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The article explores The southern limit of the Golden Eagle's (Aquila chrysaetos) breeding range in North America is Mexico, where the eagle is the national symbol yet designated as a threatened, high priority species for conservation action. Movement information needed for conserving Mexico's Golden Eagles is sparse; knowledge of dispersal from natal areas is essential to understand the eagle's ecology and help provide for its management.

The development and installation of renewable energy comes with environmental cost, including the death of wildlife. These costs occur locally, and seem small compared to the global loss of biodiversity. However, failure to acknowledge uncertainties around these costs affects local conservation, and may lead to the loss of populations or species. Working with these uncertainties can result in adaptive management plans designed to benefit renewable energy development and conservation. An example is the U.S. government's policy for managing bald (Haliaeetus leucocephalus) and golden (Aquila chrysaetos) eagle deaths at terrestrial wind facilities. Using records from 422 U.S. wind facilities we improved the precision of estimates of exposure (8.79 eagle minutes hr−1 km−3, SD: 13.64) and collision probability (0.0058 birds per minute of exposure, SD: 0.0038) currently used in U.S. policy. The new estimates for bald (exposure: 3.19 eagle minutes hr−1 km−3, SD: 2.583; collision probability: 0.007025 eagles per minute of exposure, SD: 0.004379) and golden (exposure: 1.21 eagle minutes hr−1 km−3, SD: 0.352; collision probability: 0.005648 birds per minute of exposure, SD: 0.004413) eagles had a smaller mean and standard deviation. Thus, their implementation within the government's adaptive management framework could help refine the balance between energy consumption and conservation. [ABSTRACT FROM AUTHOR]

The United States is rapidly expanding production of renewable energy to meet increased energy demands and reduce greenhouse gas emissions. Wind energy is at the forefront of this transition. A central challenge is understanding the nexus between wind energy development and its capacity for negative effects on wildlife causing population declines and habitat loss. Collaboration among conservationists and developers, early in the planning process, is crucial for minimizing wind-wildlife conflicts. Such collaborations require data showing where wind and wildlife impacts occur. To meet this challenge and inform decision-making, we provide natural resource agencies and stakeholders information regarding where future wind turbines may occur, and the potential affects on natural resource management, including the conservation of priority species and their habitats. We developed a machine learning model predicting suitability of wind turbine occurrence (hereafter, wind turbine suitability) across an eight-state region in the United States, representing some of the richest areas of wind potential. Our model incorporates predictor variables related to infrastructure, land ownership, meteorology, and topography. We additionally created a constraint layer indicating areas where wind would likely not be developed because of zoning, protected lands, and restricted federal agency proximity guidelines. We demonstrate how the predictive wind turbine suitability model informs conservation planning by incorporating animal movement models, relative abundance models coupled with spatial conservation planning software, and population density models for three exemplar, high priority species often affected by wind energy: whooping cranes (Grus americana), golden eagles (Aquila chrysaetos), and lesser prairie-chickens (Tympanuchus pallidicinctus). By merging the wind turbine and biological models, we identified conservation priority areas (i.e., places sharing high suitability for wind turbines and species use), and places where wind expansion could minimally affect these species. We use our "species-wind turbine occurrence relationships" to demonstrate applications, illustrating how forecasting areas of wind turbine suitability promotes wildlife conservation. These relationships inform wind energy siting to reduce negative ecological impacts while promoting environmental and economic viability. [ABSTRACT FROM AUTHOR]