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Chironomid-Based Inference Models for Estimating Mean July Air Temperature and Water Depth from Lakes in Yakutia, Northeastern Russia

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Chironomid-Based Inference Models for Estimating Mean July Air Temperature and Water Depth from Lakes in Yakutia, Northeastern Russia J Paleolimnol (2011) 45:57–71 DOI 10.1007/s10933-010-9479-4 ORIGINAL PAPER Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia, northeastern Russia Larisa Nazarova • Ulrike Herzschuh • Sebastian Wetterich • Thomas Kumke • Ludmila Pestryakova Received: 7 April 2009 / Accepted: 21 October 2010 / Published online: 27 November 2010 Ó Springer Science+Business Media B.V. 2010 Abstract We investigated the subfossil chironomid within the zone of continuous permafrost. Mean July fauna of 150 lakes situated in Yakutia, northeastern temperature (TJuly) varied from 3.4°C in the Laptev Russia. The objective of this study was to assess the Sea region to 18.8°C in central Yakutia near Yakutsk. relationship between chironomid assemblage compo- Water depth (WD) varied from 0.1 to 17.1 m. TJuly sition and the environment and to develop chirono- and WD were identified as the strongest predictor mid inference models for quantifying past regional variables explaining the chironomid communitiy climate and environmental changes in this poorly composition and distribution of the taxa in our data investigated area of northern Russia. The environ- set. Quantitative transfer functions were developed mental data and sediment samples for chironomid using two unimodal regression calibration tech- analysis were collected in 5 consecutive years, niques: simple weighted averaging (WA) and 2003–2007, from several regions of Yakutia. The weighted averaging partial least squares (WA-PLS). lakes spanned wide latitudinal and longitudinal The two-component TJuly WA-PLS model had the ranges and were distributed through several environ- best performance. It produced a strong coefficient of 2 mental zones (arctic tundra, typical tundra, steppe- determination (r boot = 0.87), root mean square error tundra, boreal coniferous forest), but all were situated of prediction (RMSEP = 1.93), and max bias (max biasboot = 2.17). For WD, the one-component 2 WA-PLS model had the best performance (r boot = Electronic supplementary material The online version of 0.62, RMSEP = 0.35, max biasboot = 0.47). this article (doi:10.1007/s10933-010-9479-4) contains supplementary material, which is available to authorized users. Keywords Chironomids Á Transfer function Á Russian Arctic Á Temperature Á Water depth L. Nazarova (&) Á U. Herzschuh Á S. Wetterich Alfred Wegener Institute for Polar and Marine Research, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam, Germany Introduction e-mail: [email protected] T. Kumke The study of Arctic palaeoenvironmental records Schwarz BioSciences GmbH, Alfred-Nobel Street, enables qualitative and quantitative estimations of 10, 40789 Monheim am Rhein, Germany past climate changes and provides a basis for prediction of future changes in the region (Andreev L. Pestryakova Water System Laboratory, North-Eastern Federal et al. 2004). The timing of Holocene climate events in University, 58 Belinsky Street, 677891 Yakutsk, Russia the North Atlantic region is relatively well studied 123 58 J Paleolimnol (2011) 45:57–71 (Bond et al. 2001; Solignac et al. 2006). In contrast, set. The application also shows that reconstruction of there are few quantitative palaeoclimatic data for the mean air temperature of the warmest month eastern Siberia, such that hypotheses regarding the (TJuly) before 10,650 cal. year BP and after 7,000 cal. timing and spatial coverage of important climate year BP during the Holocene is problematic because events, like Holocene Thermal Maximum remain of existing taxonomic incompatibility between the untested. In addition, paleolimnological records from fossil and the training set assemblages (Andreev et al. northern Eurasia mostly document environmental 2005). Therefore, the necessity of a regional data set changes at low temporal resolution and are derived containing information on the composition, distribu- from pollen studies (Anderson et al. 2002; Andreev tion and ecological preferences of the modern et al. 2004). Due to the relatively small magnitude of chironomid fauna of Russian north remains very temperature changes throughout the Holocene, recon- high. structions based on a single variable must be Until now, only two studies of chironomid ecology interpreted with caution. Furthermore, current global quantifying the influence of modern environmental climate warming can be challenging for the lakes and conditions on midge distributions in northern Russia aquatic fauna of eastern Siberia since this region is are known (Porinchu and Cwynar 2000; Nazarova among the most sensitive areas affected by extreme et al. 2008). Both of them covered only short climatic changes in climate (Smol et al. 2005). gradients: D Tair = 1.6°C and D WD = 10 m in Many studies have explored the potential of Central Yakutian lakes (Nazarova et al. 2008); aquatic organisms, including chironomids (Order: D Tair = 8.5°C and D WD = 9.85 m in lower Lena Diptera, Family: Chironomidae), as Quaternary pal- Delta data set (Porinchu and Cwynar 2000). These aeoclimate indicators (Porinchu and Cwynar 2002; were not sufficient for generating inference models Dieffenbacher-Krall et al. 2007). Chironomid cali- for reconstructing temperature or other ecological bration data sets and inference models for recon- parameters in Holocene. structing mean July temperature (Larocque et al. We investigated subfossil chironomids from lakes 2001), lake depth (Korhola et al. 2000), salinity and other periglacial waters of the permafrost zone in (Eggermont et al. 2007) and lake production (Wood- Yakutia, northeastern Russia. The region that spreads ward and Shulmeister 2006), have been developed over 2,500 km from east to west and 2,000 km from successfully for Western Europe (Olander et al. 1999; north to south over different geographical zones: Brooks and Birks 2001), North America (Walker tundra at the Arctic Ocean coast, mountains in the et al. 1997; Barley et al. 2006), Africa (Eggermont east and south (up to 2,000–3,000 m a.s.l.) and taiga et al. 2007), New Zealand (Woodward and forests in the west (Tahtadzhan 1978). Nearly the Shulmeister 2006) and Tasmania (Rees et al. 2008). whole territory of Yakutia is underlain by permafrost But still no calibration data sets and inference models (Geocriology of USSR 1989). Due to a harsh have been established for the Russian high latitudes, environment and extreme difficulty in getting access including Arctic Siberia. to the lakes, only limited chironomid studies have It is known that most of the models have limited been undertaken in this region. The existing studies application outside of the regions where they have focused on estimation of productivity of benthic been developed. Differences in faunal composition communities (Karationis et al. 1956; Streletzkaja between sites included in the calibration set and the 1972; Ogay 1979; Salova 1993; Tjaptirgjanov et al. studied site make data difficult to interpret and results 1992) or solely on taxonomy or karyotaxonomy of are sometimes unreliable (Lotter et al. 1999). chironomids (Zelentsov and Shilova 1996; Kiknadze Attempts to apply a transfer function based on a et al. 1996; Shobanov et al. 2002). northern Sweden calibration data set (Larocque et al. The objective of this study is to assess the 2001) to chironomid records from the northern relationship between chironomid assemblage compo- Russia: Nikolay Lake, northern Yakutia (Andreev sition and their environment and to develop chiron- et al. 2004) and Lake Lyadhej-To, northern Ural omid inference models for quantifying past regional (Andreev et al. 2005), have shown that only climate and environmental changes in this poorly 50–59.5% of the taxa from fossil lake sediment investigated area of northern Russia. We intend to assemblages appear in the modern calibration data address specific research questions such as: When did 123 J Paleolimnol (2011) 45:57–71 59 temperature begin to increase in Yakutia, signalling -60°C. Oymjakon (Region 7, Fig. 1) is the coldest the end of the last Ice Age? What is the variation in place in the Northern Hemisphere, with the lowest the timing, magnitude and spatial coverage of the winter temperatures reaching -71.2°C (Gavrilova Holocene Thermal Maximum across eastern Siberia? 1998). Average July temperature varies from about How has the hydrology of this region changed over 2–4°C on the New Siberian Islands in the Laptev Sea the past millennium? These models will provide data (Region 9, Fig. 1) to about 18–19°C in Central independent of existing palaeoclimatic inferences, Yakutia near Yakutsk (Region 2, Fig. 1) with max- which are largely pollen based, and can be used in imum summer temperatures ranging from 38 to 40°C. testing of hypotheses related to these and other Annual temperatures in Yakutia average between questions. -10 and -12°C (Gavrilova 1998). Annual precipi- tation ranges between 141 and 546 mm, which is less than annual evaporation in most areas (Gavrilova Study area and study sites 1998). The driest area is the Central Yakutian lowland, where summer evaporation is four times Yakutia is located in the northeastern part of Russia higher than precipitation. Most of the lakes in (between 55°290 and 76°460 N and 105°320 and Yakutia originate by thermokarst processes that cause 162°550 E; Fig. 1). Periglacial landscape is domi- extensive melting of the underlying permafrost and nated by tundra and taiga vegetation, deep-reaching large, water-filled depressions result. This lakes are frozen ground and widespread lake districts (Geoc- rather shallow (1–3 m) and characterized by specific riology of USSR 1989). This part of Eurasia repre- thermal and chemical regimes, making them sensitive sents one of the Earth’s most extreme semi-arid to recent climate changes. Other types of periglacial continental settings, whose role in the Arctic climate waters in patterned tundra landscapes are polygonal system so far is poorly understood (Kumke et al. ponds and thaw lakes whose formation is directly 2007). It is characterized by pronounced seasonal connected to permafrost processes like ice wedge gradients. The amplitude of temperature variations growth and initial thermokarst (Wetterich et al.
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