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.
Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary ...variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100.
A new framework for understanding the macro‐scale variations in spring phenology is developed by using new data from the USA National Phenology Network. Changes in spring budburst for the United ...States are predicted by using Coupled Model Intercomparison Project phase 5 outputs. Macro‐scale budburst simulations for the coming century indicate that projected warming leads to earlier budburst by up to 17 days. The latitudinal gradient of budburst becomes less pronounced due to spatially varying sensitivity of budburst to climate change, even in the most conservative emissions scenarios. Currently existing interspecies differences in budburst date are predicted to become smaller, indicating the potential for secondary impacts at the ecosystem level. We expect that these climate‐driven changes in phenology will have large effects on the carbon budget of U.S. forests and these controls should be included in dynamic global vegetation models.
First framework for understanding large‐scale phenology using species level data
Climate driven ecological variation within and between species
Implications for dynamic global vegetation models improvement
Tropical dry forests are distinct from wet and moist tropical forests by the presence of a strong dry season. This collection of papers explores the unique biodiversity, plant functional traits, ...coupling between carbon and water cycles, and threats to these important ecosystems. These studies have relevance for conservation and management of tropical dry forests.
Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme ...heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling–experimental conceptual framework.
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.
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.
Vegetation phenology is closely connected to the terrestrial carbon budget, and interacts with the atmosphere through surface water and energy exchange. A comprehensive and detailed characterization ...of the spatio-temporal pattern of vegetation phenology can be used to improve the understanding of interactions between vegetation and climate in Africa. This research provides an approach to derive phenology time and trajectory parameters by optimally fitting a double-logistic curve to daily remotely sensed leaf area index (LAI) from the spinning enhanced visible and infrared imager. The proposed algorithm can reconstruct the temporal LAI trajectory based on the optimized parameters with a high accuracy, and provides user-defined phenological timing information (e.g., start/end of the growing season) and trajectory information (e.g., leaf emergence/senescence rate and length) using these fitted parameters. Both single and double growing-season cases have been considered with a spatial classification scheme implemented over Africa. The newly derived vegetation phenology of Africa exhibits emerging spatial patterns in growing season length, asymmetric green-up and green-off length/rate, and distinctive phenological features of cropland and natural vegetation. This approach has the potential to be applied globally, and the derived vegetation phenological information will improve dynamic vegetation modeling and climate prediction.
There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either ...singly or in a combination, of distinctive intra-seasonal characteristics -i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (−20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability-'chronic water stress', 'acute water stress' and 'minimum water stress' - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests.
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.
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