Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the western USA

Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the... We used multiple logistic regression to model how different landscape conditions contributed to the probability of human–grizzly bear conflicts on private agricultural ranch lands. We used locations of livestock pastures, traditional livestock carcass disposal areas (boneyards), beehives, and wetland-riparian associated vegetation to model the locations of 178 reported human–grizzly bear conflicts along the Rocky Mountain East Front, Montana, USA during 1986–2001. We surveyed 61 livestock producers in the upper Teton watershed of north-central Montana, to collect spatial and temporal data on livestock pastures, boneyards, and beehives for the same period, accounting for changes in livestock and boneyard management and beehive location and protection, for each season. We used 2032 random points to represent the null hypothesis of random location relative to potential explanatory landscape features, and used Akaike’s Information Criteria (AIC/AIC C ) and Hosmer–Lemeshow goodness-of-fit statistics for model selection. We used a resulting “best” model to map contours of predicted probabilities of conflict, and used this map for verification with an independent dataset of conflicts to provide additional insights regarding the nature of conflicts. The presence of riparian vegetation and distances to spring, summer, and fall sheep or cattle pastures, calving and sheep lambing areas, unmanaged boneyards, and fenced and unfenced beehives were all associated with the likelihood of human–grizzly bear conflicts. Our model suggests that collections of attractants concentrated in high quality bear habitat largely explain broad patterns of human–grizzly bear conflicts on private agricultural land in our study area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biological Conservation Elsevier

Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the western USA

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Publisher
Elsevier
Copyright
Copyright © 2005 Elsevier Ltd
ISSN
0006-3207
D.O.I.
10.1016/j.biocon.2005.12.001
Publisher site
See Article on Publisher Site

Abstract

We used multiple logistic regression to model how different landscape conditions contributed to the probability of human–grizzly bear conflicts on private agricultural ranch lands. We used locations of livestock pastures, traditional livestock carcass disposal areas (boneyards), beehives, and wetland-riparian associated vegetation to model the locations of 178 reported human–grizzly bear conflicts along the Rocky Mountain East Front, Montana, USA during 1986–2001. We surveyed 61 livestock producers in the upper Teton watershed of north-central Montana, to collect spatial and temporal data on livestock pastures, boneyards, and beehives for the same period, accounting for changes in livestock and boneyard management and beehive location and protection, for each season. We used 2032 random points to represent the null hypothesis of random location relative to potential explanatory landscape features, and used Akaike’s Information Criteria (AIC/AIC C ) and Hosmer–Lemeshow goodness-of-fit statistics for model selection. We used a resulting “best” model to map contours of predicted probabilities of conflict, and used this map for verification with an independent dataset of conflicts to provide additional insights regarding the nature of conflicts. The presence of riparian vegetation and distances to spring, summer, and fall sheep or cattle pastures, calving and sheep lambing areas, unmanaged boneyards, and fenced and unfenced beehives were all associated with the likelihood of human–grizzly bear conflicts. Our model suggests that collections of attractants concentrated in high quality bear habitat largely explain broad patterns of human–grizzly bear conflicts on private agricultural land in our study area.

Journal

Biological ConservationElsevier

Published: Jun 1, 2006

References

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