Global remote-sensing derived measures of habitat heterogeneity for biodiversity modeling

Background/Question/Methods

Habitat heterogeneity has long been recognized as an important determinant of species distributions and biodiversity patterns. However, different to many environmental factors, such as climatic variables and productivity, currently no standardized compilation of multiple characteristics of habitat heterogeneity exists. Due to a lack of direct measures of habitat heterogeneity across large areas, most broad-scale studies use metrics derived from categorical land cover or use topographic variability as a surrogate. While the former approach ignores heterogeneity within a land cover type, the later provides a spatially inconsistent and temporally static surrogate. To address this issue, here we present a suite of 1 km global data layers that capture key aspects of habitat heterogeneity based on the textural features of the Enhanced Vegetation Index (EVI) imagery from MODIS. Using the Breeding Bird Survey data and functional traits of bird species, we evaluated the ability of these heterogeneity metrics to model community attributes, such as species richness and functional diversity, across the conterminous U.S.

Results/Conclusions

The EVI-based heterogeneity metrics capture the composition and spatial configuration of habitat patches with different EVI values. Because EVI measures biophysical characteristics of vegetation at a continuous scale, these metrics capture habitat heterogeneity not only among, but also within land cover types. The evaluation shows that the EVI-based heterogeneity metrics exceed the conventional topography- and land cover-based metrics in terms of their ability to explain the variation in both bird species richness and functional diversity among communities. While the heterogeneity metrics, compared to productivity (i.e., NPP), generally explain less variation in the community attributes, their relative importance increases at a larger scale. This study provides readily-useable data layers capturing the global patterns of habitat heterogeneity and demonstrates their usefulness for broad-scale biodiversity modeling. We expect the advanced and standardized heterogeneity measurements to help support the comparability and effectiveness of multiple potential uses in biodiversity research.