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Development and evaluation of predictive models for measuring the biological integrity of streams

机译：开发和评估用于测量河流生物完整性的预测模型

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The ratio of the number of observed taxa to that expected to occur in the absence of human-caused stress (O/E) is an intuitive and ecologically meaningful measure of biological integrity. We examined how O/E ratios derived from stream invertebrate data varied among 234 unimpaired reference sites and 254 test sites potentially impaired by past logging. Data were collected from streams in three montane ecoregions in California. Two sets of River invertebrate Prediction and Classification System (RIVPACS) predictive models were built: one set of models was based on near-species taxonomic resolution; the other was based on family identifications. Two models were built For each level of taxonomic resolution: one calculated O and E based on all taxa with probabilities of capture (P-c) > 0; the other calculated O and E based on only those taxa with P-c greater than or equal to 0.5. Evaluations of the performance of each model were based on three criteria: (1) how well models predicted the taxa found at unimpaired sites, (2) the degree to which O/E values differed among unimpaired reference sites and potentially impaired test sites, and (3) the degree to which test site O/E values were correlated with independent measures of watershed alteration. Predictions of species models were more accurate than those of family models, and predictions of the P-c greater than or equal to 0.5 species model were more robust than predictions of the P-c greater than or equal to 0 model. O/E values derived from both species models were related to land use variables, but only assessments based on the P-c greater than or equal to 0.5 model were insensitive to naturally occurring differences among streams, ecoregions, and years. [References: 70]
机译：在没有人为压力（O / E）的情况下，观察到的分类单元数量与预期发生的分类单元之比是对生物完整性的直观且具有生态学意义的度量。我们研究了从无脊椎动物数据中获得的O / E比如何在234个未受损的参考位点和254个受过去记录可能受损的测试位点之间变化。数据来自加利福尼亚州三个山区生态区的溪流。建立了两套河流无脊椎动物预测和分类系统（RIVPACS）预测模型：一套基于近物种分类学分辨率的模型;另一套基于近乎物种分类学的模型。另一个是基于家庭身份。针对分类学分辨率的每个级别，构建了两个模型：一个是基于所有具有捕获概率（P-c）> 0的分类单元计算的O和E;另一个是基于捕获概率（P-c）> 0的O。另一个仅基于P-c大于或等于0.5的分类单元计算O和E。每个模型的性能评估均基于以下三个标准：（1）模型如何预测未受损部位的分类单元;（2）在未受损参考部位和潜在受损的测试部位之间O / E值的差异程度;以及（3）测试地点O / E值与流域变更的独立度量相关的程度。物种模型的预测比家族模型的预测更为准确，P-c大于或等于0.5物种模型的预测比P-c大于或等于0模型的预测更可靠。从这两种物种模型得出的O / E值都与土地利用变量有关，但是只有基于P-c大于或等于0.5模型的评估对溪流，生态区域和年份之间自然发生的差异不敏感。 [参考：70]

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