Modelling bird species richness with neural networks for forest

Assu Gil-Tena, Cristina Vega-García, Lluís Brotons, Santiago Saura


For preserving biodiversity of European-Mediterranean forest ecosystems in current and future scenarios of global change by means of sustainable forest management it is necessary to determine how environment and forest characteristics correlate with biodiversity. For this purpose, neural networks were used to model forest bird species richness as a function of environment and forest structure and composition at the 1x1 km scale in Catalonia (NE Spain). Univariate and multivariate models respectively allowed exploring individual variable response and obtaining a parsimonious (ecologically meaningful) and accurate neural network. Forest area (with a canopy cover above 5%), mean forest canopy cover, mean annual temperature and summer precipitation were the best predictors of forest bird species richness. The resultant multivariate network had a good generalization capacity that failed however in the locations with highest species richness. Additionally, those forests with different degrees of canopy closure that were more mature and presented a more diverse tree species composition were also associated with higher bird species richness. This allowed us to provide management guidelines for forest planning in order to promote avian diversity in this European-Mediterranean region.


global change, fires, forest biodiversity, forest landscape composition and structure, forest planning, landscape diversity

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DOI: 10.5424/fs/201019S-9290