Disentangling Vereda Wetlands determinants across a wide geographic extent

Disentangling Vereda Wetlands determinants across a wide geographic extent | 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 Disentangling Vereda Wetlands determinants across a wide geographic extent Rogério Victor Soares Gonçalves, Nathan Felipe Alves, Raquel Franco Cassino, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7790212/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Veredas are wetlands from the Brazilian Cerrado hotspot biome, noted for their rich biodiversity and ecosystem services, including water provision and carbon storage. They are characteristically found in gently sloping, low-lying valleys, where the water table emerges and flows slowly. However, their distribution and abiotic drivers remain poorly understood. Thus, we tested the hypotheses that water availability (i.e., precipitation) has a positive effect on Veredas ’ distribution, while steep terrains (i.e., slope variance) have a negative effect. We used a grid-based approach to capture fine-scale variation across the Triângulo Mineiro and Alto Paranaíba (TMAP) region. We also investigated the effects of multiple climate, terrain, and soil variables in explaining Veredas occurrence. Our results supported the hypothesis regarding water availability, as the precipitation of the driest month positively influenced the probability of Veredas occurrence, explaining 5.4% of the variance. Furthermore, our results supported the hypothesis regarding slope variance, as it negatively influenced both the probability of occurrence and the abundance of Veredas , explaining 8.8% and 9.4% of the variance, respectively. Microregions with Veredas differed from those without across 23 terrain, soil, and climatic variables, indicating that additional predictors contribute to explaining Veredas ’ distribution. In contrast with previous descriptive, climate-zone comparisons, this study represents the first hypothesis-driven, landscape-scale evaluation of the determinants of Veredas occurrence, suggesting that water availability recharges the water table and flat terrains facilitate the formation of hydromorphic soil and slow water drainage. These findings provide a mechanistic basis for identifying priority areas for conservation and water security, highlighting the need for management strategies that anticipate the vulnerability of Veredas to ongoing climate change in the Cerrado biome. abiotic drivers climate change distribution abundance Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Wetlands are ecosystems in which soil is permanently or seasonally saturated with water, characterised by water-tolerant plants and hydromorphic soil (Hu et al 2017a ). These ecosystems support high biodiversity, including unique species and processes with intrinsic ecological value (De Groot et al 2018 ). However, their societal importance is most clearly recognised through the ecosystem services they provide (Mitsch and Gosselink 2000 ). For example, they are key environments for water emergence, regulation, and purification (Zedler and Kercher 2005 ). In addition to storing carbon and maintain local climate stability, wetlands support economically important plant and animal species (Mitsch and Gosselink 2000 ). The economic value of wetlands is closely linked to their spatial distribution, as human settlements in their proximity directly benefit from their ecological functions (Mitsch and Gosselink 2000 ). Accordingly, assessing wetlands’ spatial distribution and the factors influencing their occurrence is essential for effective conservation and management (Maltby 2006 ). Globally, the distribution of wetlands reflects interactions among climate, topography, and soil conditions, which determine water availability, retention, and movement through landscapes (Hu et al 2017b ; Yuan et al 2025 ). Some countries harbour extensive wetland areas, such as Brazil, which contains the largest wetland coverage worldwide, estimated at ca. 27,000,000 hectares (Ramsar 2020 ). Several factors contribute to this vast and diverse array of wetlands, including its status as the fifth largest country in the world, high precipitation levels, generally flat and eroded terrain, water-retaining deep soils, runoff from rivers originating in other countries, and an extensive coastline. Among all wetlands in Brazil, the Veredas are the most common and widely distributed, occurring throughout the Cerrado biome, Brazil’s neotropical savanna (da Cunha et al 2014 ; Fig. 1 ). The term Vereda is refers to shallow valleys or headwaters of watercourses characterised by gentle concave slopes with sandy soils and a waterlogged flat central zone, typically occupied by rows of the buriti ( Mauritia flexuosa ) palm (Boaventura 2007 ). These inland wetlands are marked by permanently hydromorphic soils rich in organic matter, where the water table either surfaces or lies very close to the surface across their extent. This feature differentiates Veredas from other Cerrado wetland types, such as wet grasslands, marshes, and peatlands. The typical vegetation structure of Veredas comprises two main zones: an outer grassy-herbaceous zone and a central shrubby-arboreal zone with the emblematic M. flexuosa palm (Ramos et al 2006 ; Ribeiro and Walter 2008 ; Rodrigues Bijos et al 2017 ). Because of groundwater upwelling and their capacity to retain large volume of water in porous soils, Veredas are known as “the cradle of waters” (Boaventura 2007 ). They are important for supplying water within the Cerrado and adjacent biomes (Junk et al 2014 ). Located at higher altitudes, water from Veredas flows through major basins in all directions, serving millions of people, including residents of Brazil's most populous cities. As such, Veredas provide water for basic human needs, livestock, agriculture, and industry (Wantzen et al 2006 ; Eloy et al 2016 ). They are also one of the few wetland formations capable of long-term organic matter storage, with stable carbon deposits as old as 30,000 years (Barberi et al 2000 ; Wantzen et al 2012 ). Additionally, Veredas are important to traditional communities, providing resources from key species for food, timber, and handicrafts (Eichemberg and Scatena 2011 ). Contrasting with their widespread importance and occurrence, most studies on Veredas have focused on spatially restricted areas (Araújo et al 2002 ; Fagundes and Ferreira 2016 ; Rodrigues Bijos et al 2017 ; Silva et al 2017 ). Previous research have characterised their occurrence to be related to groundwater upwelling, and environmental factors such as precipitation, temperature seasonality, soil texture, and slope (Gonçalves et al 2022 ). However, these assumptions are speculative, and, to our knowledge, no studies have examined causal drivers of Veredas distribution. Gonçalves et al. ( 2022 ) provided a first step in addressing this gap by studying the Veredas of Triângulo Mineiro and Alto Paranaíba (TMAP) region, which has one of the highest density of Veredas in the Cerrado (Cardoso et al., in prep). Veredas occurrence was compared across three Köppen climate zones: Aw (tropical zone with dry winters), Cwa (humid subtropical zone with dry winters and hot summers), and Cwb (humid subtropical zone with dry winters and temperate summers). This ecosystem was most frequent in the western TMAP region, characterised by Aw climate, lower elevation, pronounced precipitation seasonality, slope, soil cation exchange capacity, and silt and sand contents. Although this study provided important insights into Veredas distribution, it also has limitations. Climate was treated as a categorical variable across large areas, which obscures subtle variation, especially given Veredas continuous distribution. Moreover, the analysis was restricted to descriptive associations between regional characteristics and the presence or absence of Veredas , making it difficult to establish causal relationships with specific fine-scale variables or to infer patterns about Veredas’ abundance, including the direct influence of water availability and flat terrains. Veredas have been associated with water availability and gently sloping, low-lying landscapes that favour slow water flow (Boaventura 2007 ). Thus, we hypothesised that water availability (precipitation) would positively influence their distribution, while steep terrains (slope variance) would have a negative effect. To test these hypotheses, we investigated the probability of occurrence and the abundance of Veredas using a grid-based approach. The TMAP region was divided into smaller grids to analyse the effects of abiotic factors across microregions. Additionally, we examined the combined influence of other factors, including climate, terrain, and soil variables, allowing us to infer relationships among them. This study represents the first hypothesis-driven, landscape-scale assessment of the impact of abiotic variables on Veredas occurrence and abundance. These findings are crucial for identifying priority areas for conservation and management, as well as for proposing effective intervention strategies. Moreover, the methods applied here may inspire analyses on larger scales or other wetlands of the world. Material and Methods Study site The Triângulo Mineiro and Alto Paranaíba (TMAP) region in central Brazil has a high density of Veredas (Gonçalves et al 2022 ) within the Cerrado biome, a globally recognised biodiversity hotspot. The region experiences mean annual temperatures ranging from 22°C to 26°C and annual precipitation between 1100 to 1750 mm (Novais et al 2018 ). According to the Köppen-Geiger Climate Classification (Alvares et al 2013 ), three climatic categories occur in the region: Aw (tropical zone with dry winters), Cwa (humid subtropical zone with dry winters and hot summers), and Cwb (humid subtropical zone with dry winters and temperate summers). Procedures Veredas data for the TMAP region were obtained from the Brazilian Rural Environmental Register (Cadastro Ambiental Rural – CAR), which provides public access to rural property boundaries, including permanent preservation areas stored in separate files, such as Veredas . After downloading and merging the data for each municipality, we generated a topology feature to exclude small polygons created by layer intersections and overlapping polygons. This step was performed in ArcGIS Desktop using the Error Inspector and Fix Topology Error tools from the Topology toolbar, applying the rule Polygons Must Not Overlap . To ensure accuracy, the topology feature was validated again with Error Inspector . Centroid points were generated within each Vereda polygon using the Feature to Point tool, to estimate Veredas abundance. The TMAP region was then divided into 9 km² grids ( i.e. microregion) by creating tessellations in the Sampling toolbox. A spatial join was performed to associate centroid points with the grids, which were subsequently classified as either containing Veredas or not. Grids overlapping urban areas, identified using the census sector shapefile (IBGE 2021), were excluded from the analysis. From the remaining grids, 1000 points were randomly subsampled (500 with Veredas presence and 500 with absence) using the Create Random Points function in the Sampling toolbox (Fig. S1 ). To predict Veredas occurrence, we used climate, terrain, and soil variables (Table S1 ). Climatic variables were obtained from WorldClim at a spatial resolution of 30 arc-seconds (~ 1000 m) (Fick and Hijmans 2017 ), and terrain variables were sourced from EarthExplorer at a spatial resolution of 1 arc-second (~ 30 m) (Kretsch 2000 ). Because the predictor layers had a finer resolution than the 9 km² grids, they were resampled, and the mean, minimum, and maximum, or sum was calculated depending on the characteristics of each variable, with each grid treated as a single analysis unit. All spatial operations were performed in ArcMap 10.5. Statistical analyses We analysed all 23 predictor variables using principal component analysis (PCA) to identify the variables that contributed most to Veredas presence and their associations. The analysis was performed with the R-package FactoMineR (Husson et al 2018 ), specifying a correlation matrix to account for the different measurement scales of the variables (Abdi and Williams 2010 ). The presence or absence of Veredas was included as a supplementary categorical variable in the biplot for further analysis. We then investigated whether microregions with Veredas differed in Euclidean space according to the 23 variables by conducting a PERMANOVA (Permutational Multivariate Analysis of Variance; 10,000 iterations) implemented in the R-package package vegan (Oksanen 2019 ). Later, three separate PERMANOVAs were conducted to test whether Veredas' presence varied according to terrain, soil, and climatic variables, each using a Euclidean distance and 10,000 iterations. Multicollinearity was not tested in these models, as PERMANOVA is robust to this issue (Anderson 2017 ). To evaluate he individual contributions of the predictor variables, we first assessed multicollinearity by calculating variance inflation factor (VIF) values using the R-package usdm version 1.1–18 (Naimi 2017 ). Variables with VIF value ≥ 3.0 were considered multicollinear (Zuur et al. 2009 ; 2010 ). Following stepwise removal of variables with the highest VIFs values, 10 predictor variables were retained and 13 multicollinear variables were eliminated. To investigate factors influencing the probability of Vereda occurrence in the microregions ( i.e. presence-absence data), we fitted a generalised linear model (GLM) with a binomial error distribution using the selected predictor variables, implemented in the lme4 package (Bates 2018 ). Model diagnostics included an assessment of residual spatial autocorrelation to determine whether a spatial autoregressive model was required. Using the geographical coordinates of the grid centroids, Moran's I test was conducted with the R-package spdep (Bivand et al. , 2018), which indicated no significant spatial autocorrelation (Moran's I SD = 0.57; p = 0.285). The significance of variables was assessed using likelihood ratio (LR) tests (Zuur et al 2009 ). For significant variables, the amount of variance explained was calculated with Tjur’s R 2 ( i.e. coefficient of discrimination for GLMs with binary outcomes; Tjur 2009 ) using the R-package performance version 0.4.2. (Lüdecke et al 2019 ). We assessed which predictor variables were associated to the abundance of Veredas across microregions. Due to the high frequency of zeros in the dataset, we fitted a zero-inflated generalised linear model (ZIGLM) with Poisson distribution using the R-package glmmTMB version 1.0.0 (Magnusson et al 2020 ). Spatial autocorrelation was evaluated and not detected in this model (Moran's I SD = -0.725; p = 0.766). The significance of variables was assessed using LR tests. For significant predictors, the variance explained was calculated using the “R 2 for models with zero-inflation component” implemented in the R-package performance . Exploratory analyses were performed following the protocol of Zuur et al. ( 2010 ). Model validation was conducted by inspecting the homogeneity of fitted vs. residual values plots, quantile-quantile plots, and histograms (Zuur et al 2009 ). All analyses were conducted in R software version 3.6.2. Results Principal components analysis Together, PC1 (42.00%), PC2 (14.94%) and PC3 (10.87%) explained 67.80% of the total variance in the matrix (Fig. 2 A–C). Along PC1, contributions were broadly distributed across the variables, with the most influential being precipitation of the wettest month, altitude, diurnal temperature range, annual temperature, sand content, clay content, temperature annual range, annual precipitation, silt content, and soil organic carbon stock (in decreasing order), all with contributions above the mean (Table 1 ). Along PC2, slope variance, maximum slope, mean slope, difference of altitude, variance of annual temperature, absolute depth to bedrock, minimum depth to bedrock, and temperature seasonally contributed above the mean (Table 1 ). Along PC3, the variables contributing above the mean were cation exchange capacity, soil organic carbon content, pH, and soil organic carbon stock (Table 1 ). Based on PCA eigenvectors, three groups of correlated variables were evident ( i.e. each pointing in different directions) (Fig. 2 C). Table 1 Contributions by variable (in %) for the first three PC axes. Explanations sum 100% on each column. Variables in bold are those whose contribution is above the average on that given dimension. Variables PC1 PC2 PC3 Climate Mean of annual precipitation 5.76 1.88 0.28 Mean of annual temperature 6.64 0.04 2.68 Mean of diurnal temperature range 6.74 0.02 2.48 Mean of precipitation of the driest month 1.64 0.42 2.90 Mean of precipitation of the wettest month 7.49 1.21 2.63 Mean of precipitation seasonality 4.04 0.66 3.62 Mean of temperature annual range 6.14 0.49 1.86 Mean of temperature seasonally 2.37 4.40 0.56 Variance of annual temperature 2.24 12.27 0.03 Relief Difference of altitude 3.81 14.45 0.15 Maximum of slope 3.48 15.24 0.23 Mean of altitude 7.40 0.34 3.34 Mean of slope 3.87 14.58 0.33 Variance of slope 2.69 16.53 0.00 Soil Mean of absolute depth to bedrock 4.14 6.95 0.55 Mean of cation exchange capacity 1.92 0.05 24.10 Mean of clay content 6.25 2.90 3.32 Mean of pH 0.11 0.01 13.76 Mean of sand content 6.47 1.47 2.34 Mean of silt content 5.36 0.00 7.63 Mean of soil organic carbon content 2.42 0.11 13.92 Mean of soil organic carbon stock 5.01 0.19 13.10 Minimum of absolute depth to bedrock 3.99 5.80 0.18 When considering all the 23 predictor variables, microregions with Veredas differed significantly from those without (PERMANOVA: pseudo-F 1,997 = 58.92, p < 0.001; R 2 = 0.056). Significant differences were also detected when variables were analysed by subset: terrain (pseudo-F 1,997 = 155.95, p < 0.001; R 2 = 0.135), soil (pseudo-F 1,997 = 54.03, p < 0.001; R 2 = 0.051) and climatic variables (pseudo-F 1,997 = 72.28, p < 0.001; R 2 = 0.068). Individual contributions of the predictor variables The calculated VIF values indicated that ten predictor variables were non-multicollinear (VIF ≤ 2.58): mean of cation exchange capacity, mean of clay content, mean of pH, mean of precipitation of the driest month, mean of soil organic carbon content, mean of temperature annual range, mean of temperature seasonality, minimum distance of bedrock, variance of annual temperature, variance of slope. Considering the factors determining the probability of Veredas occurrence across microregions (Table 2 ), eight out of these ten variables were significant. Results are reported as the percentage of explained variance ( i.e. the R 2 values*100) to highlight the relative importance of each variable. Among climate predictors, the probability of Veredas occurrence increased with precipitation of the driest month, explaining 5.4% of the variance (Fig. 3 B), and with temperature annual range explaining 11% (Fig. 3 C). Temperature seasonality also showed a positive relationship but explained only 0.4% (Fig. 3 D). In contrast, variance of annual temperature was negatively associated with Veredas occurrence, explaining 3.9% of the variance (Fig. 3 E). For terrain, slope variance was negatively associated with Veredas presence, explaining 8.8% of the variance (Fig. 3 F). Among soil variables, higher cation exchange capacity was linked to a lower probability of occurrence, explaining 5% (Fig. 3 G). Soil pH was positively related but explained only 1.7% (Fig. 4 H). Minimum depth to bedrock explained 5.4% of the variance, with the probability of Vereda occurrence decreasing as bedrock depth increased (Fig. 3 I). Clay content and soil organic carbon content were not significantly associated with Veredas occurrence (Table 2 ). Table 2 Results from modeling on the probability of Veredas’ occurrence (binomial) and their abundance across microregions (zero-inflated Poisson) according to climate, relief, and soil variables. Significant p-values are highlighted in bold. Degrees of freedom = 1 in all cases. Variables Model binomial zero-inflated Poisson χ 2 p R 2 χ 2 p R 2 Climate Mean of precipitation of the driest month 14.63 < 0.001 0.054 3.02 0.082 – Mean of temperature annual range 33.71 < 0.001 0.110 16.81 < 0.001 0.097 Mean of temperature seasonality 21.56 < 0.001 0.004 12.92 < 0.001 0.010 Variance of annual temperature 5.40 0.020 0.039 2.87 0.090 – Relief Variance of slope 24.92 < 0.001 0.088 40.35 < 0.001 0.094 Soil Mean of cation exchange capacity 12.31 < 0.001 0.050 13.70 < 0.001 0.065 Mean of clay content 0.11 0.742 – 0.067 0.796 – Mean of pH 13.53 < 0.001 0.017 4.33 0.037 0.003 Mean of soil organic carbon content 2.26 0.133 – 6.93 0.008 0.058 Minimum distance of bedrock 5.91 0.015 0.054 2.25 0.133 – For the abundance of Veredas , six of the ten predictor variables were significant (Table 2 ). Among the climate variables, abundance was positively associated with temperature annual range and temperature seasonality, explaining 9.7% and 10% of the variance, respectively (Fig. 4 B, C). Slope variance was negatively associated with the abundance, explaining 9.4% of the variance (Fig. 4 D). Among soil variables, cation exchange capacity was negatively associated with the Veredas abundance, explaining 6.5% (Fig. 4 E), whereas soil pH was positively associated but explained only 0.3% (Fig. 4 F). Soil organic carbon content was also negatively associated with the abundance, explaining 5.8% of the variance (Fig. 4 G). Precipitation of the driest month, variance of annual temperature, clay content, and minimum distance of bedrock were not significantly associated with Veredas abundance (Table 2 ). Discussion We demonstrate that Veredas occur in areas with specific hydrological and topographical balances, where high precipitation throughout the year and flat terrain ensure hydromorphic soil formation, as well as water retention and availability. Consistent with our hypotheses, precipitation of the driest month positively influenced the probability of Veredas occurrence, while slope variance was negatively associated with both occurrence and abundance. Microregions with and without Veredas are different, indicating that climate, terrain, and soil variables jointly shape their distribution. Among these, temperature annual range, precipitation of the driest month, and slope variance were the most influential factors, highlighting their importance in determining Veredas occurrence and abundance. Climatic stability and topographic uniformity are critical in creating the conditions necessary for these ecosystems to thrive, maintaining hydrological balance and soil water retention (Barberi et al 2000 ). Similarly, the significant effects of soil properties, such as cation exchange capacity and depth to bedrock, highlight the importance of subsurface water dynamics in shaping both the occurrence and abundance of Veredas . Veredas occurrence depends on specific interactions between climate and topography, while soil characteristics may largely reflect these interactions (Zedler and Kercher 2005 ; Certini and Scalenghe 2023 ). Our analysis showed that cation exchange capacity (CEC) and depth to bedrock significantly influence Veredas occurrence. Lower CEC and shallower bedrock, respectively explaining 5% and 5.4% of the variance, indicate a preference for nutrient-poor soils and stable hydrological conditions. Soils with low CEC are consistent with the need for high permeability and continuous water flow to sustain wetland flora. In contrast, soils with high CEC may retain nutrients but can also impede water drainage, reducing the likelihood of Vereda formation (Nunes et al 2022 ). Similarly, shallow bedrock favours water retention and stable water table, whereas deeper bedrock can facilitate drainage, limiting the hydrological conditions that Veredas require (De-Campos et al 2013 ). These findings highlight the dual role of soil as both an outcome of and a contributor to Veredas occurrence and abundance. Disruptions to the climate-topography-soil balance, such as through farming or urbanisation, could substantially alter soil properties, leading to cascading impacts on Veredas distribution and functionality. For example, agricultural expansion that compacts soils or alters hydrological flows may reduce soil permeability and organic matter content, thereby diminishing the capacity of Veredas to sustain biodiversity and provide ecosystem services. Veredas abundance was determined by environmental gradients rather than spatial clustering. Typically, wetlands might be expected to occur in clusters, leading to areas of accumulation and non-accumulation, but our results diverge from this expectation. The absence of spatial autocorrelation indicates that the distribution of Veredas is not simply influenced by their proximity but is primarily driven by environmental variables in the model. This finding is consistent with Mitsch & Gossilink (2000), who argued that wetland distribution is more closely linked to a combination of abiotic factors rather than spatial clustering alone. The abundance analysis, based on presence probability, further reinforced the significance of environmental determinants. By focusing on probability, the study mitigated potential research biases from localised overrepresentation, providing a more comprehensive view of Veredas distribution. A distinguishing aspect of this study is its emphasis on causality, a dimension often overlooked in previous research. Whereas studies such as Gonçalves et al. ( 2022 ) mainly examined correlations, this study moves beyond correlation to assess causal drivers of Veredas distribution. While novel for Veredas , this probabilistic and causal perspective aligns with broader wetland research, including Cunha et al. ( 2014 ), who also emphasised the value of probabilistic approaches in understanding wetland ecosystems. Veredas wetlands have been the focus of numerous studies on plant biodiversity and the role of abiotic factors in shaping these ecosystems (Rodrigues Bijos et al 2017 ; Cassino et al 2018 ; Nunes et al 2022 ; Horák-Terra et al 2022 ; Nogueira et al 2022 ). However, most research has been confined to localised areas (Araújo et al 2002 ; Silva et al 2022 ; Luna et al 2024 ), overlooking the broader geographic drivers of Veredas distribution. By examining abiotic factors across a wide geographic extent, our study demonstrates how integrating climate, terrain, and soil variables provides new insights into these ecosystems. Although the SoilGrids dataset provided valuable information on soil characteristics, its coarse resolution and data structure may have overlooked finer-scale soil heterogeneity, which could be critical for Vereda ecosystems. Future studies should incorporate higher-resolution datasets and field-based measurements to capture local soil variability more accurately. This broader-scale perspective establishes a foundation for exploring more complex interactions influencing Veredas . While our study addresses the spatial patterns of abiotic drivers, future research should incorporate dynamic processes such as climate variability, land-use change, and hydrological fluctuations. Understanding these temporal dynamics is crucial for capturing how Veredas respond to pressures over time. Long-term monitoring that combines remote sensing with field surveys could provide essential data to complement the spatial analyses, offering a clearer view of ecosystem responses to both natural and anthropogenic impacts. This temporal focus naturally leads to the consideration of predictive tools. Advanced modelling approaches, including machine learning, will be critical for forecasting Veredas distribution under scenarios of climate change and land-use alteration. These predictive models could help identify priority areas for conservation and restoration, particularly in regions facing high degradation risks. However, effective conservation requires more than predictive modelling. Collaboration with local communities, including Indigenous groups, is essential for integrating traditional ecological knowledge into conservation strategies. Such participatory approach can strengthen the effectiveness of conservation efforts and ensure their long-term sustainability by aligning environmental objectives with social priorities. In conclusion, this study advances our understanding of Veredas and their distribution across a broad geographic scale. Here we show that higher water availability from precipitation, associated with flat terrains in low-lying valleys are associated with the occurrence of these important environments. These factors facilitate the formation of typical hydromorphic soils critical for water storage and slow flow. Additionally, by integrating a range of abiotic variables and adopting a multifaceted analytical framework, this work offers a comprehensive view of the determinants that shape these ecosystems. Given the strong influence of climatic and terrain factors in determining Vereda distributions, conservation efforts should prioritise regions with favourable conditions, particularly in areas predicted to experience shifts in precipitation or temperature under climate change. These findings provide a mechanistic basis for guiding targeted conservation actions to preserve the ecological integrity and long-term resilience of Veredas . Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This study was financed in part by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG - RED-00253-16/RED-00039-23 and APQ-03249-22). JCFC is grateful to Fundo Brasileiro para a Biodiversidade (FUNBIO) for his postdoctoral grant (Nº 071/2024 – Project Biodiversidade Rio Doce). Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rogério Victor Soares Gonçalves and João Custódio Fernandes Cardoso. The first draft of the manuscript was written by Rogério Victor Soares Gonçalves and all authors commented on all versions of the manuscript. Nathan Felipe Alves, Raquel Franco Cassino, Yule Roberta Ferreira Nunes, Paulo Eugênio Oliveira contributed to study design, discussion and final draft. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Abdi H, Williams LJ (2010) Principal component analysis. 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Methods in Ecology and Evolution 1:3–14. doi: 10.1111/j.2041-210X.2009.00001.x Zuur AF, Ieno EN, Walker N, et al (2009) Mixed effects models and extensions in ecology with R. doi: 10.1007/978-0-387-87458-6 Supplementary Files Figs1.png Fig. S1. Representation of the grid design, including those with Vereda present and absent, and the sampling points randomly subsampled (500 with Veredas presence and 500 with absence). TableS1.docx Table S1. Variables used in the analysis and resampling mode applied. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 10 Oct, 2025 Editor invited by journal 08 Oct, 2025 Editor assigned by journal 08 Oct, 2025 First submitted to journal 06 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:01:26","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153146,"visible":true,"origin":"","legend":"","description":"","filename":"WELAD25003890structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/a42c1d648b328ae91de61cea.xml"},{"id":94169164,"identity":"af932296-5d8e-4821-ae21-495f1f3bcf46","added_by":"auto","created_at":"2025-10-23 07:01:26","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161448,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/0191de0aba20e801e0e7a8e5.html"},{"id":94169130,"identity":"cbb2cfb2-98a1-4ddb-b6fb-1e8fbbd79145","added_by":"auto","created_at":"2025-10-23 07:01:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1815255,"visible":true,"origin":"","legend":"\u003cp\u003eAerial photograph showing a \u003cem\u003eVereda\u003c/em\u003e ecosystem in Uberlândia city, Minas Gerais state, Brazil.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/723511a77479228e89da81bd.png"},{"id":94169853,"identity":"481bd89c-060f-4364-a729-50af3dcae2c5","added_by":"auto","created_at":"2025-10-23 07:09:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182829,"visible":true,"origin":"","legend":"\u003cp\u003ePCA results on 23 variables. (A) 3D and (B) 2D representations of the first three and the first two PC axes, respectively. The presence/absence of \u003cem\u003eVeredas\u003c/em\u003e was used as a categorical supplementary variable. Ellipses comprise 0.95 CIs. (C) Contributions (expressed in percentages) of variables for the first two PC axes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/74edff07dd290b8f84e73221.png"},{"id":94169132,"identity":"7d0dc554-fb85-426b-bbbb-61c86b548dc2","added_by":"auto","created_at":"2025-10-23 07:01:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360707,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Categorical distribution of \u003cem\u003eVeredas\u003c/em\u003e in the Triângulo Mineiro region. Blue, red, and grey grids indicate microregions with \u003cem\u003eVereda\u003c/em\u003e present, \u003cem\u003eVereda\u003c/em\u003e absent, and unsampled grids, respectively. Probability of occurrence of \u003cem\u003eVeredas\u003c/em\u003e on plots according to (B) mean of precipitation of the driest month, (C) mean of temperature annual range, (D) mean of temperature seasonality, (E) variance of annual temperature, (F) variance of slope, (G) mean of cation exchange capacity, (H) mean of Ph and (I) minimum distance of bedrock. 0 and 1 on the y-axes indicate the presence and absence of \u003cem\u003eVeredas\u003c/em\u003e, respectively, while lines (binomial adjusted) represent predicted probability and their respective 95 % CIs. Points were jittered (\u003cem\u003ei.e.\u003c/em\u003e added random variation) to avoid overplotting.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/d18a7c91671353d6660ed7d8.png"},{"id":94169855,"identity":"4075fdd0-dc42-437b-b01b-9c293cb8f27e","added_by":"auto","created_at":"2025-10-23 07:09:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":243881,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Continuous distribution (\u003cem\u003ei.e.\u003c/em\u003e Kernel density) of \u003cem\u003eVeredas\u003c/em\u003e in the Triângulo Mineiro region. The color ramp varies between red (0.00-0.01 \u003cem\u003eVeredas\u003c/em\u003e/km²), purple (0.01-0.07\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/78a16916094e63b057e1f9df.png"},{"id":94170254,"identity":"ffc4e223-a1af-47e6-9bfa-f00238a2f11b","added_by":"auto","created_at":"2025-10-23 07:17:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3215338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/b2462705-b5b6-4188-b810-d883e6c2a740.pdf"},{"id":94169854,"identity":"0e1ff0bc-2fa9-44c3-913e-d2b684c12e4a","added_by":"auto","created_at":"2025-10-23 07:09:24","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":218953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1. \u003c/strong\u003eRepresentation of the grid design, including those with \u003cem\u003eVereda\u003c/em\u003e present and absent, and the sampling points randomly subsampled (500 with \u003cem\u003eVeredas\u003c/em\u003e presence and 500 with absence).\u003c/p\u003e","description":"","filename":"Figs1.png","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/265cc948acdea4bd6e60d400.png"},{"id":94169134,"identity":"9106ff0a-bec5-47ab-bab2-63dac62fcc4d","added_by":"auto","created_at":"2025-10-23 07:01:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e Variables used in the analysis and resampling mode applied.\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7790212/v1/a69781f7e6192ea3646d538e.docx"}],"financialInterests":"","formattedTitle":"Disentangling Vereda Wetlands determinants across a wide geographic extent","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands are ecosystems in which soil is permanently or seasonally saturated with water, characterised by water-tolerant plants and hydromorphic soil (Hu et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). These ecosystems support high biodiversity, including unique species and processes with intrinsic ecological value (De Groot et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, their societal importance is most clearly recognised through the ecosystem services they provide (Mitsch and Gosselink \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For example, they are key environments for water emergence, regulation, and purification (Zedler and Kercher \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In addition to storing carbon and maintain local climate stability, wetlands support economically important plant and animal species (Mitsch and Gosselink \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The economic value of wetlands is closely linked to their spatial distribution, as human settlements in their proximity directly benefit from their ecological functions (Mitsch and Gosselink \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Accordingly, assessing wetlands\u0026rsquo; spatial distribution and the factors influencing their occurrence is essential for effective conservation and management (Maltby \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Globally, the distribution of wetlands reflects interactions among climate, topography, and soil conditions, which determine water availability, retention, and movement through landscapes (Hu et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Yuan et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Some countries harbour extensive wetland areas, such as Brazil, which contains the largest wetland coverage worldwide, estimated at ca. 27,000,000 hectares (Ramsar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several factors contribute to this vast and diverse array of wetlands, including its status as the fifth largest country in the world, high precipitation levels, generally flat and eroded terrain, water-retaining deep soils, runoff from rivers originating in other countries, and an extensive coastline.\u003c/p\u003e\u003cp\u003eAmong all wetlands in Brazil, the \u003cem\u003eVeredas\u003c/em\u003e are the most common and widely distributed, occurring throughout the Cerrado biome, Brazil\u0026rsquo;s neotropical savanna (da Cunha et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The term \u003cem\u003eVereda\u003c/em\u003e is refers to shallow valleys or headwaters of watercourses characterised by gentle concave slopes with sandy soils and a waterlogged flat central zone, typically occupied by rows of the \u003cem\u003eburiti\u003c/em\u003e (\u003cem\u003eMauritia flexuosa\u003c/em\u003e) palm (Boaventura \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These inland wetlands are marked by permanently hydromorphic soils rich in organic matter, where the water table either surfaces or lies very close to the surface across their extent. This feature differentiates \u003cem\u003eVeredas\u003c/em\u003e from other Cerrado wetland types, such as wet grasslands, marshes, and peatlands. The typical vegetation structure of \u003cem\u003eVeredas\u003c/em\u003e comprises two main zones: an outer grassy-herbaceous zone and a central shrubby-arboreal zone with the emblematic \u003cem\u003eM. flexuosa\u003c/em\u003e palm (Ramos et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ribeiro and Walter \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rodrigues Bijos et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Because of groundwater upwelling and their capacity to retain large volume of water in porous soils, \u003cem\u003eVeredas\u003c/em\u003e are known as \u0026ldquo;the cradle of waters\u0026rdquo; (Boaventura \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They are important for supplying water within the Cerrado and adjacent biomes (Junk et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Located at higher altitudes, water from \u003cem\u003eVeredas\u003c/em\u003e flows through major basins in all directions, serving millions of people, including residents of Brazil's most populous cities. As such, \u003cem\u003eVeredas\u003c/em\u003e provide water for basic human needs, livestock, agriculture, and industry (Wantzen et al \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Eloy et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). They are also one of the few wetland formations capable of long-term organic matter storage, with stable carbon deposits as old as 30,000 years (Barberi et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wantzen et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, \u003cem\u003eVeredas\u003c/em\u003e are important to traditional communities, providing resources from key species for food, timber, and handicrafts (Eichemberg and Scatena \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eContrasting with their widespread importance and occurrence, most studies on \u003cem\u003eVeredas\u003c/em\u003e have focused on spatially restricted areas (Ara\u0026uacute;jo et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Fagundes and Ferreira \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rodrigues Bijos et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Silva et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Previous research have characterised their occurrence to be related to groundwater upwelling, and environmental factors such as precipitation, temperature seasonality, soil texture, and slope (Gon\u0026ccedil;alves et al \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these assumptions are speculative, and, to our knowledge, no studies have examined causal drivers of \u003cem\u003eVeredas\u003c/em\u003e distribution. Gon\u0026ccedil;alves et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provided a first step in addressing this gap by studying the \u003cem\u003eVeredas\u003c/em\u003e of \u003cem\u003eTri\u0026acirc;ngulo Mineiro and Alto Parana\u0026iacute;ba\u003c/em\u003e (TMAP) region, which has one of the highest density of \u003cem\u003eVeredas\u003c/em\u003e in the Cerrado (Cardoso et al., in prep). \u003cem\u003eVeredas\u003c/em\u003e occurrence was compared across three K\u0026ouml;ppen climate zones: Aw (tropical zone with dry winters), Cwa (humid subtropical zone with dry winters and hot summers), and Cwb (humid subtropical zone with dry winters and temperate summers). This ecosystem was most frequent in the western TMAP region, characterised by Aw climate, lower elevation, pronounced precipitation seasonality, slope, soil cation exchange capacity, and silt and sand contents. Although this study provided important insights into \u003cem\u003eVeredas\u003c/em\u003e distribution, it also has limitations. Climate was treated as a categorical variable across large areas, which obscures subtle variation, especially given \u003cem\u003eVeredas\u003c/em\u003e continuous distribution. Moreover, the analysis was restricted to descriptive associations between regional characteristics and the presence or absence of \u003cem\u003eVeredas\u003c/em\u003e, making it difficult to establish causal relationships with specific fine-scale variables or to infer patterns about \u003cem\u003eVeredas\u0026rsquo;\u003c/em\u003e abundance, including the direct influence of water availability and flat terrains.\u003c/p\u003e\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e have been associated with water availability and gently sloping, low-lying landscapes that favour slow water flow (Boaventura \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, we hypothesised that water availability (precipitation) would positively influence their distribution, while steep terrains (slope variance) would have a negative effect. To test these hypotheses, we investigated the probability of occurrence and the abundance of \u003cem\u003eVeredas\u003c/em\u003e using a grid-based approach. The TMAP region was divided into smaller grids to analyse the effects of abiotic factors across microregions. Additionally, we examined the combined influence of other factors, including climate, terrain, and soil variables, allowing us to infer relationships among them. This study represents the first hypothesis-driven, landscape-scale assessment of the impact of abiotic variables on \u003cem\u003eVeredas\u003c/em\u003e occurrence and abundance. These findings are crucial for identifying priority areas for conservation and management, as well as for proposing effective intervention strategies. Moreover, the methods applied here may inspire analyses on larger scales or other wetlands of the world.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy site\u003c/h2\u003e\u003cp\u003eThe \u003cem\u003eTri\u0026acirc;ngulo Mineiro and Alto Parana\u0026iacute;ba\u003c/em\u003e (TMAP) region in central Brazil has a high density of \u003cem\u003eVeredas\u003c/em\u003e (Gon\u0026ccedil;alves et al \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) within the Cerrado biome, a globally recognised biodiversity hotspot. The region experiences mean annual temperatures ranging from 22\u0026deg;C to 26\u0026deg;C and annual precipitation between 1100 to 1750 mm (Novais et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the K\u0026ouml;ppen-Geiger Climate Classification (Alvares et al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), three climatic categories occur in the region: Aw (tropical zone with dry winters), Cwa (humid subtropical zone with dry winters and hot summers), and Cwb (humid subtropical zone with dry winters and temperate summers).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e data for the TMAP region were obtained from the Brazilian Rural Environmental Register (Cadastro Ambiental Rural \u0026ndash; CAR), which provides public access to rural property boundaries, including permanent preservation areas stored in separate files, such as \u003cem\u003eVeredas\u003c/em\u003e. After downloading and merging the data for each municipality, we generated a topology feature to exclude small polygons created by layer intersections and overlapping polygons. This step was performed in ArcGIS Desktop using the \u003cem\u003eError Inspector\u003c/em\u003e and \u003cem\u003eFix Topology Error\u003c/em\u003e tools from the Topology toolbar, applying the rule \u003cem\u003ePolygons Must Not Overlap\u003c/em\u003e. To ensure accuracy, the topology feature was validated again with \u003cem\u003eError Inspector\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eCentroid points were generated within each \u003cem\u003eVereda\u003c/em\u003e polygon using the \u003cem\u003eFeature to Point\u003c/em\u003e tool, to estimate \u003cem\u003eVeredas\u003c/em\u003e abundance. The TMAP region was then divided into 9 km\u0026sup2; grids (\u003cem\u003ei.e.\u003c/em\u003e microregion) by creating tessellations in the \u003cem\u003eSampling\u003c/em\u003e toolbox. A spatial join was performed to associate centroid points with the grids, which were subsequently classified as either containing \u003cem\u003eVeredas\u003c/em\u003e or not. Grids overlapping urban areas, identified using the census sector shapefile (IBGE 2021), were excluded from the analysis. From the remaining grids, 1000 points were randomly subsampled (500 with \u003cem\u003eVeredas\u003c/em\u003e presence and 500 with absence) using the \u003cem\u003eCreate Random Points\u003c/em\u003e function in the \u003cem\u003eSampling\u003c/em\u003e toolbox (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo predict \u003cem\u003eVeredas\u003c/em\u003e occurrence, we used climate, terrain, and soil variables (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Climatic variables were obtained from WorldClim at a spatial resolution of 30 arc-seconds (~\u0026thinsp;1000 m) (Fick and Hijmans \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and terrain variables were sourced from EarthExplorer at a spatial resolution of 1 arc-second (~\u0026thinsp;30 m) (Kretsch \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Because the predictor layers had a finer resolution than the 9 km\u0026sup2; grids, they were resampled, and the mean, minimum, and maximum, or sum was calculated depending on the characteristics of each variable, with each grid treated as a single analysis unit. All spatial operations were performed in ArcMap 10.5.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eWe analysed all 23 predictor variables using principal component analysis (PCA) to identify the variables that contributed most to \u003cem\u003eVeredas\u003c/em\u003e presence and their associations. The analysis was performed with the R-package \u003cem\u003eFactoMineR\u003c/em\u003e (Husson et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), specifying a correlation matrix to account for the different measurement scales of the variables (Abdi and Williams \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The presence or absence of \u003cem\u003eVeredas\u003c/em\u003e was included as a supplementary categorical variable in the biplot for further analysis.\u003c/p\u003e\u003cp\u003eWe then investigated whether microregions with \u003cem\u003eVeredas\u003c/em\u003e differed in Euclidean space according to the 23 variables by conducting a PERMANOVA (Permutational Multivariate Analysis of Variance; 10,000 iterations) implemented in the R-package package \u003cem\u003evegan\u003c/em\u003e (Oksanen \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Later, three separate PERMANOVAs were conducted to test whether \u003cem\u003eVeredas'\u003c/em\u003e presence varied according to terrain, soil, and climatic variables, each using a Euclidean distance and 10,000 iterations. Multicollinearity was not tested in these models, as PERMANOVA is robust to this issue (Anderson \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo evaluate he individual contributions of the predictor variables, we first assessed multicollinearity by calculating variance inflation factor (VIF) values using the R-package \u003cem\u003eusdm\u003c/em\u003e version 1.1\u0026ndash;18 (Naimi \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Variables with VIF value\u0026thinsp;\u0026ge;\u0026thinsp;3.0 were considered multicollinear (Zuur et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Following stepwise removal of variables with the highest VIFs values, 10 predictor variables were retained and 13 multicollinear variables were eliminated.\u003c/p\u003e\u003cp\u003eTo investigate factors influencing the probability of \u003cem\u003eVereda\u003c/em\u003e occurrence in the microregions (\u003cem\u003ei.e.\u003c/em\u003e presence-absence data), we fitted a generalised linear model (GLM) with a binomial error distribution using the selected predictor variables, implemented in the \u003cem\u003elme4\u003c/em\u003e package (Bates \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Model diagnostics included an assessment of residual spatial autocorrelation to determine whether a spatial autoregressive model was required. Using the geographical coordinates of the grid centroids, Moran's I test was conducted with the R-package \u003cem\u003espdep\u003c/em\u003e (Bivand \u003cem\u003eet al.\u003c/em\u003e, 2018), which indicated no significant spatial autocorrelation (Moran's I SD\u0026thinsp;=\u0026thinsp;0.57; p\u0026thinsp;=\u0026thinsp;0.285). The significance of variables was assessed using likelihood ratio (LR) tests (Zuur et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For significant variables, the amount of variance explained was calculated with Tjur\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e (\u003cem\u003ei.e.\u003c/em\u003e coefficient of discrimination for GLMs with binary outcomes; Tjur \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) using the R-package \u003cem\u003eperformance\u003c/em\u003e version 0.4.2. (L\u0026uuml;decke et al \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe assessed which predictor variables were associated to the abundance of \u003cem\u003eVeredas\u003c/em\u003e across microregions. Due to the high frequency of zeros in the dataset, we fitted a zero-inflated generalised linear model (ZIGLM) with Poisson distribution using the R-package \u003cem\u003eglmmTMB\u003c/em\u003e version 1.0.0 (Magnusson et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Spatial autocorrelation was evaluated and not detected in this model (Moran's I SD = -0.725; p\u0026thinsp;=\u0026thinsp;0.766). The significance of variables was assessed using LR tests. For significant predictors, the variance explained was calculated using the \u0026ldquo;R\u003csup\u003e2\u003c/sup\u003e for models with zero-inflation component\u0026rdquo; implemented in the R-package \u003cem\u003eperformance\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eExploratory analyses were performed following the protocol of Zuur et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Model validation was conducted by inspecting the homogeneity of fitted \u003cem\u003evs.\u003c/em\u003e residual values plots, quantile-quantile plots, and histograms (Zuur et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). All analyses were conducted in R software version 3.6.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003ePrincipal components analysis\u003c/h2\u003e\n \u003cp\u003eTogether, PC1 (42.00%), PC2 (14.94%) and PC3 (10.87%) explained 67.80% of the total variance in the matrix (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C). Along PC1, contributions were broadly distributed across the variables, with the most influential being precipitation of the wettest month, altitude, diurnal temperature range, annual temperature, sand content, clay content, temperature annual range, annual precipitation, silt content, and soil organic carbon stock (in decreasing order), all with contributions above the mean (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Along PC2, slope variance, maximum slope, mean slope, difference of altitude, variance of annual temperature, absolute depth to bedrock, minimum depth to bedrock, and temperature seasonally contributed above the mean (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Along PC3, the variables contributing above the mean were cation exchange capacity, soil organic carbon content, pH, and soil organic carbon stock (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on PCA eigenvectors, three groups of correlated variables were evident (\u003cem\u003ei.e.\u003c/em\u003e each pointing in different directions) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eContributions by variable (in %) for the first three PC axes. Explanations sum 100% on each column. Variables in bold are those whose contribution is above the average on that given dimension.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of annual precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of annual temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of diurnal temperature range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of precipitation of the driest month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of precipitation of the wettest month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of precipitation seasonality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of temperature annual range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of temperature seasonally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance of annual temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference of altitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum of slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of altitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance of slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of absolute depth to bedrock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of cation exchange capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of clay content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of sand content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of silt content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of soil organic carbon content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of soil organic carbon stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum of absolute depth to bedrock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen considering all the 23 predictor variables, microregions with \u003cem\u003eVeredas\u003c/em\u003e differed significantly from those without (PERMANOVA: pseudo-F\u003csub\u003e1,997\u003c/sub\u003e = 58.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.056). Significant differences were also detected when variables were analysed by subset: terrain (pseudo-F\u003csub\u003e1,997\u003c/sub\u003e = 155.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.135), soil (pseudo-F\u003csub\u003e1,997\u003c/sub\u003e = 54.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.051) and climatic variables (pseudo-F\u003csub\u003e1,997\u003c/sub\u003e = 72.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.068).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eIndividual contributions of the predictor variables\u003c/h2\u003e\n \u003cp\u003eThe calculated VIF values indicated that ten predictor variables were non-multicollinear (VIF\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;2.58): mean of cation exchange capacity, mean of clay content, mean of pH, mean of precipitation of the driest month, mean of soil organic carbon content, mean of temperature annual range, mean of temperature seasonality, minimum distance of bedrock, variance of annual temperature, variance of slope. Considering the factors determining the probability of \u003cem\u003eVeredas\u003c/em\u003e occurrence across microregions (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), eight out of these ten variables were significant. Results are reported as the percentage of explained variance (\u003cem\u003ei.e.\u003c/em\u003e the R\u003csup\u003e2\u003c/sup\u003e values*100) to highlight the relative importance of each variable. Among climate predictors, the probability of \u003cem\u003eVeredas\u003c/em\u003e occurrence increased with precipitation of the driest month, explaining 5.4% of the variance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB), and with temperature annual range explaining 11% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Temperature seasonality also showed a positive relationship but explained only 0.4% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). In contrast, variance of annual temperature was negatively associated with \u003cem\u003eVeredas\u003c/em\u003e occurrence, explaining 3.9% of the variance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). For terrain, slope variance was negatively associated with \u003cem\u003eVeredas\u003c/em\u003e presence, explaining 8.8% of the variance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). Among soil variables, higher cation exchange capacity was linked to a lower probability of occurrence, explaining 5% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG). Soil pH was positively related but explained only 1.7% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH). Minimum depth to bedrock explained 5.4% of the variance, with the probability of \u003cem\u003eVereda\u003c/em\u003e occurrence decreasing as bedrock depth increased (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eI). Clay content and soil organic carbon content were not significantly associated with \u003cem\u003eVeredas\u003c/em\u003e occurrence (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults from modeling on the probability of \u003cem\u003eVeredas\u0026rsquo;\u003c/em\u003e occurrence (binomial) and their abundance across microregions (zero-inflated Poisson) according to climate, relief, and soil variables. Significant p-values are highlighted in bold. Degrees of freedom\u0026thinsp;=\u0026thinsp;1 in all cases.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ebinomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ezero-inflated Poisson\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of precipitation of the driest month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of temperature annual range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of temperature seasonality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance of annual temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance of slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of cation exchange capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of clay content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of soil organic carbon content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum distance of bedrock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFor the abundance of \u003cem\u003eVeredas\u003c/em\u003e, six of the ten predictor variables were significant (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the climate variables, abundance was positively associated with temperature annual range and temperature seasonality, explaining 9.7% and 10% of the variance, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). Slope variance was negatively associated with the abundance, explaining 9.4% of the variance (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Among soil variables, cation exchange capacity was negatively associated with the \u003cem\u003eVeredas\u003c/em\u003e abundance, explaining 6.5% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE), whereas soil pH was positively associated but explained only 0.3% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). Soil organic carbon content was also negatively associated with the abundance, explaining 5.8% of the variance (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG). Precipitation of the driest month, variance of annual temperature, clay content, and minimum distance of bedrock were not significantly associated with \u003cem\u003eVeredas\u003c/em\u003e abundance (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe demonstrate that \u003cem\u003eVeredas\u003c/em\u003e occur in areas with specific hydrological and topographical balances, where high precipitation throughout the year and flat terrain ensure hydromorphic soil formation, as well as water retention and availability. Consistent with our hypotheses, precipitation of the driest month positively influenced the probability of \u003cem\u003eVeredas\u003c/em\u003e occurrence, while slope variance was negatively associated with both occurrence and abundance. Microregions with and without \u003cem\u003eVeredas\u003c/em\u003e are different, indicating that climate, terrain, and soil variables jointly shape their distribution. Among these, temperature annual range, precipitation of the driest month, and slope variance were the most influential factors, highlighting their importance in determining \u003cem\u003eVeredas\u003c/em\u003e occurrence and abundance. Climatic stability and topographic uniformity are critical in creating the conditions necessary for these ecosystems to thrive, maintaining hydrological balance and soil water retention (Barberi et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Similarly, the significant effects of soil properties, such as cation exchange capacity and depth to bedrock, highlight the importance of subsurface water dynamics in shaping both the occurrence and abundance of \u003cem\u003eVeredas\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e occurrence depends on specific interactions between climate and topography, while soil characteristics may largely reflect these interactions (Zedler and Kercher \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Certini and Scalenghe \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our analysis showed that cation exchange capacity (CEC) and depth to bedrock significantly influence \u003cem\u003eVeredas\u003c/em\u003e occurrence. Lower CEC and shallower bedrock, respectively explaining 5% and 5.4% of the variance, indicate a preference for nutrient-poor soils and stable hydrological conditions. Soils with low CEC are consistent with the need for high permeability and continuous water flow to sustain wetland flora. In contrast, soils with high CEC may retain nutrients but can also impede water drainage, reducing the likelihood of \u003cem\u003eVereda\u003c/em\u003e formation (Nunes et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, shallow bedrock favours water retention and stable water table, whereas deeper bedrock can facilitate drainage, limiting the hydrological conditions that \u003cem\u003eVeredas\u003c/em\u003e require (De-Campos et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings highlight the dual role of soil as both an outcome of and a contributor to \u003cem\u003eVeredas\u003c/em\u003e occurrence and abundance. Disruptions to the climate-topography-soil balance, such as through farming or urbanisation, could substantially alter soil properties, leading to cascading impacts on \u003cem\u003eVeredas\u003c/em\u003e distribution and functionality. For example, agricultural expansion that compacts soils or alters hydrological flows may reduce soil permeability and organic matter content, thereby diminishing the capacity of \u003cem\u003eVeredas\u003c/em\u003e to sustain biodiversity and provide ecosystem services.\u003c/p\u003e\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e abundance was determined by environmental gradients rather than spatial clustering. Typically, wetlands might be expected to occur in clusters, leading to areas of accumulation and non-accumulation, but our results diverge from this expectation. The absence of spatial autocorrelation indicates that the distribution of \u003cem\u003eVeredas\u003c/em\u003e is not simply influenced by their proximity but is primarily driven by environmental variables in the model. This finding is consistent with Mitsch \u0026amp; Gossilink (2000), who argued that wetland distribution is more closely linked to a combination of abiotic factors rather than spatial clustering alone. The abundance analysis, based on presence probability, further reinforced the significance of environmental determinants. By focusing on probability, the study mitigated potential research biases from localised overrepresentation, providing a more comprehensive view of \u003cem\u003eVeredas\u003c/em\u003e distribution. A distinguishing aspect of this study is its emphasis on causality, a dimension often overlooked in previous research. Whereas studies such as Gon\u0026ccedil;alves et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) mainly examined correlations, this study moves beyond correlation to assess causal drivers of \u003cem\u003eVeredas\u003c/em\u003e distribution. While novel for \u003cem\u003eVeredas\u003c/em\u003e, this probabilistic and causal perspective aligns with broader wetland research, including Cunha et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who also emphasised the value of probabilistic approaches in understanding wetland ecosystems.\u003c/p\u003e\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e wetlands have been the focus of numerous studies on plant biodiversity and the role of abiotic factors in shaping these ecosystems (Rodrigues Bijos et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cassino et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nunes et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hor\u0026aacute;k-Terra et al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nogueira et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, most research has been confined to localised areas (Ara\u0026uacute;jo et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Silva et al \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Luna et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), overlooking the broader geographic drivers of \u003cem\u003eVeredas\u003c/em\u003e distribution. By examining abiotic factors across a wide geographic extent, our study demonstrates how integrating climate, terrain, and soil variables provides new insights into these ecosystems. Although the SoilGrids dataset provided valuable information on soil characteristics, its coarse resolution and data structure may have overlooked finer-scale soil heterogeneity, which could be critical for \u003cem\u003eVereda\u003c/em\u003e ecosystems. Future studies should incorporate higher-resolution datasets and field-based measurements to capture local soil variability more accurately. This broader-scale perspective establishes a foundation for exploring more complex interactions influencing \u003cem\u003eVeredas\u003c/em\u003e. While our study addresses the spatial patterns of abiotic drivers, future research should incorporate dynamic processes such as climate variability, land-use change, and hydrological fluctuations. Understanding these temporal dynamics is crucial for capturing how \u003cem\u003eVeredas\u003c/em\u003e respond to pressures over time. Long-term monitoring that combines remote sensing with field surveys could provide essential data to complement the spatial analyses, offering a clearer view of ecosystem responses to both natural and anthropogenic impacts. This temporal focus naturally leads to the consideration of predictive tools. Advanced modelling approaches, including machine learning, will be critical for forecasting \u003cem\u003eVeredas\u003c/em\u003e distribution under scenarios of climate change and land-use alteration. These predictive models could help identify priority areas for conservation and restoration, particularly in regions facing high degradation risks. However, effective conservation requires more than predictive modelling. Collaboration with local communities, including Indigenous groups, is essential for integrating traditional ecological knowledge into conservation strategies. Such participatory approach can strengthen the effectiveness of conservation efforts and ensure their long-term sustainability by aligning environmental objectives with social priorities.\u003c/p\u003e\u003cp\u003eIn conclusion, this study advances our understanding of \u003cem\u003eVeredas\u003c/em\u003e and their distribution across a broad geographic scale. Here we show that higher water availability from precipitation, associated with flat terrains in low-lying valleys are associated with the occurrence of these important environments. These factors facilitate the formation of typical hydromorphic soils critical for water storage and slow flow. Additionally, by integrating a range of abiotic variables and adopting a multifaceted analytical framework, this work offers a comprehensive view of the determinants that shape these ecosystems. Given the strong influence of climatic and terrain factors in determining \u003cem\u003eVereda\u003c/em\u003e distributions, conservation efforts should prioritise regions with favourable conditions, particularly in areas predicted to experience shifts in precipitation or temperature under climate change. These findings provide a mechanistic basis for guiding targeted conservation actions to preserve the ecological integrity and long-term resilience of \u003cem\u003eVeredas\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was financed in part by Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de Minas Gerais (FAPEMIG - RED-00253-16/RED-00039-23 and APQ-03249-22). JCFC is grateful to Fundo Brasileiro para a Biodiversidade (FUNBIO) for his postdoctoral grant (N\u0026ordm; 071/2024 \u0026ndash; Project Biodiversidade Rio Doce).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rog\u0026eacute;rio Victor Soares Gon\u0026ccedil;alves and Jo\u0026atilde;o Cust\u0026oacute;dio Fernandes Cardoso. The first draft of the manuscript was written by Rog\u0026eacute;rio Victor Soares Gon\u0026ccedil;alves and all authors commented on all versions of the manuscript. Nathan Felipe Alves, Raquel Franco Cassino, Yule Roberta Ferreira Nunes, Paulo Eug\u0026ecirc;nio Oliveira contributed to study design, discussion and final draft. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi H, Williams LJ (2010) Principal component analysis. 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Revista Brasileira de Bot\u0026acirc;nica 25:475\u0026ndash;493. doi: 10.1590/S0100-84042002012000012\u003c/li\u003e\n\u003cli\u003eBarberi M, Salgado-Labouriau ML, Suguio K (2000) Paleovegetation and paleoclimate of \u0026ldquo;Vereda de \u0026Aacute;guas Emendadas\u0026rdquo;, central Brazil. Journal of South American Earth Sciences 13:241\u0026ndash;254. doi: 10.1016/S0895-9811(00)00022-5\u003c/li\u003e\n\u003cli\u003eBates D (2018) lme4: Linear mixed‐effects models using Eigen and S4. R package version 1:1.\u003c/li\u003e\n\u003cli\u003eBoaventura RS (2007) Vereda ber\u0026ccedil;o das \u0026aacute;guas. Ecodin\u0026acirc;mica\u003c/li\u003e\n\u003cli\u003eCassino RF, Martinho CT, Da Silva Caminha SAF (2018) A Late Quaternary palynological record of a palm swamp in the Cerrado of central Brazil interpreted using modern analog data. Palaeogeography, Palaeoclimatology, Palaeoecology 490:1\u0026ndash;16. doi: 10.1016/j.palaeo.2017.08.036\u003c/li\u003e\n\u003cli\u003eCertini G, Scalenghe R (2023) The crucial interactions between climate and soil. Science of The Total Environment 856:159169. doi: 10.1016/j.scitotenv.2022.159169\u003c/li\u003e\n\u003cli\u003eda Cunha CN, Piedade MTF, Junk WJ (2014) Classifica\u0026ccedil;\u0026atilde;o e delineamento das \u0026aacute;reas \u0026uacute;midas brasileiras e de seus macrohabitats. EdUFMT\u003c/li\u003e\n\u003cli\u003eDe Groot D, Brander L, Finlayson CM (2018) Wetland Ecosystem Services. In: Finlayson CM, Everard M, Irvine K, et al (eds) The Wetland Book. Springer Netherlands, Dordrecht, pp 323\u0026ndash;333\u003c/li\u003e\n\u003cli\u003eDe-Campos AB, De Cedro DAB, Tejerina-Garro FL, et al (2013) Spatial distribution of tropical wetlands in Central Brazil as influenced by geological and geomorphological settings. Journal of South American Earth Sciences 46:161\u0026ndash;169. doi: 10.1016/j.jsames.2011.12.001\u003c/li\u003e\n\u003cli\u003eEichemberg MT, Scatena VL (2011) Handicrafts from Jalap\u0026atilde;o (TO), Brazil, and their relationship to plant anatomy. The Journal of the Torrey Botanical Society 138:34\u0026ndash;40. doi: 10.3159/TORREY-D-10-00005.1\u003c/li\u003e\n\u003cli\u003eEloy L, Aubertin C, Toni F, et al (2016) On the margins of soy farms: traditional populations and selective environmental policies in the Brazilian Cerrado. The Journal of Peasant Studies 43:494\u0026ndash;516. doi: 10.1080/03066150.2015.1013099\u003c/li\u003e\n\u003cli\u003eFagundes NCA, Ferreira EJ (2016) Veredas da regi\u0026atilde;o sudeste: peculiaridades flor\u0026iacute;sticas e estruturais e situa\u0026ccedil;\u0026atilde;o de conserva\u0026ccedil;\u0026atilde;o. Neotropical Biology and Conservation 11:178\u0026ndash;183. doi: 10.4013/nbc.2016.113.07\u003c/li\u003e\n\u003cli\u003eFick SE, Hijmans RJ (2017) WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302\u0026ndash;4315. doi: 10.1002/joc.5086\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves RVS, Cardoso JCF, Oliveira PE, et al (2022) The role of topography, climate, soil and the surrounding matrix in the distribution of Veredas wetlands in central Brazil. Wetlands Ecology and Management 30:1261\u0026ndash;1279. doi: 10.1007/s11273-022-09895-z\u003c/li\u003e\n\u003cli\u003eHor\u0026aacute;k-Terra I, Terra FDS, Lopes AKA, et al (2022) Soil characterization and drainage effects in a savanna palm swamp (vereda) of an agricultural area from Central Brazil. Revista Brasileira de Ci\u0026ecirc;ncia do Solo 46:e0210065. doi: 10.36783/18069657rbcs20210065\u003c/li\u003e\n\u003cli\u003eHu S, Niu Z, Chen Y (2017a) Global Wetland Datasets: a Review. Wetlands 37:807\u0026ndash;817. doi: 10.1007/s13157-017-0927-z\u003c/li\u003e\n\u003cli\u003eHu S, Niu Z, Chen Y, et al (2017b) Global wetlands: Potential distribution, wetland loss, and status. Science of The Total Environment 586:319\u0026ndash;327. doi: 10.1016/j.scitotenv.2017.02.001\u003c/li\u003e\n\u003cli\u003eHusson F, Josse J, Le S, Mazet J (2018) FactoMineR: multivariate exploratory data analysis and data mining. R package version 1.41. 2018. \u003c/li\u003e\n\u003cli\u003eIBGE IB de G e E (2021) Malha de Setores Censitations. https://www.ibge.gov.br/. \u003c/li\u003e\n\u003cli\u003eJunk WJ, Piedade MTF, Lourival R, et al (2014) Brazilian wetlands: their definition, delineation, and classification for research, sustainable management, and protection. Aquatic Conservation: Marine and Freshwater Ecosystems 24:5\u0026ndash;22. doi: 10.1002/aqc.2386\u003c/li\u003e\n\u003cli\u003eKretsch JL (2000) Shuttle radar topography mission overview. Proceedings 29th Applied Imagery Pattern Recognition Workshop. IEEE Computer Society, pp 276\u0026ndash;276\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;decke D, Makowski D, Ben-Shachar MS, et al (2019) Performance: assessment of regression models performance. CRAN: Contributed Packages \u003c/li\u003e\n\u003cli\u003eLuna ALL, Souza CS, Neves JGS, et al (2024) Temporal and spatial variation of floral resources of woody species in a vereda ecosystem: Uniformity and habitat complementarity. Flora 310:152425. doi: 10.1016/j.flora.2023.152425\u003c/li\u003e\n\u003cli\u003eMagnusson A, Skaug H, Nielsen A, et al (2020) Generalized linear mixed models using template model builder. Package glmmTMB. Version 1:\u003c/li\u003e\n\u003cli\u003eMaltby E (2006) Wetland Conservation and Management: Questions for Science and Society in Applying the Ecosystem Approach. In: Bobbink R, Beltman B, Verhoeven JTA, Whigham DF (eds) Wetlands: Functioning, Biodiversity Conservation, and Restoration. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 93\u0026ndash;116\u003c/li\u003e\n\u003cli\u003eMitsch WJ, Gosselink JG (2000) The value of wetlands: importance of scale and landscape setting. Ecological Economics 35:25\u0026ndash;33. doi: 10.1016/S0921-8009(00)00165-8\u003c/li\u003e\n\u003cli\u003eNaimi B (2017) Usdm: uncertainty analysis for species distribution models. R package. \u003c/li\u003e\n\u003cli\u003eNogueira EV, Bijos NR, Trindade VL, et al (2022) Differences in soil properties influence floristic changes in the Veredas of the Brazilian Cerrado. Brazilian Journal of Botany 45:763\u0026ndash;774. doi: 10.1007/s40415-022-00795-3\u003c/li\u003e\n\u003cli\u003eNovais GT, Brito JLS, Sanches FDO (2018) UNIDADES CLIM\u0026Aacute;TICAS DO TRI\u0026Acirc;NGULO MINEIRO/ALTO PARANA\u0026Iacute;BA. Revista Brasileira de Climatologia. doi: 10.5380/abclima.v23i0.58520\u003c/li\u003e\n\u003cli\u003eNunes YRF, Souza CS, Azevedo IFPD, et al (2022) Vegetation structure and edaphic factors in veredas reflect different conservation status in these threatened areas. Forest Ecosystems 9:100036. doi: 10.1016/j.fecs.2022.100036\u003c/li\u003e\n\u003cli\u003eOksanen J (2019) Vegan: community ecology package. R package version 2.2-0. http://vegan. r-forge. r-project. org/ \u003c/li\u003e\n\u003cli\u003eRamos MVV, Curi N, Motta PEFD, et al (2006) Veredas do tri\u0026acirc;ngulo mineiro: solos, \u0026aacute;gua e uso. Ci\u0026ecirc;ncia e Agrotecnologia 30:283\u0026ndash;293. doi: 10.1590/S1413-70542006000200014\u003c/li\u003e\n\u003cli\u003eRamsar C (2020) The list of wetlands of international importance. RAMSAR Secretariat: Gland, Switzerland \u003c/li\u003e\n\u003cli\u003eRibeiro JF, Walter BMT (2008) As principais fitofisionomias do bioma Cerrado. Cerrado: ecologia e flora 1:151\u0026ndash;212.\u003c/li\u003e\n\u003cli\u003eRodrigues Bijos N, Ulysses Orlando Eug\u0026ecirc;nio C, De Roure Bandeira Mello T, et al (2017) Plant species composition, richness, and diversity in the palm swamps ( veredas ) of Central Brazil. Flora 236\u0026ndash;237:94\u0026ndash;99. doi: 10.1016/j.flora.2017.10.002\u003c/li\u003e\n\u003cli\u003eSilva DPD, Amaral AG, Bijos NR, Munhoz CBR (2017) Is the herb-shrub composition of veredas (Brazilian palm swamps) distinguishable? Acta Botanica Brasilica 32:47\u0026ndash;54. doi: 10.1590/0102-33062017abb0209\u003c/li\u003e\n\u003cli\u003eSilva ND, Ferreira ME, Cunha CND, Nunes GM (2022) Object-Based Classification of the veredas wetland macrohabitat using multispectral imagery from a Remotely Piloted Aircraft System. doi: 10.21203/rs.3.rs-2022860/v1\u003c/li\u003e\n\u003cli\u003eTjur T (2009) Coefficients of Determination in Logistic Regression Models\u0026mdash;A New Proposal: The Coefficient of Discrimination. The American Statistician 63:366\u0026ndash;372. doi: 10.1198/tast.2009.08210\u003c/li\u003e\n\u003cli\u003eWantzen KM, Couto EG, Mund EE, et al (2012) Soil carbon stocks in stream-valley-ecosystems in the Brazilian Cerrado agroscape. Agriculture, Ecosystems \u0026amp; Environment 151:70\u0026ndash;79. doi: 10.1016/j.agee.2012.01.030\u003c/li\u003e\n\u003cli\u003eWantzen KM, Siqueira A, Cunha CND, Pereira De S\u0026aacute; MDF (2006) Stream‐valley systems of the Brazilian Cerrado: impact assessment and conservation scheme. Aquatic Conservation: Marine and Freshwater Ecosystems 16:713\u0026ndash;732. doi: 10.1002/aqc.807\u003c/li\u003e\n\u003cli\u003eYuan L, Wang J, Liu R, et al (2025) Soil properties, climate, and topography jointly determine plant community characteristics in marsh wetlands. Journal of Plant Research 138:37\u0026ndash;50. doi: 10.1007/s10265-024-01593-6\u003c/li\u003e\n\u003cli\u003eZedler JB, Kercher S (2005) WETLAND RESOURCES: Status, Trends, Ecosystem Services, and Restorability. Annual Review of Environment and Resources 30:39\u0026ndash;74. doi: 10.1146/annurev.energy.30.050504.144248\u003c/li\u003e\n\u003cli\u003eZuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems: \u003cem\u003eData exploration\u003c/em\u003e. Methods in Ecology and Evolution 1:3\u0026ndash;14. doi: 10.1111/j.2041-210X.2009.00001.x\u003c/li\u003e\n\u003cli\u003eZuur AF, Ieno EN, Walker N, et al (2009) Mixed effects models and extensions in ecology with R. doi: 10.1007/978-0-387-87458-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"abiotic drivers, climate change, distribution, abundance","lastPublishedDoi":"10.21203/rs.3.rs-7790212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7790212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eVeredas\u003c/em\u003e are wetlands from the Brazilian Cerrado hotspot biome, noted for their rich biodiversity and ecosystem services, including water provision and carbon storage. They are characteristically found in gently sloping, low-lying valleys, where the water table emerges and flows slowly. However, their distribution and abiotic drivers remain poorly understood. Thus, we tested the hypotheses that water availability (i.e., precipitation) has a positive effect on \u003cem\u003eVeredas\u003c/em\u003e\u0026rsquo; distribution, while steep terrains (i.e., slope variance) have a negative effect. We used a grid-based approach to capture fine-scale variation across the Tri\u0026acirc;ngulo Mineiro and Alto Parana\u0026iacute;ba (TMAP) region. We also investigated the effects of multiple climate, terrain, and soil variables in explaining \u003cem\u003eVeredas\u003c/em\u003e occurrence. Our results supported the hypothesis regarding water availability, as the precipitation of the driest month positively influenced the probability of \u003cem\u003eVeredas\u003c/em\u003e occurrence, explaining 5.4% of the variance. Furthermore, our results supported the hypothesis regarding slope variance, as it negatively influenced both the probability of occurrence and the abundance of \u003cem\u003eVeredas\u003c/em\u003e, explaining 8.8% and 9.4% of the variance, respectively. Microregions with \u003cem\u003eVeredas\u003c/em\u003e differed from those without across 23 terrain, soil, and climatic variables, indicating that additional predictors contribute to explaining \u003cem\u003eVeredas\u003c/em\u003e\u0026rsquo; distribution. In contrast with previous descriptive, climate-zone comparisons, this study represents the first hypothesis-driven, landscape-scale evaluation of the determinants of \u003cem\u003eVeredas\u003c/em\u003e occurrence, suggesting that water availability recharges the water table and flat terrains facilitate the formation of hydromorphic soil and slow water drainage. These findings provide a mechanistic basis for identifying priority areas for conservation and water security, highlighting the need for management strategies that anticipate the vulnerability of \u003cem\u003eVeredas\u003c/em\u003e to ongoing climate change in the Cerrado biome.\u003c/p\u003e","manuscriptTitle":"Disentangling Vereda Wetlands determinants across a wide geographic extent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 07:01:20","doi":"10.21203/rs.3.rs-7790212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-01T09:48:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T04:54:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2025-10-08T19:09:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T07:32:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2025-10-06T06:03:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a8810b91-4828-45c8-b97a-3ce20c9aa58a","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T14:30:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 07:01:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7790212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7790212","identity":"rs-7790212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}