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首页> 外文期刊>Ecological Applications >Predictive models attribute effects on fish assemblages to toxicity and habitat alteration
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Predictive models attribute effects on fish assemblages to toxicity and habitat alteration

机译：预测模型将对鱼类的影响归因于毒性和生境的改变

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Biological assessments should both estimate the condition of a biological resource (magnitude of alteration) and provide environmental managers with a diagnosis of the potential causes of impairment. Although methods of quantifying condition are well developed, identifying and proportionately attributing impairment to probable causes remain problematic. Furthermore, analyses of both condition and cause have often been difficult to communicate. We developed an approach that (1) links fish, habitat, and chemistry data collected from hundreds of sites in Ohio (USA) streams, (2) assesses the biological condition at each site, (3) attributes impairment to multiple probable causes, and (4) provides the results of the analyses in simple-to-interpret pie charts. The data set was managed using a geographic information system. Biological condition was assessed using a RIVPACS (river invertebrate prediction and classification system)-like predictive model. The model provided probabilities of capture for 117 fish species based on the geographic location of sites and local habitat descriptors. Impaired biological condition was defined as the proportion of those native species predicted to occur at a site that were observed. The potential toxic effects of exposure to mixtures of contaminants were estimated using species sensitivity distributions and mixture toxicity principles. Generalized linear regression models described species abundance as a function of habitat characteristics. Statistically linking biological condition, habitat characteristics including mixture risks, and species abundance allowed us to evaluate the losses of species with environmental conditions. Results were mapped as simple effect and probable-cause pie charts (EPC pie diagrams), with pie sizes corresponding to magnitude of local impairment, and slice sizes to the relative probable contributions of different stressors. The types of models we used have been successfully applied in ecology and ecotoxicology, but they have not previously been used in concert to quantify impairment and its likely causes. Although data limitations constrained our ability to examine complex interactions between stressors and species, the direct relationships we detected likely represent conservative estimates of stressor contributions to local impairment. Future refinements of the general approach and specific methods described here should yield even more promising results.
机译：生物评估不仅应估计生物资源的状况（变化幅度），还应向环境管理人员提供对潜在损害原因的诊断。尽管量化条件的方法已经很成熟，但是识别损害并按比例将其归因于可能的原因仍然存在问题。此外，对病情和病因的分析通常很难传达。我们开发了一种方法（1）链接从俄亥俄州（美国）河流中数百个站点收集的鱼类，栖息地和化学数据，（2）评估每个站点的生物状况，（3）将损伤归因于多种可能的原因，以及（4）以易于理解的饼图提供了分析结果。使用地理信息系统管理数据集。使用类似RIVPACS（河流无脊椎动物预测和分类系统）的预测模型评估生物学状况。该模型根据地点的地理位置和当地栖息地描述符提供了117种鱼类的捕获概率。受损的生物学条件定义为预计在观察到的某个位置发生的那些天然物种的比例。使用物种敏感性分布和混合物毒性原理估算了接触污染物混合物的潜在毒性作用。广义线性回归模型将物种丰度描述为栖息地特征的函数。从统计学角度将生物条件，栖息地特征（包括混合风险）和物种丰富度联系起来，使我们能够评估环境条件下物种的损失。将结果绘制为简单效果图和可能原因饼图（EPC饼图），饼图大小对应于局部损伤的幅度，而切片大小对应于不同压力源的相对可能贡献。我们使用的模型类型已成功应用于生态学和生态毒理学，但之前尚未用于量化损伤及其可能原因的模型。尽管数据限制限制了我们检查应激源和物种之间复杂相互作用的能力，但是我们检测到的直接关系可能代表了应激源对局部损害的贡献的保守估计。此处描述的通用方法和特定方法的将来改进将产生更可喜的结果。

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