Abstract
AbstractAimDistribution 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.”LocationNorway.MethodsWe used data from field‐based ecosystem‐type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data.ResultsMost ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models.Main conclusionsOur study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.
Highlights
Human impact transforms nature all over the world (Ellis, Goldewijk, Siebert, Lightman, & Ramankutty, 2010), and the need for sustainable management of ecosystems is increasing (Díaz et al, 2019)
Number of predictor variables included in each model direct predictors that represent multiple drivers, operating over long time spans, are difficult or impossible to represent by adequate proxies, the aggregated patterns that result from such processes (CLGs) may in some cases serve as better surrogates for these processes than, for example, the current climate so often used for species distribution modelling
Our study demonstrates that complex landscape gradient” (CLG) extracted by ordination of landscape data may be of predictive significance for single ecosystem types that were not subject to prior ordination
Summary
Human impact transforms nature all over the world (Ellis, Goldewijk, Siebert, Lightman, & Ramankutty, 2010), and the need for sustainable management of ecosystems is increasing (Díaz et al, 2019). Analyses of data from Norway indicate that response curves of landscape elements (including ecosystems) along CLGs bear resemblance to species response curves along local environmental complex gradients (see Figure 1; Erikstad, Halvorsen, & Simensen, 2019); most ecosystems appear to have distinct optima along CLGs, that is, intervals in which they reach maximum occurrence probability If this is the case, such landscape gradients may potentially be useful as predictors of ecosystem types in EDMs. The aim of this study was threefold: (a) to explore how well distributions of ecosystem types can be predicted; (b) to compare the predictive power of different sets of predictors in EDM; and (c) to test if EDM can be improved by using of composite “landscape predictors” (CLGs and landscape types) as predictors
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