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.

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

We examined avian community ecology in the Klamath Ecoregion and determined that individual bird species co-exist spatially to form 29 statistically distinguishable bird groups. We identified climate, geography, and vegetation metrics that are correlated with these 29 bird groups at three scales: Klamath Ecoregion, vegetation formation (agriculture, conifer, mixed conifer/hardwood, shrubland), and National Park Service unit. Two climate variables (breeding season mean temperature and temperature range) and one geography variable (elevation) were correlated at all scales, suggesting that for some vegetation formations and park units there is sufficient variation in climate and geography to be an important driver of bird communities, a level of variation we expected only at the broader scale. We found vegetation to be important at all scales, with coarse metrics (environmental site potential and existing vegetation formation) meaningful across all scales and structural vegetation patterns (e.g. succession, disturbance) important only at the scale of vegetation formation or park unit. Additionally, we examined how well six National Park Service units represent bird communities in the broader Klamath Ecoregion. Park units are inclusive of most bird communities with the exception of the oak woodland community; mature conifer forests are well represented, primarily associated with conifer canopy and lacking multi-layered structure. Identifying environmental factors that shape bird communities at three scales within this region is important; such insights can inform local and regional land management decisions necessary to ensure bird conservation in this globally significant region.

A power analysis for multivariate tests of temporal trend in species composition.

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.