Late Holocene fire history and vegetation

dynamics: a pollen and charcoal record from

a southern Finnish lake.

Michelle Buchan

Master’s Thesis

University of Helsinki

Department of Geosciences and Geology

Palaeobiology and Palaeoclimatology

31/10/2017

Tiedekunta/Osasto Fakultet/Sektion – Faculty Laitos/Institution– Department

Faculty of Science Department of Geosciences and Geography

Tekijä/Författare – Author

Michelle Salkeld Buchan

Työn nimi / Arbetets titel – Title

Late Holocene fire history and vegetation dynamics: a pollen and charcoal record from a

southern Finnish lake.

Työn laji/Arbetets art – Level Aika/Datum–Month and year Sivumäärä/ Sidoantal – Number of pages

Master’s Thesis 10/2017 61

Tiivistelmä/Referat – Abstract

Lake sediments from Valkea–Kotinen, a small lake in the Evo area of southern Finland,

were analysed to provide a high–resolution reconstruction of the fire episode and

vegetation structure histories of the past ca. 1700 years at the site. Mixing of sediment

was a problem in approximately the top half of the core, affecting the accuracy of the

chronology and the correct interpretation of pollen and charcoal accumulation. Six local

fire episodes were identified using the CharAnalysis program with a mean fire return

interval of 312 years. None these episodes could be attributed to human activities with

the exception of the episode at ca. 15 cal yr BP which may have been due to the

controlled burning of forest for regeneration elsewhere in Evo. The vegetation structure

was typical for southern Finland with Picea, Pinus, Alnus, and Betula dominating the site.

Signals of human activity such as an increase in Cerealia pollen alongside an increase in

microcharcoal from ca. 1000 cal yr BP indicate regional slash and burn activity. An

episodic Alnus decline is evident from ca. 1400 cal yr BP with a subsequent recovery at

around 1000 cal yr BP. The results show that the local fire regime at Valkea–Kotinen is

likely natural and the vegetation structure has been consistent with little anthropogenic

influence over the last ca. 1700 years.

Avainsanat – Nyckelord – Keywords

Pollen analysis · Microcharcoal · Macrocharcoal · CharAnalysis · Vegetation Structure · Pollen Accumulation Rate

Säilytyspaikka – Förvaringställe – Where deposited

University of Helsinki, Department of Geosciences and Geography, Division of Geology Muita tietoja – Övriga uppgifter – Additional information

Table of Contents

1. Introduction ................................................................................................... 1

2. Background and Study Site ............................................................................ 4

2.1 The Boreal Forest ......................................................................................................................4

2.2 Lake Basin Characteristics........................................................................................................5

2.3 Vegetation at Valkea–Kotinen ................................................................................................6

2.4 Climate at Valkea–Kotinen ......................................................................................................7

3. Materials and Methods .................................................................................. 7

3.1 Field Methods ............................................................................................................................7

3.2 Laboratory Methods..................................................................................................................8

3.2.1 Pollen and Microscopic Charcoal Analyses ...................................................................8

3.2.2 Macroscopic Charcoal Analysis .......................................................................................9

3.2.3 CharAnalysis Methodology ..............................................................................................9

3.2.4 AMS 14C Radiocarbon Dating ...................................................................................... 10

3.2.5 210Pb and 137Cs Dating .................................................................................................... 10

3.2.6 Cross Correlation ............................................................................................................ 11

4. Results .......................................................................................................... 12

4.1 210Pb and 137Cs Dating ............................................................................................................ 12

4.2 AMS 14C Radiocarbon Dating and Age–depth Model ..................................................... 13

4.3 Pollen–Stratigraphic Age Anchor Point ............................................................................. 14

4.4 Macrocharcoal Analysis ......................................................................................................... 15

4.4.1 CharAnalysis Results ...................................................................................................... 15

4.4.2 Macrocharcoal Accumulation Rate .............................................................................. 16

4.4.3 Local Fire Episodes ........................................................................................................ 16

4.5 Fossil Pollen Data................................................................................................................... 18

4.6 Microcharcoal Accumulation ............................................................................................... 19

4.7 Cross–correlation ................................................................................................................... 19

5. Discussion ................................................................................................... 26

5.1 Lake Sedimentation Processes ............................................................................................. 26

5.2 Fire History at Valkea–Kotinen ........................................................................................... 27

5.3 Vegetation History at Valkea–Kotinen ............................................................................... 32

5.4 The Alnus Decline 1400 – 1000 Cal yr BP ....................................................................... 35

5.5 PARs vs Percentages as Tools for Detecting Environmental Change.......................... 39

6. Conclusions ................................................................................................. 42

7. Acknowledgements ...................................................................................... 44

8. References ................................................................................................... 45

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1. Introduction

A great deal of research has, in the past, focused on the complex interrelationships between

vegetation, climate and other ecological processes and on how these interactions have

influenced past and present environments. Lake sediments are known to be valuable

archives of palaeoenvironmental information, commonly providing a continuous record

with the potential for high temporal resolution. Palaeoecological studies based on fossil

pollen and charcoal analyses from lake sediments can provide important information and

evidence to enable the reconstruction of past vegetation and fire regimes. Using these

proxy indicators as a means of reconstructing the past can give us an increased

understanding about the complex nature of landscapes and ecological processes as well as

giving us the means to assess the long term implications of fire, vegetation and climate

interactions (Gavin et al. 2007).

Fossil pollen analysis is a useful source for reconstructing and interpreting local and

regional vegetation dynamics and climatic events, particularly during the Quaternary period

(Seppä 2013). Alongside fossil pollen analysis, microcharcoal and macrocharcoal are used

to reconstruct long–term variances in fire regime and to examine the relationship between

climate, vegetation, fire and anthropogenic activities (Whitlock and Larsen 2002) as after

climate, fire is one of the main drivers of change in species composition in boreal forests

(Lehtonen and Kolström 2000; Ryan 2002).

Macrocharcoal and microcharcoal are both important proxies for reconstructing past fire

occurrence. The majority of macrocharcoal falls close to its source, meaning that peaks of

macrocharcoal in the sediment record are representative of ‘local’ fire episodes (Clark 1989)

and regional fire occurrences are reflected by the microcharcoal record as microcharcoal is

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able to travel greater distances through the air. One important development in the study of

macrocharcoal has been the development of a charcoal peak screening process (Gavin et al

2006) by which an identified threshold of background charcoal ‘noise’ can be deducted

from the overall charcoal occurrence to identify peaks in the record which are reflective of

past fire episode occurrences. One such program which has this capacity is CharAnalysis

(Higuera et al. 2009), and is therefore employed in this study to identify past local fire

episodes at Valkea–Kotinen.

Lake sediment studies using pollen analysis as a proxy often use pollen percentage values to

infer vegetation composition but the disadvantage of using percentage data is that it does

not take into account facts such as the difference between pollen productivity in different

taxa and differences in pollen dispersal through the air (Sugita 1994; Broström et al. 2008;

Marquer et al. 2014). To circumvent the inherent problems associated with using

percentage data for vegetation reconstructions, pollen accumulation rates (PARs), which do

not rely on the relative measure of species to species, have been used to more accurately

assess vegetation change (e.g. Davis and Deevey 1964; Giesecke and Fontana 2008; Seppä

et al 2009; O’Connell et al. 2014). Therefore the PAR method has been employed for the

Valkea–Kotinen sediment record to provide the most accurate reflection of vegetation

dynamics at the site.

This thesis describes the palaeoecological research undertaken from lake sediments in

southern Finland. It adds to the growing body of research of past boreal forest vegetation

dynamics on a local and regional scale in Finland and throughout Fennoscandia as a whole

through the study of a ca. 1700 year sediment record from Lake Valkea–Kotinen in the

Kotinen State Forest Reserve, southern Finland. The primary objective is to provide

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charcoal and pollen evidence of past fire regime and vegetation structure at Valkea–

Kotinen, where the surroundings are currently spruce dominated mesic forest.

This study adds to the growing body of research of boreal forest fire vegetation dynamics

on a local and regional scale in Finland and throughout Fennoscandia as a whole.

Therefore with that in mind, the analyses of charcoal and pollen in the Valkea–Kotinen

sediment core were done so in an attempt to address the following research questions:

What was the past fire regime at Valkea–Kotinen? How many fire episodes can be

identified and how have these affected the vegetation?

How do the macrocharcoal and microcharcoal records at Valkea–Kotinen

compare? What do they reveal about the fire history on a local and regional scale?

Have there been any changes in vegetation structure at Valkea–Kotinen through

time?

What has driven vegetation changes?

How useful are pollen accumulation rates for assessing past changes in vegetation

structure?

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2. Background and Study Site

2.1 The Boreal Forest

The boreal forest is the largest terrestrial biome on earth, extending throughout northern

Eurasia and North America. Although extensive, the area can be described as being

‘floristically simple’ (Stocks et al. 2008) as it is dominated by only a few coniferous and

deciduous taxa. In Finland, the main boreal taxa include Picea abies (Norway spruce) and

Pinus sylvestris (Scots pine). Present to a lesser extent are also Betula pubescens (downy birch),

Betula pendula (silver birch), and Alnus incana (grey alder). Becoming more common towards

the southern edge of the boreal forest are also more thermophilous broad–leaf deciduous

species such as Quercus robur (pedunculated oak), Corylus avellana (hazel), Tilia cordata (small–

leaved lime), Ulmus glabra

(wych elm) and Alnus glutinosa

(common alder). Herbaceous

plants including Vaccinium

myrtillus (bilberry), Vaccinium

vitis–idaea (lingonberry), and

Rubus idaeus (Raspberry)

amongst others are also very

common.

Figure 1: Mesic spruce forest on the west side of Valkea-

Kotinen looking East. Photo author’s own.

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2.2 Lake Basin Characteristics

Valkea–Kotinen (61° 14' N, 25° 04' E.) (Figure 2), is a small 3.59 ha headwater lake,

situated within the Valkea–Kotinen environmental monitoring area, a 30 hectare area of

pristine forest, bogs and waterbodies (Jylhä et al. 2013) located in the Evo region in the

Padasjoki municipality, southern Finland. The mean and maximum depths are 3.0 m and

6.5 m respectively (Niinioja and Villa 1995). There is no inlet to the lake but there is a small

outlet on the south side.

Figure 2: Map overview (O = study site) (© Esri 2017), photograph of Valkea-

Kotinen (author’s own) and map of Valkea-Kotinen (X = approx. location of coring

site) (Modified from © National Land Survey of Finland 2017).

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There is no human settlement, industry or agricultural practices present in the catchment

area (Bergström 1995). The Valkea–Kotinen catchment was protected in 1955 by the

National Board of Forestry (later the Finnish Forest and Park Service) as part of the

Kotinen State Forest Reserve. This area was expanded in 1987 and was then made a nature

reserve as part of a protection scheme for primeval forests in 1994 (Mäkelä 1995). The

nature reserve makes up part of the larger Evo Hiking Area, a 47 km2 area of hiking trails

in the Kanta–Häme region, established in 1994.

2.3 Vegetation at Valkea–Kotinen

Forest covers around 66%, and

peatlands around 22% of the total

area immediately surrounding the

lake. The dominant forest vegetation

site types at Valkea–Kotinen are

mesic and rich heaths (Hämet–Ahti

1989). The forest is mainly old virgin

forest with several canopy layers. The

dominant trees are 80 to 150 year old

Norway spruce (Figure 1). Birch, aspen and Scots pine are also present. The oldest trees

surrounding the lake are Scots pines dating to over 350 years. The area is currently

administered by Metsähallitus who have undertaken intensive forest management practices

on the south side of the lake as a result the trees in that area date to on average 40 to 50

years old.

Figure 3: Vegetation types and coverage at Valkea-

Kotinen (Mäkelä 1995)

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A zone of dwarf shrub pine bog surrounds the lake, becoming a treeless fen zone towards

the south end (Figure 3). On the east side of the lake beyond the dwarf shrub pine bog,

there is a strip of thin–peat spruce forest. On the west side of the lake there is an extensive

area of Vaccinium myrtillus drained peatland forest (Eurola et al. 1984).

2.4 Climate at Valkea–Kotinen

The region is located in the southern boreal vegetation zone according to a global scale

climate classification system (Ahti et al. 1968; Solantie 2005). It is characterised by cold

winters, where the lake is frozen over for around 170 days of the year (Niinioja and Villa

1995), with high precipitation. In winter the coldest average air temperature is –3°C.

Summers are short and mild with an average temperature above 10°C occurring only

between June and August (Critchfield 1966; Jylhä et al. 2010). The annual mean

precipitation is 618 mm (Mäkelä 1995).

3. Materials and Methods

3.1 Field Methods

In June 2016, the fieldwork was carried out. A small inflatable dinghy (Figure 4) was used

to row approximately 50 metres from the west shore of the lake where a 37.5 cm long

sediment core was extracted using a 50 cm long, 66 mm diameter HTH sediment corer

(Figure 4) (Renberg and Hansson 2008). The core was sub–sampled at site, by scraping off

5 mm increments of sediment from the top down and bagging them individually to provide

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a high resolution series.. A total of 73

samples were obtained and were then

stored below 5°C at the University of

Helsinki.

3.2 Laboratory Methods

3.2.1 Pollen and Microscopic Charcoal Analyses

Pollen and microscopic charcoal were analysed from continuous sub–samples. Pollen

preparation followed the process outlined in Bennett and Willis (2003) with the elimination

of the sieving step to retain small grains. Three Lycopodium tablets were added to each

sample to allow the calculation of pollen concentrations (Stockmarr 1971). The samples

were mounted with clear silicone oil and a minimum of 500 terrestrial pollen grains were

counted per sample (Bennett and Willis 2003) to at least family level and any microcharcoal

>20 µm identified during each count was also recorded. Lycopodium spores were counted

alongside pollen to calculate the pollen concentration, which was then divided by

deposition time (yr cm−1) to calculate the pollen accumulation rate (PAR) (grains cm−2 yr−1).

Pollen and microcharcoal diagrams were plotted using TILIA and TILIA.GRAPH v. 2.0.41

software (Grimm 2015).

Figure 4: Left: Dinghy used to collect

sediment core in. Right: HTH sediment

corer. Both photographs taken at

Valkea-Kotinen by author.

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3.2.2 Macroscopic Charcoal Analysis

1 cm3 of sediment from each sample was treated with dilute 14% NaOCI to promote

sediment disaggregation before being sieved at 150 µm. The sediment residue was added to

20 ml distilled water and decanted to a petri dish for charcoal analysis. Using a stereo

microscope, charcoal was identified as brittle, black crystalline particles with angular broken

edges (Swain 1973, 1978).

3.2.3 CharAnalysis Methodology

The macroscopic charcoal record underwent statistical analysis using the stand–alone

program CharAnalysis through MATLAB (Higuera et al. 2009), which decomposed the

record into peak (Cpeak) and background (Cbackground) components in order to

determine ‘fire episodes’. Charcoal concentration was interpolated to constant time steps

(using the age–depth model as shown in Figure 2), representing the average sample

resolution of the record (24 yr cm–1 at Valkea–Kotinen) to produce equally spaced intervals

and then calculated the macrocharcoal accumulation rate (CHAR particles cm–2 yr–1). The

non–log transformed CHAR series was then fitted with a Lowess smoother, robust to

outliers, to smooth the series and as such identify Cbackground. Cpeak was what remained

after the Cbackground, smoothed over a period of 500 years, was subtracted from the

CHAR time series (Higuera et al. 2010).

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3.2.4 AMS 14C Radiocarbon Dating

From the samples at 29.5 cm and 35 cm of the core, 1 cm3 of bulk sediment was taken to

be sent for 14C accelerator mass spectrometry (AMS) dating at the Poznań Radiocarbon

Laboratory, Poland. The results are displayed below in Table 1.

Depth (cm) Lab

Code AMS 14C

Age Material

Cal BP Range

σ Median Age Cal BP

35.0–35.5 Poz–87154

1865±30 BP Bulk, gyttja

1874–1720 95.4 1803

29.5–30.0 Poz–87157

1325±30 BP Bulk, gyttja

1300–1182 95.4 1268

3.2.5 210Pb and 137Cs Dating

The top 20 cm of the core was analysed for 210Pb and 137Cs content. This procedure works

well for dating sediments accumulated in the last 200 years (Appleby 2001, 2008). The

technique is based on the analyses of the decay of constantly accumulating 210Pb (half–life

22.3 years) from the atmosphere into lake sediments. Alongside this, 137Cs, an artificial

nuclide indicative of human activity such as the nuclear weapons testing in the 1950’s and

1960’s and the Chernobyl disaster in 1986, deposited in lake sediments in a similar manner

to 210Pb is often also analysed and the technique has also been employed in this study.

The sediment from each sample of the top 20 cm (40 samples in total) was freeze dried and

compacted in the Department of Geosciences and Geology at the University of Helsinki.

The freeze dried samples ranged in weight from 0.14 grams to 2.56 grams. The samples

were then sent to Department of Chemistry at the University of Helsinki to be analysed

using gamma spectrometry. The results can be seen in Table 2.

Table 1: AMS 14C radiocarbon dates obtained from Valkea-Kotinen.

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Depth [cm]

Pb–210 [Bq/g]

±2σ % Cs–137 [Bq/g]

±2σ % Age [y] Year Uncertaint

y [y]

0.75 0.71 0.17 0.24 4.63 0.47 0.10 6.4 2010 3

1.25 0.60 0.08 0.14 4.56 0.46 0.14 10.7 2005 4

1.75 0.63 0.10 0.15 4.29 0.43 0.10 15.0 2001 6

2.25 0.62 0.13 0.21 3.97 0.40 0.10 19.3 1997 8

2.75 0.49 0.10 0.20 3.66 0.37 0.10 23.5 1992 10

3.25 0.42 0.08 0.19 3.74 0.37 0.10 27.8 1988 12

3.75 0.28 0.09 0.34 3.65 0.37 0.10 32.1 1984 13

4.25 0.35 0.07 0.21 3.80 0.39 0.10 36.4 1980 15

4.75 0.44 0.10 0.22 3.92 0.40 0.10 40.7 1975 17

5.25 0.39 0.06 0.17 3.63 0.37 0.10 44.9 1971 19

5.75 0.36 0.06 0.17 3.60 0.36 0.10 49.2 1967 20

6.25 0.37 0.07 0.19 3.17 0.32 0.10 53.5 1963 22

6.75 0.35 0.06 0.18 2.88 0.29 0.10 57.8 1958 24

7.25 0.31 0.08 0.25 2.66 0.27 0.10 62.0 1954 26

7.75 0.32 0.09 0.28 2.69 0.27 0.10 66.3 1950 27

8.25 0.10 0.06 0.61 2.02 0.21 0.10 70.6 1945 29

8.75 0.15 0.05 0.33 1.64 0.17 0.10 74.9 1941 31

9.25 0.16 0.05 0.29 1.48 0.15 0.10 79.2 1937 33

9.75 0.21 0.09 0.41 1.37 0.14 0.10 83.4 1933 35

10.25 0.06 0.04 0.62 1.07 0.11 0.10 87.7 1928 36

12.75 – – – 0.59 0.06 0.10 109.1 1907 45

15.25 0.01 0.05 6.97 0.45 0.05 0.10 130.5 1885 54

19.75 – – – 0.37 0.04 0.10 169.0 1847 70

3.2.6 Cross–correlation

Cross–correlation coefficients of macroscopic charcoal and selected pollen taxa were

calculated using the Minitab® 18 Statistical Software to identify any potential links between

fire episodes and vegetation dynamics (Green, 1981; 1983; Colombaroli et al. 2008). This

method compares the values for two variables i.e. charcoal and pollen by shifting the value

chains against one another for a specified number of time lags and then calculating

correlation coefficients for each time lag (Bahrenberg et al. 1992). Non–transformed pollen

Table 2: 210Pb and 137Cs results obtained from Valkea-Kotinen.

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percentage data and charcoal counts were used. The 35 samples below the 19 cm mark in

the core were used to compute cross–correlation coefficients at ±25 lags. Each lag

represents a period of 32.9 years. The 95% interval of the cross–correlation coefficients

was estimated by computing ±SE (standard error) of the correlation coefficients (0.36 and

–0.36) (Bahrenberg et al. 1992). This corresponds to a test for significant correlation

between two variables (Tinner et al. 1999).

4. Results

4.1 210PB and 137Cs Dating

The results obtained from the 210Pb dating were determined unreliable as the uncertainty

limits were quite large (Table 2). The sedimentation rate was calculated to be quite quick at

0.117 ±0.049 cm per year (95 % confidence interval) which in comparison to the 0.020 cm

per year calculated using the AMS 14C dates, adding to the unreliability of the results.

Figure 5: 210Pb and 137Cs profiles at Valkea-Kotinen.

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The 137Cs profile at Valkea–Kotinen (Figure 5) indicates mixing of the sediments

(Lempinen 2017; Appleby 1997). Theoretically, two peaks in 137Cs should be witnessed in

the core; one to reflect the 1950’s and 1960’s nuclear bomb tests and one around 1986 to

reflect the fallout from the Chernobyl disaster. Neither of these peaks is present at Valkea–

Kotinen.

Sediment mixing is a common problem encountered through this method and usually

results in a dampening of peaks in 210Pb and 137Cs. As such, these results were omitted from

the age–depth model, with the exception of the surface date which confirmed the top of

the core was modern, as they were considered to be too unreliable.

4.2 AMS 14C Radiocarbon Dating and Age–depth Model

The two samples sent for radiocarbon dating were dated successfully (Table 1) and were

then calibrated using the IntCal13 calibration dataset (Reimer et al. 2013) and alongside the

modern 210Pb date, an age–depth model (Figure 6) was created using the Clam 2.2

programme deposition model in R software (Blaauw 2010) with a linear interpolation

confidence level of 2 σ (95.4%). Each of the samples was calculated to represent a median

of 24 years and the average sedimentation rate was calculated at 0.020 cm per year.

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4.3 Pollen–Stratigraphic Age Anchor Point

One factor, which is discussed further in section 5.4, was a decline in Alnus pollen

percentage and accumulation rate which began at ca. 1400 cal yr BP and ended at ca. 1000

cal yr BP (Stivrins et al. 2017). This was a phenomenon observed throughout many areas of

Northern Europe at the time. The occurrence of this decline and subsequent recovery in

the Valkea–Kotinen sediment core can be used as a pollen–stratigraphic age anchor point

in addition to the 14C AMS dates. This indicator also gives credence to the accuracy of the

age–depth model at this stage of the core as above 19 cm (ca. 1000 cal yr BP) the age–

depth model become ambiguous towards the surface (Figure 7).

Figure 6: Age versus depth model for Valkea-Kotinen.

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As the chronology of the age–depth model was based on so few dates, it is unlikely to be

fully accurate, especially after ca. 1000 cal yr BP. Any interpretation or discussion regarding

the timing of fire episodes or vegetation changes must take this into consideration.

4.4 Macrocharcoal Analysis

4.4.1 CharAnalysis Results

Decomposition of macrocharcoal accumulation records (CHAR; particles cm2 yr–1) from

Lake Valkea–Kotinen indicate fire–episode occurrence and magnitude over the last ca.

1700 years (Figure 8) The term ‘Fire episode’ is used rather than ‘fire’ to describe the

Figure 7: Alnus pollen percentage and accumulation curves alongside the age–

depth model from Valkea-Kotinen. Orange dashed line denotes the 19 cm (ca.

1000 cal yr BP) mark.

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statistically significant peaks in the CHAR record because more than one fire could have

occurred within the duration of a peak (Long et al. 1998). The term ‘peak magnitude’ refers

to the total charcoal accumulation in a peak and is hereafter referred to as ‘magnitude’.

The term ‘magnitude’ can refer to the fire episode size/severity, the proximity of a fire

episode to a site and the taphonomic processes by which charcoal is deposited into the

lake, or a combination of all these factors (Clark et al. 1998; Whitlock and Millspaugh 1996;

Lynch et al. 2004; Higuera et al. 2005; Higuera et al. 2007). If a fire episode is followed by a

vegetation change it is more likely that magnitude is recording fire size/severity rather than

any other factor.

4.4.2 Macrocharcoal Accumulation Rate

CHAR for the record’s interpolated intervals (Cint) had a mean of 0.31 particles cm–2 yr–1

and a range of 0.00 particles cm–2 yr–1 to 0.79 particles cm–2 yr–1 (Figure 8a.). The maximum

CHAR level was the highest amount recorded as part of a fire episode occurring at around

900 BP. The background CHAR level (Cback) had an average of 0.26 particles cm–2 yr–1 and

a range of 0.00 particles cm–2 yr–1 to 0.63 particles cm–2 yr–1.

4.4.3 Local Fire Episodes

Analysis of the macrocharcoal record at Valkea–Kotinen identified six local fire episodes at

around 1570, 1290, 900, 615, 470 and 15 cal yr BP (Figure 8b.). The fire return interval

(FRI) was on average 312 years (range 221 – 408 years).

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The first recorded local fire episode occurred at ca.1570 cal yr BP, and had a peak

magnitude of 9.2 charcoal pieces cm–2 peak–1 (Figure 8c.) and was immediately followed by

a decline in Alnus, Betula and Picea accumulation (Figure 10). The next fire episode,

recorded at ca. 1290 cal yr BP had the lowest peak magnitude of all the episodes at 2.2

pieces cm–2 peak–1 (Figure 8c.) and had no apparent effect on the vegetation. The fire

episode recorded at ca. 900 cal yr BP had a peak magnitude of 6.6 pieces cm–2 peak–1

(Figure 8c.) and was followed by a reduction in Betula, Pinus (Figure 10) and overall total

pollen accumulation rates (Figure 13).

At ca. 615 cal yr BP the largest magnitude fire recorded in the core occurs with a peak

magnitude of 14.5 pieces cm–2 peak–1 (Figure 8c.). There is no real vegetation response at

this time meaning that the fire episode likely affected only a small area but happened in

very close proximity to the lake. A fire episode occurred at ca. 470 cal yr BP and was

recorded as low magnitude (3.3 pieces cm–2 peak–1) (Figure 8c.). Although recorded as low

magnitude, wind direction at the time may have affected charcoal deposition into the lake

resulting in a bias in the record. This episode is followed by a reduction Betula, Picea and

Pinus pollen accumulation (Figure 10), and Cerealia (Figure 11) pollen accumulation.

The final recorded fire episode in the chronology occurs at ca. 15 cal yr BP and is the

second highest magnitude of all the fire episodes at 13.0 pieces cm–2 peak–1 (Figure 8c.). It is

unlikely that this result is correct as there are no documented fires at Valkea–Kotinen at

this time. This is discussed further in section 5.2.

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4.5 Fossil Pollen Data

Pollen percentage and accumulation rate diagrams for Valkea–Kotinen spanning the past

ca. 1700 years are illustrated in Figures 9–13. A total of 41 pollen and spore types were

identified from the lake core. Trees and shrubs dominated the local taxa with Pinus (average

58 %), Betula (average 22 %), Picea (average 10.9 %), and Alnus (average 4.4 %) of the total

pollen. Herbaceous taxa consisted 0 – 9.4 % and aquatics and spores consisted 0 – 2.4 % of

the total assemblage (Figure 9). Total tree and shrub accumulation rates ranged from 2907

– 8870 grains cm2 yr–1 (Figure 10), total herb accumulation rates ranged from 6 – 399 grains

Figure 8: a. Charcoal accumulation rate (CHAR-log scale; particles cm-2

yr-1), b. fire episodes (peaks; + symbols), and c. fire-episode magnitude for

Valkea-Kotinen plotted against age (Cal yr BP).

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cm2 yr–1 (Figure 11), and aquatic and spore accumulation rates ranged from 0 to 140 grains

cm2 yr–1 (Figure 12). Overall pollen accumulation values for the whole record ranged from

2952 to 9373 grains cm2 yr–1 (Figure 13).

4.6 Microcharcoal Accumulation

Microcharcoal accumulation was lowest towards the bottom of the core and increased

towards the top of the core. The accumulation rate ranged from 88 particles cm–2 yr–1 at ca.

1100 cal yr BP to 671 particles cm–2 yr–1 ca. 480 cal yr BP (Figure 9).

4.7 Cross–correlation

The cross–correlation analysis for the Valkea–Kotinen core was only applied to those

samples below the 19 cm mark to try to gain the most accurate result regarding the

temporal relationship between charcoal and pollen at the site. It was not applied to Alnus as

cross–correlation is not a suitable statistical analysis for a taxon which has experienced a

decline (see section 5.4) (Tinner et al. 1999). Results are displayed in Figures 14 and 15. The

analysis reveals that an increase in microcharcoal due to a fire episode (at lag 0) lead to a

significant negative correlation in Picea at lag +11 (Figure 14). Increases in macrocharcoal

also lead to a significant negative correlation with Picea, at lag +12 (Figure 15). Neither

Pinus, nor Betula, nor Poaceae were significantly correlated with either microcharcoal or

macrocharcoal. Cerealia pollen was significantly positively correlated with microcharcoal at

lag 0 but had no significant correlation with macrocharcoal (Figures 14 and 15).

P a g e | 20

0

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)

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rcoa

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20 40

Betula

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pinu

s

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ylus

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Picea

20 40 60 80

Pinus

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rcus

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Salix

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Tilia

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us

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Calluna

vulga

ris

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Eric

acea

e un

diff.

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us

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ula alnu

s

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phae

rha

mno

ides

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bus

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ealia

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isia

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acea

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ssicac

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naba

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-typ

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taur

ea cf. C. c

yanu

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nopo

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riace

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erac

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Filipe

ndula

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Galium

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inac

eae

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Poa

ceae

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uncu

lace

ae

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ex

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Apiac

eae

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Urtica

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Viola palus

tris

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Dro

sera

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Men

yanthe

s trifo

liata

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Spa

rgan

ium

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Dryop

teris

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Equ

isetum

10

Pterid

ium aqu

ilinu

m

10

Sph

agnu

m

Trees and Shrubs Herbs Aquatics Spores

Percentage Pollen Count (%)Particles cm -2 yr-1

Figure 9: Pollen and microcharcoal diagram from lake Valkea-Kotinen. Pollen abundances are expressed as percentages. Charcoal accumulation is

expressed as particles cm-2 yr-1. Exgenerations have been applied to some taxa. Note change in scale.

P a g e | 21

Figure 10: Pollen diagram showing accumulation rates of tree and shrub taxa at Valkea-Kotinen. Pollen values are expressed as accumulation rates

(grains cm-2 yr-1). Red lines represent local fire episodes as determined using CharAnalysis. Exgenerations have been applied to some taxa. Note

change in scale.

0

100

200

300

400

500

600

700

800

900

1000

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1200

1300

1400

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1700

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Cal Y

ears

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th (c

m)

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1000 2000 3000

Bet

ula

500 1000 1500

Picea

2000 4000 6000

Pinus

20 40

Cor

ylus

20 40

Que

rcus

20 40 60

Salix

10

Tilia

20

Ulm

us

Accumulation Rate (Grains cm -2 yr -1)

P a g e | 22

Figure 11: Pollen diagram showing accumulation rates of herb taxa at Valkea-Kotinen. Pollen values are expressed as accumulation rates (grains cm-2

yr-1). Red lines represent local fire episodes as determined using CharAnalysis. Exgenerations have been applied to some taxa. Note change in scale.

0

100

200

300

400

500

600

700

800

900

1000

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th (cm

)

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Poa

ceae

50 100 150

Cer

ealia

20

Api

acea

e

20 40 60

Artem

isia

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Ast

erac

eae

20 40

Callu

na vulga

ris

20

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ssicac

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naba

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-type

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taur

ea c

f. C. c

yanu

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nopo

diac

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riace

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erac

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ntag

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ex

20 40 60

Sor

bus

20

Urtica

10

Vio

la p

alus

tris

Accumulation Rate (Grains cm -2 yr -1)

P a g e | 23

Figure 12: Pollen diagram showing accumulation rates of aquatics and

spores. Pollen values are expressed as accumulation rates (grains cm-2 yr-1).

Red lines represent local fire episodes as determined using CharAnalysis.

Exgenerations have been applied to some taxa. Note change in scale.

0

100

200

300

400

500

600

700

800

900

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ears B

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Depth

(cm

)

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Dro

sera

15 30

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opte

ris

15 30

Equise

tum

15 30

Spargani

um

40 80 120

Sphagnu

m

10

Men

yanth

es tri

folia

ta

10

Pterid

ium

aquilin

um

Accumulation Rate (Grains cm -2 yr -1)

0

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th (cm

)

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Total T

rees

and

Shr

ubs

100 200 300 400

Total H

erbs

4000 8000

Total P

ollen

50 100 150

Total A

quatics an

d Spo

res

Accumulation Rate (Grains cm-2

yr -1

)

Figure 13: Pollen accumulation rates of total pollen, total tree and shrub

pollen, total herb pollen and total aquatics and spores at Valkea-Kotinen.

Pollen values are expressed as accumulation rates (grains cm-2 yr-1). Red

lines represent local fire episodes as determined using CharAnalysis.

Exgenerations have been applied to some taxa. Note change in scale.

P a g e | 24

Figure 14: Cross-correlation plots for microcharcoal vs. selected pollen. Dashed red line denotes significance level (0.36, -0.36). The horizontal axis

shows the lag time (1 lag = 32.9 years).

P a g e | 25

Figure 15: Cross-correlation plots for macrocharcoal vs. selected pollen. Dashed red line denotes significance level (0.36, -0.36). The horizontal axis

shows the lag time (1 lag = 32.9 years).

P a g e | 26

5. Discussion

5.1 Lake Sedimentation Processes

The mechanisms surrounding sediment accumulation into lakes are complicated. A multi–

core study of Holocene sediment accumulation patterns at Mirror Lake in New Hampshire,

USA, showed that pollen accumulation could be up to five times higher at one area of a

lake in comparison to another. Sediment generally focuses at the deepest point of the lake

so in palynological studies it is normally best to retrieve several sediment cores from

different areas throughout a lake as no single core provides an accurate record of pollen

input to a lake. The main reason for the within–lake variability in PAR is temporally and

spatially uneven patterns of sedimentation and pollen deposition which is typical to many

of the lakes in cold and temperate regions of the world (Davis et al. 1984).

As such the general changes in pollen accumulation over the past ca. 1700 years at Valkea–

Kotinen are likely to be mainly a result of the complex sedimentation and deposition

factors at work. For example, we can see in Figures 10 and 13 the accumulation rate at

Valkea–Kotinen follows a similar trend of high values at the bottom of the core, which

then reduce, rise slightly in the middle of the core before reducing again before increasing

at the top of the core, for the main tree taxa. This blanket trend is likely the effect of

sedimentation processes which affect the accumulation rate of each pollen type equally.

Other factors, including climate changes or human influences, may play a small part in the

accumulation rate but sedimentary processes are likely the driving force behind the changes

seen at Valkea–Kotinen.

The one sediment core used in this study is unlikely to give a full representation of the fire

and vegetation dynamics at the lake. Also, the results of the 210Pb and 137Cs analysis, whilst

P a g e | 27

erroneous and unsuitable for inclusion in the age–depth curve, do give us the advantage of

knowing that sediment mixing has occurred towards the surface of the core. Therefore the

following discussion regarding fire and vegetation dynamics at the lake is done so with the

knowledge that the results of this study are likely to be somewhat imprecise and not a true

reflection of the local and regional scale factors which have affected the Valkea–Kotinen

area over the past ca. 1700 years.

5.2 Fire History at Valkea–Kotinen

The local fire record at Valkea–Kotinen over the past 1740 years is quite sparse with only

six significant fire episodes and a fire return interval (FRI) of on average 312 years (range

144 – 456 years). This is also low in comparison to other areas of southern Finland. For

example, the FRI at Sudenpesä between 1700 and 400 cal yr BP was 200 years and at

Vesijako the FRI between 2000 and 750 cal yr BP was 180 years (Clear et al. 2013; 2015). It

is also low in comparison to other areas in Finland. For example, in eastern Finland the

average FRI is between 80–200 years depending on the site and vegetation type and period

of interest (Haapanen and Siitonen 1978: Pitkänen and Huttunen 1999). However the low

FRI at Valkea–Kotinen could be due to the fact that not every local fire episode,

particularly low intensity fire episodes, may have been identified (Higuera et al. 2005).

Another factor which may have affected the FRI is that wet, mesic Picea dominated forest,

like that at Valkea–Kotinen tend to burn less frequently than dry, Pinus dominated forests.

Picea dominated forest is more likely to experience high intensity, low frequency fires

(Tanskanen, 2007) whereas with Pinus dominated forest is more susceptible to high

frequency, low intensity surface fires (Zackrisson, 1977; Engelmark, 1984). Zackrisson

P a g e | 28

(1977) found that in northern Sweden found that Picea dominated mesic forests had an

average FRI of 80–120 years in comparison to Pinus dominated forests which had a FRI of

around on average 50 years. It is difficult to determine exactly whether the identified local

fire episodes were due to the effects of anthropogenic activities or were natural in nature

but the long FRI suggests that the local fire regime was not strongly influenced by

anthropogenic activity as a long FRI is quite typical for a natural forest (Hörnberg et al.

1998; Pitkänen et al. 2003).

Research into tree fire scar evidence by

Tuominen (1990) on the west side of the lake

found that there had been several local fire

episodes; at 1771, 1807, 1827, 1857, and 1886

but there is no indication of these local fires

in the sedimentary record at Valkea–Kotinen.

Just as sediment mixing towards the surface

of the core dampened the peaks in 210Pb and

137Cs it is possible that mixing resulted in

dampened macrocharcoal accumulation

peaks in the sediment after the local fire

episode identified with CharAnalysis at

around 470 cal yr BP meaning that the fire

episodes recorded by tree scars were lost in

the mixed sedimentary record.

0

100

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300

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700

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1300

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Cal Y

ears

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25

30

35

Dep

th (c

m)

200 400 600 800

>20

µm

200 400 600

>150

µm

Accumulation Rate (Particles cm -2 yr-1)

Figure 16: Micro- and macrocharcoal

accumulation rates Charcoal values are

expressed as accumulation rates (Particles

cm-2 yr-1). Red lines represent local fire

episodes.

P a g e | 29

Microcharcoal in lake sediments is generally assumed to reflect fires on a regional scale,

whilst macrocharcoal reflects fires on a local scale (Whitlock and Larsen 2001). However,

in their study at Lake Pönttölampi in eastern Finland, Pitkänen et al. (1999) demonstrated

that fire–scars on trees around the lake coincided with peaks in microcharcoal in the

sediment, suggesting that a significant proportion of microcharcoal particles counted from

pollen slides can originate from low intensity local fires. However the microcharcoal has a

potentially large source area of within a few kilometres of the site.

This may be similar to the accumulation of microcharcoal at Valkea–Kotinen as we can see

from Figure 16 that microcharcoal tends to peak around the same time that there is a local

fire episode, suggesting that a significant proportion of the microcharcoal deposited into

the lake is of local origin. After around 470 cal yr BP when there is little macrocharcoal,

there is an abundance of microcharcoal which may be somewhat representative of the local

fire events recorded by Tuominen (1990). The exact cause of these fire events are uncertain

but are most likely the result of human activity. The use of fire in forests not highly

regulated in the 18th and 19th Centuries so the fire scars could be a result of human

negligence, leading to, for example, escaped camp fires (Parviainen 1996; Aakala 2017).

Therefore these local fires may be similar to the low–intensity local fires recorded in the

microcharcoal record at Lake Pönttölampi in by Pitkänen et al. (1999) but with a dampened

macrocharcoal record from sediment mixing or the fires may also have been too small to

appear as a peak in the macrocharcoal record.

The 1827 fire scar record recorded by Tuominen (1990) is likely a representation of a large

fire documented at Evo in 1826 if one allows for some dating error (Aakala 2017).

However again, there is no local fire episode recorded at this time by CharAnalysis which

also suggests the possibility of dampening of the macrocharcoal record through sediment

P a g e | 30

mixing. The wind direction at the time of a fire episode plays a large part in determining the

distribution of charcoal particles to a lake but it is unlikely that wind direction was the

factor preventing the deposition of macrocharcoal particles in the lake for all of the fire

episode recorded by Tuominen (1990) and as such the lack of macrocharcoal peaks in the

record is may be a result of sedimentary processes.

A scarcity in macrocharcoal towards the upper end could be explained as a result of active

fire suppression. The newly–established Forest and Parks Service introduced a fire

suppression policy in the 1860’s in Finland and other areas of Fennoscandia (Niklasson and

Granström 2000; Ruuttula–Vasari 2004; Halme et al. 2013) in the midst of the growing

forestry industry as a means of protecting timber resources. Fire suppression practices

included regulating the use of fire in Finnish forests, educating people of the effects of fires

and extinguishing fires as quickly as possible if they did occur (Wallenius 2011).

Logging was carried out in Evo in the 1800’s, until the area became a crown park under the

imperial senate in 1856. A forestry institute, the first of its kind in Finland, was established

in Evo in 1862 (Metsähallitus 2017) around 5.5 km from Valkea–Kotinen so it is likely that

active fire suppression was practiced in the forest surrounding the lake at this time.

However, at Valkea–Kotinen we can see that before the establishment of the forestry

institute in 1862, the last recorded local fire episode occurred at ca. 470 cal yr BP, long

before the advent of fire suppression to reduce, in part human induced fires, in Southern

Finland. This too gives credence to the assumption that the local fire history at Valkea–

Kotinen was not strongly influenced by anthropogenic activities.

Interestingly, despite active fire suppression in the Evo region, CharAnalysis identified a

local fire episode very close to the surface of the core, apparently occurring around 15 years

P a g e | 31

ago. There are no recorded fires at Valkea–Kotinen at this time so this peak in

macrocharcoal may be a result of the sediment mixing which has occurred in the top of the

core.

However, the fire episode is the second largest in intensity of the 6 recorded so it seems

unlikely that sediment mixing would create such a large peak in macrocharcoal especially

after the absence in macrocharcoal immediately preceding this peak. One possible

explanation could be that the macrocharcoal originates from controlled burning in the area.

The reduction of fires due to fire suppression resulted in the endangerment of ecological

processes reliant on fire regimes (Rassi 2003. As such, controlled burning has been carried

out in Evo since the 1950’s (Metsähallitus 2017) in an attempt to preserve the habitats of

organisms such as some insects which thrive in areas of burned wood and also to promote

vegetation growth.

Controlled burning was carried out at 16 sites within the Lammi commune of Evo between

1960 and 1982 (Lindholm and Vasander 1987). The closest of these sites to Valkea–

Kotinen were; Vähä Ruuhijärvi, burned in 1968, around 1.4 km away and Lapinjärvi,

burned in 1963, around 1 km away. Also, as part of the Evo Life Project, five spruce

dominated stands, each two hectares in size, were burned in the summer of 2002 and two

pine dominated stands, a total area of 9 hectares were burned in 2003 for regenerative

purposes (HAMK 2005). One of these burned stands was approximately 1.3 km away from

Valkea–Kotinen.

Due to the large size of macrocharcoal particles, the local fire episodes have always been

thought to be indicative of macrocharcoal deposition within 500 m to 1 km of the source.

(Gavin et al. 2003; Lynch et al. 2004; Higuera et al. 2007). However, some studies have

P a g e | 32

found that macrocharcoal has the potential to be distributed further than this. Higuera et

al. (2007) note that depending on the charcoal injection height to the atmosphere, the

source area for macroscopic charcoal can range between 300 m and 20 km. Similarly, in

their study of long distance charcoal transportation, Oris et al. (2014) identified a

macrocharcoal (particles 150 μm and over) peak at one of their study sites, 32 km away

from where the fire event occurred.

In addition to this, studies of modern lake charcoal accumulation indicate that charcoal

deposition into sediments can occur several years after a fire episode. For example,

Whitlock & Millspaugh (1996) discovered that lakes in burned and unburned watersheds in

Yellowstone National Park, USA, received charcoal during fires episodes in 1988, but the

amount deposited continued to increase significantly for five years after in burned

watersheds. Also, Anderson et al. (1986) observed that a lake in Maine, USA, received

charcoal for several decades following a fire episode.

As such it is possible that the macrocharcoal peak near the surface of the Valkea–Kotinen

record originates from the delayed charcoal deposition of prescribed burning, further than

1 km away, undertaken in the Evo area since the 1960’s.

5.3 Vegetation History at Valkea–Kotinen

The percentages and accumulation rates of pollen from Valkea–Kotinen over the past ca.

1700 years are indicative of a fairly stable vegetation dynamic with only slight fluctuations.

The dominance of conifers such as Pinus and Picea as well as broadleaved trees such as

Betula and Alnus in the Valkea–Kotinen is very typical for the South of Finland (Metla

2012).

P a g e | 33

As discussed in section 5.1, the general trend in pollen accumulation is most likely

attributed to changing sedimentation rates rather than any other factors. A study of the past

5000 years of water table variability at Kontolanrahka bog (Väliranta 2007) in southwestern

Finland shows that southern Finland has been subject to wetter and dryer climatic periods.

The past c 1700 years of water table variability is shown in Figure 17. Vegetation dynamics

do not seem to be influenced by the changing water table suggesting that climatic changes

were not strong enough over the past ca. 1700 years to impact upon the vegetation regime.

However some changes in vegetation structure can be identified. The most significant

change that occurred was a sudden episodic decline in Alnus beginning at c.a. 1400 cal yr

BP and is discussed further in section 5.4. Whilst fire episodes have led to a decline in

Betula and relatively fire resistant Pinus accumulation, the cross correlation analysis of both

microcharcoal and macrocharcoal show that the fires did not have a significant impact on

the population of either taxa al a local or regional scale.

Picea is a fire susceptible species (Bradshaw et al. 2010) as it has thin bark, low branches

and a superficial root system (Heikinheimo, 1915; Cajander, 1941). This susceptibility is

also seen in the Valkea–Kotinen record. Picea pollen correlates negatively with both

macrocharcoal and microcharcoal, although the negative relationship with macrocharcoal is

greater suggesting that local fires had more influence over the spruce population at Valkea–

Kotinen. However, it is well known that Picea is favoured by the absence of fire (Bradshaw

1993) and the long FRI (average 312 years) at Valkea–Kotinen is likely the reason why Picea

is the dominant taxon at the lake. The expansion of Picea has the effect of reducing

wildfires by increasing shade, moss cover and soil moisture (Ohlson et al. 2011). Therefore

the fires at Valkea–Kotinen have negatively impacted on Picea abundance but not to the

extent that the taxon could not recover, creating a positive feedback where the expansion

P a g e | 34

of Picea has potentially reduced the fire activity and in turn allowed for the dominance of

Picea in the area.

Indicators of human activity such as Poaceae, Filipendula and Cerealia are present throughout

the core but they begin to increase towards the present, suggesting a growing

anthropogenic influence on the vegetation. Cerealia pollen increases to a maximum of 115

grains cm−2 yr−1 at c. 160 cal yr BP, beginning around 650 years ago (Figure 11). There is

also a general increasing trend of microcharcoal and accumulation of other taxa related to

slash–and–burn cultivation i.e. Poaceae at the same time (Figure 11). This is

contemporaneous with the more permanent settlement of the Evo region, where Valkea–

Kotinen is located, in the 14th Century (Metsähallitus 2017). Slash–and–burn cultivation

was practiced in Southern Finland from 2400–2000 cal yr BP (Tanskanen 2007; Huurre

2003) until the 19th Century (Luoto et al. 2009). Huttunen (1980), however, notes that the

Evo region comprises only 1–2% arable land so slash–and–burn cultivation was rarely

practiced there but a round 20 km towards the south at Lammi, the land becomes more

fertile and as such more suitable for cultivation.

None of the local fire episodes show evidence of being related to slash–and–burn activities,

and Cerealia pollen is not significantly correlated with macrocharcoal increases (Figure 15)

but it is significantly positively correlated with microcharcoal increases (Figure 14).

Therefore, the increase in microcharcoal alongside Cerealia pollen and other slash and burn

related taxa is likely to be an indicator of regional forest clearance for cultivation through

slash–and–burn activity (Sarmaja–Korjonen 1992), perhaps in the Lammi area or elsewhere

in the region.

P a g e | 35

There is also no documented decline in Picea pollen accumulation (Figure 11) which is

often witnessed as a result of slash–and–burn (Alenius et al. 2008; Luoto et al. 2009;

Pitkänen et al. 2002). Due to their relatively large size (70–130 µm), Picea pollen grains do

not travel far from their source. Simulations have shown that Picea has a considerably

smaller pollen source area compared to light pollen types such as Betula and Alnus (Sugita

1994). In their study of modern pollen dispersal, Pidek et al. (2010) concluded that the

source area for Picea pollen is within 1 km of the parent tree. This also adds to the evidence

suggesting that the Cerealia grains are of regional descent as if cereal cultivation were

practiced near the lake; we would expect to see a gradual decline in Picea pollen

accumulation as Cerealia pollen accumulation increases but this does not occur at Valkea–

Kotinen.

There is however a sharp decline in Picea pollen accumulation towards the surface of the

core, beginning ca. 200 years ago. This is accompanied by a decline on both Pinus and

Alnus, and to a lesser extent Betula. These declines could be explained by the intensive

logging which took place in Evo in the 1800’s as part of the country’s shipbuilding and tar

industries (Metla 2012a).

5.4 The Alnus Decline 1400 – 1000 Cal yr BP

A decline in Alnus pollen values beginning at ca. 1400 cal yr BP can be witnessed at

Valkea–Kotinen and has been discussed along with several other sites in Southern Finland

in a paper by Stivrins et al. (2017). This is a decline that has been witnessed in pollen

diagrams throughout Northern Europe at ca. 1400 – 1000 cal yr BP and has historically

been attributed to anthropogenic influences (Sarmaja–Korjonen 2003; Saarse et al. 2010).

P a g e | 36

The Alnus decline was different from other decreases in tree pollen values during the

Holocene, because it was sharp and episodic, in most cases was followed by a recovery

after <300 years and the other tree taxa showed no decrease in values during the event. At

Valkea–Kotinen Alnus percentages decline from 6 to 10% before the event to 2–3% during

the event and rise to almost the same level as before the decline. Pollen accumulation data

indicate a rapid decrease in Alnus biomass and that no other tree pollen types declined

during this period (Figure 10). There is no increase in pollen taxa which would indicate

human influence on the vegetation during the decline. Very low rates of Cerealia pollen

occur (Figure 11) but, they do not increase as Alnus pollen declines and no charcoal peak is

present in the sediment record at this time. Thus, the Alnus decline was not associated with

an increase in crop cultivation, forest clearance, fires or other types of human activity. A

credible explanation for the decline must account for the fact that (i) the decline is Alnus

specific, (ii) the decline is episodic, and (iii) the decline is witnessed in many different

environments, including those which were not subject to intense human influence.

In the past, the Alnus decline has been attributed to human influences. Sarmaja–Korjonen

(2003) and Saarse et al. (2010) noted that fire frequency and Cerealia pollen increased

during the decline and claimed that extensive forest–clearing slash–and–burn was necessary

for agriculture purposes. Sarmaja–Korjonen (2003) also claimed that Alnus wood was

needed for specific constructions, as fuel for iron smelting, or for smoking fish and meat

but this explanation does not account for the decline in Alnus in areas where there is no

evidence of human influence. Moreover, a simultaneous decline and subsequent recovery

of populations of one tree species or genus over a large region is an unlikely response to

gradually increasing late Holocene human impact (Davis, 1981; Peglar and Birks, 1993).

P a g e | 37

An abrupt climate event could also explain the Alnus decline. The best recorded episodic

decline of Alnus and other thermophilous tree species was the ‘8.2 ka event’, a cold event at

8200 cal yr BP (Seppä et al. 2007). However, a cold climate between ca. 1400 and 1000 cal

yr BP is not indicated in boreal European high–resolution temperature reconstructions

(Helama et al. 2010; Esper et al. 2012). There is some tree–ring based evidence for a

cooling in some parts of Europe at AD 536–660 (Büntgen et al. 2016) but it is unclear

whether this cold event occurred in boreal Europe (Helama et al. 2017). Even if it did, it

still fails to explain why it would have influenced only the Alnus population and no other,

more thermophilous tree species.

As Alnus is a species that requires wet soils, a decline could be caused by drought. Water

table reconstructions from the Baltic Sea region indicate a dry period during the Alnus

decline (Väliranta et al. 2007; Gałka et al. 2017), if one allows a minor offset due to

uncertainties in radiocarbon–dated sediment sequences (Figure 17). However, a major

Figure 17: Alnus pollen percentages, Alnus accumulation rate and macrocharcoal

accumulation rate from Valkea-Kotinen alongside water table variability from

Kontolanrahka bog, southern Finland (Väliranta et al. 2007). Adapted from Stivrins et al.

(2017).

P a g e | 38

drought should have affected many other tree species so the fact that the decline is specific

to Alnus does not support the climatic explanation. For example, Picea is sensitive to

drought and severe droughts cause high spruce mortality in the boreal forest (Aakala et al.

2011; Lévesque et al. 2013), but there is little change in Picea values throughout the period

of the Alnus decline. Moreover, the soil moisture records show varying humidity and

dryness throughout the late Holocene, and the dry conditions at AD 500–1000 are not

exceptional (Gałka et al. 2017).

A third possible factor which may explain the event is a pathogen outbreak. Such an

outbreak would cause a widespread, synchronous and rapid (10–20 years) decline of a

species influenced by the pathogen (Peglar and Birks, 1993). This is consistent with the

evidence for Alnus decline in this study. Alnus species are sensitive to several fungal

pathogens and an ongoing rapid Alnus population dieback caused by an infection of

Phytophthora alni (P. alni) has been observed since the 1990s in Europe and recently in North

America. P. alni is a fungal disease which causes lethal root and collar rot (Aguayo et al.

2013; Bjelke et al. 2016). In Sweden, a recent survey showed that 28% of 168 Alnus stands

were infected by P. alni, with 45% of the trees in the infected stands showing symptoms of

decline, often followed by trees dying within a few years to decade (Bjelke et al. 2016). P.

alni is a new fungal pathogen complex, which has originated from a recent hybridization

event (Bjelke et al. 2016) and is thus unlikely to be the cause of the medieval Alnus decline.

However, Phytophthora are well–known plant pathogens causing diseases that can affect

Alnus and other tree populations. It is therefore possible that a fungal disease comparable

with the ongoing Phytophthora outbreak may have caused the decline during the medieval

period. To firmly connect the cause of the decline with a pathogen would require finding

direct evidence of the pathogen, such as remains of sexual or asexual propagules or ancient

P a g e | 39

DNA of the pathogen causing the epidemic from the same sediment records as the pollen

data.

P. alni oospores are very thin walled and delicate and it is possible that they are not

preserved in the sediment. Unpublished experiments with Phytophthora oospores indicate

that they do not survive the standard chemical treatment used in pollen analysis, suggesting

that they should be sought from untreated sediment samples (Stivrins et al. 2017). As such,

the episodic and widespread Alnus decline in the boreal forest was most likely caused by

either a drought or pathogen or possibly both, but these explanations remain inconclusive

in the absence of direct evidence.

5.5 PARs vs Percentages as Tools for Detecting Environmental Change

The wide availability and unrivalled detail make pollen and charcoal sediment records an

invaluable source of information about past vegetation and fire regimes. A great number of

pollen studies express taxon in terms of percentages of the total sum of taxa and while

percentage data provide a general overview of the species present and the forest structure,

they do not give an accurate reflection of species population size (Davis et al. 1984;

Prentice 1988).

Percentage data are interdependent. The abundance ratio of one particular species is

dependent on the abundance of the other species. As such the development and

implementation of pollen analyses has, in the past, been hindered by the inherent relative

nature of percentage pollen counts (Davis 2000). Another disadvantage of using percentage

data is that it does not take into account facts such as the difference between pollen

productivity in different species and differences in pollen dispersal through the air (Sugita

P a g e | 40

1994; Broström et al. 2008; Marquer et al. 2014). Percentage pollen data can therefore

misrepresent past vegetation densities and as such have a detrimental effect upon the

reliable and accurate reconstruction of past environments (Davis 2000; Seppä 2013).

To circumvent the inherent problems associated with using percentage data for vegetation

reconstructions, Davis and Deevey (1964) and Davis (1967, 1969) initiated the use of

pollen accumulation rates (PARs) in palaeoecological studies, and began to apply the

technique to lake sediment analysis to more accurately assess vegetation change. PARs are

the calculation of the accumulation of pollen in the sediment, calculated for a specified area

(in this study, per cm2) per year. This results in an individual figure for each pollen or spore

type, so the PAR of a given species is not dependent on the abundance of other taxa and

therefore avoids the problems arising from the interdependent nature of relative records

i.e. percentage values. Pollen accumulation rates are used mainly to identify key species

surrounding a study site (Hicks 2001; Seppä et al. 2002; Seppä and Hicks 2006; Jensen et al.

2007) and as a proxy for measuring and reconstructing past species population growth rates

and fluctuations (MacDonald 1993; Giesecke 2005).

As discussed in 5.1, a disadvantage of the PAR method is that pollen accumulation can

vary greatly within a lake. As such, where possible, it is best to undertake multi–core

analysis to gain the most accurate reflection of vegetation composition and change. Two

other important criteria which must be met when using PAR records for vegetation

reconstruction are; (i) the sediment core must have a reliable chronology, with an adequate

number of dated samples for a consistent age–depth model and (ii) the pollen record must

be sampled at a high temporal resolution to reduce the risk of misinterpretation of the

record if one or more samples reflect an anomalously abundant or sparse PAR value

(Seppä and Hicks 2006).

P a g e | 41

Investigations have shown that PAR data is directly linked to the biomass and, as a result,

the population size of a particular taxon at a study site (Seppä 2006, 2008). As such, PAR

data has, in recent times, been used as a means for reconstructing past tree biomass as in

theory, the changes in PAR for an individual tree species through time reflect the

population abundance for that particular species within the source area of the pollen (Davis

and Deevey 1964; Prentice 1985; Sugita 1994; Mazier et al. 2012; Blarquez and Aleman

2016). The technique is particularly useful for vegetation reconstruction if the area under

investigation is dominated by only a few taxa.

The comparison of modern PAR values with modern biomass values can allow for the

calibration of fossil PAR values to biomass values by assuming a linear relationship

between fossil PAR and biomass values. The calibration usually involves the comparison of

modern pollen samples with standing vegetation using land surveys (Seppä et al. 2009),

trapping protocols (Blarquez et al. 2012), or remote–sensing data (Williams, 2002).

However, the technique is not straightforward and there are some factors which have to be

taken into account when undertaking this method such as careful selection of the lake basin

site, ensuring there is a precise chronology and robust age–depth curve and a careful

assessment of the complex sedimentary processes at work and their effect on pollen

accumulation rates within the study lake (Seppä 2006; Mazier et al. 2012).

In the case of this study, only one of the two criteria for successful PAR calculation and

interpretation has been met. Whilst the sediment core was sampled a high resolution (0.5

cm slices), the chronology for the age–depth model is unlikely to be accurate above 19 cm

(ca. 1000 cal yr BP). As precise dating is vital for biomass reconstructions it is unlikely that

the dating achieved at Valkea–Kotinen would be sufficient for accurate biomass

P a g e | 42

reconstruction. Also, only one core was sampled meaning that whilst the results from this

study are useful for ascertaining the vegetation coverage and general changes over time at a

local and regional scale, the results are not the most accurate representation of the

vegetation dynamics at work throughout the Late Holocene at Valkea–Kotinen.

6. Conclusions

The fossil charcoal and pollen from the Valkea–Kotinen sediment core has given new

insights into the fire and vegetation history of the area over the past ca. 1700 years.

Sediment mixing towards the surface of the core was a problem which resulted in

unsatisfactory 210Pb and 137Cs results. As such the age–depth model on which the core

chronology was based was not as accurate as desired and therefore restricted the precise

reconstruction of fire and vegetation dynamics, particularly after the Alnus pollen

stratigraphic anchor point at 19 cm (ca. 1000 cal yr BP) towards the present day.

Despite this setback, six local fire episodes with an average FRI of 312 years were identified

in the macrocharcoal record using CharAnalysis. Long FRIs are common in natural mesic

spruce forests and as such, along with scarcity of fire episodes, this suggests that these were

natural wildfires rather than due to human activities. There are no identified fire episodes

between around 470 cal yr BP and 15 cal yr BP meaning that macrocharcoal record may

have been affected by sediment mixing as there is evidence of local fire episodes during this

period through fire scar evidence. Macrocharcoal at the surface of the core may be a signal

of controlled burning in the Evo region, 1 km or more away from the site.

P a g e | 43

The pollen record at Valkea–Kotinen has only changed moderately over the past ca. 1700

years with Picea, Alnus, Betula, and Pinus always dominating the site. The main change in

vegetation was the northern European wide decline in Alnus beginning at around 1400 cal

yr BP and although it is speculated that the decline was caused by a drought or as the result

of a pathogen such as Phytophthora alni, the lack of evidence leaves the cause inconclusive.

The microcharcoal record shows increasing values towards the present day, reaching peak

accumulation from around 480 cal yr BP. Evidence of human activity was present

throughout the entire core. Cerealia pollen increased alongside macrocharcoal towards the

present from around 1000 cal yr BP, indicating regional slash and burn cultivation. Aside

from this and the expected negative affect on the fire susceptible species Picea the cross

correlation analysis showed that the fire episodes did not have a significant effect on the

vegetation at the site.

Although the use of PAR data in palaeoecological studies results in a more accurate

reflection of vegetation structure in comparison to pollen percentage data, it is still not

without its caveats. PARs require a robust chronology to ensure accurate environmental

reconstruction. Unfortunately this study does not have a robust chronology but a general

understanding of the vegetation dynamics over time at Valkea–Kotinen has been achieved.

The completion of this research, despite some inaccuracies, at Valkea–Kotinen adds to a

growing understanding of the past fire and vegetation dynamics in southern Finland and

throughout Fennoscandia as a whole.

P a g e | 44

7. Acknowledgements

I am very grateful to Heikki Seppä for giving me the opportunity to undertake this research

as part of the EBOR project and to him and Normunds Stivrins for all of their help and

support throughout the research process. Thank you to Ilkka who is always there for me

and of course to Helena for her unending supply of biscuits and banter. In the absence of

any family to thank, I would like to instead thank the Bee Gees for getting me through

some tough times. I would also like to thank the Leverhulme Trust for supporting my

studies financially.

P a g e | 45

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