Carbon allocation and trait optimality drives Amazon forest response to changing water availability

Climate change induced shifts in precipitation threaten the future carbon balance of Amazon rainforests. Our understanding of water constraints to photosynthesis is largely limited to physiology-climate effects. Less is known about the effects of carbon allocation and trait shifts in response to water availability. Mechanisms linking carbon allocation and trait responses are not well represented in current ecosystem models, causing uncertainty in predicted carbon dynamics under future climates. We ask (i) What drives the coupling between photosynthesis and precipitation is it canopy structure (leaf area index; LAI), leaf traits, or solely a physiology-climate response? (ii) Why do LAI and leaf traits vary with precipitation? (iii) What is the role of water availability and plant traits in driving carbon allocation between leaves and roots? Process based modelling allows the links between photosynthesis, water availability, carbon allocation and traits to be quantified explicitly, exploring interaction space not available to insitu experiments. We calibrated the Soil Plant Atmosphere model (SPA) to eight permanent sample plots across an Amazon mean annual precipitation gradient (1400-2800mm), as part of the Global Ecosystems Monitoring network. The model’s representation of local carbon fluxes was evaluated against biometric estimates. We then conducted a series of model experiments to quantify the principal drivers of photosynthesis across the precipitation gradient and explore mechanisms of LAI, leaf trait and carbon allocation responses to water availability. LAI increased with precipitation (R2=0.42, p=0.08), and was the principal driver of differences in photosynthesis across the gradient, accounting for 36% of observed variation. Differences in leaf traits accounted for 20% of variance and physiology-climate interactions accounted for a further 12%. Spatial variance in LAI was underpinned by carbon economics, and best predicted by an optimality approach that maximised net canopy carbon export (R2=0.87, p<0.001). Across the precipitation gradient, leaf trait strategies shifted from fast to slow as water availability increased (where fast leaf traits are a cohort of high photosynthetic capacitance, high metabolic rate, high nitrogen content, low LMA and short lifespan and vice versa for slow leaf traits). Leaf traits had a determinate effect on LAI optimality, and higher leaf areas at wet plots were supported by longer leaf lifespans rather than an increase in leaf net primary production (NPP). At dry plots, short leaf lifespans, inherent of fast leaf trait cohorts, effected lower LAI. However, fast leaf trait strategies did prove optimal at dry plots, as carbon losses during the dry season were minimised, whilst photosynthesis during the wet season was maximised. Field estimates showed that leaf NPP was highest at dry plots and declined with increasing precipitation, whilst root NPP was highest at wet plots, converse to optimal partitioning theory, which suggests prioritisation of roots under moisture stress and leaves under light limitation. Yet model results show that leaf:root NPP across the precipitation gradient was optimal, and was similarly best predicted by the maximisation of net canopy carbon export (R2=0.60, p=0.02). Optimality was supported by concurrent shifts in leaf and root traits, which together accounted for 63% of variation in optimal leaf:root NPP. Our findings demonstrate that optimality approaches can be used to successfully predict spatial variation in LAI, leaf:root NPP and leaf traits across an Amazon precipitation gradient. Leaf traits fundamentally shaped modelled optimal responses, ultimately determining carbon assimilation. The response of Amazon forests to increased moisture stress is therefore dependent on the current spatial distribution of leaf traits, their plasticity and the likelihood of future shifts in floristic and functional trait composition. Future work should expand on the findings presented by exploring the responses of carbon allocation and traits to water availability over different timescales

Does Economic Optimisation Explain LAI and Leaf Trait Distributions Across an Amazon Soil Moisture Gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality‐based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process‐based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality‐based predictions. Yet, the predictive power of an optimality‐based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long‐lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short‐lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade‐offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between‐plant competition). In addition, we show how leaf trait trade‐offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality‐based approaches in simulating primary tropical forest functioning, evaluated against in situ data.The authors would like to thank the PhD project funding body, the UK Natural Environment Research Council E3 DTP, NERC, the GHG program GREENHOUSE (NE/K002619/1), the UK's National Centre for Earth Observation (NE/R016518/1), the UKSA project Forests 2020, a Royal Society Wolfson Award to M.W., the UK Met Office, the Newton Fund and the CSSP-Brazil project. P.M. also acknowl-edges support from NERC grant NE/J011002/1 and ARC grant DP170104091. The TRY trait database is thanked for the data used in model parameterisation and the authors would like to thank the Global Ecosystems Monitoring network team for the field data used in this study, collected through funding from NERC and the Gordon and Betty Moore Foundation, and an ERC Advanced Investigator Award to Y.M. (GEM-TRAIT). In addition, the authors would like to thank the anonymous reviewers for their constructive feedback on the manuscrip

The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests

The capacity of Amazon forests to sequester carbon is threatened by climate-change-induced shifts in precipitation patterns. However, the relative importance of plant physiology, ecosystem structure and trait composition responses in determining variation in gross primary productivity (GPP) remain largely unquantified and vary among models. We evaluate the relative importance of key climate constraints to GPP, comparing direct plant physiological responses to water availability and indirect structural and trait responses (via changes to leaf area index (LAI), roots and photosynthetic capacity). To separate these factors we combined the soil-plant-atmosphere model with forcing and observational data from seven intensively studied forest plots along an Amazon drought stress gradient. We also used machine learning to evaluate the relative importance of individual climate factors across sites. Our model experiments showed that variation in LAI was the principal driver of differences in GPP across the gradient, accounting for 33 % of observed variation. Differences in photosynthetic capacity (Vcmax and Jmax) accounted for 21 % of variance, and climate (which included physiological responses) accounted for 16 %. Sensitivity to differences in climate was highest where a shallow rooting depth was coupled with a high LAI. On sub-annual timescales, the relative importance of LAI in driving GPP increased with drought stress (R2 = 0.72), coincident with the decreased importance of solar radiation (R2 = 0.90). Given the role of LAI in driving GPP across Amazon forests, improved mapping of canopy dynamics is critical, opportunities for which are offered by new satellitebased remote sensing missions such as GEDI, Sentinel and FLEX.This research has been supported by the UK Natural Environment Research Council E3 DTP, the National Centre for Earth Observation, the UKSA project Forests 2020 and the Newton CSSP Brazil

Optimal model complexity for terrestrial carbon cycle prediction

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.</p

Carbon storage and sequestration rates of trees inside and outside forests in Great Britain

Efforts to abate climate change heavily rely on carbon sequestration by trees. However, analyses of tree carbon dynamics often neglect trees outside of forests (TOFs) and spatially detailed information about tree carbon sequestration rates are largely missing. Here we describe a new method which combines remote sensing with forest inventory data from 127 358 sites to first estimate tree age and site productivity, which we then used to estimate carbon storage and sequestration rates for all trees inside and outside forests across Great Britain. Our models estimate carbon storage and sequestration rates with R ^2 values of 0.86 and 0.56 (root-mean-square errors of 70 tCO _2 e ha ^−1 and 3.4 tCO _2 e ha ^−1 yr ^−1 ). They also reveal the important finding that 17% (165.6 MtCO _2 e) of the total carbon storage and 21% (3.4 MtCO _2 e yr ^−1 ) of the total carbon sequestration rate of all trees in Great Britain come from TOF, with particularly high contributions in England (24.3% and 34.1%), followed by Wales (12.5% and 17.6%) and Scotland (2.6% and 1.8%). Future estimates of carbon status and fluxes need to account for the significant contributions of TOF because these trees, often found in field margins and hedgerows are potentially an important carbon offset. Our novel approach enables carbon baseline assessments against which changes can be assessed at management relevant scales, improving the means to measure progress towards net zero emissions targets and associated environmental policies