The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains

Understanding the spatial scales at which environmental factors drive
species richness patterns is a major challenge in ecology. Due to the
trade-off between spatial grain and extent, studies tend to focus on a
single spatial scale, and the effects of multiple environmental variables
operating across spatial scales on the pattern of local species richness
have rarely been investigated. Here, we related variation in local species
richness of ground beetles, landbirds, and small mammals to variation in
vegetation structure and topography, regional climate, biome diversity,
and glaciation history for 27 sites across the USA at two different
spatial grains. We studied the relative influence of broad-scale
(landscape) environmental conditions using variables estimated at the site
level (climate, productivity, biome diversity, and glacial era ice cover)
and fine-scale (local) environmental conditions using variables estimated
at the plot level (topography and vegetation structure) to explain local
species richness. We also examined whether plot-level factors scale up to
drive continental scale richness patterns. We used Bayesian hierarchical
models and quantified the amount of variance in observed richness that was
explained by environmental factors at different spatial scales. For all
three animal groups, our models explained much of the variation in local
species richness (85-89%), but site-level variables explained a greater
proportion of richness variance than plot-level variables. Temperature was
the most important site-level predictor for explaining variance in
landbirds and ground beetles richness. Some aspects of vegetation
structure were the main plot-level predictors of landbird richness.
Environmental predictors generally had poor explanatory power for small
mammal richness, while glacial era ice cover was the most important
site-level predictor. Relationships between plot-level factors and
richness varied greatly among geographical regions and spatial grains, and
most relationships did not hold when predictors were scaled up to
continental scale. Our results suggest that the factors that determine
richness may be highly dependent on spatial grain, geography, and animal
group. We demonstrate that instead of artificially manipulating the
resolution to study multi-scale effects, a hierarchical approach that uses
fine grain data at broad extents could help solve the issue of scale
selection in environment-richness studies.