Data from: On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum Entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: (1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or (2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs. instream models. First, equivalence tests assessed average pairwise differences (site-specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener’s <i>D</i> and Warren’s <i>I</i> niche overlap statistics quantified range-wide similarity in predicted habitat suitability values from climate vs. instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3×10^-4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). <i>D</i> and <i>I</i> statistics reflected a high margin of overlap among climate and instream models (median <i>D</i> = 0.78, median <i>I</i> = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.河流生态系统中的物种分布模型（Species Distribution Models, SDMs）可通过将气温与降水作为河道内条件的替代指标，或是借助独立的水温水文模型将气候与河道内栖息地相联系，从而纳入气候信息。该思路虽更具优势，但受限于开发水温水文模型所需的实施工作量与成本。为此，我们评估了以气候数据作为协变量，与以河道内水温和水文数据作为协变量时，区域尺度淡水物种分布模型的预测结果是否存在本质差异。 本研究针对15种淡水鱼类构建最大熵（Maximum Entropy, MaxEnt）物种分布模型，协变量集分为两类：（1）气温、降水（气候变量）与物理栖息地变量的组合；（2）水温、水文（河道内变量）与物理栖息地变量的组合。随后采用三种方法对比两类模型的预测结果： 第一，等效性检验：评估气候与河道内模型之间的平均成对差异，即针对各物种分布范围内的所有位点开展位点特异性比较；第二，一致性检验：测定气候模型与河道内模型将相同河道片段判定为高栖息地适宜度的频率；第三，舍克纳D（Schoener’s D）与沃伦I（Warren’s I）生态位重叠统计量：量化两类模型在整个物种分布范围内预测的栖息地适宜度值的相似性。 等效性检验结果显示，两类模型的栖息地适宜度得分仅存在微小的成对差异：所有物种的最大熵原始得分平均成对差异均小于3×10^-4。一致性检验表明，气候模型与河道内模型在相同河道片段上预测高栖息地适宜度的倾向极强，中位一致性达68%。舍克纳D与沃伦I统计量则反映出两类模型的重叠度较高，其中位D值为0.78，中位I值为0.96。 整体而言，我们的研究结果并不支持“在哥伦比亚盆地尺度下，以气候协变量与河道内协变量构建的鱼类物种分布模型预测结果存在本质差异”这一假说。