# Constraining Light-Driven Plasticity in Leaf Traits With Observations Improves the Prediction of Tropical Forest Demography, Structure, and Biomass Dynamics

Yixin Ma, Paul Moorcroft, S. Joseph Wright, Alistair Rogers, Julien Lamour, Kenneth Davidson, Shawn Serbin, Matteo Detto, Xiangtao Xu

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## To cite this version:

Yixin Ma, Paul Moorcroft, S. Joseph Wright, Alistair Rogers, Julien Lamour, et al., Constraining Light-Driven Plasticity in Leaf Traits With Observations Improves the Prediction of Tropical Forest Demography, Structure, and Biomass Dynamics. Journal of Geophysical Research: Biogeosciences, 2025, 130 (6), pp.e2025JG008814. 10.1029/2025JG008814. hal-05382095

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Distributed under a Creative Commons Attribution 4.0 International License
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**Constraining light-driven plasticity in leaf traits with observations improves the prediction of tropical forest demography, structure, and biomass dynamics**

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Yixin Ma¹*, Paul R. Moorcroft², S. Joseph Wright³, Alistair Rogers⁴,⁸, Julien Lamour⁵,⁸, Kenneth J. Davidson⁸, Shawn P. Serbin⁶,⁸, Matteo Detto³,⁷, Xiangtao Xu¹,*

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¹ Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14850

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² Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138

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³ Smithsonian Tropical Research Institute, Apartado 0843-03092 Balboa, Republic of Panama

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⁴ Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720

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⁵ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, France

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⁶ Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771

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⁷ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08542

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⁸ Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973

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* To whom correspondence may be addressed.

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Corresponding author: Yixin Ma (ym524@cornell.edu); Xiangtao Xu (xx286@cornell.edu)

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21 Word count: 7878

22 Number of figures: 8

23 Number of tables: 2

24 Supplementary information: Figures S1-S5, Table S1

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27 **Key Points:**

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* We examined how intra-specific light plasticity in leaf traits affects modeled tropical tree demography and long-term forest dynamics.

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* Observation-constrained light plasticity enabled an accurate prediction of tree growth rates, forest structure, and biomass regrowth.

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* Light plasticity improved modeled forest composition and trait diversity, which cannot be achieved by adding new plant functional types.

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## 35 Abstract

Predicting tropical tree demography is a key challenge in understanding the future dynamics of tropical forests. While demographic processes are known to be regulated by leaf trait diversity, only the effect of inter-specific trait variation has been evaluated, and it remains unclear as to what degree the intra-specific trait plasticity across light gradients (hereafter light plasticity) regulates tree demography, and how this will further shape long-term community and ecosystem dynamics. By combining *in situ* trait measurements and forest census data with a terrestrial biosphere model, we evaluated the impact of observation-constrained light plasticity on demography, forest structure, and biomass dynamics in a Panamanian tropical moist forest. Modeled leaf physiological traits vary across and within plant functional types (PFT), which represent the inter-specific trait variation and the intra-specific light plasticity, respectively. The simulation using three non-plastic PFTs underestimated 20-year-average understory growth rates by 41%, leading to a biased forest size structure and leaf area profile, and a 44% underestimate in long-term biomass. The simulation using three plastic PFTs generated accurate understory growth rates, resulting in a realistic forest structure and a smaller biomass underestimate of 15%. Expanding simulated trait diversity using 18 non-plastic PFTs similarly improved the prediction of demography and biomass. However, only the plasticity-enabled model predicted realistic long-term PFT composition and within-canopy trait profiles. Our results highlight the distinct role of light plasticity in regulating forest dynamics that cannot be replaced by inter-specific trait diversity. Accurately representing light plasticity is thus crucial for trait-based prediction of tropical forest dynamics.

## 56 Plain Language Summary

Ecosystem functions such as biomass dynamics and forest structure are strongly regulated by plant diversity. In addition to species diversity, plant characteristics also vary within the same species, particularly in response to environmental gradients. However, it remains largely unclear how this intra-specific diversity across environmental gradients regulates forest dynamics. Here we used vegetation modeling to investigate how the intra-specific diversity across different light environments regulates tropical tree demography, forest
composition, and carbon cycle. We found that incorporating intra-specific diversity in the model substantially influenced tree growth rates, forest structure, and long-term carbon accumulation. Constraining intra-specific diversity with observations improved model predictions of these processes. In addition, incorporating the intra-specific diversity improved the prediction of forest composition, and such an effect cannot be replaced by solely incorporating higher species diversity. These results highlight the importance of characterizing the intra-specific diversity across environmental gradients for predicting long-term tropical forest dynamics.

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# 1 Introduction

The future fate of tropical forests, particularly the persistence of old-growth forest carbon sink and the regrowth potential of secondary forests, critically influences the global carbon cycle, biodiversity, and the realization of several Sustainable Development Goals (Anderson-Teixeira et al., 2016; Pan et al., 2011; United Nations General Assembly, 2015). Predicting the community and ecosystem dynamics of these forests fundamentally depends on a realistic representation of demographic processes such as growth and mortality. However, accurately predicting tropical tree demography remains an open challenge for process-based terrestrial biosphere models (TBM). A common modeling bias is the underestimate of the abundance of understory trees (Koven et al., 2020; Longo et al., 2019a), which can lead to further biases in the prediction of long-term canopy regeneration and forest succession.

Understory trees in closed-canopy tropical forests are limited by light availability (Chazdon & Fetcher, 1984; Clark et al., 1996), thus their demographic rates are strongly regulated by the diversity in light use-associated traits (Denslow, 1987; Detto et al., 2021; Finegan, 1984; Moorcroft et al., 2001). The inter-specific variation in these traits, particularly the trait variation between light-demanding and shade-tolerant species, has been shown to influence demographic rates and ecosystem functioning (Reich, 2014; Wright et al., 2010). In addition to the inter-specific variation, leaf traits within the same species also vary substantially across light gradients in tropical forests (Xu et al. 2017). This intra-specific trait variation largely
results from phenotypic plasticity in response to within-canopy light gradients (hereafter light plasticity), although ontogeny and other environmental factors also contribute to the variation (Cavaleri et al., 2010; Coste et al., 2009; Dang-Le et al., 2013; Detto & Xu, 2020; Wen et al., 2008). To date, it has not been evaluated as to what degree light plasticity regulates understory tree demography in tropical forests.

Both light plasticity and inter-specific variation enhance functional diversity and thus are expected to influence forest dynamics. While inter-specific trait variation characterizes the species-level tradeoff between growth in high light environment and survival in low light environment, light plasticity regulates ecosystem processes through individual-level trait adjustments within a tree's lifetime. At the physiological level, light plasticity helps to optimize whole-tree photosynthesis by reducing carbon and nitrogen allocation in shaded leaves while increasing allocation in sunlit leaves (Hikosaka, 2016; Lloyd et al., 2010). It particularly increases net carbon gain in the understory by reducing respiration cost and increasing specific leaf area (Niinemets et al., 2015; Poorter et al., 2019), which can enhance understory growth, survival, and abundance. At the community level, the plasticity-driven increase in understory survival weakens the growth-survival tradeoff, thus reducing the species-level niche differentiation in light use (Sterck et al., 2013). Therefore, light plasticity is expected to shape demographic processes and species coexistence differently than the inter-specific trait variation.

In TBMs, the inter-specific trait variation is often modeled as plant functional types (PFT), which are groups of species with similar trait values, and light plasticity has been commonly represented as intra-PFT trait variation across light gradients. While many studies have aimed to improve the model representation of inter-PFT variation (Butler et al., 2022; Pappas et al., 2016; Pavlick et al., 2013; Rius et al., 2023; Sakschewski et al., 2016), fewer studies have attempted to refine the representation of plasticity and evaluate its impact on predicting forest dynamics (Table 1). In fact, TBMs often incorrectly assume that the extent of light plasticity is identical across different PFTs and traits (Table 1), despite field observations demonstrating that different species and traits exhibit a wide range of light plasticity (Chmura et al., 2017; Osunkoya et al., 1994; Valladares et al., 2000). For example, both leaf dark respiration rate and maximum rate of photosynthetic carboxylation ($V_{cmax}$) decrease with lower
light levels, but leaf dark respiration has a higher degree of plasticity than that of V$_{cmax}$, resulting in a lower respiration to V$_{cmax}$ ratio (Lamour et al., 2023) and thus a higher shade tolerance in the understory. The absence of difference in modeled respiration and V$_{cmax}$ plasticity likely explains the underestimate of understory abundance in plasticity-enabled TBMs (Koven et al., 2020; Longo et al., 2019a), and this is partly supported by recent research showing that a higher degree of leaf dark respiration plasticity in FATES model increases understory leaf area (Needham et al., 2025). However, it remains unknown whether a comprehensive and realistic representation of light plasticity, i.e., incorporating its variation across species and multiple traits, can fully correct for the model biases in understory growth and survival and further improve the prediction of long-term forest dynamics.

To examine the role of light plasticity in explaining and predicting tropical forest dynamics, we combined *in situ* trait measurements in Panama and forest inventories at Barro Colorado Island (BCI) with the Ecosystem Demography Model version 2.2 (ED2) (Longo et al., 2019b). The ED2 model is a trait-based, demography-enabled TBM, and it represents light plasticity in leaf physiological traits as intra-PFT parameter variation driven by light gradient. The modeled light plasticity is constrained by local observations, and the degree of light plasticity varies across PFTs and traits (Table 1; Fig. 1).

Overall, we expected that light plasticity would improve the prediction of forest structure and long-term forest succession by better capturing understory demography, and its effect would be different from that of inter-specific trait diversity. Specifically, we hypothesized that: (1) a non-plastic model containing only inter-PFT trait variation would underestimate growth rates and overestimate mortality rates in the understory, consequently underestimating tree abundance and leaf area; (2) incorporating observation-constrained light plasticity would yield more accurate demographic rates and thus a more realistic forest size structure and leaf area vertical profile; (3) during long-term secondary succession, light plasticity would promote forest biomass accumulation; (4) expanding inter-specific diversity in the non-plastic model by adding more PFTs would have a smaller positive effect on understory growth and long-term biomass than light plasticity.
<table><caption>**Table 1.** Current representation of light plasticity in TBMs and its impact on modeled ecological processes.</caption><thead><tr><th>Model</th><th>Traits that have light plasticity</th><th>Do traits differ in light plasticity</th><th>Do PFTs differ in light plasticity</th><th>Modeled impact of light plasticity</th><th>Reference</th></tr></thead><tbody><tr><td>Joint UK Land Environment Simulator (JULES)</td><td>V<sub>cmax</sub><br>J<sub>max</sub><br>Leaf dark respiration<br>Leaf nitrogen content</td><td>No.<br>All traits have the same plasticity.</td><td>No.<br>All PFTs have the same plasticity.</td><td>Small, positive effect on canopy photosynthesis</td><td>Mercado et al. 2007</td></tr><tr><td>Community Land Model version 4 (CLM4)</td><td>V<sub>cmax</sub><br>J<sub>max</sub><br>Leaf dark respiration<br>Leaf nitrogen content</td><td>No.<br>All traits have the same plasticity.</td><td>No.<br>All PFTs have the same plasticity.</td><td>Small, positive effect on gross primary productivity</td><td>Bonan et al. 2011</td></tr><tr><td>Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS)</td><td>V<sub>cmax</sub><br>J<sub>max</sub><br>Leaf dark respiration<br>Leaf nitrogen content</td><td>No.<br>All traits have the same plasticity.</td><td>No.<br>All PFTs have the same plasticity.</td><td>Not examined</td><td>Smith et al. 2014</td></tr><tr><td>Trait-based Forest Simulator version 2 (TFSv.2)</td><td>V<sub>cmax</sub><br>J<sub>max</sub><br>Leaf dark respiration<br>Specific leaf area<br>Leaf lifespan</td><td>Yes.<br>The light plasticity of leaf lifespan is different from those of other traits.</td><td>No.<br>All PFTs have the same plasticity.</td><td>Not examined</td><td>Fauset et al. 2019</td></tr></tbody></table>
150 **Table 1. (continued)**

<table><tr><td>Functionally Assembled Terrestrial Ecosystem Simulator (FATES)</td><td>V<sub>cmax</sub><br/>J<sub>max</sub><br/>Leaf dark respiration<br/>Leaf nitrogen content<br/>Specific leaf area</td><td>Yes.<br/>Leaf dark respiration is the most plastic, and all other traits have the same plasticity.</td><td>Yes.<br/>Early-successional PFT is the most plastic.</td><td>Large positive effect on leaf area and vegetation carbon</td><td><b>Needham et al. 2025</b></td></tr><tr><td>Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE)</td><td>V<sub>cmax</sub><br/>J<sub>max</sub><br/>Leaf dark respiration<br/>Leaf nitrogen content</td><td>No.<br/>All traits have the same plasticity.</td><td>No.<br/>All PFTs have the same plasticity</td><td>Not examined</td><td>Zhang et al. 2020</td></tr><tr><td>Ecosystem demography Model version 2.2 (ED2)</td><td>V<sub>cmax</sub><br/>J<sub>max</sub><br/>Leaf dark respiration<br/>Specific leaf area<br/>Leaf lifespan</td><td>Yes.<br/>Each trait plasticity is parameterized separately.</td><td>Yes.<br/>Early- successional PFT is the most plastic.</td><td>Large effect on tree demography, forest structure, composition, trait diversity, and biomass</td><td>This study</td></tr></table>
Intra- & inter-PFT trait variation

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Within-canopy trait variation

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**Figure 1.** Representation of plant functional diversity in ED2 model for four traits of interest: leaf dark respiration rate at 25°C, maximum rate of photosynthetic carboxylation ($V_{cmax}$) at 25°C, specific leaf area, and leaf lifespan. The upper panel is a conceptual diagram of the different components of plant functional diversity in each model. Open and closed circles represent the trait variation between top-of-canopy cohorts and understory cohorts. ED2-baseline only contains trait variation across three PFTs, without intra-PFT variation. ED2-plastic contains both trait variation across three PFTs and intra-PFT light plasticity. ED2-static only contains trait variation across 18 PFTs, without intra-PFT variation. The lower panel shows the model parameterization of within-canopy trait variation. ED2-baseline, ED2-plastic, ED2-static are represented by purple, orange, and green colors, and each point or line represents a different PFT. Light plasticity in ED2-plastic was parameterized based on top-of-canopy and understory trait values from the W03 dataset (described in section 2.4). Although not shown here, simulated maximum rate of photosynthetic electron transport ($J_{max}$) in ED2 is proportional to $V_{cmax}$ by a constant factor, therefore, it is also a plastic trait and it has the same light plasticity as $V_{cmax}$.

## 2 Materials and Methods

### 2.1 Study site

The study site is a long-term, 50-ha forest plot at Barro Colorado Island (BCI) in Panama.

The BCI site is an old-growth moist tropical forest with a mean annual precipitation of 2660 mm
and a four-month dry season. Mean air temperature is 26°C with minimal seasonal variation. The plot was established in 1981. The species identity, spatial coordinates within the plot, and diameter at breast height (DBH) of all stems with DBH ≥ 1 cm were first inventoried in 1982 and then recorded every five years since 1985 (Condit et al., 2017).

## 2.2 Model description

The ED2 model is a cohort-based TBM that simulates vegetation dynamics and land surface processes (Longo et al., 2019b). In this model, the smallest spatial unit is a patch, which is a collection of areas with similar disturbance histories, and its size is usually comparable to a forest gap (~20m*20m). Within each patch, there are multiple cohorts, which are individuals of similar size and same PFT. Each cohort is always shaded by all other taller cohorts within the same patch, therefore, cohorts experience height-structured competition for light, which further drives vegetation dynamics.

Cohort-level growth and mortality rates are simulated based on ecophysiological principles and are tracked explicitly throughout the simulation. DBH growth rate is determined by cohort-level net carbon balance, which is governed by photosynthesis, respiration, and carbon allocation among different plant compartments. The cohort-level mortality rate is modeled as the sum of growth-independent and growth-dependent components and is described by the following equation based on Camac et al. (2018).

$$ M = M_b + M_d + \alpha \times e^{\beta \times growth}, \quad (1) $$

M is the total cohort-level mortality rate. M_b is baseline mortality rate, a prescribed parameter that differs across PFTs. M_d is disturbance-related background mortality, and it has the same value for all PFTs. The last term in this equation describes the growth-dependent mortality rate. α and β are both PFT-dependent parameters, and β is always negative, meaning that this growth-dependent mortality rate declines exponentially with larger DBH growth rates.

To isolate the effect of light plasticity and inter-specific trait variation, we generated three ED2 versions that have different representations of intra-PFT light plasticity and inter-PFT
trait variation (Fig. 1). ED2-baseline characterizes three PFTs (described in section 2.5) that do not have light plasticity, meaning that traits do not vary with light levels. ED2-plastic has three plastic PFTs, and their traits respond to light gradients (described in section 2.3 and 2.4). ED2-static characterizes 18 PFTs that do not have light plasticity, but its inter-PFT trait variation covers a similar range of variation as the intra-PFT light plasticity in ED2-plastic (described in section 2.5).

## 2.3 Model representation of light-driven plasticity

Five leaf traits vary across cohorts and are modeled as plastic: leaf dark respiration rate, $V_{cmax}$, maximum rate of photosynthetic electron transport ($J_{max}$), specific leaf area (SLA), and leaf lifespan. These traits are selected because they are available in a local trait dataset (described in section 2.4) and they are important in determining leaf-level carbon balance and leaf turnover rates. The values of these plastic traits vary both across and within PFTs, and the intra-PFT variation across cohorts depends on the cohort-level overtopping leaf area index (oLAI). For each cohort of interest, oLAI is the total leaf area index (LAI) for all cohorts that grow in the same patch and are taller than the cohort of interest. Leaf area is modeled by a power-law function of DBH, and this function is estimated from the leaf area profile measured by LiDAR (light detection and ranging) at the BCI 50-ha plot (Detto et al., 2015). We used oLAI rather than absolute light levels to characterize the light environment for the following reasons. First, there was no direct measurement of the light environment at the BCI plot. Second, oLAI characterizes neighborhood shading, which is a major source of within-canopy light environment variation.

We assumed that plasticity-induced trait changes only occur through leaf turnover, and there is no other sources of within-crown trait variation such as leaf ageing. The trait value of a new leaf is calculated based on the current oLAI (equation 2) and cohort-level trait value is an average across new and existing leaves (equation 3). As described previously, oLAI is calculated from DBH, which is updated monthly, thus cohort-level oLAI and trait values are both updated monthly.
The cohort-level trait value is calculated based on the following equations:

$$X_{i,j,t} = X_{i,0} \times e^{k_i \times oLAI_{i,j,t}}, \qquad (2)$$

$$Y_{i,j,t+1} = (1 - r_{i,j,t}) Y_{i,j,t} + r_{i,j,t} X_{i,j,t} \qquad (3)$$

Equation 2 is based on Lloyd et al. (2010). $X_{i,j,t}$ is the trait value of a new leaf in cohort $j$ within PFT $i$ at month $t$. $X_{i,0}$ is the trait value for a top-of-canopy cohort (oLAI=0) of PFT $i$. $k_i$ is the light plasticity coefficient for PFT $i$. $X_{i,0}$ and $k_i$ are prescribed and constant for each trait and each PFT. $oLAI_{i,j,t}$ is the LAI overtopping cohort $j$ within PFT $i$ at month $t$. The parameterization of $k_i$ is described in section 2.4, and the parameterization of $X_{i,0}$ is described in section 2.5.

In Equation 3, $Y_{i,j,t+1}$ and $Y_{i,j,t}$ are the cohort-level trait value at month $t+1$ and month $t$. $r_{i,j,t}$ is the fraction of leaf drop between month $t$ and month $t+1$ in cohort $j$ within PFT $i$, and its numeric value equals the reciprocal of leaf lifespan (measured by month). Larger values of $r_{i,j,t}$ suggest that leaf turnover occurs at a faster rate and that traits more closely track changes in the light environment.

## 2.4 Parameterization of PFT-level light plasticity

To parameterize the PFT-level light plasticity coefficient $k_i$, we first calculated the observed species-level light plasticity based on a trait dataset collected during 1999-2002 at the San Lorenzo site and the Parque Natural Metropolitano site in Panama (hereafter W03 dataset; more detail is available in Xu et al., 2017). These sites are equipped with cranes that are 42 m and 52 m, respectively. For 64 measured species, leaf samples were collected from both their understory saplings and sun-exposed branches of top-of-canopy trees, and we assumed that the measured vertical trait variation is completely explained by light plasticity. Neither height nor light level was measured at the site of the sampling, so samples were labeled categorically as “understory” (oLAI > 0) or “top-of-canopy” (oLAI = 0). Measured traits include SLA, leaf lifespan, light-saturated leaf photosynthesis rate at ambient temperature, and leaf dark respiration rate at ambient temperature. We calculated $V_{cmax}$ at 25°C from the photosynthesis
rate using the Farquhar-von Caemmerer-Berry photosynthesis model (Farquhar et al., 1980; Xu et al., 2017).

Based on equation 2, we generated the following equation to estimate species-level light plasticity:

$$k_p = \frac{1}{oLAI_{und}} \log_e \left( \frac{X_{p,und}}{X_{p,toc}} \right), \qquad (4)$$

where $k_p$ is the light plasticity coefficient of species p, $X_{p,und}$ is the average understory leaf trait value of species p, $X_{p,toc}$ is the average top-of-canopy leaf trait value of species p. We estimated $oLAI_{und}$ as 5 m² m⁻² based on the measured leaf area profile at BCI (Detto et al., 2015). The across-species medians of $k_p$ were -0.233, -0.227, 0.184, and 0.139 for leaf dark respiration rate, $V_{cmax}$, SLA, and leaf lifespan, respectively (Fig. 2A).

To parameterize PFT-level $k_i$ from observed species-level $k_p$, we analyzed the relationship between species-average, top-of-canopy traits and their $k_p$ values (Fig. 2B-2E). We found that $V_{cmax}$ plasticity was positively and linearly related to top-of-canopy $V_{cmax}$ across species, so it was parameterized as a linear function of top-of-canopy $V_{cmax}$ (equation 5). Similarly, leaf dark respiration plasticity was parameterized as a linear function of top-of-canopy leaf dark respiration rate (equation 6).

$$k_{v,i} = -(0.0024 \times V_{cmax,i,0} + 0.06212) \qquad (5)$$

$$k_{r,i} = -(0.18974 \times R_{d,i,0} + 0.11744) \qquad (6)$$

Where $k_{v,i}$ is the $V_{cmax}$ plasticity of PFT i, $V_{cmax,i,0}$ is the $V_{cmax}$ at 25°C for a top-of-canopy cohort within PFT i. $k_{r,i}$ is the leaf dark respiration plasticity of PFT i, $R_{d,i,0}$ is the leaf dark respiration at 25°C for a top-of-canopy cohort within PFT i. SLA plasticity and leaf lifespan plasticity were not significantly related to their top-of-canopy trait values, so we calculated the average $k_{SLA}$ (0.199) and $k_{LL}$ (0.240) across all species and assigned them to all PFTs. $J_{max}$ in ED2 is modeled as proportional to $V_{cmax}$ by a constant factor, therefore, we assumed that $k_J$ equals $k_v$ for all PFTs (Table S1).
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<figure><img src="image_9.png" /><figcaption>Figure 2. Observed species-level light plasticity of leaf dark respiration rate, V~cmax~, specific leaf area, and leaf lifespan in the W03 dataset (described in section 2.4). (A) shows the distribution of species-level light plasticity coefficient (k~p~ in equation 4). Dashed horizontal lines indicate k~p~ values at which traits increase or decrease by fivefold across an oLAI gradient of 0-5 m² m⁻². (B)–(E) show the relationship between observed top-of-canopy leaf traits and their light plasticity coefficients. A solid line indicates a significant linear relationship (*p* < 0.05), whereas a dashed line indicates an insignificant relationship. Green lines and numbers indicate the regression results fitted without outlier (leaf dark respiration > 2 µmol m⁻² s⁻¹ or specific leaf area > 20 m² kg⁻¹).</figcaption></figure>

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## 2.5 PFT definition and parameterization

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Tropical tree species are commonly classified into PFTs based on plant physiological traits, particularly wood density, because these traits are associated with a species' position on a growth-mortality tradeoff axis (Wright et al., 2010). However, 35% of all inventoried species at the BCI plot do not have wood density information, and only 8% of all species have local measurements for all leaf traits of interest (leaf dark respiration rate, Vcmax, SLA, and leaf lifespan), thus a trait-based PFT definition is limited by data availability. To overcome this data limitation, we developed a metric named demographic niche score to define PFTs based on demography rather than physiological traits, following previous practices to classify tropical species using demographic rates (Condit & Rüger, 2022; Rüger et al., 2020).

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First, we grouped all inventoried individuals into 20-meter patches based on their spatial locations and calculated oLAI for each individual within each patch. oLAI values were calculated from a DBH-based leaf area allometric function as described in section 2.3. Second, for each species, we calculated the relative DBH growth rate of individuals under high light (the smallest 25% oLAI) and the mortality rate of individuals under low light (the largest 25% oLAI). Third, we performed principal component analysis (PCA) for the species-level high light growth rates and low light mortality rates (both were log transformed before the PCA analysis). We used the first principal component as the demographic niche score, which explained 71% of the total variation in demographic rates (Fig. S1).

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Standard major axis regression revealed a strong positive relationship between species-level demographic niche score and wood density (Fig. S2A), implying that our demographic niche score can reasonably represent species' position on the growth-mortality tradeoff axis.

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In ED2-baseline and ED2-plastic, there are three PFTs: early-successional (species with lowest 33% demographic niche score), late-successional (species with top 33% demographic niche score), and mid-successional (intermediate score). From 1990-2010, the BCI plot on average has 6.65, 9.40, and 15.1 cm² m⁻² (measured by basal area) of early-, mid- and late-
successional PFT. To parameterize top-of-canopy leaf trait values ($X_{i,0}$ in equation 2) for these three PFTs, we performed standardized major axis regression between species-level demographic niche score and top-of-canopy $V_{cmax}$ at 25°C, SLA, and leaf lifespan in W03 dataset (Fig. S2), then we parameterized $X_{i,0}$ based on the median demographic niche score of each PFT. Based on the regression models, early-successional PFT has the highest top-of-canopy $V_{cmax}$, largest SLA, and shortest leaf lifespan. For $R_{d,i,0}$, we parameterized it to be proportional to $V_{cmax,i,0}$ by a factor of 0.015. When $oLAI > 0$, the simulated respiration to $V_{cmax}$ ratio will deviate from 0.015 because leaf dark respiration is more plastic than $V_{cmax}$ (Table S1), resulting in a lower ratio with increasing $oLAI$ (Fig. S3E).

The three PFTs described above are parameterized by top-of-canopy leaf trait values. For ED2-static, we defined an additional 15 PFTs which are parameterized by understory leaf trait values. Specifically, for each of the three original PFTs, we calculated leaf dark respiration at 25°C, $V_{cmax}$ at 25°C, SLA, and leaf lifespan values at $oLAI$ of 1, 2, 3, 4, 5 m² m⁻² based on equation 2, then we generated five new PFTs by assigning these leaf trait values as new $X_{i,0}$ and keeping all other traits (e.g., wood density, mortality parameters) the same as the original PFT. In total, ED2-static includes six early-successional PFTs, six mid-successional PFTs, and six late-successional PFTs. The variation in $X_{i,0}$ across these 18 PFTs is identical to the plasticity-driven variation in $X_{i,j,t}$ across an $oLAI$ gradient of 0-5 m² m⁻².

## 2.6 Simulation protocol

To test our first two hypotheses, we initialized the model with the BCI forest census in 1990, ran the model from 1989 to 2010 (hereafter short-term simulation), and compared demographic rates and forest structure simulated by ED2-baseline, ED2-plastic, and ED2-static. Since we were interested in the dynamics of tree species, we excluded herbaceous species and only used the census information of woody species in the initialization. To test the third and fourth hypotheses, we simulated 300-year forest secondary succession from a near bare-ground condition (hereafter long-term simulation), and compared the forest composition and functioning simulated by the three models. A near bare-ground condition means that there are only a few tree seedlings of each PFT at the start of the simulation. The forest was simulated for
300 years because total basal area and aboveground biomass reached steady state during this time frame. We used an *in situ* climate dataset collected at BCI from 1985-2012 as meteorological forcing (available through the Smithsonian Tropical Research Institutes' Physical Monitoring Program), and all simulations used repeated cycles of this multi-year climate dataset. The long-term simulations used a disturbance rate of 0.014 year⁻¹, which is the default value in ED2 (Moorcroft et al., 2001).

## 2.7 Model benchmarking and analysis

To evaluate the prediction of demographic rates and forest structure, we calculated the simulated growth rate, mortality rate, plant density, and leaf area averaged during 1990-2010 in short-term simulations, then compared it to observations. Specifically, we calculated observed annual DBH growth rates and mortality rates for every two BCI censuses and then calculated average demographic rates during 1990-2010. We didn't use census data earlier than 1990, since DBH values smaller than 5.5 cm were rounded down to the nearest 5 mm in earlier censuses. In terms of forest structure, we calculated the size distribution of plant density averaged during 1990-2010. We also used a leaf area vertical profile estimated by airborne LiDAR (Detto et al., 2015) as a model benchmark.

To measure the simulated tree shade tolerance, we calculated the PFT-level growth compensation point (GCP), defined as the oLAI at which DBH growth rate declines to 0.01 cm year⁻¹ (Fig. 3A). A higher GCP value suggests a higher shade tolerance. The community-level GCP is calculated using all cohorts across all PFTs with DBH ≥ 1 cm.

To evaluate the prediction of long-term forest functioning, we calculated the plant density, basal area, LAI, and gross primary productivity (GPP) averaged across the last 20 years of long-term simulations. We also compared the simulated aboveground biomass (AGB) trajectories with two sets of field-based AGB estimates: one is AGB estimates across a 300-year chronosequence in Panama (Batterman et al., 2013), and the other is a BCI plot-level estimate calculated based on the 2010 census and a local height allometry (Cano et al., 2019). Besides,
we compared simulated GPP to flux tower-based GPP measurements at BCI (Detto & Pacala, 2022).

To evaluate the prediction of trait diversity, we calculated a metric named community-level vertical trait gradient for both observations and simulations. Using ten vertical leaf trait profiles measured in Panama, Lamour et al. (2023) fitted power law equations between observed trait values (pooled across multiple species) and oLAI. The fitted scaling exponent (reported in Table S3 and S4 of Lamour et al. 2023) represents the observed community-level vertical trait gradient, which is the result of both intra-specific light plasticity and vertical stratification of species composition. We similarly calculated the modeled community-level vertical trait gradient by fitting a power law equation between cohort-level trait values and modeled oLAI.

<figure><img src="image_10.png" /><figcaption>**Figure 3.** The definition of growth compensation point and the simulated relationship between DBH growth rate and overtopping LAI. (A) shows a conceptual diagram of how growth compensation point (GCP) is defined. GCP is a measure of simulated shade tolerance, and it is calculated as the overtopping LAI at which modeled DBH growth rate declines to 0.01 cm year⁻¹, and a larger GCP indicates a higher shade tolerance. (B) Simulated relationships between DBH growth rate and overtopping LAI for all DBH ≥ 1 cm cohorts during short-term simulations. ED2-baseline, ED2-plastic, and ED2-static are represented by purple, orange, and green lines, respectively. Inserted texts report simulated GCP for each model.</figcaption></figure>

## 3 Results

### 3.1 Tree demographic rates in short-term simulations
We compared 20-year-average growth rates predicted by ED2-baseline and ED2-plastic in short-term simulations with the census data. In ED2-baseline, the DBH growth rate of understory cohorts (DBH: 1-10cm) was 0.0429 cm year⁻¹, and incorporating observation-constrained light plasticity in ED2-plastic increased simulated understory growth rate to 0.0865 cm year⁻¹, closer to the observed 0.0723 cm year⁻¹ (Fig. 4A). At the PFT level, ED2-baseline predicted understory growth to be 0.0107, 0.00447, and 0.0456 cm year⁻¹ for early-, mid-, and late-successional PFTs, lower than the observed growth rates of 0.203, 0.0637, and 0.0679 cm year⁻¹ (Fig. 4B). Light plasticity improved model-data agreement by increasing the PFT-level understory growth to 0.145, 0.0878, and 0.0847 cm year⁻¹. This increase was substantially larger in early- and mid-successional PFT than in late-successional PFT (1255% and 1864% compared to 85.7%).

We also tested the growth effect of increasing inter-specific diversity. The average understory growth rate simulated by ED2-static was 0.0949 cm year⁻¹, which more than doubled the estimate of ED2-baseline and better aligned with the observation (Fig. 4A). At the PFT level, simulated growth rates were 0.104, 0.0415, and 0.111 cm year⁻¹ for early-, mid-, and late-successional PFT, and these PFT-level estimates were higher than ED2-baseline and comparable to ED2-plastic (Fig. 4B).

Although both light plasticity and inter-specific diversity improved model-data agreement of understory growth rates, only ED2-plastic correctly predicted that the understory growth rate of early-successional PFT was highest among all PFTs (Fig. 4B), whereas ED2-baseline and ED2-static wrongly predicted late-successional PFT to have the fastest growth.

The understory mortality rate simulated by ED2-baseline was 126% higher than the census observation (0.0531 year⁻¹ compared to 0.0235 year⁻¹). ED2-plastic and ED2-static reduced the model overestimate to 0.0380 and 0.0388 year⁻¹, respectively (Fig. 5).
<figure><img src="image_11.png" /></figure>

**Figure 4.** Light plasticity effect on 20-year-average tree growth during short-term simulations. (A) shows modeled and observed DBH growth rates for different size classes. ED2-baseline, ED2-plastic, and ED2-static are represented by purple, orange, and green bars, respectively. (B) shows modeled and observed understory DBH growth rates for different PFTs. Understory is defined as trees or cohorts with DBH between 1-10 cm. Error bars indicate 95% confidence intervals calculated by bootstrapping.

<figure><img src="image_12.png" /></figure>

**Figure 5.** Light plasticity effect on 20-year-average mortality rate during short-term simulations. (A) shows modeled and observed mortality rates for different size classes. ED2-baseline, ED2-plastic, and ED2-static are represented by purple, orange, and green bars, respectively. (B) shows modeled and observed understory mortality rates for different PFTs. Understory is defined as trees or cohorts with DBH between 1-10 cm. Error bars indicate 95% confidence intervals calculated by bootstrapping.

## 3.2 Forest structure in short-term simulations
We analyzed the 20-year-average size distribution of plant density and leaf area profile in the short-term simulations. Simulated understory plant density in ED2-baseline was 0.0931 plant m⁻², and light plasticity increased community-level understory plant density to 0.260 plant m⁻², bringing it closer to the observed 0.391 plant m⁻² (Fig. 6A). At the PFT level, the understory plant density of early-, mid-, and late-successional PFT were 0.00106, 0.0110, and 0.0847 plant m⁻² in ED2-baseline, which were an order of magnitude lower than the observed 0.0272, 0.161, and 0.203 plant m⁻². Light plasticity increased plant densities to 0.00301, 0.0972, and 0.159 plant m⁻², reducing the underestimates (Fig. 6B). The increase in mid-successional PFT abundance contributed the most to the improved model-data agreement of community-level understory plant density.

ED2-static increased community-level understory plant density to a more realistic 0.217 m⁻², accompanied by 86.9-252% PFT-level increases relative to ED2-baseline. Community- and PFT-level understory plant density predictions were comparable between ED2-static and ED2-plastic (Fig. 6).

Underestimates of plant density in ED2-baseline further led to underestimated leaf area in the understory (Fig. 6C). Meanwhile, the leaf area vertical profile simulated by ED2-plastic and ED2-static aligned with an airborne LiDAR-based estimate (Detto et al., 2015). The total LAI predicted by ED2-plastic and ED2-static (5.86 and 5.24 m² m⁻²) was higher than ED2-baseline prediction of 3.11 m² m⁻², and ED2-plastic estimate best aligned with a total LAI estimate of 5.9 ± 0.4 m² m⁻² based on hemispherical photos (Detto et al., 2018).

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<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

**Figure 6.** Light plasticity effect on 20-year-average forest structure during short-term simulations. (A) shows modeled and observed plant density for different size classes. ED2-baseline, ED2-plastic, and ED2-static are represented by purple, orange, and green bars, respectively. The inset figure is an enlarged version of the modeled and observed plant density for cohorts with DBH ≥ 30 cm. Error bars indicate 95% confidence intervals calculated by bootstrapping. (B) shows modeled and observed understory plant density for different PFTs. The inset figure is an enlarged version of the modeled and observed plant density for early successional PFT. Error bars indicate 95% confidence intervals calculated by bootstrapping. (C) shows the vertical profile of overtopping LAI. Inserted texts report the observed and modeled total LAI. Airborne-LiDAR based estimate is 5.4 m² m⁻² (Detto et al., 2015), and hemispherical

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photo-based estimate is 5.9 m² m⁻² (Detto et al., 2018). Gray shaded area indicates 95% confidence interval of the LiDAR-based estimate.

### 3.3 Forest dynamics in long-term simulations

We examined AGB, GPP, PFT composition, and trait composition during simulations of 300-year forest secondary succession. At the 12th and 80th years of succession, the forest simulated by ED2-baseline stored 32.4 and 123 Mg ha⁻¹ of AGB, lower than chronosequence-based estimates of 88.0 and 160 Mg ha⁻¹ (Batterman et al., 2013), and ED2-plastic generated higher and more realistic estimates of 50.7 and 175 Mg ha⁻¹ (Fig. 7A; Table 2). At the 300th year, ED2-plastic predicted AGB to be 224 Mg ha⁻¹ (Table 2), which was 50.3% higher than ED2-baseline estimate, though still lower than the census-based estimate of 263-266 Mg ha⁻¹ (Cano et al., 2019). ED2-static predicted total AGB to be 57.5, 154, and 233 Mg ha⁻¹ after 12, 80, and 300 years of succession, which were similar to or higher than the predictions with light plasticity.

During the last 20 years of succession, ED2-plastic produced the highest GPP estimate of 3.39 kg C m⁻² year⁻¹, whereas ED2-static predicted the lowest GPP of 2.23 kg C m⁻² year⁻¹. The prediction of ED2-baseline (2.83 kg C m⁻² year⁻¹) best aligned with the observed value of 2.8 kg C m⁻² year⁻¹ (Detto & Pacala, 2022).

All models exhibited a similar temporal trend of community composition, where early-successional PFT initially dominated the forest and was eventually outcompeted by late-successional PFT (Fig. 8). During the last 20 years of succession, all models underestimated the basal area of early-successional PFT relative to the abundance observed in BCI forest censuses (Fig. 7B), but the ED2-plastic estimate of 3.07 cm² m⁻² best aligned with the observed 6.65 cm² m⁻², whereas ED2-baseline and ED2-static underestimated the basal area by an order of magnitude (0.417 and 0.618 cm² m⁻²).

The simulated community-level trait diversity also differed across models. In ED2-plastic, the community-level vertical trait gradient (defined in section 2.7) of leaf dark respiration, Vcmax,
and SLA were -0.195, -0.141, and 0.149, which were comparable to the -0.14, -0.08, and 0.12 reported in Lamour et al. (2023) based on in situ trait measurements (Fig. 7C). In contrast, both ED2-baseline and ED2-static showed vertical trait gradients that were close to zero, i.e., leaf dark respiration, V<sub>cmax</sub>, and SLA were largely constant within the canopy. Aside from trait profiles, community-average leaf traits (weighted by cohort-level leaf area) during the last 20 years of succession were also different between models. Average leaf dark respiration and V<sub>cmax</sub> were 104% and 64.1% higher in ED2-plastic than in ED2-static, while SLA, leaf lifespan, and wood density were 40.3%, 50.6%, 3.72% higher in ED2-static than in ED2-plastic (Table 2).

<figure><img src="image_16.png" /><figcaption>**Figure 7.** Light plasticity effect on 300-year forest secondary succession in long-term simulations. (A) shows the trajectory of aboveground biomass (AGB) modeled by ED2-baseline (purple line), ED2-plastic (orange line), and ED2-static (green line). Gray points represent observations from a chronosequence in Panama (Batterman et al., 2013). The black point represents the plot-level AGB estimate based on censuses and local allometric equations (Cano et al., 2019). (B) shows the simulated and modeled PFT abundance averaged during the last 20 years of succession. (C) shows modeled and observed community-level vertical trait gradients of leaf dark respiration rate at 25°C, V<sub>cmax</sub> at 25°C, specific leaf area, and leaf lifespan. The definition of community-level vertical trait gradient is described in section 2.7. ED2-baseline, ED2-plastic, and ED2-static are represented by purple, orange, and green bars, respectively. Error bars for these simulated results are 95% confidence intervals calculated by bootstrapping. Gray bars represent the observed community-level vertical trait gradients and their confidence</figcaption></figure>
intervals reported in Table S3 and S4 in Lamour et al. (2023). The observed leaf lifespan
gradient is marked as “NA” because Lamour et al. (2023) did not collect leaf lifespan data.

<figure><img src="image_17.png" /><figcaption>B: Long-term PFT composition in ED2-plastic</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>C: Long-term PFT composition in ED2-static</figcaption></figure>

<figure><img src="image_19.png" /></figure>

**Figure 8.** Light plasticity effect on community composition in long-term simulations. (A)-(C) show PFT composition in ED2-baseline, ED2-plastic, and ED2-static. Early-, mid, and late-successional PFTs are shown in yellow, gray, and blue areas, respectively.
<table><caption>**Table 2.** Modeled forest structure, composition, trait diversity, and functioning in long-term simulations. Trait values are calculated as community-level averages weighted by cohort-level leaf area during the last 20 years of simulation. All other variables are calculated as ecosystem-level averages during the last 20 years of simulation, except for AGB. AGB is an annual average. For example, AGB at the 12th year is calculated as the ecosystem-level, annual average during the 12th year of the simulation.</caption><thead><tr><th></th><th>ED2-baseline</th><th>ED2-plastic</th><th>ED2-static</th><th>Observation</th></tr></thead><tbody><tr><td colspan="5"><strong>Forest structure and composition</strong></td></tr><tr><td>Total plant density (individuals m<sup>-2</sup>)</td><td>0.206</td><td>0.424</td><td>0.367</td><td>0.433 (forest census)</td></tr><tr><td>Understory plant density (individuals m<sup>-2</sup>)</td><td>0.181</td><td>0.378</td><td>0.325</td><td>0.391 (forest census)</td></tr><tr><td>Total basal area (cm<sup>2</sup> m<sup>-2</sup>)</td><td>16.9</td><td>28.7</td><td>28.7</td><td>30.7 (forest census)</td></tr><tr><td>Total LAI (m<sup>2</sup> m<sup>-2</sup>)</td><td>3.16</td><td>6.25</td><td>5.81</td><td>5.42 (Detto et al., 2018);<br>5.9 (Detto et al., 2015)</td></tr><tr><td>Basal area of early- successional PFT (cm<sup>2</sup> m<sup>-2</sup>)</td><td>0.417</td><td>3.07</td><td>0.618</td><td>6.65 (forest census)</td></tr><tr><td colspan="5"><strong>Community-level trait diversity</strong></td></tr><tr><td>Leaf dark respiration rate at 25°C (µmol m<sup>-2</sup> s<sup>-1</sup>)</td><td>0.760</td><td>0.417</td><td>0.204</td><td>NA</td></tr><tr><td>V<sub>cmax</sub> at 25°C (µmol m<sup>-2</sup> s<sup>-1</sup>)</td><td>50.7</td><td>32.5</td><td>19.8</td><td>NA</td></tr><tr><td>Specific leaf area (m<sup>2</sup> kg<sup>-1</sup>)</td><td>11.2</td><td>21.6</td><td>30.3</td><td>NA</td></tr><tr><td>Leaf lifespan (month)</td><td>12.0</td><td>26.9</td><td>40.5</td><td>NA</td></tr><tr><td>Wood density (g cm<sup>-3</sup>)</td><td>0.622</td><td>0.619</td><td>0.642</td><td>NA</td></tr></tbody></table>
Forest functioning

<table><tr><td>AGB at 12<sup>th</sup> year (Mg ha<sup>-1</sup>)</td><td>32.4</td><td>50.7</td><td>57.5</td><td>88.0 (Batterman et al., 2013)</td></tr><tr><td>AGB at 80<sup>th</sup> year (Mg ha<sup>-1</sup>)</td><td>123</td><td>175</td><td>154</td><td>160 (Batterman et al., 2013)</td></tr><tr><td>AGB at 300<sup>th</sup> year (Mg ha<sup>-1</sup>)</td><td>149</td><td>224</td><td>233</td><td>263-266 (Cano et al., 2019)</td></tr><tr><td>Gross primary productivity (kg C m<sup>-2</sup> year<sup>-1</sup>)</td><td>2.85</td><td>3.39</td><td>2.23</td><td>2.8 (Detto & Pacala, 2022)</td></tr></table>

4 Discussion

4.1 Light plasticity and inter-specific diversity similarly correct for model biases in understory growth and forest structure

Consistent with our first hypothesis, growth rates of understory trees were underestimated by 40.7% when they were not plastic and were parameterized by top-of-canopy leaf traits (Fig. 4A). Incorporating observation-based light plasticity corrected for this growth underestimate and further enabled accurate prediction of tree size distribution and leaf area profile (Fig. 4 and 6), which supports our second hypothesis. These findings provide the first quantitative evidence that light plasticity is critical for explaining demographic processes and forest structure, and that observation-constrained light plasticity largely corrects for the model underestimate of understory growth and abundance in tropical forests.

The better representation of understory growth rate, tree size distribution, and leaf area profile arises from a more realistic characterization of plant shade tolerance, which is highlighted by the increase in GCP. The simulated community-level GCP doubled with light plasticity (Fig. 3B), with the largest increase in early-successional PFT (Fig. S4). Field experiments have similarly shown that compared to plants in high light treatment, plants in low
light treatment substantially reduce their light compensation point (LCP), which is the minimum
light level required to maintain a positive net carbon balance. For example, Kitajima (1994)
showed that tropical tree seedlings grown in the shaded treatment reduced their LCP by more
than 50% compared to conspecifics grown in the full sun. Sterck et al. (2013) showed that the
plant-level LCP of tropical tree seedlings grown at the low light level decreased to one-third of
conspecifics grown at the high light level. The magnitude of increase in shade tolerance is
comparable between field observations and our modeling results, suggesting that the simulated
physiological consequences of light plasticity are realistic.

Meanwhile, ED2-static with higher inter-specific diversity predicted similar understory
growth and forest structure as ED2-plastic during short-term simulations (Fig. 4 and 6).
Although ED2-static did not incorporate light plasticity, it generated within-canopy trait profiles
and GCPs that were similar to ED2-plastic (Fig. S3 and S4) by including additional PFTs.
Therefore, incorporating within-canopy variation in leaf traits, either by including intra-specific
light plasticity or expanding inter-specific diversity, is necessary to predict realistic understory
growth and tropical forest structure.

## 4.2 Light plasticity effect on long-term functional composition and within-canopy trait profiles can not be compensated by inter-specific diversity

The demographic effects of light plasticity and inter-specific diversity further modulate
long-term forest succession and regrowth. We found that ED2-plastic and ED2-static similarly
enhanced modeled long-term biomass accumulation relative to ED2-baseline, but the effect of
light plasticity on improving the prediction of functional composition can not be compensated
by inter-specific diversity (Table 2; Fig. 7 and 8).

Consistent with our third hypothesis, either including light plasticity or a higher inter-
specific diversity increased AGB, but AGB predictions at the 300th year across all models were
still lower than the plot-level estimate based on local allometry (Cano et al., 2019). This is
attributed to an underestimate of large tree growth and an overestimate in their mortality
rates (Fig. 4A and 5A), which may be related to the inaccurate representation of size
dependence in reproductive allocation and aboveground versus belowground allocation (Xu et al., 2024).

Although ED2-plastic and ED2-static generated similar predictions of AGB (Table 2; Fig. 7A), their predictions of functional composition were different. Early-successional PFT was largely outcompeted by mid- and late-successional PFTs in ED2-static, whereas it coexisted with other PFTs at a substantially higher abundance in ED2-plastic (Table 2). This difference arises because inter-specific diversity and light plasticity shape community assembly differently. In ED2-static, light limitation imposes a strong selection pressure on understory trees, particularly for early-successional PFTs. In contrast, intra-PFT plasticity enhances individual fitness, especially the fitness of early-successional PFT which is the most plastic (Table S1), eventually promoting coexistence.

The unique effect of light plasticity on composition is also evident at the trait level. During long-term simulations, ED2-plastic generated a realistic community-level vertical trait gradient (defined in section 2.7), whereas leaf traits simulated by ED2-static did not vary significantly across the vertical gradient (Fig. 7C). This is because in ED2-static, only trees with shade-tolerant traits can survive the understory stage and then reach the top canopy, thus ED2-static simulated values of leaf dark respiration rate and Vcmax were always low, and values of SLA and leaf lifespan were always high, regardless of the canopy position (Fig. S5). In contrast, trees in ED2-plastic dynamically adjust their traits based on light levels throughout their lifetime. As a result, ED2-static had 51.1% and 39.1% lower community-level leaf dark respiration and Vcmax than ED2-plastic, further leading to a 34.2% lower GPP (Table 2). The community-average leaf dark respiration and Vcmax were the highest in ED2-baseline because all cohorts in this model had top-of-canopy trait values.

Interestingly, GPP simulated by ED2-baseline best aligned with the flux tower-based estimate during 2012-2017 (Detto & Pacala, 2022). This model-data agreement in GPP does not arise from an accurate representation of ecophysiological processes, rather, it arises from the compensatory effect of an underestimated LAI and a high Vcmax. Meanwhile, ED2-plastic predicted realistic trait profiles and LAI, but overestimated GPP by 21.1%. This may be
explained by a lack of hydraulic limitation (Xu et al., 2016) in the version of ED2 used for our study and possible biases in stomatal parameters and photosynthetic temperature dependence.

Results of PFT composition and community-level trait diversity suggest that light plasticity regulates tropical forest functional composition differently from inter-specific trait variation, and this finding is relevant to recent efforts to improve the representation of fine-scale functional diversity in TBMs. For example, hyperspectral imaging has been used to initialize PFT composition at a high spatial resolution (Bogan et al., 2019). Although this approach captures spatial heterogeneity at the PFT level, it does not account for the intra-PFT trait variation. Our results emphasize that increasing inter-PFT trait variation is insufficient for predicting long-term forest dynamics, instead, the intra-PFT trait variation across microenvironmental gradients is required for realistic prediction of forest functional composition. Future research should extend beyond our plot-level findings to quantify the ecological consequences of intra-specific, within-canopy trait diversity at a larger spatial scale and across different tropical regions.

Based on the demonstrated role of light plasticity in demography, forest structure, and composition, we further hypothesize that light plasticity will regulate ecosystem responses to light environment variability, particularly gap formations and post-disturbance forest regeneration. First, light plasticity maintains the abundance of light-demanding species prior to gap formation and thus facilitates future gap colonization and regrowth of these species. Second, light plasticity will increase the photosynthesis and growth of previously suppressed understory trees after gap formation. These hypotheses, although yet to be tested, are particularly relevant for mechanistic understanding of tropical secondary forests, which now occupy more than half of all tropical forests (Food and Agriculture Organization of the United Nations, 2010) and are predicted to have large regrowth potential (Anderson-Teixeira et al., 2016; Pan et al., 2011; Pugh et al., 2019; Shevliakova et al., 2009). Future research should investigate the role of light plasticity in predicting the multi-dimensional recovery of tropical secondary forests (Poorter et al., 2021).
633 4.3 Toward mechanistic modeling of trait plasticity

634 By incorporating the variation in light plasticity across different species and traits, our
635 study provides a more realistic representation of light plasticity than previous TBMs (**Table 1**).
636 However, our representation of light plasticity has several assumptions about the degree,
637 timescale, and ecological consequences of trait plasticity. These assumptions, which have not
638 been thoroughly evaluated, highlight the key challenges and opportunities for further
639 developing a more mechanistic characterization of trait plasticity in TBMs.

640 We assumed that intra-specific light plasticity is the only driver of observed within-
641 canopy trait gradient, which may lead to an overestimate of the degree of light plasticity. While
642 light is a primary cue of the vertical variation, ontogeny can also contribute significantly to leaf
643 trait plasticity, particularly for non-pioneer species (Wen et al., 2008). In addition, thermal
644 stress, water stress, and herbivory can all contribute to the vertical trait profile (Cavaleri et al.,
645 2010; Coste et al., 2009; Dang-Le et al., 2013). Field-based trait measurements across a wider
646 range of ontogenetic stages and microenvironmental gradients will be instrumental in
647 disentangling the contribution of trait plasticity from these other sources of variation.

648 Another key assumption is that light plasticity only occurs at the time of leaf turnover
649 and that traits remain constant within a leaf's lifetime, and such assumption may have
650 underestimated the rate of plasticity adjustment. Results from warming experiments have
651 shown that the timescale of temperature acclimation in leaf dark respiration varies from two
652 weeks to two months (Reich et al., 2021; Ren et al., 2024), and a similar analysis is yet to be
653 conducted to determine the timescale of plasticity-induced trait adjustments driven by other
654 environmental factors.

655 Our results emphasized the benefits of light plasticity, but phenotypic plasticity is not
656 necessarily adaptive. For example, the expression of plasticity can be energetically costly, and
657 plasticity may lower fitness under certain scenarios (DeWitt et al., 1998). We did not model
658 these processes because field-based quantitative assessments are rare, and the few studies did
659 not find consistent evidence for the costs and tradeoffs associated with plasticity (Avramov et
al., 2007; McIntyre & Strauss, 2014; Liu et al., 2016). On the other hand, TBM can serve as a useful tool to quantify the cost of plasticity. For example, the metabolic cost of trait plasticity can be incorporated as a model parameter, and optimizing the parameter against observed forest demography and carbon fluxes may serve as a first-order estimate of the cost.

While we focused on the light-driven plasticity of leaf physiological traits in this study, trait plasticity is a widespread phenomenon observed in other traits and across other environmental gradients (Poorter et al., 2019; Siefert et al., 2015). For example, plant structural traits such as leaf angle vary substantially within the canopy, which can influence carbon and energy fluxes (Yang et al., 2023). In addition to leaf traits, plant allometry and root traits are also known to be plastic (Poorter et al., 2019; Yaffar et al., 2024). A mechanistic understanding and representation of trait plasticity is thus a research frontier in vegetation modeling, and it will ultimately benefit from field-based trait sampling accompanied by comprehensive measurements of the microenvironment. Recent years have already seen increasing field campaigns that measure plant morphological and physiological traits across different microenvironments (Lamour et al., 2023; Poorter et al., 2018). These datasets attempt to characterize the variation of both functional traits and environmental factors at a scale that is ecologically relevant to individual plant performance, and they will provide useful information for both quantitative characterization of trait plasticity and its incorporation in trait-based models.

## 5 Conclusions

Plant functional diversity in traits associated with light use, including both inter-specific and intra-specific variation, critically shapes tropical forest dynamics by modulating tree demography. By combining trait measurements, long-term census data, and trait-based ecosystem modeling, we showed that observation-constrained light plasticity enhances understory growth and abundance. This demographic effect further increases long-term tropical forest biomass accumulation and strongly modulates forest structure and composition.
Importantly, the community and ecosystem effects of light plasticity cannot be fully compensated by increasing inter-specific functional diversity, particularly in terms of community composition and within-canopy trait gradients. These findings suggest that light plasticity is crucial for trait-based prediction of tropical forest regrowth and resilience, especially in secondary forests which experience high variability and heterogeneity in the light environment. Future research should quantify phenotypic plasticity across a broader range of traits and environmental gradients and evaluate their community and ecosystem-level impact.

694

## Acknowledgments

XX and YM were supported by DOE Environmental System Science Program grant DE-SC0023048 and NSF grant 2140581. AR, JL, KD, and SS were supported by the Next Generation Ecosystem Experiments-Tropics project funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, and by U.S. Department of Energy Contract No. DE-SC0012704 to Brookhaven National Laboratory. AR was also supported by U.S. Department of Energy Contract No. DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory. SS was also supported by the NASA Surface Biology and Geology Mission. MD was supported by the Carbon Mitigation Initiative at Princeton University and NSF grant 2017804.

The authors declare no conflict of interest.

705

## Open Research

Census data used for model benchmarking are publicly available at https://datadryad.org/stash/dataset/doi:10.15146/5xcp-0d46. Previously published trait data is described in Lamour et al. (2023). All scripts for ED2 model simulations are available at https://github.com/yixin98/ED2/tree/new_plasticity

711

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943
Figure.
# Intra- & inter-PFT trait variation

Top of canopy
cohort

Model 1: ED2-baseline

Model 2: ED2-plastic

Model 3: ED2-static

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

# Within-canopy trait variation

Leaf dark respiration
at 25°C

<figure><img src="image_23.png" /></figure>

$V_{cmax}$
at 25°C

<figure><img src="image_24.png" /></figure>

Specific leaf area

<figure><img src="image_25.png" /></figure>

Leaf lifespan

<figure><img src="image_26.png" /></figure>
Figure.
A: Across-species distribution of light plasticity coefficient

<figure><img src="image_27.png" /></figure>

B: Leaf dark respiration at 25°C

<figure><img src="image_28.png" /></figure>

C: V<sub>cmax</sub> at 25°C

<figure><img src="image_29.png" /></figure>

D: Specific leaf area

<figure><img src="image_30.png" /></figure>

E: Leaf lifespan

<figure><img src="image_31.png" /></figure>
Figure.
A: Definition of growth compensation point

<figure><img src="image_32.png" /></figure>

B: Light-DBH growth relationship

<figure><img src="image_33.png" /></figure>
Figure.
A: Size-dependent DBH growth rate

<figure><img src="image_34.png" /></figure>

B: PFT-level understory growth rate

<figure><img src="image_35.png" /></figure>
Figure.
<figure><img src="image_36.png" /><figcaption>B: PFT-level understory mortality rate</figcaption></figure>

<figure><img src="image_37.png" /></figure>
Figure.
<figure><img src="image_38.png" /><figcaption>B: PFT composition during 280th-300th year</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>C: Community-level vertical trait gradient</figcaption></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>
Figure.
<figure><img src="image_42.png" /><figcaption>B: Long-term PFT composition in ED2-plastic</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>C: Long-term PFT composition in ED2-static</figcaption></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>
Figure.
A: Size-dependent plant density

<figure><img src="image_46.png" /></figure>

B: PFT-level understory plant density

<figure><img src="image_47.png" /></figure>

C: Leaf area profile

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>