Northern Russian chironomid-based modern summer temperature data set and inference models
Introduction
Biotic proxies from lake sediments provide a powerful means of quantifying past climate change in terrestrial contexts. In addition, analysis of biotic remains from lake sediments provides an indication of the rate and magnitude of the response of animals and plants to past climate change and how they may respond in the future. Climatic inferences from palaeorecords are based on modern or near-modern analogues (training sets) from which the empirical reconstruction models (i.e. the transfer function) are established. By using inference models, which link the present distribution and abundance of chironomids to contemporary climate, past climates can be quantified from fossil chironomid assemblages (Kienast et al., 2011, Self et al., 2011). Chironomids (Insecta: Diptera) are well-proven to be among the most reliable quantitative proxies of mean July air temperature (Brooks, 2006). They are a diverse and nearly ubiquitous family of holometabolous two-winged flies and play vital roles in freshwater ecosystems as primary consumers (Coffman and Ferrington, 1996). The abundance and distribution of most chironomid taxa are temperature-dependent (Walker et al., 1991), reflecting the effect of air and water temperatures on all stages of their life cycles (Oliver, 1971) and they respond rapidly to climate change by virtue of the winged adult stage. The larval head capsules preserve well in lake sediment deposits and the subfossils are readily identifiable in most cases at least to species morphotype (Brooks et al., 2007).
Chironomid based inference models for reconstructing mean July air temperature have been developed successfully for Western Europe (Olander et al., 1999, Brooks and Birks, 2001), North America (Walker et al., 1997, Barley et al., 2006), Africa (Eggermont et al., 2007), New Zealand (Woodward and Shulmeister, 2006) and Tasmania (Rees et al., 2008).
Recently, data on the distribution and abundance of chironomids in lakes along environmental gradients in eastern and western Siberia were used to develop modern chironomid-based calibration data sets (training sets) and quantitative transfer functions for reconstructing mean July air temperature (T July), water depth (WD) and continentality (CI) in eastern (ES) and western Siberia (WS) (Nazarova et al., 2011, Self et al., 2011). Numerical analysis showed that T July is the most significant variable explaining contemporary chironomid distribution and abundance in both data sets. These data sets and transfer functions have provided a new tool for quantitative assessment of the past environment in north-eastern Eurasia and were applied in several studies of Holocene palaeoclimate in Siberia (Jones et al., 2011, Kienast et al., 2011, Self et al., 2011, Mackay et al., 2012, Nazarova, 2012, Nazarova et al., 2013a, Nazarova et al., 2013b, Engels et al., 2014). Climate inference models have limited application outside the regions in which they have been developed, so the new Russian models are an improvement over the Swedish and Norwegian inference models, which have been used previously for chironomid-inferred temperature reconstructions in northern Russia. Solovieva et al. (2005) reconstructed T July in north-east European Russia using a chironomid July 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 chironomid temperature-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 the Polar Urals (Solovieva et al., 2005).
In this paper we present the results of the work we have done to re-analyse and standardise the taxonomy between our already published chironomid calibration sets from East Siberia (Nazarova et al., 2011) and West Siberia (Self et al., 2011) with the addition of new regions to the data set: Bunge Land (Laptev Sea), 31 lakes from Kolyma River region, 10 lakes from Indigirka River region and 13 lakes from Kamchatka (Fig. 1). Following taxonomic standardisation we have merged the data sets. This has the advantage of extending the geographical and environmental gradients and increasing the representation of taxa in the calibration set. This can be expected to further improve the performance and applicability of the chironomid-temperature inference models by providing better estimates of the environmental optimum of taxa and increasing the probability of analogues between present and past assemblages.
The main objectives of our investigation are to compare the faunal composition of the WS and ES data sets, to examine the environmental factors which influence chironomid distribution and abundance in the combined data set, to identify which climate variable has the most potential for development of a chironomid-based inference model and to develop chironomid inference models for quantifying past regional climate and environmental changes in northern and north-eastern Russia.
Section snippets
Study regions
Study sites included in this investigation span wide latitudinal and longitudinal ranges in northern Russia: from Komi Republic in the West (50.50 E, part of the WS) to Kamchatka in the East (163.15 E, new data) and from Novosibirsk Islands in the Laptev Sea in the North (75.40 N, part of the ES) to southern Kamchatka (53.03 N, new data) in the South (Fig. 1).
The most western part of the data set includes the Komi Republic (region Komi, part of WS, Fig. 1) and Bolshezemelskaya tundra (region
Field methods and derivation of climate variables
Surface sediments and environmental data for the West Siberian (WS) data set (100 lakes in total; Self et al., 2011) were collected between 1998 and 2007 in north-east European Russia (Solovieva et al., 2002, Solovieva et al., 2005, Sarmaja-Korjonen et al., 2003), Lower Lena River (Porinchu and Cwynar, 2000), in Putorana Plateau, in the Komi Republic (Self et al., 2011). The sediment samples and environmental data for the East Siberian (ES; Nazarova et al., 2005, Nazarova et al., 2008, Nazarova
Distribution of the lakes in the Russian training sets along the T July gradient
The distribution of the sampled lakes along the T July gradient is shown in Fig. 2. The West Siberian data set (WS) covers the T July gradient from 10.8 to 18.3 °C with a high proportion of lakes between 11.5 and 12.0 °C and very few lakes in the range between 15.0 and 16.4 °C (Fig. 2). The East Siberian data set (ES) covers a longer T July gradient, from 3.4 to 18.8 °C, but has two major gaps in the sampled gradient. There are no lakes from 4.0 to 9.0 °C and from 13.5 to 16.6 °C. Merging the two
Ordination of the full set of data
VIF and CCAs show that latitude, longitude, T Jan, altitude and continentality are intercorrelated and are subsequently eliminated from the analysis. A set of CCAs constrained to individual environmental variables and Monte Carlo permutation tests reveal that four variables explain significant proportions (p < 0.05) of variance in the data set: T July explains 5.1% of the variance in the data, conductivity 3.7%, pH and WD explain 2.8% each. CCA with these four variables had CCA axis 1 of 0.185
Development of inference models
T July models with all 268 lakes and 174 taxa (the full data set model, or FM) yielded relatively high coefficients of determination (rJack2 = 0.66–0.73), high root mean squared errors of prediction (RMSEP = 2.3–2.7) and max biasesjack (2.28–3.50) (Table 4, Fig. 6a).
In order to improve statistical parameters of the FM we used first an ecological approach to lake selection. For the 193 lakes reduced data set, which we refer to as the North Russia (NR), we found the same significant environmental
Taxon specific T July optima in the NR and FE data sets
In the FM data set 94 taxa have more than 10 occurrences, 89 of them have more than 10 occurrences in the reduced NR data set and 49 in the FE data set. The generalised linear models demonstrate that in the full data set 86.1% of the taxa have a significant relationship to T July (63.8% of the taxa have a highly significant relationship (p ≤ 0.001) and 22.3% have a significant relationship (p ≤ 0.05)). 76.5% of the taxa had a significant relationship to T July in NR model and 79.6% of taxa in FE
Taxonomic composition of the data sets
In this study we combined data from geographically remote areas. The distance between the most western (Komi, lake K7) and the most eastern (Kolyma, lake KO29) sampling sites is more than 5290 km. Two data sets and 55 new sites were merged based on several preconditions: high taxonomic similarity of the combined data sets; distribution of chironomid taxa in both data sets is driven mainly by the same ecological factors; responses of individual taxa to the measured environmental variables are
Conclusions
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Following taxonomic standardisation between chironomid WS and ES calibration sets we have merged the data sets and added 55 lakes from three new East Siberian regions to the combined data set.
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High taxonomic similarity was found between the WS and ES data sets. The most taxonomically distinctive regions in the combined data set are the coldest region Laptev, and the continental areas of CY, NY and Komi. The highest taxonomic similarity was found between the neighbouring regions and between the
Acknowledgements
We thank all Russian and German colleagues who helped us during the fieldwork in north-eastern Russia over several years. Many thanks to Antje Eulenberg and Ute Bastian (AWI) for the help in laboratory work, and to Dr. Evgenia Vinogradova for the help with chironomid slides preparation. Sincere thanks to the reviewers for their valuable comments. This study was supported by Alexander von Humboldt Foundation AvH: 3.4-RUS/1117171STP, Deutsche Forschungsgemeinschaft (DFG: NA 760/2 and DI 655/9),
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2023, Quaternary InternationalMetrics of structural change as indicators of chironomid community stability in high latitude lakes
2020, Quaternary Science ReviewsCitation Excerpt :We begin by generating presence-absence data on simulated chironomid taxa and lakes to produce null expectations of community composition and structural change across temperature gradients using three structural metrics: beta diversity, compositional disorder and network skewness. We then apply these metrics to three empirical datasets of chironomids across regional gradients of ambient temperature from northern North America (Fortin et al., 2015), Norway (Brooks and Birks, 2001, 2004), and Russia (Nazarova et al., 2011, 2015). Comparison of empirical to modelled outcomes reveals deviations from the null expectations, enabling us to assess whether the lakes have undergone additional stress from climate change or secondary drivers.