Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the U.S.

Species loss is occurring globally at unprecedented rates, and effective
conservation planning requires an understanding of landscape
characteristics that determine biodiversity patterns. Habitat
heterogeneity is an important determinant of species diversity, but is
difficult to measure across large areas using field-based methods that are
costly and logistically challenging. Satellite image texture analysis
offers a cost-effective alternative for quantifying habitat heterogeneity
across broad spatial scales. We tested the ability of texture measures
derived from 30-m resolution Enhanced Vegetation Index (EVI) data to
capture habitat heterogeneity and predict bird species richness across the
conterminous U.S. We used Landsat 8 satellite imagery from 2013-2017 to
derive a suite of texture measures characterizing vegetation heterogeneity
(available at http://silvis.forest.wisc.edu/webmap/landsat8-evi-textures).
Individual texture measures explained up to 21% of the variance in bird
richness patterns in North American Breeding Bird Survey (BBS) data during
the same time period. Texture measures were positively related to total
breeding bird richness, but this relationship varied among forest,
grassland, and shrubland habitat specialists. Multiple texture measures
combined with mean EVI explained up to 41% of the variance in total bird
richness, and models including EVI-based texture measures explained up to
10% more variance than those that included only EVI. Models that also
incorporated topographic and land cover metrics further improved
predictive performance, explaining up to 51% of the variance in total bird
richness. A texture measure contributed predictive power and characterized
landscape features that EVI and forest cover alone could not, even though
the latter two were overall more important variables. Our results
highlight the potential of texture measures for mapping habitat
heterogeneity and species richness patterns across broad spatial extents,
especially when used in conjunction with vegetation indices or land cover
data. By generating 30-m resolution texture maps and modeling bird
richness at a near-continental scale, we expand on previous applications
of image texture measures for modeling biodiversity that were either
limited in spatial extent or based on coarse resolution imagery.
Incorporating texture measures into broad-scale biodiversity models may
advance our understanding of mechanisms underlying species richness
patterns and improve predictions of species responses to rapid global
change.