Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

• Published in: International journal of applied earth observation and geoinformation Vol. 55; pp. 1 - 8
• Main Authors: Gastón, Aitor, Ciudad, Carlos, Mateo-Sánchez, María C., García-Viñas, Juan I., López-Leiva, César, Fernández-Landa, Alfredo, Marchamalo, Miguel, Cuevas, Jorge, de la Fuente, Begoña, Fortin, Marie-Josée, Saura, Santiago
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2017
• Subjects: Brown bear; Habitat selection; High-resolution data; LiDAR; Multi-scale habitat modelling; Remote sensing; High-resolution data; Brown bear; Habitat selection; Multi-scale habitat modelling; Remote sensing; LiDAR
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2016.10.007

•Habitat models for bears were developed using different spatial resolution data.•We compared vegetation data from CORINE, forest map at 1:50,000 scale and LiDAR.•Resolution affects spatial scale at which bear is estimated to perceive landscape.•Reliability of coarse-resolution models depends on indirect non-vegetation factors.•Model performance and credibility increases with resolution and is best with LiDAR. Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC>0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.