R.B. Boone | Search USGS

USGS2001

Partitioning sources of variation in vertebrate species richness

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

Journal of Biogeography↗

USGS2000

Predicting broad-scale occurrences of vertebrates in patchy landscapes

Spatially explicit landscape-scale models that predict species distributions, where patches of habitat are shown as having potential to be occupied or unoccupied, are increasingly common. To successfully use such data, one should understand how these predicted distributions are created and how their relative accuracies are assessed. Geographic ranges, defined upon observations (e.g., atlases), literature review, and expert review, are a primary data layer. A map of land cover is created, often from interpretation of satellite imagery or other remotely-sensed data. Species/habitat associations are defined based upon a literature review and expert review, describing associations for habitats derived from the cover map. Included as ancillary associations are how species relate to physical features, where appropriate, such as elevation and hydrography. The three layers of information (range, land cover, and associations) are merged, often using raster-based algebraic statements that exclude unused habitats or patches outside the range of a species. The accuracy of predictions for a suite of species is typically assessed with surveys by comparing the species predicted to occur in an area to the species observed. Omission (i.e., present in species lists but not predicted) and commission (i.e., predicted but not present in lists) errors are reported. Errors may be due to many sources. For example, ranges of species change, cover types may be misidentified, species/habitat associations may be incorrect or change, or species may be rare and unlikely to be seen in surveys and judged in-error even though the species may be present. An example is given of an appropriate use of broad-scale species predicted distributions, in which patterns and threats to Maine terrestrial vertebrate diversity are summarized.

Landscape Ecology↗