Assessing habitat loss and fragmentation and their effects on population viability of forest specialist birds: linking biogeographical and population approaches

Abstract

Aim: Biogeographic approaches usually have been developed apart from population ecology, resulting in predictive models without key parameters needed to account for reproductive and behavioural limitations on dispersal. Our aim was to incorporate fully spatially explicit population traits into a classic species distribution model (SDM) using Geographic Information Systems (GIS), aiming at conservation purposes.

Location: Southern South America.

Methods: Our analysis incorporates the effects of habitat loss and fragmentation on population viability and therefore provides insights into how much spatially explicit population traits can improve the SDM prediction of habitable habitat. We utilized a well-studied focal endemic bird of South American temperate rainforests (Scelorchilus rubecula). First, at a large scale, we assessed the historical extent habitat based on climate envelopes in an SDM. Second, we used a land cover change analysis at a regional scale to account for recent habitat loss and fragmentation. Third, we used empirically derived criteria to predict population responses to fragmented forest landscapes to identify actual losses of habitat and population. Then we selected three sites of high conservation value in southern Chile and applied our population model. Finally, we discuss the degree to which spatially explicit population traits can improve the SDM output without intervening in the modelling process itself.

Results: We found a historical habitat loss of 39.12% and an additional forest cover loss of 3.03% during 2000-2014; the latter occurred with a high degree of fragmentation, reducing the overall estimation of (1) carrying capacity by -82.4%, -33.1% and -45.1% and (2) estimated number of pairs on viable populations by -84.1%, -33.0% and -54.6% on the three selected sites.

Main conclusion: We conclude that our approach sharpened the SDM prediction on environmental suitability by 54.4%, adjusting the habitable area by adding population parameters through GIS, and allowing to incorporate other phenomena as fragmentation and habitat loss.