Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference cond...栖息地适宜性与物种独特的迁移能力，是阐释与理解河流鱼类分布格局的核心关键。近年来，河流水文地貌学的全面数据已可在低至100米的空间尺度下完成测绘，有望为构建高分辨率的物种-栖息地模型（如鱼类相关模型）提供支撑。然而，学界对特定水文地貌与河道内栖息地变量的相对重要性，及其影响的空间尺度仍知之甚少。本研究采用提升回归树（Boosted Regression Trees）方法，基于河流地貌与河道内栖息地数据，为某沙质底质低地河流中的13种鱼类构建了物种-栖息地模型。首先，我们针对5个距离区间（采样点上下游各延伸4000米范围内）的预测变量计算平均值，以识别可最优预测鱼类物种出现概率的空间尺度。其次，我们对比了实测变量与自然参照条件相关的评估评分的适宜性……