Environmental favourability as a cost‐efficient tool to estimate carrying capacity

• Published in: Diversity & distributions 2015-12, Vol.21 (12), p.1388-1400
• Main Authors: Muñoz, Antonio‐Román, Jiménez‐Valverde, Alberto, Márquez, Ana Luz, Moleón, Marcos, Real, Raimundo, Robertson, Mark
• Format: Article
• Language: English
• Subjects: Aquila fasciata; Biodiversity; BIODIVERSITY RESEARCH; Biogeography; carrying capacity; Conservation; cost effectiveness; eagles; Endangered & extinct species; environmental factors; local density; prediction; probability; probability of occurrence; quantile regression; regression analysis; species distribution modelling; surveys; threatened species; topography; vegetation
• ISSN: 1366-9516; 1472-4642
• DOI: 10.1111/ddi.12352

AIM: The way in which environmental conditions determine the distribution and abundance of species is a crucial topic in ecology, biogeography and conservation. It is especially important to understand the nature of this relationship regarding threatened species. The ability to forecast local densities over the geographic range of species provides a way to link population ecology and biogeography. Particularly, our aim was to test whether predictions derived from species distribution modelling data provide useful information on spatial patterns of abundance and, consequently, may act as surrogates for local density estimates. LOCATION: Andalusia (southern Spain). METHODS: Logistic regression and the favourability function were applied as modelling tools to presence–absence data to compare the predicted results with current abundance. This approach is useful in the identification of local priorities for the target species whenever broad‐scale surveys need to be performed. RESULTS: The model included variables related to topography, vegetation and spatial location to explain the presence of Bonelli's eagle. A positive relationship was found between both probability and favourability and the density of this species, but a triangular fit only with favourability, suggesting that the physiological and ecological requirements of this species in the study area are better reflected in the favourability model. MAIN CONCLUSIONS: We suggest that favourability models derived from presence–absence data provide insights into abundance data and valuable information on carrying capacity over large scales. To minimize costs, maximize output and optimize results, priority could be given to detecting the presence of the species, instead of investing resources aimed at estimating abundance, which is more expensive.