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首页> 外文期刊>Archiv fur Hydrobiologie >Development of a prediction system for lake stony-bottom littoral macro invertebrate communities
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Development of a prediction system for lake stony-bottom littoral macro invertebrate communities

机译：湖石质底部沿海无脊椎动物群落预测系统的开发

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Assessing the biological integrity of aquatic habitats often requires a comparison of observed conditions with those conditions expected to occur in the absence of human-generated stress. Using data from least impaired lakes, models were developed to predict the macroinvertebrate community composition of stony littoral habitats of relatively small (median surface area = 0.26 km(2)) boreal lakes. In brief, three steps were used in model calibration: (i) identification of biological groups by clustering, (ii) selection of environmental variables that explain among-group variance using ordination, and (iii) model calibration using discriminant function analysis. RIVPACS-type predictive models were individually developed for the three major regions of Sweden; namely, the mixed forest, coniferous forest and arctic/alpine regions. Geographic position (latitude, longitude, altitude) and variables indicative of substratum (e.g. cobble and vegetation type) and water color were often found to discriminate among-group variance and were used in model calibrations. The lowest errors associated with observed to expected (O/E) scores for taxon richness were found for models developed for the mixed forest region (mean O/E score = 0.997 +/- 0.254, CV = 25.5%), and the highest errors were associated with models developed for the arctic/alpine region ((mean O/E score = 1.011 +/- 0.337, CV = 33.3%). The use of RIVPACS-type models in ecological assessment is increasing, principally because the site-specific predictions that are produced are intuitive and rather straightforward. This study shows that the RIVPACS approach may be used for assessing and monitoring the ecological quality of boreal lake ecosystems using littoral macroinvertebrate communities. [References: 46]
机译：评估水生生境的生物完整性通常需要将观察到的条件与在没有人为压力的情况下预期发生的条件进行比较。使用来自受损最少的湖泊的数据，开发了模型来预测相对较小（中位数表面积= 0.26 km（2））的北方湖泊石质沿海栖息地的大型无脊椎动物群落组成。简而言之，在模型校准中使用了三个步骤：（i）通过聚类识别生物组;（ii）选择使用排序来解释组间差异的环境变量;以及（iii）使用判别函数分析的模型校准。 RIVPACS类型的预测模型是针对瑞典的三个主要地区分别开发的。即混交林，针叶林和北极/高山地区。人们经常发现地理位置（纬度，经度，海拔）和指示底层的变量（例如鹅卵石和植被类型）和水色可以区分组间差异，并用于模型校准。在针对混合森林地区开发的模型中，发现与分类单元丰富度的观测到的期望值（O / E）相关的最低误差（平均O / E分数= 0.997 +/- 0.254，CV = 25.5％），并且最高误差与北极/高山地区开发的模型相关（（平均O / E分数= 1.011 +/- 0.337，CV = 33.3％）。RIVPACS型模型在生态评估中的使用正在增加，这主要是因为特定地点所产生的预测是直观而直接的。这项研究表明，RIVPACS方法可用于利用沿岸大型无脊椎动物群落评估和监测北方湖泊生态系统的生态质量[参考文献：46]

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