Bird Communities and Environmental Correlates in Southern Oregon and Northern California, USA

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

1. 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. 2. 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. 3. 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. 4. 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. 5. 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.

Established and empirically derived landbird focal species lists correlate with vegetation and avian metrics

Surrogate species are commonly used in conservation science due to the fact that it is not feasible to measure and manage each component of biodiversity independently; yet, there is much debate about their efficacy. We use long-term monitoring data from six national park units in northern California and southern Oregon to test the focal species approach, wherein a suite of species is selected whose habitat requirements collectively encompass those of co-occurring species. Specifically, we examine how well existing Partners in Flight (PIF) habitat-based focal species lists and empirically derived focal species lists represent vegetation and three avian assemblages of interest: the entire assemblage, species of concern, and common species in steep decline. Existing PIF focal species lists were significantly correlated with the three alternative matrices of avian assemblages and vegetation, but not all parks and alternate matrices performed with equal correlative strength and/or significance. For example, existing PIF focal species lists were significantly correlated to the entire assemblage at five of the six parks and had ecologically meaningful correlations (>0.70) at four. However, PIF focal species list correlations with park specific species of concern and common species in steep declined varied widely, with correlations between 0.040-0.943 and 0.210-0.556, respectively. Averaged across park units the empirical focal species lists developed to represent both vegetation metrics and species of concern improved correlation with all alternative matrices of avian assemblages and vegetation metrics. We found that the focal species approach generally represented the entire avian community, but did not adequately represent suites of species of concern or common species in decline. Empirical testing is a critical step in validating or refining suites of focal species at management relevant scales, and in some instances, a more refined focal species list may increase overall utility of the surrogate species approach.