Quaternary International 306 (2013) 88e96
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A testate amoeba-based transfer function for paleohydrological
reconstruction from boreal and subarctic peatlands in northeastern
Canada
A. Lamarre a, b, *, G. Magnan a, b, M. Garneau a, b, c, É. Boucher b, c
a
b
c
Peatland Ecosystems Dynamics and Climatic Change, CP 8888, Succ. Centre-Ville, Montréal, Québec, Canada, H3C 3P8
GEOTOP e Geochemistry and Geodynamics Research Center, CP 8888, Succ. Centre-Ville, Montréal, Québec, Canada, H3C 3P8
Department of Geography, Université du Québec à Montréal, CP 8888, Succ. Centre-Ville, Montréal, Québec, Canada, H3C 3P8
a r t i c l e i n f o
a b s t r a c t
Article history:
Available online 7 June 2013
Testate amoebae are common proxy for water table depth in peatlands and are commonly used to
reconstruct past hydroclimatic conditions. In northeastern America, previous transfer function development was mostly limited to ombrotrophic peatlands from continental and/or oceanic contexts. This
study provides a greater range of modern analogues (n ¼ 206) from ombrotrophic to poor minerotrophic
peatlands along an ecoclimatic gradient from boreal to subarctic in both continental and oceanic regions.
Multivariate analysis confirmed that water table depth is the dominant control on species distribution,
and a new transfer function was developed for this environmental parameter. The WA.inv model has a
RMSEP of 5.44 cm and R2 of 0.80. The effect of spatial autocorrelation on the predictive power and
statistical significance of this transfer function was also tested using newly developed statistical tests. We
also improved modern analogues for two problematic species (Hyalophenia subflava and Difflugia pulex).
This new testate amoeba transfer function will improve paleohydrological reconstructions from highlatitude peatlands (e.g. subarctic fens) and increase our ability to evaluate their sensitivity to ongoing
climate change.
Ó 2013 Elsevier Ltd and INQUA. All rights reserved.
1. Introduction
Over recent decades, testate amoebae (single-celled protists)
have become an important paleoecological and paleohydrological
indicator in peatlands. These organisms, which thrive in the upper
peat layers, are among the most sensitive proxies for water table
depth (WTD) (Booth, 2008; Mitchell et al., 2008) and have been
widely used in paleohydroclimatic reconstructions from peat records in Europe (e.g. Charman et al., 2006; Langdon et al., 2012).
Testate amoebae-based transfer functions have been developed
using modern training sets from a variety of peatlands in different
bioclimatic regions in Europe (Mitchell et al., 1999; Lamentowicz
and Mitchell, 2005; Charman et al., 2007; Payne and Mitchell,
2007; Mitchell et al., 2008). In northeastern America, testate
amoebae transfer function development has been mostly restricted
* Corresponding author. Research Chair on Peatland Ecosystems Dynamics and
Climatic Change, CP 8888, Succ. Centre-Ville, 1690 Cuvillier, Montréal, Québec,
Canada, H3C 3P8.
E-mail addresses: [email protected] (A. Lamarre), magnangabriel@
gmail.com (G. Magnan), [email protected] (M. Garneau), boucher.etienne@
uqam.ca (É. Boucher).
1040-6182/$ e see front matter Ó 2013 Elsevier Ltd and INQUA. All rights reserved.
http://dx.doi.org/10.1016/j.quaint.2013.05.054
to ombrotrophic peatlands in temperate and oceanic regions of the
Canadian Atlantic provinces and Eastern United States (Warner and
Charman, 1994; Charman and Warner, 1997; Hughes et al., 2006;
Booth, 2008; Elliott et al., 2011). Regional comparisons suggest
that most taxa have similar positions along the hydrological
gradient (e.g. Amphitrema wrightianum, Trigonopyxis arcula type),
while some others with poor modern analogues (e.g. Hyalosphenia
subflava, Difflugia pulex) tend to vary significantly between regions
(Mitchell et al., 2008).
In Canada, ombrotrophic peatlands are widespread throughout
the boreal biome, and are gradually replaced by poor minerotrophic
systems (fen) northward as peat accumulation is impeded by
shorter growing seasons and the influence of permafrost (Payette,
2001). Testate amoebae have been increasingly used as proxies of
paleohydrological conditions within the boreal and subarctic
peatlands of northeastern Canada (Loisel and Garneau, 2010; van
Bellen et al., 2011; Bunbury et al., 2012; Lamarre et al., 2012). The
paleoecological interpretations of testate amoeba records are often
limited within these ecosystems by the lack of modern analogues
covering this biogeographic context. More knowledge is required
on the ecology of taxa typical of minerotrophic conditions (fens) to
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
89
support the spatial (climatic gradient) and temporal (fen to bog
transition) ecological interpretations. Although the environmental
control on testate amoeba communities in fens remains poorly
understood, Payne (2011) showed that they can also be used to
quantify substrate-moisture in peatlands. However, these results
must be taken with caution as this study covered a large variety of
trophic conditions from rich minerotrophic peatlands to reed
swamps and even drained fens. In the present study, we developed
the first testate amoeba-based transfer function that includes
modern training sets from boreal and subarctic regions from both
minerotrophic and ombrotrophic peatlands. This study aims to (1)
evaluate the relationship between testate amoebae and environmental variables; (2) build a new transfer function to improve the
paleohydrological reconstructions in high-latitude peatlands; (3)
evaluate the differences in terms of species optima from northern
regions to other published testate amoeba studies; and (4) improve
knowledge on taxa with poor modern analogues such as H. subflava
and D. pulex (Mitchell et al., 2008).
2. Methods
2.1. Regional setting
A total of 18 peatlands have been selected for testate amoeba
modern analogue sampling (Fig. 1; Table 1). The sites are located
across Québec and belong to the boreal and subarctic bioclimatic
regions. They are distributed along southenorth (from 45 570 to
55130 N) and westeeast gradients (from 78 120 to 63 320 W) to
cover a broad range of peatland types developed within continental
to oceanic climate conditions.
Table 1
Details on the sites included in the transfer function.
#
Nom
Code
Latitude
( N)
Longitude
( W)
Peatland n
WTD
type
sites range
(cm)
1
2
3
4
Kuujjuarapik
La Grande
La Grande
Matagami
55 130 32.1
53 400 38.8
53 390 01.1
49 410 06.5
77 410 44.4
78 120 58.0
77 430 32.3
77 430 54.3
Fen
Fen
Bog
Bog
10
10
10
10
3e19
2e23
0.1e28
7.5e47
5
Matagami
KUJU
LG2 Fen
LG2 Bog
MTG
Bog2
MTG
Bog1
VER
VLD
MORTS
49 450 05.6 77 390 24.5 Bog
6
13e51
47 18 09.2 76 51 16.4 Fen
48 110 13.2 77 350 43.7 Bog
50 150 51.6 63 400 08.7 Bog
4
10
17
1e19
9e44
1e39.5
BAIE
PYL
ABE
FRONT
MANIC
LEBEL
LEBEL-G
BAIE-M
PLAINE
ROMA
49 050 47.4
53 470 50.4
54 060 54.7
45 570 59.3
49 070 17.9
49 060 32.1
49 060 02.6
49 050 36.0
50 160 27.3
50 170 51.8
7
3
6
10
25
10
10
11
15
25
0.5e28
0.1e14.5
0.1e45
0.1e21.5
0e43
3e38
5.5e41.5
0e34
6e57
1e40
6
7
8
9
10
11
12
13
14
15
16
17
18
Verendrye
Val-d’Or
Pointe des
Morts
Baie
Pylône
Abeille
Frontenac
Manic
Lebel
Lebel
Baie
Plaine
Roma
0
0
68 140 37.6
73 190 38.4
72 300 03.2
71 080 21.8
68 180 02.5
68 140 23.5
68 130 07.9
68 140 34.0
63 320 28.5
63 410 35.1
Bog
Fen
Fen
Fen
Bog
Bog
Bog
Bog
Bog
Bog
2.2. Modern training set collection and laboratory analyses
A total of 206 modern surface peat samples were collected and
treated for transfer function modeling following the ACCROTELM
protocol (http://www2.glos.ac.uk/accrotelm/fieldtst.html). In order
to minimize the statistical bias associated with the clustered design
of modern training sets (Payne et al., 2011), we sampled a large
number of sites (n ¼ 18), integrated a wide diversity of peatlands
(poor fen to domed bog) over significant latitudinal gradients, and
covered the full microtopographic gradient (from high and low
Fig. 1. Location of the study sites in Québec. Details for each site are listed in Table 1.
hummocks to pool margins; no high hummocks in fens). Surface
peat samples (10 10 10 cm) were cut with a serrated knife
before being placed in sealed plastic bags. Peat temperature (upper
5e10 cm) was measured using a VWR 23609-234 digital thermometer equipped with a 10-cm probe rod. Surface moisture was
evaluated with a HH2 Moisture Meter (Delta-T devices) from the
oceanic sites only (Saint-Lawrence North Shore; n ¼ 90). Water
table depth was measured relative to the surface in the cavity left
by the sampling after at least 30 min (Payne et al., 2006) while pH
was obtained from interstitial water of surface peat. In the laboratory, each peat sample was cut in half, electric conductivity was
measured from the squeezed water of one half of the sample and
the other half was treated for testate amoeba analysis. Due to the
vertical distribution of testate amoeba communities (Mitchell and
Gilbert, 2004), only the upper 3e5 cm of the Sphagnum stems
under the capitulum was treated (Payne et al., 2006; Booth, 2008).
Tests were extracted following a standard protocol (Hendon and
Charman, 1997; Charman et al., 2000). Peat subsamples were
gently boiled in distilled water for 10 min and wet-sieved through
355 and 15 mm mesh screens. The remaining portion was then
mixed with glycerinated water (30%) in 15-ml tubes and centrifuged at 3000 rpm for 10 min. Residual material was stained and
mounted on glass slides and analyzed at a 400 magnification with
an optical microscope. A minimal count of 100 individuals was
achieved, as suggested by Payne and Mitchell (2009). Species
identification was based on test morphology detailed in Charman
et al. (2000). Along with testate amoebae, rotifer shells of Habrotrocha angusticollis were also counted, but were not included in the
transfer function database.
2.3. Multivariate analysis
Ordination analyses were conducted with PC-ORD 6 software
(McCune and Mefford, 2011). Specieseenvironment relationships
were explored using canonical correspondence analysis (CCA), a
technique which performs well with unusual sampling design and
highly correlated environmental variables (Palmer, 1993). CCA
90
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
analyses were conducted on a reduced dataset of 181 samples for
which water table depth, conductivity, pH and peat temperature
were available. Taxa with less than three occurrences were
excluded from the CCA ordinations. Further partial CCAs were
conducted on each environmental variable to estimate the variance
partitioning. A Monte Carlo permutation test was used to determine the statistical significance of the specieseenvironment
relationships.
2.4. Transfer function development
A total of 64 different testate amoeba taxa were identified
from the surface samples but those with less than five occurrences and that never reached 5% in abundance were removed
from the dataset, leaving a total of 47 taxa. Transfer functions
were built using the Rioja (Juggins, 2012) and palaeoSig (Telford,
2011) packages available in R CRAN (R core Team). As it was
previously shown that Weighted average (WA) based models are
the most robust for spatial autocorrelation (Telford and Birks,
2005; Amesbury et al., 2012), we developed four WA models
(WA.inv, WA.cla, WA.inv.tol, WA.cla.tol). Their performances were
evaluated using bootstrap cross-validation (1000 cycles). These
models were optimized by removing samples with high residual
values (20% of the entire WTD range), based on the screening of
the predicted versus observed values (Charman et al., 2007;
Payne and Mitchell, 2007). Additionally, samples with score
values superior to two (2) on the secondary axis of the CCA analyses (Fig. 3) were removed from the transfer function, as we
assumed that these samples were primarily influenced by pH.
The evaluation of model performances was based on R2, root
mean square error of prediction (RMSEP) and maximum bias
(MaxBias). Tests for the influence of spatial autocorrelation on
the transfer functions were also conducted, as suggested by
Telford and Birks (2009) and Amesbury et al. (2012). To test its
applicability to reconstruct past WTD, we applied our transfer
function to the fossil testate amoeba record of Lac Le Caron
peatland from the James Bay region. We finally compared our
WTD reconstruction to those performed by Amesbury et al.
(2012) and Booth (2008). Finally, in order to show that our
transfer model is statistically significant, our reconstruction was
compared to 999 simulations trained on randomly generated
WTD (Telford and Birks, 2011b).
Fig. 2. Canonical Correspondence Analysis (CCA) of the reduced dataset of 181 modern samples showing the specieseenvironment relationship.
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
91
statistics for R2 and RMSEP (Fig. 4, Table 2). Although each WA
model could be used in theory, the capacity of the WA-inv to predict WTD on LLC was optimal, we have thus retained this model for
further statistical testing (Fig. 5).
Table 2
Models performances before and after (.) screening of samples with high residual
values and taxa with low occurrences.
Fig. 3. Canonical Correspondence Analysis (CCA) of the reduced dataset of 181 modern
samples, A) modern samples from bogs (open circle) and from fens (filled circles), B)
environmental vectors: water-table depth (WTD), electrical conductivity (CONP), peat
temperature (TEMP) and pH.
3. Results
3.1. Canonical correspondence analysis (CCA)
Constrained ordination with CCA shows that the main axis of
species variation (axis 1) is strongly correlated to water table
depth (r ¼ 0.85, p < 0.001) (Fig. 2). The Monte Carlo permutation test showed that axis 1 is highly significant (p < 0.001, 999
random permutations). CCA axis 2 is mainly correlated to pH
(r ¼ 0.67, p < 0.001). Axis 1 and 2 explain 11.7% of the variability
in the contemporary testate amoeba dataset and 71% of the specieseenvironment relationship. Partial CCAs show that WTD and
pH are both significant (p < 0.001) and explain respectively 7.6%
and 4.2% of the species variability. Further CCAs of the subgroup
of modern samples from the maritime region (Saint-Lawrence
North Shore) (n ¼ 90) shows that surface moisture is also strongly
correlated to axis 1 (r ¼ 0.86) and explains 8.7% of the variability
in these assemblages.
The constrained ordination of the samples with CCA shows
some overlap in the assemblages from different sites but distinct
pattern between bog and fen samples (Fig. 3). The bog assemblages are preferentially controlled by the hydrological gradient
(axis 1) whereas the fen assemblages are more influenced by pH
along axis 2. Further CCAs performed separately on the fen and
bog subsets (n ¼ 45 and n ¼ 136 respectively) show that although
pH explains more species variability in fens (5.8%), WTD remains
the most important controlling variable in these samples (variance explained ¼ 9.5%).
Models
Number of
samples
R2
WA.inv
WA.cla
WA.inv.tol
WA.cla.tol
199
199
199
199
0.63
0.63
0.67
0.67
(165)
(165)
(165)
(165)
(0.80)
(0.80)
(0.81)
(0.82)
Max bias
RMSEP
24.49
19.24
25.98
22.40
7.89
8.99
7.48
8.27
(9.40)
(5.75)
(9.20)
(5.98)
(5.44)
(5.80)
(5.34)
(5.65)
Additional cross-validation tests were conducted to estimate the
influence of spatial autocorrelation within the dataset (Telford and
Birks, 2009). Fig. 6 presents R2 decreases associated with the
removal of geographical neighbors (0, 1, 50, 250, 500 km) compared
to the removal of randomly-selected sites (0e90% sites deleted at
random). The R2 drops from 0.80 to 0.74 both with the removal of
geographical neighbors and the random deletion of sites. Differences between the two R2 drops are systematically inferior to 5% for
the full geographical gradient. This suggests that spatial autocorrelation in not an issue in our dataset, an aspect that reinforces the
validity of the performance statistics.
3.3. Evaluating the transfer function on a fossil sequence
We have tested the statistical validity of our transfer function to
reconstruct past WTD on Lac le Caron (LLC) record by comparing
our reconstructions from our models to the one inferred from to
999 transfer functions based on randomly generated WTD data
(Telford and Birks, 2011b) (Fig. 5). The statistical significance of the
transfer function model is confirmed by that procedure as weighted
correlations between species optima and the first ordination axis
scores are higher or very close to 95% of the reconstructions
generated from random WTD data (Fig. 5).
We applied the new WA.inv model on Lac Le Caron record
(James Bay lowlands) and compared the inferred WTD with those
produced with the modern datasets of Booth (2008) (van Bellen
et al., 2011) and Amesbury et al. (2012) (Table 3). Our WA.inv
reconstruction is very similar to the one of Booth (2008): the only
difference is that our model produced lower WTD values in some
sections (Fig. 7). The reconstruction from Amesbury et al. (2012)
shows the same overall tendencies but with much lower amplitude of variations.
Table 3
Correlation coefficients between our model, Booth (2008) and Amesbury et al.
(2012) water table reconstructions of LLC core. (see Fig. 7).
Models
This study
WA.inv
Booth
(2008) WA
Amesbury et al.
(2012) WA.inv.tol
WA.inv this study
WA Booth (2008)
WA.inv.tol Amesbury
et al. (2012)
e
0.77
0.43
e
0.75
e
4. Discussion
3.2. Transfer function development
4.1. Specieseenvironment relationship
The multivariate analysis shows that modern testate amoeba
communities are significantly correlated to water table depth and
confirms that a transfer function can be developed for this variable.
Overall, the optimized WA models show good performance
The constrained ordination using CCA corroborates the results
of previous studies showing that hydrological variables (WTD
and moisture) are the strongest environmental control on the
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
Inferred WTD (cm)
92
40
40
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
-5
-5
-10
-10
-16
-8
0
8
16
24
32
40
48
56
64
Observed WTD (cm)
-16
-8
0
8
16
24
32
40
48
56
64
Observed WTD (cm)
Fig. 4. Observed versus estimated water table depth values from the WA.inv model. Left: on the complete dataset and Right: after the screening of samples with high residual values
and taxa with low occurrences.
distribution of testate amoebae (e.g. Payne et al., 2006; Charman
et al., 2007; Payne and Mitchell, 2007; Booth, 2008; Lamentowicz
et al., 2008; Swindles et al., 2009; Amesbury et al., 2012). In the
minerotrophic samples, water table depth was also the most
important environmental factor, but the CCA ordination (Fig. 3)
suggests that pH has a greater influence on the testate amoeba
communities within these environments. Although no taxa were
exclusive to fens, the distribution of some species such as Centropyxis platystoma type, Nebela tubulosa type, Quadrulella symetrica and Sphenoderia lenta seems clearly related to a nutrient status
associated with pH (Fig. 2). These results globally support those of
Payne et al. (2011), who found that testate amoebae are sensitive
surface-moisture proxies in poor minerotrophic environments as
well. However, our results may not be extended to nutrient-rich
environments since most of our minerotrophic sites had a poor
nutrient status (poor fens). Testate amoeba communities are sensitive to water table in fens, but transfer functions developed for
these systems may result in models with lower predictive
performance considering the importance of environmental factors
related to water chemistry (e.g. pH). Hence, more sampling efforts
are clearly needed from different types of minerotrophic peatlands
to fully explore the species ecology within these environments.
However, considering the small gradients of water table depths in
minerotrophic environments, the inclusion of their samples could
amplify clustering effect and spatial autocorrelation within the
dataset and make it impossible to build statistically valid transfer
functions.
There remains a high percentage of unexplained variance in the
modern testate amoeba dataset, which is partially explained by
inter-correlations between variables or unmeasured environmental
factors. As shown by Sullivan and Booth (2011), testate amoebae
distribution in peatlands is also influenced by short-term environmental variability, a factor which may be particularly important
in boreal and subarctic peatlands influenced by considerable seasonal hydroclimatic variations. Although vegetation types do not
implicitly control testate amoeba distribution (Woodland et al.,
Fig. 5. Histograms showing the weighted correlation between species optima and the first ordination axis scores for each WA model tested with the LLC dataset. Grey histograms
represent the distribution of the correlations obtained from 999 reconstructions calibrated on randomly generated WTD. The dashed line represents the 95th percentile of the
distribution. The solid line represents the value of the correlation when the observed WTD values are used.
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
93
1998), the short-term environmental variability seems to be buffered by vegetation structure and density (Sullivan and Booth, 2011)
and may also differ between microforms, peatland types (bogs and
fens) and latitude. In northern peatlands, seasonal freezing or
permafrost within the subsurface peat generates drier conditions
which in turn impact on thermal conductivity and influence its
duration. Hence, seasonal freezing or permafrost may significantly
increase the subsurface environmental variability (from dry when
frozen to wet when melting) and this factor needs to be further
investigated in nordic systems.
4.2. A new transfer function for water table depth reconstructions
adapted to high-latitude peatlands
Fig. 6. Plot showing effect of spatial autocorrelation on R2 after the removal of
randomly selected sites (open circles) and sites in the geographical neighbourhood of
the test site (filled circles).
Our transfer function models show very good performance
statistics to predict water table depth. The RMSEP is similar to those
obtained by Amesbury et al. (2012). Our results also show that
spatial autocorrelation has a very limited impact on the predictive
power of the WA model (Fig. 6) and that our paleohydrological
reconstruction is statistically significant (Fig. 5). Therefore, the
statistical techniques developed by Telford and Birks (2009, 2011a)
have greatly helped in evaluating the robustness of our transfer
Fig. 7. Comparison of the different water table depth (WTD) reconstructions on the fossil testate amoebae record of Lac Le Caron (van Bellen et al., 2011).
94
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
Fig. 8. Water table depth (WTD) optima and tolerances of the 47 remaining taxa of our WA.inv model.
A. Lamarre et al. / Quaternary International 306 (2013) 88e96
function while choosing most appropriate WA model to reconstruct
long-term WTD variability at LLC.
4.3. Testing the transfer function models on a fossil record
The WTD reconstruction produced by our WA.inv model on the
fossil record of Lac Le Caron peatland (van Bellen et al., 2011) was
very similar to the one based on Booth (2008) (Fig. 7). However,
there were clear discrepancies with Amesbury et al. (2012) that
show much lower amplitude of WTD variations. This difference is
mainly explained by the poor modern analogue for some taxa such
as D. pulex, as underlined by Amesbury et al. (2012). Their results
suggest that this species has a relatively wet optima which differs
significantly from our study and other published transfer functions
showing intermediate (Charman et al., 2007; Booth, 2008) and
even very dry WTD optima (Turner et al., 2013) for D. pulex (Fig. 8).
These differences may arise from poor modern analogues or potential identification confusion that may result in difficulties in
inferring proper range of species WTD optima and tolerances.
Considering that Lac Le Caron peat record, as in many other Holocene peat sequences, contains significant abundances of D. pulex
(van Bellen et al., 2011), it was crucial to improve modern analogues
for this species.
4.4. Improving modern analogues from boreal and subarctic
regions
In the present study, most testate amoeba species occupy
similar positions along the water table depth gradient compared to
previously published transfer functions (Booth, 2008; Amesbury
et al., 2012; Turner et al., 2013) (Fig. 8). This suggests that the
testate amoeba response to water table depth does not vary
significantly between the bioclimatic contexts. Species such as
Difflugia globulosa type and A. wrightianum have very wet optima in
different peatland types of all regions while T. arcula type and
Bullinularia indica are confirmed as reliable indicators of very dry
conditions. We have improved modern analogues for D. pulex (44
occurrences) and H. subflava (51 occurrences) although their
abundance was relatively low in most samples. D. pulex has a dry to
intermediate WTD optima (20.9 cm), whereas H. subflava is
confirmed as a dry indicator (WTD optima ¼ 24.5 cm). However,
both species seem to have a complex distribution along the watertable gradient as they were found in wet fen environments as well
as dry bog hummocks. Sullivan and Booth (2011) have suggested
that these two taxa are characteristics of sites with highly variable
environmental conditions.
Additional research is required to increase our knowledge on
the response of testate amoeba communities to seasonal variations.
Little is known about the impact of seasonal freezing and permafrost on testate amoeba communities particularly on their relation
with active layer thickness and surface-wetness during the summer. Other environmental factors also need to be further considered in nordic conditions such as wind exposure which increases
peat surface desiccation without directly affecting the water table
depth. Improving the understanding of species response to environmental stress like permafrost, seasonal frost and wind-exposure
will eventually improve paleoecological interpretations for some
species like D. pulex and H. subflava which are dominant in fossil
assemblages but poorly represented in modern analogues.
5. Conclusion
We developed and tested a new paleohydrological tool adapted
to both Sphagnum-dominated bogs and poor fens from boreal and
subarctic environments. The results of our study supplied a greater
95
range of analogues over a wide geographic and ecoclimatic gradient
in northeastern Canada. Hence, this new testate amoeba transfer
function provides a reference for further climate reconstruction
from nordic environments. As northern peatland ecosystems are
undergoing significant transformations (Payette et al., 2004;
Turetsky et al., 2007; Vallée and Payette, 2007), the development
of adapted paleohydrological tools will improve our ability to
evaluate their sensitivity. In the future, particular attention should
be given to the ecology of testate amoebae in sites experiencing a
wide range of moisture variability particularly in subarctic environments where frost dynamics play an important role in peatland
hydrology. Further data from such environments will greatly
improve the comprehension of hydrological variations on these
ecosystems and their impact on the carbon dynamics. Considering
the lack of modern analogues from northern peatlands, this
transfer function is the first step towards the development of a fully
adapted and functional tool to infer paleohydrological conditions in
these environments.
Acknowledgments
We thank Dr. Dan J. Charman (UK), Dr. Robert K. Booth (U.S.A.)
and Dr. Edward A.D. Mitchell (CH) for their precious collaboration
with the identification of certain species and their useful tips for
the sampling and the elaboration of the transfer function. We also
thank all students, lab assistants and colleagues who have
contributed to the sampling and the analysis of surface peat samples (Hans Asnong, Antoine Thibault, Noémie Cliche-Trudeau,
Maxime Huot, Sébastien Lacoste and Marilou Hayes). Finally, we
would like to thank "Les Tourbeux" for their support and their
advice throughout this study. Thank to M. Amesbury and G.
Swindles for their fruitful comments on the manuscript.
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