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Assessing the Utility of a Temporally Dynamic Wind Index in Alaskan Moose Resource Selection
Jyoti
Jennewein, University of Idaho, jjennewein@uidaho.edu
Peter
Mahoney, University of Washington, pmahoney29@gmail.com
Arjan
JH
Meddens, University of Idaho, ameddens@uidaho.edu
Sophie
Gilbert, University of Idaho, sophiegilbert@uidaho.edu
Mark
Hebblewhite, University of Montana, mark.hebblewhite@umontana.edu
Natalie
Boelman, Lamont-Doherty Earth Observatory, Columbia Univ., nboelman@ldeo.columbia.edu
Scott
Brainerd, Alaska Department of Fish and Game, scott.brainerd@alaska.gov
Kyle
Joly, National Park Service, kyle_joly@nps.gov
Kim
King Jones, Alaska Department of Fish and Game, kim.jones@alaska.gov
Kalin
Seaton, Alaska Department of Fish and Game, kalin.seaton@alaska.gov
Lee
A.
Vierling, University of Idaho, leev@uidaho.edu
Jan
UH
Eitel, University of Idaho, jeitel@uidaho.edu
(Presenter)
Accelerated warming in high northern latitudes (HNL) has led to warmer, wetter, and more variable environments. Wildlife living in these systems must adapt to these changes. For instance, moose (Alces alces) are cold-adapted ungulates who can experience heat stress year-round. In winter, moose begin experiencing heat stress at -5C, and temperatures exceeding 0C require excess energy to be expended to maintain normal body conditions. To combat heat stress, moose adopt thermoregulatory behaviors that include selection of taller and denser forest canopies, conductive cooling from lying on snow, and convective cooling from exposure to wind. Despite its notable role in thermoregulation, wind parameters are rarely included in resource selection studies because of difficulties associated with spatial and temporal interpolation of weather station wind data. One possible approach to estimate the effect of wind in resource selection modeling is the use of dynamic terrain attributes, such as a wind shelter index, which model long-term, highly-dynamic meteorological data across a digital elevation model. To investigate the utility of dynamic terrain attributes in winter moose resource selection, we modelled a wind shelter index as a thermoregulation mechanism in three game management units in Alaska. Next, we compared a null winter model that focuses on structural habitat features related to thermoregulation to a windshelter model using conditional-logistic regression to account for the dynamics of changing resource availability throughout the winter.
Three moose datasets (n=128 total; 93 cows, 35 bulls), were analyzed independently to assess location-specific resource selection. The ArcticDEM (5m pixels) served as the terrain input, which enables dynamic, fine-scale topographic features to be resolved. Wind shelter was programmed with meteorological data from the North American Regional Analysis (NARR) database. A resource selection function in a used-available sampling design estimated the proportional probability of resource use. A step-selection function generated ten available locations per used location. Matched case-control logistic regression contrasted the used and available points. Results from this analysis suggest that the dynamic wind shelter index is a statistically significant predictor (p<0.01) in Alaskan winter moose resource selection. Additionally, in four out of five models wind shelter enhances model fit (delta BIC from -24 to -195). Taken together, these results suggest that the wind shelter index provide insight into winter moose resource selection and thermoregulation that could not be gained when using static landscape characteristics. Future applications of such dynamic terrain indices that incorporate time-varying meteorological data may be increasingly important in modelling habitat selection under continued climate change scenarios.
Associated Project(s):
Poster Location ID: 21
Session Assigned: Wildlife and Ecosystem Services
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