Temporal trends in breeding bird diversity across North America: a linear mixed-effects model analysis.
Keywords:
Breeding Bird Survey; biodiversity monitoring; linear mixed-effects models; alpha diversity; beta diversity; species richness; biotic homogenization; North America; long-term trends; climate effectsAbstract
Long-term biodiversity monitoring data require analytical frameworks that can simultaneously capture continental-scale trends and local variation. We applied linear mixed-effects models (LMMs) to data from the North American Breeding Bird Survey (BBS) spanning 440 survey routes and 24 years (1984–2007) to assess temporal trends in six biodiversity indices – species richness (S), total abundance (N), Shannon entropy (H), and Simpson-based indices (D, 1−D, 1/D) – and to evaluate whether climate (temperature, precipitation) and vegetation (NDVI) can account for the observed trends. Fixed effects in LMMs estimate trends in mean local (alpha) diversity, while random effects quantify heterogeneity among locations (beta diversity). All biodiversity indices showed statistically significant directional trends over the study period. Species richness, Shannon entropy, and evenness-related indices increased moderately, while total abundance declined. Intraclass correlation coefficients (ICC = 0.75–0.96) confirmed that location identity accounted for the dominant share of variance in all indices. Climate and vegetation explained negligible fractions of the temporal trends: adding temperature, precipitation, and NDVI to the models caused no meaningful attenuation of the year coefficient for any index except abundance. LMMs offer substantial advantages over location-by-location regression, including partial pooling of information across sites, formal decomposition of variance between alpha and beta levels, and valid inference under the clustered structure of monitoring data.
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