New model sheds light on wildlife interactions

By Blake Jackson

Researchers at Cornell University, along with their colleagues, have developed a new statistical model that provides a deeper understanding of wildlife species interactions and their abundance in a region. This model offers valuable insights that will assist in wildlife management and conservation efforts. The model, tested on coyotes, fishers, and American martens in northern New York, can be applied to a variety of wildlife systems, including mammals, birds, fish, and insects.

The research, titled "Abundance-Mediated Species Interactions," was published in Ecology. Joshua Twining, now an assistant professor at Oregon State University, was the lead author, with Angela Fuller, professor at Cornell University, as a co-author. The new model goes beyond traditional models that focus solely on species co-occurrence, which only tells us if two species are present at a site. In contrast, the new model accounts for how the abundance of different species influences their interactions, such as competition, predation, and disease.

To develop the model, the researchers used data from multiple years of camera trap surveys in the Adirondacks and Tug Hill region of New York. These areas are home to various species, including coyotes, which are apex predators, and fishers and martens, which are smaller mesopredators. The study revealed unexpected results: larger coyote populations were linked to higher numbers of fishers but lower populations of martens. The reasons behind this are still unclear but may involve differences in size and vulnerability to coyotes or other environmental factors.

The research also highlighted the potential threats climate change poses to marten populations, as these animals thrive in areas with deep snow. As snow depth decreases due to climate change, martens may face additional challenges. The new model can be used to examine interactions across various species and environments, providing essential data for effective wildlife management strategies, especially in light of emerging challenges like disease dynamics.

Photo Credit: cornell-university