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Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics

机译：利用多尺度景观数据模拟淡水鱼类分布：以六个窄范围地方病为例

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Species distribution models (SDMs) have become integral tools in scientific research and conservation planning. Despite progress in the assessment of various statistical models for use in SDMs, little has been done in way of evaluating appropriate ecological models. In this paper, we evaluate the multiscale filter framework as a suitable theoretical model for predicting freshwater fish distributions in the upper Green River system (Ohio River drainage), USA. The spatial distributions of six fishes with contrasting biogeographies were modeled using boosted regression trees and multiscale landscape data. Species biogeography did not appear to affect predictive performance and all models performed well statistically with receiver operating characteristic area under the curve (AUC) ranging from 0.87 to 0.98. Predictive maps show accurate estimations of ranges for five of six species based on historical collections. The relative influence of each type of environmental feature and spatial scale varied markedly with between species. A hierarchical effect was detected for narrowly distributed species. These species were highly influenced by soil composition at larger spatial scales and land use/land cover (LULC) patterns at more proximal scales. Conversely, WLC pattern was the most influential feature for widely distributed at all spatial scales. Using multiscale data capable of capturing hierarchical landscape influences allowed production of accurate predictive models and provided further insight into factors controlling freshwater fish distributions.
机译：物种分布模型（SDM）已成为科学研究和保护规划中不可或缺的工具。尽管在SDM中使用的各种统计模型的评估方面取得了进展，但是在评估适当的生态模型方面却做得很少。在本文中，我们将多尺度过滤器框架评估为预测美国上游绿河系统（俄亥俄州河流排水）中淡水鱼分布的合适理论模型。使用增强的回归树和多尺度景观数据对具有不同生物地理学的六种鱼类的空间分布进行了建模。物种生物地理学似乎并未影响预测性能，并且所有模型的统计性能均良好，曲线下的接收器工作特征区域（AUC）为0.87至0.98。预测性地图显示了基于历史收藏的六个物种中五个物种的准确范围估计。每种环境特征和空间尺度的相对影响随着物种之间的变化而显着变化。对于狭窄分布的物种，检测到等级效应。这些物种在较大的空间尺度上受到土壤成分的强烈影响，在更近的尺度上受到土地利用/土地覆盖（LULC）模式的强烈影响。相反，WLC模式是在所有空间尺度上广泛分布的最具影响力的特征。使用能够捕获分层景观影响的多尺度数据可以产生准确的预测模型，并进一步了解控制淡水鱼分布的因素。

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