A field test of R package <scp>GPSeqClus</scp>: For establishing animal location clusters

Cluff, H. Dean; Mech, L. David

doi:10.1002/2688-8319.12204

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…size of the resulting clusters as well as the subsequent inference made on the data collected (Cluff & Mech, 2023; Figure 2).…”

Section: Tab 2 Adjust Cluster Analysis Parametersmentioning

confidence: 95%

“…If (a) these buffers overlap (so at a maximum distance of x × 2), (b) there are y numbers of GPS locations within these overlapping buffers, and (c) the buffered GPS locations are within the study period, then they will generate a cluster. The specific research question and therefore the criteria for how a GPS cluster is developed have to be defined beforehand, as the chosen parameters can affect number and size of the resulting clusters as well as the subsequent inference made on the data collected (Cluff & Mech, 2023 ; Figure 2 ).…”

Section: Workflowmentioning

confidence: 99%

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“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data

Märtz,

Tallian,

Wikenros

et al. 2024

Ecology and Evolution

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The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. “GPS cluster studies” are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the “ClusterApp” Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the “ClusterApp” with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus).

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“…size of the resulting clusters as well as the subsequent inference made on the data collected (Cluff & Mech, 2023; Figure 2).…”

Section: Tab 2 Adjust Cluster Analysis Parametersmentioning

confidence: 95%

Section: Workflowmentioning

confidence: 99%

“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data

Märtz,

Tallian,

Wikenros

et al. 2024

Ecology and Evolution

View full text Add to dashboard Cite

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Wolf Predation on White‐tailed Deer Before, During, and After a Historically Mild Winter in Northern Minnesota

Gable,

Homkes,

Bump

2024

Ecology and Evolution

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In many southern boreal ecosystems of North America, wolves are the primary predators of white‐tailed deer, and white‐tailed deer are the primary prey of wolves. Furthermore, wolf–deer systems have and will continue to become more common as white‐tailed deer range continues expanding northward in North America. Despite this, there is little information on kill rates of wolves on deer (i.e., the number of deer killed per wolf per unit of time)—a fundamental metric of wolf predation on deer—and how kill rates vary with deer density, wolf density, and environmental conditions. We estimated kill rates of wolves on deer before, during, and after a historically mild winter in the Greater Voyageurs Ecosystem, Minnesota, USA. Kill rates of wolves on deer were low (0.009–0.018 deer/wolf/day) in fall, peaked in February (0.050 deer/wolf/day), and quickly declined to 0 deer/wolf/day by April. The kill rates of wolves on deer we observed in winter were some of the lowest kill rates of wolves on deer that have been documented. Wolves in the Greater Voyageurs Ecosystem appeared unable to catch and kill a sufficient number of deer to meet their daily energetic requirements during Winter 2023–2024, and thus most wolves likely lost weight during winter, a period when wolves are typically in peak physical condition. The rates of wolf predation we observed appeared to be well below those needed to decrease deer population density in the GVE. Thus, our work, in combination with numerous other studies, indicates winter conditions are the primary driver of deer population change in northern climates.

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Wolf density and predation patterns in the Canadian High Arctic

Anderson,

Cluff,

Mech

et al. 2024

J Wildl Manag

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The Arctic wolf (Canis lupus arctos) is a predator of muskoxen (Ovibos moschatus), Arctic hares (Lepus arcticus), and endangered Peary caribou (Rangifer tarandus pearyi) in the Canadian High Arctic. Although Arctic wolves potentially affect the dynamics of muskoxen and Peary caribou populations, knowledge about their abundance, distribution, and predation patterns is limited. Inuit and Inuvialuit communities value these species for sociocultural and subsistence reasons, and community members are concerned about how interactions among these species and their environment may change in a warming, unpredictable Arctic. We conducted a study from 2014–2018 of wolves in the rolling tundra of central Ellesmere Island (Fosheim Peninsula) and eastern Axel Heiberg Island. This area supported relatively high densities of muskoxen and Arctic hares, and previously supported Peary caribou, although caribou were mostly absent in the area during our study. We deployed global positioning system (GPS) radio‐collars on 10 adult wolves in 6 packs on Ellesmere and Axel Heiberg islands to describe wolf density and predation patterns. Wolves were neither nomadic nor migratory; they remained on territories year‐round, with summer densities of 2.5–8.0 adult wolves/1,000 km2 and 3.7–10.4 wolves/1,000 km2 including pups. Based on a ground search of 312 of 868 location clusters over a 340‐day period, wolves in a focal study pack killed approximately 0.12 muskoxen/day, equivalent to a predation rate of 5.5–17.0% of the estimated muskox population (older than 10 months old). This predation rate is likely sustainable given that calves and yearlings rather than reproductive adults comprised most documented kills, and that muskox populations can increase at rates up to 20%. The kill rate for this pack also implied a biomass intake deficit of 0.82–1.63 kg/wolf/day that could have been offset by each wolf consuming 115–228 Arctic hares annually. The decline of Peary caribou in the study area precluded any assessment of wolf predation influences on their population, but annual telemetry data confirming the year‐round presence of a wolf–ungulate–hare system with relatively high wolf densities suggests that apparent competition could present a challenge to Peary caribou recovery efforts.

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A field test of R package GPSeqClus: For establishing animal location clusters

Cited by 3 publications

References 14 publications

“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data

“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data

Wolf Predation on White‐tailed Deer Before, During, and After a Historically Mild Winter in Northern Minnesota

Wolf density and predation patterns in the Canadian High Arctic

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