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When species simultaneously compete with two or more species of competitor, higher-order interactions (HOIs) can lead to emergent properties not present when species interact in isolated pairs. To extend ecological theory to multi-competitor communities, ecologists must confront the challenges of measuring and interpreting HOIs in models of competition fit to data from nature. Such efforts are hindered by the fact that different studies use different definitions, and these definitions have unclear relationships to one another. Here, we propose a distinction between 'soft' HOIs, which identify possible interaction modification by competitors, and 'hard' HOIs, which identify interactions uniquely emerging in systems with three or more competitors. We show how these two classes of HOI differ in their motivation and interpretation, as well as the tests one uses to identify them in models fit to data. We then show how to operationalise this structure of definitions by analysing the results of a simulated competition experiment underlain by a consumer resource model. In the course of doing so, we clarify the challenges of interpreting HOIs in nature, and suggest a more precise framing of this research endeavour to catalyse further investigations.

Many tropical regions are experiencing an intensification of drought, with increasing severity and frequency. The ecosystem response to these changes is still highly uncertain. On short time scales (from diurnal to seasonal), tropical forests respond to water stress by physiological controls, such as stomatal regulation and phenological adjustment, to cope with increasing atmospheric water demand and reduced water supply. However, the interactions among biological processes and co-varying environmental factors that determine the ecosystem-level fluxes are still unclear. Furthermore, climate variability at longer time scales, such as that generated by ENSO, produces less predictable effects because it depends on a highly stochastic combination of factors that might vary among forests and even between events in the same forest. This study will present some emerging patterns of response to water stress from 5 years of water, carbon, and energy fluxes observed on a seasonal tropical forest in central Panama, including an increase in productivity during the 2015 El Niño. These responses depend on the combination of environmental factors experienced by the forest throughout the seasonal cycle, in particular, increase in solar radiation, stimulating productivity, and increasing vapor pressure deficit (VPD) and decreasing soil moisture, limiting stomata opening. These results suggest a critical role of plant hydraulics in mediating the response to water stress over a broad range of temporal scales (diurnal, intraseasonal, seasonal, and interannual), by acclimating canopy conductance to light and VPD during different soil moisture regimes. A multilayer photosynthesis model coupled with a plant hydraulics scheme can reproduce these complex responses. However, results depend critically on parameters regulating water transport efficiency and the cost of water stress. As these costs have not been properly identified and quantified yet, more empirical research is needed to elucidate physiological mechanisms of hydraulic failure and recover, for example embolism repair and xylem regrowth.

Both competition for water and phenological variation are important determinants of plant community structure, but ecologists lack a synthetic theory for how they affect coexistence outcomes. We developed an analytically tractable model of water competition for Mediterranean annual communities and demonstrated that variation in phenology alone can maintain high diversity in spatially homogenous assemblages of water-limited plants. We modelled a system where all water arrives early in the season and species vary in their ability to grow under drying conditions. As a consequence, species differ in growing season length and compete by shortening the growing season of their competitors. This model replicates and offers mechanistic explanations for patterns observed in empirical studies of how phenology influences coexistence among Mediterranean annuals. Additionally, we found that a decreasing, concave-up trade-off between growth rate and access to water can maintain high diversity under simple but realistic assumptions. High diversity is possible because: (1) later plants escape competition after their earlier season competitors have gone to seed and (2) early-season species are more than compensated for their shortened growing season by a growth rate advantage. Together, these mechanisms provide an explanation for how phenologically variable annual plant species might coexist when competing only for water.

Jesse D. Jenkins is an assistant professor at Princeton University in the department of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. He is a macro-scale energy systems engineer with a focus on the rapidly evolving electricity sector and leads the Princeton ZERO Lab, which focuses on improving and applying optimization-based energy systems models to evaluate low-carbon energy technologies and generate insights to guide policy and planning decisions. Jesse earned a PhD and SM from the Massachusetts Institute of Technology and was previously a postdoctoral environmental fellow at Harvard University. Erin N. Mayfield is an assistant professor at the Thayer School of Engineering at Dartmouth College focusing her research on sustainable-systems engineering and public policy. She develops and applies multi-objective computational models that integrate techno-economic, environmental, and social equity objectives to inform energy and industrial infrastructure transitions. Prior to her academic research career, her work with other organizations included regulatory assessments of environmental rule-makings, hazardous waste remediation in environmental justice communities, and ecosystem services valuation related to petrochemical and pesticide contamination. Erin has a PhD in engineering and public policy from Carnegie Mellon University and was previously a postdoctoral scholar at Princeton University. Eric Larson is a senior research engineer at Princeton University leading the Energy Systems Analysis Group in the Andlinger Center for Energy and the Environment. He holds courtesy appointments with the High Meadows Environmental Institute and the Center for Policy Research on Energy and the Environment in the School of Public and International Affairs. His research, intersecting engineering, environmental science, economics, and public policy, aims to identify sustainable, engineering-based solutions to major energy-related problems and to help inform public and private decision-making in the U.S. and elsewhere. Eric has a PhD in mechanical engineering from the University of Minnesota. Stephen Pacala is the Frederick D. Petrie professor in ecology and evolutionary biology at Princeton University and director of the Princeton Carbon Mitigation Initiative. His research interests are in the design and testing of mathematical models to investigate interactions between greenhouse gases, climate, and the biosphere and to predict their effects on local and global ecosystems. He is a member of the U.S. National Academy of Sciences, the American Association for the Advancement of Science, and the American Academy of Arts and Sciences. Steve has a PhD in biology from Stanford University. Chris Greig is the Theodora D. and William H. Walton III senior research scientist in the Andlinger Center for Energy and the Environment at Princeton University. His research interests lie in energy transitions, economics, and policy; capital project investment decision-making and implementation; and carbon capture, utilization, and storage. Chris has a PhD in chemical engineering from the University of Queensland and is a fellow of the Australian Academy of Technology and Engineering. His academic career was preceded by 25 years in senior project and executive roles in the resources and energy sectors.

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO 2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree–grass competition. Enhanced productivity due to CO 2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

[Purpose] A large fraction of a plant’s biomass is belowground, especially in shrublands that typically occur in episodically water-limited climates. Nonetheless, we have no standardized method to map the distribution of the root density (i.e., biomass per soil volumetric unit) of plant individuals (hereafter, Individual-level Root Density Distribution, IRDD). This type of information is difficult to collect, especially in woody plant communities in natural conditions where roots of different individuals can be highly intermingled., [Methods] We assess three methods to map IRDD of field shrubs: soil drilling to extract roots, plant injection with dyes, and microsatellite analysis for individual-level root identification. Using the resulting data, we fitted IRDD models obtaining comparable predictions of the root density of shrubs for each method., [Results] The proportion of identified roots was higher using plan injection, but the cost per linked roots was two orders of magnitude higher using microsatellite. Model results show that microsatellite markers had a similar success as compared to plant injection for those plant individuals for which it worked well, but it failed completely for several genotypes or individuals., [Conclusion] Core drilling machines and plant injection with dyes of different colors to link root fragments from the sample pool to plant individuals represent an affordable, reliable way to study the foraging behavior of woody plants which roots are highly intermingled., This work was funded by Princeton University’s High Meadows Environmental Institute-Carbon Mitigation Initiative (HMEI-CMI). CC acknowledges funding from The May Fellowship in the Department of Ecology and Evolutionary Biology, Princeton University.

International audience; The 2015−2016 El Niño was one of the strongest on record, but its influence on the carbon balance is less clear. Using Northern Hemisphere atmospheric CO2 observations, we found both detrended atmospheric CO2 growth rate (CGR) and CO2 seasonal-cycle amplitude (SCA) of 2015−2016 were much higher than that of other El Niño events. The simultaneous high CGR and SCA were unusual, because our analysis of long-term CO2 observations at Mauna Loa revealed a significantly negative correlation between CGR and SCA. Atmospheric inversions and terrestrial ecosystem models indicate strong northern land carbon uptake during spring but substantially reduced carbon uptake (or high emissions) during early autumn, which amplified SCA but also resulted in a small anomaly in annual carbon uptake of northern ecosystems in 2015−2016. This negative ecosystem carbon uptake anomaly in early autumn was primarily due to soil water deficits and more litter decomposition caused by enhanced spring productivity. Our study demonstrates a decoupling between seasonality and annual carbon cycle balance in northern ecosystems over 2015−2016, which is unprecedented in the past five decades of El Niño events.

Purpose: A large fraction of a plant’s biomass is thought to be belowground, especially in shrublands that typically occur in episodically water-limited climates. Nonetheless, we have no standardized method to map individual plant’s root density distribution (IRDD) and lack of easily obtainable allometric predictors of shrubland belowground biomass. This type of information is difficult to collect, especially in woody plant communities in natural conditions where roots of different individuals can be highly intermingled.Methods: We assess three methods to map IRDD of field shrubs: soil drilling to extract roots, and plant injection with dyes and microsatellite analysis for individual-level root identification. Using the resulting data, we fitted IRDD models and integrated root density predictions from the models across three-dimensional space obtaining total root biomass of shrubs. We use the resulting individual-level data to establish allometric relations based on two easy-to-measure predictors: crown biovolume and stem diameter at the ground level.Results: We found plant injection to be a highly cost-effective technique to link root fragments from a soil sample to plant individuals. We fitted power law distributions predicting aboveground and belowground biomass.Conclusions: Core drilling machines and plant injection with dyes of different colors to link root fragments from the sample pool to plant individuals represent an affordable, reliable way to study the foraging behavior of woody plants which roots are highly intermingled. The resulting allometric relations allowed us to conclude that crown biovolume is the best proxy for shrub biomass, especially belowground.

Lianas are found in virtually all tropical forests and have strong impacts on the forest carbon cycle by slowing tree growth, increasing tree mortality and arresting forest succession. In a few local studies, ecologists have successfully differentiated lianas from trees using various remote sensing platforms including satellite images. This demonstrates a potential to use remote sensing to investigate liana dynamics at spatio-temporal scales beyond what is currently possible with ground-based inventory censuses. However, why do liana-infested tree crowns and forest stands display distinct spectral signals? And is the spectral signal of lianas only locally unique or consistent across continental and global scales? Unfortunately, we are not yet able to answer these questions, and without such an understanding the limitations and caveats of large-scale application of automated classifiers cannot be understood. Here, we tackle the questions of why we can detect lianas from airborne and spaceborne remote sensing platforms. We identify whether a distinct spectral distribution exists for lianas, when compared to their tree hosts, at the leaf, canopy and stand scales in the solar spectrum (400 to 2500 nm). To do so, we compiled databases of (i) leaf reflectance spectra for over 4771 individual leaves of 539 species, (ii) fine-scale (∼1m2) surface reflectance from 999 tree canopies characterized by different levels of liana infestation in Panama and Malaysia, and (iii) coarse-scale (>100 m2) surface reflectance from hundreds of hectares of heavily infested liana forest stands in French Guiana and Bolivia. Using these data, we find consistent spectral signal of liana-infested canopies across sites with a mean inter-site correlation of 89% (range 74-94%). However, as we find no consistent difference between liana and tree leaves, a distinct liana spectral signal appears to only manifests at the canopy and stand scales (>1m2). To better understand this signal, we implement mechanistic radiative transfer models capable of modeling the vertically stratificatied non-linear mixing of spectral signals intrinsic to lianas infestation of forest canopies. Next, we inversely fit the models to observed spectral signals of lianas at all scales to identify key biochemical or biophysical processes. We then corroborate our model results with field data on liana leaf chemistry and canopy structural properties. Our results suggest that a liana-specific spectral distribution arises due to the combination of cheaply constructed leaves and efficient light interception. A model experiment revealed that the spectral distribution was most sensitive to lower leaf and water mass per unit area, affecting the absorption of NIR and SWIR radiation, and a more planophile (flatter) leaf angle distribution. Finally, we evaluate the theoretical discernibility of lianas from trees and how this varies with remote sensing platforms and resolution. We end by discussing the potential, limitations and risks of applying automated classifiers to detect lianas from remotely sensed data at large scales.

Plant roots determine carbon uptake, survivorship, and agricultural yield and represent a large proportion of the world's vegetation carbon pool. Study of belowground competition, unlike aboveground shoot competition, is hampered by our inability to observe roots. We developed a consumer-resource model based in game theory that predicts the root density spatial distribution of individual plants and tested themodel predictions in a greenhouse experiment. Plants in the experiment reacted to neighbors as predicted by the model's evolutionary stable equilibrium, by both overinvesting in nearby roots and reducing their root foraging range. We thereby provide a theoretical foundation for belowground allocation of carbon by vegetation that reconciles seemingly contradictory experimental results such as root segregation and the tragedy of the commons in plant roots., This work was supported by the Princeton University May Fellowship in the department of Ecology and Evolutionary Biology, the Gordon and Betty Moore Foundation through grant GBMF2550.06,Instituto Serrapilheira through grant Serra-1911-31200, FAPESP through grants ICTP-SAIFR 2016/01343-7, and Programa Jovens Pesquisadores em Centros Emergentes 2019/24433-0, the Simons Foundation, the Spanish 235 Ministry for Science, Innovation and Universities (COMEDIAS grant CGL2017-83170-R), and the Princeton Environmental Institute Carbon Mitigation Initiative.

Regression dilution is a statistical inference bias that causes underestimation of the strength of dependency between two variables when the predictors are error-prone proxies (EPPs). EPPs are widely used in plant community studies focused on negative density-dependence (NDD) to quantify competitive interactions. Because of the nature of the bias, conspecific NDD is often overestimated in recruitment analyses, and in some cases, can be erroneously detected when absent. In contrast, for survival analyses, EPPs typically cause NDD to be underestimated, but underestimation is more severe for abundant species and for heterospecific effects, thereby generating spurious negative relationships between the strength of NDD and the abundances of con- and heterospecifics. This can explain why many studies observed rare species to suffer more severely from conspecific NDD, and heterospecific effects to be disproportionally smaller than conspecific effects. In general, such species-dependent bias is often related to traits associated with likely mechanisms of NDD, which creates false patterns and complicates the ecological interpretation of the analyses. Classic examples taken from literature and simulations demonstrate that this bias has been pervasive, which calls into question the emerging paradigm that intraspecific competition has been demonstrated by direct field measurements to be generally stronger than interspecific competition.

© 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Drought-induced tree mortality is expected to increase in future climates with the potential for significant consequences to global carbon, water, and energy cycles. Xylem embolism can accumulate to lethal levels during drought, but species that can refill embolized xylem and recover hydraulic function may be able to avoid mortality. Yet the potential controls of embolism recovery, including cross-biome patterns and plant traits such as nonstructural carbohydrates (NSCs), hydraulic traits, and nocturnal stomatal conductance, are unknown. We exposed eight plant species, originating from mesic (tropical and temperate) and semi-arid environments, to drought under ambient and elevated CO 2 levels, and assessed recovery from embolism following rewatering. We found a positive association between xylem recovery and NSCs, and, surprisingly, a positive relationship between xylem recovery and nocturnal stomatal conductance. Arid-zone species exhibited greater embolism recovery than mesic zone species. Our results indicate that nighttime stomatal conductance often assumed to be a wasteful use of water, may in fact be a key part of plant drought responses, and contribute to drought survival. Findings suggested distinct biome-specific responses that partially depended on species climate-of-origin precipitation or aridity index, which allowed some species to recover from xylem embolism. These findings provide improved understanding required to predict the response of diverse plant communities to drought. Our results provide a framework for predicting future vegetation shifts in response to climate change.

Tree allometric relationships are widely employed for estimating forest biomass and production and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to capture changes in scaling during ontogeny and physical limits to maximum tree size and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter for 162 species from the Barro Colorado Nature Monument, Panama. We fit nonlinear, hierarchical models informed by species traits – wood density, mean sapling growth, or sapling mortality – and assessed the performance of three alternative functional forms: the scale-invariant power function and the saturating Weibull and generalized Michaelis–Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach for improving estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical plant functional types in vegetation models.

Representing biological nitrogen fixation (BNF) is an important challenge for coupled carbon (C) and nitrogen (N) land models. Initial representations of BNF in land models applied simplified phenomenological relationships. More recent representations of BNF are mechanistic and include the dynamic response of symbiotic BNF to N limitation of plant growth. However, they generally do not include the competitive dynamics between N-fixing and non-fixing plants, which is a key ecological mechanism that determines ecosystem-scale symbiotic BNF. Furthermore, asymbiotic BNF is generally not included in land models. Here, we present LM4.1-BNF, a novel representation of BNF (asymbiotic and symbiotic) and an updated representation of N cycling in the Geophysical Fluid Dynamics Laboratory Land Model 4.1 (LM4.1). LM4.1-BNF incorporates a mechanistic representation of asymbiotic BNF by soil microbes, a representation of the competitive dynamics between N-fixing and non-fixing plants, and distinct asymbiotic and symbiotic BNF temperature responses derived from corresponding observations. LM4.1-BNF makes reasonable estimations of major carbon (C) and N pools and fluxes and their temporal dynamics, in comparison to the previous version of LM4.1 with N cycling (LM3-SNAP) and to previous representations of BNF in land models generally (phenomenological representations and those without competitive dynamics between N-fixing and non-fixing plants and/or asymbiotic BNF) at a temperate forest site. LM4.1-BNF effectively reproduces asymbiotic BNF rate (13 kgNha-1yr-1) in comparison to observations (11 kgNha-1yr-1). LM4.1-BNF effectively reproduces the temporal dynamics of symbiotic BNF rate: LM4.1-BNF simulates a symbiotic BNF pulse in early succession that reaches 73 kgNha-1yr-1 at 15 years and then declines to ∼0 kgNha-1yr-1 at 300 years, similarly to observed symbiotic BNF, which reaches 75 kgNha-1yr-1 at 17 years and then declines to ∼0 kgNha-1yr-1 in late successional forests. As such, LM4.1-BNF can be applied to project the dynamic response of vegetation to N limitation of plant growth and the degree to which this will constrain the terrestrial C sink under elevated atmospheric CO2 concentration and other global change factors.

The exploitative segregation of plant roots (ESPR) is a theory that uses a game-theoretical model to predict plant root foraging behavior in space. The original model returns the optimal root distribution assuming exploitative competition between a pair of identical plants in soils with homogeneous resource dynamics. In this short communication, we explore avenues to develop this model further. We discuss: (i) the response of single plants to soil heterogeneity; (ii) the variability of the plant response under uneven competition scenarios; (iii) the importance of accounting for the constraints and limitations to root growth that may be imposed from the plant shoot; (iv) the importance of root functional traits to predict root foraging behavior; (v) potential model extensions to investigate facilitation by incorporating facilitative traits to roots, and (vi) the possibility of allowing plants to tune their response by accounting for non-self and non-kin root recognition. For each case, we introduce the topic briefly and present possible ways to encode those ingredients in the mathematical equations of the ESPR model, providing preliminary results when possible., We thank four anonymous reviewers for their constructive comments on previous versions of this text. CC was supported by the Princeton University May Fellowship in the Department of Ecology and Evolutionary Biology and by the Princeton Environmental Institute Carbon Mitigation Initiative (PEI-CMI); RMG by Instituto Serrapilheira (grant Serra-1911-31200), FAPESP (grants ICTP-SAIFR 2016/01343-7 Programa Jovens Pesquisadores em Centros Emergentes 2019/24433-0, 2019/05523-8) and the Simons Foundation; FV by the Spanish Ministry for Science, Innovation and Universities (COMEDIAS grant CGL2017-83170-R).

Risks to mitigation potential of forests Much recent attention has focused on the potential of trees and forests to mitigate ongoing climate change by acting as sinks for carbon. Anderegg et al. review the growing evidence that forests' climate mitigation potential is increasingly at risk from a range of adversities that limit forest growth and health. These include physical factors such as drought and fire and biotic factors, including the depredations of insect herbivores and fungal pathogens. Full assessment and quantification of these risks, which themselves are influenced by climate, is key to achieving science-based policy outcomes for effective land and forest management. Science , this issue p. eaaz7005

Tropical forest responses are an important feedback on global change, but changes in forest composition with projected increases in CO2 and drought are highly uncertain. Here we determine shifts in the most competitive plant hydraulic strategy (the evolutionary stable strategy or ESS) from changes in CO2 and drought frequency and intensity. Hydraulic strategies were defined along a spectrum from drought avoidance to tolerance by physiology traits. Drought impacted competition more than CO2 , with elevated CO2 reducing but not reversing drought-induced shifts in the ESS towards more tolerant strategies. Trait plasticity and/or adaptation intensified these shifts by increasing the competitive ability of the drought tolerant relative to the avoidant strategies. These findings predict losses of drought avoidant evergreens from tropical forests under global change, and point to the importance of changes in precipitation during the dry season and constraints on plasticity and adaptation in xylem traits to forest responses.

Plants influence the atmosphere through fluxes of carbon, water and energy1, and can intensify drought through land–atmosphere feedback effects2–4. The diversity of plant functional traits in forests, especially physiological traits related to water (hydraulic) transport, may have a critical role in land–atmosphere feedback, particularly during drought. Here we combine 352 site-years of eddy covariance measurements from 40 forest sites, remote-sensing observations of plant water content and plant functional-trait data to test whether the diversity in plant traits affects the response of the ecosystem to drought. We find evidence that higher hydraulic diversity buffers variation in ecosystem flux during dry periods across temperate and boreal forests. Hydraulic traits were the predominant significant predictors of cross-site patterns in drought response. By contrast, standard leaf and wood traits, such as specific leaf area and wood density, had little explanatory power. Our results demonstrate that diversity in the hydraulic traits of trees mediates ecosystem resilience to drought and is likely to have an important role in future ecosystem–atmosphere feedback effects in a changing climate.

The mechanisms governing tree drought mortality and recovery remain a subject of inquiry and active debate given their role in the terrestrial carbon cycle and their concomitant impact on climate change. Counter-intuitively, many trees do not die during the drought itself. Indeed, observations globally have documented that trees often grow for several years after drought before mortality. A combination of meta-analysis and tree physiological models demonstrate that optimal carbon allocation after drought explains observed patterns of delayed tree mortality and provides a predictive recovery framework. Specifically, post-drought, trees attempt to repair water transport tissue and achieve positive carbon balance through regrowing drought-damaged xylem. Furthermore, the number of years of xylem regrowth required to recover function increases with tree size, explaining why drought mortality increases with size. These results indicate that tree resilience to drought-kill may increase in the future, provided that CO2 fertilisation facilitates more rapid xylem regrowth.

Tree allometric relationships are widely employed to estimate forest biomass and production, and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to reflect finite size effects at the smallest and largest sizes, and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter using species-specific morphological and life history data of 162 species from Barro Colorado Island, Panamá. We fit nonlinear, hierarchical models informed by species traits and assessed the performance of three alternative functional forms: the scale-invariant power function, and the saturating Weibull and generalized Michaelis-Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach to improve estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical tree functional types in vegetation models.

Understanding variation in leaf functional traits-including rates of photosynthesis and respiration and concentrations of nitrogen and phosphorus-is a fundamental challenge in plant ecophysiology. When expressed per unit leaf area, these traits typically increase with leaf mass per area (LMA) within species but are roughly independent of LMA across the global flora. LMA is determined by mass components with different biological functions, including photosynthetic mass that largely determines metabolic rates and contains most nitrogen and phosphorus, and structural mass that affects toughness and leaf lifespan (LL). A possible explanation for the contrasting trait relationships is that most LMA variation within species is associated with variation in photosynthetic mass, whereas most LMA variation across the global flora is associated with variation in structural mass. This hypothesis leads to the predictions that (i) gas exchange rates and nutrient concentrations per unit leaf area should increase strongly with LMA across species assemblages with low LL variance but should increase weakly with LMA across species assemblages with high LL variance and that (ii) controlling for LL variation should increase the strength of the above LMA relationships. We present analyses of intra- and interspecific trait variation from three tropical forest sites and interspecific analyses within functional groups in a global dataset that are consistent with the above predictions. Our analysis suggests that the qualitatively different trait relationships exhibited by different leaf assemblages can be understood by considering the degree to which photosynthetic and structural mass components contribute to LMA variation in a given assemblage.

In tropical regions, fires propagate readily in grasslands but typically consume only edges of forest patches. Thus, forest patches grow due to tree propagation and shrink by fires in surrounding grasslands. The interplay between these competing edge effects is unknown, but critical in determining the shape and stability of individual forest patches, as well the landscape-level spatial distribution and stability of forests. We analyze high-resolution remote-sensing data from protected Brazilian Cerrado areas and find that forest shapes obey a robust perimeter-area scaling relation across climatic zones. We explain this scaling by introducing a heterogeneous fire propagation model of tropical forest-grassland ecotones. Deviations from this perimeter-area relation determine the stability of individual forest patches. At a larger scale, our model predicts that the relative rates of tree growth due to propagative expansion and long-distance seed dispersal determine whether collapse of regional-scale tree cover is continuous or discontinuous as fire frequency changes.

Fire-prone ecosystems contain plants that are both fire-adapted and flammable. It has been hypothesized that these plants were under selection to become more flammable, but it is unclear whether this could be adaptive for an individual plant. We propose arrested succession as a robust mechanism that supports the evolution of flammability in surface fire ecosystems without the need to invoke group selection or additional fitness benefits. We used the natural history of lodgepole pine (Pinus ponderosa) forests, longleaf pine (Pinus palustris) forests, and tall grass prairies to create a general mathematical model of surface fire ecosystems and solved for the evolutionarily stable strategy (ESS) level of flammability. In our model, fires always kill understory plants and only sometimes kill overstory plants. Thus, more flammable plants suffer increased mortality due to fires, but also more frequently arrest succession by clearing their understory of late successional competitors. Increased flammability was selected for when the probability of an overstory plant dying from an individual fire was below a maximum threshold and the rate of succession relative to fires was above a minimum threshold. Future studies can test our model predictions and help resolve whether or not plants have been selected to be more flammable.

Potential variability in nutrient limitation among tree size classes, functional groups and species calls for an integrated community- and ecosystem-level perspective of lowland tropical rainforest nutrient limitation. In particular, canopy trees determine ecosystem nutrient conditions, but competitive success for nutrients and light during the sapling bottleneck determines canopy composition. We conducted an in situ multi-nutrient sapling fertilization experiment at La Selva Biological Station, Costa Rica, to determine how functional group identity, species identity and light availability can impact nutrient limitation of stem growth in three functional groups and nine species. Despite high soil fertility, we found nutrient-light limitation in two functional groups and four species. Unexpectedly, the nitrogen-fixing (“N2 fixers”) and shade-tolerant functional groups were significantly nutrient limited, while the light-demanding functional group was not. This was partially explained by species-level variation in nutrient limitation within these functional groups, with only some species conforming to the prediction of stronger nutrient limitation in light demanders compared to shade-tolerants. Most surprisingly, we found strong nutrient limitation at low-light levels in the N2 fixers (which were shade-tolerant), but not in the shade-tolerant non-fixers. We hypothesize that the N2 fixers were actually nitrogen limited at low-light levels because of their nitrogen-rich leaves and the high carbon cost of their symbionts. This finding suggests a highly shade-tolerant, N2 fixation strategy, in addition to the perception that N2 fixation is mostly advantageous in high-light environments during early and gap succession. The shade-tolerant, N2 fixation strategy may be part of a sapling and canopy tree feedback, where the canopy N2 fixers enrich the soil N, enhancing growth of their shade-tolerant saplings relative to non-fixing competitors, enabling further canopy domination by shade-tolerant N2 fixers, as seen at La Selva. Synthesis. The pervasiveness of functional group- and species-specific nutrient and light co-limitation in our saplings indicates that these interactions likely play an important role in the dynamics of lowland tropical rainforest nutrient limitation, potentially via other such sapling and canopy tree feedbacks as the one hypothesized.

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the 'ageing rate:' the rate at which leaf photosynthetic capacity declines with age. Here, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimality model with in situLL data for 105 species in two Panamanian forests. We show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.

Fire regimes in savannas and forests are changing over much of the world. Anticipating the impact of these changes requires understanding how plants are adapted to fire. In this study, we test whether fire imposes a broad selective force on a key fire-tolerance trait, bark thickness, across 572 tree species distributed worldwide. We show that investment in thick bark is a pervasive adaptation in frequently burned areas across savannas and forests in both temperate and tropical regions where surface fires occur. Geographic variability in bark thickness is largely explained by annual burned area and precipitation seasonality. Combining environmental and species distribution data allowed us to assess vulnerability to future climate and fire conditions: tropical rainforests are especially vulnerable, whereas seasonal forests and savannas are more robust. The strong link between fire and bark thickness provides an avenue for assessing the vulnerability of tree communities to fire and demands inclusion in global models.

Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.

Competition is a major driver of carbon allocation to different plant tissues (e.g., wood, leaves, fine roots), and allocation, in turn, shapes vegetation structure. To improve their modeling of the terrestrial carbon cycle, many Earth system models now incorporate vegetation demographic models (VDMs) that explicitly simulate the processes of individual-based competition for light and soil resources. Here, in order to understand how these competition processes affect predictions of the terrestrial carbon cycle, we simulate forest responses to elevated atmospheric CO2 concentration [CO2] along a nitrogen availability gradient, using a VDM that allows us to compare fixed allocation strategies vs. competitively optimal allocation strategies. Our results show that competitive and fixed strategies predict opposite fractional allocation to fine roots and wood, though they predict similar changes in total net primary production (NPP) along the nitrogen gradient. The competitively optimal allocation strategy predicts decreasing fine root and increasing wood allocation with increasing nitrogen, whereas the fixed strategy predicts the opposite. Although simulated plant biomass at equilibrium increases with nitrogen due to increases in photosynthesis for both allocation strategies, the increase in biomass with nitrogen is much steeper for competitively optimal allocation due to its increased allocation to wood. The qualitatively opposite fractional allocation to fine roots and wood of the two strategies also impacts the effects of elevated [CO2] on plant biomass. Whereas the fixed allocation strategy predicts an increase in plant biomass under elevated [CO2] that is approximately independent of nitrogen availability, competition leads to higher plant biomass response to elevated [CO2] with increasing nitrogen availability. Our results indicate that the VDMs that explicitly include the effects of competition for light and soil resources on allocation may generate significantly different ecosystem-level predictions of carbon storage than those that use fixed strategies.

Methane mitigation is essential for addressing climate change, but the value of rapidly implementing available mitigation measures is not well understood. In this paper, we analyze the climate benefits of fast action to reduce methane emissions as compared to slower and delayed mitigation timelines. We find that the scale up and deployment of greatly underutilized but available mitigation measures will have significant near-term temperature benefits beyond that from slow or delayed action. Overall, strategies exist to cut global methane emissions from human activities in half within the next ten years and half of these strategies currently incur no net cost. Pursuing all mitigation measures now could slow the global-mean rate of near-term decadal warming by around 30%, avoid a quarter of a degree centigrade of additional global-mean warming by midcentury, and set ourselves on a path to avoid more than half a degree centigrade by end of century. On the other hand, slow implementation of these measures may result in an additional tenth of a degree of global-mean warming by midcentury and 5% faster warming rate (relative to fast action), and waiting to pursue these measures until midcentury may result in an additional two tenths of a degree centigrade by midcentury and 15% faster warming rate (relative to fast action). Slow or delayed methane action is viewed by many as reasonable given that current and on-the-horizon climate policies heavily emphasize actions that benefit the climate in the long-term, such as decarbonization and reaching net-zero emissions, whereas methane emitted over the next couple of decades will play a limited role in long-term warming. However, given that fast methane action can considerably limit climate damages in the near-term, it is urgent to scale up efforts and take advantage of this achievable and affordable opportunity as we simultaneously reduce carbon dioxide emissions.

Earth system models are incorporating plant trait diversity into their land components to better predict vegetation dynamics in a changing climate. However, extant plant trait distributions will not allow extrapolations to novel community assemblages in future climates, which will require a mechanistic understanding of the trade-offs that determine trait diversity. In this study, we show how physiological trade-offs involving leaf mass per unit area (LMA), leaf lifespan, leaf nitrogen, and leaf respiration may explain the distribution patterns of evergreen and deciduous trees in the temperate and boreal zones based on (1) an evolutionary analysis of a simple mathematical model and (2) simulation experiments of an individual-based dynamic vegetation model (i.e., LM3-PPA). The evolutionary analysis shows that these leaf traits set up a trade-off between carbon- and nitrogen-use efficiency at the scale of individual trees and therefore determine competitively dominant leaf strategies. As soil nitrogen availability increases, the dominant leaf strategy switches from one that is high in nitrogen-use efficiency to one that is high in carbon-use efficiency or, equivalently, from high-LMA/long-lived leaves (i.e., evergreen) to low-LMA/short-lived leaves (i.e., deciduous). In a region of intermediate soil nitrogen availability, the dominant leaf strategy may be either deciduous or evergreen depending on the initial conditions of plant trait abundance (i.e., founder controlled) due to feedbacks of leaf traits on soil nitrogen mineralization through litter quality. Simulated successional patterns by LM3-PPA from the leaf physiological trade-offs are consistent with observed successional dynamics of evergreen and deciduous forests at three sites spanning the temperate to boreal zones.

A plant lineage can compete for resources in a spatially variable environment by colonizing new areas, exploiting resources in those areas quickly before other plants arrive to compete with it, or tolerating competition once other plants do arrive. These specializations are ubiquitous in plant communities, but all three have never been derived from a spatial model of community dynamics-instead, the possibility of rapid exploitation has been either overlooked or confounded with colonization. We use moment equations, equations for the mean densities and spatial covariance of competing plant populations, to characterize these strategies in a fully spatial stochastic model. The moment equations predict endogenous spatial pattern formation and the efficacy of spatial strategies under different conditions. The model shows that specializations for colonization, exploitation, and tolerance are all possible, and these are the only possible spatial strategies; among them, they partition all of the endogenous spatial structure in the environment. The model predicts two distinct short-dispersal specializations where parents disperse their offspring locally, either to exploit empty patches quickly or to fill patches to exclude competitors.

Tropical forest responses are an important feedback on global change, but changes in forest composition with projected increases in CO