Debora Nigro | Geology Reports

USGS2018

Incorporating productivity as a measure of fitness into models of breeding area quality of Arctic peregrine falcons

Using empirical location data from individuals to model habitat quality and species distributions is valuable towards understanding habitat use of wildlife, especially for conservation and management planning. Incorporating measures of reproductive success or survival into these models helps address the role of vital rates (a surrogate of fitness) in affecting a species’ distribution. We used 24-year datasets of Arctic peregrine falcon ( Falco peregrinus tundrius ) nest-site locations and productivity from the Colville River Special Area, Alaska, USA to model suitability of breeding habitat and the relative quality of used and potential nest sites. We used zero-inflated negative binomial regression models and covariates describing nest-site productivity, area of surrounding prey habitat, geology, topography, and land-cover type to model and predict intensity of Arctic peregrine falcon nest-site use along the Colville River, and developed a predictive map of intensity of nest-site use. Regions of higher predicted intensity of use were characterized by steeper slopes, greater area of prey habitat, and higher average productivity, which are likely attributed to minimizing predation risk, gaining advantages for hunting, having sufficient prey resources, site quality, and overall fitness. Including productivity in intensity of nest-site use models improved the models, supporting our supposition that adding a fitness parameter enhanced the predictive capability of the species distribution model. Areas predicted to have higher intensity of use by our model can be used to focus efforts of continued protection of areas with frequently occupied and productive nest sites, and conversely, identify areas where protection of nest sites is likely to have few conservation benefits.

Alaska↗

USGS2015

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

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.

Alaska↗