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Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one-out cross-validation R

A major uncertainty in the land carbon cycle is whether symbiotic nitrogen fixation acts to enhance the tropical forest carbon sink. Nitrogen-fixing trees can supply vital quantities of the growth-limiting nutrient nitrogen, but the extent to which the resulting carbon–nitrogen feedback safeguards ecosystem carbon sequestration remains unclear. We combine (i) field observations from 112 plots spanning 300 years of succession in Panamanian tropical forests, and (ii) a new model that resolves nitrogen and light competition at the scale of individual trees. Fixation doubled carbon accumulation in early succession and enhanced total carbon in mature forests by ~10% (~12MgC ha−1) through two mechanisms: (i) a direct fixation effect on tree growth, and (ii) an indirect effect on the successional sequence of non-fixing trees. We estimate that including nitrogen-fixing trees in Neotropical reforestation projects could safeguard the sequestration of 6.7 Gt CO2 over the next 20 years. Our results highlight the connection between functional diversity of plant communities and the critical ecosystem service of carbon sequestration for mitigating climate change., The contribution of symbiotic dinitrogen fixation to the forest carbon sink could change throughout forest succession. Here the authors model nitrogen cycling and light competition between trees based on data from Panamanian forest plots, showing that fixation contributes substantially to the carbon sink in early successional stages.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Leaf habit has been hypothesized to define a linkage between the slow-fast plant economic spectrum and the drought resistance-avoidance trade-off in tropical forests ('slow-safe vs fast-risky'). However, variation in hydraulic traits as a function of leaf habit has rarely been explored for a large number of species. We sampled leaf and branch functional traits of 97 tropical dry forest tree species from four sites to investigate whether patterns of trait variation varied consistently in relation to leaf habit along the 'slow-safe vs fast-risky' trade-off. Leaf habit explained from 0% to 43.69% of individual trait variation. We found that evergreen and semi-deciduous species differed in their location along the multivariate trait ordination when compared to deciduous species. While deciduous species showed consistent trait values, evergreen species trait values varied as a function of the site. Last, trait values varied in relation to the proportion of deciduous species in the plant community. We found that leaf habit describes the strategies that define drought avoidance and plant economics in tropical trees. However, leaf habit alone does not explain patterns of trait variation, which suggests quantifying site-specific or species-specific uncertainty in trait variation as the way forward.

Lianas, or woody vines, are abundant throughout forests worldwide, but are especially common in the tropics. Their presence can strongly suppress tree wood production, and presumably also reduce the strength of the tropical forest carbon sink. In intact neotropical forests, liana presence has been increasing over the past few decades, though the mechanisms remain under debate. Vexingly, lianas are not represented at all in current-day climate models. Better knowledge of liana morphology and allocation is required to unravel the mechanisms of below- and aboveground liana-tree competition in tropical forests. Such knowledge is also an essential step toward incorporating lianas into mechanistic forest dynamics models. To address these liana knowledge gaps, we have initiated a new project that integrates empirical and modeling work. Our objectives in this presentation are to compare observed liana allocation patterns to allocation patterns predicted by theory, and then to demonstrate how these results can be integrated into a numerical model.Empirical measurements are being carried out in tropical dry forests in Guanacaste, Costa Rica. These measurements will eventually include excavations of ~80 entire trees and lianas, which will enable measurements of belowground and aboveground biomass of co-occurring trees and lianas, coarse and fine root vertical distribution, and lateral root spread. Also being measured are liana traits (including several critical hydraulic traits), above- and belowground productivity, and species-level fine root productivity. The modeling work includes the incorporation of lianas into the TROLL model, which is a mechanistic, individual-based forest dynamics model. The model will simulate the unique features of lianas, accounting for their structural parasitism and their different allocation strategies and morphology compared to trees. The simulated trees and lianas will compete aboveground for light and belowground for water. Thus, the model will integrate above- and belowground processes and couple the carbon and water cycles. Traits measured as part of this project are being used to parameterize the model.Thus far, 33 mature, canopy-exposed individuals (18 trees and 15 lianas) have been harvested and analyzed. For both trees and lianas, biomass partitioning to roots, stems, and leaves were consistent with the predictions of allometric biomass partitioning theory. This result thwarted our initial expectation that lianas, with their narrow-diameter stems, would allocate proportionally less to stems than trees. We also found that vertical root profiles varied across life forms: lianas had the shallowest roots, evergreen trees had the deepest roots, and deciduous trees had intermediate rooting depths. The liana root systems also had notably broader lateral extents than the tree root systems. These results run contrary to previous work that reported that lianas were relatively deeply-rooted.Our empirical results have helped to motivate model development. Each of our modeled liana individuals is assigned a laterally-widespread root system that can potentially extend beneath many trees. The liana root system is then permitted to put up aboveground shoots that associate with trees within the footprint of the root system. Comparisons of simulated and observed above- and belowground productivity are currently being conducted to help evaluate model assumptions.

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.

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.

Increases in atmospheric carbon dioxide (CO2 ) concentrations are expected to lead to increases in the rate of tree biomass accumulation, at least temporarily. On the one hand, trees may simply grow faster under higher CO2 concentrations, preserving the allometric relations that prevailed under lower CO2 concentrations. Alternatively, the allometric relations themselves may change. In this study, the effects of elevated CO2 (eCO2 ) on tree biomass and allometric relations were jointly assessed. Over 100 trees, grown at Duke Forest, NC, USA, were harvested from eight plots. Half of the plots had been subjected to CO2 enrichment from 1996 to 2010. Several subplots had also been subjected to nitrogen fertilization from 2005 to 2010. Allometric equations were developed to predict tree height, stem volume, and aboveground biomass components for loblolly pine (Pinus taeda L.), the dominant tree species, and broad-leaved species. Using the same diameter-based allometric equations for biomass, it was estimated that plots with eCO2 contained 21% more aboveground biomass, consistent with previous studies. However, eCO2 significantly affected allometry, and these changes had an additional effect on biomass. In particular, P. taeda trees at a given diameter were observed to be taller under eCO2 than under ambient CO2 due to changes in both the allometric scaling exponent and intercept. Accounting for allometric change increased the treatment effect of eCO2 on aboveground biomass from a 21% to a 27% increase. No allometric changes for the nondominant broad-leaved species were identified, nor were allometric changes associated with nitrogen fertilization. For P. taeda, it is concluded that eCO2 affects allometries, and that knowledge of allometry changes is necessary to accurately compute biomass under eCO2 . Further observations are needed to determine whether this assessment holds for other taxa.

Drought-related tree mortality is now a widespread phenomenon predicted to increase in magnitude with climate change. However, the patterns of which species and trees are most vulnerable to drought, and the underlying mechanisms have remained elusive, in part due to the lack of relevant data and difficulty of predicting the location of catastrophic drought years in advance. We used long-term demographic records and extensive databases of functional traits and distribution patterns to understand the responses of 20 to 53 species to an extreme drought in a seasonally dry tropical forest in Costa Rica, which occurred during the 2015 El Niño Southern Oscillation event. Overall, species-specific mortality rates during the drought ranged from 0% to 34%, and varied little as a function of tree size. By contrast, hydraulic safety margins correlated well with probability of mortality among species, while morphological or leaf economics spectrum traits did not. This firmly suggests hydraulic traits as targets for future research.

Author(s): Xu, Xiangtao; Medvigy, David; Trugman, Anna T; Guan, Kaiyu; Good, Stephen P; Rodriguez-Iturbe, Ignacio

Natural and anthropogenic disturbances influence ecological succession and impact the carbon cycle. Understanding disturbance effects and ecosystem recovery is essential to carbon modeling. We hypothesized that (1) species-specific disturbances impact the carbon cycle differently from nonspecific disturbances. In particular, disturbances that target early-successional species will lead to higher carbon uptake by the postrecovery, middle- and late-successional community and (2) disturbances that affect the midsuccessional deciduous species have more intense and long-lasting impacts on carbon uptake than disturbances of similar intensity that only affect the early-successional species. To test these hypotheses, we employed a series of simulations conducted with the Ecosystem Demography model version 2 to evaluate the sensitivity of a temperate mixed-deciduous forest to disturbance intensity and type. Our simulation scenarios included a control (undisturbed) case, a uniform disturbance case where we removed 30% of all trees regardless of their successional status, five cases where only early-successional deciduous trees were removed with increasing disturbance intensity (30%, 70%, 85%, and 100%), and four cases of midsuccessional disturbances with increasing intensity (70%, 85%, and 100%). Our results indicate that disturbances affecting the midsuccessional deciduous trees led to larger decreases in carbon uptake as well as longer recovery times when compared to disturbances that exclusively targeted the early-successional deciduous trees at comparable intensities. Moreover, disturbances affecting 30% to 100% of early-successional deciduous trees resulted in an increased carbon uptake, beginning 6 years after the disturbance and sustained through the end of the 100 year simulation.

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.

Rapid warming and changes in water availability at high latitudes alter resource abundance, tree competition, and disturbance regimes. While these changes are expected to disrupt the functioning of boreal forests, their ultimate implications for forest composition are uncertain. In particular, recent site-level studies of the Alaskan boreal forest have reported both increases and decreases in productivity over the past few decades. Here, we test the idea that variations in Alaskan forest growth and mortality rates are contingent on species composition. Using forest inventory measurements and climate data from plots located throughout interior and south-central Alaska, we show significant growth and mortality responses associated with competition, midsummer vapor pressure deficit, and increased growing season length. The governing climate and competition processes differed substantially across species. Surprisingly, the most dramatic climate response occurred in the drought tolerant angiosperm species, trembling aspen, and linked high midsummer vapor pressure deficits to decreased growth and increased insect-related mortality. Given that species composition in the Alaskan and western Canadian boreal forests is projected to shift toward early-successional angiosperm species due to fire regime, these results underscore the potential for a reduction in boreal productivity stemming from increases in midsummer evaporative demand.

This paper presents a continental-scale phenological analysis of African savannas and woodlands. We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green-up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem-water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an “optimal rainy season length,” after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change.

Aim Previous studies have shown that warming temperatures can affect the phenology of cold deciduous forests, delaying the timing of leaf coloration. However, these works have principally been based on observations from a small number of sites. Consequently it has been challenging to infer continental-scale variations in the phenology of individual deciduous forest species and the extent to which there may be underlying climate drivers for these variations. To address that problem, this study evaluated and predicted the large-scale variations of leaf colouring by using macroscale observations and models. Location North America. Methods We developed leaf colouring models using select observations (1) from Harvard Forest only and (2) from both Harvard Forest and a new, ground-based, Alaskan dataset from the USA National Phenology Network (USA-NPN). Both model types were evaluated using reserved observations from the continental-scale USA-NPN that were not used in model calibration. Validated models were then used to assess the spatial scaling and interspecies variation in the timing of leaf coloration. The sensitivity of the models to projected climate change was also evaluated. Results Using a model calibrated only with data from Harvard Forest, significant biases were found in predictions of leaf colouring date for species with broad habitat ranges in the temperate to boreal regions. When calibration data from both Harvard Forest and Alaska were used, model performance improved throughout the whole continent. It was also found that species with similar shade tolerance could be described by similar models. Finally, the models indicated that climate change over the next century will affect leaf coloration in Alaska less than in the Harvard Forest region. Main conclusions For a given species, continental-scale variations in the timing of autumn leaf coloration can be predicted using a model driven by photoperiod and daily temperature. The temperature sensitivity of the leaf colouring date is nonlinear, such that warmer regions have a larger temperature sensitivity than cooler regions. Species-specific measurements from multiple environments are essential for model parameterization.

[1] Seasonal variation in photosynthetic capacity is an important part of the overall seasonal variability of temperate deciduous forests. However, it has only recently been introduced in a few terrestrial biosphere models, and many models still do not include it. The biases that result from this omission are not well understood. In this study, we use the Ecosystem Demography 2 model to simulate an oak-dominated stand in the New Jersey Pine Barrens. Two alternative model configurations are presented, one with seasonal variation of photosynthetic capacity (SPC-ON) and one without seasonal variation of photosynthetic capacity (SPC-OFF). Under typical climate conditions, the two configurations simulate values of monthly gross primary productivity (GPP) as different as 0.05 kg C m−2 month−1 in the early summer and 0.04 kg C m−2 month−1 in the fall. The differences between SPC-ON and SPC-OFF are amplified when there is temporal correlation between photosynthetic capacity and climate anomalies or disturbances. Warmer spring temperatures enhance GPP in SPC-ON more than in SPC-OFF, but warmer fall temperatures enhance GPP in SPC-OFF more than in SPC-ON. Defoliation by gypsy moth, a class of disturbance that typically happens in late spring in the New Jersey Pine Barrens, has a disproportionately negative impact on GPP in SPC-ON. It is concluded that including seasonal variation of photosynthetic capacity in models will improve simulations of monthly scale ecosystem functioning as well as of longer-term responses to climate change and disturbances.

Summary 1. Seed dispersal is a short-term phenomenon with long-term consequences for population survival and spread. Physiological mechanisms that target the release of seeds to particular sets of environmental conditions that maximize the probability of long-distance dispersal should evolve if long dispersal distance enhances fitness. 2. In this study, we use high-frequency censuses of seeds actually dispersed, high-frequency within-canopy meteorological observations and long-term measurements of above-canopy wind to investigate the environmental conditions that control the timing of seed abscission at different time-scales for a wind-dispersed tropical tree, Luehea seemannii. 3. We show that seed abscission follows a typical seasonal pattern, is rare at night and is most prevalent during periods of prolonged updrafts, higher temperature, with negative feedback when the heat canopy flux is relatively high. 4. We use phenomenological (super-WALD) and mechanistic (coupled Eulerian–Lagrangian closure) models to estimate the relative effects of the timing of seed release at different subannual temporal scales (seconds–hours) on the resulting long-term (season–decade) dispersal kernels. We find that periods of high wind speed increase the probability of long-distance dispersal between 100–1000 m, but decrease the probability at distances further than 1000 m relative to unbiased environmental conditions. We also find abscission during updrafts to increase the probability of long-distance dispersal at distances greater than 100 m. 5. Synthesis: We observe preferential abscission during updrafts in a tropical wind-dispersed tree. We use mechanistic models and long-term wind statistics to estimate the dispersal consequences of preferential seed release in specific environmental conditions. We find that the timing of the dispersal season may be influenced by wind conditions that maximize long-distance dispersal; however, there are likely other environmental factors essential for their determination. Our approach provides a method to bridge between small turbulence scales and large ecosystem scales to predict dispersal kernels. These findings shed light on the evolutionary processes that drive optimization of the timing of seed abscission and may be incorporated into plant population movement models to increase their accuracy and predictive power.

Satellite-based observations indicate that seasonal patterns in canopy greenness and productivity in the Amazon are negatively correlated with precipitation, with increased greenness occurring during the dry months. Flux tower measurements indicate that the canopy greening that occurs during the dry season is associated with increases in net ecosystem productivity (NEP) and evapotranspiration (ET). Land surface and terrestrial biosphere model simulations for the region have predicted the opposite of these observed patterns, with significant declines in greenness, NEP, and ET during the dry season. In this study, we address this issue mainly by developing an empirically constrained, light-controlled phenology submodel within the Ecosystem Demography model version 2 (ED2). The constrained ED2 model with a suite of field observations shows markedly improved predictions of seasonal ecosystem dynamics, more accurately capturing the observed patterns of seasonality in water, carbon, and litter fluxes seen at the Tapajos National Forest, Brazil (2.86°S, 54.96°W). Long-term simulations indicate that this light-controlled phenology increases the resilience of Amazon forest NEP to interannual variability in climate forcing.

Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5° N, 72.1° W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions.

We assess the significance of high-frequency variability of environmental parameters (sunlight, precipitation, temperature) for the structure and function of terrestrial ecosystems under current and future climate. We examine the influence of hourly, daily, and monthly variance using the Ecosystem Demography model version 2 in conjunction with the long-term record of carbon fluxes measured at Harvard Forest. We find that fluctuations of sunlight and precipitation are strongly and nonlinearly coupled to ecosystem function, with effects that accumulate through annual and decadal timescales. Increasing variability in sunlight and precipitation leads to lower rates of carbon sequestration and favors broad-leaved deciduous trees over conifers. Temperature variability has only minor impacts by comparison. We also find that projected changes in sunlight and precipitation variability have important implications for carbon storage and ecosystem structure and composition. Based on Intergovernmental Panel on Climate Change model estimates for changes in high-frequency meteorological variability over the next 100 years, we expect that terrestrial ecosystems will be affected by changes in variability almost as much as by changes in mean climate. We conclude that terrestrial ecosystems are highly sensitive to high-frequency meteorological variability, and that accurate knowledge of the statistics of this variability is essential for realistic predictions of ecosystem structure and functioning.

We assessed whether diversity in plant hydraulic traits can explain the observed diversity in plant responses to water stress in seasonally dry tropical forests (SDTFs). The Ecosystem Demography model 2 (ED2) was updated with a trait-driven mechanistic plant hydraulic module, as well as novel drought-phenology and plant water stress schemes. Four plant functional types were parameterized on the basis of meta-analysis of plant hydraulic traits. Simulations from both the original and the updated ED2 were evaluated against 5 yr of field data from a Costa Rican SDTF site and remote-sensing data over Central America. The updated model generated realistic plant hydraulic dynamics, such as leaf water potential and stem sap flow. Compared with the original ED2, predictions from our novel trait-driven model matched better with observed growth, phenology and their variations among functional groups. Most notably, the original ED2 produced unrealistically small leaf area index (LAI) and underestimated cumulative leaf litter. Both of these biases were corrected by the updated model. The updated model was also better able to simulate spatial patterns of LAI dynamics in Central America. Plant hydraulic traits are intercorrelated in SDTFs. Mechanistic incorporation of plant hydraulic traits is necessary for the simulation of spatiotemporal patterns of vegetation dynamics in SDTFs in vegetation models.

Tree abundance in tropical savannas exhibits large and unexplained spatial variability. Here, we propose that differentiated tree and grass water use strategies can explain the observed negative relation between maximum tree abundance and rainfall intensity (defined as the characteristic rainfall depth on rainy days), and we present a biophysical tree–grass competition model to test this idea. The model is founded on a premise that has been well established in empirical studies, namely, that the relative growth rate of grasses is much higher compared with trees in wet conditions but that grasses are more susceptible to water stress and lose biomass more quickly in dry conditions. The model is coupled with a stochastic rainfall generator and then calibrated and tested using field observations from several African savanna sites. We show that the observed negative relation between maximum tree abundance and rainfall intensity can be explained only when differentiated water use strategies are accounted for. Numerical experiments reveal that this effect is more significant than the effect of root niche separation. Our results emphasize the importance of vegetation physiology in determining the responses of tree abundance to climate variations in tropical savannas and suggest that projected increases in rainfall intensity may lead to an increase in grass in this biome.

Methane (CH4) emission by carbon-rich cryosols at the high latitudes in Northern Hemisphere has been studied extensively. In contrast, data on the CH4 emission potential of carbon-poor cryosols is limited, despite their spatial predominance. This work employs CH4 flux measurements in the field and under laboratory conditions to show that the mineral cryosols at Axel Heiberg Island in the Canadian high Arctic consistently consume atmospheric CH4. Omics analyses present the first molecular evidence of active atmospheric CH4-oxidizing bacteria (atmMOB) in permafrost-affected cryosols, with the prevalent atmMOB genotype in our acidic mineral cryosols being closely related to Upland Soil Cluster α. The atmospheric (atm) CH4 uptake at the study site increases with ground temperature between 0 °C and 18 °C. Consequently, the atm CH4 sink strength is predicted to increase by a factor of 5–30 as the Arctic warms by 5–15 °C over a century. We demonstrate that acidic mineral cryosols are a previously unrecognized potential of CH4 sink that requires further investigation to determine its potential impact on larger scales. This study also calls attention to the poleward distribution of atmMOB, as well as to the potential influence of microbial atm CH4 oxidation, in the context of regional CH4 flux models and global warming.

[1] In temperate regions, the budburst date of deciduous trees is mainly regulated by temperature variation, but the exact nature of the temperature dependence has been a matter of debate. One hypothesis is that budburst date depends purely on the accumulation of warm temperature; a competing hypothesis states that exposure to cold temperatures is also important for budburst. In this study, variability in budburst is evaluated using 15 years of budburst data for 17 tree species at Harvard Forest. We compare two budburst hypotheses through reversible jump Markov chain Monte Carlo. We then investigate how uncertainties in budburst date mapped onto uncertainties in ecosystem carbon using the Geophysical Fluid Dynamics Laboratory's LM3 land model. For 15 of 17 species, we find that more complicated budburst models that account for a chilling period are favored over simpler models that do not include such dependence. LM3 simulations show that the choice of budburst model induces differences in the timing of carbon uptake commencement of ∼11 days, in the magnitude of April–May carbon uptake of ∼1.03 g C m−2 day−1, and in total ecosystem carbon stocks of ∼2 kg C m−2. While the choice of whether to include a chilling period in the budburst model strongly contributes to this variability, another important factor is how the species-dependent field data gets mapped onto LM3's single deciduous plant functional type (PFT). We conclude budburst timing has a strong impact on simulated CO2 fluxes, and uncertainty in the fluxes can be substantially reduced by improving the model's representation of PFT diversity.

[1] Insights into how terrestrial ecosystems affect the Earth’s response to changes in climate and rising atmospheric CO2 levels rely heavily on the predictions of terrestrial biosphere models (TBMs). These models contain detailed mechanistic representations of biological processes affecting terrestrial ecosystems; however, their ability to simultaneously predict field-based measurements of terrestrial vegetation dynamics and carbon fluxes has remained largely untested. In this study, we address this issue by developing a constrained implementation of a new structured TBM, the Ecosystem Demography model version 2 (ED2), which explicitly tracks the dynamics of fine-scale ecosystem structure and function. Carbon and water flux measurements from an eddy-flux tower are used in conjunction with forest inventory measurements of tree growth and mortality at Harvard Forest (42.5N, 72.1W) to estimate a number of important but weakly constrained model parameters. Evaluation against a decade of tower flux and forest dynamics measurements shows that the constrained ED2 model yields greatly improved predictions of annual net ecosystem productivity, carbon partitioning, and growth and mortality dynamics of both hardwood and conifer trees. The generality of the model formulation is then evaluated by comparing the model’s predictions against measurements from two other eddy-flux towers and forest inventories of the northeastern United States and Quebec. Despite the markedly different composition throughout this region, the optimized model realistically predicts observed patterns of carbon fluxes and tree growth. These results demonstrate how TBMs parameterized with field-based measurements can provide quantitative insight into the underlying biological processes governing ecosystem composition, structure, and function at larger scales.

Tropical forests provide important ecosystem services in maintaining biodiversity, sequestering carbon and regulating climate regionally and globally. Climate triggers the seasonal transitions of vegetation structure and function in tropical forests. In turn, the seasonal cycles of structure and function in tropical forests feed back to the climate system through the control of land-atmosphere exchange of carbon, water and energy fluxes. Large uncertainties exist in the carbon and water budgets of tropical forests, and environmental controls on phenology are among the least understood factors. Although field studies have identified patterns in the environmental controls on local-scale species-level phenology in the tropics, there is little consensus on large-scale top-down environmental controls on whole-ecosystem seasonality. In this paper, we use both optical and microwave remote sensing to investigate the seasonality of vegetation canopy structure and function in three distinct tropical African forest types, and identify environmental triggers or controls of their variability. For most tropical forests that have a closed canopy and high leaf biomass, optical remote sensing (e.g., vegetation indices) captures canopy photosynthetic capacity (i.e., canopy function), while small-wavelength microwave remote sensing characterizes the leaf biomass and leaf water content of the upper canopy (i.e., canopy structure). Our results reveal a strong coupling of canopy structure with canopy function in the tropical deciduous forests and woody savannas, and their seasonalities are both controlled by precipitation rather than solar radiation. By contrast, tropical evergreen forests in Africa exhibit a decoupling of canopy structure from canopy function revealed by different sensors: canopy photosynthetic capacity shown by the optical remote sensing is linked to the seasonal variation of precipitation, while microwave remote sensing captures semi-annual leaf-flushing that is synchronous with peak insolation intensity at the top of the atmosphere, which is bimodal. The differential coupling of canopy structure and function in tropical forests observed from remote sensing highlights differences inherent in distinct vegetation types within the tropics that may originate in the different life histories of their respective floras. This satellite-based finding encourages more field-based studies to clarify the interpretation of these large scale patterns.

Many temperate and boreal forests are subject to insect epidemics. In the eastern US, over 41 million meters squared of tree basal area are thought to be at risk of gypsy moth defoliation. However, the decadal-to-century scale implications of defoliation events for ecosystem carbon dynamics are not well understood. In this study, the effects of defoliation intensity, periodicity and spatial pattern on the carbon cycle are investigated in a set of idealized model simulations. A mechanistic terrestrial biosphere model, ecosystem demography model 2, is driven with observations from a xeric oak‐pine forest located in the New Jersey Pine Barrens. Simulations indicate that net ecosystem productivity (equal to photosynthesis minus respiration) decreases linearly with increasing defoliation intensity. However, because of interactions between defoliation and drought effects, aboveground biomass exhibits a nonlinear decrease with increasing defoliation intensity. The ecosystem responds strongly with both reduced productivity and biomass loss when defoliation periodicity varies from 5 to 15 yr, but exhibits a relatively weak response when defoliation periodicity varies from 15 to 60 yr. Simulations of spatially heterogeneous defoliation resulted in markedly smaller carbon stocks than simulations with spatially homogeneous defoliation. These results show that gypsy moth defoliation has a large effect on oak‐pine forest biomass dynamics, functioning and its capacity to act as a carbon sink.