Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes

Patrice Descombes,Niklaus E Zimmermann,Damaris Zurell,Dirk N Karger,Lorenz Walthert,Andri Baltensweiler,Christian Ginzler,Reto Giulio Meuli

doi:10.1111/ecog.05117

Abstract

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge.Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert‐based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing meso‐climate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species.All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation.Combining large citizen science databases to expert‐based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.

Highlights

Predicting the potential distribution of plants has become an important approach in conservation and biodiversity assessments (Guisan and Thuiller 2005, Guisan et al 2013)
We found strong positive correlations and relationships between ecological indicator values (EIVs)-R and soil pH measured in the soil profiles in both independent soil databases (NABO: r = 0.768, n = 1103, R2 = 0.652; were moderately positively correlated (WSL): r = 0.819, n = 1156, R2 = 0.628; Fig. 2a, d)
We found a moderate positive correlation between EIV-F and the modelled drought index (WSL: r = 0.393, n = 1139, R2 = 0.307; Fig. 2c), and a moderate negative correlation between EIV-D and hydromorphy (WSL: r = −0.439, n = 1156, R2 = 0.239, Fig. 2f )

Summary

Introduction

Predicting the potential distribution of plants has become an important approach in conservation and biodiversity assessments (Guisan and Thuiller 2005, Guisan et al 2013). -called species distribution models (SDMs; Guisan and Zimmermann 2000, Guisan et al 2017), which relate species occurrences or abundances to spatially explicit ecological variables, allow predicting the occurrence probability of a species at a given location across the landscape. The quality of such predictions depends on the input predictors, which should ideally reflect physiological constraints of a species (Guisan and Thuiller 2005, Soberón 2007, Thuiller 2013). There is a need for more complete and direct predictors enabling to capture a larger spectrum of the species’ ecological niche conditions (Austin and Meyers 1996)

Methods

Results

Discussion

Conclusion