Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning.

中文翻译：

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栖息地划分改善了多生境物种的区域分布模型：以欧洲越桔为例

对多生境物种的空间分布进行建模具有挑战性，因为它们显示了多维环境响应，这些响应可能因生境而异。因此，对于这些物种，在保护规划中有用的现实模型的实现可能取决于模型校准中栖息地信息的适当考虑。我们旨在评估不同类型的栖息地预测器的作用，以及对栖息地进行分区，以改善模型推论，检测跨越栖息地的非平稳响应并模拟抽样偏差对空间预测的影响。作为案例研究，我们模拟了多生境植物越桔（越桔越桔）的发生）（位于西班牙西北部的坎塔布连山脉），它代表着濒临灭绝的棕熊和喀麦隆的基本营养资源。我们使用MaxEnt将使用地形气候变量校准的基线模型方法与使用基于植被图和遥感数据的显式栖息地信息的三种替代方法进行了比较。对于每种方法，我们都运行非分区（所有生境一起）和生境分区模型（每个生境一个），并评估模型性能，过度拟合和外推。对于包括栖息地预测器在内的栖息地分区模型，性能最高。最低的过度拟合是针对基线非分区模型的，其代价是获得了最高的预测分数面积。栖息地分区模型的外推成功率很低，具有基准方法最高的性能。我们的结果突出表明，多生境物种对跨生境的反应不稳定，而生境偏向的数据导致较弱的空间预测。在对区域尺度上的多生境物种分布进行建模时，我们建议使用生境分区模型，包括生境预测因子，植被图或遥感数据，以改善空间输出的真实性及其在区域保护规划中的适用性。