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Leaf dark respiration (Rdark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (Vcmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed Rdark variability and its associations with Vcmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site Rdark than commonly used traits like LMA, N and P concentrations, but univariate trait-Rdark relationships were always weak (r2 ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-Rdark relationships, accurately predicted cross-site Rdark (r2 = 0.65) and pinpointed the factors contributing to Rdark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding Rdark, challenging the use of empirical trait-Rdark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating Rdark, which could ultimately improve process modeling of terrestrial plant respiration.

Lianas, climbing woody plants, influence the structure and function of tropical forests. Climbing traits have evolved multiple times, including ancestral groups such as gymnosperms and pteridophytes, but the genetic basis of the liana strategy is largely unknown. Here, we use a comparative transcriptomic approach for 47 tropical plant species, including ten lianas of diverse taxonomic origins, to identify genes that are consistently expressed or downregulated only in lianas. Our comparative analysis of full-length transcripts enabled the identification of a core interactomic network common to lianas. Sets of transcripts identified from our analysis reveal features related to functional traits pertinent to leaf economics spectrum in lianas, include upregulation of genes controlling epidermal cuticular properties, cell wall remodeling, carbon concentrating mechanism, cell cycle progression, DNA repair and a large suit of downregulated transcription factors and enzymes involved in ABA-mediated stress response as well as lignin and suberin synthesis. All together, these genes are known to be significant in shaping plant morphologies through responses such as gravitropism, phyllotaxy and shade avoidance.

Transpiration in humid tropical forests modulates the global water cycle and is a key driver of climate regulation. Yet, our understanding of how tropical trees regulate sap flux in response to climate variability remains elusive. With a progressively warming climate, atmospheric evaporative demand [i.e., vapor pressure deficit (VPD)] will be increasingly important for plant functioning, becoming the major control of plant water use in the twenty-first century. Using measurements in 34 tree species at seven sites across a precipitation gradient in the neotropics, we determined how the maximum sap flux velocity (vmax) and the VPD threshold at which vmax is reached (VPDmax) vary with precipitation regime [mean annual precipitation (MAP); seasonal drought intensity (PDRY)] and two functional traits related to foliar and wood economics spectra [leaf mass per area (LMA); wood specific gravity (WSG)]. We show that, even though vmax is highly variable within sites, it follows a negative trend in response to increasing MAP and PDRY across sites. LMA and WSG exerted little effect on vmax and VPDmax, suggesting that these widely used functional traits provide limited explanatory power of dynamic plant responses to environmental variation within hyper-diverse forests. This study demonstrates that long-term precipitation plays an important role in the sap flux response of humid tropical forests to VPD. Our findings suggest that under higher evaporative demand, trees growing in wetter environments in humid tropical regions may be subjected to reduced water exchange with the atmosphere relative to trees growing in drier climates.

Abiotic environments and biotic neighbourhoods interact to influence plant growth and community assembly. However, the nature of this interaction depends very much on how biotic neighbourhoods are measured, including their relatedness to focal plants. In a tropical seasonal rainforest, we examine the growth of a dominant canopy species in response to environmental factors, the densities and relatedness of conspecific and heterospecific neighbours, and their interactions. We find significant environmental effects and conspecific negative density dependence on growth. Furthermore, conspecific neighbour density has stronger negative effects on growth under high light and soil water resource levels, but weaker negative effects under low light and soil water resource levels. In addition, more closely related heterospecifics in the neighbourhood have negative effects on growth under high soil phosphorus availability, but positive effects under low soil phosphorus availability. In contrast, more closely related conspecifics in the neighbourhood have negative effects on growth under low soil potassium availability, but positive effects under high soil potassium availability. Our study emphasizes the importance of both intra- and interspecific neighbourhood composition and their interactions with resource levels for understanding tree growth. This enhances our understanding of the complex processes in community assembly and species coexistence within forest communities. [ABSTRACT FROM AUTHOR]

Much ecological research aims to explain how climate impacts biodiversity and ecosystem-level processes through functional traits that link environment with individual performance. However, the specific climatic drivers of functional diversity across space and time remain unclear due largely to limitations in the availability of paired trait and climate data. We compile and analyze a global forest dataset using a method based on abundance-weighted trait moments to assess how climate influences the shapes of whole-community trait distributions. Our approach combines abundance-weighted metrics with diverse climate factors to produce a comprehensive catalog of trait-climate relationships that differ dramatically-27% of significant results change in sign and 71% disagree on sign, significance, or both-from traditional species-weighted methods. We find that (i) functional diversity generally declines with increasing latitude and elevation, (ii) temperature variability and vapor pressure are the strongest drivers of geographic shifts in functional composition and ecological strategies, and (iii) functional composition may currently be shifting over time due to rapid climate warming. Our analysis demonstrates that climate strongly governs functional diversity and provides essential information needed to predict how biodiversity and ecosystem function will respond to climate change.

Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees have been linked to mechanisms that maintain biodiversity across tropical forests. How species survival rates change with size offers insight into the links between biodiversity and ecosystem function across tropical forests. We tested patterns of size-dependent tree survival across the tropics using data from 1,781 species and over 2 million individuals to assess whether tropical forests can be characterized by size-dependent life-history survival strategies. We found that species were classifiable into four 'survival modes' that explain life-history variation that shapes carbon cycling and the relative abundance within forests. Frequently collected functional traits, such as wood density, leaf mass per area and seed mass, were not generally predictive of the survival modes of species. Mean annual temperature and cumulative water deficit predicted the proportion of biomass of survival modes, indicating important links between evolutionary strategies, climate and carbon cycling. The application of survival modes in demographic simulations predicted biomass change across forest sites. Our results reveal globally identifiable size-dependent survival strategies that differ across diverse systems in a consistent way. The abundance of survival modes and interaction with climate ultimately determine forest structure, carbon storage in biomass and future forest trajectories.

The nutrient demands of regrowing tropical forests are partly satisfied by nitrogen-fixing legume trees, but our understanding of the abundance of those species is biased towards wet tropical regions. Here we show how the abundance of Leguminosae is affected by both recovery from disturbance and large-scale rainfall gradients through a synthesis of forest inventory plots from a network of 42 Neotropical forest chronosequences. During the first three decades of natural forest regeneration, legume basal area is twice as high in dry compared with wet secondary forests. The tremendous ecological success of legumes in recently disturbed, water-limited forests is likely to be related to both their reduced leaflet size and ability to fix N2, which together enhance legume drought tolerance and water-use efficiency. Earth system models should incorporate these large-scale successional and climatic patterns of legume dominance to provide more accurate estimates of the maximum potential for natural nitrogen fixation across tropical forests.

AIM: To test the latitudinal gradient in plant species diversity for self‐similarity across taxonomic scales and amongst taxa. LOCATION: North America. METHODS: We used species richness data from 245 local vascular plant floras to quantify the slope and shape of the latitudinal gradients in species diversity (LGSD) across all plant species as well as within each family and order. We calculated the contribution of each family and order to the empirical LGSD. RESULTS: We observed the canonical LGSD when all plants were considered with floras at the lowest latitudes having, on average, 451 more species than floras at the highest latitudes. When considering slope alone, most orders and families showed the expected negative slope, but 31.7% of families and 27.7% of orders showed either no significant relationship between latitude and diversity or a reverse LGSD. Latitudinal patterns of family diversity account for at least 14% of this LGSD. Most orders and families did not show the negative slope and concave‐down quadratic shape expected by the pattern for all plant species. A majority of families did not make a significant contribution in species to the LGSD with 53% of plant families contributing little to nothing to the overall gradient. Ten families accounted for more than 70% of the gradient. Two families, the Asteraceae and Fabaceae, contributed a third of the LGSD. MAIN CONCLUSIONS: The empirical LGSD we describe here is a consequence of a gradient in the number of families and diversification within relative few plant families. Macroecological studies typically aim to generate models that are general across taxa with the implicit assumption that the models are general within taxa. Our results strongly suggest that models of the latitudinal gradient in plant species richness that rely on environmental covariates (e.g. temperature, energy) are likely not general across plant taxa.

Understanding the role of biodiversity (B) in maintaining ecosystem function (EF) is a foundational scientific goal with applications for resource management and conservation. Two main hypotheses have emerged that address B-EF relationships: niche complementarity (NC) and the mass-ratio (MR) effect. We tested the relative importance of these hypotheses in a subtropical old-growth forest on the island nation of Taiwan for two EFs: aboveground biomass (ABG) and coarse woody productivity (CWP). Functional dispersion (FDis) of eight plant functional traits was used to evaluate complementarity of resource use. Under the NC hypothesis, EF will be positively correlated with FDis. Under the MR hypothesis, EF will be negatively correlated with FDis and will be significantly influenced by community-weighted mean (CWM) trait values. We used path analysis to assess how these two processes (NC and MR) directly influence EF and may contribute indirectly to EF via their influence on canopy packing (stem density). Our results indicate that decreasing functional diversity and a significant influence of CWM traits were linked to increasing AGB for all eight traits in this forest supporting the MR hypothesis. Interestingly, CWP was primarily influenced by NC and MR indirectly via their influence on canopy packing. Maximum height explained more of the variation in both AGB and CWP than any of the other plant functional traits. Together, our results suggest that multiple mechanisms operate simultaneously to influence EF, and understanding their relative importance will help to elucidate the role of biodiversity in maintaining ecosystem function.

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. Detailed maps of tree species can be used for forest analysis, biogeographic research and ecosystem monitoring. This study provides individual tree species maps for 24 sites in the United States National Ecological Observatory Network (NEON), covering 80 tree species and 100 million individuals. [ABSTRACT FROM AUTHOR]

The opportunity to reflect broadly on the accomplishments, prospects, and reach of a field may present itself relatively infrequently. Each biennial meeting of the International Biogeography Society showcases ideas solicited and developed largely during the preceding year, by individuals or teams from across the breadth of the discipline. Here, we highlight challenges, developments, and opportunities in biogeography from that biennial synthesis. We note the realized and potential impact of rapid data accumulation in several fields, a renaissance for inter-disciplinary research, the importance of recognizing the evolution-ecology continuum across spatial and temporal scales and at different taxonomic, phylogenetic and functional levels, and re-exploration of classical assumptions and hypotheses using new tools. However, advances are taxonomically and geographically biased, key theoretical frameworks await tools to handle, or strategies to simplify, the biological complexity seen in empirical systems. Current threats to biodiversity require unprecedented integration of knowledge and development of predictive capacity that may enable biogeography to unite its descriptive and hypothetico-deductive branches and establish a greater role within and outside academia.