High spatial resolution bioclimatic variables to support ecological modelling in a Mediterranean biodiversity hotspot

Understanding the effects of climate on biodiversity and its different levels of response to climatic variation is important for addressing conservation-based questions: the use of bioclimatic variables and species modelling tools is common in environmental, agricultural and biological sciences. Unfortunately, most of the ecological local studies are limited to the use of global data with coarse spatial resolutions, while fine-grain climate data are necessary to capture environmental variability and perform reliable modelling. We propose a high-resolution dataset (40 m grid) of the suite of original coarse-grain bioclimatic variables proposed by WorldClim 2 for the island of Sardinia (Italy); variations amongst our dataset and WorldClim 2 were calculated and mapped to show the spatial distribution of differences between all pairs of variables. We observed relevant differences for the bioclimatic variables related to rainfall (mean RMSE = 39.79; mean nRMSE = 0.21) compared to the temperature ones (mean RMSE = 4.81; mean nRMSE = 0.11). Moreover, discrepancies are not evenly distributed in the territory: the greater differences correspond to the areas characterized by complex orographic systems. Results recommend caution in making ecological assessments based on bioclimatic variables derived from global data with coarse spatial resolutions in physiographically complex landscapes, especially in the Mediterranean regions, characterized by seasonal climatic variations and high levels of biodiversity and biogeographical complexity. These new data will support a new generation of research studies in a broad array of ecological applications at a much finer scale than previously possible.

Automated summary

Understanding the effects of climate on biodiversity and its responses to climatic variation is crucial for conservation; using fine-grain climate data is necessary for reliable modeling in physiographically complex landscapes, with caution recommended when using global data-derived bioclimatic variables in such regions.