia Hills (VH-10) 23 13 27 10

Mariana Lakes (ML-13) 10 15 15 13

Mitsue (MI-10) 14 15 19 16

Total 106 103 127 171

a Pre-fire cover type was determined using Alberta Phase 3 inventory (Alberta Forest Service 1985), theinventory is based on 1:15,000 scale aerial photographs, the mapped forested stands are areas of uniformcanopy attributes; Canopy species composition is expressed as proportions of estimated merchantablevolume for stands [12 m in height, and otherwise by proportion of canopy cover

New Forests (2014) 45:215–234 219

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0.3 m and no deciduous[0.3 m, deciduous (‘D’) plots had at least one deciduous[0.3 m

and no conifer [0.3 m, mixed (‘MX’) plots had at least one conifer [0.3 m and one

deciduous [0.3 m, while not sufficiently restocked (‘NSR’) plots had no trees [0.3 m.

Species composition of regeneration

To examine variation in species composition of the regeneration we used unconstrained

ordination on the dataset of number of trees ([0.3 m height) per species for each plot. Plots

with no regeneration were removed, leaving a total of 439 plots. We used non-metric

multidimensional scaling (NMDS, Oksanen et al. 2010) based on chord distance and 20

random starts. A step-across procedure was used to replace the dissimilarities for plots

which had no species in common, and zero dissimilarities were changed to 0.0012 (a

randomly selected number lower than all non-zero dissimilarities).

To evaluate the influence of pre-fire cover type, fire and their interaction on composition

of the regeneration we conducted a permutational multivariate analysis of variance

(PERMANOVA) (Anderson 2001). The analysis is partitioning sums of squares of a

multivariate data set analogous to the multivariate analysis of variance but uses semi-

metric and metric distance matrices. Significance tests (a = 0.05) were done using F tests

based on sequential sums of squares from permutations of the raw data (Oksanen et al.

2010).We used the multiple-response permutation procedure (MRPP) to test for pairwise

differences in regeneration composition between the two subregions and between pre-fire

cover types, applying the chord distance and 999 permutations with group size (number of

plots) as a weighting factor. All vegetation analyses were done in R using the ‘‘vegan’’

package 1.17-3 (Oksanen et al. 2010).

Factors influencing regeneration

To determine which factors were influencing regeneration composition we used classifi-

cation and regression trees. Recursive partitioning was used to derive classification trees

for categorical response variables: stocking type and presence/absence of white spruce

regeneration. We used regression trees for continuous response variables (density of

conifer, density of deciduous trees[0.3 m height, height of Populus sp.[0.3 m) within a

conditional inference framework to explain variation in the response variables as a function

of the explanatory variables. We accomplished this with the non-parametric conditional

inferences tree using the ‘‘ctree’’ function of the r package ‘‘party’’ (Hothorn et al. 2010).

At each step of the analysis, one explanatory variable was selected from all the available

variables, based on the best separation of two homogenous groups using a permutation test;

this point is determined by a numerical value (threshold) of the explanatory variable

(Hothorn et al. 2006a, b). The relationships between the response variable and explanatory

variables are presented by a dichotomous tree diagram with nodes that represent split

points, branches that connect nodes, and leaves or terminal nodes that represent the final

groups.

Explanatory variables included in these analyses were: the percent cover of the different

ground layers [grass, forb, shrub, tree, moss, lichen, mineral, rock, organic, downed wood

(CWD)]; crown closure of alder (Alder %) and willow (Willow %); average height of

alder (Alder-ah) and willow (Willow-ah); fire, pre-fire cover type, time since fire, and time

to first white spruce mast crop after the fire (based on Peters et al. 2005; Martin-DeMoor

et al. 2010). For the analyses of density of conifer and deciduous trees [0.3 m height, as

220 New Forests (2014) 45:215–234

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well as the height of Populus sp.[0.3 m we included the number of trees of other species

within the plot as an explanatory ‘‘competition’’ variable.

Results

Range of variation in regeneration composition

There was considerable variation in regeneration composition within and between fires,

stand ages and pre-fire cover types (Supplementary 1).

We next examined the regeneration composition based on which species were ‘potential

crop trees’; i.e., the tallest conifer and/or deciduous tree above 0.3 m height (Supple-

mentary 1). The two Populus species were dominant in this regard, being the only potential

crop tree in 60 % of the plots in the younger fires and in 47 % of plots in the older fire.

Only stands that were ‘pure’ conifer pre-fire had white spruce as the only potential crop

tree, and there were very few of these (2.2–7.7 % of plots). In the younger pre-fire conifer

stands, 13 % of plots had only white birch as the crop tree.

Despite the fact that we avoided sampling areas that had pine pre-fire, many plots

regenerated to pine. In stands that were ‘pure’ white spruce pre-fire 8 % of the plots in the

younger fires had pine as the only crop tree (either lodgepole or jack pine, depending on the

region). For plots that regenerated to a mixture (i.e., had both deciduous and conifer trees

[0.3 m) the proportion that included some pine was even higher; overall for stands that

were ‘pure’ white spruce pre-fire, 34 % of plots regenerated to a pine-deciduous mixture.

Averaged over all pre-fire cover types, a deciduous—pine mixture was found in 10 % of

the plots in the younger stands and 32 % of plots in the older fire. In contrast, white

spruce–deciduous (aspen, poplar or birch) mixtures were found in only 5 % of the plots in

the younger fires and 11 % of plots in the older fire.

The regeneration composition was highly variable and the NMDS ordination showed no

clear differentiation among the four pre-fire stand types. The most obvious pattern in the

ordination diagram was the association of plots that were ‘pure’ white spruce pre-fire with

tree species that did not regenerate in the other three cover types (e.g., tamarack, black

spruce, balsam fir) along with pine and white spruce (Fig. 1). The linear pattern of plots

from the pre-fire mixed and pure deciduous stands on the right side of the first axis (Fig. 1)

is a result of aspen dominance in terms of frequency and density, which defines the upper

limit of the first NMDS axis. Aspen regeneration was present in most of the plots which

had contained a deciduous component pre-fire (D, DC, CD); thus, there was little differ-

entiation among these three pre-fire cover types in terms of regeneration composition (see

also results of MRPP below).

The PERMANOVA showed a significant effect of fire, pre-fire cover type and their

interaction on regeneration composition (Table 3). The subsequent MRPP showed sig-

nificant differences in post-fire composition between stands that were ‘pure’ conifer pre-

fire versus the other three cover types, which did not differ from one another (Table 4).

There were also significant differences between the Central Mixedwoods (CM) and the

Lower Foothills (LF) in terms of regeneration composition (by MRPP, Table 4). In the

Lower Foothills fires (CL-10, OC-20, VH-10, upper part of the ordination diagram in

Fig. 1) there was more regeneration of balsam poplar, tamarack and lodgepole pine. In

contrast, more regeneration of balsam fir, paper birch, jack pine and white spruce was

observed in the Central Mixedwood fires (MI-10, ML-13, lower part of the ordination

diagram in Fig. 1).

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Factors influencing regeneration

Stocking

The notable difference between pre-fire ‘pure’ conifer stands and the other cover types, as

described above, was further emphasized by the results of the classification tree; the most

important factor determining the post-fire stocking was whether the pre-fire stand was

‘pure’ conifer or not (Fig. 2). Within the pre-fire conifer stands, the percent grass cover

-1.0 -0.5 0.0

-1.0

-0.5

0.0

0.5

0.5

NMDS1

C

CDD

DC

CM

LF CL-10

MI-10

ML-13OC-20

VH-10

Potr

Bepa

Lala

Abba

Poba

Piba

Pico

Pima

Pigl

CCDDCD

NM

DS

2

Fig. 1 Results of NMDS ordination examining regeneration composition. The composition dataset was basedon number of individuals ([0.3 m height) per plot by species (the ordination used Chord distance, 20 randomstarts, was iterated for two dimensions, Stress: 19.04, non-metric fit r2 0.97, linear fit r2 0.91). Symbolsrepresent individual plots coded by pre-fire cover type [‘pure’ conifer (C), conifer dominated (CD), deciduousdominated (DC), ‘pure’ deciduous (D)], 95 % ellipses around pre-fire cover type centroids are given (goodnessof fit: r2 0.13, p = 0.001). Centroids are given for the fires: Chip Lake (CL-10), Mariana Lakes (ML-13),Mitsue Lake (MI-10), O0Chiese (OC-20), Virginia Hills (VH-10), (goodness of fit: r2 0.11, p = 0.001) andecological subregions: Central Mixedwoods (CM), Lower Foothills (LF) (goodness of fit: r2 0.06, p = 0.001).Species codes show their location in ordination space [Potr trembling aspen (Populus tremuloides), Pobabalsam poplar (Populus balsamifera), Bepa white birch (Betula papyrifera), Piba jack pine (Pinus banksiana),Pico lodgepole pine (Pinus contorta), Pima black spruce (Picea mariana), Pigl white spruce (Picea glauca),Abba balsam fir (Abies balsamea), Lala tamarack (Larix laricina)]. Species were included in the ordinationdiagram by weighted averages of the plot scores. Environmental factors (pre-fire cover type, fire, ecologicalsubregion) show the averages of factor levels (class centroids). Confidence limits (0.95) for the cover typeswere included in the ordination diagram based on the standard error of the (weighted) averages of the plots

Table 3 Results of PERMA-NOVA testing for the effects ofpre-fire cover type, fire and theirinteraction on regenerationcomposition

Df F R2 p

Pre-fire cover type 3 22.14 0.10 \0.001

Fire 4 19.81 0.12 \0.001

Pre-fire cover type x fire 12 8.88 0.16 \0.001

222 New Forests (2014) 45:215–234

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(mostly Calamagrostis canadensis), was the most influential factor determining whether

the plots regenerated to the mixed (MX) versus the deciduous (D) stocking type Fig. 2).

When grass cover was higher than 50 %, 49 % of the plots were stocked to D and about

35 % were NSR. In contrast, when grass cover was lower than 50 % there was a higher

probability of plots regenerating to the MX stocking type. The subsequent influential factor

for pre-fire ‘pure’ conifer stands with low grass cover was the cover of alder. When alder

cover was \20 %, 62 % of the plots regenerated to the MX stocking type while 22 %

regenerated to the C stocking type. Of the 8 plots with alder cover [20 %, none regen-

erated to the C stocking type, while there were fairly similar proportions of plots regen-

erating to the MX or D stocking type or being identified as NSR (*25–37 %).

In the other three pre-fire cover types (D, DC, CD; right split of Node 1 in Fig. 2) plots

most often regenerated to the D stocking type but there were differences among the fires in

terms of the proportions of MX versus NSR plots (Node 7 in Fig. 2). The 10 year old fires

separated from the two other fires due to the latter having a much higher proportion of plots

regenerating to the MX stocking type (right split of Node 7 in Fig. 2). Among the 10 year-

old fires one (VH) had a much higher proportion of NSR plots (split at Node 9, Fig. 2).

White spruce presence and density

Since white spruce regeneration was so infrequent in our plots, we used another classifi-

cation tree (Fig. 3) to examine factors influencing its presence/absence in a plot regardless

of whether it was the tallest conifer. The most important factor influencing presence of

white spruce trees taller than 0.3 m was the mast year following the fire which separated

the older fire (O’Chiese, 20 years, mast in 1991)—which had a higher percentage of plots

containing a white spruce ([0.3 m height)—from the four younger fires (10 or 13 years;

mast in 1998). The next most important factor in the older (O’Chiese) fire was the pre-fire

Table 4 Results of pairwise comparisons of regeneration composition by means of multi-response per-mutation procedure tests (MRPP) between ecological subregions and pre-fire cover types

Comparison Ac p

Ecological subregionsa

CM versus LF 0.04 0.001

Pre-fire cover typesb

C versus CD 0.08 0.001

C versus DC 0.08 0.001

C versus D 0.09 0.001

CD versus DC \0.001 0.216

CD versus D \0.001 0.176

DC versus D \0.001 0.125

a Ecological Subregions: Central Mixedwoods (CM) and Lower Foothills (LF)b pre-fire cover type: ‘pure’ conifer (C), conifer dominated (CD), deciduous dominated (DC) and ‘pure’deciduous (D)c A: chance-corrected within-group agreement which ranges from 0 to 1 where 1 indicates that the com-position of all plots are identical within groups. With significant separation of groups, A values \0.1 arecommon with community data (McCune et al. 2002)

Significant differences are indicated in bold

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cover type; pre-fire ‘pure’ deciduous stands had a very low frequency of plots with a white

spruce (\5 %) compared to pre-fire ‘pure’ conifer or mixedwood stands in this fire ([50 %

frequency of plots with a white spruce). For the four younger fires (right split of Node 1;

Fig. 3) the next mast year was in 1998—either in the same year as (CL-10, VH-10, MI-10)

or 3 years after the fire (ML-13). For these, grass cover was the most important factor

determining the presence of spruce regeneration[0.3 m height. If grass cover was B60 %

the chance of finding a white spruce was 15 %; with higher grass cover, this dropped to

about 3 %.

Even considering presence of white spruce of any size (including the semi-quantitative

data on spruce\0.3 m) our results showed that 79–91 % of the plots had no white spruce

in the older and younger fires, respectively (Supplement 2). Most of the small white spruce

trees were found in the stands that were pure white spruce pre-fire. We noticed very few

seed trees left standing in any of the fires; most of the established white spruce would have

come from seed sources 500 m or further away—or from seed trees that had fallen before

the time of our data collection.

Fig. 2 Classification tree to predict post-fire stocking type based on the conditional inference tree (cTree)model. The encircled explanatory variables are those showing the strongest association to the responsevariable. Values on lines connecting explanatory variables indicate splitting criteria; for example, the firstsplit separated plots in the pre-fire ‘pure’ conifer (C) cover type (left split) from those in the other threecover types (right side of the split). Numbers in boxes above the explanatory variable indicate the nodenumber. ‘‘n =’’ next to terminal nodes indicate the number of plots classified in that node. Bar graphsillustrate the proportion of plots in that node that regenerated to a stocking type. C = conifer, MX = mixed,D = deciduous, NSR = not sufficiently restocked. See Methods for detailed definitions of the stockingtypes. Explanatory variables: pre-fire CoverType: ‘pure’ conifer (C), conifer dominated (CD), deciduousdominated (DC), ‘pure’ deciduous (D), ‘‘Grass %’’: percent cover of graminoid species in the plot, ‘‘Alder%’’: percent cover of alder in the plot, ‘‘Fire’’: Chip Lake (CL), Mariana Lakes (ML), Mitsue Lake (MI),O0Chiese (OC), Virginia Hills (VH)

224 New Forests (2014) 45:215–234

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In concordance with the results for stocking, pre-fire stand type was the most important

factor influencing conifer regeneration density (Fig. 4). Pre-fire conifer stands had higher

densities of conifer regeneration than did the other three cover types. Within the pre-fire

conifer stands, the amount of grass cover was the most influential factor. There was little or

no conifer regeneration in plots where grass cover was [30 %. In plots with lower grass

cover, shrub cover had an additional influence; when shrub cover was B5 %, the median

conifer density was about 25 trees per 10 m2 plot, whereas with higher shrub cover this

dropped to 5 per plot.

In the pre-fire cover types with a deciduous component (D, DC, CD) the timing of white

spruce mast after the fire was an important factor determining the density of conifer

regeneration. In the three youngest (10 years old) fires, for which the mast was in the same

year as the fire (1998), conifer regeneration density was very low (right split at Node 7 in

Fig. 4). In the 13 and 20 year older fires, for which the mast occurred 3 years after the burn

conifer regeneration densities were higher. For these fires, shrub cover was a further

controlling factor; with shrub cover B15 % (left split at Node 8; Fig. 4) the median conifer

density was about 20 trees/plot whereas higher shrub cover resulted in lower conifer

regeneration densities (right split at Node 8, Fig. 4).

Deciduous density and height

The first factor influencing density of deciduous trees was the fire (Fig. 5). Deciduous

regeneration density was, overall, considerably greater in CL-10 and ML-13 than in the

other three fires (split at Node 1; Fig. 5). In these two fires the next influential factor was

firstmastp < 0.001

1

1991 1998

CoverTypep = 0.001

2

C, CD, DC D

Node 3 (n = 48)

Sw

no

0

0.2

0.4

0.6

0.8

1Node 4 (n = 28)

Sw

no

0

0.2

0.4

0.6

0.8

1

Grass%p = 0.003

5

<60 >60

Node 6 (n = 273)

Sw

no

0

0.2

0.4

0.6

0.8

1Node 7 (n = 154)

Sw

no

0

0.2

0.4

0.6

0.8

1

Fig. 3 Classification tree to predict the presence (dark proportion of bar) or absence (light grey proportionof the bar) of white spruce regeneration ([0.3 m height) based on the conditional inference tree model. SeeFig. 2. Explanatory variables: ‘‘first mast’’ after the fire: 1991 (3 years after for the 1988 fire) or 1998(3 years after for the 1995 fire and in the same year for 1998 fires); pre-fire ‘‘CoverType’’ ‘pure’ conifer (C),conifer dominated (CD), deciduous dominated (DC), ‘pure’ deciduous (D), ‘‘Grass %’’: cover of graminoidspecies in the plot

New Forests (2014) 45:215–234 225

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pre-fire cover type; stands with a greater deciduous component pre-fire (D, DC) had higher

densities of deciduous regeneration post-fire than did stands with more conifer pre-fire (C,

DC) (split at Node 2; Fig. 5). For stands of the D or DC pre-fire cover type, the final

influential factor was grass cover (split at node 3; Fig. 5). The median deciduous regen-

eration density was 28 stems per plot when grass cover was B10 % but about half that (15

trees per plot) with higher grass cover. For stands that had a greater conifer component pre-

fire (right split of Node 2; Fig. 5), the next important factor was whether their pre-fire

cover type was CD or C (split at Node 6; Fig. 5). The CD stands had higher densities of

deciduous regeneration (median *10 per plot) than did the ‘pure’ conifer stands (median

of 3).

For the other three fires (right split at Node 1; Fig. 5), which had generally lower

deciduous regeneration density, grass cover was also an important factor. Plots with

B30 % grass cover had higher deciduous regeneration density (median 8 per plot) than

plots with greater grass cover (split at Node 9, Fig. 5). In plots with grass cover [30 %

there was a significant influence of pre-fire cover type; pre-fire D and CD stands had higher

deciduous regeneration densities than did DC and C stands (split at Node 11; Fig. 5).

As mentioned above, aspen was not only the most frequent and abundant regenerating

species, but also the tallest. For other species we did not have enough trees to examine

factors impacting height. We examined factors influencing aspen height only in the four

Fig. 4 Regression tree to predict conifer regeneration density (trees per 10 m2 plot, height [ 0.3 m) withinthe conditional inference framework. See Fig. 2. The p values listed at each node represent the significanceof the influence of the listed independent variable on the response variable. Box plots at the terminal nodesshow the distribution of regeneration density (number of trees per plot) for plots within that branch. Boxesrepresent the inner-quartile range (25th–75th percentiles) of the data, dark horizontal lines within the boxesrepresent the median, while whiskers represent the extent of data within the 1.5 9 inner-quartile range.Circles above and below the whiskers represent data points outside of this range. The number of plots thatfall within each branch (n) is listed above the box plots. Explanatory variables: pre-fire ‘‘CoverType’’: ‘pure’conifer (C), conifer dominated (CD), deciduous dominated (DC), ‘pure’ deciduous (D); ‘‘Grass %’’: percentcover of graminoid species in the plot; ‘‘Shrub %’’: percent cover of shrubs in the plot; ‘‘firstmast’’: in yearsafter the fire

226 New Forests (2014) 45:215–234

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younger fires because 20 years post-fire, the trees were much taller. Aspen regeneration in

the Virginia Hills (VH-10) fire was shorter (median 2.8 m) than in the other three fires

(split at Node 1; Fig. 6), for which there was evidence of a significant influence of com-

petition from other regeneration or from tall shrubs. Aspen were, overall, shorter when

there were B10 stems per plot of deciduous regeneration (left split at Node 2; Fig. 6). In

this case aspen were taller when willow cover was B10 % than with higher willow cover

(median height of 4.5 vs. 3 m respectively). In more densely stocked stands ([10 stems per

plot of deciduous regeneration), aspen were taller (right split at Node 2; Fig. 6) and also

showed evidence of a significant influence of alder presence/absence. Median aspen

regeneration height was greater when alder was present than absent (6.8 vs. 5 m

respectively).

Discussion

Post-fire regeneration composition

Our results provide some support for the direct regeneration hypothesis (Yih et al. 1991)

and are in accordance with previous predictions for the boreal forest (Greene and Johnson

1999; Chen et al. 2009; Ilisson and Chen 2009a, b) of North America. The composition of

post-fire stands tended to correspond to their pre-fire cover type—but there were notable

exceptions. The composition of post-fire regeneration was dominated by Populus, mostly

Firep < 0.001

1

CL-10, ML-13 MI-10, OC-20, VH-10

CoverTypep < 0.001

2

D, DC C, CD

Grass%p < 0.001

3

<10 >10

Node 4 (n = 26)

0

20

40

60

Node 5 (n = 154)

0

20

40

60

CoverTypep = 0.024

6

CD C

Node 7 (n = 57)

0

20

40

60

Node 8 (n = 56)

0

20

40

60

Grass%p < 0.001

9

<30 >30

Node 10 (n = 105)

0

20

40

60

CoverTypep = 0.039

11

CD, D C, DC

Node 12 (n = 34)

0

20

40

60

Node 13 (n = 71)

0

20

40

60

Fig. 5 Regression tree to predict the density of juvenile deciduous (trembling aspen, balsam poplar andwhite birch) regeneration (per 10 m2 plot, height [ 0.3 m) within the conditional inference framework. SeeFigs. 2, 4. Box plots at the terminal nodes show the distribution of regeneration density (number of trees perplot) for plots within that branch. Explanatory variables: ‘‘Fire’’: Chip Lake (CL-10), Mariana Lakes (ML-13), Mitsue Lake (MI-10), O0Chiese (OC-20), Virginia Hills (VH-10); pre fire ‘‘CoverType’’: ‘pure’ conifer(C), conifer dominated (CD), deciduous dominated (DC), ‘pure’ deciduous (D); ‘‘Grass %’’: percent coverof graminoid species

New Forests (2014) 45:215–234 227

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aspen; this was expected because of their strong vegetative reproduction ability and fast

early growth following fire (Frey et al. 2003; Chen et al. 2009). Plots in which Populus spp.

were a part of the pre-fire cover (D, DC, CD cover types) were almost always stocked with

Populus spp. post-fire—either exclusively or as a mixture with conifers. More conifer

regeneration was observed in pre-fire stands that were conifer-dominated or ‘pure’ conifer

pre-fire (CD or C cover types) and ‘pure’ white spruce stands pre-fire had the highest

occurrence of restocking to conifer. However, both pre-fire CD and C cover types were

most often stocked, post-fire, to the deciduous or mixed type, rather than to conifer only.

There was a wide variety of conifer species regenerating in the pre-fire conifer stands

and—surprisingly—the leading (tallest) conifer was most often a pine. Even when we

considered white spruce regeneration under 0.3 m, there was a very high percentage of

plots that had no white spruce. The amount of pine regeneration was variable among fires

but was, overall, quite surprising considering that they were not included in the pre-fire

stand attribute data (Phase 3 forest inventory cover type call) as even a minor species.

Since pine were not detected in the pre-fire inventory because of low cover, the relatively

high densities of pine regeneration found in four of our fires (OC, CL, VH, ML) must be

attributable to a few mature pre-fire pine. Jack pine and Lodgepole pine are shade intol-

erant and are described as ‘‘obligate seeders’’ because of their serotinous cones (Chen et al.

2009). As such, they are adapted to regenerate after fire (Ilisson and Chen 2009b). Lavoie

and Sirois (1998) reported a change in species composition from black spruce to jack pine

post-fire in the eastern boreal forest and suggested that an increase in fire frequency could

trigger the expansion of jack pine in the region.

Firep < 0.001

1

CL-10, MI-10, ML-13 VH-10

allDp < 0.001

2

<10 >10

Willow%p = 0.022

3

<10 >10

Node 4 (n = 38)

0

2

4

6

8

Node 5 (n = 21)

0

2

4

6

8

Alder-ahp < 0.001

6

<0 >0

Node 7 (n = 93)

0

2

4

6

8

Node 8 (n = 22)

0

2

4

6

8

Node 9 (n = 28)

0

2

4

6

8

Fig. 6 Regression tree to predict the height of aspen (m) within the conditional inference framework. SeeFigs. 2, 4. Box plots at the terminal nodes show the distribution of average regeneration height (m) for plotswithin that branch. Explanatory variables: ‘‘Fire’’: Chip Lake (CL-10), Mariana Lakes (ML-13), Mitsue Lake(MI-10), O0Chiese (OC-20), Virginia Hills (VH-10); ‘‘allD’’: number of additional deciduous individuals inthe plot; ‘‘Willow %’’: percent cover of willow in the plot; ‘‘Alder-ah’’: average height of alder in the plot

228 New Forests (2014) 45:215–234

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In general, white spruce recruitment was poor in the five fires we studied even when

fires coincided with a spruce mast (the CL, MI, VH fires). Stands that were ‘pure’ white

spruce pre-fire, regenerated to a diversity of conifer species—including pine, tamarack,

balsam fir, and black spruce—and to a mixedwood (conifer–deciduous) type. Thus man-

agement regimes that aim to regenerate ‘pure’ white spruce stands to ‘pure’ white spruce

through planting and selective herbicides do not represent the typical post-fire regeneration

composition and could cause a loss in tree species diversity on these upland sites, relative

to what occurs following wildfire.

An important finding was the relatively high percentage of plots that did not meet any

regeneration standard and that this varied among pre-fire cover types. Heterogeneity in fire

intensity and severity (consumption of organic matter) can influence post-fire regeneration

(Simard et al. 1998; Charron and Greene 2002; Greene et al. 2005; Burton et al. 2008).

Spruce regenerate best in mineral soil microsites which are more available following

higher severity fires (Charron and Greene 2002; Purdy et al. 2002; Greene et al. 2005). We

found the highest occurrence of unstocked plots (regeneration gaps) in pre-fire ‘pure’ white

spruce stands in the younger fires (23 % of plots) and in the 20-year old fire in the pre-fire

‘pure’ deciduous and deciduous dominated mixedwoods (11–12 % of plots). These results

suggest that some areas within fires likely have stocking gaps which could persist over the

long term. It should be noted that the regeneration tree height limit we used (0.3 m for both

conifer and deciduous) was lower than for past (*1 m thresholds; ASRD 2007; WAS

2008) or current (0.3 m for conifers, 1.3 m for deciduous; ASRD 2012) performance

surveys in Alberta. If we had used a 1 m height limit, the proportion of not sufficiently

stocked plots would have been 40 % in the pre-fire ‘pure’ spruce stands and 9–29 % in

stands with a pre-fire deciduous component. Similarly, MacIsaac et al. (2006) found

unstocked gaps covered 29 % of the stand area in five 14 year-old aspen stands that were

regenerating naturally after harvesting in northern Alberta.

Factors affecting conifer regeneration

Post-fire conifer regeneration (stocking and density) was affected by grass and shrub cover.

Bluejoint (C. canadensis) grass is known to be a serious competitor to regeneration and

growth of both white spruce and aspen (Lieffers et al. 1993; Lieffers and Stadt 1994;

Greenway and Lieffers 1997; Landhausser and Lieffers 1998; Shropshire et al. 2001;

MacIsaac et al. 2006). The stronger influence of grass competition in the pre-fire conifer

stands may be explained by the fact that there was less vegetative regeneration of Populus

spp., which otherwise shades out the competing grass and shrubs (MacIsaac et al. 2006;

Man et al. 2008). Our results suggest a threshold of 50 % grass cover, above which white

spruce regeneration is very low and the proportion of unstocked plots is as high as 35 %. In

plots with lower grass cover, alder had a strong inhibitory effect on regeneration; in plots

with [20 % alder cover there were no plots stocked to conifer and 37 % of plots were

unstocked. Bartemucci et al. (2002) found shrub-dominated gaps in boreal forests of

British Columbia occupied 5.2 % of the area and persisted over a long period.

Timing of the mast year relative to the year of the fire is known to be key to successful

recruitment of white spruce (Purdy et al. 2002; Peters et al. 2005, 2006; Martin-DeMoor

et al. 2010). We expected good regeneration of white spruce in the three fires that burned in

1998, since this was reported to be a widespread mast year in northern Alberta, and

previous studies had found good white spruce regeneration in stands burned in 1998 (Purdy

et al. 2002). However, we found poor white spruce regeneration in these fires, particularly

for stands which had a pre-fire deciduous component. Our results do conform to those of

New Forests (2014) 45:215–234 229

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Purdy et al. (2002) who found low densities of regenerating white spruce in the Virginia

Hills fire (28 trees/ha). The poor regeneration we observed could be due to a lack of seed

sources. Most of the potential seed trees that were still standing when we sampled were

more than 500 m away from our plots; too far for adequate seed rain (Stewart et al. 1998,

2001; Martin-DeMoor et al. 2010). However, seed trees could have died and fallen since

they released their seed. It is also likely that post-fire salvage logging in areas adjacent to

our study sites removed white spruce seed trees.

Surprisingly, we found higher densities of white spruce in the 13 and 20 year old burns,

which had a 3 year delay before the first mast year, than in the 1998 burns. These higher

densities could be due to an accumulation of recruitment from the first mast year and

subsequent high seed years, despite the rapid deterioration of seedbeds following a fire

(Purdy et al. 2002). This is supported by the fact that the oldest (20 years) burn had the

highest density of white spruce regeneration and the most small (\0.3 m) seedlings.

Previous studies showed peak white spruce sapling densities 5–20 years post-fire (Galipeau

et al. 1997; Awada et al. 2004; Johnstone et al. 2004). Together, the evidence supports the

importance of delayed or on-going recruitment of white spruce (Lieffers et al. 1996; Peters

et al. 2006).

The poor white spruce regeneration we observed could also be due to the fact that spring

or early summer fires consume less organic matter and thus have lower availability of the

mineral soil microsites that are critical for white spruce recruitment (Moore and Wein

1977; Charron and Greene 2002; Miyanishi and Johnson 2002; Purdy et al. 2002; Peters

et al. 2006; Gartner et al. 2011). However, we chose these early season fires because they

are historically predominant in this part of the boreal forest (Johnson et al. 1999) and this is

expected to continue under future climates (Albert-Green et al. 2013).

Factors promoting deciduous regeneration

Broadleaved dominance not only increased in frequency following fire but deciduous

species also had much higher densities than coniferous species. This confirms earlier

findings that broadleaved tree species are promoted by wildfires in the boreal forest (Bartos

and Mueggler 1981; Brown and Debyle 1987; Johnson et al. 1998; Frey et al. 2003; Greene

et al. 2004; Brassard et al. 2008; Chen et al. 2009; Ilisson and Chen 2009a, b). Density of

the deciduous regeneration and height of the aspen regeneration were highly variable

among fires. Our two fires with the most deciduous regeneration had densities

(*14,000–19,000 trees/ha) similar to those reported 5 years after a fire in Wyoming

(14,000–20,000 trees/ha; Bartos and Mueggler 1981). Our other fires had densities about

half that. The lower density in the 20 year old fire could indicate that self-thinning had

begun (Chen et al. 2009). As for white spruce, fire severity likely also influence aspen

regeneration by removal of too much or too little of the organic layer (Fraser et al. 2004).

The condition of the aspen root system likely also influenced density and height of

regeneration. Higher vitality and density of the parental root system in stands that were

deciduous-dominated pre-fire (D or DC) had higher deciduous regeneration density than

other pre-fire cover types (DesRochers and Lieffers 2001; Frey et al. 2003). The variability

in deciduous regeneration density among fires could also be partly due to the impact of

grass cover, as described above.

The tallest aspen (*6.5 m) were found 10–13 years post-fire, in plots that had more

than ten other deciduous trees, and in which alder was present. This apparent beneficial

effect of alder could be attributed to its effects on soil nitrogen availability (Landhausser

et al. 2010). The negative effect of willow on aspen height could be due to competition or

230 New Forests (2014) 45:215–234

123

might suggest that willow abundance is higher on wetter and cooler sites, on which aspen

growth would be poorer (Landhausser et al. 2003).

Conclusions

We found wide variation in post-fire regeneration outcomes—in terms of stocking, com-

position, densities and heights of deciduous and conifer. However, stands with a pre-fire

deciduous component were strongly deciduous-dominated post-fire, demonstrating the

highest resilience and conforming to the direct regeneration hypothesis. In contrast, rela-

tively ‘pure’ white spruce stands appear to have poorer resilience to fire, with a surprising

proportion of plots that regenerated to pine or deciduous species, or which were unstocked.

Both the obligate resprouting species, like Populus spp., and the fire-adapted pine species

seem to take advantage of fire by increasing their dominance in the early post-fire land-

scape. This is in contrast to many studies which document that if the right conditions

coincide, white spruce has a chance to regenerate well after fire. It’s worth noting that these

results were obtained at a rather early stage in succession. Stands that were pure deciduous

post-fire could still possibly develop into mixedwoods with on-going recruitment of white

spruce over time, and succeed to spruce dominance when the deciduous species senesce.

Overall our results suggest that regeneration gaps and pre- to post-fire switches in

composition occur naturally. ‘Pure’ pre-fire white spruce stands rarely regenerate just to

white spruce. Rather, they seem to provide an opportunity for establishment of natural tree

species mixtures during early successional stages in the post-fire western boreal mixed-

wood landscape. The high natural variability in regeneration should be taken into con-

sideration when developing approaches to post-harvest regeneration according to natural-

disturbance based management.

Acknowledgments We thank the Forest Resource Improvement Association of Alberta (FRIAA) and theMixedwood Management Association (MWMA) for financial support. We appreciated the assistance ofVern Peters and Lowell Lyseng in fire selection, and Farrah Gilchrist for her help in the field. We aregrateful to Alberta Pacific Forest Industries Ltd., Alberta Plywood, Blue Ridge Lumber, Millar WesternForest Products, Weyerhaeuser, and the Western Boreal Growth and Yield Association (WESBOGY) fortheir assistance.

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