USGS ScienceSearch

USGS · 70197436

The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles

Abstract

Wind energy development is rapidly expanding in North America, often accompanied by requirements to survey potential facility locations for existing wildlife. Within the USA, golden eagles ( Aquila chrysaetos ) are among the most high-profile species of birds that are at risk from wind turbines. To m inimize golden eagle fatalities in areas proposed for wind development, modified point count surveys are usually conducted to estimate use by these birds. However, it is not always clear what drives variation in the relationship between on-site point count data and actual use by eagles of a wind energy project footprint. We used existing GPS-GSM telemetry data, collected at 15 min intervals from 13 golden eagles in 2012 and 2013, to explore the relationship between point count data and eagle use of an entire project footprint. To do this, we overlaid the telemetry data on hypothetical project footprints and simulated a variety of point count sampling strategies for those footprints. We compared the time an eagle was found in the sample plots with the time it was found in the project footprint using a metric we called “error due to sampling”. Error due to sampling for individual eagles appeared to be influenced by interactions between the size of the project footprint (20, 40, 90 or 180 km 2 ) and the sampling type (random, systematic or stratified) and was greatest on 90 km 2 plots. However, use of random sampling resulted in lowest error due to sampling within intermediate sized plots. In addition sampling intensity and sampling frequency both influenced the effectiveness of point count sampling. Although our work focuses on individual eagles (not the eagle populations typically surveyed in the field), our analysis shows both the utility of simulations to identify specific influences on error and also potential improvements to sampling that consider the context-specific manner that point counts are laid out on the landscape.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 33.4333° to 36.1333° latitude; -118.5667° to -115.3833° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maitreyi Sur, James R. Belthoff, Emily R. Bjerre, Brian A. Millsap, Todd E. Katzner. 2018. The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles. https://doi.org/10.1016/j.ecolind.2018.01.024

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

A tool to monitor hydrologic conditions on tree islands in the Everglades

Tree islands are patchy upland forested habitats in Florida's Everglades that face degradation and disappearance due to altered hydrologic patterns. The U.S. Geological Survey coordinated with the Miccosukee Tribe of Indians of Florida and the Seminole Tribe of Florida to co-develop a decision-support tool based on tree-island hydrologic conditions. Everglades managers can use this tool to help with restoration planning and water operations decisions that affect tree-island conditions. After a series of organized workshops and meetings, a list of hydrologic metrics was selected as indicators of tree-island health, including hydroperiod, number of days since last dry, and maximum water depth at the head of the island. As a result, a web application tool, called ETree, has been developed and is publicly available online. This web application provides data on daily metrics for the current Everglades water year and annual summaries for past years, beginning in 2000.

Florida

Vulnerability of different Colorado Plateau land types to drivers of change

Public lands are challenged by a range of pressures—changing climate, increasing visitation, resource extraction—and their effects can span spatial scales, often crossing land management jurisdictional boundaries. Research approaches which explicitly span jurisdictions can support strategies to contend with regional pressures. We assess management-relevant drivers of change—aridification, livestock grazing, invasive species, surface disturbance, and fire—across a patchwork of land management units and agencies on the Colorado Plateau, focusing on southeastern Utah, USA. We use vulnerability analysis, first evaluating exposure to drivers across the landscape, then quantifying sensitivity to each driver across different land types, defined by mapped Ecological Site Groups, a system for classifying landscapes according to physical factors including climate, soils, and topographic setting. We address the questions: 1) how are drivers spatially distributed across the study region; and, 2) based on exposure and sensitivity, are certain land types more vulnerable to these drivers? We find that the study region has high exposure and sensitivity—and thus high vulnerability—to aridification and grazing, but low exposure and vulnerability to other drivers. Although more sensitive land types were not generally more exposed, identifying which areas are most sensitive can guide adaptive measures, like where new uses or disturbances would be least harmful and which areas could be prioritized for restoration. The method we demonstrate is a flexible tool for assessing landscape-scale impacts, is built on nationally available datasets, and can be tailored to different datasets and sensitivity metrics.

Utah

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico