Naz2011a

Naz2011a
Quaternary Science Reviews 30 (2011) 1122e1141
Contents lists available at ScienceDirect
Quaternary Science Reviews
journal homepage: www.elsevier.com/locate/quascirev
The distribution and abundance of chironomids in high-latitude Eurasian lakes
with respect to temperature and continentality: development and application
of new chironomid-based climate-inference models in northern Russia
A.E. Self a, b, *, S.J. Brooks a, H.J.B. Birks b, c, d, L. Nazarova e, D. Porinchu f, A. Odland g, H. Yang b, V.J. Jones b
a
Department of Entomology, Natural History Museum, Cromwell Road, London SW7 5BD, UK
Environmental Change Research Centre, Department of Geography, University College London, Gower Street, London WC1E 6BT, UK
Department of Biology, Bjerknes Centre for Climate Research, University of Bergen, P.O. Box 7803, N-5020 Bergen, Norway
d
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
e
Alfred Wegener Institute for Polar and Marine Research, Telegrafenberg A 43, 14473 Potsdam, Germany
f
Department of Geography, The Ohio State University, 1036 Derby Hall, 154 N. Oval Mall, Columbus, OH 43210, USA
g
Institute of Environmental Studies, Telemark University College, N-3800 Bø, Norway
b
c
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 10 June 2010
Received in revised form
18 January 2011
Accepted 19 January 2011
Available online 26 February 2011
The large landmass of northern Russia has the potential to influence global climate through amplification
of climate change. Reconstructing climate in this region over millennial timescales is crucial for
understanding the processes that affect the global climate system. Chironomids, preserved in lake
sediments, have the potential to produce high resolution, low error, quantitative summer air temperature
reconstructions. Canonical correspondence analysis of modern surface sediments from high-latitude
lakes, located in northern European Russia and central Siberia, suggests that mean July air temperature is
the most significant variable explaining chironomid distribution and abundance. This strong relationship
enabled the development of a chironomid-based mean July air temperature-inference model based on 81
lakes and 89 taxa which has a r2jack ¼ 0.92 and RMSEP ¼ 0.89 C. Comparison of taxon responses to July
temperature between this Russian and existing Norwegian data-sets shows that the temperature optima
of individual taxa were between 1 and 3 C higher in the Russian data regardless of modelling technique.
Reconstructions based on fossil assemblages from a Russian tundra lake core (VORK5) using a Norwegian
chironomid-based inference model provide mean July air temperature estimates that are 1.0e2.7 C
colder than from the 81-lake Russian model and are also lower than the instrumental record from
a nearby meteorological station. The Norwegian model also did not reconstruct decadal-scale fluctuations in temperature seen in the instrumental record. These observations suggest that chironomid-based
inference models should only be applied to sediment cores which have similar climate regimes to the
geographic area of the training set. In addition a 149 lake, 120 taxa chironomid-based continentality
inference model was also developed from the modern Norwegian and Russian training sets. A 2component WA-PLS model was the minimal adequate model with r2jack ¼ 0.73 and RMSEP ¼ 9.9 using the
Gorczynski continentality index. Comparison of reconstructed continentality indices from the tundra
lake, VORK5, show close agreement with local instrumental records over the past 70 years and suggest
that the model is reliable. Recent warming in the Arctic has been spatially and seasonally heterogeneous;
in many areas warming is more pronounced in the spring and autumn leading to a lengthening of the
summer, while summer temperatures have remained relatively stable. A continentality inference model
has the potential to detect these seasonal changes in climate.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
Chironomids
Palaeolimnology
Russia
Climate change
Transfer function
Continentality
Species responses
WA-PLS
1. Introduction
* Corresponding author. Department of Entomology, Natural History Museum,
Cromwell Road, London SW7 5BD, UK. Tel.: þ44 20 7942 5595.
E-mail address: [email protected] (A.E. Self).
0277-3791/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.quascirev.2011.01.022
The Arctic is particularly sensitive to perturbations in climate;
average arctic temperatures have increased at almost twice the
global average rate over the past 100 years and this trend is predicted to continue over the twenty first century (IPCC, 2007). The
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
large landmass of northern Eurasia plays an important role in
global climate dynamics and in the amplification of climate change
by positive feedbacks. For example, enhanced snow pack and
permafrost melting increase freshwater discharge from Eurasian
rivers and hence heat advection from low latitudes (Peterson et al.,
2002; McClelland et al., 2004). The scarcity of observational and
proxy records has meant that northern Russia is often poorly represented in the global perspective of climate trends and their
effects on ecosystems (for example Smol et al., 2005). However,
given its potential as an important driver of global climates, studies
of northern Russia are important for understanding the timing and
magnitude of past climate change and the mechanisms underlying
those changes.
Instrumental climate records in arctic Russia are sparse, of short
duration and often intermittent (Rigor et al., 2000). Meteorological
stations are often located in the most benign habitats, for example
at lower, warmer elevations, which are unrepresentative of the
regional climate (Rawlins and Willmott, 2003). Satellite data have
become available over recent decades and suggest mean annual
temperatures have increased since AD 1981 in some areas of
northern Russia whereas other regions have cooled (Comiso, 2003).
In the absence of long-term instrumental or satellite data, palaeolimnological techniques can be used to determine whether these
regional responses are a persistent feature of Holocene climate
change across northern Russia or a short-term non-equilibrium
response to present climate forcing.
Subfossil remains of chironomid midge larvae (Insecta, Diptera,
Chironomidae) are abundant and well-preserved in lake sediments (Brooks, 2003). Chironomids are sensitive indicators of
environmental conditions (Lindegaard, 1995). Their distribution is
influenced by many factors including lake trophic status (Brundin,
1958; Wiederholm, 1983), water depth (Heiri, 2004) and oxygen
stress (Quinlan and Smol, 2001b). However, the analysis of
training sets of present-day assemblages and associated environmental data from lake surface sediments has shown mean
summer temperature to be the dominant factor determining
chironomid distribution and abundance over wide geographical
regions (for example Walker et al., 1991; Brooks and Birks, 2001;
Larocque et al., 2001; Barley et al., 2006). The strength of the
relationship has enabled the development of quantitative
chironomid-based temperature-inference models which have
been used to reconstruct past temperatures in many areas of
Western Europe and North America (see reviews in Brooks,
2006a,b; Barley et al., 2006), however relatively little data are
available from Russia.
Quantitative chironomid-based reconstructions of palaeotemperature have been derived for northern Russia using European
training sets. Solovieva et al. (2005) reconstructed mean July air
temperatures in north-east European Russia using a chironomideJuly air temperature-inference model based on a modern
training set of 153 Norwegian lakes (Brooks and Birks, 2001 and
unpublished data) supplemented with data from lakes within the
study area. The chironomidetemperature-inference model developed for northern Sweden (Larocque et al., 2001) has also been
used for temperature reconstructions in the Lena River Delta
(Andreev et al., 2004), the Kola Peninsula (Ilyashuk et al., 2005)
and Polar Urals (Andreev et al., 2005). However as more easterly
sites are investigated the use of these European-based training sets
may not be applicable due to the restricted geographic ranges of
certain taxa and potential obstacles to dispersal. A number of
important taxa in Russian subfossil assemblages, such as Constempellina and Mesocricotopus (Andreev et al., 2005), are absent or
poorly represented in the European data-sets leading to poor
analogues and potentially unreliable reconstructions for the
Russian sequences. Additionally, the selective pressure resulting
1123
from the extreme continental climate of central Eurasia, where
annual temperatures vary from a winter minimum of 71.2 C to
a summer maximum of 38e40 C (Nazarova et al., 2008), may elicit
physiological or behavioural adaptations in the chironomid fauna
or changes in the faunal composition in response to the extreme
environment.
The relationship between the distance from the open ocean,
climate and vegetation was first recognised by von Humboldt
(1827) and the use of continentality indices is well established in
agriculture, geography, meteorology and ecology as a means of
describing the climate regime, by quantifying the influence of the
ocean on the climate. Plant species are often most sensitive to the
effects of continentality along their northern limits and Giesecke
et al. (2008) used the distribution of temperate tree species in
Fennoscandia to develop a pollen-based continentality inference
model. Although the influence of continentality has been less well
studied in insects than plants a number of studies suggest it may
also be important in insect distribution. For example, outbreaks of
forest-defoliating insects are more frequent and intense in eastern
than western Ukraine (Meshkova, 2002) as the greater continentality of eastern Ukraine results in a more rapid increase in
spring air temperature which accelerates larval development. Over
the Late Quaternary the climate regime of the Russian Arctic may
have changed due to variations in solar radiation, changes in
atmospheric circulation around ice sheets or changes in sea level.
The latter is particularly important in Siberia where the coastline at
the start of the Holocene was approximately 170 km north of its
present location (Bauch et al., 2001) resulting in a more continental
environment than at the same latitudes today. Increased continentality caused by eustatically lower sea level has been identified
as a potential influence on Holocene tree-line dynamics within this
region (MacDonald et al., 2000). Therefore the continentality of
a location may have changed whilst the summer temperatures
remained unaltered. As chironomids are abundant and widespread
throughout the Russian arctic the potential for developing
a chironomid-based continentality model was investigated in this
study.
In this paper, the environmental factors which influence
chironomid distribution and abundance in northern Russia are
examined with the aim of developing chironomid-based inference
models for climate reconstructions in this climatically sensitive
region. The faunal composition and species responses are
compared with data from Norway; this is a large data-set of
modern chironomid distribution and abundance in surface sediments from 157 high-latitude lakes from Svalbard and mainland
Norway (Brooks and Birks, 2001 and unpublished data). In theory
combining the data-sets would improve the distribution of lakes
along the July temperature gradient and the representation of taxa
which are uncommon in the Norwegian fauna but is only justifiable
if the chironomid assemblages, and responses to environmental
variables, are similar.
There are three main aims:
1. To examine the environmental factors which influence
chironomid distribution and abundance in northern Russia and
assess whether these vary from the factors affecting the
distribution and abundance of chironomids in Norway.
2. To determine whether the Norwegian and Russian training sets
could be combined to develop a northern Eurasia training set to
reconstruct past climates or whether the training sets should
be limited to specific geographic areas.
3. To identify climate variables which have the potential for
palaeoenvironmental reconstructions and develop chironomid-based inference models for climate reconstructions in
north-east Russia.
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A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
2. Regional setting
Surface sediments were collected from 100 lakes in arctic and
subarctic Russia between 61 and 72 N and 50e132 E (Fig. 1). The
study sites span major biomes such as the boundaries between the
continuous and the discontinuous permafrost zones and from
boreal coniferous forests in the south to tundra vegetation in north.
Most of the lakes are shallow (mean water depth 4.1 m) and
remote. The lakes represent a range of different climatic conditions
and whilst the physical and chemical characteristics, (e.g. pH,
conductivity) are similar for the majority of the lakes, the underlying geology varies. Quaternary deposits are widely distributed
throughout the Pechora, Lena, and Yenisey river valleys (Nalvikin,
1973). The lakes in the Komi Republic are formed on a sedimentary sequence of Palaeozoic carbonate and deep marine shale
(Lindquist, 1999). The remaining lakes from the Putorana Plateau,
the southern Lena Delta and near Vilyuysk, in central Yakutia, lie on
the Siberian Traps, a large igneous province extruded in the late
Permian ca 240e220 Myrs (Zolotukhin and Almukhamedov, 1988).
The altitude of the lakes varies from 2 m to 805 m above sea level
(a.s.l.). The mean July air temperature ranges from 8.8 to 18.9 C and
the mean annual precipitation from 240 to 640 mm (Table 1,
Appendix A).
3. Material and methods
3.1. Site selection and field methods
Surface sediments were collected from 22 lakes in north-east
European Russia between AD 1998 and 2001 as part of the EUfunded TUNDRA and SPICE projects as described by Solovieva et al.
(2002, 2005) and Sarmaja-Korjonen et al. (2003), 21 lakes from the
Lower Lena River as described by Porinchu and Cwynar (2000), and
36 lakes from Central Yakutia by Kumke et al. (2007). Additional
surface samples were collected from 10 lakes at Igarka and on the
Putorana Plateau in July 2006, 5 lakes near Vorkuta, north-east
European Russia in April 2007 and 6 lakes in the Komi Republic, in
August 2007. Chironomid assemblage and environmental data from
Russia were compared to data from 157 Norwegian lakes from
Svalbard and mainland Norway collected between AD 1995 and AD
1999 (Brooks and Birks, 2000, 2001 and unpublished data). The
Table 1
Summary of the environmental data for the 100 Russian lakes (complete data in
Appendix A).
Russian Lakes (100 lakes)
Minimum
Latitude (N)
Longitude (E)
Altitude (m a.s.l)
Distance to
coast (km)
pH
Conductivity
(mS cm1)
Water depth (m)
Tjuly ( C)
Tjan ( C)
Continentality Index
Mean annual
precipitation (mm)
Cl (meq l1)
1
SO2
4 (meq l )
Ca2þ (meq l1)
Mg2þ (meq l1)
Naþ (meq l1)
Mean
Median
Maximum
61.214
50.5029
2
16
66.4951
99.4822
151
485
67.1167
121.6545
111
503
71.9015
131.2273
805
5000
Std dev
176
566
5.14
2.4
7.7
160.1
7.5
58
9.92
2980
0.89
356.4
0.7
8.8
39.4
31
239
3.8
14.5
29.7
62
364
2.2
13.4
34.4
58
283
25
19
15.5
91
640
4.3
3.5
8.8
20
121
2
1
18
15
10
141
46
518
849
579
30
25
344
157
84
5896
760
1921
17270
25141
621
90
462
2479
2666
sampling methods from all the locations are broadly similar to the
following method. Sediment cores were collected from the deepest
point of each lake using an 80 mm diameter HON-Kajak corer
(Renberg, 1991) with a 0.5 m Perspex coring tube. Cores were
extruded in the field at 0.25e1.0 cm intervals. For the palaeolimnological study a short core from an unnamed tundra lake,
coded VORK5 (67.856972 N, 59.025722 E) (Fig. 1) was subsampled at 0.5 cm intervals from 0 to 5 cm depth, then at 1 cm
intervals to 19 cm depth. Samples were stored in whirl-pak bags
and kept cool and dark in the field prior to storage at 4 C. The
conductivity and pH of the lakes were measured in the field using
portable meters.
3.2. Laboratory and desktop methods
3.2.1. Chironomid analyses
Sediment samples for subfossil chironomid analysis were
prepared using standard methods. Wet sediments were deflocculated in 10% KOH at 70 C for 5 min then left to stand in hot water for
Fig. 1. Map of north-east Russia showing the location of the sampled lakes.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
20 min (Brooks and Birks, 2000). Samples were washed through 212
and 90 mm sieves and the re-suspended sediment sorted in
a grooved Bogorov tray using a dissecting microscope at 25
magnification. Head capsules were separated and mounted in
EuparalÒ mounting medium after progressive dehydration in 80%
and 100% ethanol. Chironomids were identified with reference to
Wiederholm (1983), Schmid (1993), Makarchenko and
Makarchenko (1999), Rieradevall and Brooks (2001), Brooks et al.
(2007) and the national Chironomidae collection at the Natural
History Museum, London, UK. The subfossil assemblage was determined in the uppermost 0e1 cm for the training-set lakes. Between
50 and 560 head capsules were identified from the majority of
surface sediments. However only 36e49 head capsules were isolated from seven high-latitude lakes, but the low taxonomic diversity of these assemblages suggests these abundances are acceptable
for inclusion in the training set (Quinlan and Smol, 2001a). Between
51 and 194 head capsules were identified from the VORK5 sediment
core.
3.2.2. Water chemistry analyses
Water samples from Putorana and north-east European Russia
were analysed for major cations and anions at the Komi Science
Centre, Syktyvkatr, Russia. Sodium (Naþ) and potassium (Kþ) were
determined by flame emission and magnesium (Mg2þ) and calcium
(Ca2þ) by atomic absorption spectroscopy. Nitrate (NO
3 ) concentrations were determined colorimetrically by cadmium reduction.
Sulphate (SO2
4 ) was analysed photomerically and chloride (Cl )
determined by potentiomeric titration. Analysis of total phosphorus (Ptot) in unfiltered water followed Murphy and Riley (1962).
Analyses of water samples from Central Yakutia are described by
Kumke et al. (2007) and from the Lena Delta by Duff et al. (1998).
3.2.3. Chronology
The VORK5 sediment core was dated using 210Pb and 137Cs at the
Bloomsbury Environmental Isotope Facility, UCL. Subsamples from
the sediment cores were analysed for 210Pb, 226Ra, 137Cs and 241Am
by direct gamma assay using an ORTEC HPGe GWL series well-type
coaxial low background intrinsic germanium detector. 210Pb was
determined via its gamma emissions at 46.5 keV, and 226Ra by the
295 keV and 352 keV gamma rays emitted by its daughter isotope
214
Pb following 3 weeks storage in sealed containers to allow
radioactive equilibration. 137Cs and 241Am were measured by their
emissions at 662 keV and 59.5 keV (Appleby et al., 1986). Radiometric dates were calculated using the CRS 210Pb dating models
(Appleby, 2001) and corrected by the AD 1963 depths determined
from the 137Cs and 241Am stratigraphic records.
3.2.4. Modern climate and derivation of climate variables
Previous studies suggest that chironomid abundance and
distribution are strongly influenced by mean air temperatures of
the warmest summer month (e.g. Brooks, 2003, 2006b). Within the
study area, July is the warmest month and mean July air temperature is included as an environmental variable. It was estimated for
each lake by spatial interpolation of meteorological records from
the three nearest weather stations, corrected for altitude. Sampling
took place over a 9 year period in north-east European Russia and
records from Syktyvkar suggest July air temperatures increased by
0.5 C during the period 1977e2006 compared with 1968e97.
Therefore mean July air temperatures were estimated from the
instrumental data for the 30 years preceding sampling. Gorczynski
(1920) derived a continentality index in which the annual difference in temperature is divided by the sine of latitude. Latitude is
included as a parameter as solar radiation varies greatly with latitude. However near the equator, the sine of latitude approaches
zero resulting in extremely high continentality indices, so later
1125
modifications of the index include the addition of constants (for
example Grieser et al., 2006). The Grieser et al. (2006) modification
of the Gorczynski’s index (CI) was used in this study as it is easily
computed and applicable to high latitudes:
CI ¼ 1:7ðA=sin fÞ 20:4
where A is the annual range of average monthly temperatures in C
and 4 the latitude of the lake. Within the Russian study area the
annual temperature range was based on mean July and January
temperatures. CI values ranged from 0 to 29 in the Norwegian dataset and 31e91 in the 100-lake Russian data-set. Mean annual and
summer (JJA) precipitation were estimated, for the 30-year period
prior to sampling, from monthly observations in the Global
Precipitation Climatology Centre (GPCC) database version 3, based
on 0.5 grid intervals (Rudolf et al., 2003).
3.2.5. Numerical methods
The major cation and anion concentrations were strongly
skewed and were therefore log transformed to normalise their
distribution and to stabilise their variances (Birks, 1998). Detrended
correspondence analysis (DCA), with detrending by segments, was
used to assess the most appropriate numerical technique for
further analyses of the chironomid data. Canonical correspondence
analysis (CCA) was used to explore the relationship between the
chironomid assemblages and the environmental variables. Several
of the environmental variables were strongly correlated, and variables with the highest variance inflation factors (VIFs) were
sequentially removed from the CCA until the VIFs were less than 20.
Significant environmental variables were identified by forward
selection and tested using a Monte Carlo permutation test (499
unrestricted permutations). Variables were considered significant if
p < 0.05. CCAs were then run with each forward-selected variable
individually. In this analysis, the ratio of the first constrained
eigenvalue (l1) to the second unconstrained eigenvalue (l2) indicates the potential significance of the variable in explaining the
cumulative variance in the taxon data. Inference models derived
from explanatory variables with high ratios are therefore likely to
have greater predictive power. Both DCA and CCA were performed
using CANOCO 4.5 (ter Braak and Smilauer,
2002) and in both
analyses percentage species abundance data were square-root
transformed and rare taxa down-weighted.
Two-way indicator species analysis (TWINSPAN) was under
taken using TWINSPAN for Windows version 2.3 (Hill and Smilauer,
2005). TWINSPAN pseudospecies cut levels were set at 0, 2, 5, 10
and 20% to allow differences in taxon abundance to influence
classification. The response of individual taxa to specific environmental variables was evaluated by fitting Huisman, Olff and Fresco
(HOF) models (Oksanen and Minchin, 2002). These are a series of
hierarchical models which are used to describe species responses to
environmental variables. Temperature optima of taxa with more
than 10 occurrences in both the Norwegian and Russian data-sets
were estimated by Gaussian logit regression (GLR). Gaussian
response curves were fitted to the data using the program GLR
(Version 1.1, Juggins, 1994). The weighted averaging (WA) temperature optimum of each taxon was estimated using C2 (Juggins,
2005). General linear models (GLM) and general additive models
(GAM), based on presenceeabsence data, were developed for taxa
with more than 10 occurrences in both the Norwegian and Russian
data-sets from the subset using R software (R Development Core
Team, 2004). Models were fitted using the mgcv package for R,
version 1.3e24 (Wood, 2007) and the statistical significance of
additional parameters was tested using the Akiake Information
Criterion (AIC) and Bayes Information Criterion (BIC).
The 100-lake Russian and 157-lake Norwegian data-sets were
analysed to examine the relationship between the environmental
1126
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
variables, including mean July air temperature and continentality,
and chironomid distribution and abundance. Nineteen lakes with
extremes of conductivity, pH or TP (total phosphorus) were
removed to produce a second Russian 81-lake data-set with
a more even distribution of lakes along the environmental gradients. A summary of the environmental variables in the 81-lake
Russian data-set is given in Table 2 and the Norwegian data-set in
Table 3. Chironomid-based temperature-inference models were
developed from all three data-sets using weighted averaging (WA)
and weighted averaging partial least squares (WA-PLS) methods
(ter Braak and Looman, 1986; ter Braak and Juggins, 1993) using
the program C2 version 1.4.3 (Juggins, 2005). The performance of
the models and optimal number of components in the transfer
function were assessed using leave-one-out, jack-knifed crossvalidation.
To explore the relationship between chironomid distribution
and continentality the 100-lake Russian data-set was combined
with the Norwegian training set compiled by Brooks and Birks
(2000, 2001) and unpublished data. Mean July air temperature is
the main factor influencing the distribution of chironomids.
Therefore to minimise the risk of wrongly attributing a temperature-driven response to continentality a subset of 149 lakes were
selected from the Norwegian and Russian lakes. Lakes for inclusion
were selected by stratified sampling; lakes were stratified by mean
July temperature (at 0.5 C intervals), then continentality at 1 CI
intervals and finally by geographical location (longitude and latitude). If more that one lake from a similar location had the same
mean July air temperature and same CI, a single representative lake
was chosen by random selection. In the combined data-set the two
extremes of the temperature gradient were restricted to a narrow
range of continentality indices, the warmest lakes (18e18.9 C)
were located in central Yakutia with extremely high continentality
and the coldest lakes (3.5e6.3 C) in Svalbard have very low CIs of
10e15 so these two extremes were excluded from the continentality data-set by the stratification. The stratification process
followed by selection of a single lake will minimise the risk of
spatial autocorrelation and produce a data-set in which lakes with
mean July air temperatures of 8e16 C have a range of continentality indices (from 0 to 70). A summary of the environmental
variables for this 149 NorwegianeRussian lake data-set is given in
Table 4. A chironomid-based continentality inference model was
produced and the performance evaluated as described for the
temperature models.
Table 2
Summary of environmental data for the subset of 81 Russian lakes.
Table 3
Summary of environmental data for the 157 Norwegian lakes.
Norwegian lakes (157 lakes)
Latitude (N)
Longitude (E)
Altitude (m a.s.l)
Distance to
coast (km)
pH
Conductivity
(mS cm1)
Water depth (m)
Tjuly ( C)
Tjan ( C)
Continentality index
Mean annual
precipitation (mm)
Cl (meq l1)
1
SO2
4 (meq l )
Ca2þ (meq l1)
Mg2þ (meq l1)
Naþ (meq l1)
Latitude (N)
Longitude (E)
Altitude (m a.s.l)
Distance to
coast (km)
pH
Conductivity
(mS cm1)
Water depth (m)
Tjuly ( C)
Tjan ( C)
Continentality Index
Mean annual
precipitation (mm)
Cl (meq l1)
1
SO2
4 (meq l )
Ca2þ (meq l1)
Mg2þ (meq l1)
Naþ (meq l1)
Median
Mean
Maximum
Std dev
58.08
5.005
5
0
61.54
8.862
260
42
64.71
11.27
484
70
79.8
31.038
1594
250
473
71
4.66
4
6.37
33
6.4
51.4
8.4
367
0.7
55.5
0.5
3.5
15.6
1
390
7
10.5
7.6
12
796
8.1
10.3
7.5
14
995
29
16
2.6
29
2700
5.9
3.5
4.8
7
631
2
7
20
7
7
67
46
99
44
78
147
72
233
95
151
2342
1226
3004
930
2127
254
112
355
140
224
4. Results
4.1. Specieseenvironment relationships in the Russian lakes
Concentrations of sodium, magnesium and chloride ions, and
consequently the conductivity of the lakes, are related to climatic
parameters such as summer temperature, precipitation and continentality (Table 1, Appendix A). High concentrations of these ions
and the associated high conductivity measured in lakes from
Central Yakutia suggest their hydrology is strongly affected by the
extreme continentality and negative hydrological balance of the
region (Kumke et al., 2007). The extremes in electrical conductivity,
chloride and sodium ions result primarily from extreme values in
two lakes (Y1740 and Y1741). As expected, July air temperature is
strongly negatively correlated to latitude (Pearson productmoment correlation coefficient, r ¼ 0.96). Longitude is negatively
correlated to January air temperatures, continentality index and
annual and summer precipitation (r > 0.77). Annual precipitation
shows strong positive correlation with summer precipitation
(r ¼ 0.98). Mg2þ and Naþ ion concentrations and conductivity are
Table 4
Summary of environmental data for the 149-lakes in the CI data-set.
Russian lakes (81 lakes)
Minimum
Minimum
CI data-set lakes (149 lakes)
Mean
Median
Maximum
Std dev
61.2140
50.5029
2
16
67.2364
93.4761
154
408
67.8667
92.2031
102
259
71.9015
130.7293
805
5000
195
602
5.1
2.4
7.5
73.7
7.4
40.0
9.5
356.0
0.8
83.9
0.7
8.8
39.4
31
242
4.1
13.5
27.8
56
390
2.6
13.2
29.4
55
418
25.0
18.9
15.5
91
640
4.4
3.2
8.8
18
121
2
1
18
15
10
43
50
409
261
115
27
28
284
123
52
288
760
1587
1727
813
49
97
379
359
156
Minimum
Latitude (N)
Longitude (E)
Altitude (m a.s.l)
Distance to
coast (km)
pH
Conductivity
(mS cm1)
Water depth (m)
Tjuly ( C)
Tjan ( C)
Continentality Index
Mean annual
precipitation (mm)
Cl (meq l1)
1
SO2
4 (meq l )
Ca2þ (meq l1)
Mg2þ (meq l1)
Naþ (meq l1)
Mean
Median
Maximum
Std dev
58.0767
5.0767
2
1
64.7492
39.1043
345
116
62.3833
11.4467
206
75
71.9015
128.8992
1194
548
4.7694
42.1657
341
137
4.66
2.4
6.64
42.6
6.68
34.0
9.10
173.0
0.75
31.8
0.5
8.8
36.5
0
269
7.3
11.8
13.2
26
921
6.2
11.7
10.3
20
570
26.0
15.3
0.8
70
2700
5.6
1.9
11.3
19
674
2
5
26
11
10
96
69
243
93
106
41
44
144
62
62
677
760
1585
609
654
127
84
258
97
111
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
also positively correlated (r > 0.78). These relationships suggest
there is considerable redundancy within the environmental
variables.
In total, 132 chironomid taxa were identified from the surface
sediment samples of the 100 Russian lakes. Of these 42 taxa occurred
in 3 or fewer lakes. The majority of these rare taxa occur at less than
5% abundance in any lake, the exceptions are Propsilocerus lacustristype with a maximum abundance of 7.4%, Parachaetocladius (6.2%),
Paralauterborniella (6.7%) and Propsilocerus jacuticus-type (6.5%).
Whilst none of the taxa occurred in all the lakes, the most common
are Psectrocladius sordidellus-type which occurred in 87% of the
lakes, Procladius (77%), Tanytarsus lugens-type (75%), Cladotanytarsus mancus-type (71%) and Chironomus anthracinus-type (66%).
Hydrobaenus conformis-type, Corynocera oliveri-type, Zalutschia
type B and Abiskomyia are predominately associated with mean July
air temperatures of less than 12 C (Fig. 2). Chironomus plumosustype, Paratanytarsus penicillatus-type, Glyptotendipes pallens-type
and Cricotopus laricomalis-type are more abundant in, and Einfeldia
restricted to, lakes with July air temperatures greater than 16 C.
Detrended correspondence analysis (DCA) of the species data
produces an axis 1 gradient length of 3.78 standard deviation units,
with an eigenvalue of 0.468, suggesting that unimodal-based
numerical methods are most appropriate for further analysis (Birks,
1998). Canonical correspondence analysis (CCA) was undertaken on
the 100-lake data-set with 18 environmental variables. Forward
selection, using Monte Carlo permutation tests with 499 unrestricted permutations, indicates that six environmental variables
(mean July air temperature, continentality, mean annual precipitation, tundra vegetation, water depth and Ca2þ concentrations)
explain significant proportions (p < 0.05) of the explained variance
(Fig. 3a, Table 5). The six selected variables were then used as single
variables in a set of CCAs. The chironomid assemblages are most
strongly correlated with mean July air temperature and continentality which give eigenvalue ratios of 1.169 (l1/l2 ¼ 0.374/
0.320) and 0.730 (l1/l2 ¼ 0.267/0.366), respectively. These high
1127
ratios indicate that these variables may be suitable for the development of inference models.
Fig. 3b presents correlation biplots of the CCA ordination based
on 100 lakes, 132 taxa, and 6 significant environmental variables.
The CCA results suggest Zalutschia zalutschicola-type and Limnophyes are closely associated with deep lakes and high mean annual
precipitation (Fig. 3b). Zalutschia (Moller Pillot and Buskens, 1990)
and some species of Limnophyes (Brodin, 1986) are associated with
aquatic macrophytes. The majority of Russian lakes are on gently
undulating tundra, so increasing precipitation may result in
increased water depth and lake area, thereby increasing the size of
the littoral zone and the area available for macrophyte growth.
Limnophyes typically occurs in very shallow water (Kansanen, 1985;
Hofmann, 1998), Massaferro and Brooks (2002) suggested that high
abundances, in a sediment core from southern Chile, were associated with periods of low lake levels resulting from a decline in
precipitation. The terrain in Chile is mountainous with steep-sided,
tectonically controlled valleys. Therefore the impact of changing
precipitation on water depth and the size of the littoral zone may
depend on lake morphometry and the surrounding topography.
From the CCA (Fig. 3b) it is difficult to distinguish if individual taxa
are responding to mean July air temperatures and/or continentality
as taxa such as Einfeldia, C. plumosus-type and C. laricomalis-type
are most abundant in the Yakutian lakes with the warmest July air
temperatures and most extreme continentality.
4.2. Comparisons of the Russian and Norwegian assemblages
The Norwegian training set comprises 157 lakes from Svalbard
and mainland Norway. Fewer lakes were sampled in Russia (100
lakes) due to economic and logistic constraints. Potentially
combining the data-sets could improve the representation of
chironomid assemblages and individual taxa along the environmental gradients. But this would only be appropriate if the
composition of the chironomid assemblages and responses of
Fig. 2. Russian chironomid data-set ordered by mean July air temperature with the coldest lake at the top and the warmest at the bottom. Chironomids are shown as percentage
abundance, for all taxa with abundances greater than 10% (100 lakes).
1128
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Fig. 3. CCA biplots of (a) 100 Russian lakes and significant environmental variables (black crosses: Yakutia, black diamonds; Komi Republic, grey filled circles; Pechora, grey
triangles; Putoran, black unfilled circles Lena River) and (b) common taxa and significant environmental variables (circle size proportional to N2). (Abbreviations: Tjuly; mean July air
temperature, CI; continentality index, MAP; mean annual precipitation, tundra; tundra vegetation and lg Ca; log of calcium ion concentrations).
Table 5
Significant environmental variables identified by forward selection in CCA of 100
Russian lakes and the variance they explain.
Variable
Variance
explained
% total
variance
explained
Significance
level
Mean July air temperature (Tjuly)
Continentality (CI)
Mean annual precipitation (MAP)
Tundra vegetation (tundra)
Water depth
Log Ca2þ conc (lg Ca)
Total variance explained
Total variance
0.37
0.16
0.09
0.08
0.08
0.08
0.86
3.99
43.0
18.6
10.5
9.3
9.3
9.3
p < 0.002
p < 0.002
p < 0.002
p < 0.004
p < 0.006
p < 0.002
individual taxa are similar in the two geographic regions. The fauna
from Birgervatnet, one of the Svalbard lakes, is composed of a single
taxon Hydrobaenus lugubris-type so this lake was deleted from the
Norwegian data-set reducing the gradient length of the first DCA
axis from 6.6 to 4.4 SD units in the Norwegian set.
In compiling the Norwegian data-set, lakes were carefully selected
to maximise the July temperature gradient and provide an even
distribution of lakes over that gradient whilst minimising variation in
the other parameters (Brooks and Birks, 2000). The standard deviations of variables such as pH, conductivity, and major cations are,
therefore, lower in the Norwegian data-set compared to the Russian
data (Table 3) as primarily naturally acid or circum-neutral, clearwater lakes were selected. However the maxima and standard deviation of altitude and annual precipitation are greater in the Norwegian
data-set. These variables are spatially correlated as the main mountain
range in Norway runs along the coast which results in high altitude
lakes receiving greater orographic precipitation.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
In total, 142 chironomid taxa or morphotypes were identified in
surface sediments from the 156 Norwegian lakes. Some taxa
identified in the Russian surface sediments, such as Constempellina
brevicosta and Einfeldia, are not found in the sampled Norwegian
lakes and other taxa including Diamesa aberrata-type are found in
the Norwegian lakes only. Taxa recorded in both Norway and
Russia, or the two data-sets individually, are listed in Appendix B.
Several taxa found only in the Norwegian lakes are associated with
springs, streams or running water; for example Krenosmittia
(Schmid, 1993), Rheotanytarsus (Pinder and Reiss, 1983), Rheocricotopus spp. (Cranston et al., 1983) and Parochlus (Brundin, 1983).
Their occurrence may reflect the greater importance of surface
inflow into the Norwegian lakes due to the high precipitation and
mountainous terrain compared to the Russian lakes. Many of the
common morphotypes, such as P. sordidellus-type T. lugens-type,
Procladius and C. anthracinus-type, appear to be widespread
throughout Norway and Russia. The taxa may either have
a cosmopolitan distribution due to wide environmental tolerances
or the species composition, and therefore possibly the temperature
optima of the morphotypes may vary between geographical
regions, indicating the existence of distinct regional morphotypes.
Canonical correspondence analysis (CCA), with forward selection, suggest there is a relationship between chironomid distribution and abundance in the Norwegian lakes and July air temperature
(not shown) with distance to the coast, chloride ion concentrations,
and continentality also being important environmental variables.
Chironomid distribution and abundance, in the combined NorwegianeRussian data-set, show strongest relationships with mean July
air temperature and continentality (Fig. 4, Table 6) giving eigenvalue
ratios in constrained CCA of 0.783 (l1/l2 ¼ 0.311/0.397) for mean July
air temperature and 0.479 (l1/l2 ¼ 0.231/0.482) for continentality.
July air temperature and continentality explain similar percentages
of the variance in the chironomid data in both the Russian and
combined data-sets, approximately 40e43% attributed to July air
temperature and 19e24% to continentality.
1129
Table 6
Significant environmental variables identified by forward selection in CCA of the
combined Norwegian and Russian lakes and the variance they explain.
<Variable
Variance
explained
% total
variance
explained
Significance
level
Mean July air temperature (Tjuly)
Continentality (CI)
Log conductivity (lgcond)
Water depth (depth)
Polar desert vegetation (polar desert)
Mean annual precipitation (MAP)
Log Cl conc (lgCl)
pH
Total variance explained
Total variance
0.31
0.18
0.08
0.06
0.06
0.04
0.02
0.03
0.78
3.78
39.7
23.1
10.3
7.7
7.7
5.1
2.6
3.8
p < 0.002
p < 0.002
p < 0.002
p < 0.002
p < 0.002
p < 0.002
p < 0.036
p < 0.012
4.3. Comparisons of species composition in Russian and Norwegian
data-sets
Using squared chord distance as a measure of dissimilarity in an
analogue analysis, Norwegian lakes were selected as the closest
analogue for 14% of the Russian lakes. By comparison only 5% of
Norwegian lakes selected a Russian lake as the closest analogue,
probably reflecting the greater number and geographically close
proximity of sampled Norwegian lakes compared to Russian lakes.
TWINSPAN of the chironomid assemblages from Norway and Russia,
excluding Birgervatnet, suggests the Svalbard (Groups 1 and 2) and
Yakutian lakes (Group 3) have distinctive faunas (Fig. 5). The first
level of the TWINSPAN division separates assemblages with greater
than 2% Orthocladius type S and an absence of Tanytarsus mendaxtype from the remaining lakes. Lakes with significant abundances of
Orthocladius type S are on Svalbard and three lakes in northern
Norway (B98-3, 99-23 and 99-25). The second division separates
these three Norwegian lakes and lake N from Svalbard from the
remaining Svalbard lakes, with the majority of Svalbard lakes are
dominated by Diamesa zernyi/cinerella-type and Pseudodiamesa. The
Svalbard lakes experience the coldest July air temperatures
(3.5e6.5 C) of the Norwegian data-set and these taxa are cold
stenotherms (Brooks et al., 2007). In the fifth division the presence of
Einfeldia and C. laricomalis-type at greater than 2% abundance and
the absence of Sergentia coracina-type separates the Yakutian lakes
and lake K-7 (36 lakes) from a larger group of 76 of Russian and
Norwegian lakes. S. coracina-type is a cold stenotherm (Brundin,
1956; Brodin, 1986) and Einfeldia is usually indicative of eutrophic
water (Sæther, 1979; Brooks et al., 2001). Eutrophic conditions are
more common in warm lakes but Einfeldia is also found in relatively
cool conditions in Switzerland (Heiri, 2001). With the exception of
the assemblages from the Yakutian lakes and lake K-7, the remaining
Russian assemblages are compositionally similar to the Norwegian
assemblages at the fourth TWINSPAN level.
4.4. SpecieseJuly air temperature relationships
Fig. 4. CCA of the combined Norwegian and Russian lakes (157 from Norway and 100
from Russia) showing the significant environmental variables. Ovals highlight the
compositionally distinct faunas from Svalbard (grey filled circles) and Yakutia (black
unfilled circles). (Abbreviations: Tjuly; mean July air temperature, CI; continentality
index, lgcond: log of conductivity, lgCl; log of chloride ion concentration, polar desert;
polar desert vegetation, MAP; mean annual precipitation and depth; water depth).
Taxon responses to July air temperature were examined in the
Norwegian data-set (excluding Birgervatnet) and 100-lake Russian
data-set by fitting response models to species abundance and
presence/absence data for taxa with 10 or more occurrences using
HOF and GLR models. Species mean July air temperature optima
were also estimated by weighted averaging. The HOF models
suggest 85% of the taxa in the Russian data-set with 10 or more
occurrences have a statistically significant response to mean July air
temperature, however only 47% have a unimodal response
compared with 76% in the Norwegian data-set. A number of taxa
were assigned zero values or values which clearly lay outside the
environmental range of the study areas, for example 99.8 C or
1130
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Einfeldia,
GROUP 3
Cladotanytarsus mancus-type,
Cladopelma lateralis-type,
Chironomus plumosus-type
Zalutschia type B
GROUP 4
Sergentia coracina-type
Parakiefferiella type A,
GROUP 5
Tanytarsus mendax-type
Pseudorthocladius, Limnophyes
Microtendipes pedellus-type,
Corynocera ambigua,
GROUP 6
GROUP 7
Heterotrissocladius maeaeri-type,
Heterotrissocladius marcidus-type
Tanytarsus lugens-type > 2%
Mesocricotopus, Pseudodiamesa,
GROUP 8
GROUP 9
GROUP 2
Orthocladius type S >2%
Diamesa zernyi/cinerella type,
Pseudodiamesa
Group 2
N
B98-3
99-23
99-25
A
B
D
E
G
O
Q
R
S
T
U
Scur
C
F
H
I
J
K
M
P
Arsj
Group 1
GROUP 1
Y1718
Y1719
Y1720
Y1722
Y1724
Y1725
Y1726
Y1730
Y1733
Y1737
Y1741
Y1746
K7
Y1704
Y1707
Y1709
Y1711
Y1713
Y1716
Y1717
Y1721
Y1723
Y1727
Y1729
Y1732
Y1735
Y1739
Y1743
Y1700
Y1701
Y1703
Y1705
Y1706
Y1708
Y1712
Y1731
Group 3
97-35
99-28
99-29
99-30
99-38
IGAR
LS-12
LS-16
LS-17
LS-24
K4
K5
K6
K10
K11
97-12
F3-2
F3-3
F3-6
F4-2
F4-4
F4-5
F7-3
F7-4
F7-5
F8-2
T D RC 2
T D RD 2
TDRU 42a
LS-25
96-11
B98-11
B98-14
B98-16
B98-17
9 9- 4
99- 6
99-14
99-15
99-17
99-19
99-20
99-24
99-43
99-45
99-46
99-50
Mitro
WILD
ARTE
PTHE
PFOR
VORK3
VORK5
F3-5
F3-12
AFOX
PONE
Group 4
Group 6
96-1
96-7
97-16
97-27
97-32
99-9
9 9 - 22
99 -2 6
99-27
99-31
99-32
9 9 -3 4
9 9- 3 7
9 9- 4 0
9 9 -4 1
9 9 -4 2
99-44
Y1740
9 6- 2 6
9 6 -3 1
9 6 - 61
96-63
9 7- 1 7
9 7 -1 9
97-21
97-28
9 6 -2 5
9 6-2 9
9 6- 39
9 6 -5 2
9 6- 58
96-60
96-72
9 6 - 77
97-8
9 7- 1 0
97 - 13
9 7- 15
97 - 1 8
97-23
9 7 - 24
9 7 -2 5
97 - 2 6
9 7 - 31
9 7 -3 3
B98-13
9 9 - 33
9 9 - 35
9 6 - 15
9 6 - 21
9 6 - 24
96- 2 8
9 6 - 32
9 6 - 35
96 - 3 8
9 6 - 49
96 -5 1
96 - 53
96-56
96-65
96 - 7 4
Group 5
Group 8
96-47
99-2
99-3
99-8
99-10
99-12
99-18
96-2
96-13
96-20
96-36
96-44
96-67
96-70
B98-12
B98-15
B98-23
99-1
99-7
99-11
99-47
PFIV
F6-2
F8-4
TDRA 2
TDRE 1
TDRU 11a
Vanuk-ty
N ER U
96-10
96-12
96-54
96-71
B98-1
B98-10
B98-18
B98-21
B98-22
99-5
99-21
PTWO
GYXO
KHAR
SAND
LS-1
LS-2
LS-3
LS-4
LS-5
LS-6
LS-7
LS-8
LS-10
LS-11
LS-13
LS-15
LS-19
LS-27
LS-28
LS-30
Group 7
96-14
96-37
96-45
96-78
B98-2
B98-4
B98-5
B98-6
B98-7
B98-8
B98-9
B98-19
B98-20
N98-1
99-13
99-16
Group 9
Fig. 5. TWINSPAN analysis of combined Norwegian and Russian lakes based on chironomid percentage abundance (Russian lakes highlighted in grey).
þ47.5 C in the Russian data-set (Table 7). GLR assumes all taxa
have Gaussian responses to environmental variables and can give
unrealistic results when unimodal responses are imposed on taxa
that do not respond unimodally. When GLR-derived July air
temperature optima for taxa shown to have a unimodal response
(HOF model IV or V) in both data-sets and WA optima for all taxa
are compared, the estimated optima are typically 1e3 C higher in
the Russian data-set than in the Norwegian data. The exceptions are
S. coracina-type and Parakiefferiella triquetra-type which have
higher GLR optima in the Norwegian data-set, although the WA
optimum for S. coracina-type is 0.5 C higher in the Russia data. The
higher optima for the Russian taxa may be an artefact reflecting the
gradient length and distribution of lakes along the July air
temperature gradient in both data-sets. However, the higher
optima for the Russian taxa remained after the coldest Svalbard
lakes were removed from the analysis. Taxon response curves to
July air temperature based on presence/absence data, modelled
using GLMs and GAMs, also confirm the trend of higher optima for
taxa in the Russian data-set. Examples of GLM response curves are
shown in Fig. 6 for taxa in both data-sets.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
1131
Table 7
Number of occurrences (Count), maximum abundance (Max %), N2, and response to July air temperature (HOF model, GLR optima (GLR opt), and WA optima (WA opt)) for taxa
with more than 10 occurrences and present in both Norwegian and Russian data-sets. (HOF model I shows no response to July air temperature; II a sigmoidal increasing or
decreasing response, III a response which reaches a plateau; IV a unimodal response and V a skewed unimodal response).
Norway (156 lakes)
Russia (100 lakes)
GLR opt C
WA opt C
Count
Max %
N2
HOF
GLR opt C
WA opt C
Count
Max %
N2
HOF
Tanypodinae
Ablabesmyia
Procladius
98
118
15.2
12.7
64.3
82.8
IV
IV
13.1
12.6
12.4
11.4
46
77
8.2
13.6
32.5
50.3
V
IV
15.8
15.1
15.7
14.8
Chironomini
Chironomus anthracinus-type
Chironomini 1st instar larvula
Chironomus plumosus-type
Cladopelma
Dicrotendipes
Microtendipes pedellus-type
Parachironomus varus-type
Phaenopsectra flavipes-type
Polypedilum nubeculosum-type
Sergentia coracina-type
Stictochironomus
Pseudochironomus
60
41
12
49
72
74
15
16
51
119
21
20
20.1
9.8
11.3
7.4
20.7
46.2
4.9
4.7
12.6
55.0
3.8
5.6
23.0
19.4
3.6
27.6
28.5
20.5
7.2
9.6
21.5
53.8
15.0
11.4
II
II
IV
II
IV
IV
II
IV
IV
IV
IV
IV
0
0
13.7
20.3
13.2
12.5
0
14.0
15.4
12.8
11.5
15.9
12.6
13.6
13.6
12.5
12.2
12.0
14.2
14.0
13.3
11.5
11.3
14.5
66
57
49
50
60
43
28
15
52
50
29
18
26.7
18.6
39.4
11.2
23.6
27.2
6.6
2.8
8.3
30.8
8.3
9.5
29.2
26.4
21.2
27.3
33.0
20.3
18.3
12.2
36.5
25.2
17.7
12.7
II
I
II
II
II
IV
II
IV
IV
II
II
II
0
0
0
0
0
14.6
0
14.6
16.3
10.1
11.4
18.2
14.1
15.4
17.3
15.2
16.6
14.3
17.0
14.0
16.8
12.0
12.3
18.3
Tanytarsini
Cladotanytarsus mancus-type
Corynocera ambigua
Corynocera oliveri-type
Micropsectra insignilobus-type
Micropsectra radialis-type
Paratanytarsus penicillatus-type
Paratanytarsus undiff.
Stempellinella e Zavrelia
Tanytarsus lugens-type
Tanytarsus mendax-type
Tanytarsus pallidicornis-type
42
38
11
79
54
72
89
28
88
94
64
23.4
52.4
47.6
30.5
96.3
26.0
38.1
4.1
37.6
18.8
15.8
13.5
18.0
2.3
32.1
20.7
30.2
36.7
18.8
39.0
55.6
31.9
IV
IV
IV
IV
II
IV
IV
IV
IV
IV
IV
12.5
9.9
8.0
9.5
2.4
11.7
8.6
12.6
8.9
12.0
13.2
11.6
10.1
8.7
9.7
5.2
11.3
9.3
11.9
9.6
11.5
12.1
71
60
20
39
14
64
41
41
75
63
58
16.7
56.5
41.8
20.1
18.2
24.1
18.6
9.4
28.8
22.5
33.3
40.1
23.9
8.5
18.2
4.8
36.7
23.2
26.2
39.6
35.8
29.5
II
IV
IV
IV
IV
II
II
IV
V
II
IV
16.9
12.4
10.2
12.2
13.0
29.2
0
14.4
48.3
47.5
14.8
15.4
13.8
10.2
12.3
12.8
17.1
11.1
14.2
12.7
15.4
14.8
62
51
30
29
58
48
19
85
34
54
12
22
46
77
133
24
42
23
6.7
19.0
7.5
2.5
17.4
69.3
46.1
18.8
22.9
9.3
1.4
8.0
11.3
24.8
30.8
7.3
24.6
41.8
35.8
18.3
17.4
22.2
21.8
15.2
4.0
42.1
8.6
28.3
10.4
8.1
23.3
33.2
91.9
13.1
13.7
11.4
IV
IV
V
IV
IV
IV
II
IV
I
IV
IV
IV
IV
IV
IV
II
IV
V
13.5
13.3
9.4
11.1
11.5
9.1
0
0
0
12.3
12.3
11.5
12.7
11.6
13.7
22.1
12.3
11.4
11.9
12.8
10.0
11.0
11.2
9.6
5.4
12.6
10.2
11.9
11.8
11.3
12.3
11.4
11.4
12.7
11.6
11.2
31
49
54
24
13
25
17
55
29
40
16
12
14
21
87
13
47
24
11.3
11.3
24.4
6.2
17.9
42.2
11.2
30.4
11.0
7.4
7.4
4.9
13.0
5.7
27.0
3.8
61.3
24.3
17.4
30.1
26.4
15.2
5.8
9.8
8.4
17.6
17.1
27.3
10.4
8.3
4.1
11.0
57.2
9.9
10.9
10.5
II
II
II
IV
IV
IV
II
II
II
I
II
IV
IV
IV
I
IV
II
IV
99.8
17.4
0
12.7
14.0
12.8
0
12.6
0
0
10.1
17.4
0
14.0
0
14.6
0
12.7
11.3
16.6
17.8
12.6
13.6
12.7
10.4
13.2
11.5
14.5
11.2
15.6
15.5
13.5
15.1
14.5
10.8
12.7
Orthocladiinae
Corynoneura arctica-type
Cricotopus cylindraceus-type
Cricotopus laricomalis-type
Cricotopus type P
Heterotrissocladius grimshawi-type
Heterotrissocladius maeaeri-type
Hydrobaenus conformis-type
Limnophyes e Paralimnophyes
Orthocladius oliveri-type
Parakiefferiella bathophila-type
Parakiefferiella triquetra-type
Paraphaenocladius
Psectrocladius barbatipes-type
Psectrocladius septentrionalis-type
Psectrocladius sordidellus-type
Pseudosmittia
Zalutschia type B
Zalutschia zalutschicola
4.5. Speciesecontinentality relationships
HOF models of the taxonecontinentality relationship suggest
77% of the Russian taxa have a statistically significant response to
the continentality index (CI). Fewer taxa (66%) have a significant
response in the Norwegian lakes which may reflect the short CI
gradient in Norway. Continentality is calculated from, and therefore
is not independent of, Tjuly. Therefore to minimise the risk of attributing a response to Tjuly instead of continentality, the Norwegian
and Russian data-sets were combined and a subset of 149 lakes
selected. This excluded samples from the extremities of the Tjuly
gradient as all the coldest lakes (in Svalbard) have low CIs and all
the warmest lakes (in Yakutia) have high CIs and, for these lakes, it
is difficult to separate out the effects of temperature and continentality on chironomid distribution and abundance. Additional
samples were excluded to even out the distribution of samples
along the continentality gradient. For each 0.5 C interval along the
July air temperature gradient, samples were selected so that the
number of Norwegian lakes approximately equalled the number of
Russian lakes for each interval, the total number of lakes in each
0.5 C interval did not exceed 10 and the widest range of CI was
retained in each temperature interval.
Taxon responses to CI were examined in the 149 lake data-set by
fitting response models to species abundance and presence/
absence data for taxa with 10 or more occurrences using HOF and
GAM models. The fitted HOF models suggested 12 of the 82 taxa
tested (14.6%) have no statistically significant relationship to continentality (Table 7). However, the GAM models for an additional 25
taxa show fluctuations in abundance, rather than a definite trend,
along the CI gradient which mean that additional data are required
to determine whether the inferred-HOF relationships are reliable.
The results vary as the taxon responses are more complex than the
five specified HOF models; GAMs are data-driven and therefore not
limited to an a priori model as in HOF.
1132
HOF model I null response
Show HOF response: but
insufficient data for GAM model
HOF model II sigmoidal
decreasing response
HOF model II sigmoidal
increasing response
HOF models IV and V
unimodal response
Cricotopus cylindraceus-type
Cricotopus sylvestris-type
Cryptochironomus
Dicrotendipes
Heterotrissocladius maeaeri-type
Heterotrissocladius grimshawi-type
Limnophyes e Paralimnophyes
Orthocladius type S
Pagastiella
Paratanytarsus austriacus-type
Parakiefferiella bathophila-type
Phaenopsectra flavipes-type
Zalutschia zalutschicola
Cladopelma lateralis-type
Corynocera ambigua
Corynoneura arctica-type
Corynoneura edwardsi-type
Corynoneura lobata-type
Cricotopus type C
Cricotopus type P
Glyptotendipes pallens-type
Macropelopia
Mesocricotopus
Microtendipes pedellus-type
Micropsectra insignilobus-type
Micropsectra radialis-type
Monodiamesa
Parakiefferiella nigra-type
Paratanytarsus penicillatus-type
Parachironomus varus-type
Paracladius
Paramerina
Paratanytarsus undiff.
Polypedilum nubeculosum-type
Stictochironomus rosenschoeldi-type
Tanytarsus ‘no spur’
Zavrelia e Stempellinella
Ablabesmyia
Heterotanytarsus apicalis-type
Orthocladius undiff.
Paracladopelma
Paraphaenocladius
Protanypus
Psectrocladius septentrionalis-type
Psectrocladius sordidellus-type
Pseudorthocladius
Pseudosmittia
Sergentia coracina-type
Smittia e Parasmittia
Synorthocladius
Tanytarsus chinyensis-type
Thienemanniella clavicornis-type
Thienemannimyia-group
Abiskomyia
Chironomus anthracinus-type
Chironomini 1st instar larvula
Chironomus plumosus-type
Cricotopus intersectus-type
Hydrobaenus conformis-type
Orthocladius oliveri-type
Tanytarsus lugens-type
Zalutschia type B
Cladotanytarsus mancus-type
Corynoneura type A
Endochironomus albipennis-type
Endochironomus impar-type
Eukiefferiella claripennis-type
Heterotrissocladius marcidus-type
Lauterborniella
Micropsectra pallidula-type
Parakiefferiella triquetra-type
Parakiefferiella type A
Phaenopsectra type A
Psectrocladius barbatipes-type
Pseudochironomus
Tanytarsus pallidicornis-type
Zavrelimyia
HOF model III plateau response
Constempellina e Thienemanniola
Corynocera oliveri-type
Cricotopus laricomalis-type
Procladius
Tanytarsus mendax-type
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Fig. 6. Probability of occurrence of (a) Heterotrissocladius maeaeri-type and (b) Paratanytarsus penicillatus-type in the Norwegian (solid line) and Russian (dashed line)
data-sets.
Nine taxa have a statistically significant sigmoidal increasing
response to continentality (Table 8). These include Abiskomyia,
H. conformis-type, T. lugens-type and C. oliveri-type which are cold
stenothermic species (Brooks et al., 2007). Sixteen taxa have
a statistically significant sigmoidal decreasing response to continentality, the majority of these taxa are terrestrial, semi-terrestrial
or associated with the splash or very shallow littoral zone, for
example Paraphaenocladius (Cranston et al., 1983) and Thienemannimyia-type (Fittkau and Roback, 1983). Fifteen taxa have
statistically significant unimodal response to continentality and
again many of these are associated with the shallow littoral or surf
zones of lakes, for example Psectrocladius barbatipes-type
(Lindegaard, 1992) and Eukiefferiella claripennis-type (Cranston
Table 8
HOF model responses to continentality in the combined Norwegian and Russian data-set (149 lakes, 82 taxa).
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
et al., 1983; Lindegaard, 1992). Some thermophilic taxa such as
Heterotrissocladius marcidus-type and Micropsectra pallidula-type
(Brooks et al., 2007) have low CI optima of 12 and 13, respectively,
whereas cold stenotherms such as P. triquetra-type have high CI
optima, CI ¼ 40 (Table 9). Many of the genera which show no
statistically significant response to continentality have wide
geographical distributions. Limnophyes (Cranston et al., 1983) and
Dicrotendipes (Pinder and Reiss, 1983), for example, have worldwide distributions. Therefore each genus may be represented by
different species along the continentality gradient.
4.6. Development of inference models
4.6.1. Chironomid-inferred July air temperature model
Chironomid assemblages show a strong relationship with mean
July air temperature in both the Russian and Norwegian data-sets.
The higher July temperature optima for the Russian taxa suggest
that it is inappropriate to combine the two data-sets. A chironomidbased mean July air temperature transfer function was developed
based on the 100 Russian lakes and 132 taxa. Performance statistics
indicated the weighted average partial least squares (WA-PLS)
2-component model (Table 10a) performed best with a high coefficient of determination (r2jack ¼ 0.90) and low RMSEP (1.09 C). The
lakes in the Russian data-set are unevenly distributed along the July
air temperature gradient as 36 of 100 lakes were located in Central
Yakutia with air temperatures of 18e19 C. Many of these lakes
have extremely high conductivity due to the low precipitation and/
or have high nutrient levels (Table 1, Appendix A) and all are
faunistically distinct from the majority of the remaining Russian
lakes (Fig. 3). Therefore 19 lakes from Yakutia were excluded from
the training set and a second chironomid-inferred July air
temperature (C-IT) transfer function was constructed, based on 81
lakes. Only the 89 taxa with 3 or more occurrences were included in
this inference model. To generate robust inference models the
apparent and cross-validated predictive powers (RMSE/RMSEP
ratio) must be similar. To achieve this the number of taxa should
approximately equal the number of lakes (Racca et al., 2003).
Performance statistics indicated the weighted average partial least
squares (WA-PLS) 2-component model (Table 10b) performed
better than the 100-lake model with a higher coefficient of determination (r2jack ¼ 0.92) and lower RMSEP (0.89 C). The model overpredicts temperatures below 13 C and under-predicts above 13 C
and has a maximum bias of 1.28 C (Fig. 7a). These performance
statistics compare favourably with other chironomid-based
temperature-inference models (Brooks, 2006a).
Table 9
GLR and WA derived continentality indices optima for taxa showing a unimodal
response to continentality. Abbreviations follow Table 7.
Unimodal response
Count
Max %
N2
HOF
GLR
opt.CI
WA
opt CI
Cladotanytarsus mancus-type
Corynoneura type A
Endochironomus albipennis-type
Endochironomus impar-type
Eukiefferiella claripennis-type
Heterotrissocladius marcidus-type
Lauterborniella
Micropsectra pallidula-type
Parakiefferiella triquetra-type
Parakiefferiella type A
Phaenopsectra type A
Psectrocladius barbatipes-type
Pseudochironomus
Tanytarsus pallidicornis-type
Zavrelimyia
65
24
17
14
40
63
20
20
27
27
24
52
16
82
26
4.8
2.9
1.7
1.9
4.4
5.0
2.9
2.1
2.7
2.8
1.8
3.4
2.4
5.8
2.6
48.7
20.1
15.4
12.0
30.4
50.2
16.7
17.6
22.8
21.9
21.1
44.3
13.5
65.0
22.2
IV
IV
V
IV
IV
IV
IV
IV
IV
IV
IV
IV
IV
IV
IV
39
20
42
36
0
9
18
15
54
21
18
21
16
35
10
33
16
38
34
16
12
16
13
40
17
15
21
15
28
11
1133
Table 10
Performance statistics for the chironomid-inferred mean July air temperature
transfer functions for (a) the 100 Russian lake data-set, (b) the 81 Russian lake dataset and (c) the 157-lake Norwegian lake data-set. The model with the statistically
best performance is shown in bold. Max ¼ maximum, inv ¼ inverse, cla ¼ classical,
TOL ¼ tolerance.
Model
r2jack
RMSEPjack Mean biasjack Max biasjack Reduction
in prediction
error (%)
a
Inverse
WA (inv)
0.85 0.01
WA e TOL (inv) 0.81 0.25
2.17
2.47
1.36
1.53
e
e
Classical
WA (cla)
WA e TOL (cla)
WA-PLS (1)
WA-PLS (2)
WA-PLS (3)
WA-PLS (4)
WA-PLS (5)
0.85
0.81
0.85
0.90
0.90
0.88
0.86
0.01
0.27
0.04
0.00
0.01
0.01
0.03
2.36
2.62
2.15
1.19
1.07
0.90
1.08
1.39
1.59
1.36
1.09
1.12
1.19
1.29
e
e
e
19.43
1.91
6.71
8.00
b
Inverse
WA (inv)
WA-TOL (inv)
0.86
0.80
1.19
1.47
0.02
0.16
2.02
2.56
e
e
Classical
WA (cla)
WA-TOL (cla)
WA-PLS (1)
WA-PLS (2)
WA-PLS (3)
WA-PLS (4)
WA-PLS (5)
0.86
0.80
0.86
0.92
0.93
0.93
0.92
1.21
1.60
1.19
0.89
0.86
0.87
0.90
0.02
0.19
0.05
L0.02
0.02
0.02
0.03
2.06
2.68
2.05
1.28
0.82
0.74
0.78
e
e
e
24.73
3.31
1.20
3.06
c
Inverse
WA (inv)
WA-TOL (inv)
0.80
0.80
1.52
1.65
0.01
0.01
2.29
2.62
e
e
Classical
WA (cla)
WA-TOL (cla)
WA-PLS (1)
WA-PLS (2)
WA-PLS (3)
WA-PLS (4)
WA-PLS (5)
0.83
0.84
0.80
0.89
0.90
0.89
0.87
1.39
1.44
1.56
1.14
1.10
1.15
1.22
0.02
0.02
0.05
0.01
0.03
0.02
0.02
1.81
1.39
2.03
1.16
1.05
1.01
1.06
e
e
e
26.88
3.95
4.95
6.00
4.6.2. Chironomid-inferred continentality model
The ordinations suggest that although there is a strong relationship between the distribution and abundance of chironomids
and mean July air temperature, continentality is also an important
factor. A chironomid-inferred continentality (C-IC) transfer function was constructed from a combined NorwegianeRussian dataset of 149 lakes. The 149 lakes, which include 120 taxa, were
selected to ensure a range of continentality indices (CI) for each
temperature interval (see Section 4.5). Within this data-set Pearson
productemoment correlation coefficients suggest CI is weakly
correlated with mean July air temperature (r ¼ 0.11) and water
depth (r ¼ 0.38), but very strongly correlated to longitude
(r ¼ 0.92) and mean January air temperature (r ¼ 0.98). Continentality increases with distance from the ocean (Atlantic and
Arctic Oceans), but as the effect is dominated by the Atlantic Ocean
this approximates to longitude in this data-set. CI is calculated from
the annual temperature range (mean JanuaryeJuly temperatures).
The extremely strong negative correlation to mean January air
temperature results from the larger variation in January air temperatures than July temperatures within the data-set. However as
the lakes in the training set are covered in ice and thermally
insulated throughout the winter, coldest month temperatures are
1134
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Fig. 7. Relationship between observed vs estimated and the residual (inferred observed) for (a) the chironomid-inferred mean July air temperatures 2-component WA-PLS model
and (b) chironomid-inferred continentality 2-component WA-PLS model. Trends in residuals are highlighted with a LOESS smoother (span ¼ 0.45).
unlikely to have a direct impact on the chironomids. Variance
partitioning by partial CCAs (Leps and Smilauer,
2003) showed that
31.6% of the total variation in the 149-lake species data can be
explained by CI and mean July air temperature. Of this, 17.7% can be
solely explained by the effects of continentality and 12.0% solely by
July air temperature. The remaining 1.9% cannot be attributed solely
to either environmental variable. Therefore the relationship with
continentality in the 149-lake data-set appears to be independent
of mean July air temperature.
The 2-component WA-PLS model represents the minimal
adequate model (Birks, 1998) (Table 11) with a combination of high
r2jack (0.73) and low RMSEPjack (9.88 CI units). Additional components did not reduce the prediction error by more than 5% and were
not included in the model (Birks, 1998). The model predicts
reasonably well over the CI range 10e40 but under-predicts above
an index of 50, which is typical of locations east and south of the
Ural Mountains (Fig. 7b). Although the use of C-IT models for
reconstructing July air temperatures is well established, the use of
a chironomideinference model to reconstruct continentality has
not been attempted before.
4.7. C-IT and C-IC reconstructions from unnamed tundra lake
VORK5
July air temperature and CI are estimated from chironomid
assemblages prepared from a 15 cm short core collected from an
unnamed tundra lake in NE European Russia, codenamed VORK5.
The 210Pb chronology suggested the core was deposited over the
past 150 years, including the period from AD 1936 to present
enabling comparison of inferred values with instrumental records.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Table 11
Performance statistics for the chironomid-inferred continentality transfer function.
The model with the statistically best performance is shown in bold. Max ¼
maximum, inv ¼ inverse, cla ¼ classical, TOL ¼ tolerance.
4
Depth (cm)
6
8
10
12
0.6
0
RMSEPjack
Mean biasjack
Max biasjack
Reduction
in prediction
error (%)
0.5
20
0.4
Inverse
WA (inv)
WA-TOL (inv)
0.65
0.57
11.16
12.50
0.06
0.06
25.39
20.72
e
e
Classical
WA (cla)
WA-TOL (cla)
WA-PLS (1)
WA-PLS (2)
WA-PLS (3)
WA-PLS (4)
WA-PLS (5)
0.65
0.58
0.65
0.73
0.73
0.71
0.69
12.69
15.00
11.16
9.88
9.83
10.23
10.69
0.07
0.05
0.30
0.08
0.03
0.04
0.07
18.49
14.65
25.12
19.26
20.06
19.97
18.79
e
e
e
11.53
0.52
4.07
4.51
4.7.1. VORK5 chronology
Unsupported 210Pb activities in VORK5 show a non-monotonic
relationship with depth (Table 12) which indicates that changes
have occurred in the initial 210Pb concentrations supplied to the
sediment over time. This precludes the use of the Constant Initial
Concentration (CIC) model (Appleby, 2001) and, therefore, 210Pb
chronologies were calculated using the Constant Rate of Supply
(CRS) dating model (Appleby and Oldfield, 1978). The raw CRS
dating model places the AD 1963 layer at c.6 cm, above the AD 1963
layer suggested by the 137Cs and 241Am records. The final dates
were calculated using the CRS model and corrected from the
137
Cs/241Am records (Fig. 8).
4.7.2. Biostratigraphy
The diversity of the chironomid fauna in VORK5 is low and
dominated by a small number of taxa (Fig. 9). Cold stenotherms such
as H. lugubris-type (Cranston et al., 1983) and T. lugens-type (Brundin,
1956; Brodin, 1986) decline in abundance from the mid-twentieth
century whilst thermophilic taxa such as C. mancus-type (Sæther,
1979; Brodin, 1986) increase in abundance. Corynocera ambigua
increases from 3% in AD 1850 to a peak of 37% in AD 1962. Abundances of C. ambigua remain high but fluctuating until approximately
AD 1990 before declining to late 19th century levels. Short-lived
increases in C. ambigua in stratigraphic sequences are often associated with periods of environmental change within the lake.
4.7.3. Reconstructions and comparison with instrumental data
Despite the better performance statistics of the inference model
based on the 81 Russian lake data-set the chironomid-inferred July
air temperatures (C-IT) for VORK5 show little difference between
Table 12
Summary table showing the
210
Pb,
241
Am and
40
0.3
60
80
0.2
100
Sediment accumulation rate (cm/yr)
r2jack
2
-20
Age (yrs BP)
Model
0
1135
0.1
120
0
140
Fig. 8. Ageedepth model and sediment accumulation rates for core VORK5 (sediment
accumulation rates are shown in grey and ages in black with 2 SD error).
the reconstructions based on the 81-lake data-set and those based
on the 100-lake data (Fig. 10a). Individual reconstructions vary by
less than 0.6 C and remain relatively stable at approximately 12 C
throughout the period covered by the instrumental record (AD
1938 to AD 2006), although the decline in cold stenotherms since
AD 1940 would suggest the climate had warmed. Minor cool
fluctuations occur in the AD 1950s and late AD 1990s when
temperatures decline by approximately 0.8 C, whilst temperatures warm by 0.4 C during the AD 1960s. However all these
oscillations are within the prediction error of the C-IT models.
Chironomid-based July air temperature reconstructions based on
the Norwegian data-set reconstruct approximately 1.0e2.7 C
colder than the Russian models throughout the core. The variation
between the reconstructions is greater than the prediction errors
of the respective models.
Chironomid-inferred continentality reconstructions suggest the
period from AD 1930s to AD 1970s was, in general, more continental than present (CI ¼ 50, present-day CI ¼ 39) with the exception of a period of low CI in the AD 1940se50s. CI declined from 50
in AD 1971 to 32 in AD 1993, suggesting a more maritime climate.
Over the last 25 years the continentality index has increased but
shows greater inter-annual variation than earlier in the record.
Chironomid-inferred values are compared to instrumental
records and the continentality index was calculated from the
European Climate Assessment and Data-set records (Klein Tank
et al., 2002) for Hoseda Hard (67.08 N, 59.38 E), approximately
90 km south of VORK5 (Fig. 10). The higher sampling resolution and
sediment accumulation rates towards the top of the core mean the
137
Cs counting results, with respective errors, for the core VORK5.
Sample
depth (cm)
210
Pb activity
(Bq kg1)
Counting error
(Bq kg1)
210
Pb unsupported
activity (Bq kg1)
137
Cs activity
(Bq kg1)
Counting error
(Bq kg1)
241
Am activity
(Bq kg1)
Counting error
(Bq kg1)
0.25
1.25
2.75
4.25
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
13.5
223.54
155.93
167.67
134.57
115.76
84.47
70.03
60.72
47.51
54.11
47.82
43.07
23.17
32.85
17.47
19.84
21.38
13.79
14.08
13.67
12.23
10.9
12.78
10.87
11.17
10.43
175.1
118.98
137.76
105.28
85.71
43.58
37.17
19.93
20.66
23.22
13.23
10.62
3.43
18.83
17.29
15.16
26.53
38.61
45.52
65.5
82.67
58.37
24.23
6.56
0
0
4.3
2.48
2.71
3.31
2.41
2.64
2.68
2.83
2.05
2.02
1.52
0
0
0
0
0
0
0
0
0
2.14
0
0
0
0
0
0
0
0
0
0
0
0
1.17
0
0
0
0
0
1136
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Fig. 9. Biostratigraphy of VORK5 showing taxa with >5% abundance.
subsamples from 0 to 0.5 cm and 1.0e1.5 cm depth typically
represent 2e3 years accumulation of chironomid subfossils. The
C-IT and C-IC estimates are in close agreement with the instrumental record for the mid-point, a single year, for these top two
samples. Further down the core the lower sampling resolution and
the greater uncertainty associated with the 210Pb dating means the
subsamples represent an amalgamation of several years. The
sample from 9 to 10 cm depth, for example, accumulated over
approximately 14 years from AD 1945 to AD 1959. Therefore the
values represent a general trend for the time interval. In general the
chironomid-inferred temperatures are 0.4e2.5 C cooler and the CI
values more continental than the instrumental records for Hoseda
Hard which probably reflects the more northerly location of the
lake. The chironomid-inferred July temperature reconstructions
based on the Norwegian data-set are lower than those of the
instrumental record. The Norwegian-based reconstruction also fails
to reconstruct many of the decadal-scale fluctuations in the
instrumental record. These results would suggest that the use of
the Norwegian inference model would be inappropriate for this
region of Russia.
On a decadal scale, the chironomid-inferred July air temperature
(C-IT) reconstructions for VORK5 based on the Russian inference
model show similar trends to the instrumental temperature over
the period AD 1936e2006. Sample-specific prediction errors from
the C-IT reconstructions are lower, 1.0 C than the high interannual variability in the instrumental record. Similarly the
chironomid-inferred continentality (CI-IC) reconstructions from
VORK5 show broad agreement to the instrument-derived record;
both have a general trend to a more maritime climate over the last
25 years.
5. Discussion
The results suggest there is a strong relationship between
chironomid distribution and abundance in north-west Russia and
mean July air temperatures and continentality which dominate axes
1 and 2 respectively of the CCA plot (Fig. 3). These strong relationships also appear to persist over the wider geographical area of
north-west Eurasia (i.e. Norway and north-west Russia) (Fig. 4). Of
these, mean July air temperature is the most significant variable
affecting chironomid distribution. The close relationship between
chironomid assemblages and mean air temperature of the warmest
month is well documented and has been used to develop chironomid-inferred temperature transfer functions in northern Eurasia
(Lotter et al., 1997; Olander et al., 1999; Larocque et al., 2001; Luoto,
2008). The July air temperature gradient (8.8e19.0 C) in the Russian
data-set is comparable in length to the Swiss data-set (6.6e17.3 C)
(Lotter et al., 1997), but includes warmer temperatures than the
Swedish (Larocque et al., 2001) or Norwegian data-sets (7.0e14.7 C
and 3.5e16.0 C respectively). Water depth and mean annual
precipitation were also statistically significant (p 0.05) in the
Russian (Fig. 3) and NorwegianeRussian ordinations (Fig. 4).
Although the range of water depths were similar in the Russian and
Norwegian data-sets, the lakes in the 100-lake Russian data were
skewed towards shallower depth (mean ¼ 3.8 m, median ¼ 2.2 m).
Chironomids in shallow lakes are probably more prone to be directly
affected by winter ice and by winter temperatures than larvae living
in deeper lakes. Furthermore, a negative water balance, which
affects the continental interior such as central Yakutia in the study
area, may also lead to shallow lakes. These relationships could
provide a potential problem for the continentality inference model.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Mean July air temperature °C
a
22
Instrumental data
81 Russian lake model
100 Russian lake model
Norwegian model
20
18
meaned instrument data
16
14
12
10
8
6
1930
b
1137
1940
1950
1960
1970
Year
1980
1990
2000
2010
70
Continentality indices
Instrumental data
meaned instrument data
C-IC reconstruction
50
30
10
1930
1940
1950
1960
1970
1980
1990
2000
2010
Year
Fig. 10. Comparisons between chironomid-inferred reconstructions from VORK5 and instrumental records from Hoseda Hard showing (a) mean July air temperatures and (b)
continentality indices. Annual instrumental values or CI calculated from the instrumental record are shown in grey. Sample-specific prediction errors for chironomid-inferred
reconstructions are shown along the y-axis and the time interval represented by the sediment sample along the x-axis. Annual instrumental values are shown in grey, smoothed
instrumental record (averaged instrumental data) is derived from the mean of the time interval represented by the sediment slice used to derive the chironomid-inferred
reconstruction to give a comparable temporal resolution.
However, in both the 257-lake NorwegianeRussian data-set and
149-lake subset the correlation coefficients between water depth
and CI were r ¼ 0.40 and r ¼ 0.38, respectively, which suggests
only a moderate to weak correlation between the variables.
Previous studies have suggested that total phosphorus (TP)
(Brooks et al., 2001), dissolved oxygen (Quinlan and Smol, 2001b),
organic carbon (Langdon et al., 2008) and chlorophylla (Brodersen
and Lindegaard, 1999) are potentially important environmental
variables in influencing chironomid distribution and abundance.
The data-sets used in this study were compiled by different
researchers. Therefore the above variables were either not recorded
or not measured consistently so could not be used in the ordinations. The relationship with any of these variables may be stronger
than the observed relationship with continentality. However no
data are available for comparison as no previous chironomid
studies have included continentality as an environmental variable.
The 100-lake and 81-lake Russian data-sets were used to
produce C-IT transfer functions as mean July air temperature was
the most significant variable in explaining chironomid distribution
in NW Russia (Table 5). The use of chironomid-inferred temperature (C-IT) models is well established (Brooks, 2006b; Heiri, 2006)
and the performance of the C-IT models developed in the current
study are similar to previously published models in terms of
RMSEP, r2 and maximum bias (Brooks, 2006a). Chironomid-inferred mean July air temperature reconstructions for a short core from
a tundra lake, VORK5, varied by less than 0.6 C between the
reconstructions based on the 81-lake data-set and those based on
the 100-lake data (Fig. 10a). Nineteen lakes with high conductivity,
salinity or eutrophic status were removed from the 100-lake data-
set to give values more similar to the Norwegian data-set. The
deleted lakes were also from the warmest lakes (18.0e19.0 C),
therefore their deletion may have a more significant impact on
reconstructions from warmer lakes than VORK5 where reconstructed temperatures varied between 10.9 and 12.5 C. Chironomid-based July air temperature reconstructions based on the
Norwegian data-set reconstruct approximately 1.0e2.7 C colder
than the Russian models and are lower than the instrumental
record. The Norwegian-based reconstructions also fail to reconstruct many of the decadal-scale fluctuations in July temperature
seen in the instrumental record, which are reflected in the Russianbased reconstructions.
Additionally, a combined RussianeNorwegian training set was
used to produce a C-IC transfer function as continentality indices
are statistically significant in explaining chironomid distribution
and abundance in these countries (Table 6). The development of the
chironomid-inferred continentality (C-IC) model represents a novel
approach to palaeoenvironmental reconstructions. However the
relationship with CI and the performance of the C-IC transfer
function is considerably weaker than the relationship with July air
temperature, r2jack is 0.92 for C-IT and 0.73 for C-IC. Gorczynski’s
continentality index (CI) is calculated from the difference between
summer and winter temperatures but also provides an ‘estimate of
the influence of the ocean on the local climate’ (Grieser et al., 2006).
The climate regime experienced by a lake in Norway with a July air
temperature of approximately 14 C varies greatly from one in
eastern Siberia with the same July temperature. For example Bergen (60.38 N, 5.33 E, CI ¼ 5.6, mean July temperature 14.6 C) had
an average of 317 days per year with air temperatures above 0 C,
1138
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
between AD 1981e90, and Olenyok (68.5 N, 112.43 E, CI ¼ 73.6,
mean July temperature 14.0 C) an average of 107 days per year
above 0 C (Royal Netherlands Meteorological Institute, 2009,
http://climexp.knmi.nl/). Fig. 11 shows mean monthly air temperatures for Bergen and Olenyok. Although July air temperatures are
similar, spring and autumn temperatures, the rates of spring
warming and autumn cooling, and the duration of the warmest
(summer) period vary considerably, in addition to the annual
temperature range. Therefore, although continentality indices are
calculated from the annual temperature range, continentality
encapsulates more aspects of the climate regime than just the
annual temperature range.
Chironomids in arctic environments generally overwinter in
diapause or a quiescent state in frozen lakes in which water
temperatures remain at or above 0 C. Therefore it is unlikely they
respond to winter temperature directly and may be responding to
other climate variations. However, Larocque et al. (2001) showed
that mean January air temperature also accounted for significant
variation in chironomid assemblages in Sweden. Insects in the
arctic are affected by the severity of the climate (cold temperatures,
limited heat accumulation for growth), its seasonality (wide
differences between summer and winter), short-term unpredictability (such as the potential for sudden changes in temperature
even in summer) and variability (year-to-year changes in the
factors governing development and survival) (Danks, 2004).
Chironomids show a number of adaptations to arctic and subarctic
environments. The number of generations per year is negatively
correlated to latitude (Tokeshi, 1985) and extended life cycles of
2e7 years are common in many species at high latitudes (Welch,
1976; Butler, 1982) Chironomids, therefore, need to complete part
of their life cycle during the summer and reach a resistant overwintering state before the onset of winter. The developmental rates
of both eggs (Iwakuma, 1986) and larvae (Mackey, 1977; Edwards,
1986) are positively correlated to temperature, whereas daily
growth rates appear to reach a maxima at a temperature optima
(Storey, 1987; Hauer and Benke, 1991). This may ensure that larvae
mature and pupate as fast as possible regardless of size. Variations
in mean spring temperature, the rate of warming following ice20
Mean air temperatures (°C)
10
0
-10
-20
-30
-40
Jan
Feb
Mar
Apr
May
June July
Aug
Sep
Oct
Nov
Dec
Fig. 11. Mean monthly air temperature for Bergen, 60.38 N, 5.33 E, (solid line) and
Olenyok, 68.5 N, 112.43 E, (dashed line) between AD 1981e90 (Royal Netherlands
Meteorological Institute, 2009, http://climexp.knmi.nl/).
melt and the duration of the summer period due to differences in
continentality may all affect the ability of species to complete part
of their life cycle within the summer period. In areas with the
continuous daylight of the polar summer the emergence of adults is
governed by thresholds in water temperature (Danks and Oliver,
1972a). Data from four taxa, Corynoneura arctica, Stictochironomus
sp., Micropsectra natvigi and Procladius culiciformis on Bathhurst
and Ellesmere Islands, Canada suggest thresholds of 4e5 C for
pupation and 7 C for pupal ecdysis (Danks, 1971; Danks and Oliver,
1972b). These thresholds, particularly for pupal ecdysis, are relatively high and often close to the lake water maximum temperature
in high arctic lakes and for completion of pupation the temperatures need to be sustained for periods of up to 21 days. If these
thresholds are not met then the species will not develop further
that year. Therefore any change in the time interval above the
threshold may influence the composition of the chironomid
assemblage. The duration of lake ice may also affect the oxygen
tension of the water column and surface sediments. Chironomid
taxa vary in their oxy-regulatory capabilities (Brodersen et al.,
2004) and prolonged ice cover may favour taxa able to tolerate
low oxygen tensions. Further work is required to determine the
relative importance of these mechanisms.
Taxa showing a statistically significant sigmoidal increasing
response to continentality include cold stenothermic species such
as Abiskomyia, H. conformis-type, T. lugens-type and C. oliveri-type
(Brooks et al., 2007). Their increased occurrence in more continental climates (CI above 40) suggests these species are ecologically
or behaviourally adapted to completing part of their life cycle
within the short continental summers and surviving the long icecovered winters. The majority of taxa demonstrating a statistically
significant sigmoidal decreasing response to continentality are
terrestrial, semi-terrestrial or associated with the splash zone or
very shallow littoral zone. Most species of Smittia and the larvae of
many Paraphaenocladius species are terrestrial (Cranston et al.,
1983) while Pseudorthocladius (Strenzlke, 1950; Saether and
Sublette, 1983), Synorthocladius (Cranston et al., 1983), Thienemanniella clavicornis-type (Cranston et al., 1983) and Thienemannimyia-type (Fittkau and Roback, 1983) occur in littoral, splash
zones or streams. Although terrestrial habitats and the splash zone
derive thermal insulation from the snow layer this can melt and
refreeze throughout the winter period or be removed by strong
winds which could leave chironomids in these environments
vulnerable to cellular damage from repeated freeze-thawing.
Therefore the decline in these species in more continental areas
may reflect their inability to survive in these environments during
the long cold winters rather than their failure to complete part of
their life cycle within the short summer. The decline of stream
dwelling taxa, such as Pseudorthocladius, Synorthocladius, Thienemanniella and Thienemannimyia, with increased continentality may
reflect a decline in the number of stream-fed lakes and an increase
in ground-water fed, thermokarst lakes across the continentality
gradient.
Whereas increasing or decreasing sigmoidal responses may
reflect an adaptation to, or tolerance of, differences in the length of
the summer or intensity of the winter; the concept of a continentality optimum is more difficult to explain in an ecologically
meaningful way. Taxa with unimodal responses (Table 9) may have
a competitive advantage under certain climate regimes but are outcompeted by better adapted species in more extreme maritime or
continental climates. Alternatively the taxa may be restricted
geographically for reasons unrelated to the present-day climate
such as past dispersal patterns. Nevertheless of the 15 taxa showing
unimodal responses, 12 occur in both the Norwegian and Russian
data-sets. Some of these taxa, such as P. triquetra-type have
a Holarctic distribution and have also been found in Patagonian
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
lakes (Massaferro and Brooks, 2002). Their widespread distribution
suggests they may be restricted by local environmental factors,
which may or may not be related to climate such as pH. The
remaining 3 taxa, Corynoneura type A, M. pallidula-type and Zavrelimyia were recorded in Norway only. This restricted distribution
may reflect differences in biogeography, climate or the greater
number of lakes in the Norwegian data-set. The eastern Palaearctic
fauna includes a number of chironomid taxa which are not found in
the western Palaearctic. Whilst this may be because climatic
conditions are different in the eastern than western Palaearctic,
a number of other insect groups have similar differences due to
historical reasons such as glaciation, climate history and postglacial dispersal (Konstantinov et al., 2009).
The ordinations suggest that the distribution and abundance of
chironomids in both the Norway and Russian data-sets are influenced by many of the same environmental variables. The TWINSPAN
results indicate the composition of the faunal assemblages from the
two data-sets are similar and many taxa show similar responses to
July air temperature. These characteristics are sometimes used to
justify the merging of training sets or the application of inference
models to areas outside the geographical region used to compile the
data. However, the speciesetemperature optima are consistently
higher, with 1e2 exceptions, in the Russian lakes than the Norwegian data-set regardless of the statistical modelling technique used.
The July temperature reconstruction of the short core VORK5 based
on the Norwegian inference model was consistently lower than that
based on the Russian C-IT model and the instrumental record. This
suggests continentality may play a role in the chironomid response
either directly through climate parameters such as the duration of
the ice-free period or spring temperatures or indirectly through the
occurrence of ground-water fed thermokarst or stream-fed lakes.
Further work is needed to confirm the relationship between
chironomid distribution and continentality, and possible mechanisms for the response. However if confirmed this would imply that
inference models should be considered specific to a geographical
region with a similar continentality regime. Application to sites tens
to hundreds of kilometres outside the geographic area covered by
the training set may be acceptable but the use of western European
inference models to cores collected in central Siberia is unlikely to
yield reliable reconstructions. However, the use of transfer functions
from geographically distinct areas may be justified when fossil taxa
are poorly represented in local training sets. For example, LarocqueTobler (2010) used North American and Swedish transfer functions
to reconstruct July air temperatures at Egelsee, Switzerland as these
data-sets had better representation of fossil taxa than the Swiss
transfer function. The Swedish and North American-based reconstructions varied by up to 4 C, suggesting that whilst the general
pattern of temperature change could be inferred using a transfer
function from another region, estimates of temperature and the
amplitude of change may be less reliable (Larocque-Tobler, 2010).
6. Conclusions
Numerical analysis of environmental variables and subfossil
head capsules remains in the surface sediments of 100 lakes
showed that mean July air temperature is the most significant
variable explaining contemporary chironomid distribution and
abundance in northern Russia. This has been shown to be an
important environmental variable in a number of previous studies,
leading to the development of chironomidetemperature transfer
functions (e.g. in Northern Europe by Olander et al., 1999; Brooks
and Birks, 2001; Larocque et al., 2001). The performance of the
WA-PLS temperature inference in the present study, with RMSEP of
0.89 C and r2 of 0.92, is comparable to these models. However, the
Russian inference model improves the representation of a number
1139
of taxa, such as C. oliveri-type, Constempellina and Paracladius,
which frequently occur in subfossil assemblages from arctic
Russian lakes, but are poorly represented in European training sets.
These are cold-adapted taxa and their absence from the training
sets could lead to overestimations of July temperatures, using
European inference models, in fossil samples where these taxa
form a major component (for example see Andreev et al., 2005).
Ordinations also suggest that continentality, expressed as the R
continentality index, is also a statistically significant variable
influencing the distribution and abundance of chironomid assemblages and also the temperature optima of chironomid species.
Temperature optima are consistently higher in the Russian data
than in the Norwegian data which implies that chironomid-based
temperature reconstructions for subfossil assemblages in Russian
sediment samples using the Norwegian inference model, or visa
versa, are likely to be reliable. This was confirmed by reconstructions of the Russian core, VORK5, based on the Norwegian C-IT
inference model which underestimated July air temperatures at
this lake and did not reconstruct decadal-scale oscillations in
temperatures. Therefore inference models should only be considered applicable to sediment cores collected within the broad
geographic source area of the training set and to areas with similar
continentality regimes. The relationships with continentality
enabled the development of chironomid-inference models to
reconstruct continentality over north-east Eurasia and July
temperature in north European Russia to central Siberia.
July air temperature and continentality reconstructions from an
unnamed lake in north-east European Russia gave similar value and
trends to instrumental data over the past 70 years. The ability of the
Russian models to estimate accurately past climate suggests the
Russian chironomideJuly air temperature-inference model represents an improvement over the Swedish and Norwegian inference
models, for chironomid-inferred temperature reconstructions in
northern Russia. The continentality inference model represents the
first attempt to reconstruct past continentality using chironomids.
This has the potential to make a valuable contribution towards
reconstructing past climates in Arctic environments where recent
summer (June, July and August) warming appears dampened
compared to other months (Hirschi et al., 2007). However further
work is necessary to confirm the observed relationship with continentality and establish the biological mechanisms for the
response. Continentality reconstructions over the Holocene where
seasonal insolation has varied and the variations are known may
help to elucidate the relationship.
Acknowledgements
The work by AS was funded by a NERC CASE studentship with
the NHM (NER/S/A/2005/13227); additional funding was provided
by CARBO-North funded under the EU Sixth Framework Programme Global Change and Ecosystems sub-programme (project
number 036993). The sediment core was 210Pb dated by HY at the
Bloomsbury Environmental Isotope Facility (BEIF) at UCL. Help with
fieldwork was provided by Nadia Solovieva from UCL, London;
Heikki Seppa and Sebastien Seboni from the University of Helsinki;
Mikhail Brezin and Elena Tkacheva from Moscow Zoo and Vasily
Ponomorov of the Komi Science Centre. In addition we would also
like to thank the anonymous reviewers for their valuable
comments on the manuscript.
Appendix. Supplementary data
Supplementary data associated with this article can be found, in
the on-line version, at doi:10.1016/j.quascirev.2011.01.022.
1140
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
References
Andreev, A., Tarasov, P., Schwamborn, G., Ilyashuk, B., Ilyashuk, E., Bobrov, A.,
Klimanov, V., Rachold, V., Hubberten, H.-W., 2004. Holocene paleoenvironmental records from Nikolay Lake, Lena River Delta, Arctic Russia. Palaeogeography, Palaeoclimatology, Palaeoecology 209, 197e217.
Andreev, A., Tarasov, P., Ilyashuk, B., Ilyashuk, E., Cremer, H., Hermichen, W.-D.,
Wischer, F., Hubberten, H.-W., 2005. Holocene environmental history recorded
in Lake Lyadhej-To sediments, Polar Urals, Russia. Palaeogeography, Palaeoclimatology, Palaeoecology 223, 181e203.
Appleby, P., Oldfield, F., 1978. The calculation of 210Pb dates assuming a constant rate
of supply of unsupported 210Pb to the sediment. Catena 5, 1e8.
Appleby, P.G., Nolan, P.J., Gifford, D.W., Godfrey, M.J., Oldfield, F., Anderson, N.J.,
Battarbee, R.W., 1986. 210Pb dating by low background gamma counting.
Hydrobiologia 141, 21e27.
Appleby, P.G., 2001. Chronostratigraphic techniques in recent sediments. In:
Last, W.M., Smol, J.P. (Eds.), Tracking Environmental Change Using Lake Sediments. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 171e203.
Barley, E.M., Walker, I.R., Kurek, J., Cwynar, L.C., Mathewes, R.W., Gajewski, K.,
Finney, B.P., 2006. A northwest North American training set: distribution of
freshwater midges in relation to air temperature and lake depth. Journal of
Paleolimnology 36, 295e314.
Bauch, H.A., Mueller-Lupp, T., Taldenkova, E., Spielhagen, R.F., Kassens, H.,
Grootes, P.M., Thiede, J., Heinemeier, J., Petryashova, V.V., 2001. Chronology of
the Holocene transgression at the North Siberian margin. Global and Planetary
Change 31, 125e139.
Birks, H.J.B., 1998. Numerical tools in palaeolimnology e progress, potentialities and
problems. Journal of Paleolimnology 20, 307e332.
Brodersen, K.A., Lindegaard, C., 1999. Classification, assessment and trophic
reconstruction of Danish lakes using chironomids. Freshwater Biology 42,
143e157.
Brodersen, K.P., Pedersen, O., Lindegaard, C., Hamburger, K., 2004. Chironomids
(Diptera) and oxy-regulatory capacity: an experimental approach to paleolimnological interpretation. Limnology and Oceanography 49, 1549e1559.
Brodin, Y.W., 1986. The postglacial history of Lake Flarken, southern Sweden,
interpreted from subfossil insect remains. Internationale Revue der Gesamten
Hydrobiologie 71, 371e432.
Brooks, S.J., Birks, H.J.B., 2000. Chironomid-inferred late-glacial and early-Holocene
mean July air temperatures for Kråkenes Lake, western Norway. Journal of
Paleolimnology 23, 77e89.
Brooks, S.J., Bennion, H., Birks, H.J.B., 2001. Tracing lake trophic history with
a chironomid-total phosphorus inference model. Freshwater Biology 46,
513e533.
Brooks, S.J., Birks, H.J.B., 2001. Chironomid-inferred air temperatures from Lateglacial and Holocene sites in north-west Europe: progress and problems.
Quaternary Science Reviews 20, 1723e1741.
Brooks, S.J., 2003. Chironomidae (Insecta: Diptera). In: MacKay, A., Battarbee, R.W.,
Birks, H.J.B. (Eds.), Global Change in the Holocene. Arnold, London, pp. 328e341.
Brooks, S.J., 2006a. Fossil midges (Diptera: Chironomidae) as palaeoclimatic indicators for the Eurasian region. Quaternary Science Reviews 25, 1894e1910.
Brooks, S.J., 2006b. Chironomid records: Late Pleistocene of Europe. In: Elias, S.A.
(Ed.), Encyclopedia of Quaternary Science. Elsevier Science, pp. 377e390.
Brooks, S.J., Langdon, P.G., Heiri, O., 2007. The Identification and Use of Palaearctic
Chironomidae Larvae in Palaeoecology. Quaternary Research Association, London, 276 pp.
Brundin, L., 1956. Zur Systematik der Orthocladiinae (Dipt., Chironomidae). Report.
Institute of Freshwater Research, Drottningholm, pp. 5e185.
Brundin, L., 1958. The bottom faunistical lake type system and its application to the
southern hemisphere. Moreover a theory of glacial erosion as a factor of
productivity in lakes and oceans. Verhandlungen der Interntionalen Vereinigung fur Theoretische und Angewandte Limnologie 13, 288e297.
Brundin, L., 1983. The larvae of Podonominae (Diptera: Chironomidae) of the
Holarctic region e keys and diagnoses. In: Wiederholm, T. (Ed.), Chironomidae
of the Holarctic Region. Keys and Diagnoses. Part 1-Larvae. Entomologica
Scandinavica Supplement 19, 23e31. Lund, Sweden.
Butler, M.G., 1982. A 7-year life cycle for two Chironomus species in arctic Alaskan
tundra ponds (Diptera: Chironomidae). Canadian Journal of Zoology 60, 58e70.
Comiso, J.C., 2003. Warming trends in the Arctic from clear sky satellite observations. Journal of Climate 16, 3498e3509.
Cranston, P.S., Oliver, D.R., Sæther, O.A., 1983. The larvae of the Orthocladiinae
(Diptera: Chironomidae) of the Holarctic region: keys and diagnoses. In:
Wiederholm, T. (Ed.), Chironomidae of the Holarctic Region. Keys and Diagnoses. Part 1-Larvae. Entomologica Scandinavica Supplement 19, 149e291.
Lund, Sweden.
Danks, H.V., 1971. Spring and early summer temperatures in a shallow arctic pond.
Arctic 24, 113e123.
Danks, H.V., Oliver, D.R., 1972a. Diel periodicities of emergence of some high Arctic
Chironomidae (Diptera). Canadian Entomologist 104, 903e916.
Danks, H.V., Oliver, D.R., 1972b. Seasonal emergence of some high Arctic Chironomidae (Diptera). Canadian Entomologist 104, 661e686.
Danks, H.V., 2004. Seasonal Adaptations in Arctic Insects. Integrative and
Comparative Biology 44, 85e94.
Duff, K., Laing, T.E., Smol, J.P., Lean, D.R.S., 1998. Limnological characteristics of lakes
located across arctic tree-line in northern Russia. Hydrobiologia 391, 205e222.
Edwards, D.H.D., 1986. Chironomidae (Diptera) of Australia. In: De Deckker, P.,
Williams, W.D. (Eds.), Limnology in Australia. CSIRO, Dordrecht, Melbourne,
pp. 159e173.
Fittkau, E.J., Roback, S.S., 1983. The larvae of the Tanypodinae (Diptera: Chironomidae) of the Holarctic region e keys and diagnoses. In: Wiederholm, T. (Ed.),
Chironomidae of the Holarctic Region. Keys and Diagnoses. Part 1-Larvae.
Entomologica Scandinavica Supplement 19, 33e112. Lund, Sweden.
Giesecke, T.A.E.B., Chiverrell, R.C., Seppä, H., Ojala, A.E.K., Birks, H.J.B., 2008.
Exploring Holocene continentality changes in Fennoscandia using present and
past tree distributions. Quaternary Science Reviews 27, 1296e1308.
Gorczynski, W., 1920. Sur le calcul du degre du continentalisme et son application
dans la climatologie. Geografiska Annaler 2, 324e331.
Grieser, J., Gommes, R., Cofield, S., Bernardi, M., 2006. Data Sources for FAO
Worldmaps of Koeppen Climatologies and Climatic Net Primary Production. The
Agromet Group, SDRN, Food and Agriculture Organization of the United
Nations, Rome, Italy.
Hauer, F.R., Benke, A.C., 1991. Rapid growth of snag-dwelling chironomids in
a blackwater river: the influence of temperature and discharge. Journal of the
North American Benthological Society 10, 154e164.
Heiri, O., 2001. Holocene palaeolimnology of Swiss mountain lakes reconstructed
using subfossil chironomid remains: past climate and prehistoric human impact
on lake ecosystems. Ph.D. thesis, University of Bern, Bern, Switzerland, p. 113.
Heiri, O., 2004. Within-lake variability of subfossil chironomid assemblages in
shallow Norwegian lakes. Journal of Paleolimnology 32, 67e84.
Heiri, O., 2006. Chironomid records: postglacial Europe. In: Elias, S.A. (Ed.), Encyclopedia of Quaternary Science. Elsevier, Amsterdam, pp. 390e398.
Hill, M.O., Smilauer,
P., 2005. TWINSPAN for Windows Version 2.3. Centre for
Ecology and Hydrology & University of South Bohemia., Huntingdon & Ceske
Budejovice.
Hirschi, J.J.-M., Sinha, B., Josey, S.A., 2007. Global warming and changes in continentality since 1948. Weather 62, 215e221.
Hofmann, W., 1998. Cladocerans and chironomids as indicators of lake level changes
in north temperate lakes. Journal of Paleolimnology 19, 55e62.
Ilyashuk, E., Ilyashuk, B., Hammarlund, D., Larocque, I., 2005. Holocene climatic and
environmental changes inferred from midge (Diptera: Chironomidae, Chaoboridae, Ceratopogonidae) records at Lake Berkut, southern Kola Peninsula,
Russia. The Holocene 15, 897e914.
IPCC, 2007. Summary for policymakers. In: Solomon, S., Qin, D., Manning, M.,
Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change
2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA.
Iwakuma, T., 1986. Ecology and production of Tokunagayusurika akamusi (Tokunga)
and Chironomus plumosus (L.) (Diptera: Chironomidae) in a shallow eutrophic
lake. Ph.D. thesis, Kyushu University, Kyushu, Japan.
Juggins, S., 1994. Gaussian Logit Regression. Unpublished Computer Programme.
Department of Geography, University of Newcastle, UK.
Juggins, S., 2005. C2 Version 1.4.3. Software for Ecological and Palaeoecological Data
Analysis and Visualisation. Newcastle University, Newcastle upon Tyne, UK.
Kansanen, P.H., 1985. Assessment of pollution history from recent sediments in Lake
Vanajavesi, southern Finland. II. Changes in Chironomidae, Chaoboridae and
Ceratopogonidae (Diptera) fauna. Annales Zoologici Fennici 22, 57e90.
Klein Tank, A.M.G., Wijngaard, J.B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A.,
Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P.,
Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzferald, D.,
Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R.,
Engelen, A.F.V.V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V.,
Nieplova, E., Cegnar, T., López, J.A., Dahlström, B., Moberg, A., Kirchhofer, W.,
Ceylan, A., Pachaliuk, O., Alaxander, L.V., Petrovic, P., 2002. Daily dataset of
20th-century surface air temperature and precipitation series for the European
Climate Assessment. International Journal of Climatology 22, 1441e1453.
Konstantinov, A.S., Korotyaev, B.A., Volkovitsh, M.G., 2009. Insect biodiversity in the
Palearctic region. In: Foottit, R.G., Adler, P.H. (Eds.), Insect Biodiversity: Science
and Society. Wiley-Blackwell, p. 656.
Kumke, T., Ksenofontova, M., Pestryakova, L., Nazarova, L., Hubberten, H.-W., 2007.
Limnological characteristics of lakes in the lowlands of Central Yakutia, Russia.
Journal of Limnology 66, 40e53.
Langdon, P.G., Holmes, N., Caseldine, C.J., 2008. Environmental controls on modern
chironomid faunas from NW Iceland and implications for reconstructing
climate change. Journal of Paleolimnology 40, 273e293.
Larocque-Tobler, I., 2010. Reconstructing temperature at Engelsee, Switzerland
using North American and Swedish chironomid transfer functions: potential
and pitfalls. Journal of Paleolimnology 44, 243e251.
Larocque, I., Hall, R.I., Grahn, E., 2001. Chironomids as indicators of climate change:
a 100-lake training set from a subarctic region of northern Sweden (Lapland).
Journal of Paleolimnology 26, 307e322.
Leps, J., Smilauer,
P., 2003. Multivariate Analysis of Ecological Data Using CANOCO.
Cambridge University Press, Cambridge, 269 pp.
Lindegaard, C., 1992. Zoobenthos ecology of Thingvallavatn: vertical distribution,
abundance, population, dynamics and production. Oikos 64, 257e304.
Lindegaard, C., 1995. Classification of water-bodies and pollution. In: Armitage, P.,
Cranston, P.S., Pinder, L.C.V. (Eds.), The Chironomidae: the Biology and Ecology
of Non-biting Midges. Chapman and Hall, London, pp. 385e404.
Lindquist, S.J., 1999. The Timan-Pechora Basin Province of Northwest Arctic Russia:
Domanik e Paleozoic Petroleum System.
A.E. Self et al. / Quaternary Science Reviews 30 (2011) 1122e1141
Lotter, A.F., Birks, H.J.B., Hofmann, W., Marchetto, A., 1997. Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators
for the reconstruction of past environmental conditions in the Alps. I. Climate.
Journal of Paleolimnology 18, 395e420.
Luoto, T.P., 2008. Subfossil Chironomidae (Insecta: Diptera) along a latitudinal
gradient in Finland: development of a new temperature inference model.
Journal of Quaternary Science 24, 150e158.
MacDonald, G.M., Kremenetski, C.V., Velichko, A.A., Cwynar, L.C., Riding, R.T.,
Goleva, A.A., Andreev, A., Borisova, O.K., Edwards, T.W.D., Hammarlund, D.,
Szeicz, J.M., Forman, S.L., Gataullin, V., 2000. Holocene treeline history and
climate change across northern Eurasia. Quaternary Research 53, 302e311.
Mackey, A.P., 1977. Trophic dependencies of some larval Chironomidae (Diptera)
and fish species in the River Thames. Hydrobiologia 62, 241e247.
Makarchenko, E.A., Makarchenko, M.A., 1999. Chironomidae. In: Tsalolikhin, S.J.
(Ed.), Key to Freshwater Invertebrates of Russia and Adjacent Lands. Zoological
Institute RAS, St. Petersburg, pp. 210e295.
Massaferro, J.I., Brooks, S.J., 2002. Response of chironomids to Late Quaternary
environmental change in the Taitao Peninsula, southern Chile. Journal of
Quaternary Science 17, 101e111.
McClelland, J., Holmes, R.M., Peterson, B.J., Stieglitz, M., 2004. Increasing river
discharge in the Eurasia Arctic: consideration of dams, permaforst thaw and
fires as potential agents of change. Journal of Geophysical Research e Atmospheres 109, D18102.
Meshkova, V., 2002. Dependency of outbreaks distribution from insects-defoliators’
seasonal development. In: McManus, M.L., Liebhold, A.M. (Eds.), Proceedings of
the Ecology, Survey and Management of Forest Insects. USDA Forest Service,
Kraków, Poland, pp. 52e60.
Moller Pillot, H.K.M., Buskens, R.F.M., 1990. De larven der Nederlandse Chironomidae. Autoecologie en verspreiding. Nederlandse Faunistische Medelelingen
1c, 1e87.
Murphy, J., Riley, J., 1962. A modified single solution for the determination of
phosphate in natural waters. Analytica Chimica Acta 27 31e29.
Nalvikin, D.V., 1973. The geology of the U.S.S.R.
Nazarova, L.B., Pestryakova, L.A., Ushnitskaya, L.A., Hubberten H., W., 2008.
Chironomids (Diptera: Chironomidae) in Lakes of Central Yakutia and their
indicative potential for paleoclimatic research. Contemporary Problems of
Ecology 15, 141e150.
Oksanen, J., Minchin, P.R., 2002. Continuum theory revisited: what shape are
species responses along ecological gradients? Ecological Modelling 157,
119e129.
Olander, H., Birks, H.J.B., Korhola, A., Blom, T., 1999. An expanded calibration model
for inferring lakewater and air temperatures from fossil chironomid assemblages in northern Fennoscandia. The Holocene 9, 279e294.
Peterson, B.J., Holmes, R.M., McClelland, J., Vorosmarty, C.J., Lammers, R.B.,
Shiklomanov, A.I., Shiklomanov, I.A., Rahstorf, S., 2002. Increasing river
discharge to the Arctic Ocean. Science 298, 2171e2173.
Pinder, L.C.V., Reiss, F., 1983. The larvae of Chironominae (Diptera: Chironomidae) of
the Holarctic region e keys and diagnoses. In: Wiederholm, T. (Ed.), Chironomidae of the Holarctic Region. Keys and Diagnoses. Part 1-Larvae. Entomologica
Scandinavica Supplement 19, 149e292. Lund, Sweden.
Porinchu, D.F., Cwynar, L.C., 2000. The distribution of freshwater Chironomidae
(Insecta: Diptera) across the treeline near the lower Lena River, northeast
Siberia. Arctic, Antarctic and Alpine Research 32, 429e437.
Quinlan, R., Smol, J.P., 2001a. Setting minimum head capsule abundance and taxa
deletion criteria in chironomid-based inference models. Journal of Paleolimnology 26, 327e342.
Quinlan, R., Smol, J.P., 2001b. Chironomid-based inference models for estimating
end-of-summer hypolimnetic oxygen from south-central Ontario shield lakes.
Freshwater Biology 46, 1529e1551.
R Development Core Team, 2004. R: a language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3900051-00-3. http://www.R-project.org.
Racca, J.M.J., Wild, M., Birks, H.J.B., Prairie, Y.T., 2003. Separating wheat from chaff:
diatom taxon selection using an artificial neural network pruning algorithm.
Journal of Paleolimnology 29, 123e133.
Rawlins, M.A., Willmott, C.J., 2003. Winter air temperature change over the Terrestrial
Arctic, 1961e1990. Arctic, Antarctic and Alpine Research 35, 530e537.
1141
Renberg, I., 1991. The HON-Kajak sediment corer. Journal of Paleolimnology 6,
167e170.
Rieradevall, M., Brooks, S.J., 2001. An identification guide to subfossil Tanypodinae
larvae (Insecta: Diptera: Chironomidae) based on cephalic setation. Journal of
Paleolimnology 25, 81e99.
Rigor, I.G., Colony, R.L., Martin, S., 2000. Variations in surface air temperature
observations in the Arctic, 1979e1997. Journal of Climate 13, 896e914.
Rudolf, B., Fuchs, T., Schneider, U., Meyer-Christoffer, A., 2003. Introduction of the
Global Precipitation Climatology Centre (GPCC). Deutscher Wetterdienst,
Offenbach a.M, 16 pp.
Sæther, O.A., 1979. Chironomid communities as water quality indicators. Holarctic
Ecology 2, 65e74.
Saether, O.A., Sublette, J.E., 1983. A review of the genera Doithrix n. gen., Georthocladius Strenzke, Parachaetocladius Wülker, and Pseudorthocladius Goetghebuer
(Diptera: Chironomidae: Orthocladiinae). Entomologica Scandinavica Supplement 20, 1e100.
Sarmaja-Korjonen, K., Kultti, S., Valiranta, M., Solovieva, N., 2003. Mid-Holocene
palaeoclimatic and palaeohydrological conditions in northeastern European
Russia: a multi-proxy study of Lake Vankavad. Journal of Paleolimnology 30,
415e426.
Schmid, P.E., 1993. A Key to the Larval Chironomidae and Their Instars from Austrian
Danube Region Streams and Rivers. Part 1. Diamesinae, Prodiamesinae and
Orthocladininae. Federal Institute for Water Quality, Wien.
Smol, J.P., Wolfe, A.P., Birks, H.J.B., Douglas, M.S.V., Jones, V.J., Korhola, A., Pienitz, R.,
Ruhland, K., Sorvari, S., Antoniades, D., Brooks, S.J., Fallu, M.-A., Hughes, M.,
Keatley, B.E., Laing, T.E., Michelutti, N., Nazarova, L., Nyman, M., Paterson, A.,
Perren, B., Quinlan, R., Rautio, M., Saulnier-Talbot, E., Siitonen, S., Solovieva, N.,
Weckstrom, J., 2005. Climate-driven regime shifts in the biological communities
of arctic lakes. PNAS 102, 4397e4402.
Solovieva, N., Jones, V.J., Appleby, P.G., Kondratenok, B.M., 2002. Extent, environmental impact and long-term trends in atmospheric contamination in the Usa
basin of east-European Russian Arctic. Water, Air and Soil Pollution 139,
237e260.
Solovieva, N., Jones, V.J., Nazarova, L., Brooks, S.J., Birks, H.J.B., Grytnes, J.-A.,
Appleby, P.G., Kauppila, T., Kondratenok, B., Renberg, I., Ponomarev, V., 2005.
Palaeolimnological evidence for recent climatic change in lakes from the
northern Urals, arctic Russia. Journal of Paleolimnology 33, 463e482.
Storey, A.W., 1987. Influence of temperature and food quality on the life history of
an epiphytic chironomid. Entomologica Scandinavica Supplement 29, 339e347.
Strenzlke, K., 1950. Systematik, Morphologie und Ökologie der terrestrischen Chironomiden. Archiv für Hydrobiologie Supplement 18, 207e414.
ter Braak, C.J.F., Looman, C.W.N., 1986. Weighted averaging, logistic regression and
the Gaussian response model. Vegetatio 65, 3e11.
ter Braak, C.J.F., Juggins, S., 1993. Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269, 485e502.
P., 2002. CANOCO Reference Manual and User’s Guide to
ter Braak, C.J.F., Smilauer,
CANOCO for Windows: Software for Canonical Community Ordination Version
4.5. Microcomputer Power, Ithaca, New York.
Tokeshi, M., 1985. Life cycles and population dynamics. In: Armitage, P.,
Cranston, P.S., Pinder, L.C.V. (Eds.), The Chironomidae: Biology and Ecology of
Non-biting Midges. Chapman and Hall, London, pp. 225e268.
von Humboldt, A., 1827. Über die Haupt-Ursachen der temperature-verschiedenheit
auf dem Erdkörper. Abhandlungen der Königlichen Akademie der Wissenschaften zu Berlin, Physikalische Klasse, 295e316.
Walker, I.R., Smol, J.P., Engstrom, D.R., Birks, H.J.H., 1991. An assessment of Chironomidae as quantitative indicators of past climatic change. Canadian Journal of
Fisheries and Aquatic Science 48, 975e987.
Welch, H.E., 1976. Ecology of Chironomidae (Diptera) in a polar pond. Journal of the
Fisheries Research Board of Canada 33, 227e247.
Wiederholm, T., 1983. Chironomidae of the Holarctic. Keys and diagnoses. Part 1.
Larvae. Entomologica Scandinavica Supplement 19, Lund, Sweden, 457 pp.
Wood, S., 2007. GAMs with GCV Smoothness Estimation and GAMMs by REML/PQL.
The mgcv package. [email protected]
Zolotukhin, V.V., Almukhamedov, A.I., 1988. Traps of the Siberian platform. In:
MacDougall, J.D. (Ed.), Continental Flood Basalts. Kluwer Academic Publishers,
Dordrecht, pp. 273e310.
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