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Topographic wetness index predicts the occurrence of bird species in floodplains


Correspondence: J. Secondi, LUNAM University, University of Angers, GECCO (Group Ecology and Conservation of Vertebrates), 49045 Angers, France.




Selecting informative variables is crucial for species distribution modelling and ecological studies in general. Proxies quantifying water accumulation may have suitable properties because hydromorphy partly determines plant and animal communities. Topographic wetness index (TWI) was developed to locate wetlands but has largely been ignored from ecological studies despite the value of these areas for biodiversity and the ecosystem services they provide. We assessed here the ability of TWI to predict the occurrence of grassland passerines and tested different settings to determine which was the best predictor for our dataset.


Floodplain meadows in the Loire valley, France, Western Europe.


We recorded the occurrence of four grassland passerines on 64 transects in large hay meadow patches. We computed four TWIs based on digital elevation models (DEMs). TWIs compute water accumulation as a function of slope and catchment. We tested two DEM resolutions (50 m and 250 m) and four TWI algorithms to identify which combination yielded the best model fits to our dataset.


Results depended on the predictor settings and the species considered. TWI predicted the occurrence of the Whinchat, the most specialized species, and the combined occurrence of the others three passerines. One TWI algorithm (SWI) yielded the poorest fit, and we could not determine the best algorithm among the others three. The coarser DEM resolution (250 m pixel size) also yielded better fitting model than the finer resolution (50 m).

Main conclusions

Topographic wetness index appears as an informative predictor of species occurrence, at least for the Whinchat, and a useful proxy to detect suitable areas for floodplain grassland birds. This family of indices may improve our ability to model the habitat of wetlands species. However, DEM resolution and algorithm should be selected with caution as they may impact the predictive potential of the proxy.