Open Access. Powered by Scholars. Published by Universities.®
![Digital Commons Network](http://assets.bepress.com/20200205/img/dcn/DCsunburst.png)
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Extreme Fire As A Management Tool To Combat Regime Shifts In The Range Of The Endangered American Burying Beetle, Alison K. Ludwig, Daniel R. Uden, Dirac Twidwell
Extreme Fire As A Management Tool To Combat Regime Shifts In The Range Of The Endangered American Burying Beetle, Alison K. Ludwig, Daniel R. Uden, Dirac Twidwell
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
This study is focused on the population of federally-endangered American burying beetles in south-central Nebraska. It is focused on changes in land cover over time and at several levels of spatial scale, and how management efforts are impacting both the beetle and a changing landscape. Our findings are applicable to a large portion of the Great Plains, which is undergoing the same shift from grassland to woodland, and to areas where the beetle is still found.
Evaluating Potential Effects Of 2019 Australian Bushfires On Animal Species, Protected Land, And Land Cover, Alyssa J. Kaewwilai
Evaluating Potential Effects Of 2019 Australian Bushfires On Animal Species, Protected Land, And Land Cover, Alyssa J. Kaewwilai
Student Publications
The 2019-2020 Australian bushfire event had exceptionally dry, hot conditions as well as high potential impacts on the country’s wildlife and natural resources. The purpose of the study was to analyze the potential impacts of the 2019 Australian bushfire event on animal species, protected land, and varied land cover types. The research question of this project is: how does the location of the Australian Bushfires of 2020 potentially impact animal species, protected land and national parks, as well as different land covers? Raster calculator was used to combine and classify layers from the MODIS Burned Area Product of burned (1) …
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
USDA Wildlife Services: Staff Publications
Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera …