Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia, northeastern Russia
Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia, northeastern Russia
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DOI：
10.1007/s10933-010-9479-4
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发表时间：
2011
影响因子：
2.1
通讯作者：
L. Nazarova;U. Herzschuh;S. Wetterich;T. Kumke;L. Pestryakova
中科院分区：
文献类型：
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作者：
L. Nazarova;U. Herzschuh;S. Wetterich;T. Kumke;L. Pestryakova
我们调查了位于俄罗斯东北部雅库特的150个湖泊的亚化石摇蚊动物群。本研究的目的是评估摇蚊组合组成和环境之间的关系，并制定摇蚊推理模型量化过去的区域气候和环境变化，在这个调查不充分的地区，俄罗斯北方。2003-2007年，从雅库特的几个地区连续5年收集了环境数据和沉积物样品进行摇蚊分析。这些湖泊跨越广泛的纬度和经度范围，分布在几个环境区（北极苔原，典型苔原，草原苔原，北方针叶林），但都位于连续多年冻土带内。7月平均气温（TJuly）从拉普捷夫海地区的3.4°C到雅库特中部雅库茨克附近的18.8°C不等。水深（WD）从0.1米到17.1米不等。TJuly和WD被确定为解释摇蚊群落组成和分类群分布的最强预测变量。定量传递函数的开发使用两个单峰回归校准技术：简单加权平均（WA）和加权平均偏最小二乘（WA-PLS）。两组分TJulyWA-PLS模型的预测效果最好。它产生了很强的决定系数（r2 boot = 0.87），预测均方根误差（RMSEP = 1.93）和最大偏差（max biasboot= 2.17）。对于WD，单组分WA-PLS模型具有最佳性能（r2 boot = 0.62，RMSEP = 0.35，max biasboot= 0.47）。
We investigated the subfossil chironomid fauna of 150 lakes situated in Yakutia, northeastern Russia. The objective of this study was to assess the relationship between chironomid assemblage composition and the environment and to develop chironomid inference models for quantifying past regional climate and environmental changes in this poorly investigated area of northern Russia. The environmental data and sediment samples for chironomid analysis were collected in 5 consecutive years, 2003–2007, from several regions of Yakutia. The lakes spanned wide latitudinal and longitudinal ranges and were distributed through several environmental zones (arctic tundra, typical tundra, steppe-tundra, boreal coniferous forest), but all were situated within the zone of continuous permafrost. Mean July temperature (TJuly) varied from 3.4°C in the Laptev Sea region to 18.8°C in central Yakutia near Yakutsk. Water depth (WD) varied from 0.1 to 17.1 m. TJulyand WD were identified as the strongest predictor variables explaining the chironomid communitiy composition and distribution of the taxa in our data set. Quantitative transfer functions were developed using two unimodal regression calibration techniques: simple weighted averaging (WA) and weighted averaging partial least squares (WA-PLS). The two-component TJulyWA-PLS model had the best performance. It produced a strong coefficient of determination (r2boot= 0.87), root mean square error of prediction (RMSEP = 1.93), and max bias (max biasboot= 2.17). For WD, the one-component WA-PLS model had the best performance (r2boot= 0.62, RMSEP = 0.35, max biasboot= 0.47).