Estimating species’ potential distribution is one of the main objectives of macroecology, especially when sampling biases can affect knowledge on how environmental variables affect species distribution. Ecological niche models estimate species’ environmental niches from different variables and their occurrences. Using the presence‐only data from eight Amazonian fish species, which inhabit rivers and streams, we aimed to (a) explore the effect of different sets variables on the spatial distributions of target species and (b) evaluate the predictive responses of MaxEnt to sets of variables with different degrees of complexity. MaxEnt has high flexibility in relation to the input data and its performance is influenced by a moderate number of adjustable parameters, allowing for high precision results when balancing underestimation and overestimation errors. We used environmental predictors in MaxEnt the principal components of climatic, topographic and edaphic variables as inputs. The combination of topographic and edaphic variables produced more precise and spatially restricted distribution ranges for all species when compared to those generated with climatic variables. All models reached high AUC values, especially for stream species. Modelled range sizes were broader for the river species, suggesting different tolerance thresholds and habitat preferences when compared to stream species. The complexity of the different variables sets did not affect MaxEnt's prediction capacity. However, for stream species, MaxEnt showed a greater predictive power. This work increases the knowledge with regards to the influence of different environmental predictors on the spatial patterns of the distribution of Amazonian fish.

中文翻译：

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不同变量集对亚马逊河流域鱼种潜在分布的影响

估计物种的潜在分布是宏观生态学的主要目标之一，尤其是当采样偏差会影响有关环境变量如何影响物种分布的知识时。生态位模型从不同的变量及其发生情况估计物种的环境位。我们使用来自居住在河流和溪流中的八个亚马逊鱼类的仅存在数据，我们旨在（a）探索不同集变量对目标物种空间分布的影响，以及（b）评估MaxEnt对集的预测响应具有不同复杂程度的变量。MaxEnt在输入数据方面具有高度的灵活性，并且其性能会受到适当数量的可调参数的影响，平衡低估和高估误差时可提供高精度结果。我们在MaxEnt中使用环境预测因子作为气候变量，地形变量和水文变量的主要成分。与使用气候变量生成的变量相比，地形变量和前瞻性变量的组合对所有物种产生了更精确且受空间限制的分布范围。所有模型都达到了较高的AUC值，特别是对于河流物种。河流物种的模型范围更广，表明与河流物种相比，不同的耐受阈值和栖息地偏好。不同变量集的复杂性不会影响MaxEnt的预测能力。但是，对于河流物种，MaxEnt表现出更大的预测能力。