| Literature DB >> 23613910 |
Joseph K Bump1, Chelsea M Murawski, Linda M Kartano, Dean E Beyer, Brian J Roell.
Abstract
BACKGROUND: The influence of policy on the incidence of human-wildlife conflict can be complex and not entirely anticipated. Policies for managing bear hunter success and depredation on hunting dogs by wolves represent an important case because with increasing wolves, depredations are expected to increase. This case is challenging because compensation for wolf depredation on hunting dogs as compared to livestock is less common and more likely to be opposed. Therefore, actions that minimize the likelihood of such conflicts are a conservation need. METHODOLOGY/PRINCIPALEntities:
Mesh:
Year: 2013 PMID: 23613910 PMCID: PMC3629141 DOI: 10.1371/journal.pone.0061708
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Distinct wolf conflict patterns in the upper Great Lakes region, USA.
Poisson log-linear relationship between annual totals of wolf depredation (i.e. kill or injury) on bear-hunting dogs (y-axis) and annual estimates of wolf abundance (x-axis) in Wisconsin (1990–2010; closed symbols) and Michigan (1996–2010; open symbols).
Comparison of best performing models explaining trends in wolf depredation on bear-hunting dogs in the Wisconsin and Michigan, USA, by Akaike’s information criterion & weight.
| Explanatory factors | AICC
| ΔAICC
| W |
| Wolves (0.531), State (<0.001),Bait total (0.28) | 36.30 | 0.00 | 0.30 |
| State (<0.0001), Bait only (0.018) | 36.77 | 0.47 | 0.24 |
| State (<0.0001), Dogs total (0.016) | 37.16 | 0.86 | 0.20 |
| State (<0.0001), Encounter (0.028),Bait total (0.948) | 37.74 | 1.44 | 0.15 |
| State (<0.0001), Days afield (0.588),Encounter (0.032) | 38.17 | 1.87 | 0.12 |
Encounter = the ratio of bear hunting permits sold per wolf (see methods). Numbers in parenthesis under explanatory factors are p-values for the five best-performing models.
AICC is Akaike’s information criterion, corrected for small sample size.
ΔAICC is AICC for the model of interest minus the smallest AICC for the set of models being considered. We only considered models with ΔAICC ≤2.
W is the Akaike’s weight of each model. The ratio of one model’s weight to another estimates how many times more support the data provide for that model over the other.
Figure 2Wolf conflict timing and bear hunting in the upper Great Lakes region, USA.
Lower panel: cumulative wolf depredations on bear-hunting dogs (y-axis) each month (x-axis) from 1980–2010 in Wisconsin (closed bars) and Michigan (open bars). Upper panel: General timing of bear-baiting, training, and hunting regulations (y-axis) in each state; x-axis and bar symbols are the same as in lower panel. In Wisconsin there is a pre-training baiting period of ∼2.5 months that does not exist in Michigan and baiting in Michigan begins ∼4 months later than in Wisconsin.
Logistic regression analysis of the probability of wolf depredation on hunting dogs in relation to time since training with bait in Wisconsin and Michigan, USA.
| Predictor | β |
| Wald’s χ2 |
|
|
|
| Constant | 0.8119 | 0.3115 | 4.79 | 1 | 0.091 | NA |
| Time since training with bait | −0.0241 | 0.0048 | 25.11 | 1 | <0.0001 | 0.9762 |
| State | 1.2732 | 0.1696 | 44.16 | 1 | <0.0001 | 3.5723 |
| Test | χ2 |
|
| |||
| Overall model evaluation | ||||||
| Likelihood ratio test | 39.01 | 1 | <0.0001 | |||
| Wald test | 44.16 | 1 | <0.0001 | |||
| Goodness-of-fit test | ||||||
| Likelihood ratio test | 249.21 | 243 | 0.3785 | |||
Note. NA = not applicable.
Figure 3Wolf conflict and the duration of bear-baiting in the upper Great Lakes region, USA.
Predicted probability of a wolf depredation on bear-hunting dogs (y-axis) versus the number of days since training with bait began (x-axis) in Wisconsin (upper line) and Michigan (lower line). Each point represents a day since training with bait began in Wisconsin (closed symbols) and Michigan (open symbols). Note that open symbols for Michigan are offset from (0) and (1) probability so as to not overlap symbols for Wisconsin. The odds of a depredation event occurring in Wisconsin were 3.57× greater than the odds in Michigan; a relative depredation risk 2.12–7.22× greater in Wisconsin.