Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data. Issue 5 (9th May 2022)

• Record Type: Journal Article
• Authors: Graves, Tabitha A.; Yarnall, Michael J.; Johnston, Aaron N.; Preston, Todd M.; Chong, Geneva W.; Cole, Eric K.; Janousek, William M.; Cross, Paul C.
• Is Part Of: Ecological applications. Volume 32:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-09
• Subjects: adaptive management -- animal aggregation -- cervids -- chronic wasting disease -- contact rates -- disease ecology -- drone -- elk -- harvest -- imagery -- supplemental feeding -- winter range; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2600
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost‐effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non‐feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite‐based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground‐based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.