McCall, ID, United States
McCall, ID, United States

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Mitchell M.S.,U.S. Geological Survey | Gude J.A.,Montana Fish | Ausband D.E.,University of Montana | Sime C.A.,Montana Fish | And 6 more authors.
Wildlife Biology | Year: 2010

Model-based predictors derived from historical data are rarely evaluated before they are used to draw inferences. We performed a temporal validation, (i.e. assessed the performance of a predictive model using data collected from the same population after the model was developed) of a statistical predictor for the number of successful breeding pairs of wolves Canis lupus in the northern Rocky Mountains (NRM). We predicted the number of successful breeding pairs, β, in Idaho, Montana and Wyoming based on the distribution of pack sizes observed through monitoring in 2006 and 2007 (β̂,), and compared these estimates to the minimum number of successful breeding pairs, βMIN, observed through intensive monitoring. βMIN was consistently included within the 95 confidence intervals of β̂? for all states in both years (except for Idaho in 2007), generally following the pattern β̂L (lower 95 prediction interval for β̂?) < β̂MIN < β̂. This evaluation of β̂ estimates for 2006 and 2007 suggest it will be a robust model-based method for predicting successful breeding pairs of NRM wolves in the future, provided influences other than those modeled in β̂? (e.g. disease outbreak, severe winter) do not have a strong effect on wolf populations. Managers can use β̂ models with added confidence as part of their post-delisting monitoring of wolves in NRM. © 2010 Wildlife Biology.

Ausband D.E.,University of Montana | Mitchell M.S.,University of Montana | Doherty K.,University of Montana | Zager P.,316 16th Street | And 2 more authors.
Journal of Wildlife Management | Year: 2010

We used rendezvous site locations of wolf (Canis lupus) packs recorded during 19962006 to build a predictive model of gray wolf rendezvous site habitat in Idaho, USA. Variables in our best model included green leaf biomass (Normalized Difference Vegetation Index), surface roughness, and profile curvature, indicating that wolves consistently used wet meadow complexes for rendezvous sites. We then used this predictive model to stratify habitat and guide survey efforts designed to document wolf pack distribution and fecundity in 4 study areas in Idaho. We detected all 15 wolf packs (32 wolf pack-yr) and 20 out of 27 (74) litters of pups by surveying <11 of the total study area. In addition, we were able to obtain detailed observations on wolf packs (e.g., hair and scat samples) once we located their rendezvous sites. Given an expected decrease in the ability of managers to maintain radiocollar contact with all of the wolf packs in the northern Rocky Mountains, rendezvous sites predicted by our model can be the starting point and foundation for targeted sampling and future wolf population monitoring surveys. © 2010 The Wildlife Society.

Ausband D.E.,University of Montana | Rich L.N.,University of Montana | Rich L.N.,Virginia Polytechnic Institute and State University | Glenn E.M.,University of Montana | And 7 more authors.
Journal of Wildlife Management | Year: 2014

The behavioral patterns and large territories of large carnivores make them challenging to monitor. Occupancy modeling provides a framework for monitoring population dynamics and distribution of territorial carnivores. We combined data from hunter surveys, howling and sign surveys conducted at predicted wolf rendezvous sites, and locations of radiocollared wolves to model occupancy and estimate the number of gray wolf (Canis lupus) packs and individuals in Idaho during 2009 and 2010. We explicitly accounted for potential misidentification of occupied cells (i.e., false positives) using an extension of the multi-state occupancy framework. We found agreement between model predictions and distribution and estimates of number of wolf packs and individual wolves reported by Idaho Department of Fish and Game and Nez Perce Tribe from intensive radiotelemetry-based monitoring. Estimates of individual wolves from occupancy models that excluded data from radiocollared wolves were within an average of 12.0% (SD = 6.0) of existing statewide minimum counts. Models using only hunter survey data generally estimated the lowest abundance, whereas models using all data generally provided the highest estimates of abundance, although only marginally higher. Precision across approaches ranged from 14% to 28% of mean estimates and models that used all data streams generally provided the most precise estimates. We demonstrated that an occupancy model based on different survey methods can yield estimates of the number and distribution of wolf packs and individual wolf abundance with reasonable measures of precision. Assumptions of the approach including that average territory size is known, average pack size is known, and territories do not overlap, must be evaluated periodically using independent field data to ensure occupancy estimates remain reliable. Use of multiple survey methods helps to ensure that occupancy estimates are robust to weaknesses or changes in any 1 survey method. Occupancy modeling may be useful for standardizing estimates across large landscapes, even if survey methods differ across regions, allowing for inferences about broad-scale population dynamics of wolves. © 2014 The Wildlife Society.

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