PUBLICATION on data quality in habitat suitability modelling

Fine scale waterbody data improve prediction of waterbirds occurrence despite coarse species data

 Petra Šímová¹, Vítězslav Moudrý¹, Jan Komárek¹, Karel Hrach¹, Marie-Josée Fortin²

¹Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha – Suchdol, 165 00, Czech Republic

² Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks, Toronto (Ontario) M5S 3B2 Canada

 Corresponding author: Petra Šímová simova@fzp.czu.cz

Abstract

While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental of datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes of area 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10,000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global inland waterbody dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a sufficient resolution to be able to capture the smallest patches of the habitat suitable for given species presence for both fine and coarse grain habitat suitability and distribution modelling.