The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation.

中文翻译：

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栖息地作为物种分布模型中的预测因子：我们应该使用连续数据还是二元数据？

一个区域内土地覆盖类型（即栖息地）的表示通常用作物种分布模型中的解释变量。然而，合适栖息地的简单二元存在/不存在可能是某些物种存在/不存在的最重要决定因素，因此，它比连续参数（面积）更好地预测物种出现。我们假设二元预测器更适合相对稀有的栖息地（例如湿地），而对于常见的栖息地（例如森林），焦点栖息地的数量是更好的预测器。我们使用捷克共和国第三个繁殖鸟类地图集作为物种分布数据的来源，并使用 CORINE 土地覆盖清单作为土地覆盖信息的来源。为了检验我们的假设，我们拟合了 32 种水鸟和 32 种森林鸟类的广义线性模型。我们的结果表明，对于水鸟物种，使用二元预测变量（栖息地的存在/不存在）的模型比使用连续预测变量（即栖息地的数量）的模型表现更好；然而，对于森林物种，我们观察到相反的情况。因此，未来使用栖息地作为物种发生预测因子的研究应考虑栖息地在景观中的普遍性，以及栖息地类型在特定物种生活史中的生物学作用。此外，在变量选择期间，在建模之前对栖息地预测器的二元和连续版本的性能（例如使用信息标准）进行初步比较可能是有益的。