140 pages, b/w illustrations, tables
Monitoring the impacts of resource use and landscape change on wildlife habitat over large areas is a daunting assignment. Forest land managers could benefit from linking the frequent decisions of resource use (timber harvesting) with a system of wildlife habitat accounting, but to date these tools are not widely available. I examined aspects of wildlife habitat modeling that: (in Chapter 2) could potentially lead to the establishment of wildlife habitat accounting within a resource decision support tool, (in Chapter 3) improve our theoretical understanding and methods to interpret the accuracy of wildlife habitat models, (in Chapter 4) explore the effects of vegetation classification systems on wildlife habitat model results, and (in Chapter 5) show that forest structural estimates from satellite imagery can improve potential habitat distribution models (GAP) for forest bird species.
The majority of the analyses in this dissertation were done using a forest resource inventory developed by the State of Michigan (IFMAP). Paired with field vegetation and bird samples from sites across the lower peninsula of Michigan, we compared the relative accuracy of wildlife habitat relationship models built with plot-scale vegetation samples and stand-scale forest inventory maps. Recursive partitioning trees were used to build wildlife habitat models for 30 bird species. The habitat distribution maps from the Michigan Gap Analysis (MIGAP) were used as a baseline for comparison of model accuracy results. Both the plot and stand-scale measurements achieved high accuracy and there were few large differences between plot and stand-scale models for any individual species. Where the plot and stand-scale models were different, they tended to be species associated with mixed habitats. This may be evidence that scale of vegetation measurement has a larger influence on species associated with edges and ecotones. Habitat models that were built solely with land cover data were less accurate than models that included detailed vegetation composition and structure information. This result was supported in multiple analyses, including forest structural estimates generated from satellite imagery.
There are distinct patterns of model accuracy and especially commission and omission errors that are linked to species ecological traits and method of error calculation. These patterns are illustrated with figures that relate the model results to a conceptual relationship between a species' probability of presence at a given location and the suitability of the habitat at that location. The correct application of accuracy assessment is key to correctly understanding the utility of a model and to avoid discounting a model as useless when it is in fact informative. I also compared the relative accuracy of wildlife habitat relationship models built with three different hierarchical vegetation classifications. Despite major differences in the distribution of field sites among the classes, there was little difference in terms of bird habitat model accuracy between the classifications at any given level. The number of classes (level of the hierarchy) appeared to be more important to bird habitat model accuracy than did the nature of the classification itself.
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