摘要：Recent advances in remote sensing and ecological modeling warrant a timely and robust investigation of the ecological variables that underlie large-scale patterns of breeding bird species richness, particularly in the context of intensifying land use and climate change. Our objective was to address this need using an array of bioclimatic and remotely sensed data sets representing vegetation properties and structure, and other aspects of the physical environment. We first build models of bird species richness across breeding bird survey (BBS) routes, and then spatially predict richness across the coterminous US at moderately high spatial resolution (1 km). Predictor variables were derived from various sources and maps of species richness were generated for four groups (guilds) of birds with different breeding habitat affiliation (forest, grassland, open woodland, scrub/shrub), as well as all guilds combined. Predictions of forest bird distributions were strong (R2 = 0.85), followed by grassland (0.76), scrub/shrub (0.63) and open woodland (0.60) species. Vegetation properties were generally the strongest determinants of species richness, whereas bioclimatic and lidar-derived vertical structure metrics were of variable importance and dependent upon the guild type. Environmental variables (climate and the physical environment) were also frequently selected predictors, but canopy structure variables were not as important as expected based on more local to regional scale studies. Relatively sparse sampling of canopy structure metrics from the satellite lidar sensor may have reduced their importance relative to other predictor variables across the study domain. We discuss these results in the context of the ecological drivers of species richness patterns, the spatial scale of bird diversity analyses, and the potential of next generation space-borne lidar systems relevant to vegetation and ecosystem studies. This study strengthens current understanding of bird species–climate–vegetation relationships, which could be further advanced with improved canopy structure information across spatial scales.
