Constraining long-term model predictions for woody growth using tropical tree rings.

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO2 (Ca ). When forced with historical Ca , ED2.2-hydro reproduced the magnitude of increases in intercellular CO2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing Ca based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to Ca and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing Ca . More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by Ca rise predicted by biosphere models.

The role of memory-based movements in the formation of animal home ranges.

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

Experimental evidence of memory-based foraging decisions in a large wild mammal.

Many animals restrict their movements to a characteristic home range. This constrained pattern of space use is thought to result from the foraging benefits of memorizing the locations and quality of heterogeneously distributed resources. However, due to the confounding effects of sensory perception, the role of memory in home-range movement behavior lacks definitive evidence in the wild. Here, we analyze the foraging decisions of a large mammal during a field resource manipulation experiment designed to disentangle the effects of memory and perception. We parametrize a mechanistic model of spatial transitions using experimental data to quantify the cognitive processes underlying animal foraging behavior and to predict how individuals respond to resource heterogeneity in space and time. We demonstrate that roe deer (Capreolus capreolus) rely on memory, not perception, to track the spatiotemporal dynamics of resources within their home range. Roe deer foraging decisions were primarily based on recent experience (half-lives of 0.9 and 5.6 d for attribute and spatial memory, respectively), enabling them to adapt to sudden changes in resource availability. The proposed memory-based model was able to both quantify the cognitive processes underlying roe deer behavior and accurately predict how they shifted resource use during the experiment. Our study highlights the fact that animal foraging decisions are based on incomplete information on the locations of available resources, a factor that is critical to developing accurate predictions of animal spatial behavior but is typically not accounted for in analyses of animal movement in the wild.

Memory drives the formation of animal home ranges: Evidence from a reintroduction.

Most animals live in home ranges, and memory is thought to be an important process in their formation. However, a general memory-based model for characterising and predicting home range emergence has been lacking. Here, we use a mechanistic movement model to: (1) quantify the role of memory in the movements of a large mammal reintroduced into a novel environment, and (2) predict observed patterns of home range emergence in this experimental setting. We show that an interplay between memory and resource preferences is the primary process influencing the movements of reintroduced roe deer (Capreolus capreolus). Our memory-based model fitted with empirical data successfully predicts the formation of home ranges, as well as emergent properties of movement and spatial revisitation observed in the reintroduced animals. These results provide a mechanistic framework for combining memory-based movements, resource preferences, and the formation of home ranges in nature.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250 m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2 model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737 km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 trees km-2  year-1 in 2010-2011, to ~535 trees km-2  year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2  = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison.

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Knowing your neighbours: How memory-mediated conspecific avoidance influences home ranges.

In Focus: Ellison, N., Hatchwell, B. J., Biddiscombe, S. J., Napper, C. J., & Potts, J. R. (2020). Mechanistic home range analysis reveals drivers of space use patterns for a non-territorial passerine. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13292. Most animals for which space use has been studied restrict their movements into a constrained spatial area: their home range. The ubiquity of this space-use pattern suggests that home ranges are adaptive in a wide range of ecological contexts, and that they likely arise from general biological mechanisms. In this issue, Ellison et al. use a mechanistic home range analysis (MHRA) to uncover the drivers underlying home range patterns in a passerine that is non-territorial. They show that a model integrating both resource preferences (specifically, an attraction to woodland centre), and memory-mediated conspecific avoidance can capture the space-use patterns observed in a wild population of long-tailed tits Aegithalos caudatus. In doing so, their analysis extends the applicability of MHRA to capturing and predicting home range patterns beyond the previously studied cases where spatially exclusive home ranges emerge from scent mark-mediated avoidance responses to neighbouring groups.

Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change.

Amazon forests, which store ∼ 50% of tropical forest carbon and play a vital role in global water, energy, and carbon cycling, are predicted to experience both longer and more intense dry seasons by the end of the 21st century. However, the climate sensitivity of this ecosystem remains uncertain: several studies have predicted large-scale die-back of the Amazon, whereas several more recent studies predict that the biome will remain largely intact. Combining remote-sensing and ground-based observations with a size- and age-structured terrestrial ecosystem model, we explore the sensitivity and ecological resilience of these forests to changes in climate. We demonstrate that water stress operating at the scale of individual plants, combined with spatial variation in soil texture, explains observed patterns of variation in ecosystem biomass, composition, and dynamics across the region, and strongly influences the ecosystem's resilience to changes in dry season length. Specifically, our analysis suggests that in contrast to existing predictions of either stability or catastrophic biomass loss, the Amazon forest's response to a drying regional climate is likely to be an immediate, graded, heterogeneous transition from high-biomass moist forests to transitional dry forests and woody savannah-like states. Fire, logging, and other anthropogenic disturbances may, however, exacerbate these climate change-induced ecosystem transitions.

Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts.

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Drivers and mechanisms of tree mortality in moist tropical forests.

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.

Vegetation demographics in Earth System Models: A review of progress and priorities.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Land cover change explains the increasing discharge of the Paraná River.

Over the past 40 years, the discharge in South America's Paraná River basin has increased despite no evidence of significant rainfall increases in the basin. In this analysis, we show that the observed multi-decadal increase in discharge can be explained by concomitant changes in land cover that have occurred within the basin during this period. Our analysis also indicates that the peak discharge timing may have shifted concurrently from January/February in the 1970s to March in more recent decades. While land-use effect dominantly alters the long-term temporal dynamics of the river discharge over multi-decades, the change in the seasonality of the discharge can be attributable to the combined effect of the land-use and climate variability. This study suggests that the mean annual discharge is likely to change in the other South American River basins where land transformation is currently taking place, and the shift of the month of peak discharge needs to be taken into consideration to forecast the hydropower generation under changing climate and land conversion.

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees.

Considerable uncertainty surrounds the impacts of anthropogenic climate change on the composition and structure of Amazon forests. Building upon results from two large-scale ecosystem drought experiments in the eastern Brazilian Amazon that observed increases in mortality rates among some tree species but not others, in this study we investigate the physiological traits underpinning these differential demographic responses. Xylem pressure at 50% conductivity (xylem-P50 ), leaf turgor loss point (TLP), cellular osmotic potential (πo ), and cellular bulk modulus of elasticity (ε), all traits mechanistically linked to drought tolerance, were measured on upper canopy branches and leaves of mature trees from selected species growing at the two drought experiment sites. Each species was placed a priori into one of four plant functional type (PFT) categories: drought-tolerant versus drought-intolerant based on observed mortality rates, and subdivided into early- versus late-successional based on wood density. We tested the hypotheses that the measured traits would be significantly different between the four PFTs and that they would be spatially conserved across the two experimental sites. Xylem-P50 , TLP, and πo , but not ε, occurred at significantly higher water potentials for the drought-intolerant PFT compared to the drought-tolerant PFT; however, there were no significant differences between the early- and late-successional PFTs. These results suggest that these three traits are important for determining drought tolerance, and are largely independent of wood density-a trait commonly associated with successional status. Differences in these physiological traits that occurred between the drought-tolerant and drought-intolerant PFTs were conserved between the two research sites, even though they had different soil types and dry-season lengths. This more detailed understanding of how xylem and leaf hydraulic traits vary between co-occuring drought-tolerant and drought-intolerant tropical tree species promises to facilitate a much-needed improvement in the representation of plant hydraulics within terrestrial ecosystem and biosphere models, which will enhance our ability to make robust predictions of how future changes in climate will affect tropical forests.

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison.

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, 'soil water stress' and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re ) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models.

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO2, and land use.

There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO2 levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models (ED2, IBIS, and JULES) forced by three bias-corrected IPCC AR4 climate projections (PCM1, CCSM3, and HadCM3) under two land-use change scenarios. We assess the relative roles of climate change, CO2 fertilization, land-use change, and fire in driving the projected changes in Amazonian biomass and forest extent. Our results indicate that the impacts of climate change are primarily determined by the direction and severity of projected changes in regional precipitation: under the driest climate projection, climate change alone is predicted to reduce Amazonian forest cover by an average of 14%. However, the models predict that CO2 fertilization will enhance vegetation productivity and alleviate climate-induced increases in plant water stress, and, as a result, sustain high biomass forests, even under the driest climate scenario. Land-use change and climate-driven changes in fire frequency are predicted to cause additional aboveground biomass loss and reductions in forest extent. The relative impact of land use and fire dynamics compared to climate and CO2 impacts varies considerably, depending on both the climate and land-use scenario, and on the terrestrial biosphere model used, highlighting the importance of improved quantitative understanding of all four factors - climate change, CO2 fertilization effects, fire, and land use - to the fate of the Amazon over the coming century.

Movement responses of caribou to human-induced habitat edges lead to their aggregation near anthropogenic features.

The assessment of disturbance effects on wildlife and resulting mitigation efforts are founded on edge-effect theory. According to the classical view, the abundance of animals affected by human disturbance should increase monotonically with distance from disturbed areas to reach a maximum at remote locations. Here we show that distance-dependent movement taxis can skew abundance distributions toward disturbed areas. We develop an advection-diffusion model based on basic movement behavior commonly observed in animal populations and parameterize the model from observations on radio-collared caribou in a boreal ecosystem. The model predicts maximum abundance at 3.7 km from cutovers and roads. Consistently, aerial surveys conducted over 161,920 km(2) showed that the relative probability of caribou occurrence displays nonmonotonic changes with the distance to anthropogenic features, with a peak occurring at 4.5 km away from these features. This aggregation near disturbed areas thus provides the predators of this top-down-controlled, threatened herbivore species with specific locations to concentrate their search. The edge-effect theory developed here thus predicts that human activities should alter animal distribution and food web properties differently than anticipated from the current paradigm. Consideration of such nonmonotonic response to habitat edges may become essential to successful wildlife conservation.

Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought.

Considerable uncertainty surrounds the fate of Amazon rainforests in response to climate change. Here, carbon (C) flux predictions of five terrestrial biosphere models (Community Land Model version 3.5 (CLM3.5), Ecosystem Demography model version 2.1 (ED2), Integrated BIosphere Simulator version 2.6.4 (IBIS), Joint UK Land Environment Simulator version 2.1 (JULES) and Simple Biosphere model version 3 (SiB3)) and a hydrodynamic terrestrial ecosystem model (the Soil-Plant-Atmosphere (SPA) model) were evaluated against measurements from two large-scale Amazon drought experiments. Model predictions agreed with the observed C fluxes in the control plots of both experiments, but poorly replicated the responses to the drought treatments. Most notably, with the exception of ED2, the models predicted negligible reductions in aboveground biomass in response to the drought treatments, which was in contrast to an observed c. 20% reduction at both sites. For ED2, the timing of the decline in aboveground biomass was accurate, but the magnitude was too high for one site and too low for the other. Three key findings indicate critical areas for future research and model development. First, the models predicted declines in autotrophic respiration under prolonged drought in contrast to measured increases at one of the sites. Secondly, models lacking a phenological response to drought introduced bias in the sensitivity of canopy productivity and respiration to drought. Thirdly, the phenomenological water-stress functions used by the terrestrial biosphere models to represent the effects of soil moisture on stomatal conductance yielded unrealistic diurnal and seasonal responses to drought.

Responses of terrestrial ecosystems and carbon budgets to current and future environmental variability.

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.