DOI: 10.5061/dryad.rjdfn2zjq
title: Flooding drives tropical dry forest tree community assembly in southeast Brazil

description: In this study, we characterized and compared vegetation types associated
 with geomorphological units susceptible to distinct flooding levels.
 Differences in vegetation are related to landform variations. We aimed to
 (i) characterize the vegetation structure and quantify community
 compositional differences among landforms and (ii) compare landforms soil
 characteristics and how these correlate with the tree vegetation. The
 study area is located in the Brazilian Caatinga Domain, near the Verde
 Grande River, a tributary of São Francisco River (coordinates 14º 54’ 38’’
 S 43º 42’ 53’’ W). We allocated six plots in each landform sampled from
 wettest to driest sites: (i) Marginal Dike (RF – Riparian Forest), (ii)
 Upper terrace (RWF – Riparian Wetland Forest), (iii) Lower Terrace (WF –
 Wetland Forest), (iv) Lower Plain (OFF – Occasionally Flooded Forest), and
 (v) Upper Plain (UF - Unflooded Forest). We conducted multivariate
 analyses (non-metric multidimensional scaling and Principal component
 analyses) to determine if the five sampled environments formed distinct
 floristic and environmental groups across the flooding gradient. We used
 ANOVA and Tukey post-hoc tests to assess soil variable differences among
 vegetation types (where relationships between edaphic variations and tree
 communities' floristic-structural patterns are the purpose of each
 analysis). A total of 1422 individuals, 26 families, 70 genera, and 89
 species were recorded. The NMDS revealed two distinct floristic groups:
 one group is associated with landforms with assumed higher flood frequency
 (RF, RWF, WF) and one with less frequently flooded landforms (OFF and UF).
 The RF, OFF, and UF landforms contained exclusive species (that only
 occurred in the plots of a particular landform). The species Geofroea
 spinosa (Fabaceae) was responsible for 70% of the total biomass recorded
 in the landforms RWF and WF. The soil analysis showed a gradient of soil
 acidity and fertility related to water saturation, whereby the most
 frequently flooded plots had the highest acidity values and highest
 fertility. We found that flood-related conditions significantly influence
 tree community structure and species distribution in this floodplain in
 the Brazilian Caatinga Domain.
description: <em><strong>Study
 site and sampling</strong></em>
 Our study was conducted near
 the Verde Grande River, in northern Minas Gerais, near the state of Bahia
 in Brazil (coordinates 14º 54’ 38’’ S 43º 42’ 53’’ W). The Verde Grande
 River (557 km of extension), which belongs to the São Francisco river
 basin (area of 641.000 km²), begins in the village of Alto Belo in the
 Bocaiúva municipality, northern Minas Gerais, and flows into the São
 Francisco River in Malhada, Bahia state (Inema). According to Köppen’s
 climate classification, the region falls into the Aw category (hot and
 humid tropical climate), experiencing hot summers and dry winters with
 mild temperatures. Seasonality is another important factor in the basin’s
 climate, with well-defined rainy (October to March) and dry seasons (April
 to September) (ANA). The average total annual precipitation is 785 mm,
 with values ranging from 700 mm in certain parts of the basin to over
 1,300 mm at the headwaters (ANA). The study sites belong to the Caatinga
 biogeographic domain, in a region where seasonally dry tropical forests
 predominate. More specifically, the sites are located in flood-prone areas
 of the Verde Grande River that can be differentiated into five vegetation
 types based on landforms flood frequency from wettest to driest: (i)
 Marginal Dike (RF – Riparian Forest), (ii) Upper terrace (RWF – Riparian
 Wetland Forest), (iii) Lower Terrace (WF – Wetland Forest), (iv) Lower
 Plain (OFF – Occasionally Flooded Forest), and (v) Upper Plain (UF -
 Unflooded Forest) (Figure 1). The “Marginal Dike” can be described as a
 sedimentary ridge along the riverbanks. The bank’s width varies between
 half and four times the channel’s width, while the bank’s height varies
 from a few meters to more than 10 meters and tends to be topographically
 higher closest to the river. Soils are sandier than in other floodplain
 sites and its tree-dominated vegetation is distributed in forest
 fragments. The marginal
 lagoons are flat, often water-saturated, and flood-prone. In these
 environments, the soils are usually clay-rich, and the vegetation is
 dominated by flood-adapted herbaceous plants (trees are rare in this
 environment) (Christofoletti 1980, Lobo and Joly 2000, Silva et al.
 2012). The terraces represent former floodplains and
 can be subdivided into a lower terrace and an upper terrace according to
 flood frequency. The “Lower Terrace” is under the considerable influence
 of paleochannel flooding due to lateral overflow of the main bed and/or
 rainwater accumulation during the rainy season. The “Upper Terrace” is
 located above the floodplain level and, therefore, less susceptible to
 flooding (Christofoletti 1980, Pereira 2013). 
 Floodplains are located near
 shoreline ponds or in depressions and vary in susceptibility to flooding
 depending on topographical position or proximity to the main bed or
 shoreline pond. The “Lower Plain” is located in gentle depressions along
 the floodplain, generally close to the river and/or the marginal lagoons,
 while the “Upper Plain” is located at higher elevations further from the
 main riverbed or marginal lagoons and thus is not affected by flooding
 regimes. <em><strong>Data
 collection</strong></em>
 Woody vegetation on each of
 the five landform types was sampled in 2014. Three plots of 400 m² (20x20
 m) were allocated on each side of the river, a total of six plots per
 site. Our classification of flooding regime of each site was based on
 local knowledge and observable landscape signs. For example, flooded
 forest sites are located in riparian habitats or depressions and have
 evidence of sedimentation and short-lived ponds of standing water. The
 occasionally flooded sites are characterized by shallow depressions and
 are flooded at about once every 30 years. Finally, sites that are never
 flooded are typically 500-1900 m from watercourses, with no evidence of
 temporary ponds.  In each plot, the circumference at breast height
 (1.3 m above the soil) of every living tree that met the inclusion
 criterion (minimum circumference: 10 cm) was recorded. For trees with
 multiple stems, we included those of which the square root of the sum of
 squares of each stem's circumference met the inclusion criterion
 (Scolforo and Mello 1997, Souza et al. 2021). Tree identification was
 performed by a specialist in the field or by comparisons with herbarium
 specimens. Family names were standardized based on the Angiosperm
 Phylogeny Group (APG IV 2016), and species names were based on Reflora
 database
 (reflora.jbrj.gov.br/reflora/herbarioVirtual).
 For the soil data, we
 collected superficial samples of 0.5 liters to 10 cm of depth at three
 points in each plot (beginning, middle, and end). Each sample was stored
 in a plastic bag and later sent to the Federal University of Lavras
 Laboratory of Soil Analysis.  The following variables were analyzed:
 proportions of clay, silt, and sand; pH in water, potassium (K),
 phosphorus (P), calcium (Ca), magnesium (Mg), aluminum (Al), organic
 matter (OM); potential acidity (H+Al); sum of bases (SB), base saturation
 (BS); aluminum saturation (AS); effective Cation Exchange Capacity (CEC);
 Cation Exchange Capacity at pH 7.0 (CECpH7) and remnant phosphorus (rem
 P). <em><strong>Data
 analysis</strong></em> For the tree data, we calculated the
 Shannon-Wiener (H’) and Pielou equability (J) indices (Durigan, 2003). For
 taxonomic distinctness analysis (Clarke and Warwick, 2001), six taxonomic
 categories (subclass, superorder, order, family, genus, and species) of
 trees were used (Supporting information). The value of Δ+ (average
 taxonomic distinctness - average taxonomic path length between two
 randomly chosen species) and Λ+ (variation in taxonomic distinctness,
 which reflects the unevenness in the taxonomic tree of a given species’
 list and represents the variance of these pair-wise path lengths) were
 calculated for each patch and plotted against the corresponding number of
 species. The index was calculated by the ‘taxa2dist’ and ‘taxondive’
 functions of the R package ‘vegan’ (Oksanen et al. 2019). To assess
 whether the five sampled environments across the flooding gradient formed
 distinguishable floristic groups, we employed non-metric multidimensional
 scaling (NMDS) (Gotelli and Ellison, 2011) using abundance data
 (Bray-Curtis distance) and the function "metaMDS" from the vegan
 package (Oksanen et al. 2020). We also evaluated floristic specificity and
 species sharing among the environments. For this, we used the packages
 venn diagram (Chen 2011) and tidyverse (Wickham et al. 2019) in the R
 environment (&lt;www.r-project.org&gt;). 
 We compared the vegetation of
 the five environments in terms of abundance, above-ground woody biomass
 (AGB), and canopy cover (CC). We calculated each species' AGB with
 the pantropical modified equation of Chave et al. (2014), using the
 biomass package (Rejou-Mechain et al. 2017). This allometric equation
 estimates AGB based on stem diameter, a bioclimatic stressor variable E
 (derived from the WorldClim database and based on plot geographic
 coordinates, aiming to compensate for the lack of height information
 through the relationship of this variable with macro-scale climatic
 patterns), and mean wood density values. When available, we used
 species-level values of wood density, otherwise (if species-level
 information was not available), we used genera- or family-level means.
 These values were obtained from the Global Wood Density database (Zanne
 2009). We calculated the proportion of family, species,
 and abundance with the highest biomass in each vegetation type. In each
 landform, we summed the total AGB per species, starting with the most
 dominant species and arranged them in descending order, to add up to 70%
 of each vegetation type total biomass. For the structural analysis of the
 forests, we calculated the average diameter and average biomass/stem/plot
 in the landforms. On a microscale, the vegetation changes sharply between
 environments. To investigate these changes, we analyzed how environmental
 harshness relates to the flooding regime and how the vegetation types
 differ from each other. In this analysis, we only considered the data on
 exclusive species (that only occurred in the plots of a particular
 landform). To determine the abundance, richness, and AGB of these
 exclusive species for each vegetation type we calculated the percentage of
 each of these variables (abundance, richness, and AGB) attained by the
 exclusive species relative to the rest of the vegetation type community in
 the landform.  We assessed whether the environments differ
 significantly in terms of each one of the edaphic variables. We used ANOVA
 and Tukey tests for differences in the original soil variables among the
 vegetation types. We employed Principal Component Analysis (PCA) in the R
 environment with the soil variables collected in the 30 plots to assess
 soil variations among the landforms. Due to intercorrelation, we removed
 Ca, Mg, and Cation Exchange Capacity (CEC) from the
 analysis.
description: # FLOODING DRIVES TROPICAL DRY FOREST TREE COMMUNITY ASSEMBLY IN SOUTHEAST
 BRAZIL Cite this dataset Madeira, Denise et al. (2023). Flooding drives
 tropical dry forest tree community assembly in southeast Brazil [Dataset].
 Dryad.
 [https://doi.org/10.5061/dryad.rjdfn2zjq](https://doi.org/10.5061/dryad.rjdfn2zjq) ## Abstract In this study, we characterized and compared vegetation types associated with landform units susceptible to distinct flooding levels, from tropical dry forests fragments located in the Brazilian *Caatinga* Domain. Each response variable was measured during plot sampling in sites. ## Methods Our study was conducted near the Verde Grande River, in northern Minas Gerais, near Bahia state in Brazil. We allocated six plots in each woody vegetation type sampled from wettest to driest sites where differences in vegetation are related to landform variations. More specifically, sites are located in flood-prone areas of the Verde Grande River that can be differentiated into five vegetation types based on landforms flood frequency from wettest to driest: (i) Marginal Dike (RF – Riparian Forest), (ii) Upper terrace (RWF – Riparian Wetland Forest), (iii) Lower Terrace (WF – Wetland Forest), (iv) Lower Plain (OFF – Occasionally Flooded Forest), and (v) Upper Plain (UF - Unflooded Forest) In each plot (400 m²), the circumference at breast height (1.3 m above the soil) of every living tree that met the inclusion criterion (minimum circumference: 10 cm) was recorded. For trees with multiple stems, we included those of which the square root of the sum of squares of each stem's circumference met the inclusion criterion. Tree identification was performed by a specialist in the field. Family names were standardized based on The Angiosperm Phylogeny Group (APG IV 2016), and species names were based on Reflora database (reflora.jbrj.gov.br/reflora/herbarioVirtual). For the soil data, we collected superficial samples of 0.5 liters to 10 cm of depth in three points in each plot (beginning, middle and end). Each sample was stored in a plastic bag and later sent to the Federal University of Lavras Laboratory of Soil Analysis ## Usage notes These datasets were collected in the landscape surrounding the Verde Grande River, São Francisco basin, northern Minas Gerais state, Brazil (2014). Details for each dataset are provided in README file. ## Datasets included: 1. Tree species identified in the plots; 2. Phytosociological and NMDS analysis (abundance matrix); 3. Venn diagram analysis data ( packages *venn* *diagram* (Chen 2011) and *tidyverse* (Wickham et al. 2019) used in the R environment; 4. Soil variables for ANOVA and Tukey tests for differences in the original soil variables among the vegetation types per plot; 5. Soil variables for Principal Component Analysis (PCA) analysis; 6. Aboveground biomass (AGB) (t ha-¹) per tree, per plot and per vegetation type; 7. Average diameter (cm) per tree, per plot and per vegetation type; In addition, a list of species with their respective families, abundances, aboveground biomass (AGB) (t ha-¹), canopy cover (CC) (%) and taxonomic classification table information (data for taxonomic and variation in taxonomic distinctness analysis) are available in the Supporting information of the manuscript. (Madeira et al. 2022 - Nordic Journal of Botany - 10.1111/njb.03913) ## SHARING/ACCESS INFORMATION 1. Links to publications that cite or use the data: Madeira, Denise et al. (2023). Flooding drives tropical dry forest tree community assembly in southeast Brazil. Oikos. 2. Links to other publicly accessible locations of the data: None 3. Links/relationships to ancillary data sets: None 4. Was data derived from another source? No ## DATA & FILE OVERVIEW 1. File List: A. field.csv B. nmds.csv C. venn.csv D. soil\_analysis.csv E. agbabd.csv F. ave\_diam.csv 2. Relationship between files, if important: None 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA ### Tree data DATA-SPECIFIC INFORMATION FOR: field.csv 1. Number of variables: 7 2. Number of cases/rows: 1423 3. Variable List: * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * ind = tree marked with numbered aluminium plates. * family, genus, species; * wd = wood density (Chave et al. (2014)(for R BIOMASS package (Rejou-Mechain et al. 2017); * dq = quadractic diameter(trees with multiple stems) (cm); * agb = tree individual above ground biomass (ton. ha -¹); * yr = year of sampling 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None <br> ### Phytosociological and NMDS analysis (abundance matrix) DATA-SPECIFIC INFORMATION FOR: nmds.csv 1. Number of variables: 92 (abundance matrix) 2. Number of cases/rows: 30 3. Variable List: * plot\_code = plots; * landform = There are 5 different landforms in the study area (i) Marginal Dike, Upper terrace, Lower Terrace, Lower Plain and Upper Plain; * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * species. 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None =========================================================== ### Venn diagram analysis (R package venn diagram (Chen 2011) DATA-SPECIFIC INFORMATION FOR: venn.csv 1. Number of variables: 6 2. Number of cases/rows: 1423 3. Variable List: * plot\_code = plots; * landform = There are 5 different landforms in the study area (i) Marginal Dike, Upper terrace, Lower Terrace, Lower Plain, and Upper Plain * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * family; * species * dq = for presence /absence use only 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None =========================================================================================================== ### Soil variables for ANOVA and Tukey tests for differences in the original soil variables among the vegetation types per plot DATA-SPECIFIC INFORMATION FOR: soil\_analysis.csv 1. Number of variables: 19 2. Number of cases/rows: 31 3. Variable List: * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * plot\_code = plots; * pH in water; * phosphorus (P) (mg/cm³); * potassium (K) (mg/cm³); * calcium (Ca) (mg/dm³); * magnesium (Mg) (mg/dm³); * aluminum (Al) (mg/dm³); * potential acidity (H+Al); * sum of bases (SB)(cmolc/dm³); * effective Cation Exchange Capacity (CEC)(cmolc/dm³); * Cation Exchange Capacity at pH 7.0 (CECpH7)(cmolc/dm³); * base saturation (BS) (%); * aluminum saturation (AS)(%); * organic matter (OM) (dag/kg); * remnant phosphorus (rem P)(mg/L); * clay (dag/kg); * silt (dag/kg); * sand (dag/kg); 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None ===================================================================================================== ### Soil variables for Principal Component Analysis (PCA) (R\_Rescaled) DATA-SPECIFIC INFORMATION FOR: soil\_pca.csv 1. Number of variables: 7 2. Number of cases/rows: 31 3. Variable List: * plot\_code = plots; * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * pH in water; * phosphorus (P) (mg/cm³); * aluminum (Al) (mg/dm³); * sum of bases (SB)(cmolc/dm³); * organic matter (OM) (dag/kg); 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None =================================================================================================== ### Aboveground biomass (AGB) per plot and per tree (t ha-¹) DATA-SPECIFIC INFORMATION FOR: agbabd.csv 1. Number of variables: 5 2. Number of cases/rows: 31 3. Variable List: * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * plot\_code = plots; * agb = above ground biomass(t ha-¹); * abund = tree abundance per plot; * agb\_tree = average agb per tree in the plot (t ha-¹); 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None ====================================================================================================== ### Average diameter (cm) DATA-SPECIFIC INFORMATION FOR: ave\_diam.csv 1. Number of variables: 4 2. Number of cases/rows: 31 3. Variable List: * vg\_type = ((i) Marginal Dike **(RF – Riparian Forest)**, (ii) Upper terrace **(RWF – Riparian Wetland Forest)**, (iii) Lower Terrace **(WF – Wetland Forest)**, (iv) Lower Plain **(OFF – Occasionally Flooded Forest)**, and (v) Upper Plain **(UF - Unflooded Forest)**; * plot\_code = plots; * ave\_diam = average diameter (cm); * abund = tree abundance per plot; 4. Missing data codes: None 5. Specialized formats or other abbreviations used: None ## Funding: This research was supported by: Fundação de Amparo à Pesquisa do Estado de Minas Gerais FAPEMIG. Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq Coordenação de Aperfeiçoamento de Pessoal de Nível Superior <br>