Developing and Validating Species Distribution Models for Wetland Plants Across Europe

TL;DR

This study creates and validates models to predict the distribution of wetland plant species across Europe, showing they can effectively forecast species occurrences based on climate and other environmental factors.

Contribution

The study introduces and validates species distribution models for 265 wetland plant species, including mosses, with a focus on broad-scale predictions and incorporating hydrological variables.

Findings

SDMs for diagnostic mosses showed high predictive performance with median AUC of 0.93 and TSS of 0.73.

Climate variables, especially the mean temperature of the coldest month, were the strongest predictors of species occurrence.

SDMs for non-diagnostic plants had lower performance compared to diagnostic species.

Abstract

Drainage, agricultural conversion, and climate change threaten wetlands and their unique biodiversity. Species distribution models (SDMs) can help to identify effective conservation measures. However, existing SDMs for wetland plants are often geographically limited, miss variables representing hydrological conditions, and neglect moss species, essential to many wetlands. Here, we developed and validated SDMs for 265 vascular plant and moss species characteristic of European wetlands, using environmental variables representing climate, soil, hydrology, and anthropogenic pressures. We validated the spatial predictions of the SDMs through cross‐validation and against independent data from the Global Biodiversity Information Facility (GBIF). Further, we validated the niche optima of the species, as obtained from the modelled species response curves, with empirical niche optima. The spatial validation revealed good predictive power of the SDMs, especially for diagnostic mosses, for which we obtained median cross‐validated values of the area under the curve (AUC) and true skill statistic (TSS) of 0.93 and 0.73, respectively, and a median true positive rate (TPR) based on GBIF records of 0.77. SDMs of diagnostic vascular plants performed well, too, with median AUC, TSS, and TPR of 0.91, 0.69, and 0.67, respectively. SDMs of non‐diagnostic plants had the lowest performance, with median AUC, TSS, and TPR values of 0.84, 0.53, and 0.62, respectively. Correlations between modelled and empirical niche optima were typically in the expected direction. Climate variables, particularly the mean temperature of the coldest month, were the strongest predictors of species occurrence. At the same time, groundwater table depth was a significant predictor for diagnostic vascular plants but not for mosses. We concluded that our SDMs are suitable for predicting broad‐scale patterns of wetland plant species distributions as governed by climatic conditions. Alternative or additional variables or a different modelling approach might be needed to represent better the local heterogeneity in the hydrological conditions of wetlands.

We fitted species distribution models (SDMs) for 265 wetland plant species in Europe. We validated predicted occurrences using independent data. The SDMs showed a high predictive performance, such that our SDMs can be used to predict broad‐scale distributions of wetland plants.

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