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Effective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes (Grus americana) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat. [ABSTRACT FROM AUTHOR]

Electricity generation from renewable‐energy sources has increased dramatically worldwide in recent decades. Risks associated with wind‐energy infrastructure are not well understood for endangered Whooping Cranes (Grus americana) or other vulnerable Crane populations. From 2010 to 2016, we monitored 57 Whooping Cranes with remote‐telemetry devices in the United States Great Plains to determine potential changes in migration distribution (i.e., avoidance) caused by presence of wind‐energy infrastructure. During our study, the number of wind towers tripled in the Whooping Crane migration corridor and quadrupled in the corridor's center. Median distance of Whooping Crane locations from nearest wind tower was 52.1 km, and 99% of locations were >4.3 km from wind towers. A habitat selection analysis revealed that Whooping Cranes used areas ≤5.0 km (95% confidence interval [CI] 4.8–5.4) from towers less than expected (i.e., zone of influence) and that Whooping Cranes were 20 times (95% CI 14–64) more likely to use areas outside compared to adjacent to towers. Eighty percent of Whooping Crane locations and 20% of wind towers were located in areas with the highest relative probability of Whooping Crane use based on our model, which comprised 20% of the study area. Whooping Cranes selected for these places, whereas developers constructed wind infrastructure at random relative to desirable Whooping Crane habitat. As of early 2020, 4.6% of the study area and 5.0% of the highest‐selected Whooping Crane habitat were within the collective zone of influence. The affected area equates to habitat loss ascribed to wind‐energy infrastructure; losses from other disturbances have not been quantified. Continued growth of the Whooping Crane population during this period of wind infrastructure construction suggests no immediate population‐level consequences. Chronic or lag effects of habitat loss are unknown but possible for long‐lived species. Preferentially constructing future wind infrastructure outside of the migration corridor or inside of the corridor at sites with low probability of Whooping Crane use would allow for continued wind‐energy development in the Great Plains with minimal additional risk to highly selected habitat that supports recovery of this endangered species. [ABSTRACT FROM AUTHOR]

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Copyright of African Journal of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)