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Abstract Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co‐occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well‐characterized by the set of functional traits generally measured by ecologists.To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co‐occurrence in the hyper‐diverse genusFicus. Our study focused on over 800 genes related to drought and defence, providing detailed information on how these genes may contribute to the diversity ofFicusspecies.We find widespread and consistent evidence of the importance of defence gene dissimilarity in co‐occurring species, providing genetic support for what would be expected under the Janzen‐Connell mechanism. We also find that drought‐related gene sequence similarity is related toFicusco‐occurrence, indicating that similar responses to drought promote co‐occurrence.Synthesis. We provide the first detailed functional genomic evidence of how drought‐ and defence‐related genes simultaneously contribute to the local co‐occurrence in a hyper‐diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co‐occurrence in hyper‐diverse tropical tree genera. 

Abstract Forest trees provide critical ecosystem services for humanity that are under threat due to ongoing global change. Measuring and characterizing genetic diversity are key to understanding adaptive potential and developing strategies to mitigate negative consequences arising from climate change. In the area of forest genetic diversity, genetic divergence caused by large-scale changes at the chromosomal level has been largely understudied. In this study, we used the RNA-seq data of 20 co-occurring forest trees species from genera including Acer, Alnus, Amelanchier, Betula, Cornus, Corylus, Dirca, Fraxinus, Ostrya, Populus, Prunus, Quercus, Ribes, Tilia, and Ulmus sampled from Upper Peninsula of Michigan. These data were used to infer the origin and maintenance of gene family variation, species divergence time, as well as gene family expansion and contraction. We identified a signal of common whole genome duplication events shared by core eudicots. We also found rapid evolution, namely fast expansion or fast contraction of gene families, in plant–pathogen interaction genes amongst the studied diploid species. Finally, the results lay the foundation for further research on the genetic diversity and adaptive capacity of forest trees, which will inform forest management and conservation policies. 

The search for simple principles that underlie the spatial structure and dynamics of plant communities is a long-standing challenge in ecology. In particular, the relationship between species coexistence and the spatial distribution of plants is challenging to resolve in species-rich communities. Here we present a comprehensive analysis of the spatial patterns of 720 tree species in 21 large forest plots and their consequences for species coexistence. We show that species with low abundance tend to be more spatially aggregated than more abundant species. Moreover, there is a latitudinal gradient in the strength of this negative aggregation–abundance relationship that increases from tropical to temperate forests. We suggest, in line with recent work, that latitudinal gradients in animal seed dispersal and mycorrhizal associations may jointly generate this pattern. By integrating the observed spatial patterns into population models8, we derive the conditions under which species can invade from low abundance in terms of spatial patterns, demography, niche overlap and immigration. Evaluation of the spatial-invasion condition for the 720 tree species analysed suggests that temperate and tropical forests both meet the invasion criterion to a similar extent but through contrasting strategies conditioned by their spatial patterns. Our approach opens up new avenues for the integration of observed spatial patterns into ecological theory and underscores the need to understand the interaction among spatial patterns at the neighbourhood scale and multiple ecological processes in greater detail. 

Abstract Metabolomics provides an unprecedented window into diverse plant secondary metabolites that represent a potentially critical niche dimension in tropical forests underlying species coexistence. Here, we used untargeted metabolomics to evaluate chemical composition of 358 tree species and its relationship with phylogeny and variation in light environment, soil nutrients, and insect herbivore leaf damage in a tropical rainforest plot. We report no phylogenetic signal in most compound classes, indicating rapid diversification in tree metabolomes. We found that locally co‐occurring species were more chemically dissimilar than random and that local chemical dispersion and metabolite diversity were associated with lower herbivory, especially that of specialist insect herbivores. Our results highlight the role of secondary metabolites in mediating plant–herbivore interactions and their potential to facilitate niche differentiation in a manner that contributes to species coexistence. Furthermore, our findings suggest that specialist herbivore pressure is an important mechanism promoting phytochemical diversity in tropical forests. 

Abstract Background and Aims The composition and dynamics of plant communities arise from individual-level demographic outcomes, which are driven by interactions between phenotypes and the environment. Functional traits that can be measured across plants are frequently used to model plant growth and survival. Perhaps surprisingly, species average trait values are often used in these studies and, in some cases, these trait values come from other regions or averages calculated from global databases. This data aggregation potentially results in a large loss of valuable information that probably results in models of plant performance that are weak or even misleading. Methods We present individual-level trait and fine-scale growth data from >500 co-occurring individual trees from 20 species in a Chinese tropical rain forest. We construct Bayesian models of growth informed by theory and construct hierarchical Bayesian models that utilize both individual- and species-level trait data, and compare these models with models only using individual-level data. Key Results We show that trait–growth relationships measured at the individual level vary across species, are often weak using commonly measured traits and do not align with the results of analyses conducted at the species level. However, when we construct individual-level models of growth using leaf area ratio approximations and integrated phenotypes, we generated strong predictive models of tree growth. Conclusions Here, we have shown that individual-level models of tree growth that are built using integrative traits always outperform individual-level models of tree growth that use commonly measured traits. Furthermore, individual-level models, generally, do not support the findings of trait–growth relationships quantified at the species level. This indicates that aggregating trait and growth data to the species level results in poorer and probably misleading models of how traits are related to tree performance.