Landscape Models to Predict the Influence of Forest Structure on Tassel-Eared Squirrel Populations.
Authors:
Prather, J.W. Dodd, N.L. Dickson, B.G. Hampton, H.M. Xu, Y. Aumack, E.N. Sisk, T.D. USDA, FS
Source:
Journal of wildlife management. 2006 June, v. 70, no. 3, p. 723-731.
NALT Subjects:
Pinus ponderosa national forests stand structure Sciurus squirrels population density spatial data prediction linear models environmental models forest-wildlife relations basal area canopy forest thinning forest fire management remote sensing image analysis thematic maps Arizona
Other Subjects:
Sciurus aberti
Issue Date:
Jun-2006
Abstract:
The tassel-eared squirrel (Sciurus aberti) is often used as an indicator species in southwestern ponderosa pine (Pinus ponderosa) forests. Because of more than a century of fire suppression, grazing, and timber harvest, these forests have become increasingly prone to catastrophic wildfire, resulting in pressure to implement large-scale treatments to reduce fire threat and restore ecosystem function. However, such treatments could have dramatic effects on tassel-eared squirrels and other wildlife. Because of emerging plans for thinning southwestern forests to reduce fire threat, we undertook a modeling effort to produce spatial data to examine the results of proposed management actions on squirrel habitat. We used squirrel density and recruitment data from 9 study areas located in the Flagstaff region of northern Arizona, USA, linked with spatial data on forest structure developed from remote-sensing imagery. We used a multiscale approach to analyze relationships between forest structure and squirrel density and recruitment. We then used an information-theoretic approach to identify the most parsimonious models for both squirrel density and recruitment. The most strongly supported models of squirrel density included local-scale basal area and .60% canopy cover at the 65-ha spatial scale. For squirrel recruitment, 4 different models that included both local-scale basal area (m2/ha) and variations of canopy cover over extents of approximately 160-305 ha were strongly supported. Using the most parsimonious models, we created spatial data layers representing both squirrel density and recruitment across an 800,000-ha landscape in northern Arizona. Our approach resulted in spatially explicit models that can be used in efforts to predict the effects of forest management on squirrel populations.
