Scholarship & Research
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Item Increasing equitable access to graduate education through competitive hiring in the life sciences(Wiley, 2023-06) Carroll, Kathleen A.; Lance, Michael J.; Smithers, Brian V.; Debinski, Diane M.Many professions necessitate a graduate-level education, and research conducted by graduate students is integral in many fields, particularly those in the life science programs like ecology and environmental sciences. However, practices for recruiting and selecting graduate students are inconsistent among and within institutions. Although some institutions, departments, or faculty members hire graduate students through open and competitive graduate student hiring processes, graduates are frequently selected through inconsistent processes that limit the pool of applicants and do not maximize the potential for increasing workforce diversity. Here, we review and evaluate six approaches to graduate recruitment processes common in ecology and environmental science degree programs in the US to determine which approaches, or combinations of approaches, could increase equity in career development opportunities, promote workforce diversity, and provide clear justifications to funding bodies. We compiled our list of recruitment methods through informal interviews with recruiters, administrators, faculty, and graduate students in ecology, natural resources, and environmental sciences. We determined that three of the six approaches examined were most effective in supporting equitable graduate student hiring practices, and three were not. While life science fields were the primary focus of this review, our approach to evaluating graduate recruitment methods is widely applicable across disciplines where graduate students conduct research.Item Evaluating the importance of wolverine habitat predictors using a machine learning method(Oxford University Press, 2021-12) Carroll, Kathleen A.; Hansen, Andrew J.; Inman, Robert M.; Lawrence, Rick L.In the conterminous United States, wolverines (Gulo gulo) occupy semi-isolated patches of subalpine habitats at naturally low densities. Determining how to model wolverine habitat, particularly across multiple scales, can contribute greatly to wolverine conservation efforts. We used the machine-learning algorithm random forest to determine how a novel analysis approach compared to the existing literature for future wolverine conservation efforts. We also determined how well a small suite of variables explained wolverine habitat use patterns at the second- and third-order selection scale by sex. We found that the importance of habitat covariates differed slightly by sex and selection scales. Snow water equivalent, distance to high-elevation talus, and latitude-adjusted elevation were the driving selective forces for wolverines across the Greater Yellowstone Ecosystem at both selection orders but performed better at the second order. Overall, our results indicate that wolverine habitat selection is, in large part, broadly explained by high-elevation structural features, and this confirms existing data. Our results suggest that for third-order analyses, additional fine-scale habitat data are necessary.