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The relative importance of tree mortality risk factors remains unknown, especially in diverse tropical forests where species may vary widely in their responses to particular conditions. We present a new framework for quantifying the importance of mortality risk factors and apply it to compare 19 risks on 31 203 trees (1977 species) in 14 one-year periods in six tropical forests. We defined a condition as a risk factor for a species if it was associated with at least a doubling of mortality rate in univariate analyses. For each risk, we estimated prevalence (frequency), lethality (difference in mortality between trees with and without the risk) and impact ('excess mortality' associated with the risk, relative to stand-level mortality). The most impactful risk factors were light limitation and crown/trunk loss; the most prevalent were light limitation and small size; the most lethal were leaf damage and wounds. Modes of death (standing, broken and uprooted) had limited links with previous conditions and mortality risk factors. We provide the first ranking of importance of tree-level mortality risk factors in tropical forests. Future research should focus on the links between these risks, their climatic drivers and the physiological processes to enable mechanistic predictions of future tree mortality.

The growth and survival of individual trees determine the physical structure of a forest with important consequences for forest function. However, given the diversity of tree species and forest biomes, quantifying the multitude of demographic strategies within and across forests and the way that they translate into forest structure and function remains a significant challenge. Here, we quantify the demographic rates of 1961 tree species from temperate and tropical forests and evaluate how demographic diversity (DD) and demographic composition (DC) differ across forests, and how these differences in demography relate to species richness, aboveground biomass (AGB), and carbon residence time. We find wide variation in DD and DC across forest plots, patterns that are not explained by species richness or climate variables alone. There is no evidence that DD has an effect on either AGB or carbon residence time. Rather, the DC of forests, specifically the relative abundance of large statured species, predicted both biomass and carbon residence time. Our results demonstrate the distinct DCs of globally distributed forests, reflecting biogeography, recent history, and current plot conditions. Linking the DC of forests to resilience or vulnerability to climate change, will improve the precision and accuracy of predictions of future forest composition, structure, and function.

The effects of climate change on tropical forests will depend on how diverse tropical tree species respond to drought. Current distributions of evergreen and deciduous tree species across local and regional moisture gradients reflect their ability to tolerate drought stress, and might be explained by functional traits. We measured leaf water potential at turgor loss (i.e. ‘wilting point’; πtlp), wood density (WD) and leaf mass per area (LMA) on 50 of the most abundant tree species in central Panama. We then tested their ability to explain distributions of evergreen and deciduous species within a 50 ha plot on Barro Colorado Island and across a 70 km rainfall gradient spanning the Isthmus of Panama. Among evergreen trees, species with lower πtlp were associated with drier habitats, with πtlp explaining 28% and 32% of habitat association on local and regional scales, respectively, greatly exceeding the predictive power of WD and LMA. In contrast, πtlp did not predict habitat associations among deciduous species. Across spatial scales, πtlp is a useful indicator of habitat preference for tropical tree species that retain their leaves during periods of water stress, and holds the potential to predict vegetation responses to climate change.

International audience; A better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) drive spatial variations in species’ baseline growth rates, whereas deviations from these averages over time (anomalies) can create growth variation around the local baseline. However, the rarity of long-term tree census data spanning climatic gradients has so far limited our understanding of their respective role, especially in tropical systems. Furthermore, tree growth sensitivity to climate is likely to vary widely among species, and the ecological strategies underlying these differences remain poorly understood. Here, we utilize an exceptional dataset of 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how multiannual tree growth responds to both climate means and anomalies, and how species’ functional traits mediate these growth responses to climate. We show that anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both climate means and anomalies. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests and that species traits can provide insights into understanding these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

Tree rings provide an invaluable long-term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree-ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3-month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3-month seasonal windows), with concave-down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.

A better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) and short-term deviations from these averages (anomalies) both influence tree growth, but the rarity of long-term data integrating climatic gradients with tree censuses has so far limited our understanding of their respective role, especially in tropical systems. Here, we combined 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how tree growth responds to both climate means and anomalies, and how species functional traits mediate these tree growth responses to climate. We showed that short-term, anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both long-term and short-term climate variations. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests, and that species traits can be leveraged to understand these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

ForestGEO is a network of scientists and long-term forest dynamics plots (FDPs) spanning the Earth's major forest types. ForestGEO's mission is to advance understanding of the diversity and dynamics of forests and to strengthen global capacity for forest science research. ForestGEO is unique among forest plot networks in its large-scale plot dimensions, censusing of all stems ≥1 cm in diameter, inclusion of tropical, temperate and boreal forests, and investigation of additional biotic (e.g., arthropods) and abiotic (e.g., soils) drivers, which together provide a holistic view of forest functioning. The 71 FDPs in 27 countries include approximately 7.33 million living trees and about 12,000 species, representing 20% of the world's known tree diversity. With >1300 published papers, ForestGEO researchers have made significant contributions in two fundamental areas: species coexistence and diversity, and ecosystem functioning. Specifically, defining the major biotic and abiotic controls on the distribution and coexistence of species and functional types and on variation in species' demography has led to improved understanding of how the multiple dimensions of forest diversity are structured across space and time and how this diversity relates to the processes controlling the role of forests in the Earth system. Nevertheless, knowledge gaps remain that impede our ability to predict how forest diversity and function will respond to climate change and other stressors. Meeting these global research challenges requires major advances in standardizing taxonomy of tropical species, resolving the main drivers of forest dynamics, and integrating plot-based ground and remote sensing observations to scale up estimates of forest diversity and function, coupled with improved predictive models. However, they cannot be met without greater financial commitment to sustain the long-term research of ForestGEO and other forest plot networks, greatly expanded scientific capacity across the world's forested nations, and increased collaboration and integration among research networks and disciplines addressing forest science.

Atmospheric carbon dioxide concentration ([CO

© 2019 John Wiley & Sons Ltd Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

No claim to original US government works New Phytologist © 2018 New Phytologist Trust Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO2 fertilization-induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large-scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

The prediction of vegetation responses to climate requires a knowledge of how climate-sensitive plant traits mediate not only the responses of individual plants, but also shifts in the species and functional compositions of whole communities. The emission of isoprene gas - a trait shared by one-third of tree species - is known to protect leaf biochemistry under climatic stress. Here, we test the hypothesis that isoprene emission shapes tree species compositions in tropical forests by enhancing the tolerance of emitting trees to heat and drought. Using forest inventory data, we estimated the proportional abundance of isoprene-emitting trees (pIE) at 103 lowland tropical sites. We also quantified the temporal composition shifts in three tropical forests - two natural and one artificial - subjected to either anomalous warming or drought. Across the landscape, pIE increased with site mean annual temperature, but decreased with dry season length. Through time, pIE strongly increased under high temperatures, and moderately increased following drought. Our analysis shows that isoprene emission is a key plant trait determining species responses to climate. For species adapted to seasonal dry periods, isoprene emission may tradeoff with alternative strategies, such as leaf deciduousness. Community selection for isoprene-emitting species is a potential mechanism for enhanced forest resilience to climatic change.

ISI Document Delivery No.: GJ5TETimes Cited: 0Cited Reference Count: 60Chitra-Tarak, Rutuja Ruiz, Laurent Dattaraja, Handanakere S. Kumar, M. S. Mohan Riotte, Jean Suresh, Hebbalalu S. McMahon, Sean M. Sukumar, RamanMinistry of Environment, Forest and Climate Change, Govt. of India; CSIR, India; French Institute of Research for Development (IRD), CNRS-INSU, Toulouse University, France; NSF [1137366, 1046113]Ministry of Environment, Forest and Climate Change, Govt. of India; CSIR, India; French Institute of Research for Development (IRD), CNRS-INSU, Toulouse University, France; NSF, Grant/Award Numbers: 1137366, 1046113WileyHoboken; 1. Drought-induced tree mortality is expected to increase globally due to climate change, with profound implications for forest composition, function and global climate feedbacks. How drought is experienced by different species is thought to depend fundamentally on where they access water vertically below-ground, but this remains untracked so far due to the difficulty of measuring water availability at depths at which plants access water (few to several tens of metres), the broad temporal scales at which droughts at those depths unfold (seasonal to decadal), and the difficulty in linking these patterns to forest-wide species-specific demographic responses. 2. We address this problem through a new eco-hydrological framework: we used a hydrological model to estimate below-ground water availability by depth over a period of two decades that included a multi-year drought. Given this water availability scenario and 20year long-records of species-specific growth patterns, we inversely estimated the relative depths at which 12 common species in the forest accessed water via a model of water stress. Finally, we tested whether our estimates of species relative uptake depths predicted mortality in the multi-year drought. 3. The hydrological model revealed clear below-ground niches as precipitation was decoupled from water availability by depth at multi-annual scale. Species partitioned the hydrological niche by diverging in their uptake depths and so in the same forest stand, different species experienced very different drought patterns, resulting in clear differences in species-specific growth. Finally, species relative water uptake depths predicted species mortality patterns after the multi-year drought. Species that our method ranked as relying on deeper water were the ones that had suffered from greater mortality, as the zone from which they access water took longer to recharge after depletion. 4. Synthesis. This research changes our understanding of how hydrological niches operate for trees, with a trade-off between realized growth potential and survival under drought with decadal scale return time. The eco-hydrological framework highlights the importance of species-specific below-ground strategies in predicting forest response to drought. Applying this framework more broadly may help us better understand species coexistence in diverse forest communities and improve mechanistic predictions of forests productivity and compositional change under future climate.

Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25ha), all stems 1cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25 degrees S-61 degrees N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 degrees C), changes in precipitation (up to +/- 30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8g Nm(-2)yr(-1) and 3.1g Sm(-2)yr(-1)), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS-ForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Understanding how species and ecosystems respond to climate change has become a major focus of ecology and conservation biology. Modelling approaches provide important tools for making future projections, but current models of the climate-biosphere interface remain overly simplistic, undermining the credibility of projections. We identify five ways in which substantial advances could be made in the next few years: (i) improving the accessibility and efficiency of biodiversity monitoring data, (ii) quantifying the main determinants of the sensitivity of species to climate change, (iii) incorporating community dynamics into projections of biodiversity responses, (iv) accounting for the influence of evolutionary processes on the response of species to climate change, and (v) improving the biophysical rule sets that define functional groupings of species in global models.

In response to our paper (1) showing a recent increase in forest-biomass growth in an eastern United States forest, Foster et al. (2) emphasize the importance of considering biomass loss caused by mortality when inferring biomass rate changes and modeling uncertainty in these analyses. Although decreases in biomass loss caused by mortality could lead to an observed increase in biomass, we believe that growth and not mortality is the critical driver of this documented biomass rate. Fig. 1 shows that, although changes in both net primary productivity (NPP) and mortality could combine to create the observed mean increase in stand growth in ref. 1, mortality is an unlikely contributor for three primary reasons.

Forests and their soils contain the majority of the earth’s terrestrial carbon stocks. Changes in patterns of tree growth can have a huge impact on atmospheric cycles, biogeochemical cycles, climate change, and biodiversity. Recent studies have shown increases in biomass across many forest types. This increase has been attributed to climate change. However, without knowing the disturbance history of a forest, growth could also be caused by normal recovery from unknown disturbances. Using a unique dataset of tree biomass collected over the past 22 years from 55 temperate forest plots with known land-use histories and stand ages ranging from 5 to 250 years, we found that recent biomass accumulation greatly exceeded the expected growth caused by natural recovery. We have also collected over 100 years of local weather measurements and 17 years of on-site atmospheric CO 2 measurements that show consistent increases in line with globally observed climate-change patterns. Combined, these observations show that changes in temperature and CO 2 that have been observed worldwide can fundamentally alter the rate of critical natural processes, which is predicted by biogeochemical models. Identifying this rate change is important to research on the current state of carbon stocks and the fluxes that influence how carbon moves between storage and the atmosphere. These results signal a pressing need to better understand the changes in growth rates in forest systems, which influence current and future states of the atmosphere and biosphere.

Forests are one of Earth's critical biomes. They have been shown to respond strongly to many of the drivers that are predicted to change natural systems over this century, including climate, introduced species, and other anthropogenic influences. Predicting how different tree species might respond to this complex of forces remains a daunting challenge for forest ecologists. Yet shifts in species composition and abundance can radically influence hydrological and atmospheric systems, plant and animal ranges, and human populations, making this challenge an important one to address. Forest ecologists have gathered a great deal of data over the past decades and are now using novel quantitative and computational tools to translate those data into predictions about the fate of forests. Here, after a brief review of the threats to forests over the next century, one of the more promising approaches to making ecological predictions is described: using hierarchical Bayesian methods to model forest demography and simulating future forests from those models. This approach captures complex processes, such as seed dispersal and mortality, and incorporates uncertainty due to unknown mechanisms, data problems, and parameter uncertainty. After describing the approach, an example by simulating drought for a southeastern forest is offered. Finally, there is a discussion of how this approach and others need to be cast within a framework of prediction that strives to answer the important questions posed to environmental scientists, but does so with a respect for the challenges inherent in predicting the future of a complex biological system.

The flora of California, a global biodiversity hotspot, includes 2387 endemic plant taxa. With anticipated climate change, we project that up to 66% will experience >80% reductions in range size within a century. These results are comparable with other studies of fewer species or just samples of a region's endemics. Projected reductions depend on the magnitude of future emissions and on the ability of species to disperse from their current locations. California's varied terrain could cause species to move in very different directions, breaking up present-day floras. However, our projections also identify regions where species undergoing severe range reductions may persist. Protecting these potential future refugia and facilitating species dispersal will be essential to maintain biodiversity in the face of climate change.