Using a GIS model to assess terrestrial salamander response to alternative forest management plans.
A GIS model predicting the spatial distribution of terrestrial salamander abundance based on topography and forest age was developed using parameters derived from the literature. The model was tested by sampling salamander abundance across the full range of site conditions used in the model. A regression of the predictions of our GIS model against these sample data showed that the model has a modest but significant ability to predict both salamander abundance and mass per unit area. The model was used to assess the impacts of alternative management plans for the Hoosier National Forest (Indiana, USA) on salamanders. These plans differed in the spatial delineation of management areas where timber harvest was permitted, and the intensity of timber harvest within those management areas. The spatial pattern of forest openings produced by alternative forest management scenarios based on these plans was projected over 150 years using a timber-harvest simulator (HARVEST). We generated a predictive map of salamander abundance for each scenario over time, and summarized each map by calculating mean salamander abundance and the mean colonization distance (average distance from map cells with low predicted abundance to those with relatively high abundance). Projected salamander abundance was affected more by harvest rate (area harvested each decade) than by the management area boundaries. The alternatives had a varying effect on the mean distance salamanders would have to travel to colonize regenerating stands. Our GIS modeling approach is an example of a spatial analytical tool that could help resource management planners to evaluate the potential ecological impact of management alternatives.
Gustafson
E J
EJ
USDA Forest Service, North Central Research Station, 5985 Highway K, Rhinelander, WI 54501, USA. egustafson@fs.fed.us
Murphy
N L
NL
Crow
T R
TR
D000818:Animals; D003247:Conservation of Natural Resources; D004777:Environment; D004784:Environmental Monitoring; D005260:Female; D005544:Forecasting; D005843:Geography; D007256:Information Systems; D008297:Male; D008962:Models, Theoretical; D011157:Population Dynamics; D018570:Risk Assessment; D014197:Trees; D014562:Caudata