Microhabitat selection models are frequently used in rivers to evaluate anthropogenic effects on aquatic organisms. Fish models are generally developed from few rivers, with debatable statistical treatments for coping with overdispersed abundance distributions. Analyses of data from multiple rivers are needed to test their transferability and increase their relevance for stakeholders. Using 3,528 microhabitats sampled in nine French rivers during 129 surveys, we developed models for 35 specific size classes of 22 fish species. We used mixed‐effects generalized linear models (accounting for multiple surveys), involving B‐spline transformations (accounting for nonlinear responses) and assuming a negative binomial distribution (accounting for abundance overdispersion). We compared models of increasing complexity: no selection (M1), an “average” selection similar in all surveys (M2), two models with different selection across surveys (M3–M4). Of 132 univariate cases (specific size classes by habitat), 63% indicated selection for depth, 71% for velocity, 45% for substratum size and 13% for substratum heterogeneity. A total of 50 models were retained, involving 26/35 specific size classes. Model fits indicated low explained deviance (R²MF < 0.19) and higher rank correlations (ρ < 0.69) between observed and modelled values. However, Bayesian posterior predictive checks validated these results since excellent fits would generate R²MF lower than 0.59 and ρ lower than 0.78. We found high transferability among rivers and dates, because (a) M2 was the most appropriate in 26/50 cases; (b) the R²MF and ρ values by M2 was, respectively, 72% and 75% of that explained by the complex M4 and (c) independent river cross‐validations showed good transferability. Bivariate models for selected specific size classes improved univariate model fits (ρ from 0.30 to 0.38). Overall, using a nonlinear mixed‐effect approach, our results confirmed the relevance of “average” models based on several rivers for developing helpful e‐flow tools. Finally, our modelling approach opens opportunities for integrating additional effects as the spatial distribution of competitors.

中文翻译：

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多个地点鱼类微生境选择的预测模型，说明了丰富的过度分散

河流中经常使用微生境选择模型来评估对水生生物的人为影响。鱼类模型通常是从​​很少的河流中开发出来的，并经过有争议的统计处理以应对过度分散的丰度分布。需要对来自多条河流的数据进行分析，以测试其可传递性并提高其与利益相关者的相关性。我们在129次调查中使用了在9条法国河流中采样的3,528个微生境，开发了针对22种鱼类的35种特定尺寸类别的模型。我们使用混合效应广义线性模型（考虑了多次调查），涉及B样条变换（考虑了非线性响应），并假设负二项式分布（考虑了丰度过度分散）。我们比较了日益复杂的模型：无选择（M1），所有调查（M2）中的“平均”选择相似，两个模型在调查中的选择不同（M3–M4）。在132个单变量病例（按栖息地的特定大小分类）中，63％表示选择深度，71％表示速度，45％表示基质大小，13％表示基质异质性。总共保留了50个模型，涉及26/35个特定尺寸的类别。模型拟合表明较低的解释偏差（R ² MF <0.19）和观测值与模型值之间的较高等级相关性（ρ<0.69）。但是，贝叶斯后验检查验证了这些结果，因为出色的拟合将产生小于0.59的R ² MF和小于0.78的ρ。我们发现河流和枣子之间的转移性很高，因为（a）M2最适合26/50的情况；（b）R ²M2的MF和ρ值分别是复杂M4和（c）独立河流交叉验证所解释的MF值和72％值，显示出良好的可转移性。选定特定尺寸类别的双变量模型改善了单变量模型拟合（ρ从0.30到0.38）。总体而言，使用非线性混合效应方法，我们的结果证实了基于多条河流的“平均”模型与开发有用的电子流工具的相关性。最后，我们的建模方法为整合其他效果（竞争对手的空间分布）带来了机遇。