Abstract
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.
Highlights
Leaf area index (LAI, the total one-sided leaf area per unit ground area) determines canopy light interception, evapotranspiration and energy exchange between the land and atmosphere, driving significant spatial and temporal variability in carbon (C) assimilation (Caldararu et al, 2012, Muraoka et al, 2010, Street et al, 2007, Xu & Baldocchi, 2004)
For question three we investigate how Net canopy C export (NCE) responds to changes in leaf traits
Soil-Plant-Atmosphere model (SPA)-simulated gross primary productivity (GPP) was within field estimate error bounds for five of the seven plots (Figure S5; the disparity between error bounds for the remaining two plots was marginal at 115 gC m-2 yr-1 and 50 gC m-2 yr-1 for KEN01 and TAM06 respectively)
Summary
Leaf area index (LAI, the total one-sided leaf area per unit ground area) determines canopy light interception, evapotranspiration and energy exchange between the land and atmosphere, driving significant spatial and temporal variability in carbon (C) assimilation (Caldararu et al, 2012, Muraoka et al, 2010, Street et al, 2007, Xu & Baldocchi, 2004). Our ability to simulate spatial and temporal variation in LAI remains limited Resolving this knowledge gap is important in the tropics (De Weirdt et al, 2012, Kim et al, 2012) as its forests, for instance those in the Amazon, have a large influence on the global C cycle (Liu et al, 2017, Malhi et al, 2008, Pan et al, 2011) and climate system. Many models continue to simulate leaf NPP as a fixed fraction of total NPP (Clark et al, 2011, Thornton & Zimmermann, 2007) Such model structures lack the capacity to actively vary LAI in response to soil moisture-stress, within the context of climatic change. It is important to note that uncertainty in MODIS LAI estimates is high in tropical regions (Liu et al, 2018, Xu et al, 2018)
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