Abstract

White-tailed deer (Odocoileus virginianus) are an ecological, economical, and socially significant species that occupy a variety of ecoregions. White-tailed deer are mobile habitat generalists that prefer habitats containing woody cover. Deer have successfully adapted to habitat-fragmented, agricultural landscapes. As a result, deer are not uniformly distributed across intensively cultivated areas, which make field surveys difficult with often highly variable spatial data. To increase sampling efficiency (deer observed / sampling effort), the landscape can be stratified based upon preferred habitat types. Habitat suitability models (HSI) have been used to represent hypothesized wildlife-habitat relationships, and therefore the likelihood of deer being observed may likely vary based on HSI scores. My research objective was to improve field sampling efforts for spotlight surveys in an intensive agricultural landscape of southwest Minnesota, using HSI modeling to stratify the landscape. An HSI model previously created for white-tailed deer populations in Illinois (original HSI) and a modified HSI model that I created which included grassland habitats were utilized. Deer management unit (DMU) HSI scores were correlated with deer densities at the statewide level and the original HSI and modified HSI models explained much of the variation in DMU deer densities at the statewide level. Spotlight surveys were conducted in spring 2015 and 2016 to test both models on a local level. The modified HSI model was more efficient at predicting where deer could be in agricultural landscapes, in large part, because the original HSI model ignored grassland habitats and many deer were observed in these habitats. The modified HSI model is recommended to stratify habitats for transect surveys to better predict the distribution and abundance of white-tailed deer in agricultural landscapes, which will improve sampling efficiency.

Advisor

John Krenz

First Committee Member

Marrett Grund

Second Committee Member

Shannon Fisher

Date of Degree

2016

Language

english

Document Type

Thesis

Degree

Master of Science (MS)

Department

Biological Sciences

College

Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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