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Natal dispersal, the movement between the birth and the first breeding site, has been rarely studied in long-lived territorial birds with a long-lasting pre-breeding stage. Here we benefited from the long-term monitoring programs of six populations of Egyptian vultures (Neophron percnopterus) from Spain and France to study how the rearing environment determines dispersal. For 124 vultures, we recorded a median dispersal distance of 48 km (range 0-656 km). Linear models were used to assess the effect of population and individual traits on dispersal distance at two spatial scales. Dispersal distances were inversely related to vulture density in the natal population, suggesting that birds perceive the abundance of conspecifics as a signal of habitat quality. This was particularly true for declining populations, so increasing levels of opportunistic philopatry seemed to arise in high density contexts as a consequence of vacancies created by human-induced adult mortality. Females dispersed further than males, but males were more sensitive to the social environment, indicating different dispersal tactics. Both sexes were affected by different individual attributes simultaneously and interactively with this social context. These results highlight that complex phenotype-by-environment interactions should be considered for advancing our understanding of dispersal dynamics in long-lived organisms.

We propose an image processing method for ordering anterior chamber optical coherence tomography (OCT) images in a fully unsupervised manner. The method consists of three steps: Firstly we preprocess the images (filtering the noise, aligning and normalizing the resolution); secondly, a distance measure between images is computed for every pair of images; thirdly we apply a machine learning algorithm that exploits the distance measure to order the images in a two-dimensional plane. The method is applied to a large (~1000) database of anterior chamber OCT images of healthy subjects and patients with angle-closure and the resulting unsupervised ordering and classification is validated by two ophthalmologists.