Species packing and the latitudinal gradient in local beta-diversity

The decline in species richness at higher latitudes is among the most fundamental patterns in ecology. Whether changes in species composition across space (beta-diversity) contribute to this gradient of overall local species richness (gamma diversity) remains hotly debated. Previous studies that failed to resolve the issue suffered from a well-known tendency for small samples in areas with high gamma-diversity to have inflated measures of beta-diversity. We provide here a novel analytical test, using beta-diversity metrics that correct the gamma-diversity and sampling bias, to compare beta-diversity and species-packing across a latitudinal gradient in tree species richness of 21 large forest plots along a wide environmental gradient in East Asia. We demonstrate that after accounting for topography and correcting the gamma-diversity bias, tropical forests still have higher beta-diversity than temperate analogs. This suggests that local beta-diversity contributes to the latitudinal species richness gradient as a component of gamma-diversity. Moreover, niche specialization and niche marginality (a measure of niche spacing along a habitat gradient) also increases towards the equator, after removing the effect of topographic heterogeneity. This supports the hypothesis of tighter species packing and larger niche space in tropical forests while demonstrating the importance of local processes in controlling beta-diversity.The decline in species richness at higher latitudes is among the most fundamental patterns in ecology. Whether changes in species composition across space (beta-diversity) contribute to this gradient of overall local species richness (gamma diversity) remains hotly debated. Previous studies that failed to resolve the issue suffered from a well-known tendency for small samples in areas with high gamma-diversity to have inflated measures of beta-diversity. We provide here a novel analytical test, using beta-diversity metrics that correct the gamma-diversity and sampling bias, to compare beta-diversity and species-packing across a latitudinal gradient in tree species richness of 21 large forest plots along a wide environmental gradient in East Asia. We demonstrate that after accounting for topography and correcting the gamma-diversity bias, tropical forests still have higher beta-diversity than temperate analogs. This suggests that local beta-diversity contributes to the latitudinal species richness gradient as a component of gamma-diversity. Moreover, niche specialization and niche marginality (a measure of niche spacing along a habitat gradient) also increases towards the equator, after removing the effect of topographic heterogeneity. This supports the hypothesis of tighter species packing and larger niche space in tropical forests while demonstrating the importance of local processes in controlling beta-diversity.

We used data from 21 forest dynamics plots that are part of the ForestGEO and Chinese Forest Biodiversity Monitoring Networks (Anderson-Teixeira et al. 2015; Feng et al. 2016). All stems with diameter at breast height (DBH) ≥ 1 cm were spatially mapped, tagged, measured and identified to species (Condit 1998). To remove gamma-diversity dependence caused by sample-size bias of beta-diversity metrics, we used the correction method designed for Shannon diversity index based on the relationship between cumulative diversity and sample size (Chao et al. 2013). we used the surface to planimetric area ratio as a metric of topographic heterogeneity, which provided a useful measure of the range and roughness of the overall plot, based on the digital elevation models (DEMs) (Jenness 2004; Brown et al. 2013). Niche marginality was defined as the ecological distance between each species’ optimum and the mean habitat within the plot (illustrated in electronic supplementary material, figure S1) (Hirzel et al. 2002; Devictor et al. 2010). Specialization and marginality were calculated for each species using a multivariate approach – ecological niche factor analysis (ENFA) (Hirzel et al. 2002).,