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Conspecific negative density dependence (CNDD) promotes tree species diversity by reducing recruitment near conspecific adults due to biotic feedbacks from herbivores, pathogens, or competitors. While this process is well-described in tropical forests, tests of temperate tree species range from strong positive to strong negative density dependence. To explain this, several studies have suggested that tree species traits may help predict the strength and direction of density dependence: for example, ectomycorrhizal-associated tree species typically exhibit either positive or weaker negative conspecific density dependence. More generally, the strength of density dependence may be predictably related to other species-specific ecological attributes such as shade tolerance, or the relative local abundance of a species. To test the strength of density dependence and whether it affects seedling community diversity in a temperate forest, we tracked the survival of seedlings of three ectomycorrhizal-associated species experimentally planted beneath conspecific and heterospecific adults on the Prospect Hill tract of the Harvard Forest, in Massachusetts, USA. Experimental seedling survival was always lower under conspecific adults, which increased seedling community diversity in one of six treatments. We compared these results to evidence of CNDD from observed sapling survival patterns of 28 species over approximately 8 years in an adjacent 35-ha forestmore »plot. We tested whether species-specific estimates of CNDD were associated with mycorrhizal association, shade tolerance, and local abundance. We found evidence of significant, negative conspecific density dependence (CNDD) in 23 of 28 species, and positive conspecific density dependence in two species. Contrary to our expectations, ectomycorrhizal-associated species generally exhibited stronger (e.g., more negative) CNDD than arbuscular mycorrhizal-associated species. CNDD was also stronger in more shade-tolerant species but was not associated with local abundance. Conspecific adult trees often have a negative influence on seedling survival in temperate forests, particularly for tree species with certain traits. Here we found strong experimental and observational evidence that ectomycorrhizal-associating species consistently exhibit CNDD. Moreover, similarities in the relative strength of density dependence from experiments and observations of sapling mortality suggest a mechanistic link between negative effects of conspecific adults on seedling and sapling survival and local tree species distributions.« less

Disturbance regimes can strongly influence geographic patterns of biodiversity. The types of disturbances and their frequencies can have varying impacts on different dimensions of biodiversity and taxonomic groups, and their influence can also vary with spatial scale. Yet disturbance layers are lacking at sufficiently high spatial resolution and extent to uncover these relationships with biodiversity. We detected disturbances for the conterminous United States from Landsat time series using the established LandTrendr temporal segmentation with a novel secondary classification that incorporates spatial context. We then included these disturbance layers, aggregated to metrics at different temporal and spatial scales, into model of species richness at National Ecological Observatory Network sites.

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in theirmore »ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.« less