Widespread Decline in Vegetation Photosynthesis in Southeast Asia Due to the Prolonged Drought During the 2015/2016 El Niño

El Niño events are known to be associated with climate extremes and have substantial impacts on the global carbon cycle. The drought induced by strong El Niño event occurred in the tropics during 2015 and 2016. However, it is still unclear to what extent the drought could affect photosynthetic activities of crop and forest in Southeast Asia. Here, we used the satellite solar-induced chlorophyll fluorescence (SIF), which is a proxy of actual photosynthesis, along with traditional vegetation indices (Enhanced Vegetation Index, EVI) and total water storage to investigate the impacts of El Niño–induced droughts on vegetation productivity of the forest and crop in the Southeast Asia. We found that SIF was more sensitive to the water stress than traditional vegetation indices (EVI) to monitor drought for both evergreen broadleaf forest and croplands in Southeast Asia. The higher solar radiation partly offset the negative effects of droughts on the vegetation productivity, leading to a larger decrease of SIF yield (SIFyield) than SIF. Therefore, SIFyield had a larger reduction and was more sensitive to precipitation deficit than SIF during the drought. The comparisons of retrieved column-average dry-air mole fraction of atmospheric carbon dioxide with SIF demonstrated the reduction of CO2 uptake by vegetation in Southeast Asia during the drought. This study highlights that SIF is more beneficial than EVI to be an indicator to characterize and monitor the dynamics of drought in tropical vegetated regions.

Simulated projections of boreal forest peatland ecosystem productivity are sensitive to observed seasonality in leaf physiology†

We quantified seasonal CO2 assimilation capacities for seven dominant vascular species in a wet boreal forest peatland then applied data to a land surface model parametrized to the site (ELM-SPRUCE) to test if seasonality in photosynthetic parameters results in differences in simulated plant responses to elevated CO2 and temperature. We collected seasonal leaf-level gas exchange, nutrient content and stand allometric data from the field-layer community (i.e., Maianthemum trifolium (L.) Sloboda), understory shrubs (Rhododendron groenlandicum (Oeder) Kron and Judd, Chamaedaphne calyculata (L.) Moench., Kalmia polifolia Wangenh. and Vaccinium angustifolium Alton.) and overstory trees (Picea mariana (Mill.) B.S.P. and Larix laricina (Du Roi) K. Koch). We found significant interspecific seasonal differences in specific leaf area, nitrogen content (by area; Na) and photosynthetic parameters (i.e., maximum rates of Rubisco carboxylation (Vcmax25°C), electron transport (Jmax25°C) and dark respiration (Rd25°C)), but minimal correlation between foliar Na and Vcmax25°C, Jmax25°C or Rd25°C, which illustrates that nitrogen alone is not a good correlate for physiological processes such as Rubisco activity that can change seasonally in this system. ELM-SPRUCE was sensitive to the introduction of observed interspecific seasonality in Vcmax25°C, Jmax25°C and Rd25°C, leading to simulated enhancement of net primary production (NPP) using seasonally dynamic parameters as compared with use of static parameters. This pattern was particularly pronounced under simulations with higher temperature and elevated CO2, suggesting a key hypothesis to address with future empirical or observational studies as climate changes. Inclusion of species-specific seasonal photosynthetic parameters should improve estimates of boreal ecosystem-level NPP, especially if impacts of seasonal physiological ontogeny can be separated from seasonal thermal acclimation.

Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO₂) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO₂ flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO₂ fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO₂ exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Using the rapid A-Ci response (RACiR) in the Li-Cor 6400 to measure developmental gradients of photosynthetic capacity in poplar

The rapid A-C_i response (RACiR) technique alleviates limitations of measuring photosynthetic capacity by reducing the time needed to determine the maximum carboxylation rate (V_cmax ) and electron transport rate (J_max ) in leaves. Photosynthetic capacity and its relationships with leaf development are important for understanding ecological and agricultural productivity; however, our current understanding is incomplete. Here, we show that RACiR can be used in previous generation gas exchange systems (i.e., the LI-6400) and apply this method to rapidly investigate developmental gradients of photosynthetic capacity in poplar. We compared RACiR-determined V_cmax and J_max as well as respiration and stomatal conductance (g_s ) across four stages of leaf expansion in Populus deltoides and the poplar hybrid 717-1B4 (Populus tremula × Populus alba). These physiological data were paired with leaf traits including nitrogen concentration, chlorophyll concentrations, and specific leaf area. Several traits displayed developmental trends that differed between the poplar species, demonstrating the utility of RACiR approaches to rapidly generate accurate measures of photosynthetic capacity. By using both new and old machines, we have shown how more investigators will be able to incorporate measurements of important photosynthetic traits in future studies and further our understanding of relationships between development and leaf-level physiology.

Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity

Improving the accuracy of predicting plant productivity is a key element in planning nutrient management strategies to ensure a balance between nutrient supply and demand under climate change. A calculation based on intercepted photosynthetically active radiation is an effective and relatively reliable way to determine the climate impact on a crop above-ground biomass (AGB). This research shows that using variations in a chlorophyll content index (CCI) in a mathematical function could effectively obtain good statistical diagnostic results between simulated and observed crop biomass. In this study, the leaf CCI, which is used as a biochemical photosynthetic component and calibration parameter, increased simulation accuracy across the growing stages during 2016–2017. This calculation improves the accuracy of prediction and modelling of crops under specific agroecosystems, and it may also improve projections of AGB for a variety of other crops.

Acceleration of global vegetation greenup from combined effects of climate change and human land management

Global warming and human land management have greatly influenced vegetation growth through both changes in spring phenology and photosynthetic primary production. This will presumably impact the velocity of vegetation greenup (Vgreenup, the daily rate of changes in vegetation productivity during greenup period), yet little is currently known about the spatio-temporal patterns of Vgreenup of global vegetation. Here, we define Vgreenup as the ratio of the amplitude of greenup (Agreenup) to the duration of greenup (Dgreenup) and derive global Vgreenup from 34-year satellite leaf area index (LAI) observations to study spatio-temporal dynamics of Vgreenup at the global, hemispheric, and ecosystem scales. We find that 19.9% of the pixels analyzed (n = 1,175,453) experienced significant trends toward higher greenup rates by an average of 0.018 m²  m^-2  day^-1 for 1982-2015 as compared to 8.6% of pixels with significant negative trends (p < 0.05). Global distribution and dynamics of Vgreenup show high spatial heterogeneity and ecosystem-specific patterns, which is primarily determined by the high spatial variation in Agreenup, while the temporal dynamics of Vgreenup are directly controlled by both changes in Dgreenup and Agreenup. Areas with the largest Vgreenup and largest positive trends are both observed in deciduous and mixed forests as compared to nonforest ecosystems showing both lower Vgreenup and trends. For nonforest ecosystems, human-managed ecosystems (e.g., rangelands and rainfed croplands) exhibited higher Vgreenup and positive trends than those of natural counterparts, suggesting strong imprints of human land management on terrestrial ecosystem functioning. Globally, warming has accelerated Vgreenup in temperature-constrained high latitude forest ecosystems and arctic regions, but decelerated Vgreenup in temperate and arid/semiarid areas. These results suggest that the combined effects of climate change and human land management have greatly accelerated global vegetation greenup, with important implications for changes in terrestrial ecosystem functioning and global carbon cycling.

Causes of reduced leaf-level photosynthesis during strong El Niño drought in a Central Amazon forest

Sustained drought and concomitant high temperature may reduce photosynthesis and cause tree mortality. Possible causes of reduced photosynthesis include stomatal closure and biochemical inhibition, but their relative roles are unknown in Amazon trees during strong drought events. We assessed the effects of the recent (2015) strong El Niño drought on leaf-level photosynthesis of Central Amazon trees via these two mechanisms. Through four seasons of 2015, we measured leaf gas exchange, chlorophyll a fluorescence parameters, chlorophyll concentration, and nutrient content in leaves of 57 upper canopy and understory trees of a lowland terra firme forest on well-drained infertile oxisol. Photosynthesis decreased 28% in the upper canopy and 17% in understory trees during the extreme dry season of 2015, relative to other 2015 seasons and was also lower than the climatically normal dry season of the following non-El Niño year. Photosynthesis reduction under extreme drought and high temperature in the 2015 dry season was related only to stomatal closure in both upper canopy and understory trees, and not to chlorophyll a fluorescence parameters, chlorophyll, or leaf nutrient concentration. The distinction is important because stomatal closure is a transient regulatory response that can reverse when water becomes available, whereas the other responses reflect more permanent changes or damage to the photosynthetic apparatus. Photosynthesis decrease due to stomatal closure during the 2015 extreme dry season was followed 2 months later by an increase in photosynthesis as rains returned, indicating a margin of resilience to one-off extreme climatic events in Amazonian forests.

Post-drought decline of the Amazon carbon sink

Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3 ± 0.2 (95% CI) PgC yr⁻¹ after the 2005 mega-drought, which continued persistently over the next 3 years (2005–2008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35 ± 1.80 MgC ha⁻¹ a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.

Forests of the Amazon Basin have experienced frequent and severe droughts in recent years with significant impacts on their carbon cycling. Here, using satellite LiDAR samples from 2003 to 2008, the authors show the long-term legacy of these droughts with persistent loss of carbon stocks after the 2005 drought.

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.

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.

Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest

Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. We conducted demographic surveys of leaf age composition, and measured the age dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used these data to independently test the much-debated hypothesis - arising from satellite and tower-based observations - that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interactions between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.

Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production

Gross primary production (GPP) is a key driver of the peatland carbon cycle. Although many studies have explored the apparent GPP under natural light conditions, knowledge of the maximum GPP at light-saturation (GPP_max) and its spatio-temporal variation is limited. This information, however, is crucial since GPP_max essentially constrains the upper boundary for apparent GPP. Using chamber measurements combined with an external light source across experimental plots where vegetation composition was altered through long-term (20-year) nitrogen addition and artificial warming, we could quantify GPP_max in-situ and disentangle its biotic and abiotic controls in a boreal peatland. We found large spatial and temporal variations in the magnitudes of GPP_max which were related to vegetation species composition and phenology rather than abiotic factors. Specifically, we identified vegetation phenology as the main driver of the seasonal GPP_max trajectory. Abiotic anomalies (i.e. in air temperature and water table level), however, caused species-specific divergence between the trajectories of GPP_max and plant development. Our study demonstrates that photosynthetically active biomass constrains the potential peatland photosynthesis while abiotic factors act as secondary modifiers. This further calls for a better representation of species-specific vegetation phenology in process-based peatland models to improve predictions of global change impacts on the peatland carbon cycle.

Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016

Amazon droughts have impacted regional ecosystem functioning as well as global carbon cycling. The severe dry-season droughts in 2005 and 2010, driven by Atlantic sea surface temperature (SST) anomaly, have been widely investigated in terms of drought severity and impacts on ecosystems. Although the influence of Pacific SST anomaly on wet-season precipitation has been well recognized, it remains uncertain to what extent the droughts driven by Pacific SST anomaly could affect forest greenness and photosynthesis in the Amazon. Here, we examined the monthly and annual dynamics of forest greenness and photosynthetic capacity when Amazon ecosystems experienced an extreme drought in 2015/2016 driven by a strong El Niño event. We found that the drought during August 2015-July 2016 was one of the two most severe meteorological droughts since 1901. Due to the enhanced solar radiation during this drought, overall forest greenness showed a small increase, and 21.6% of forests even greened up (greenness index anomaly ≥1 standard deviation). In contrast, solar-induced chlorophyll fluorescence (SIF), an indicator of vegetation photosynthetic capacity, showed a significant decrease. Responses of forest greenness and photosynthesis decoupled during this drought, indicating that forest photosynthesis could still be suppressed regardless of the variation in canopy greenness. If future El Niño frequency increases as projected by earth system models, droughts would result in persistent reduction in Amazon forest productivity, substantial changes in tree composition, and considerable carbon emissions from Amazon.

Spatio-temporal dynamics of soil bacterial communities as a function of Amazon forest phenology

Most tropical evergreen rain forests are characterised by varying degrees of precipitation seasonality that influence plant phenology and litterfall dynamics. Soil microbes are sensitive to soil water:air ratio and to nutrient availability. We studied if within-year seasonality in precipitation and litterfall-derived nutrient input resulted in predictable seasonal variation in soil bacterial diversity/microbial functional groups in an Amazonian forest. We characterised the spatio-temporal dynamics of microbial communities from the plot to the stand scales and related them to precipitation seasonality and spatial variability in soil characteristics. Community composition and functional diversity showed high spatial heterogeneity and was related to variability in soil chemistry at the stand level. Large species turnover characterised plot level changes over time, reflecting precipitation seasonality-related changes in soil nutrient and moisture regimes. The abundance of decomposers was highest during the rainy season, characterised also by anaerobic saprophytes and N₂-fixers adapted to fluctuating redox conditions. In contrast, Beijerinckiaceae, likely derived from the phyllosphere, were found at higher abundances when litter inputs and accumulation were highest. We showed that in a mildly seasonal rain forest, the composition of soil microbial communities appears to be following canopy phenology patterns and the two are interlinked and drive soil nutrient availability.

Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation

The El Niño-Southern Oscillation (ENSO) is the largest source of interannual climate variability in much of the tropics. We hypothesize that tropical plants exhibit interannual variation in reproduction and resource acquisition strategies driven by ENSO that mirrors their seasonal responses. We analyze the relationship of leaf and seed fall to climate variation over 30 years in a seasonally dry tropical forest in Panama where El Niño brings warm, dry, and sunny conditions. Elevated leaf fall precedes the onset of El Niño, and elevated seed production follows, paralleling associations with dry seasons. Our results provide evidence of a shift in allocation from leafing to fruiting in response to a warming phase of ENSO. This shift may enable plants to take advantage of higher light availability, while coping with higher atmospheric water demand and lower water supply. These findings might be an indicator of adaptive strategies to optimize reproduction and resource acquisition.

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

Attribution of seasonal leaf area index trends in the northern latitudes with "optimally" integrated ecosystem models

Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1980s have been detected and attributed at annual and growing season scales. However, we presently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental changes. In this study, we first investigated trends in the seasonal mean leaf area index (LAI) at northern latitudes (north of 30°N) between 1982 and 2009 using three remotely sensed long-term LAI data sets. The most significant LAI increases occurred in summer (0.009 m²  m^-2  year^-1 , p < .01), followed by autumn (0.005 m²  m^-2  year^-1 , p < .01) and spring (0.003 m² m^-2  year^-1 , p < .01). We then quantified the contribution of elevating atmospheric CO₂ concentration (eCO₂ ), climate change, nitrogen deposition, and land cover change to seasonal LAI increases based on factorial simulations from 10 state-of-the-art ecosystem models. Unlike previous studies that used multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integration of model ensemble. The optimally integrated ensemble LAI changes are significantly closer to the observed seasonal LAI changes than the traditional MME results. The BMA factorial simulations suggest that eCO₂ provides the greatest contribution to increasing LAI trends in all seasons (0.003-0.007 m²  m^-2  year^-1 ), and is the main factor driving asymmetric seasonal LAI trends. Climate change controls the spatial pattern of seasonal LAI trends and dominates the increase in seasonal LAI in the northern high latitudes. The effects of nitrogen deposition and land use change are relatively small in all seasons (around 0.0002 m²  m^-2  year^-1 and 0.0001-0.001 m²  m^-2  year^-1 , respectively). Our analysis of the seasonal LAI responses to the interactions between seasonal changes in environmental factors offers a new perspective on the response of global vegetation to environmental changes.

The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO₂ assimilation. We developed a two-fraction leaf (sun and shade), two-layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leaf quantity, quality, and within-canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground-based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two-fraction leaf, two-layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance-derived CO₂ assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO₂ assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.

A global moderate resolution dataset of gross primary production of vegetation for 2000-2016

Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

Tropical rainforest carbon sink declines during El Niño as a result of reduced photosynthesis and increased respiration rates

Changes in tropical forest carbon sink strength during El Niño Southern Oscillation (ENSO) events can indicate future behavior under climate change. Previous studies revealed ˜6 Mg C ha^-1  yr^-1 lower net ecosystem production (NEP) during ENSO year 1998 compared with non-ENSO year 2000 in a Costa Rican tropical rainforest. We explored environmental drivers of this change and examined the contributions of ecosystem respiration (RE) and gross primary production (GPP) to this weakened carbon sink. For 1998-2000, we estimated RE using chamber-based respiration measurements, and we estimated GPP in two ways: using (1) the canopy process model MAESTRA, and (2) combined eddy covariance and chamber respiration data. MAESTRA-estimated GPP did not statistically differ from GPP estimated using approach 2, but was ˜ 28% greater than published GPP estimates for the same site and years using eddy covariance data only. A 7% increase in RE (primarily increased soil respiration) and a 10% reduction in GPP contributed equally to the difference in NEP between ENSO year 1998 and non-ENSO year 2000. A warming and drying climate for tropical forests may yield a weakened carbon sink from both decreased GPP and increased RE. Understanding physiological acclimation will be critical for the large carbon stores in these ecosystems.

Rainforest-initiated wet season onset over the southern Amazon

Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2-3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought.

Litterfall mass and nutrient fluxes over an altitudinal gradient in the coastal Atlantic Forest, Brazil

Litterfall is one of the most important pathways through which nutrients are recycled in the terrestrial biosphere. In tropical soils, which are generally low in essential nutrients such as phosphorus and cations, the flux of nutrients through litterfall is particularly important to sustaining CO2-uptake capacity; however, questions remain over the role of altitude in altering litter nutrient cycling rates among tropical forest ecosystems. Here we examine litterfall, carbon (C), nitrogen (N) and phosphorus (P) fluxes through litterfall over an altitudinal gradient in the coastal Atlantic Forest located on the northern coast of the State of São Paulo, Brazil. Litterfall was collected twice a month for 1 y (April 2007–March 2008) using 30 litter traps placed in four different forest types arrayed by altitude – coastal forest (sea level), lowland forest (50–200 m asl), submontane forest (300–500 m asl) and montane forest (1000 m asl). Litterfall mass-fluxes decreased with increasing altitude, from ~9 Mg ha−1 in lowland forests to 7 Mg ha−1 in higher-altitude ecosystems. Contribution of reproductive organs to litterfall was significantly greater in lower than in higher altitudes. Litterfall N and P fluxes were higher in the lowland forest vs. other forest types, pointing to strong altitudinal controls over nutrient cycling. Furthermore, nitrogen-use efficiency (NUE) was lower and litter δ15N was higher in the lowland site providing additional evidence for lack of N constraints to productivity in lowland of the south-eastern Atlantic Forest.

Climate drivers of the Amazon forest greening

Our limited understanding of the climate controls on tropical forest seasonality is one of the biggest sources of uncertainty in modeling climate change impacts on terrestrial ecosystems. Combining leaf production, litterfall and climate observations from satellite and ground data in the Amazon forest, we show that seasonal variation in leaf production is largely triggered by climate signals, specifically, insolation increase (70.4% of the total area) and precipitation increase (29.6%). Increase of insolation drives leaf growth in the absence of water limitation. For these non-water-limited forests, the simultaneous leaf flush occurs in a sufficient proportion of the trees to be observed from space. While tropical cycles are generally defined in terms of dry or wet season, we show that for a large part of Amazonia the increase in insolation triggers the visible progress of leaf growth, just like during spring in temperate forests. The dependence of leaf growth initiation on climate seasonality may result in a higher sensitivity of these ecosystems to changes in climate than previously thought.

Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests

Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. The model performed well for independent Brazilian sunlit and shade canopy leaves (R²  = 0.75-0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R²  = 0.27-0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment-trait linkages - either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments - we achieved a more general model that well-predicted leaf age across forests and environments (R²  = 0.79). Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments.

Climatic factors driving vegetation declines in the 2005 and 2010 Amazon droughts

Along with global climate change, the occurrence of extreme droughts in recent years has had a serious impact on the Amazon region. Current studies on the driving factors of the 2005 and 2010 Amazon droughts has focused on the influence of precipitation, whereas the impacts of temperature and radiation have received less attention. This study aims to explore the climate-driven factors of Amazonian vegetation decline during the extreme droughts using vegetation index, precipitation, temperature and radiation datasets. First, time-lag effects of Amazonian vegetation responses to precipitation, radiation and temperature were analyzed. Then, a multiple linear regression model was established to estimate the contributions of climatic factors to vegetation greenness, from which the dominant climate-driving factors were determined. Finally, the climate-driven factors of Amazonian vegetation greenness decline during the 2005 and 2010 extreme droughts were explored. The results showed that (i) in the Amazon vegetation greenness responded to precipitation, radiation and temperature, with apparent time lags for most averaging interval periods associated with vegetation index responses of 0–4, 0–9 and 0–6 months, respectively; (ii) on average, the three climatic factors without time lags explained 27.28±21.73% (mean±1 SD) of vegetation index variation in the Amazon basin, and this value increased by 12.22% and reached 39.50±27.85% when time lags were considered; (iii) vegetation greenness in this region in non-drought years was primarily affected by precipitation and shortwave radiation, and these two factors altogether accounted for 93.47% of the total explanation; and (iv) in the common epicenter of the two droughts, pixels with a significant variation in precipitation, radiation and temperature accounted for 36.68%, 40.07% and 10.40%, respectively, of all pixels showing a significant decrease in vegetation index in 2005, and 15.69%, 2.01% and 45.25% in 2010, respectively. Overall, vegetation greenness declines during the 2005 and 2010 extreme droughts were adversely influenced by precipitation, radiation and temperature; this study provides evidence of the influence of multiple climatic factors on vegetation during the 2005 and 2010 Amazon droughts.

Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales

Gross ecosystem productivity (GEP) in tropical forests varies both with the environment and with biotic changes in photosynthetic infrastructure, but our understanding of the relative effects of these factors across timescales is limited. Here, we used a statistical model to partition the variability of seven years of eddy covariance-derived GEP in a central Amazon evergreen forest into two main causes: variation in environmental drivers (solar radiation, diffuse light fraction, and vapor pressure deficit) that interact with model parameters that govern photosynthesis and biotic variation in canopy photosynthetic light-use efficiency associated with changes in the parameters themselves. Our fitted model was able to explain most of the variability in GEP at hourly (R²  = 0.77) to interannual (R²  = 0.80) timescales. At hourly timescales, we found that 75% of observed GEP variability could be attributed to environmental variability. When aggregating GEP to the longer timescales (daily, monthly, and yearly), however, environmental variation explained progressively less GEP variability: At monthly timescales, it explained only 3%, much less than biotic variation in canopy photosynthetic light-use efficiency, which accounted for 63%. These results challenge modeling approaches that assume GEP is primarily controlled by the environment at both short and long timescales. Our approach distinguishing biotic from environmental variability can help to resolve debates about environmental limitations to tropical forest photosynthesis. For example, we found that biotically regulated canopy photosynthetic light-use efficiency (associated with leaf phenology) increased with sunlight during dry seasons (consistent with light but not water limitation of canopy development) but that realized GEP was nonetheless lower relative to its potential efficiency during dry than wet seasons (consistent with water limitation of photosynthesis in given assemblages of leaves). This work highlights the importance of accounting for differential regulation of GEP at different timescales and of identifying the underlying feedbacks and adaptive mechanisms.

Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure

Light-regime variability is an important limiting factor constraining tree growth in tropical forests. However, there is considerable debate about whether radiation-induced green-up during the dry season is real, or an apparent artifact of the remote-sensing techniques used to infer seasonal changes in canopy leaf area. Direct and widespread observations of vertical canopy structures that drive radiation regimes have been largely absent. Here we analyze seasonal dynamic patterns between the canopy and understory layers in Amazon evergreen forests using observations of vertical canopy structure from a spaceborne lidar. We discovered that net leaf flushing of the canopy layer mainly occurs in early dry season, and is followed by net abscission in late dry season that coincides with increasing leaf area of the understory layer. Our observations of understory development from lidar either weakly respond to or are not correlated to seasonal variations in precipitation or insolation, but are strongly related to the seasonal structural dynamics of the canopy layer. We hypothesize that understory growth is driven by increased light gaps caused by seasonal variations of the canopy. This light-regime variability that exists in both spatial and temporal domains can better reveal the drought-induced green-up phenomenon, which appears less obvious when treating the Amazon forests as a whole.

A roadmap for improving the representation of photosynthesis in Earth system models

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO₂ assimilation (A) to key environmental variables: light, temperature, CO₂ concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.

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 (R_e ) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and R_e 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.

Extracting Plant Phenology Metrics in a Great Basin Watershed: Methods and Considerations for Quantifying Phenophases in a Cold Desert

Plant phenology is recognized as important for ecological dynamics. There has been a recent advent of phenology and camera networks worldwide. The established PhenoCam Network has sites in the United States, including the western states. However, there is a paucity of published research from semi-arid regions. In this study, we demonstrate the utility of camera-based repeat digital imagery and use of R statistical phenopix package to quantify plant phenology and phenophases in four plant communities in the semi-arid cold desert region of the Great Basin. We developed an automated variable snow/night filter for removing ephemeral snow events, which allowed fitting of phenophases with a double logistic algorithm. We were able to detect low amplitude seasonal variation in pinyon and juniper canopies and sagebrush steppe, and characterize wet and mesic meadows in area-averaged analyses. We used individual pixel-based spatial analyses to separate sagebrush shrub canopy pixels from interspace by determining differences in phenophases of sagebrush relative to interspace. The ability to monitor plant phenology with camera-based images fills spatial and temporal gaps in remotely sensed data and field based surveys, allowing species level relationships between environmental variables and phenology to be developed on a fine time scale thus providing powerful new tools for land management.

Seasonality of temperate forest photosynthesis and daytime respiration

Terrestrial ecosystems currently offset one-quarter of anthropogenic carbon dioxide (CO2) emissions because of a slight imbalance between global terrestrial photosynthesis and respiration. Understanding what controls these two biological fluxes is therefore crucial to predicting climate change. Yet there is no way of directly measuring the photosynthesis or daytime respiration of a whole ecosystem of interacting organisms; instead, these fluxes are generally inferred from measurements of net ecosystem-atmosphere CO2 exchange (NEE), in a way that is based on assumed ecosystem-scale responses to the environment. The consequent view of temperate deciduous forests (an important CO2 sink) is that, first, ecosystem respiration is greater during the day than at night; and second, ecosystem photosynthetic light-use efficiency peaks after leaf expansion in spring and then declines, presumably because of leaf ageing or water stress. This view has underlain the development of terrestrial biosphere models used in climate prediction and of remote sensing indices of global biosphere productivity. Here, we use new isotopic instrumentation to determine ecosystem photosynthesis and daytime respiration in a temperate deciduous forest over a three-year period. We find that ecosystem respiration is lower during the day than at night-the first robust evidence of the inhibition of leaf respiration by light at the ecosystem scale. Because they do not capture this effect, standard approaches overestimate ecosystem photosynthesis and daytime respiration in the first half of the growing season at our site, and inaccurately portray ecosystem photosynthetic light-use efficiency. These findings revise our understanding of forest-atmosphere carbon exchange, and provide a basis for investigating how leaf-level physiological dynamics manifest at the canopy scale in other ecosystems.