PDF(Pubmed)
Abstract：
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.
摘要：
专注于一个季节或人口部分的野生动物保护策略可能无法充分保护人口，特别是当一个物种的栖息地偏好随季节而变化时，年龄层,地理区域,或其他因素。保护金鹰（Aquilachrysaetos）就是这种复杂情况的一个例子，其中的分布，栖息地的使用，这种保护物种的迁徙策略因年龄组而异，生殖状态,区域,和季节。尽管如此，旨在绘制优先使用区域以告知北美西部金鹰管理的研究通常集中在繁殖期间的领土上，很大程度上排除了其他季节和生活史群体。为了支持金鹰在整个年度周期内的全人群保护规划，我们开发了一种分布模型,该模型适用于未进行典型评估的季节-冬季-以及美国西部内陆地区的个体,该地区是物种保护的高度优先事项.我们使用大型GPS遥测数据集和环境变量库来开发机器学习模型，以预测怀俄明州冬季金鹰的相对使用强度的空间变化。美国,和周围的生态区。基于一系列严格的评估，包括交叉验证，保留和独立数据，我们的冬季模型准确地预测了与筑巢领土无关的多个年龄和生活史群体的使用强度的空间变化(即所有年龄段的长途移民，居民非成人和成人“漂浮物”，以及成年领土持有人及其后代在其繁殖领土之外的移动）。模型中的重要预测因素是风和隆升(40.2%的贡献)，植被和土地覆盖(27.9%)，地形(14%)，气候和天气(9.4%),和生态区(8.7%)。高利用冬季栖息地的预测区域与筑巢栖息地的空间重叠相对较低，建议针对一个季节的高使用率地区的保护策略将在另一个季节捕获多达一半和四分之一的高使用率地区。大多数预测的高使用率栖息地（前10％分位数）发生在私人土地上（55％）；各州和土地管理局（BLM）管理的土地数量较低（33％），但高使用栖息地的浓度高于其区域的预期（1.5-1.6倍）。这些结果将使参与我们研究区域金鹰保护和管理的人员能够将越冬栖息地的空间优先考虑纳入其现有的监管过程中，土地利用规划任务，和保护行动。
