Integrating individual habitat choices and regional distribution of a biodiversity indicator and top predator

Aim Habitat selection studies have mainly focused on behavioural choices of individuals or on the habitat‐related regional distribution of a population, with little integration of the two approaches. This is despite the fact that traditional biogeography theory sees the geographical distribution of a species as the collective outcome of the adaptive habitat choices of individuals. Here, we integrate individual habitat choices with regional distribution through a bottom‐up Geographical Information System (GIS)‐based approach, by using a 9‐year data set on a large avian predator, the eagle owl (Bubo bubo L.). We further examine the potential population level and biodiversity consequences of this approach. Location The study was conducted in the Trento Region (central‐eastern Italian Alps) and in six other areas of the nearby Lombardia Region in the central Alps. Methods We used stepwise logistic regression to build a habitat suitability model discriminating between eagle owl territories and an equal number of random locations. The model was applied to the whole Trento region by means of a GIS so as to predict suitable habitat patches. The predicted regional distribution (presence–absence in 10‐km grid quadrats) was then compared with the observed one. Furthermore, we compared estimates of biodiversity in quadrats with and without eagle owls, so as to test whether the presence of this top predator may signal macro‐areas of high biodiversity. Results The logistic habitat suitability model showed that, compared with a random distribution, eagle owls selected low‐elevation breeding sites with high availability of prey‐rich habitats in their surroundings. Breeding performance increased with the availability of prey‐rich habitats, confirming the adaptiveness of the detected habitat choices. We applied the habitat suitability model to the 6200 km² study region by means of a GIS and found a close fit between the observed and predicted regional distribution. Furthermore, population abundance was positively related to the availability of habitat defined as suitable by the above analyses. Finally, high biodiversity levels were associated with owl presence and with the amount of suitable owl habitat, demonstrating that modelling habitat suitability of a properly chosen indicator species may provide key conservation information at the wider ecosystem level. Main conclusions Our bottom‐up modelling approach may increase the conservation‐value of habitat selection models, by (1) predicting local and regional distribution, (2) estimating regional population size, (3) stimulating further hypothesis testing, (4) forecasting the population effects of future habitat loss and degradation and (5) aiding in the identification and prioritization of high‐biodiversity areas.