Landscape heterogeneity reduces coyote predation on white-tailed deer fawns
Abstract
Coyote (Canis latrans) predation on white-tailed deer (Odocoileus virginianus) fawns in southeastern North America has led to deer population declines in some areas. Research or management efforts initiated in response to coyote predation on fawns have primarily focused on implementation of reduced antlerless deer harvest or coyote control to mitigate population declines. Vegetation characteristics may influence coyote hunting efficiency, but the potential influence of land cover at large scales in the southeastern United States is underexplored. We investigated whether mortality risk was affected by landscape characteristics within fawn home ranges for a sample of 165 fawns on the United States Department of Energy’s Savannah River Site (SRS), South Carolina, 2007–2012. We monitored fawns every 8 hours to ≥ 4 weeks of age and 1–3 times daily to 12 weeks of age. We included only surviving or coyote-predated fawns in the dataset. The most supported model describing hazard ratios included the length of edge (i.e., area where 2 land cover types joined) in fawn home ranges. Probability of coyote predation increased 1.26 times for each 968-m decrease in edge within a fawn’s simulated home range (29.1-ha circular buffer) under this model. Further, fawns with the least edge in their home rangesmore »
- Authors:
-
- Auburn Univ., AL (United States). School of Forestry and Wildlife Sciences
- USDA Forest Service, New Ellenton, SC (United States). Southern Research Station
- Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL). Warnell School of Forestry and Natural Resources; Univ. of Georgia, Athens, GA (United States)
- Publication Date:
- Research Org.:
- Savannah River Site (SRS), New Ellenton, SC (United States). USDA Forest Service
- Sponsoring Org.:
- USDOE Office of Environmental Management (EM), Office of Science and Technology (EM-50); United States Department of Agriculture (USDA)
- OSTI Identifier:
- 1352537
- Report Number(s):
- 17-01-p
Journal ID: ISSN 0022-541X; 17-01-p
- Grant/Contract Number:
- AI09-00SR22188
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Wildlife Management
- Additional Journal Information:
- Journal Volume: 81; Journal Issue: 4; Journal ID: ISSN 0022-541X
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 54 ENVIRONMENTAL SCIENCES; Canis latrans; coyote; fawn; habitat; Odocoileus virginianus; predation; survival; white-tailed deer
Citation Formats
Gulsby, William D., Kilgo, John C., Vukovich, Mark, and Martin, James A. Landscape heterogeneity reduces coyote predation on white-tailed deer fawns. United States: N. p., 2017.
Web. doi:10.1002/jwmg.21240.
Gulsby, William D., Kilgo, John C., Vukovich, Mark, & Martin, James A. Landscape heterogeneity reduces coyote predation on white-tailed deer fawns. United States. https://doi.org/10.1002/jwmg.21240
Gulsby, William D., Kilgo, John C., Vukovich, Mark, and Martin, James A. Tue .
"Landscape heterogeneity reduces coyote predation on white-tailed deer fawns". United States. https://doi.org/10.1002/jwmg.21240. https://www.osti.gov/servlets/purl/1352537.
@article{osti_1352537,
title = {Landscape heterogeneity reduces coyote predation on white-tailed deer fawns},
author = {Gulsby, William D. and Kilgo, John C. and Vukovich, Mark and Martin, James A.},
abstractNote = {Coyote (Canis latrans) predation on white-tailed deer (Odocoileus virginianus) fawns in southeastern North America has led to deer population declines in some areas. Research or management efforts initiated in response to coyote predation on fawns have primarily focused on implementation of reduced antlerless deer harvest or coyote control to mitigate population declines. Vegetation characteristics may influence coyote hunting efficiency, but the potential influence of land cover at large scales in the southeastern United States is underexplored. We investigated whether mortality risk was affected by landscape characteristics within fawn home ranges for a sample of 165 fawns on the United States Department of Energy’s Savannah River Site (SRS), South Carolina, 2007–2012. We monitored fawns every 8 hours to ≥ 4 weeks of age and 1–3 times daily to 12 weeks of age. We included only surviving or coyote-predated fawns in the dataset. The most supported model describing hazard ratios included the length of edge (i.e., area where 2 land cover types joined) in fawn home ranges. Probability of coyote predation increased 1.26 times for each 968-m decrease in edge within a fawn’s simulated home range (29.1-ha circular buffer) under this model. Further, fawns with the least edge in their home ranges were >2 times more likely to be depredated by a coyote than fawns with the greatest edge availability. Support for other models was relatively low, but informative variables (e.g., mean patch fractal dimension, Shannon’s diversity index, mean forest patch size) supported a general trend that as fawn home ranges became more homogeneous and contained larger patches with less edge and fewer cover types, predation risk increased. These findings are consistent with similar work in the midwestern United States, despite landscape differences between regions. The combined weight of evidence suggests maintenance of a heterogeneous landscape consisting of relatively small dispersed patches may reduce fawn losses to coyotes. In conclusion, this information may also be used to identify areas susceptible to greater fawn predation rates across large spatial scales. However, the relatively long forestry rotation lengths and large scale of consistent forest management on the SRS are uncommon in the southeastern United States and the mechanism for the pattern we observed is unclear. Therefore, our results may not be applicable to sites with different forest management practices.},
doi = {10.1002/jwmg.21240},
journal = {Journal of Wildlife Management},
number = 4,
volume = 81,
place = {United States},
year = {Tue Mar 07 00:00:00 EST 2017},
month = {Tue Mar 07 00:00:00 EST 2017}
}
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