# Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site

Matthew Worden, Caroline Famiglietti, Paul Levine, Shuang Ma, A. Anthony Bloom, Damien Bonal, Clément Stahl, Alexandra Konings

► To cite this version:

Matthew Worden, Caroline Famiglietti, Paul Levine, Shuang Ma, A. Anthony Bloom, et al. Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site. Global Change Biology, 2024, 30 (5), 10.1111/gcb.17287. hal-04671999

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Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

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WILEY

# Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site

Matthew A. Worden<fnref n="1" /> ID

Caroline A. Famiglietti<fnref n="1" />,<fnref n="2" /> ID

Paul A. Levine<fnref n="3" /> ID

Shuang Ma<fnref n="3" /> ID

A. Anthony Bloom<fnref n="3" /> ID

Damien Bonal<fnref n="4" /> ID

Clément Stahl<fnref n="5" /> ID

Alexandra G. Konings<fnref n="1" /> ID

### Correspondence

Matthew A. Worden, Department of Earth System Science, Stanford University, Stanford, CA, USA.

Email: mworden@stanford.edu

### Funding information

Future Investigators in NASA Earth and Space Science and Technology (FINESST), Grant/Award Number: 80NSSC21K1593; Agence Nationale de la Recherche, Grant/Award Number: ANR-10-LABX-25-01 and ANR-11-LABX-0002-01; NASA 2020 Carbon Cycle Science, Grant/Award Number: NNH20ZDA001N-CARBON; Division of Environmental Biology, Grant/Award Number: 1942133

### Abstract

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long-lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought-induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model-data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post-drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust-boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought-free conditions. Overall, allocation shifts accounted for 65% [45%-75%] of drought legacy effects in modeled NBE. In summary, drought-induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land-atmosphere CO₂ exchanges and should be accounted for in ecosystem models.

### KEYWORDS

drought, drought legacy, drought recovery, dynamic allocation, model-data fusion, plant carbon allocation

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

<footnote n="1">¹Department of Earth System Science, Stanford University, Stanford, California, USA</footnote>

<footnote n="2">²HydroSat, Washington, District of Columbia, USA</footnote>

<footnote n="3">³Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA</footnote>

<footnote n="4">⁴AgroParisTech, INRAE, UMR Silva, Université de Lorraine, Nancy, France</footnote>

<footnote n="5">⁵INRAE, UMR EcoFoG, AgroParisTech, CNRS, Cirad, Université des Antilles, Université de Guyane, Kourou, France</footnote>
# 1 INTRODUCTION

Tropical forests are the largest reservoirs of aboveground biomass and contribute significantly to the global carbon sink and its variability (e.g., Friedlingstein et al., 2020; Xu et al., 2021). However, the future of these forests amid climate change remains uncertain (Brando et al., 2019; Mitchard, 2018), partially due to uncertainties surrounding their response to the anticipated rise in frequency and severity of droughts (Huntingford et al., 2013; Lehner et al., 2017; Spinoni et al., 2018; Wang et al., 2021).

Drought, caused by soil water deficit and atmospheric aridity, reduces photosynthesis, respiration and transpiration, and increases biomass burning and forest mortality (e.g., Aragão et al., 2007; Lee et al., 2013; McDowell et al., 2022; Metcalfe et al., 2010). For example, plant stomata can close in response to drought conditions, decreasing CO₂ assimilation, photosynthesis, and transpiration (Gentine et al., 2019). Drought processes also change the size of the live vegetation and dead soil carbon states, which can influence later carbon and water exchanges over longer periods (McDowell et al., 2022; Trugman et al., 2018; Zhou et al., 2016). For instance, repeated droughts can instigate long-term reductions in tropical canopy size (Saatchi et al., 2013; Tao et al., 2022). These alterations could subsequently influence the water cycle, specifically by limiting evapotranspiration and potentially changing the timing of the wet season onset (Shi et al., 2019). Such long-term impacts of droughts that occur during the post-drought period are frequently labeled as “drought legacy effects” (Vilonen et al., 2022)—we will follow this use of the phrase throughout this paper. In the tropics, one estimate suggests that legacy effects explain 64% of the net carbon flux interannual variability (Bloom et al., 2020). However, the contributions of different mechanisms to overall drought legacy effects in different ecosystems are unclear (Kannenberg et al., 2020; Müller & Bahn, 2022). For example, beyond the processes above, drought legacy effects could also be modulated by post-drought dynamics of nutrient availability (Gessler et al., 2017), as well as pest infection and pathogen occurrence within forests (Anderegg et al., 2015).

Shifts in carbon allocation (the partitioning of photosynthesized non-structural carbohydrates to plant tissues and respiration processes) have been hypothesized to be a significant mechanism contributing to drought legacy effects on carbon fluxes and their recovery to pre-drought levels (Kannenberg et al., 2020; Müller & Bahn, 2022). In-situ studies show that plants can shift their carbon allocation to different components (e.g., root, foliage, wood) in response to water deficits from drought (Joseph et al., 2020). The resulting changes in leaf area and root water uptake in turn affect carbon fluxes through changes in photosynthesis (Doughty et al., 2014, 2015). However, despite this potential influence of drought-induced allocation shifts on drought legacies, the magnitude and direction of changes to carbon uptake and carbon pools, in response to these shifts in tropical ecosystems, are unknown.

One reason for this knowledge gap is that plant carbon allocation is hard to measure and therefore poorly quantified, with the majority

of measurements occurring at mid-latitudes (Babst et al., 2020) and few observations within the tropics (e.g., Doughty et al., 2015; Malhi et al., 2017). Even less information exists on how carbon allocation shifts in response to environmental factors such as CO₂ concentration, temperature, nutrient availability, incoming solar radiation, and, of particular interest for this study, soil water availability (Hartmann et al., 2020; Joseph et al., 2020; Poorter et al., 2012). This data scarcity persists because comprehensive measurements capturing allocation shifts, especially in belowground and nonstructural carbohydrate pools (e.g., labile pool), are labor-intensive. Current methods require recurring measurements of various plant biomass pools and frequently also require destructive sampling. For example, one technique requires continual sampling of hard-to-access root biomass and the quantification of carbon fluxes entering and exiting the soil litter system (Giardina & Ryan, 2002; Stape et al., 2008). Another technique involves isotopic labeling with ¹³C, which requires pumping ¹³C into a tree and then repeatedly sampling different tree organs and fluxes to determine where the ¹³C is allocated (Joseph et al., 2020). Furthermore, understanding how carbon allocation responds to drought requires measurements during both drought and non-drought conditions (Drake et al., 2019; Joseph et al., 2020; Rowland et al., 2014; Stape et al., 2008). These challenges in quantifying allocation shifts through direct observations suggest the need for a different approach to characterize this process, especially in places with relatively few measurements and field sites, such as the tropics.

In this paper, we demonstrate an alternative approach for determining drought-induced allocation shifts. We introduce a flexible model representation of carbon allocation that is a function of soil water availability (Section 2.3) and infer the parameters of that representation using relatively easy-to-obtain aboveground measurements, coupled with a model-data fusion approach. This approach is then tested at a wet tropical site in French Guiana, characterized by seasonal dry periods with highly varying intensities from year to year. In doing so, our study builds on previous research in the area of model-data fusion for carbon cycling (e.g., Ma, Jiang, et al., 2023; Norton et al., 2019; Sierra et al., 2021; Verbeeck et al., 2011). Specifically, our approach uses the CARbon DAta MObel fraMeWork (CARDAMOM) (Bloom et al., 2016; Bloom & Williams, 2015), and expands upon previous work by Rowland et al. (2014). Rowland et al. (2014) compared static allocation fractions derived during separate dry season and wet season runs to examine seasonal allocation shifts. We extend this approach to infer fully dynamic allocation shifts instead, introducing an approach that could be applied at any field site with sufficient observations. Furthermore, we quantified how these inferred allocation shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event.

This study both demonstrates a new method of determining drought-induced carbon allocation shifts and tests the effects of such shifts on drought legacies. Specifically, we test two hypotheses: H1 states that drought-induced allocation shifts can be inferred from measurements of carbon and water cycle states and fluxes
<figure><img src="image_3.png" /></figure>

using model-data fusion, rather than relying on direct measurements of allocation shifts, and H2 states that carbon allocation shifts significantly increase drought impacts on Net Biosphere Exchange (NBE) within the post-drought period, thereby acting as a key mechanism governing drought legacies. By testing the first hypothesis, we introduce an efficient and hence scalable alternative to previous labor-intensive techniques. Evaluating the second hypothesis provides context on the importance of drought-induced allocation shifts, especially given the variety of known mechanisms that influence drought legacies and the existing gaps in our understanding of their contributions to the overall legacy. Overall, this analysis serves to deepen our understanding of the impact of drought on the tropical land sink.

## 2 | MATERIALS AND METHODS

In Section 2.1, we describe the wet tropical flux tower site examined in this study. Section 2.2 describes the CARDAMOM model-data fusion system. Section 2.3 describes our novel allocation shift structure, which we used with CARDAMOM to test the hypotheses noted in the introduction. Section 2.4 describes the measurements used from the flux tower site and how they drive and constrain CARDAMOM. Section 2.5 describes how we used CARDAMOM, as well as longer records of meteorological data, to quantify the effect of allocation shifts on drought legacies in NBE.

### 2.1 | Site description: Guyaflux tower site in French Guiana (GF-GUY)

The Guyaflux tower is located within an old growth wet tropical forest in French Guiana, South America (5.279 N, 52.925 W) with data records from 2004 to 2020. The tower's height is 55 meters, with a tower fetch covering hilly terrain ranging from 10 to 40 meters high, a creek, and a section of bottomland (Bonal et al., 2008). The average canopy height of the forest is 35 meters with a density of 620 trees per hectare, and a diversity of around 140 species per hectare (Bonal et al., 2008). The site is exposed to seasonal variations in radiation and precipitation and periods of soil drought with varying intensities among years (from moderate to extreme conditions), which impact the ecosystem's carbon uptake, or NBE (Aguilos, Hérault, et al., 2018; Aguilos, Stahl, et al., 2018).

### 2.2 | CARDAMOM model-data fusion system

We use CARDAMOM as a framework to test our two hypotheses. CARDAMOM is a Bayesian model-data fusion system (Bloom et al., 2016; Bloom & Williams, 2015) that optimizes parameters of a land surface model called the Data Assimilation Linked Ecosystem Carbon model (DALEC) (Bloom et al., 2016; Williams et al., 2005). It has been applied at both site (e.g., Rowland et al., 2014;

Stettz et al., 2021) and global (e.g., Bloom et al., 2016; Famiglietti et al., 2023) scales. CARDAMOM can assimilate a variety of measurements to inform carbon and water states, fluxes and associated processes controlling the flow of carbon and water through an ecosystem. CARDAMOM optimizes DALEC's parameter distributions and initial conditions using Bayes' theorem (Yang et al., 2021), thus enabling the inference of plant physiological parameters, including allocation shifts. This process employs a Markov Chain Monte Carlo method with a differential evolution algorithm (Braak, 2006) to optimize a likelihood function based on the assimilated observations (Yang et al., 2021). Consequently, the Bayesian framework used allows for the determination and propagation of parametric uncertainty.

Compared to more complex global land surface models such as the organizing carbon and hydrology in dynamic ecosystems (ORCHIDEE) (Vuichard et al., 2019) or the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) models (Smith et al., 2014), DALEC maintains a simpler structure. However, unlike more complex land surface models, DALEC's use within the CARDAMOM framework enables an observation-informed representation of land biosphere dynamics, with reduced errors on empirical parameterizations of carbon and water cycling because parameters are locally optimized and spatial variability in parameters is accounted for (Quetin et al., 2020). As demonstrated in numerous recent efforts (e.g., Famiglietti et al., 2021; Norton et al., 2023; Yang et al., 2021), this methodological distinction enables testing biogeochemical process hypotheses (such as H2) using a multitude of co-located observations. CARDAMOM has been successfully tested for its ability to accurately estimate carbon and water fluxes at over 200 flux towers from the FLUXNET 2015 dataset (Yang et al., 2021). Finally, CARDAMOM has been used in many studies to constrain or estimate ecosystem processes or quantities that are not directly measured (e.g., Ma, Bloom, et al., 2023; Myrgiotis et al., 2022; Quetin et al., 2023; Smallman et al., 2021).

The DALEC version used here contains only four vegetation carbon pools (foliar carbon, labile carbon, coarse root carbon, and woody carbon), two soil carbon pools (soil organic matter and litter), and two soil water pools (plant available water and plant unavailable water). The model's allocation scheme is depicted in Figure S1a; the augmentation of the model with dynamic allocation (Figure S1b) is described in Section 2.3. We refer the reader to Ma, Bloom, et al. (2023), Bloom et al. (2020), and Williams et al. (2005) for additional information on the model structure.

### 2.3 | Novel dynamic allocation structure dependent on water availability

Most versions of DALEC, including the one we modify here to incorporate dynamic allocation (DALEC version 1012; Ma, Bloom, et al., 2023), use a static carbon allocation scheme (allocation does not change with water availability and remains constant throughout the simulation; Figure S1a). Gross primary productivity (GPP) is
either allocated into autotrophic respiration or partitioned into foli- age, labile, root, and wood carbon pools:

$$f_{\text{fol}} + f_{\text{lab}} + f_{\text{wood}} + f_{\text{root}} + f_{\text{autoresp}} = 1, \quad (1)$$

where $f_{fol}$, $f_{lab}$, $f_{wood}$, $f_{root}$, and $f_{autoresp}$ are the fraction of GPP allocated to foliage, labile carbon (non-structural carbohydrates which feed into the foliage pool), woody carbon, fine roots, and autotrophic res-piration, respectively. Many other land surface models also allocate carbon in this manner, albeit divided across a larger number of pools (Merganičová et al., 2019). These static allocation parameters within DALEC are inferred via CARDAMOM's model-data fusion.

To infer allocation shifts as a function of water availability, we im- plemented a dynamic carbon allocation structure (Figure S1b) based on a generalized logistic function (Figure 1) known as Richards' curve (Richards, 1959). We assume that there are two allocation regimes: one for wet conditions and another for dry conditions. Furthermore, we used two analogous equations to represent either increasing (Equation 2) or decreasing allocation (Equation 3) during dry periods:

$$f_c(\text{PAW}) = s_c + \frac{(d_c - s_c)}{1 + \exp(k(\text{PAW} - W_o))} \quad \text{if } d_c > s_c, \quad (2)$$

$$f_c(\text{PAW}) = d_c + \frac{(s_c - d_c)}{1 + \exp(-k(\text{PAW} - W_o))} \quad \text{if } d_c \le s_c, \quad (3)$$

where the subscript c represents three of the four plant carbon pools and plant respiration (labile, foliar, fine roots, autotrophic respiration), and $f_c$ is the fraction of GPP allocated to c. PAW is the plant available

water pool, which represents the total amount of water available to the plants, irrespective of its location beneath the surface. The param- eter $s_c$ is the carbon allocation fraction during the wettest periods, $d_c$ is the carbon allocation fraction during the driest periods, k is the rate of change of the logistic function, and $W_o$ is the plant available water at the midpoint of the logistic curve. The parameters $s_c$, $d_c$, k, $W_o$, and the initial value of PAW are optimized as part of CARDAMOM's model- data fusion. The fraction allocated to the wood pool is computed as the residual of other allocations, since the sum of allocation fractions must equal one:

$$f_{\text{wood}} = 1 - (f_{\text{fol}} + f_{\text{lab}} + f_{\text{root}} + f_{\text{autoresp}}). \quad (4)$$

The variation of $f_{wood}$ with PAW retains the logistic function shape because it is the residual of other allocations that are modeled as logistic functions.

The logistic function is a useful tool for modelling allocation shifts due to its inherent flexibility. It smoothly transitions between customizable minimum and maximum allocation values, which occur under either stressed or unstressed conditions based on water availability. The function also allows for the customization of the inflection point, which allows flexibility regarding when stress is de- tected in plant available water, and the rate of transition between minimum and maximum allocation. The adaptability of this function allows a range of behaviors to be represented. For instance, in loca- tions with low sensitivity to water stress-induced allocation shifts, CARDAMOM can adjust the 'k' rate of change parameter, enabling the logistic function to assume a less sensitive, approximately lin- ear form. Other carbon cycle models also employ functions that

<figure><img src="image_4.png" /><figcaption>FIGURE 1 Schematic of the allocation shift function: This schematic illustrates examples of the allocation scheme where (a) carbon allocation during the wettest conditions ($s_c$) is greater than during the driest conditions ($d_c$) (Equation 3), and (b) carbon allocation during the wettest conditions ($s_c$) is less than during the driest conditions ($d_c$) (Equation 2). The dashed red line shows the effect of the parameter k, which controls the steepness of the curve or the rate of change from wet to dry conditions. The dashed blue line shows the effect of the parameter $W_o$, which controls the location of the midpoint of the curve and, hence, the transition from wet to dry conditions.</figcaption></figure>
<figure><img src="image_5.png" /></figure>

identify minimum and maximum allocation fractions and include a rate of change in carbon allocation based on factors such as water availability and environmental stressors (e.g., Arora & Boer, 2005; Friedlingstein et al., 1999; Xia et al., 2017). A final advantage of the logistic function is its non-linear form, which is more effective at representing drastic climatic changes compared to linear, fixed allocation models (Ceballos-Núñez et al., 2020).

## 2.4 | CARDAMOM model-data fusion setup for dynamic and static model

We used monthly meteorological data from the flux tower site, which includes precipitation, relative humidity, CO₂ concentration, air temperature, and incoming solar radiation (Figure S2), to drive the static and dynamic versions of DALEC. The flux tower data assimilated by CARDAMOM (Table S1; Figure S5) includes monthly NBE, monthly evapotranspiration (ET), and average GPP. GPP was calculated from NBE using the Jena platform and the daytime method (Lasslop et al., 2009). Pastorello et al. (2020) provides a description of flux tower data collection and processing. Additional data incorporated into CARDAMOM (Table S1; Figure S5) consisted of in-situ measurements of average soil methane emissions (Courtois et al., 2019), wood biomass (Sousa et al., 2022), soil organic matter (Epron et al., 2006) and leaf area index (LAI) from LiCOR LAI-2000 measurements (B. Burban, unpublished data; Rowland et al., 2014). These in situ measurements each have various sources of noise and are representative of slightly different footprints; we account for such noise by assuming a sufficiently large observational uncertainty when assimilating the data into CARDAMOM (Table S1). We used the entire time-series of assimilated datasets, except for NBE, in the assimilation process for both the static and dynamic allocation model versions. To check for overfitting while including a range of drought behaviors from different segments of the time-series in the assimilation, we assimilated only alternating years of NBE data.

Each assimilated measurement is incorporated into the likelihood function of CARDAMOM during the model-data fusion process, to optimize DALEC's parameters (Ma, Bloom, et al., 2023; Yang et al., 2021). Additionally, each measurement is assigned a corresponding uncertainty (Table S1) within the likelihood function (Ma, Bloom, et al., 2023; Yang et al., 2021). NBE is assigned two distinct uncertainties (Table S1), as the cost function incorporates both the seasonal (monthly) values and the annual means to more effectively capture seasonal and annual NBE variability (Quetin et al., 2020). The uncertainty is designed to account for the combined errors originating from the observation, DALEC model structure, and meteorological drivers.

## 2.5 | Quantifying drought NBE legacy effects from allocation shifts

To quantify the effects of drought-induced plant carbon allocation shifts on NBE, we compared NBE from the dynamic allocation model

(Figure S1b) to NBE from the static allocation model (Figure S1a). To incorporate a more extensive and diverse record of droughts than what the 17-year observational record includes, we adjusted the 1981–2004 ERA-5 Land meteorology data (Muñoz Sabater, 2019) to match the flux tower meteorology, employing cumulative distribution matching techniques (Reichle & Koster, 2004). We then appended the scaled 23-year record of ERA-5 Land data to the end of the flux tower meteorology record, which spanned from 2004 to 2020, yielding a total simulation period of 40 years that uses data from 1981 to 2020. Note that we are not simulating future years; instead, we are using adjusted historical meteorological data to demonstrate the effects of different drought behaviors. Because only the period from 2004 to 2020 represents flux tower measurements, figures displaying the full 40-year time series use labeling of +4, +8, and so on, post-2020, to represent 4 and 8 simulated years after the 2020 conditions, respectively. Unlike the meteorological data, the atmospheric CO₂ concentration data from the flux tower was simply repeated across the 40 years (Figure S2). This approach to CO₂ stems from our emphasis on the effects of drought, rather than on future simulations. Additionally, CO₂ data are not available before 2004. However, we also performed a study where CO₂ increases post-2020 and found that our conclusions about drought legacy effects were unchanged (sensitivity analysis not shown).

To simulate carbon fluxes without drought while maintaining coupling among the meteorological variables, we replaced all meteorological drivers used by DALEC during drought months with their corresponding monthly averages from the entire 40-year simulation time series. These drivers included vapor pressure deficit, maximum and minimum air temperature at two meters, total precipitation, and surface solar radiation downwards. We identified drought months by calculating when the Standard Precipitation and Evapotranspiration Index (SPEI) fell below negative one for at least 3 months (Figure S3), a criterion also previously employed by Schwalm et al. (2017).

Overall, we ran four distinct DALEC model simulations (depicted in Figure 2) to calculate the drought legacies for both allocation models and to assess the overall effect of allocation shifts on drought legacies. These four NBE simulations included two extended (observed and ERA-5) meteorological datasets, reflecting conditions with and without drought ($M_{drought}$ and $M_{nodrought}$, respectively)—and the two DALEC models with static and dynamic allocation structures. Each model used its respective optimized parameters (denoted as $y_{static}$ for the static model and $y_{dynamic}$ for the dynamic model), as derived from CARDAMOM's model-data fusion. A first run with the static model simulated NBE with the unmodified meteorology ($M_{drought}$) and the observationally optimized static model parameters ($y_{static}$). This resulted in a time series of NBE that includes all the effects from drought except for allocation shifts: NBE($M_{drought}$, $y_{static}$). The second run with the static model simulated NBE with the meteorology without drought ($M_{nodrought}$) and the observationally optimized static model parameters ($y_{static}$). This resulted in a time series of NBE that excludes feedbacks from drought for the static model: NBE($M_{nodrought}$, $y_{static}$)
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 2 Schematic of the four Data Assimilation Linked Ecosystem Carbon model runs used to quantify drought legacy and the contribution of allocation shifts to drought legacy effects in Net Biosphere Exchange (NBE). Comparing the difference between the dynamic model runs (blue boxes) and the difference between the static model runs (orange boxes) quantifies the effect of allocation shifts on NBE in the post-drought period (drought legacy).</figcaption></figure>

By comparing simulated NBE from these two runs, we quantified the extent to which all the drought mechanisms within DALEC, excluding allocation shifts, affected drought legacies:

$$ \Delta \text{NBE}_{\text{static}} = \text{NBE}(M_{\text{drought}}, y_{\text{static}}) - \text{NBE}(M_{\text{nodrought}}, y_{\text{static}}). \quad (5) $$

A third run with the dynamic model simulated NBE with the unmodified meteorological record ($M_{\text{drought}}$) and the observationally optimized dynamic model parameters ($y_{\text{dynamic}}$). This gave a timeseries of modeled NBE that includes all the effects of drought within DALEC: $\text{NBE}(M_{\text{drought}}, y_{\text{dynamic}})$. The fourth run with the dynamic model simulated NBE with the drought-adjusted meteorology ($M_{\text{nodrought}}$) and observationally optimized dynamic model parameters ($y_{\text{dynamic}}$). This resulted in an NBE timeseries unaffected by drought: $\text{NBE}(M_{\text{nodrought}}, y_{\text{dynamic}})$. By comparing simulated NBE from these two runs, we quantified how much drought affected carbon uptake post-drought, including the effect of allocation shifts:

$$ \Delta \text{NBE}_{\text{dynamic}} = \text{NBE}(M_{\text{drought}}, y_{\text{dynamic}}) - \text{NBE}(M_{\text{nodrought}}, y_{\text{dynamic}}). \quad (6) $$

We further defined the drought recovery time in NBE as the duration required for the post-drought $\Delta \text{NBE}$ to revert to zero. To mitigate abrupt signal spikes in $\Delta \text{NBE}$, associated with nonlinear changes in allocation amplified during scenario comparisons, we employed a 3-month rolling average when calculating this timeframe. Additionally, the cumulative recovery magnitude was determined as the total sum of post-drought $\Delta \text{NBE}$ up until complete recovery occurred. At last, we assessed the effects of the drought-induced allocation shifts on the drought legacy by comparing the cumulative sum of the absolute value of $\Delta \text{NBE}$ for all post-drought months between the dynamic and static models (Equation 7).

Allocation contribution to drought legacies

$$ = \frac{\sum |\Delta \text{NBE}_{\text{dynamic}}| - \sum |\Delta \text{NBE}_{\text{static}}|}{\sum |\Delta \text{NBE}_{\text{dynamic}}|}. \quad (7) $$

CARDAMOM generates 2500 ensemble members for each model simulation based on posterior parameter distributions. This influences how summary metrics are calculated for each model

structure as well as when comparing output from the different model structures. In Section 2.3, we calculated individual recovery metrics for the static and dynamic models separately, by quantifying the recovery metric for each member of the ensemble. We then calculated the median, 25th, and 75th percentiles. Because the static and dynamic model parameters and pools are determined from independent MCMC runs, they are not ordered, and a given ensemble member from the static model does not have any particularly correspondence with the equivalent ensemble member for the dynamic model (and vice versa). Therefore, when comparing $\Delta \text{NBE}$ from the dynamic model to $\Delta \text{NBE}$ from the static model (Equation 7), we compared each dynamic ensemble member to each static ensemble member, resulting in $2500^2 = 6,250,000$ comparisons. From these, we derived the median, 25th, and 75th percentiles for the relative allocation shifts effect (Section 2.3).

## 3 | RESULTS

### 3.1 | Dynamic allocation model improves match to several observations

At a monthly timescale, the dynamic allocation model demonstrated improved NBE prediction compared to the assimilated NBE data (static $R^2=.38$ vs. dynamic $R^2=.46$) as shown in Figure 3 and Table S3. In contrast, when compared to withheld data, both the dynamic and static allocation models exhibited similar and poor performance (static $R^2=.18$ vs. dynamic $R^2=.17$). The relatively poor performance of both models at monthly timescales may be partly attributed to the site's complex carbon and water dynamics. The Guyaflux site boasts a rich biodiversity with over 140 canopy tree species per hectare, each featuring unique functional traits such as leaf phenology (ranging from deciduous to semi-evergreen and evergreen), rooting depth, and drought resistance (Bonal et al., 2008; Stahl et al., 2013; Ziegler et al., 2019). This complexity contrasts with the more simplified approach of the DALEC land surface model. Consequently, various versions of DALEC have generally
<figure><img src="image_8.png" /></figure>

**FIGURE 3** (a) Monthly Net Biosphere Exchange (NBE) model simulations for the dynamic (blue) and static (orange) allocation models. (b) Twelve-month rolling averages of the monthly NBE simulations for the dynamic (blue) and static (orange) allocation models. Blue and orange shaded areas represent the interquartile range (25th to 75th percentiles) for each model's simulation ensemble, whereas the lines show the ensemble median. The black line represents the flux tower NBE observations used for assimilation and prediction, with the surrounding black shaded area illustrating observational uncertainty. Light yellow regions highlight periods where observations were assimilated by CARbon DAta MOdel fraMework, while white areas denote withheld data used to assess predictive skill.

<figure><img src="image_9.png" /></figure>

demonstrated subpar performance at this site over shorter time-scales. This is illustrated by the near-zero $R^2$ in matching monthly NBE, as reported by Norton et al. (2023), and the approximate factor of three difference between modeled and observed NEE variability (equivalent to NBE), as shown by Rowland et al. (2014).

However, when considering the model's ability to capture slower-moving time scales of variability, such as a 12-month rolling average of NBE (Figure 3; Tables S2 and S3), the dynamic model shows significantly improved NBE prediction. This improvement is evident for both assimilated data (static $R^2$=.33 vs. dynamic $R^2$=.78) and withheld data (static $R^2$=.29 vs. dynamic $R^2$=.59). Comparing to 12-month rolling averages of NBE helps reduce the effect of noise in the NBE observations, providing a better test of the model's ability to capture slower-moving NBE dynamics. Indeed, previous CARDAMOM experiments have concluded that assimilating NBE into CARDAMOM is most effective when the cost function separately assimilates monthly and 12-month average NBE time series, as in Quetin et al. (2020), and implemented in our CARDAMOM setup here (refer to Section 2.4). Furthermore, these 12-month averages align with the timescale of drought impacts, as discussed in Müller and Bahn (2022). Lastly, in terms of performance during drier versus wetter conditions—given that this study focuses on drought responses—neither model exhibits a distinct bias as a function of plant available water, whether analyzing monthly values or the 12-month rolling average of NBE (Figure S4).

Beyond NBE, the dynamic model demonstrated the most skill in replicating foliar patterns and evapotranspiration data. The dynamic allocation exhibited reduced seasonal variation ($\sigma$) in LAI compared to the static model. This resulted in seasonal variation magnitude more aligned with that expected based on manual canopy observations at the site (Figure S5). Moreover, for the

years 2007–2009, the dynamic model aligned with the seasonal variability of unassimilated LAI measurements (Table S2) from in-situ photosynthetic active radar sensors (Bréda, 2003) (dynamic standard deviation $\sigma$= .49 $m^2$/m$^2$, [.49–.51], uncertainty ranges represent the 25th–75th percentiles throughout), while the static model approximately doubled the variability (static $\sigma$= .84 $m^2$/m$^2$ [.83–.85]). Furthermore, the dynamic allocation model outperformed the static model in accurately matching assimilated LAI ($R^2$ improvement of .25, RMSE improvement of .47 $m^2$/m$^2$) and ET ($R^2$ improvement of .07, RMSE improvement of .05 kg/m$^2$/day) measurements (Figure S5; Table S3).

The dynamic model also performed comparably to the static model in matching wood biomass, methane (as a constraint on heterotrophic respiration), soil organic matter, and mean GPP (Figure S5; Table S3). While this provides additional support for the realism of the models' inferences, we note that based on leave-one-out experiments, these four observational datasets had less influence on the parameter posteriors than NBE, ET, and LAI did (Figure S6).

Finally, we showed that the dynamic allocation model does not introduce significant shifts in inferred ecosystem parameters beyond processes directly affected by dynamic allocation. We compared the parameter distributions shared between the static and dynamic models. Among the 44 parameter distributions within the static model, 33 exhibited statistical indistinguishability (Gelman-Rubin score below 1.2) with the dynamic model's parameter distributions (Figure S7). The remaining 11 parameters were associated with processes that were influenced by the newly introduced water-based allocation shifts, such as wood turnover, root turnover, and the initial foliar pool size. Overall, the improved alignment of the dynamic model with ecosystem observations indicates its physical plausibility.
## 3.2 | CARDAMOM-inferred allocation shifts

CARDAMOM infers shifts in plant carbon allocation during drought that are qualitatively similar across the entire uncertainty range (Figure 4). Fine root allocation increased during drought. Wood and foliar allocation decreased during drought. Autotrophic respiration and labile carbon allocation remained relatively constant, regardless of water stress, as shown by the insignificant change in carbon allocation relative to the uncertainty band. CARDAMOM derived an inflection point for water stress (parameter $W_o$ in Equations 2 and 3) at 2412 kg/m²/day [1940–2715], while the mean plant available water was 2250 kg/m²/day [2039–2495] (Figure 4; Figure S8). Because the inflection point is near the mean plant available water, carbon allocation follows the dynamics of plant available water across the study period (Figure S9).

## 3.3 | Dynamic allocation model exhibits a greater reduction in carbon uptake post-drought than the static model

Dynamic allocation shifts due to water stress not only changed NBE during drought but had even more substantial effects on NBE during post-drought periods (Figure 5). We defined recovery in the post-drought period as the time and cumulative magnitude required for the NBE flux to return to the no-drought baseline run, that is when $\Delta$NBE returns to zero (Section 2.5). However, in instances where the subsequent drought occurred before complete recovery (as was the case with the initial four droughts), we only estimated the cumulative

<figure><img src="image_10.png" /><figcaption>FIGURE 4 Allocation shifts as a function of plant available water, based on the data-inferred $s_c$, $d_c$, k, and $W_o$, parameters in the dynamic allocation model. The shaded regions show the 25th to 75th percentile ranges of the allocation fractions for each pool. The plant available water pool range indicates the range from the median of the ensemble minimums to the median of the ensemble maximums for plant available water. Water stress ($W_o$) occurs at ~2400 kg/m².</figcaption></figure>

recovery magnitude and not the recovery time. Across six droughts, allocation shifts after drought increased the average cumulative recovery magnitude roughly threefold (4.9 gC/m²/day [3.8–6.1] vs. 13.6 gC/m²/day [5.3–18.9]). Furthermore, the cumulative recovery magnitudes of each drought varied significantly, especially for the dynamic model (Table S4). The dynamic model's median cumulative recovery magnitude ranged from 0.7 to 24.7 gC/m²/day, while the static model's median cumulative recovery magnitude ranged from 2.7 to 8.1 gC/m²/day. For the two fully recovered droughts, the dynamic model run was associated with an increase in the average recovery time by 5 months (29.0 months [25.5–32.0] vs. 34.0 months [26.0–40.0]).

The dynamic allocation model additionally exhibited post-drought behavior not observed in the static allocation model (Figure 5). Notably, the dynamic model, unlike the static model, postponed the peak of drought impacts beyond the drought period. Furthermore, for the two longest droughts (the 2020 drought and the +18 drought), the dynamic allocation shifts produced what we term here a “bustboom” trend: exhibiting first a greater decrease in carbon uptake due to the drought, followed by a subsequent increase. As a result, later in the legacy period, there is greater carbon uptake than there would have been in the absence of drought (Figure 5).

Overall, allocation shifts contributed 65% [45%–75%] to drought legacies in NBE during the post-drought period (as calculated by Equation 7). To examine whether the impact of allocation shifts remained consistent regardless of the SPEI threshold chosen for identifying droughts (Section 2.5), we conducted a sensitivity analysis. In this analysis, we varied the SPEI threshold used to define drought occurrence and to replace drought meteorology, using values that ranged from –0.8 to –1.5. Most drought-thresholds showed a relative contribution of allocation shifts to drought legacies (Equation 7) of about 68%, similar to the 65% calculated using a threshold of SPEI = –1, suggesting little sensitivity of the results (Figure S10).

## 3.4 | Allocation shifts impact NBE post-drought by adjusting the foliar and fine root pools, thereby influencing GPP and heterotrophic respiration fluxes

In the dynamic allocation model, significant shifts in foliar and fine root allocation resulted in consequential alterations to the corresponding GPP and heterotrophic respiration fluxes, thereby producing the notable drought legacies in NBE (Figure 6). These alterations occur because the plant available water pool does not revert to baseline levels within the first year after the drought, despite the return of meteorological conditions to normalcy during this period (Figure 6; Figures S11 and S13). This resulted in allocation changes persisting beyond the meteorological drought occurrence. Aboveground, allocation shifts impacted carbon fluxes by reducing the size of the foliar carbon pool post-drought (Figure 6; Figure S11). This reduction in foliage subsequently led to a decline in GPP, limiting carbon uptake in the ecosystem and reducing evapotranspiration (Figure S11). Belowground, the allocation shifts influenced recovery through a more intricate process (Figures S12 and S13). Increased
<figure><img src="image_11.png" /></figure>

root allocation enhanced root production, which subsequently expanded the litter pool as this additional root biomass died off. A larger litter pool, in turn, increased the amount of aerobic heterotrophic respiration, which served as the primary driver of changes in heterotrophic respiration (Figure S12). Unlike the dynamic model, the static model's drought legacies are driven (Figure S14) by the connection between plant available water and soil organic matter heterotrophic respiration in DALEC (Ma, Bloom, et al., 2023). To

summarize, drought reduces plant available water, which in turn reduces aerobic soil organic matter heterotrophic respiration through changes in the volumetric water content and the fraction of aerobic respiration used to calculate aerobic heterotrophic respiration. This process also exists in the dynamic model, albeit at a smaller magnitude, due to the smaller change in plant available water (Figure S14). This smaller change in plant available water in the dynamic model is due to changes in foliar allocation and corresponding changes in evapotranspiration (Figures S11 and S14).

Within the dynamic model, the shifts in foliar and root allocation exhibit notable differences while significantly influencing NBE to a similar degree (Figure 7). For one, the change in foliar allocation is much smaller than the shift in root allocation (Figures S11 and S13). Another difference is that the effects of post-drought changes in root allocation on heterotrophic respiration persist even after GPP returns to its baseline level. Finally, the shifts in foliar allocation are the main drivers in the change in autotrophic respiration (Figure 7). This connection arises because, within the DALEC model framework, autotrophic respiration is considered a proportion of GPP, and CARDAMOM did not deduce significant variability relative to plant available water (Figure 4).

As mentioned above, in the aftermath of the most prolonged droughts within our record, we discerned a unique “bust-boom” pattern, which was only exhibited by the dynamic allocation model. The pattern of ΔNBE estimated after the 2020 drought event reveals an intriguing trend: from 2020 to +4, carbon uptake was lowered by the 2020 drought, yet from +4 to +10, carbon uptake was enhanced by the 2020 drought (Figures 5 and 7). A similar pattern emerges after the +18 drought, though it ends prematurely due to meteorological

<figure><img src="image_12.png" /><figcaption>FIGURE 5 The model with dynamic allocation shifts has larger carbon emissions during recovery than the model with static allocation. The blue line illustrates the median difference between the drought and no drought meteorology Net Biosphere Exchange simulations for the dynamic model, whereas the orange line shows the difference for the static model. Blue and orange shaded areas represent the interquartile range (25th to 75th percentiles). Note that the years following 2020, labeled as +4, +8, etc., are simulations based on adjusted historical meteorological data used as model drivers beyond the end of actual observations in 2020. These are not intended to predict future conditions. For further details, refer to Section 2.5.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 6 Schematic of the impact of allocation shifts within Data Assimilation Linked Ecosystem Carbon model (DALEC) on drought recovery for drought ending in 2020/2008 for the dynamic model. We show one ensemble member that is representative of the median. The black line represents the difference between drought and non-drought scenarios for corresponding DALEC variables. The gray dashed line marks the zero level. The red line illustrates the peak difference in Net Biosphere Exchange (NBE) between drought and non-drought simulations. Fluxes are plotted with the same y axes range. Note the foliar allocation change is much smaller in magnitude than the fine root allocation change.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>FIGURE 7 Analysis of carbon fluxes to identify fluxes controlling drought legacy within Net Biosphere Exchange (NBE) for the dynamic model. Autotrophic respiration, heterotrophic respiration, and gross primary productivity all contribute to NBE perturbation during the post-drought period. A single ensemble member representative of the median difference in fluxes for drought and non-drought simulations in the dynamic model is plotted as a specific example. The vertical tan-shaded region shows the drought period, while the brown dashed vertical line marks the largest difference in NBE for drought and non-drought simulations in the dynamic model. The black line shows the difference in NBE, the green line shows the difference in GPP, the pink line shows the difference in autotrophic respiration, and the blue line shows the difference in heterotrophic respiration. Note that the years following 2020, labeled as +1, +2, and so forth, are simulations based on adjusted historical meteorological data used as model drivers beyond the end of actual observations in 2020. These are not intended to predict future conditions. For further details, refer to Section 2.5.</figcaption></figure>

record limitations. This pattern occurs because the decreased foli-
age allocation due to the drought reduces the size of the foliar pool
and associated GPP (Figure S11). This, in turn, reduces the associated
evapotranspiration in the years following the drought (Figure S11).
Within DALEC, evapotranspiration is calculated partly as a function
of GPP, due to the association between plant stomatal opening and
photosynthesis (Yang et al., 2021). Eventually, about 2 years after the
drought, the amount of plant available water is higher than it might
have been in the absence of drought. The allocation patterns then
reverse relative to those immediately following the drought and de-
scribed above (Figures S11 and S13), resulting in increased foliage
biomass and, consequently, GPP, as well as reduced root allocation,
leading to decreased heterotrophic respiration (Figure 7). This com-
bined chain of events results in the initial “bust” of carbon uptake
post-drought being followed by a “boom”.

4 | DISCUSSION

4.1 | Evaluation of inferred plant carbon allocation shifts against independent estimates

The allocation shifts we infer here align with previous studies per-
formed at Guyaflux. The steep decrease in wood allocation from
water stress is consistent with a prior study at the site that used

dendrometers to show wood growth stopped during drought peri-
ods (Wagner et al., 2011). Another CARDAMOM-based study that
inferred static allocations separately during wet and dry seasons at
Guyaflux (Rowland et al., 2014) found allocation shifting in the same
direction, but with different magnitudes. Specifically in our study,
CARDAMOM found a 0.38 [0.47–0.23] increase in fine root allo-
cation, a 0.29 [0.18–0.40] decrease in wood allocation, and a 0.09
[0.06–0.13] decrease in foliar allocation during drought. By contrast,
Rowland et al. (2014) found a smaller increase in fine root allocation
(0.15), a smaller decrease in wood allocation (0.07), and a similar de-
crease in foliar allocation (0.09) during drought.

The prioritization of carbon allocation to fine roots during water stress (the largest water-induced C allocation shift inferred by CARDAMOM) represents a plausible plant response to water limitation. It suggests an ecosystem strategy of increased root growth for enhanced water accessibility. This strategy aligns with the optimal partitioning theory, which postulates that plants strategically distribute biomass to secure the most growth-limiting resources (McCarthy & Enquist, 2007; Rowland et al., 2014). Indeed, at Guyaflux, soil water availability serves as a key driver of NEE variability and reduces ecosystem productivity during the dry season (Aguilos, Hérault, et al., 2018). Furthermore, the reallocation of carbon during water stress to improve water access has been observed in various locations worldwide, potentially contributing to root-to-shoot ratios that favor greater root biomass (e.g., Bacher et al., 2021; Fan et al., 2017; Lozano et al., 2020; Xu et al., 2015). This strategy may also help explain the observed absence of tree mortality within Guyaflux during normal and severe drought (Longo et al., 2018; Ziegler et al., 2019), despite hydraulic failure being a primary cause of mortality in neo-tropical forests (Rowland et al., 2015).

To further test the credibility of our allocation shifts, beyond
improved model performance (Section 3.1) and comparisons to
other Guyaflux studies, we compare our allocation shifts to those
observed with in-situ measurements at other tropical experiments.
Other studies on tropical forest plots have reported seasonal varia-
tion in the percentage of Net Primary Productivity (NPP) allocated
to wood (up to 37%), foliage (up to 45%), and fine roots (up to 28%)
(Doughty et al., 2015), and CARDAMOM estimates comparable
changes. Specifically, the inferred allocation parameters suggest
that NPP allocation to wood can vary by up to 42%, allocation to
foliage up to 15%, and allocation to fine roots up to 50% between
seasons (Figure S9). However, a limitation of this comparison is that
other environmental factors, such as nutrient and light availability
(Hartmann et al., 2020; Poorter et al., 2012), can also influence allo-
cation shifts and are not accounted for here.

Comparing our inferred allocation changes and their downstream
effects to tropical throughfall exclusion experiments (Metcalfe
et al., 2010; Moser et al., 2014) avoids the issue of environmentally
confounding factors and adds further support. For instance, one ex-
periment at Caxiuanã, Brazil, demonstrated that reducing throughfall
by 50% over 5 years resulted in the yearly allocation of NPP to fine
roots increasing by 8±14% compared to the control plot (Metcalfe
et al., 2010). In our simulation, a 25% reduction in precipitation,
which represents the greatest yearly reduction in our record, led to allocation of NPP to root biomass increasing by 20% [0%–32%] for the year. Similarly, an Indonesian throughfall experiment found that reducing precipitation by 60%–80% led to allocation of NPP to wood biomass decreasing by $11 \pm 5\%$ compared to the control plot in the second year of the experiment (Moser et al., 2014). In our simulation, a 25% reduction in precipitation for a year resulted in the total allocation of NPP to wood biomass decreasing by 12% [0%–23%].

The dynamic model also predicts changes in biomass growth consistent with observations from tropical throughfall exclusion experiments. Several experiments conducted elsewhere in Brazil have reported a decline in wood production in experimental plots without throughfall compared to control plots (Brando et al., 2008; Meir et al., 2015; Nepstad et al., 2002). The decrease in carbon allocation to wood inferred by CARDAMOM provides a plausible mechanism that could have contributed to this result. Additionally, one throughfall exclusion experiment found reduced growth of foliage (Nepstad et al., 2002), which is also in line with CARDAMOM's inferred decrease in allocation to foliage under drought.

Taken together, this study demonstrates the feasibility of using model-data fusion to infer drought-induced allocation shifts from existing measurements, even in species-diverse and dynamic ecosystems like Guyaflux. It suggests that satellite-derived estimates could potentially replace site-based observations, facilitating the inference of such shifts across the tropics (e.g., Wagner et al., 2013). Constraints on the carbon and water cycle, similar to those employed in this study, are available. These include GRACE equivalent water thickness (Landerer & Swenson, 2012), CMS-Flux NBE (Liu et al., 2020), and satellite and machine learning-derived estimates of aboveground biomass (Xu et al., 2021). Additionally, the CARDAMOM framework has been successfully used to infer static carbon allocations throughout the pan-tropics (Bloom et al., 2016), demonstrating the model's proven scalability in this domain.

## 4.2 | Allocation shifts prolong and increase drought legacies

Allocation shifts influenced the post-drought period for up to 10 years following the drought event (Figure 5). The complete 10-year period was only observed during the simulated post-drought phase following the 2020 drought. In our study, this represented the sole instance of a post-drought period not interrupted by another drought before the legacy effects ended. The 10-year influence aligns with the findings of Müller and Bahn (2022), who recommend that forests should be monitored for up to 15 years to capture the full impact of drought legacies. Additionally, we observed an increase in carbon uptake four (simulated) years after the 2020 drought and 4 years after the simulated +18 drought. Such simulations of enhanced carbon uptake several years after drought support the argument by Vilonen et al. (2022) that our understanding of drought effects may be skewed toward negative impacts because the long-term effects of specific droughts on ecosystems are not measured.

Allocation shifts also lead to longer and more pronounced post-drought recovery periods. Accounting for drought-induced allocation shifts increases average recovery cumulative magnitude by a factor of 2.8 [1.4–3.1] compared to a model without allocation shifts. Moreover, the dynamic model extends the average recovery time by 5 months, with an average recovery time of 2.8 years [2.2–3.3]. A comparison between net primary productivity derived from tree rings and net primary productivity from land surface models (Kolus et al., 2019) has previously shown that model-simulated drought recovery magnitudes were four times too low and four times too short. Our result, if it holds in other ecosystems, implies that accounting for drought-induced allocation shifts could considerably improve modeled drought responses, since most models use a static allocation scheme (Merganičová et al., 2019).

Allocation shifts also postpone the peak of drought effects. The delayed peak occurs because plant available water in the modeled drought simulation remains below the non-drought simulation plant available water even after the meteorological drought has ended. This condition triggers additional allocation shifts before the plant available water pool can fully recover, as depicted in Figure 6; Figures S11 and S13. Although this effect of allocation has not been previously described in the literature, some studies demonstrate that forests can exhibit such behavior. For instance, findings from the Pará throughfall experiment in Brazil (Brando et al., 2008) revealed a similar delay in the peak of drought effects. In this case, LAI had not recovered to control levels within 1 year of the experiment's conclusion. In fact, the difference had increased, with a lower LAI than observed in the control.

## 4.3 | Allocation shifts contribute significantly to drought legacies through above and belowground processes

Allocation shifts significantly influenced the post-drought period by altering both the foliage and fine root pools, which subsequently modulated GPP and heterotrophic respiration (Figure 6). Although earlier studies have attributed post-drought changes in GPP to alterations in foliage allocation across various tropical sites (Doughty et al., 2014, 2015), the exact degree of change instigated by such allocation shifts has not been quantified. Our study quantifies this change, albeit for a single site. In contrast to the known effect on GPP, changes in heterotrophic respiration post-drought attributable to shifts in fine root allocation have remained unobserved, potentially due to difficulties in monitoring carbon movement belowground (Brando et al., 2008). The significant role of root allocation in heterotrophic respiration, however, aligns with the findings of Adachi et al. (2006), who described a positive correlation between root size and soil respiration in the tropics, and Trumbore et al. (2006), who determined that decomposing roots contribute to soil respiration in these regions. Finally, the importance of these allocation shifts in relation to drought legacies was previously unclear (Kannenberg et al., 2020). Our finding that the allocation
shifts account for 65% [45%–75%] of drought legacies in modeled NBE highlights the significance of this mechanism, particularly since lagged effects can control the interannual variability of NBE in the tropics (Bloom et al., 2020).

## 4.4 | Allocation shifts create bust-boom cycles in the post-drought period

Although bust-boom behavior post-drought has been observed in some site studies, it has not been described for carbon fluxes or attributed to a specific mechanism (here, allocation shifts). For example, a bust-boom cycle was observed in soil moisture at a grassland throughfall experiment site in Colorado (Hoover et al., 2021). Comparing the treatment plot to the control plot after the treatment ended, the treatment plot exhibited lower soil moisture for ~3.5 months but subsequently higher soil moisture thereafter. Another instance of bust-boom behavior was observed in eucalyptus plantations at a throughfall exclusion experiment in Brazil (Chambi-Legoas et al., 2022). The authors reported that the annual increment in basal area of drought-affected trees was suppressed compared to the control during the 3 years of exclusion. However, the drought-impacted trees exhibited elevated basal area increments during the first 2 years post-throughfall exclusion, adding a “boom” after the “bust”. These observations support the bust-boom behavior identified in our simulations, highlighting the fact that if enough time occurs between droughts, drought-induced water stress can actually cause increased carbon uptake during part of the post-drought period. Note that this behavior contrasts with the ‘boom-bust’ dynamic often cited in literature as a structural overshoot. A structural overshoot describes a scenario where an ecosystem initially responds to favorable conditions with a ‘boom’ in growth or productivity, but then experiences a subsequent reduction or ‘bust’ when it encounters unfavorable conditions, such as a drought (Jump et al., 2017; Zavala, 2021; Zhang et al., 2021). The bust-boom cycles identified here are in some sense a temporally reversed analogue of this process, but they are driven by below-ground water and carbon dynamics instead of only canopy dynamics.

## 4.5 | Limitations

Our inferred allocation shifts might be affected by structural errors arising from processes not depicted in the dynamic model, such as changes in carbon allocation due to nutrient or radiation availability, changes in biomass resulting from tree mortality during drought, or variations in litter turnover rates. The lack of representation of other resources affecting allocation might enhance our inferred drought-induced allocation shifts, as the variability caused by those resources is incorporated into our inferred allocations. In the specific case of Guyaflux, the inference of allocation shifts is unlikely to be influenced by tree mortality or litter turnover during drought. This

conclusion is supported by research conducted at the site, which did not find evidence of drought-induced modulation of these factors (Longo et al., 2018; Ziegler et al., 2019).

The effects of drought-induced allocation shifts inferred here also depend on the quality of the model-data fusion driven inference. As with all model inversions, CARDAMOM is potentially subject to equifinality. Equifinality refers to the existence of multiple solutions capable of explaining an observational dataset, leading to subsequent inference uncertainty (Beven & Freer, 2001). Equifinality must be minimized to avoid inferring incorrect allocation shifts. Within CARDAMOM, the assimilated monthly NBE, monthly evapotranspiration, and annual LAI each contribute substantially to determining the allocation shifts that best fit observations. Smaller, but non-negligible changes are also caused by the assimilation of wood biomass, methane, and soil organic matter observations (Figure S6). The use of multiple data streams reduces the likelihood of significant equifinality here. Furthermore, CARDAMOM’s propagated uncertainty accounts for any residual equifinal solutions and our results are significant with respect to the propagated uncertainty. Nevertheless, our model results underscore the necessity of using observations from multiple components of the carbon and water cycles (water and carbon fluxes, as well as an additional carbon pool and more) to infer allocation shifts. With the application of appropriate constraints, the changes in carbon allocation caused by drought, as well as their subsequent effects on drought legacies, can be inferred.

## AUTHOR CONTRIBUTIONS

**Matthew A. Worden:** Conceptualization; data curation; formal analysis; methodology; software; writing – original draft; writing – review and editing. **Caroline A. Famiglietti:** Conceptualization; methodology; writing – original draft; writing – review and editing. **Paul A. Levine:** Methodology; writing – review and editing. **Shuang Ma:** Methodology; writing – review and editing. **A. Anthony Bloom:** Conceptualization; funding acquisition; methodology; writing – review and editing. **Damien Bonal:** Resources; validation; writing – review and editing. **Clément Stahl:** Resources; validation; writing – review and editing. **Alexandra G. Konings:** Conceptualization; funding acquisition; methodology; supervision; writing – original draft; writing – review and editing.

## ACKNOWLEDGEMENTS

This work was supported by a NASA 2020 Carbon Cycle Science grant to AAB and AGK (NNH20ZDA001N-CARBON) and by NSF DEB project 1942133 awarded to AGK. MAW and CAF were supported by a NASA FINESST grant 80NSSC21K1593. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The Guyaflux program received support from two “investissements d’avenir” grants from the Agence Nationale de la Recherche (CEBA, ref ANR-10-LABX-25-01; Lab of Excellence ARBRE, ANR-11-LABX-0002-01).
<figure><img src="image_15.png" /></figure>

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.10933643.

## ORCID

Matthew A. Worden https://orcid.org/0009-0009-0414-4187

Caroline A. Famiglietti https://orcid.org/0000-0002-6073-0457

Paul A. Levine https://orcid.org/0000-0002-1248-6920

Shuang Ma https://orcid.org/0000-0002-6494-724X

A. Anthony Bloom https://orcid.org/0000-0002-1486-1499

Damien Bonal https://orcid.org/0000-0001-9602-8603

Clément Stahl https://orcid.org/0000-0001-5411-1169

Alexandra G. Konings https://orcid.org/0000-0002-2810-1722

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Worden, M. A., Famiglietti, C. A., Levine, P. A., Ma, S., Bloom, A. A., Bonal, D., Stahl, C., & Konings, A. G. (2024). Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site. *Global Change Biology*, 30, e17287. https://doi.org/10.1111/gcb.17287