Project description:BackgroundDispersal comprises three broad stages - departure from the natal or breeding locations, subsequent travel, and settlement. These stages are difficult to measure, and vary considerably between sexes, age classes, individuals and geographically. We used tracking data from 24 golden eagles, fitted with long-lived GPS satellite transmitters as nestlings, which we followed during their first year. We estimated the timing of emigration from natal sites using ten previously published methods. We propose and evaluate two new methods. The first of these uses published ranging distances of parents as a measure of the natal home range, with the requirement that juveniles must exceed it for a minimum of 10 days (a literature-based measure of the maximum time that a juvenile can survive without food from its parents). The second method uses the biggest difference in the proportion of locations inside and outside of the natal home range smoothed over a 30 day period to assign the point of emigration. We used the latter as the standard against which we compared the ten published methods.ResultsThe start of golden eagle dispersal occurred from 39 until 250 days after fledging (based on method 12). Previously published methods provided very different estimates of the point of emigration with a general tendency for most to apparently assign it prematurely. By contrast the two methods we proposed provided very similar estimates for the point of emigration that under visual examination appeared to fit the definition of emigration much better.ConclusionsWe have used simple methods to decide when an individual has dispersed - they are rigorous and repeatable. Despite one method requiring much more information, both methods provided robust estimates for when individuals emigrated at the start of natal dispersal. Considerable individual variation in recorded behaviour appears to account for the difficulty capturing the point of emigration and these results demonstrate the potential pitfalls associated with species exhibiting complex dispersal behaviour. We anticipate that coupled with the rapidly increasing availability of tracking data, our new methods will, for at least some species, provide a far simpler and more biologically representative approach to determine the timing of emigration.

Project description:Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

Project description:In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles (Aquila chrysaetos) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66-0.74) for first-year birds to 0.90 (0.88-0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39-0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96-1.05). The PTL model estimated a median allowable take limit of ~2227 (708-4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.

Project description:Wildlife conservation strategies focused on one season or population segment may fail to adequately protect populations, especially when a species' habitat preferences vary among seasons, age-classes, geographic regions, or other factors. Conservation of golden eagles (Aquila chrysaetos) is an example of such a complex scenario, in which the distribution, habitat use, and migratory strategies of this species of conservation concern vary by age-class, reproductive status, region, and season. Nonetheless, research aimed at mapping priority use areas to inform management of golden eagles in western North America has typically focused on territory-holding adults during the breeding period, largely to the exclusion of other seasons and life-history groups. To support population-wide conservation planning across the full annual cycle for golden eagles, we developed a distribution model for individuals in a season not typically evaluated-winter-and in an area of the interior western U.S. that is a high priority for conservation of the species. We used a large GPS-telemetry dataset and library of environmental variables to develop a machine-learning model to predict spatial variation in the relative intensity of use by golden eagles during winter in Wyoming, USA, and surrounding ecoregions. Based on a rigorous series of evaluations including cross-validation, withheld and independent data, our winter-season model accurately predicted spatial variation in intensity of use by multiple age- and life-history groups of eagles not associated with nesting territories (i.e., all age classes of long-distance migrants, and resident non-adults and adult "floaters", and movements of adult territory holders and their offspring outside their breeding territories). Important predictors in the model were wind and uplift (40.2% contribution), vegetation and landcover (27.9%), topography (14%), climate and weather (9.4%), and ecoregion (8.7%). Predicted areas of high-use winter habitat had relatively low spatial overlap with nesting habitat, suggesting a conservation strategy targeting high-use areas for one season would capture as much as half and as little as one quarter of high-use areas for the other season. The majority of predicted high-use habitat (top 10% quantile) occurred on private lands (55%); lands managed by states and the Bureau of Land Management (BLM) had a lower amount (33%), but higher concentration of high-use habitat than expected for their area (1.5-1.6x). These results will enable those involved in conservation and management of golden eagles in our study region to incorporate spatial prioritization of wintering habitat into their existing regulatory processes, land-use planning tasks, and conservation actions.

Project description:BackgroundSarcoptic mange is a globally distributed parasitic disease caused by the burrowing mite Sarcoptes scabiei. This mite has a certain degree of host specificity, although interspecific transmission can occur among phylogenetically related species or through prey-predator mediated exposure. In 2018, a wild boar (Sus scrofa) with lesions compatible with sarcoptic mange was hunted in Ports de Tortosa i Beseit Natural Park (PTB, north-eastern Spain), where an active epizootic outbreak of sarcoptic mange is affecting Iberian ibexes (Capra pyrenaica) since 2014.MethodsA complete necropsy, skin scrapings and skin digestions with hydroxide potassium were performed to confirm the diagnosis. Routine histopathological analysis, toluidine blue staining and immunohistochemistry were used to characterize the lesions and the inflammatory infiltrate. Finally, 10 specific S. scabiei microsatellites were molecularly genotyped through polymerase chain reactions in mites obtained from the affected wild boar. For phylogenetic comparison, mites obtained from sympatric Iberian ibexes and allopatric wild boars and Iberian ibexes from southern Spain were analysed.ResultsSarcoptes scabiei was visually and molecularly identified in the infested wild boar from PTB, causing skin lesions with dermal inflammatory infiltrate rich in T and B cells, which indicate an adaptive immune response. Three S. scabiei genetic clusters were identified: one included mites from southern Iberian ibexes, another included mites from southern wild boars, and a third one distinctively grouped the wild boar from PTB with the sympatric ibexes.ConclusionsTo the authors' knowledge, this is the first reported case of sarcoptic mange in wild boar in Spain and the first documented case of S. scabiei cross-transmission from a wild ruminant host to a wild boar. The wild boar presented an ordinary scabies type reaction, which is typical of the self-limiting infestations reported in other cases of interspecific transmission.

Dataset Information

Knemidocoptic mange in wild golden eagles, California, USA.

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