Predict mule deer migration routes without GPS collars

The researchers used GPS collar data from a herd traveling through the red desert to Hoback Corridor, western Wyoming, to create a model that predicts where mule deer migrate, without the need to pair new animals. Credit: Tanner Warder, Wyoming Cooperative Fish and Wildlife Research Unit

How do researchers understand where large game animals migrate across the vast landscape each spring and fall? That’s the question biologists from the Department of Fish and Game at the University of Wyoming and Idaho asked in a study published in the journal. Methods in ecology and evolution.

Recent advances in technology have allowed biologists and Wildlife managers To keep track of ungulates, such as elk and gazelleWith GPS collars that detect animal migration routes. But collars are expensive and logistically difficult to deploy, making it difficult to build a comprehensive inventory of the passes required by herds.

Now, a research team has found a promising way to predict where a mule will be Spinner They are more likely to migrate, without the need to collar new animals.

“We were surprised by how well we were able to predict most deer movements, indicating that rather than moving randomly, migratory mule deer seem to follow rules that do a good job of balancing the costs of moving with the benefits of foraging access,” says Tristan Nunez, who led the work. as a postdoctoral researcher in the US Geological Survey’s Fish and Wildlife Collaborative Research Unit of Wyoming at the University of Wyoming.

Nunez tackled a question that has long plagued biologists: Can we predict immigration Trails in areas without any collared animals using GPS, using the information they have already learned about the habitats through which the tracked animals choose to migrate?

previous search It relies on GPS data to map migration corridors, which has proven to be a powerful method for science-based management and conservation. But Nunez and his colleagues hoped they could identify migration pathways based on environmental information or habitat quality alone.

To answer this question, the research team first created models to estimate the movement of a mule deer herd based on topography, snowmelt, intensity of human development, and new grass growth. Next, they compared those predicted corridors with the actual migration routes of 130 mules from three GPS collared herds in Idaho and Wyoming.

Team models worked like a route app on smartphones that determined the best route to navigate between two points. For mule deer, the predicted lane that best matched actual spring movements was not a straight line, or the shortest distance, between the summer and winter range. Instead, deer generally preferred roads with mountainous terrain, shrubby vegetation, and less human development.

Predict mule deer migration routes without GPS collars

GPS collars deployed on elk in a Tex Creek herd between 2007 and 2009 revealed that the herd migrates an average of 40 miles between its summer and winter ranges. Credit: James Brewer, Idaho Department of Fish and Game

“Being able to predict migration routes on a large scale will save an enormous amount of time and money, and ultimately be more beneficial to Idaho’s wildlife management,” says study co-author Mark Hurley of the Idaho Department of Fish and Game.

Traditionally, wildlife managers have relied on GPS data from collared animals to determine migration routes, which are critical to healthy groups of ungulates. However, detailed knowledge of seasonal migrations depends on years of costly and time-intensive data collection. “Even after the thousands of animals have been collared, it is likely that we have fully described only a fraction of the migration routes used throughout the state,” Hurley says.

Over the past few years, biologists and wildlife managers have used GPS collar data sets to map the migrations of more than a hundred herds of ungulates across the western United States.

The work, along with the current study, is made possible through a partnership known as the Corridor Mapping Team. The team was established in 2018 by the USGS in response to SC order 3362 to promote mapping and conservation of ungulates. migration corridors.

State, tribal, and state wildlife agencies use GPS motion data to plan migrations and support regional big game management and conservation. Predictive migration models are an alternative to costly and labor-intensive animal tracking, which is useful for maintaining migrations that have not yet been identified.

Hoofs move like mule deer across the western United States every spring and fall, consistent with environmental cues associated with food. But as the human footprint expands in the West, migratory flocks face increasing obstacles such as new subdivisions, power development, impermeable fences, and heavily congested roads on their long journeys.

New computer algorithms used to predict migration routes will be freely available, allowing wildlife managers in areas where herds have not been hunted at all to better understand where major migrations may occur. Certain migrations can then direct where to make fences more suitable for deer, prevent subdivisions, or build bridges to facilitate passage through busy highways to keep large landscapes open to ungulate migrations.

Working with members of the Corridor mapping team, Nunez hopes to extend the work to other types of large game, herds, and landscapes across the western United States and beyond, to better understand how their movement preferences differ. Additionally, he says, the models could help shed light on how climate change will affect future ungulate migrations.


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more information:
Tristan A. Nunez et al., A statistical framework for modeling migration corridors, Methods in ecology and evolution (2022). DOI: 10.1111 / 2041-210X.13969

Introduction of
University of Wyoming


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