Water-uptake depth mediates the effects of water-table depth on plant physiological performance in a tropical dune forest

Water-uptake depth mediates the effects of water-table depth on plant physiological performance in a tropical dune forest | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Water-uptake depth mediates the effects of water-table depth on plant physiological performance in a tropical dune forest Cristina Antunes, Sergio Chozas, Rafael Flora, Cristina Máguas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545929/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims In tropical forests both light and water influence the growth and health of woody species. In seasonally flooded dune forests, like restinga forests of the Atlantic Forest, groundwater availability may play a major role in forest functioning. This study aims to understand whether and how restinga ’s woody vegetation experience variations in physiological performance in response to water-table depth, and the mediator role of plants water-uptake depth. Methods 15 woody species were sampled along a water-table gradient, for traits related to water-use, nitrogen acquisition and photosynthetic activity. Direct, and indirect - mediated by water-uptake depth adjustments, effects of water-table depth on physiological status of restinga vegetation were tested, accounting with light and stand structure influence. Results Water-table depth strongly defined plants water-uptake depth, which mediated negative indirect effects of water-table lowering on the woody community’s photosynthetic status. Plants using deeper, possibly more abundant water resources, showed maintenance of the water status, but lower physiological performance. Besides a strong light-driven variation, water-uptake dynamics explain additional physiological variation, showing a secondary hydraulic constraint on plant photosynthetic function. Conclusion Indirectly, through water-uptake depth regulation, water-table depth can influence, at least to some degree, the physiological status of restinga plants. Despite maintained water status, greater water-uptake depth negatively influenced photosynthetic and structural leaf traits. There is a below–aboveground allocation trade-off, whereby belowground allocation possibly buffers water limitation but constrains aboveground investment, limiting leaf-level photosynthetic performance in restinga forests. plant ecophysiology restinga forest groundwater changes plant-water interactions plant functional responses below and aboveground tradeoffs Figures Figure 1 Figure 2 Introduction In tropical ecosystems, both light and water are considered to be important drivers of variations in growth and survival of tree species (Poorter 2001 ; Wright 2002 ; Poorter 2002 ; Vieira et al. 2004 ; Markesteijn et al. 2007 ; Fyllas et al. 2017 ; Fauset et al. 2017 ; Rowland et al. 2021 ). Species existence and physiological performance along gradients of water and light availability will, to a great extent, be determined by the ability of both water and light acquirement and tolerance to water shortage and/or shade (Markesteijn et al. 2007 ; Wang and Wang 2023 ). For example, if tropical plants are able to adjust their root systems to hydrological site-specific conditions, such as variations in water-table, their water stress might be alleviated (Zea-Cabrera et al. 2006 ; de Oliveira and Joly 2010 ; Fan et al. 2017 ). Also, deep rooting habit can be an important adaptation to seasonal drought, as access to more readily available water at greater depth can eventually allow the maintenance of a more favorable plant water status (Nepstad et al. 1994 ; Zencich et al. 2002 ; Oliveira et al. 2005 ; Barbeta and Peñuelas 2017 ; Antunes et al. 2018b , c ; Ding et al. 2021 ; Chitra-Tarak et al. 2021 ; Laughlin et al. 2023 ). Hence, functional traits such as root water-uptake depth are expected to vary with (ground)water availability (Schenk and Jackson 2002 ; Fan et al. 2017 ; Antunes et al. 2018a ; Miguez-Macho and Fan 2021 ). However, exploring water in top-soil layers in lowland tropics, where flooding typically occurs, is crucial for plants’ access to oxygenated soils (and avoidance of anoxia) (Stone and Kalisz 1991 ; Pavlis and Jeník 2000 ; Fan et al. 2017 ). Thus, plasticity in belowground traits could be a major driver of water resource-use and, consequently, significantly influence plant physiological status (Jackson et al. 1990 ; Kulmatiski et al. 2017 ; Guderle et al. 2018 ). Additionally, individual structural features such as tree size can strongly influence plant water requirements, plant water-use patterns and, direct or indirectly, plant physiological status (Meinzer et al. 1999 ; Meinzer 2003 ; Rossatto et al. 2012 ; Brienen et al. 2017 ; Ledo et al. 2018 ). Biotic controls related to stand structure and its functional composition can also have direct effects on plant physiological performance and primary productivity (Aerts 1999 ; Fyllas et al. 2017 ). As plants use the same basic resources (light, CO 2 , water, nutrients and space for growth), co-occurring plants would likely compete (Silvertown 2004 ; Peñuelas et al. 2011 ). In this case, competition would hence be greater with higher species overlapping, i.e. higher density, biomass and/or higher similarity of functional composition. Due to reduced competition with conspecific neighbors, plants at low density might be more likely to survive and ⁄or grow better than plants at high density (Weigelt et al. 2002 ; Skálová et al. 2013 ). However, the mechanisms of plant competition can be greatly controlled by which resource is limiting, by resources-use strategies and unavoidable trade-offs in competition for above- and below-ground resources (encompassing differential allocation of biomass to structures involved in the acquisition of a resource) and by the relative importance of traits related to resource acquisition and retention (Aerts 1999 ; Malhi et al. 2004 ). In turn, these biotic controls can be regulated by environmental factors, and thus work as ‘bridges’ between environmental controls and overall plant physiology (e.g. water, carbon and nitrogen status) (Fyllas et al. 2017 ). This means that environmental conditions can also have an indirect effect on forest productivity and physiological performance by regulating the functional responses of the community. The effect of local hydrological conditions, such as water-table depth, is prevalent in tropical regions, filtering species and influencing the diversity and composition of tropical forests (Oliveira et al. 2014 , 2019 ; Ribeiro et al. 2021 ; Sousa et al. 2022 ; Marca-Zevallos et al. 2022 ). Accounting with local hydrological factors, such as water-table depth, and mechanisms underlying forest physiological responses would contribute to better understanding plant responses and the resilience of tropical forests to increased water variations. Shallow water-table tropical forests have shown more resource-acquisitive and hydrologically vulnerable trees than deep water-table forests, and may be more sensitive to severe droughts due to shallow roots and drought-intolerant traits (Esteban et al. 2021 ; Costa et al. 2023a , b). Among key plant traits mediating the responses of plants to hydrological gradients such as water-table depth, is functional rooting depth (where roots actively absorb water), although continues to be poorly studied. Therefore, to study how forests ecophysiologically respond to changes of water-table depth, and how traits such as water-uptake depth might influence and mediate this response, is of great relevance. Environmental conditions change both in time and in space, and studies along environmental gradients can provide valuable insights into controls of ecosystem function. Specifically, studies along (spatial) gradients of groundwater could contribute to disentangling to what extent this water source influences and constrains overall physiology and vitality of vegetation. This is particularly relevant in restinga forests, which are shallow water-table tropical coastal ecosystems, that occur in poor sandy soils, where water availability changes can be very rapid, water retention is low, and little water is available within the topsoil during dry periods while flooding occurs in wet periods (Assis et al. 2011 ; Joly et al. 2012 ; Magnago et al. 2012 ). Restinga woody species have shown to seasonally re-adjust their water-sources use (Antunes et al. 2019 ), to change belowground fine root investments (Rosado et al. 2011 ; Silva et al. 2020 ) and water-uptake adjustments (Rosado et al. 2016 ) in function of hydrological constrains. However, the physiological implications of these belowground adjustments and the effects of water-table changes on community physiological performance are still unknown. In these ecosystems, habitat filtering drives the local distribution of congeneric species, and comprises flood resistant species but also flood sensitive species (Oliveira 2011 ; Ribeiro et al. 2021 ), with some species showing adaptations to deal with low water and nutrient availability (Gessler et al. 2007 ; Rosado and De Mattos 2010 ; Rosado et al. 2013 , 2016 ). Even small changes in depth to groundwater within the same low precipitation availability (intra-season spatial variation due to micro-topographic features) can have implications on plants water-use and the physiological status of the plants and the overall woody community. Additionally, restinga ’s woody species are also subjected to differential light accessibility and stand composition variations, which, as above mentioned, can also affect plant physiological status. In this study, we aim to explore whether and how restinga ’s woody vegetation experience variations on physiological performance in response to groundwater changes. By exploring plant community (15 woody species) physiological responses (i.e. variations in a suite of physiological parameters) and plant water-uptake depth, we aim to: (i) understand the direct and indirect effects of water-table changes on vegetation physiological status, while accounting with light accessibility, plant size and stand structure effects, and (ii) disentangle the mediator role of plant water-uptake depth adjustments on physiological status of restinga woody vegetation. Materials and methods Study site and species The study was conducted at Serra do Mar State Park (Atlantic Forest), in the seasonally flooded coastal forest (known as restinga forest) that occurs at Praia da Fazenda, municipality of Ubatuba, São Paulo, Brazil, in a permanent plot previously established (1ha, 23° 21’ 22” S; 44° 51’ 03” O) (Joly et al., 2012 ). Its soil is sandy, acid and poor in nutrients (Scarano 2002 ; Assis et al. 2011 ; Joly et al. 2012 ), and the system is subjected to seasonal or perennial waterlogging due to variations in precipitation along the year (de Oliveira and Joly 2010 ; Oliveira 2011 ; Antunes et al. 2019 ). The study area encompasses a gradient of water-table depth due to micro-topography (Fig. S1 – Supporting information). Within the study area (1 ha), eighteen sampling plots (10m x 10m) were randomly selected (ensuring they were not side-contiguous) (Fig. S1 ), and showed different distances from soil surface to water-table, ranging from 0.8 to 1.4 m. Species considered to be the dominant species (and occurring at least in three plots) were considered, and at least one shrub species and two tree species were sampled in each plot (Fig. S1 , Table 1 ). A total of 80 individual plants were sampled for the physiological assessment and considered for water-uptake depth estimations (see below). Table 1 Woody species sampled and their code, growth form (Type) and number of individuals sampled (n). Species Code Type n Euterpe edulis Mart. Ee tree 5 Eugenia schuechiana Berg. Es tree 4 Faramea pachyantha Müll.Arg. Fp tree 3 Guarea macrophylla ( Vell.) T.D.Penn. Gm tree 4 Guapira opposita (Vell.) Reitz Go tree 4 Guatteria sp. Gsp. tree 6 Jacaranda puberula Cham. Jp tree 5 Myrcia brasiliensis Kiaersk. Mb tree 4 Maytenus littoralis Carv-Okano Ml tree 6 Myrcia multiflora (Lam.) DC. Mm tree 3 Myrcia racemosa (O.Berg) Kiaersk. Mr tree 6 Marlierea tomentosa Cambess. Mt tree 4 Pera glabrata (Schott) Poepp. ex Baill. Pg tree 4 Psychotria sp1 Psp1 shrub 18 Psychotria sp2 Psp2 shrub 4 Total 80 Physiological parameters Ecophysiological traits measured in the selected plants (Table 1 , n = 80), in a rainless period, were: leaf C and N concentrations (%), linked to structural investment and photosynthetic activity respectively. leaf C and N isotope ratios (‰), associated with stomatal control + water-use efficiency and nitrogen cycling + N sources, respectively. Dried and milled bulk leaf samples were analysed for determining C, N, δ 13 C and δ 15 N, by continuous flow isotope ratio mass spectrometry (CF-IRMS) on a Sercon Hydra 20–22 (Sercon, UK) stable isotope ratio mass spectrometer, coupled to a EuroEA (EuroVector, Italy) elemental analyser in SIIAF (FCUL, Portugal). Uncertainty of the isotope ratio analysis, calculated using values from 6 to 9 replicates of secondary isotopic reference material interspersed among samples in every batch analysis, was ≤ 0.1‰. Spectral reflectance indices: Chlorophyll index CHL [CHL = R750/R705], correlated with leaf chlorophyll content on a number of plant species, providing information about photosynthetic potential (Peñuelas, Frederic and Filella, 1995); Photochemical index PRI [PRI = (R531-R570) / (R531 + R570)], associated with light use efficiency and photosynthetic activity (Wong and Gamon, 2015, Peñuelas, Llusia, Pinol, & Filella, 1997); Water index WI [WI = R900/R970], highly related to plant water content and water status (Claudio et al., 2006; Peñuelas et al., 1997); Normalized difference vegetation index NDVI [NDVI = (R900-R680) / (R900 + R680)], proxy of biomass "greenness" and plant photosynthetic capacity (Gamon et al., 1995). These indices were measured using a UniSpec Spectral Analysis System, PP Systems, in 6 leaves per plant and a mean value considered per plant. Water-uptake depth estimations Water uptake depth (WUD, m) was estimated for all plants considered in the physiological assessment (n = 80), using data from (Antunes et al., 2019 ) on the relative contribution of different water sources to the composition of the xylem water. Each plant WUD was calculated by a weighted average of contributions of the different soil layers to the xylem water, following (Antunes, Chozas, et al., 2018) and using information of water-table depth extracted, for each plant point, from the water-table map developed. Predictors Water-table depth (WTD, m), representing groundwater availability, calculated for the location of each sampled plant using the map developed for the study area (see Fig. S1 ). Crown illumination index (CII), representing light availability and increasing exposure along a canopy openness gradient, measured for each plant (C. Keeling and Phillips 2007; Vieira et al. 2008; Joly et al. 2012 ). Diameter breast height (DBH, cm), representing plant size, calculated by measuring each individuals’ diameter at breast height. When perimeters < 15 cm a ‘DBH’ value of 4.7 cm was attributed. Woody species plot density (Density), representing a proxy for potential competition for water-resources, calculated as the number of individuals with DBH > 4.8 cm present within the plot. Total plot biomass (Biomass, kg), representing a proxy for potential competition for water-resources, calculated as the sum of all tree biomass within the plot following Vieira et al. (2008). See ‘statistical analysis’ section below for details on how these predictors were considered in the study. Statistical analysis A multivariate principal component analyses (PCA) were performed with the individual physiological traits measured (n = 80), to check the physiological patterns among species and possibly define an integrated proxy of plant physiological condition (accounting with specific relative position within the community physiological axis). The resulting PCA (Fig. 1 ) showed a first axis (PC1) explaining 37.3% of the variance and reflected a gradient of physiological performance, from low to high values of chlorophyll content index (CHL), normalized difference vegetation index (NDVI), Photochemical Index (PRI), δ 13 C and leaf carbon content (% C), and high to low values of leaf δ 15 N (Fig. 1 ; Supporting information Table S1 ). Thus, PC1 points to a coordinated strategy of structural investment, stomatal regulation, and sustained photosynthetic capacity, collectively indicating efficient and persistent photosynthetic function (Joshi et al. 2022 ), particularly in tree species. The second axis (PC2), explaining 18.6% of the variance, mainly reflected plant water status and leaf δ 15 N, from low to high values of plant water index (WI) (Fig. 1 ; Table S1 ). As the differences in PC1 scores between trees and understory shrubs were significant (F = 134.4, p < 2.2e-16, Supporting information Fig. S2), we also checked if the physiological axis would hold-up for each growth form separately (Supporting information Fig. S3); and as it did the individual factor scores of the first principal component (PC1) of the PCA were used in the subsequent statistical analysis as an integrated proxy of plants’ photosynthetic function. As WI was not integrated in the first axis and represents a different physiological response component (water-related), it was considered for further analysis as a response variable separately. Structural equation modeling (SEM) was designed to test a specific trait-based mechanistic hypothesis concerning the drivers of belowground adjustments and their consequences for aboveground ecophysiological responses. Pathways were specified a priori based on ecological meaning and with particular emphasis on the hypothesized mediating role of water-uptake depth (WUD), and whether variation in WUD is shaped by groundwater availability, potential competition for water or both. Furthermore, the SEM was intentionally designed to test short- to medium-term ecophysiological responses and mechanisms, focusing on groundwater availability as a driver. It aimed to tackle specific causal hypotheses rather than to explore all possible interconnections among variables. Because light is a crucial factor which is expected to strongly shape photosynthetic-related responses (as the obtained PCA already points to that), and we intended to disentangle how does WUD contributes to physiological variation after accounting for light, light was included in the model as a predictor of photosynthetic function variation in the community. Although alternative causal pathways involving stand structure, water availability and light availability could be hypothesized, these typically operate over longer temporal scales associated with community assembly and community structural adjustments (e.g Ribeiro et al. 2021 ), while representing parallel mechanisms outside the scope of the present study. Such pathways were therefore not included in the SEM, to avoid conflating processes operating at different temporal scales and to maintain a parsimonious, theory-driven model. Thus, to investigate the overall effects and the indirect effects (effects mediated by water-uptake depth adjustments) on plant physiological status, the SEM model included three endogenous (plant-level) response variables: water-uptake depth (WUD), photosynthetic function (PC1, a composite index of photosynthetic status), and water status (WI). Exogenous predictors included biotic variables related to potential competition for water measured at the plot level (Density and Biomass) and plant-level characteristics (WTD, DBH, and CII). Although light availability was assessed using an ordinal index (CII), because the index represents ordered approximately equidistant classes, spans a broad range of light conditions, and showed qualitatively unchanged results when light was treated as an ordered variable in the model (ordered = "CII_ord"), it was treated as a continuous predictor in the SEM. All exogenous variables were allowed to covary, and all variables were standardized prior to analysis. WUD was modeled as a function of both WTD, Density, Biomass and DBH; PC1 as a function of WTD, WUD and CII; and WI as a function of WTD and WUD. A residual covariance between PC1 and WI was included to account for potential unexplained correlation between these two physiological variables. Indirect effects of WTD through WUD were computed for PC1 and WI. The SEM was estimated in R using the lavaan package (v0.6.15) (Rosseel 2012; Lefcheck 2016) with maximum likelihood estimation with robust standard errors (MLR). To account for the nested sampling design (plants nested within plots), standard errors were clustered by plot_ID (Fan et al. 2016 ). Model fit was evaluated using Chi-square, RMSEA, SRMR, CFI, and TLI, with robust corrections for clustering. Standardized path coefficients and R² values were reported to quantify the magnitude of direct and indirect effects. All statistical analyses were performed in R version 4.3.1 (R CoreTeam 2023 ). Results Water-table depth (WTD) influenced positively water-uptake depth (WUD) (β = 0.42; p < 0.001), while no significant direct effect was observed on photosynthetic function (PC1) or plant water status (WI) (Fig. 2 ). The model explained 26% of WUD variance, and this variable was significantly influenced not only by WTD but also by tree size (β = 0.22; p = 0.035). Biotic controls related to stand structure (and potential resource competition) did not show significant effects on WUD (Fig. 2 ). PC1 was mainly explained by light availability (CII) (β = 0.70; p < 0.001), and, while accounting for CII, WUD had a significant direct negative effect on physiological variation (β=-0.18; p = 0.011) (PC1 R 2 = 0.49) (Fig. 2 ). WTD indirectly affected PC1 through WUD, as belowground water-uptake adjustments mediated the indirect effect of WTD on PC1 (significant indirect effect, sβ= -0.08, p = 0.007) (Fig. 2 ). In contrast to PC1, WI showed very low explained variance (R²=0.048) and was not significantly related to WTD or WUD (Fig. 2 ). Discussion In this study we found deeper water-uptake combined with low illumination implied a decreased photosynthetic performance of woody species in the studied restinga forest. Athough photosynthetic function of the overall woody community, comprising tree and shrub species, was mainly affected by the access to light, we found that, after accounting for the strong light-driven variation, water-uptake dynamics explain additional physiological variation. This indicates a secondary but mechanistically consistent hydraulic constraint on plant function. Importantly, water-table depth strongly defined plant water-uptake depth, which mediated negative indirect effects of water-table lowering on the woody community’s photosynthetic status. Indirectly, through water-uptake depth regulation, water-table depth can influence, at least to some degree, the physiological status of restinga plants. Light accessibility drives the differentiation in the photosynthetic functions of restinga woody vegetation, segregating understory and canopy trees. It is expected that plants are limited by light in the shaded understory of tropical and subtropical forests and variation in leaf ecophysiological traits of woody species are expected to capture these variations in light conditions (Poorter and Rose 2005 ; Sánchez-Gómez et al. 2006 ; Markesteijn et al. 2007 ; Coble et al. 2017 ; Tripathi et al. 2020 ; Grunwald et al. 2024 ). There is a high construction investment paired with maintained photosynthetic capacity over time in plants with higher light availability, and a coordinated investment in leaf structure and photosynthetic capacity, consistent with a persistent canopy strategy (Rowland et al. 2021 ). Shrubby species able to tolerate the understory environment such as Psychotria sp. can explore specific light niches and still thrive in shade environments (Valladares et al. 2000 ; Bloor and Grubb 2004 ; Huang et al. 2016 ). Beyond the expected light effects, it is relevant to explore how particular water-resources changes and water-uptake depth patterns might coordinate with water and photosynthetic related traits and the potential consequences for energy allocation and overall plant vitality (Joshi et al. 2022 ; Tonet et al. 2024 ; Muñoz-Gálvez et al. 2025 ). In this shallow water-table sandy system, water-uptake depth imposed a negative influence on the photosynthetic-related traits, but not on the water status of the woody community. Plants using deeper, possibly more abundant water resources, showed a maintenance of favorable water status, indicating a possible hydraulic advantage in adjusting water-uptake towards deeper soil layers. Shallow-water table tropical forests have shown high embolism vulnerability (Costa et al. 2023b ), and water acquisition from deeper soil layers might provide an essential water source to mitigate water limitation impacts on plant hydraulics (Nepstad et al. 1994 ; Chitra-Tarak et al. 2021 ; Kühnhammer et al. 2023 ; Cordeiro et al. 2025 ). Root structural and functional adjustments, including increased deep root production and dynamic shifts in water uptake depth, appear to be key traits that buffer tropical trees against hydrological changes, contributing to persistence of water status and delaying hydraulic failure (under reduced surface water availability). This belowground adjustments were driven by water-table depth, in agreement with global trends of deeper root depths with increasing water-table depths (Rossatto et al. 2012 ; Kulmatiski et al. 2017 ; Evaristo and McDonnell 2017 ; Fan et al. 2017 ), and influenced by tree size. Larger tropical trees tend to access deeper soil or groundwater pools than smaller individuals, particularly during dry periods, reflecting size-dependent rooting depth and hydraulic buffering (Nepstad et al., 1994 ; Meinzer et al., 2013 ; Brum et al., 2023). Furthermore, tropical tree species can show differential leaf-level water demand depending on size, therefore requiring an increased water supply to the leaf (Chave et al. 2009 ; Markesteijn et al. 2011 )(Meinzer et al. 2013 ; Bennett et al. 2015 ; Britton et al. 2022 ). Nevertheless, even only considering shrubs, the water-table depth variations implied water-uptake adjustments, being a common response within the woody community. This further extend the idea of overlapping water-uptake among species, and similar strategies of water-resources root acquisition (seen previous for this restinga community but only exploring seasonal adjustments, not in a water-table gradient (Antunes et al. 2019 )) and potential competition for water (Schenk 2006 ). This functional convergence of the woody community would thus be sustained by enough water supply, other physiological/water-use strategies or further water-uptake plasticity (Bucci et al. 2004 ; Zea-Cabrera et al. 2006 ; Zeppel 2013 ; Schwendenmann et al. 2015 ; Guderle et al. 2018 ). Plant physiological tolerances, functional traits, and eventually their performance (growth, survival, and reproduction) are expected to be optimized over an environment. A safety-efficiency trait trade-off may underlie the belowground investments, as tree species exploring certain hydrological niches might be less prone to xylem cavitation but more vulnerable to carbon limitation (Chitra-Tarak and Warren 2023 ; Laughlin et al. 2023 ). Reducing hydraulic risk often comes at ecological and physiological cost, as investing greatly in roots and xylem safety, might trigger low photosynthetic maxima. Heavy belowground investment directly competes with aboveground demands for carbon, nutrients, and construction costs (Doughty et al. 2014 ; Cusack et al. 2021 ; Umaña et al. 2021 ). This implies carbon allocation trade-offs: carbon allocated to deeper roots, new fine roots, higher root biomass, or root maintenance respiration will mean carbon unavailable for leaves. When carbon and nutrients are diverted belowground, leaves may show lower chlorophyll concentration, and lower photosynthetic capacity even water status is maintained. Restinga forest water-uptake depth responses allow water status to be buffered, but photosynthetic function does not scale proportionally. As the relative importance of above and belowground investments may change with differential resource availability, and trade-offs related to water and carbon allocation may constrain water-use strategies (Li et al. 2024 ; Muñoz-Gálvez et al. 2025 ), our results point to the possibility of further impacts of changing groundwater-resources at the community level. Trends of community-level rooting depths and traits along spatial water-table gradients showed to be very important to study, as it can reveal specific contributions to drought resilience in tropical forests (Oliveira et al. 2019 ; Trugman 2022 ). Examining the indirect effects of water-table depth on the restinga forest revealed to be imperative, as we gained crucial insights into the intricate mechanisms and functioning of this ecosystem, filling gaps in our understanding of their dependency on this water-source and the portential impacts of groundwater changes. Conclusion Greater belowground investment in water acquisition under water-table changes can constrain aboveground allocation to photosynthetic and structural leaf traits in restinga woody species, leading to conservative leaf function despite maintained water status. Our study highlights the role that groundwater availability plays, under less-wet or drier periods, on shaping water-sources-use of restinga’s plant community. Besides the great influence of light availability, an additional indirect effect of a lowering water-table on woody species physiological performance occur through differential belowground investments. There is a belowground–aboveground allocation trade-off, whereby increased investment in water acquisition maintains plant water status, possibly buffering water limitation, but limits leaf-level photosynthetic performance. With this work we emphasize the relevance of understanding the dynamics, trade-offs and paths of influence that water resources might have in these dune systems. Differences in physiology, carbon allocation and water resources use which in function of water-table depth changes (groundwater availability) may affect, in the long term, plants vitality and their functions in the ecosystem. Declarations Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Funding This research was originally funded by Fundação de Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação para a Ciência e a Tecnologia (FCT) in the frame of the projects PTDC/AAC-CLI/118555/2010 and DOI 10.54499/UIDB/00329/2020 . CA acknowledges the support of FCT through a CEEC 5th edition Junior Research contract – FCUL 381 2022.08532.CEECIND/CP1715/CT0006). The scientific project was co-supported by the Brazilian National Research Council/CNPq (PELD Process 403710/2012-0), by the British Natural Environment Research Council/NERC and by The State of São Paulo Research Foundation/FAPESP as part of the projects Functional Gradient, PELD/BIOTA and ECOFOR (Processes 2003/12595-7, 2012/51509-8 e 2012/51872-5, within the BIOTA/FAPESP Program - The Biodiversity Virtual Institute ( www.biota.org.br ). It counted on COTEC/IF 002.766/2013 and 010.631/2013 permits for sampling. Namely, we would like to thank Yvonne Bakker for the help given during field surveys, and Andreia Anjos for the laboratory work. Author Contributions CA, SV, CM contributed to the study conception and design. Data collection and analysis were performed by CA. The first draft of the manuscript was written by CA and all authors commented on previous versions and improved the manuscript. All authors read and approved the final manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8545929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581650005,"identity":"6cf74964-c783-4ab9-bc37-e4a7132b7df4","order_by":0,"name":"Cristina Antunes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYHAD5gMfgKQMkaoTQARb4gwgyQPUS7QWHkPitPBPO/zwc+GPw3n8/Gc+NvxgOMzDIN1/AK8WidtpxtIzEg4XS87I3djYA9Iicxi/LQbSCQbSPAmHEzfc4N3+mAGkRSKZkJb0z79BWvafP/OwmUgtOWYQWxhyGInTInE7p8yaJy09ccaNNMPGHoN0HjaZwwZ4tfDPTt98m8fGOrG///DDhh8V1nL80o0P8FuD5k5gjEqQogHqVtK1jIJRMApGwfAGAEkmQJEmULfEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5934-388X","institution":"Universidade de Lisboa Faculdade de Ciencias","correspondingAuthor":true,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Antunes","suffix":""},{"id":581650006,"identity":"dbce4745-65ab-45af-b595-48e0e7cbce56","order_by":1,"name":"Sergio Chozas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Chozas","suffix":""},{"id":581650007,"identity":"8439d857-0355-4405-bc95-4ff51c53bf2c","order_by":2,"name":"Rafael Flora","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Flora","suffix":""},{"id":581650008,"identity":"8dbf92c2-d718-4338-8c85-4d26606a088d","order_by":3,"name":"Cristina Máguas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Máguas","suffix":""},{"id":581650009,"identity":"6dcefeae-cc72-4333-98ab-70547157e31d","order_by":4,"name":"Carlos Joly","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Joly","suffix":""},{"id":581650010,"identity":"d84cdf27-4f64-40c9-8dc0-8d69c60cf201","order_by":5,"name":"Simone Vieira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Vieira","suffix":""}],"badges":[],"createdAt":"2026-01-08 01:56:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8545929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8545929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101486778,"identity":"8928667c-b43d-444d-a52a-6973fb517b2b","added_by":"auto","created_at":"2026-01-30 09:21:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82049,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) based on physiological measurements (n=80), showing the individual scores by species. Species are represented by different colors as shown in the inner legend: see Table 1 for species names. For physiological parameters considered see Methods section.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545929/v1/7c6fa6cd511f79ac6f6118b5.png"},{"id":101751884,"identity":"3abb6264-ba61-465e-b622-9f7bd007d80d","added_by":"auto","created_at":"2026-02-03 10:24:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284045,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model (SEM) of direct and indirect effects of environmental and biotic controls on the photosynthetic and water status of the restinga woody community (n = 80). See Methods section for details on the variables included and the rationale for the specified paths. Green arrows indicate significant positive paths (*P ≤ 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001), red arrows indicate significant negative paths (*P ≤ 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001), and dashed black arrows indicate non-significant paths (P \u0026gt; 0.05). Arrow thickness corresponds to the magnitude of the standardized path coefficient, which is displayed on the arrows. Marginal R² values for each endogenous variable are shown close to the corresponding box. Standardized indirect effects of water-table depth via water-uptake depth (WTD → WUD) are shown for both photosynthetic and water status (bold if significant, *P \u0026lt; 0.05). The model includes a residual covariance between these two variables (which is not shown in the figure), as it was non-significant (β = -0.096, P = 0.184).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545929/v1/730a52d4c12231de5a1a0186.jpeg"},{"id":106401725,"identity":"edd45c84-5503-46b1-b3c4-54c91fde70d3","added_by":"auto","created_at":"2026-04-08 09:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":956049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8545929/v1/b06c3ced-16e4-476a-be39-3649c3497b35.pdf"},{"id":101486780,"identity":"2a60e520-3159-48c5-b544-ea7f518214ce","added_by":"auto","created_at":"2026-01-30 09:21:33","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":329566,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformationPlantESoiljan2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8545929/v1/fe9a244587274882258792db.pdf"}],"financialInterests":"","formattedTitle":"Water-uptake depth mediates the effects of water-table depth on plant physiological performance in a tropical dune forest","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn tropical ecosystems, both light and water are considered to be important drivers of variations in growth and survival of tree species (Poorter \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wright \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Poorter \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Markesteijn et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Fyllas et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fauset et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rowland et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Species existence and physiological performance along gradients of water and light availability will, to a great extent, be determined by the ability of both water and light acquirement and tolerance to water shortage and/or shade (Markesteijn et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wang and Wang \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, if tropical plants are able to adjust their root systems to hydrological site-specific conditions, such as variations in water-table, their water stress might be alleviated (Zea-Cabrera et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; de Oliveira and Joly \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, deep rooting habit can be an important adaptation to seasonal drought, as access to more readily available water at greater depth can eventually allow the maintenance of a more favorable plant water status (Nepstad et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Zencich et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Barbeta and Pe\u0026ntilde;uelas \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Antunes et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003ec\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chitra-Tarak et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Laughlin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, functional traits such as root water-uptake depth are expected to vary with (ground)water availability (Schenk and Jackson \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Antunes et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Miguez-Macho and Fan \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, exploring water in top-soil layers in lowland tropics, where flooding typically occurs, is crucial for plants\u0026rsquo; access to oxygenated soils (and avoidance of anoxia) (Stone and Kalisz \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Pavlis and Jen\u0026iacute;k \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, plasticity in belowground traits could be a major driver of water resource-use and, consequently, significantly influence plant physiological status (Jackson et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Kulmatiski et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guderle et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, individual structural features such as tree size can strongly influence plant water requirements, plant water-use patterns and, direct or indirectly, plant physiological status (Meinzer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Meinzer \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rossatto et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brienen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ledo et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiotic controls related to stand structure and its functional composition can also have direct effects on plant physiological performance and primary productivity (Aerts \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Fyllas et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As plants use the same basic resources (light, CO\u003csub\u003e2\u003c/sub\u003e, water, nutrients and space for growth), co-occurring plants would likely compete (Silvertown \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pe\u0026ntilde;uelas et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this case, competition would hence be greater with higher species overlapping, i.e. higher density, biomass and/or higher similarity of functional composition. Due to reduced competition with conspecific neighbors, plants at low density might be more likely to survive and \u0026frasl;or grow better than plants at high density (Weigelt et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sk\u0026aacute;lov\u0026aacute; et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, the mechanisms of plant competition can be greatly controlled by which resource is limiting, by resources-use strategies and unavoidable trade-offs in competition for above- and below-ground resources (encompassing differential allocation of biomass to structures involved in the acquisition of a resource) and by the relative importance of traits related to resource acquisition and retention (Aerts \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Malhi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In turn, these biotic controls can be regulated by environmental factors, and thus work as \u0026lsquo;bridges\u0026rsquo; between environmental controls and overall plant physiology (e.g. water, carbon and nitrogen status) (Fyllas et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This means that environmental conditions can also have an indirect effect on forest productivity and physiological performance by regulating the functional responses of the community.\u003c/p\u003e \u003cp\u003eThe effect of local hydrological conditions, such as water-table depth, is prevalent in tropical regions, filtering species and influencing the diversity and composition of tropical forests (Oliveira et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ribeiro et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Marca-Zevallos et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accounting with local hydrological factors, such as water-table depth, and mechanisms underlying forest physiological responses would contribute to better understanding plant responses and the resilience of tropical forests to increased water variations. Shallow water-table tropical forests have shown more resource-acquisitive and hydrologically vulnerable trees than deep water-table forests, and may be more sensitive to severe droughts due to shallow roots and drought-intolerant traits (Esteban et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Costa et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, b). Among key plant traits mediating the responses of plants to hydrological gradients such as water-table depth, is functional rooting depth (where roots actively absorb water), although continues to be poorly studied. Therefore, to study how forests ecophysiologically respond to changes of water-table depth, and how traits such as water-uptake depth might influence and mediate this response, is of great relevance.\u003c/p\u003e \u003cp\u003eEnvironmental conditions change both in time and in space, and studies along environmental gradients can provide valuable insights into controls of ecosystem function. Specifically, studies along (spatial) gradients of groundwater could contribute to disentangling to what extent this water source influences and constrains overall physiology and vitality of vegetation. This is particularly relevant in \u003cem\u003erestinga\u003c/em\u003e forests, which are shallow water-table tropical coastal ecosystems, that occur in poor sandy soils, where water availability changes can be very rapid, water retention is low, and little water is available within the topsoil during dry periods while flooding occurs in wet periods (Assis et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Joly et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Magnago et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eRestinga\u003c/em\u003e woody species have shown to seasonally re-adjust their water-sources use (Antunes et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), to change belowground fine root investments (Rosado et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and water-uptake adjustments (Rosado et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in function of hydrological constrains. However, the physiological implications of these belowground adjustments and the effects of water-table changes on community physiological performance are still unknown. In these ecosystems, habitat filtering drives the local distribution of congeneric species, and comprises flood resistant species but also flood sensitive species (Oliveira \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ribeiro et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with some species showing adaptations to deal with low water and nutrient availability (Gessler et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rosado and De Mattos \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rosado et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Even small changes in depth to groundwater within the same low precipitation availability (intra-season spatial variation due to micro-topographic features) can have implications on plants water-use and the physiological status of the plants and the overall woody community. Additionally, \u003cem\u003erestinga\u003c/em\u003e\u0026rsquo;s woody species are also subjected to differential light accessibility and stand composition variations, which, as above mentioned, can also affect plant physiological status.\u003c/p\u003e \u003cp\u003eIn this study, we aim to explore whether and how \u003cem\u003erestinga\u003c/em\u003e\u0026rsquo;s woody vegetation experience variations on physiological performance in response to groundwater changes. By exploring plant community (15 woody species) physiological responses (i.e. variations in a suite of physiological parameters) and plant water-uptake depth, we aim to: (i) understand the direct and indirect effects of water-table changes on vegetation physiological status, while accounting with light accessibility, plant size and stand structure effects, and (ii) disentangle the mediator role of plant water-uptake depth adjustments on physiological status of \u003cem\u003erestinga\u003c/em\u003e woody vegetation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and species\u003c/h2\u003e \u003cp\u003eThe study was conducted at Serra do Mar State Park (Atlantic Forest), in the seasonally flooded coastal forest (known as \u003cem\u003erestinga\u003c/em\u003e forest) that occurs at Praia da Fazenda, municipality of Ubatuba, S\u0026atilde;o Paulo, Brazil, in a permanent plot previously established (1ha, 23\u0026deg; 21\u0026rsquo; 22\u0026rdquo; S; 44\u0026deg; 51\u0026rsquo; 03\u0026rdquo; O) (Joly et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Its soil is sandy, acid and poor in nutrients (Scarano \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Assis et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Joly et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the system is subjected to seasonal or perennial waterlogging due to variations in precipitation along the year (de Oliveira and Joly \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Oliveira \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Antunes et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The study area encompasses a gradient of water-table depth due to micro-topography (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e \u0026ndash; Supporting information). Within the study area (1 ha), eighteen sampling plots (10m x 10m) were randomly selected (ensuring they were not side-contiguous) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and showed different distances from soil surface to water-table, ranging from 0.8 to 1.4 m.\u003c/p\u003e \u003cp\u003eSpecies considered to be the dominant species (and occurring at least in three plots) were considered, and at least one shrub species and two tree species were sampled in each plot (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 80 individual plants were sampled for the physiological assessment and considered for water-uptake depth estimations (see below).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWoody species sampled and their code, growth form (Type) and number of individuals sampled (n).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuterpe edulis\u003c/em\u003e Mart.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEugenia schuechiana\u003c/em\u003e Berg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFaramea pachyantha\u003c/em\u003e M\u0026uuml;ll.Arg.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGuarea macrophylla (\u003c/em\u003eVell.) T.D.Penn.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGuapira opposita\u003c/em\u003e (Vell.) Reitz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGuatteria sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGsp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJacaranda puberula\u003c/em\u003e Cham.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyrcia brasiliensis\u003c/em\u003e Kiaersk.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMaytenus littoralis\u003c/em\u003e Carv-Okano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyrcia multiflora\u003c/em\u003e (Lam.) DC.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyrcia racemosa\u003c/em\u003e (O.Berg) Kiaersk.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMarlierea tomentosa\u003c/em\u003e Cambess.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePera glabrata\u003c/em\u003e (Schott) Poepp. ex Baill.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePsychotria sp1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsp1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eshrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePsychotria sp2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsp2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eshrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhysiological parameters\u003c/h3\u003e\n\u003cp\u003eEcophysiological traits measured in the selected plants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, n\u0026thinsp;=\u0026thinsp;80), in a rainless period, were:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eleaf C and N concentrations (%), linked to structural investment and photosynthetic activity respectively.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eleaf C and N isotope ratios (\u0026permil;), associated with stomatal control\u0026thinsp;+\u0026thinsp;water-use efficiency and nitrogen cycling\u0026thinsp;+\u0026thinsp;N sources, respectively. Dried and milled bulk leaf samples were analysed for determining C, N, δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN, by continuous flow isotope ratio mass spectrometry (CF-IRMS) on a Sercon Hydra 20\u0026ndash;22 (Sercon, UK) stable isotope ratio mass spectrometer, coupled to a EuroEA (EuroVector, Italy) elemental analyser in SIIAF (FCUL, Portugal). Uncertainty of the isotope ratio analysis, calculated using values from 6 to 9 replicates of secondary isotopic reference material interspersed among samples in every batch analysis, was \u0026le;\u0026thinsp;0.1\u0026permil;.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpectral reflectance indices: Chlorophyll index CHL [CHL\u0026thinsp;=\u0026thinsp;R750/R705], correlated with leaf chlorophyll content on a number of plant species, providing information about photosynthetic potential (Pe\u0026ntilde;uelas, Frederic and Filella, 1995); Photochemical index PRI [PRI = (R531-R570) / (R531\u0026thinsp;+\u0026thinsp;R570)], associated with light use efficiency and photosynthetic activity (Wong and Gamon, 2015, Pe\u0026ntilde;uelas, Llusia, Pinol, \u0026amp; Filella, 1997); Water index WI [WI\u0026thinsp;=\u0026thinsp;R900/R970], highly related to plant water content and water status (Claudio et al., 2006; Pe\u0026ntilde;uelas et al., 1997); Normalized difference vegetation index NDVI [NDVI = (R900-R680) / (R900\u0026thinsp;+\u0026thinsp;R680)], proxy of biomass \"greenness\" and plant photosynthetic capacity (Gamon et al., 1995). These indices were measured using a UniSpec Spectral Analysis System, PP Systems, in 6 leaves per plant and a mean value considered per plant.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eWater-uptake depth estimations\u003c/h3\u003e\n\u003cp\u003eWater uptake depth (WUD, m) was estimated for all plants considered in the physiological assessment (n\u0026thinsp;=\u0026thinsp;80), using data from (Antunes et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on the relative contribution of different water sources to the composition of the xylem water. Each plant WUD was calculated by a weighted average of contributions of the different soil layers to the xylem water, following (Antunes, Chozas, et al., 2018) and using information of water-table depth extracted, for each plant point, from the water-table map developed.\u003c/p\u003e\n\u003ch3\u003ePredictors\u003c/h3\u003e\n\u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWater-table depth (WTD, m), representing groundwater availability, calculated for the location of each sampled plant using the map developed for the study area (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCrown illumination index (CII), representing light availability and increasing exposure along a canopy openness gradient, measured for each plant (C. Keeling and Phillips 2007; Vieira et al. 2008; Joly et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDiameter breast height (DBH, cm), representing plant size, calculated by measuring each individuals\u0026rsquo; diameter at breast height. When perimeters\u0026thinsp;\u0026lt;\u0026thinsp;15 cm a \u0026lsquo;DBH\u0026rsquo; value of 4.7 cm was attributed.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWoody species plot density (Density), representing a proxy for potential competition for water-resources, calculated as the number of individuals with DBH\u0026thinsp;\u0026gt;\u0026thinsp;4.8 cm present within the plot.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTotal plot biomass (Biomass, kg), representing a proxy for potential competition for water-resources, calculated as the sum of all tree biomass within the plot following Vieira et al. (2008).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eSee \u0026lsquo;statistical analysis\u0026rsquo; section below for details on how these predictors were considered in the study.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA multivariate principal component analyses (PCA) were performed with the individual physiological traits measured (n\u0026thinsp;=\u0026thinsp;80), to check the physiological patterns among species and possibly define an integrated proxy of plant physiological condition (accounting with specific relative position within the community physiological axis). The resulting PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed a first axis (PC1) explaining 37.3% of the variance and reflected a gradient of physiological performance, from low to high values of chlorophyll content index (CHL), normalized difference vegetation index (NDVI), Photochemical Index (PRI), δ\u003csup\u003e13\u003c/sup\u003eC and leaf carbon content (% C), and high to low values of leaf δ\u003csup\u003e15\u003c/sup\u003eN (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supporting information Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Thus, PC1 points to a coordinated strategy of structural investment, stomatal regulation, and sustained photosynthetic capacity, collectively indicating efficient and persistent photosynthetic function (Joshi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), particularly in tree species. The second axis (PC2), explaining 18.6% of the variance, mainly reflected plant water status and leaf δ\u003csup\u003e15\u003c/sup\u003eN, from low to high values of plant water index (WI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). As the differences in PC1 scores between trees and understory shrubs were significant (F\u0026thinsp;=\u0026thinsp;134.4, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16, Supporting information Fig. S2), we also checked if the physiological axis would hold-up for each growth form separately (Supporting information Fig. S3); and as it did the individual factor scores of the first principal component (PC1) of the PCA were used in the subsequent statistical analysis as an integrated proxy of plants\u0026rsquo; photosynthetic function. As WI was not integrated in the first axis and represents a different physiological response component (water-related), it was considered for further analysis as a response variable separately.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStructural equation modeling (SEM) was designed to test a specific trait-based mechanistic hypothesis concerning the drivers of belowground adjustments and their consequences for aboveground ecophysiological responses. Pathways were specified \u003cem\u003ea priori\u003c/em\u003e based on ecological meaning and with particular emphasis on the hypothesized mediating role of water-uptake depth (WUD), and whether variation in WUD is shaped by groundwater availability, potential competition for water or both. Furthermore, the SEM was intentionally designed to test short- to medium-term ecophysiological responses and mechanisms, focusing on groundwater availability as a driver. It aimed to tackle specific causal hypotheses rather than to explore all possible interconnections among variables. Because light is a crucial factor which is expected to strongly shape photosynthetic-related responses (as the obtained PCA already points to that), and we intended to disentangle how does WUD contributes to physiological variation after accounting for light, light was included in the model as a predictor of photosynthetic function variation in the community. Although alternative causal pathways involving stand structure, water availability and light availability could be hypothesized, these typically operate over longer temporal scales associated with community assembly and community structural adjustments (e.g Ribeiro et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while representing parallel mechanisms outside the scope of the present study. Such pathways were therefore not included in the SEM, to avoid conflating processes operating at different temporal scales and to maintain a parsimonious, theory-driven model. Thus, to investigate the overall effects and the indirect effects (effects mediated by water-uptake depth adjustments) on plant physiological status, the SEM model included three endogenous (plant-level) response variables: water-uptake depth (WUD), photosynthetic function (PC1, a composite index of photosynthetic status), and water status (WI). Exogenous predictors included biotic variables related to potential competition for water measured at the plot level (Density and Biomass) and plant-level characteristics (WTD, DBH, and CII). Although light availability was assessed using an ordinal index (CII), because the index represents ordered approximately equidistant classes, spans a broad range of light conditions, and showed qualitatively unchanged results when light was treated as an ordered variable in the model (ordered = \"CII_ord\"), it was treated as a continuous predictor in the SEM. All exogenous variables were allowed to covary, and all variables were standardized prior to analysis. WUD was modeled as a function of both WTD, Density, Biomass and DBH; PC1 as a function of WTD, WUD and CII; and WI as a function of WTD and WUD. A residual covariance between PC1 and WI was included to account for potential unexplained correlation between these two physiological variables. Indirect effects of WTD through WUD were computed for PC1 and WI. The SEM was estimated in R using the lavaan package (v0.6.15) (Rosseel 2012; Lefcheck 2016) with maximum likelihood estimation with robust standard errors (MLR). To account for the nested sampling design (plants nested within plots), standard errors were clustered by plot_ID (Fan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Model fit was evaluated using Chi-square, RMSEA, SRMR, CFI, and TLI, with robust corrections for clustering. Standardized path coefficients and R\u0026sup2; values were reported to quantify the magnitude of direct and indirect effects.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in R version 4.3.1 (R CoreTeam \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWater-table depth (WTD) influenced positively water-uptake depth (WUD) (β\u0026thinsp;=\u0026thinsp;0.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no significant direct effect was observed on photosynthetic function (PC1) or plant water status (WI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model explained 26% of WUD variance, and this variable was significantly influenced not only by WTD but also by tree size (β\u0026thinsp;=\u0026thinsp;0.22; p\u0026thinsp;=\u0026thinsp;0.035). Biotic controls related to stand structure (and potential resource competition) did not show significant effects on WUD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PC1 was mainly explained by light availability (CII) (β\u0026thinsp;=\u0026thinsp;0.70; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and, while accounting for CII, WUD had a significant direct negative effect on physiological variation (β=-0.18; p\u0026thinsp;=\u0026thinsp;0.011) (PC1 R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.49) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). WTD indirectly affected PC1 through WUD, as belowground water-uptake adjustments mediated the indirect effect of WTD on PC1 (significant indirect effect, sβ= -0.08, p\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast to PC1, WI showed very low explained variance (R\u0026sup2;=0.048) and was not significantly related to WTD or WUD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we found deeper water-uptake combined with low illumination implied a decreased photosynthetic performance of woody species in the studied restinga forest. Athough photosynthetic function of the overall woody community, comprising tree and shrub species, was mainly affected by the access to light, we found that, after accounting for the strong light-driven variation, water-uptake dynamics explain additional physiological variation. This indicates a secondary but mechanistically consistent hydraulic constraint on plant function. Importantly, water-table depth strongly defined plant water-uptake depth, which mediated negative indirect effects of water-table lowering on the woody community\u0026rsquo;s photosynthetic status. Indirectly, through water-uptake depth regulation, water-table depth can influence, at least to some degree, the physiological status of restinga plants.\u003c/p\u003e \u003cp\u003eLight accessibility drives the differentiation in the photosynthetic functions of \u003cem\u003erestinga\u003c/em\u003e woody vegetation, segregating understory and canopy trees. It is expected that plants are limited by light in the shaded understory of tropical and subtropical forests and variation in leaf ecophysiological traits of woody species are expected to capture these variations in light conditions (Poorter and Rose \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; S\u0026aacute;nchez-G\u0026oacute;mez et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Markesteijn et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Coble et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tripathi et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Grunwald et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is a high construction investment paired with maintained photosynthetic capacity over time in plants with higher light availability, and a coordinated investment in leaf structure and photosynthetic capacity, consistent with a persistent canopy strategy (Rowland et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Shrubby species able to tolerate the understory environment such as \u003cem\u003ePsychotria\u003c/em\u003e sp. can explore specific light niches and still thrive in shade environments (Valladares et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bloor and Grubb \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Beyond the expected light effects, it is relevant to explore how particular water-resources changes and water-uptake depth patterns might coordinate with water and photosynthetic related traits and the potential consequences for energy allocation and overall plant vitality (Joshi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tonet et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mu\u0026ntilde;oz-G\u0026aacute;lvez et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this shallow water-table sandy system, water-uptake depth imposed a negative influence on the photosynthetic-related traits, but not on the water status of the woody community. Plants using deeper, possibly more abundant water resources, showed a maintenance of favorable water status, indicating a possible hydraulic advantage in adjusting water-uptake towards deeper soil layers. Shallow-water table tropical forests have shown high embolism vulnerability (Costa et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), and water acquisition from deeper soil layers might provide an essential water source to mitigate water limitation impacts on plant hydraulics (Nepstad et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Chitra-Tarak et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; K\u0026uuml;hnhammer et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cordeiro et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Root structural and functional adjustments, including increased deep root production and dynamic shifts in water uptake depth, appear to be key traits that buffer tropical trees against hydrological changes, contributing to persistence of water status and delaying hydraulic failure (under reduced surface water availability). This belowground adjustments were driven by water-table depth, in agreement with global trends of deeper root depths with increasing water-table depths (Rossatto et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kulmatiski et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Evaristo and McDonnell \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and influenced by tree size. Larger tropical trees tend to access deeper soil or groundwater pools than smaller individuals, particularly during dry periods, reflecting size-dependent rooting depth and hydraulic buffering (Nepstad et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Meinzer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Brum et al., 2023). Furthermore, tropical tree species can show differential leaf-level water demand depending on size, therefore requiring an increased water supply to the leaf (Chave et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Markesteijn et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)(Meinzer et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bennett et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Britton et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, even only considering shrubs, the water-table depth variations implied water-uptake adjustments, being a common response within the woody community. This further extend the idea of overlapping water-uptake among species, and similar strategies of water-resources root acquisition (seen previous for this restinga community but only exploring seasonal adjustments, not in a water-table gradient (Antunes et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)) and potential competition for water (Schenk \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This functional convergence of the woody community would thus be sustained by enough water supply, other physiological/water-use strategies or further water-uptake plasticity (Bucci et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zea-Cabrera et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zeppel \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Schwendenmann et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guderle et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant physiological tolerances, functional traits, and eventually their performance (growth, survival, and reproduction) are expected to be optimized over an environment. A safety-efficiency trait trade-off may underlie the belowground investments, as tree species exploring certain hydrological niches might be less prone to xylem cavitation but more vulnerable to carbon limitation (Chitra-Tarak and Warren \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Laughlin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Reducing hydraulic risk often comes at ecological and physiological cost, as investing greatly in roots and xylem safety, might trigger low photosynthetic maxima. Heavy belowground investment directly competes with aboveground demands for carbon, nutrients, and construction costs (Doughty et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cusack et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uma\u0026ntilde;a et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This implies carbon allocation trade-offs: carbon allocated to deeper roots, new fine roots, higher root biomass, or root maintenance respiration will mean carbon unavailable for leaves. When carbon and nutrients are diverted belowground, leaves may show lower chlorophyll concentration, and lower photosynthetic capacity even water status is maintained. Restinga forest water-uptake depth responses allow water status to be buffered, but photosynthetic function does not scale proportionally.\u003c/p\u003e \u003cp\u003eAs the relative importance of above and belowground investments may change with differential resource availability, and trade-offs related to water and carbon allocation may constrain water-use strategies (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mu\u0026ntilde;oz-G\u0026aacute;lvez et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), our results point to the possibility of further impacts of changing groundwater-resources at the community level. Trends of community-level rooting depths and traits along spatial water-table gradients showed to be very important to study, as it can reveal specific contributions to drought resilience in tropical forests (Oliveira et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trugman \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Examining the indirect effects of water-table depth on the restinga forest revealed to be imperative, as we gained crucial insights into the intricate mechanisms and functioning of this ecosystem, filling gaps in our understanding of their dependency on this water-source and the portential impacts of groundwater changes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGreater belowground investment in water acquisition under water-table changes can constrain aboveground allocation to photosynthetic and structural leaf traits in restinga woody species, leading to conservative leaf function despite maintained water status. Our study highlights the role that groundwater availability plays, under less-wet or drier periods, on shaping water-sources-use of restinga\u0026rsquo;s plant community. Besides the great influence of light availability, an additional indirect effect of a lowering water-table on woody species physiological performance occur through differential belowground investments. There is a belowground\u0026ndash;aboveground allocation trade-off, whereby increased investment in water acquisition maintains plant water status, possibly buffering water limitation, but limits leaf-level photosynthetic performance.\u003c/p\u003e \u003cp\u003eWith this work we emphasize the relevance of understanding the dynamics, trade-offs and paths of influence that water resources might have in these dune systems. Differences in physiology, carbon allocation and water resources use which in function of water-table depth changes (groundwater availability) may affect, in the long term, plants vitality and their functions in the ecosystem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was originally funded by Funda\u0026ccedil;\u0026atilde;o de Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) and Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e a Tecnologia (FCT) in the frame of the projects PTDC/AAC-CLI/118555/2010 and DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.54499/UIDB/00329/2020\u003c/span\u003e\u003cspan address=\"10.54499/UIDB/00329/2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. CA acknowledges the support of FCT through a CEEC 5th edition Junior Research contract \u0026ndash; FCUL 381 2022.08532.CEECIND/CP1715/CT0006). The scientific project was co-supported by the Brazilian National Research Council/CNPq (PELD Process 403710/2012-0), by the British Natural Environment Research Council/NERC and by The State of S\u0026atilde;o Paulo Research Foundation/FAPESP as part of the projects Functional Gradient, PELD/BIOTA and ECOFOR (Processes 2003/12595-7, 2012/51509-8 e 2012/51872-5, within the BIOTA/FAPESP Program - The Biodiversity Virtual Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.biota.org.br\u003c/span\u003e\u003cspan address=\"http://www.biota.org.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It counted on COTEC/IF 002.766/2013 and 010.631/2013 permits for sampling. Namely, we would like to thank Yvonne Bakker for the help given during field surveys, and Andreia Anjos for the laboratory work.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eCA, SV, CM contributed to the study conception and design. Data collection and analysis were performed by CA. The first draft of the manuscript was written by CA and all authors commented on previous versions and improved the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003ePlant traits data will be deposited in DRYAD upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAerts R (1999) Interspecific competition in natural plant communities: mechanisms, trade-offs and plant-soil feedbacks. 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Ecohydrology 6:889\u0026ndash;900. https://doi.org/10.1002/eco.1377\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"plant ecophysiology, restinga forest, groundwater changes, plant-water interactions, plant functional responses, below and aboveground tradeoffs","lastPublishedDoi":"10.21203/rs.3.rs-8545929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8545929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn tropical forests both light and water influence the growth and health of woody species. In seasonally flooded dune forests, like \u003cem\u003erestinga\u003c/em\u003e forests of the Atlantic Forest, groundwater availability may play a major role in forest functioning. This study aims to understand whether and how \u003cem\u003erestinga\u003c/em\u003e’s woody vegetation experience variations in physiological performance in response to water-table depth, and the mediator role of plants water-uptake depth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e15 woody species were sampled along a water-table gradient, for traits related to water-use, nitrogen acquisition and photosynthetic activity. Direct, and indirect - mediated by water-uptake depth adjustments, effects of water-table depth on physiological status of restinga vegetation were tested, accounting with light and stand structure influence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWater-table depth strongly defined plants water-uptake depth, which mediated negative indirect effects of water-table lowering on the woody community’s photosynthetic status. Plants using deeper, possibly more abundant water resources, showed maintenance of the water status, but lower physiological performance. Besides a strong light-driven variation, water-uptake dynamics explain additional physiological variation, showing a secondary hydraulic constraint on plant photosynthetic function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndirectly, through water-uptake depth regulation, water-table depth can influence, at least to some degree, the physiological status of restinga plants. Despite maintained water status, greater water-uptake depth negatively influenced photosynthetic and structural leaf traits. There is a below–aboveground allocation trade-off, whereby belowground allocation possibly buffers water limitation but constrains aboveground investment, limiting leaf-level photosynthetic performance in restinga forests.\u003c/p\u003e","manuscriptTitle":"Water-uptake depth mediates the effects of water-table depth on plant physiological performance in a tropical dune forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 09:21:28","doi":"10.21203/rs.3.rs-8545929/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"820086a7-3848-440c-a190-32b99f5e704d","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T12:21:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 09:21:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8545929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8545929","identity":"rs-8545929","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}