Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red‐list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem‐level distribution modelling) produces results that are more directly relevant for management and decision‐making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner‐outer coast” and “land use intensity.”

中文翻译：

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复合景观预测因子​​改善了生态系统类型的分布模型

分布模型是获取有关生物多样性空间分布的知识的有用方法，例如，红名单评估。尽管分布建模方法主要应用于单个物种，但对社区和生态系统建模（EDM；生态系统级分布建模）所产生的结果与管理和决策更直接相关。尽管预测器的选择是建模过程的关键部分，但很少有研究比较不同组预测器对EDM的适用性。在这项研究中，我们比较了50个单一环境变量与11个复合景观梯度（CLG）的性能，以预测生态系统类型。CLG代表从多元分析得出的景观要素组成中的梯度，