Multi-season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population

摘要

Critical information for evaluating the effectiveness of management strategies for species of concern include distinguishing seldom occupied (or low-quality) habitat from habitat that is frequently occupied and thus contributes substantially to population trends. Using multi-season models that account for imperfect detection and a long-term (1981-2002) dataset on migratory Arctic Peregrine Falcons Falco peregrinus tundrius nesting along the Colville River, Alaska, we quantified the effects of previous year's productivity (i.e. site quality), amount of prey habitat, topography, climate, competition and year on occupancy dynamics across two spatial scales (nest-sites, cliffs) during recovery of the population. Initial occupancy probability was positively correlated with area of surrounding prey habitat and height of nest-sites above the Colville River. Colonization probability was positively correlated with nest height and negatively correlated with date of snowmelt. Local extinction probability was negatively correlated with productivity, area of prey habitat and nest height. Colonization and local extinction probabilities were also positively and negatively correlated, respectively, with year. Our results suggest that nest-sites (or cliffs) along the Colville River do not need equal protection measures. Nest-sites and cliffs with historically higher productivity were occupied most frequently and had lower probability of local extinction. These sites were on cliffs high above the river drainage, surrounded by adequate prey habitat and with southerly aspects associated with early snowmelt and warmer microclimates in spring. Protecting these sites is likely to encourage continued occupancy by Arctic Peregrine Falcons along the Colville River and other similar areas. Our findings also illustrate the importance of evaluating fitness parameters along with climate and habitat features when analysing occupancy dynamics, particularly with a long-term dataset spanning a range of annual climate variation.