Programa de Pós Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Brazil; Laboratório de Biogeografia e Ecologia Aquática (Bioecol), Universidade Estadual de Goiás, Anápolis, Goiás, Brazil.
Programa de Pós Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Brazil; Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Brazil.
Sci Total Environ. 2023 Jul 10;881:163417. doi: 10.1016/j.scitotenv.2023.163417. Epub 2023 Apr 11.
The development of multimetric indices (MMIs) to measure the biotic condition of aquatic habitats is based on metrics derived from biological assemblages. Considering fish assemblages, the inconsistencies in metrics responses outside of the places where they were developed limit MMI transferability and applicability to other locations, requiring local calibration. The factors behind the low transferability of these MMIs are still poorly understood. We investigated how environmental dissimilarity and spatial distance influence the transferability of metrics generated from local stream fish assemblages to other regions. We also tested whether functional and taxonomic metrics respond differently to the spatial distance. We used data from 239 fish assemblages from streams distributed across a Brazilian, the upper Parana basin and characterized each site according to the level of anthropogenic disturbance at the landscape scale using an Anthropogenic Pressure Index (API). We divided the upper Parana basin into sub-basins and used two of them to create template response models of the metrics in relation to the API. We used these response models to predict the responses outside the template sub-basins. Our response variable representing a metric of transferability was the absolute difference between metric's predicted and observed value for each site (prediction error). We thus modeled the prediction error in relation to the predictor variables that were i) the environmental dissimilarity between each site with the average of the sites from template sub-basins (climatic, topographic and soil type variables) and ii) the spatial distance (overland and watercourse distance) between each site and the center of the template sub-basin. We found that errors in metric predictions were associated with both environmental dissimilarity and spatial distance. Furthermore, functional and taxonomic metrics responded equally to spatial distance. These results indicate the need for local calibration of metrics when developing MMIs, especially if the protocols already available come from distant and environmentally dissimilar places.
多指标指数（MMI）的发展是基于生物组合衍生的指标来衡量水生栖息地的生物状况。考虑到鱼类组合，这些指标在其开发地点之外的反应不一致性限制了 MMI 的可转移性和适用性，需要进行本地校准。这些 MMIs 低可转移性的背后因素仍未得到很好的理解。我们调查了环境差异和空间距离如何影响从当地溪流鱼类组合生成的指标在其他地区的可转移性。我们还测试了功能和分类指标是否对空间距离有不同的反应。我们使用了分布在巴西、上巴拉那盆地的 239 个溪流鱼类组合的数据，并根据景观尺度上的人为干扰水平，使用人为压力指数（API）对每个地点进行了特征描述。我们将上巴拉那盆地划分为子流域，并使用其中的两个子流域来创建与 API 相关的指标响应模型的模板。我们使用这些响应模型来预测模板子流域之外的响应。我们的转移能力度量的响应变量是每个地点的度量预测值与观测值之间的绝对差异（预测误差）。因此，我们根据预测变量来模拟预测误差，这些预测变量是 i）每个地点与模板子流域中地点平均值之间的环境差异（气候、地形和土壤类型变量）和 ii）每个地点与模板子流域中心之间的空间距离（陆地和水道距离）。我们发现，度量预测误差与环境差异和空间距离都有关。此外，功能和分类指标对空间距离的反应相同。这些结果表明，在开发 MMIs 时，特别是如果已经可用的协议来自遥远且环境不同的地方，需要对指标进行本地校准。
