DOI: 10.5061/dryad.18931zd74
title: Data from: Divergence of vessel diameter explains interspecific variation in hydraulic safety to salinity in the Sundarbans mangrove ecosystem

description: Sundarbans (~10000 sq km), the world’s largest single-block mangrove
 ecosystem, provides important ecosystem goods and services to &gt; 7.5
 million coastal people in two neighboring countries, Bangladesh (~60%) and
 India (~40%). It has extensive variability in environmental variables,
 including siltation and salinity, which are influenced by freshwater
 damming in the north and changing seawater levels from the south. Based on
 spatial salinity gradients, this ecosystem is divided into three
 contrasting salinity zones: the low salinity zone (LSZ, eastern and
 northeastern part), the medium salinity zone (MSZ, central and southern
 part), and the high salinity zone (HSZ, western and southwestern part).
 Forest growth, such as tree height, increases from the HSZ to the LSZ and
 varies between salinity zones. However, human disruptions and rapid
 environmental changes, such as sea level rise (SLR), pose a threat to this
 sensitive ecosystem. Increasing salinity creates challenges for hydraulic
 transport in mangrove species. Therefore, studies on variation in intra-
 and interspecific hydraulic traits, and associated xylem anatomy may allow
 us to understand the adaptation of mangrove species to changing
 environmental conditions. in this study, we examined how xylem and
 hydraulic traits vary among dominant tree species such
 as Exocecaria agallocha, Xylocarpus moluccensis, and Heritiera
 fomes growing under climatically identical but heterogeneous environmental
 conditions in the Sundarbans. Although potential conductivity (KP) and
 leaf-specific conductivity (KL) showed species-specific variation, a
 notably greater conductivity was found in the low salinity zone (LSZ),
 which had lower vessel wall reinforcement (t/b)2. Xylem and hydraulic
 traits exhibited mostly strong phylogenetic signals, whereas pairwise
 relationships between traits were phylogenetically independent. The study
 species had distinct hydraulic characteristics, where vessel diameter was
 strongly related to the variation in KP and KL. Furthermore, the study
 species exhibited a weak trade-off between hydraulic efficiency and
 safety. A higher frequency of smaller vessel diameters in light-demanding
 E. agallocha indicates greater hydraulic safety under stressful conditions
 than in shade-tolerant H. fomes, followed by X. moluccensis. Although
 species characteristics place broad bounds on xylem traits, the combined
 effects of salinity, nutrient availability, and tree size modulate vessel
 diameter, which leads to hydraulic conductivity variation. The contrasting
 safety in terms of vessel diameter in mangroves suggests an important role
 in adaptation to salinity and reveals an underlying mechanism of tree
 growth and species distribution in the Sundarbans.
description: Sampling was done in the three salinity zones (LSZ, MSZ and HSZ)
 of the Bangladesh Sundarbans (21°30′ – 22°30′ N, 89° 00′ – 89°55′ E).
 Total 218 wood cores were collected (at a height of 1.3 m from the ground)
 from three widely distributed species, such as <em>Exocecaria
 agallocha </em>(105),<em> Xylocarpus moluccensis</em>
 (47) and <em>Heritiera fomes </em>(66). The DBH (cm) and tree
 height (m) were measured using a diameter tape and a Suunto clinometer
 (Suunto, Vantaa, Finland), respectively. Afterwards, the wood cores were
 placed in polythene bags separately for laboratory analyses.
 We used the outer part of the wood core (near bark) from each
 tree for microtomy to minimize the age effect. Wood sections of 15 μm in
 thickness were cut using a sliding microtome (Microm, Fisher Scientiﬁc,
 Walldorf, Germany). Afterwards, the sections were stained in a solution of
 0.1% safranin (Merck KGaA, Darmstadt, Germany) and 50% ethanol. The
 sections were subsequently washed for 5 minutes in 50, 75, 96, and 100%
 ethanol series and subsequently mounted on microscopic slides.
 Observations were performed under a light microscope, and images were
 captured using a camera system connected to the microscope. The
 microscopic images were analyzed using Fiji ImageJ software.
 We used five 500 μm resolution images for vessel density (VD)
 measurements for each tree using the following
 equation:<em> </em> <em>VD<sub>
 </sub>= number of
 </em><em>vessels/</em><em>area
 (mm<sup>2</sup>)</em> The vessel
 diameter, D (µm) was measured on the images. We randomly measured the
 diameters of 100 vessels for each tree. The grouping
 index (GI) was calculated as the ratio of the total number of vessels to
 the total number of vessel groupings in each image. The
 proportion of water-conducting vessel area (<em>F</em>) and
 vessel composition index (<em>S</em>,
 mm<sup>4</sup>) for each tree were also calculated following
 the following equations: <em>F = VA<sub>
 </sub>* VD</em> <em>S =
 VA/VD</em> where VA<sub>
 </sub>indicates the mean vessel area, and VD<sub>
 </sub>indicates the mean vessel density. Based on
 the Hagen–Poiseuille law, the hydraulically weighted diameter
 (D<sub>H,</sub> µm) was calculated using a statistic that
 weights the vessel lumen size. <em>D<sub>H
 </sub>=
 ƩD<sup>5</sup>/ƩD<sup>4</sup></em>
 Hagen–Poiseuille’s law was also used to determine potential
 conductivity (K<sub>P, </sub>kg m<sup>−1</sup>
 MPa<sup>−1</sup> s<sup>−1</sup>) based on
 hydraulic diameter (D<sub>H</sub>) and vessel density
 (VD). <em>K<sub>P</sub> =
 (</em><em>)</em><em>
 </em><em>* D<sub>H</sub><sup>4</sup> *
 VD</em> where <em>η</em> is the
 viscosity index of water (1.002 × 10<sup>−9</sup> MPa s at 20
 °C) <em>ρ</em> is the density of water at
 20 °C (998.21 kg m<sup>−3</sup>) D is the
 vessel diameter (µm) Leaf-specific conductivity
 (K<sub>L, </sub>kg m<sup>–1</sup>
 MPa<sup>–1</sup> s<sup>–1</sup>) was calculated as
 the ratio of potential conductivity (K<sub>P</sub>) to leaf
 area (LA). The double wall thickness (μm) was measured
 for 50 pairs of interconnected vessels on each tree to determine the mean
 thickness. The vessel wall reinforcement (t/b)<sup>2
 </sup>indicates the capacity of vessels to resist water pressure
 deficit and was calculated as the ratio of the paired vessel wall
 thickness (t) and the diameter of the vessel closest to the hydraulic mean
 diameter (b). All trees with DBH ≥ 4.5 cm (at a height
 of 1.3 m from the ground) were measured to calculate stand characteristics
 such as tree density (stem ha<sup>−1</sup>). The DBH (cm) and
 tree height (m) were measured using a diameter tape and a Suunto
 clinometer (Suunto, Vantaa, Finland), respectively. We also quantified the
 coefficient of variation of DBH (CoV DBH) and tree height (CoV H), which
 indicates tree size inequality in the sample plots. The third order fully
 expanded leaf pair from the apex of the plagiotropic (lateral) branches
 was selected for sampling. We measured the fresh area of the leaves using
 images (captured by a digital camera, Nikon D5500, Nikon, Tokyo, Japan) in
 Adobe Photoshop CS (Adobe, San Jose, California, USA) following Sarker et
 al. (2021).  The soil samples from each PSP were
 collected at a depth of 15 cm using a 5 cm diameter cylindrical soil core
 sampler for laboratory analysis (Sarker et al., 2016). The collected
 composite soil samples were used for these analyses. The soil salinity
 (EC, mS cm<sup>-1</sup>) was measured using a digital
 conductivity meter (Extech 341350A-P Oyster) at a 1:5 ratio in a
 suspension of soil in water (Hardie & Doyle, 2012). A digital pH
 meter (Extech RE300 ExStik) was used to measure the soil pH. The soil
 texture (silt percentage) was analyzed using the hydrometer method (Gee
 & Bauder, 1986). Highly dynamic regional hydrology influences
 sediment deposition in the Sundarbans and, consequently, at variable
 elevations, even within a small distance (&lt; 1 km) (Sarker et al.,
 2016). Five elevation readings were randomly extracted from each PSP from
 the digital elevation model (DEM), and these readings were averaged for
 each PSP to reduce the errors associated with the DEMs (Sarker et al.,
 2021). We selected resource variables such as soil
 NH<sub>4</sub>, P, and K because of their important influences
 on mangrove tree growth and development (Reef et al., 2010). Soil
 NH<sub>4</sub> was measured following the Kjeldahl method
 (Bremner & Breitenbeck, 1983). We followed the molybdovanadate
 method to measure the soil P concentration (Ueda & Wada, 1970).
 The total soil K was determined using an atomic absorption
 spectrophotometer (AA-7000).  
description: This README file was generated on 2025-01-08 by MD. Imam Hossain Imran.
 GENERAL INFORMATION 1. Title of Dataset: Data from: Divergence of vessel
 diameter explains interspecific variation in hydraulic safety to salinity
 in the Sundarbans mangrove ecosystem. 2. Author Information A. Principal
 Investigator Contact Information Name: Md. Qumruzzaman Chowdhury
 Institution: Shahjalal University of Science and Technology Address:
 Sylhet, Bangladesh Email:
 [qumrul-for@sust.edu](mailto:qumrul-for@sust.edu) 3. Date of data
 collection (single date, range, approximate date): 2021 4. Geographic
 location of data collection: Sundarban Mangrove Forest, Bangladesh
 SHARING/ACCESS INFORMATION 1. Links to publicly accessible locations of
 the data: None 2. Links/relationships to ancillary data sets: None 3. Was
 data derived from another source? No A. If yes, list source(s): NA DATA
 & FILE OVERVIEW 1. File List: A) Tree_Traits.csv B)
 Regulatory_Resources_Variables.csv C) Xylem_Traits.csv D)
 Hydraulic_Traits.csv 1. Relationship between files, if important: None 2.
 Additional related data collected that was not included in the current
 data package: None 3. 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 DATA-SPECIFIC INFORMATION FOR:
 Tree_Traits.csv This data set was used to create figure 6. 1. Number of
 variables: 8 2. Number of cases/rows: 219 3. Variable List: *
 Salinity_Zones: Three salinity zones in Sundarban Mangrove Forest from
 where the data were collected: LSZ (Low Salinity Zone), MSZ (Medium
 Salinity Zone), HSZ (High Salinity Zone) * Species: Three tree species
 (Excoecaria agallocha, Xylocarpus mekongensis, Heritiera fomes), which
 were sampled from above mentioned salinity zones. * DBH: Diameter at
 Breast Height (DBH) in centimeter (cm) was measured from each sampled tree
 using Diameter Tape and Suunto clinometer. * Height: Tree Height in meter
 (m) was measured from each sampled tree using Range Finder. * CoV_DBH: The
 coefficient of variation of DBH. * CoV_Height: The coefficient of
 variation of Height. * Tree_Density: Tree density (stem ha^-1) is the
 ratio of tree number and the area. * Leaf_Area: The fresh areas of the
 leaves were measured using images in Adobe Photoshop CS. 4. Missing data
 codes: None 5. Specialized formats or other abbreviations used: None
 DATA-SPECIFIC INFORMATION FOR: Regulatory_Resources_Variables.csv This
 data set was used to create figure 6. 1. Number of variables: 7 2. Number
 of cases/rows: 219 All the soil variables (Regulators and Resources) were
 measured from the collected soil samples under the each trees of all
 sample plots. 1. Variable List: * Species: Three tree species (Excoecaria
 agallocha, Xylocarpus mekongensis, Heritiera fomes), which were sampled
 from above mentioned salinity zones. REGULATORS: ``` * Salinity: The soil
 salinity (EC, mS cm^-1) was measured using a digital conductivity meter. *
 pH: A digital pH meter (Extech RE300 ExStik) was used to measure the soil
 pH. * Silt%: The soil texture (silt percentage) was analyzed using the
 hydrometer method. * Elevation: Elevetaion (m) of each tree collected from
 each sample plot using digital elevation model (DEM). ``` RESOURCES: ``` *
 NH4: Soil NH4 was measured following the Kjeldahl method. *
 Phosphorus_(P): Molybdovanadate method was used to measure the soil P
 concentration. * Pottasium_(K): The total soil K was calculated using an
 atomic absorption spectrophotometer. ``` 4\. Missing data codes: None 1.
 Specialized formats or other abbreviations used: None DATA-SPECIFIC
 INFORMATION FOR: Xylem_Traits.csv This data set was used to create figure
 4 and 5. 1. Number of variables: 7 2. Number of cases/rows: 219 We
 collected outer part of wood cores from all the individual trees and
 prepared those for microtpmy and microscopy to calculate all the xylem and
 hydraulic traits. 1. Variable List: * Species: Three tree species
 (Excoecaria agallocha, Xylocarpus mekongensis, Heritiera fomes), which
 were sampled from above mentioned salinity zones. * Vessel_Dia_(D): The
 vessel diameter, D (both tangential and radial, µm) was measured on the
 thin sectioned microscopic images of wood cores. * Vessel_Density_(VD):
 The vessel Density (VD) was measured on the thin sectioned microscopic
 images of wood cores. * GI: The Vessel Grouping Index (GI) is a measure of
 vessel aggregation in cross sections (the ratio of the total number of
 vessels to the total number of vessel groupings in each image) * F: It is
 the total water-conducting vessel area (F) derived from the VA and VD * S:
 Vessel composition index (S, mm4) is theratio of VA and VD. . * VA: The
 vessel area(VA) was measured on the thin sectioned microscopic images of
 wood cores. 2. Missing data codes: None 3. Specialized formats or other
 abbreviations used: None DATA-SPECIFIC INFORMATION FOR:
 Hydraulic_Traits.csv This data set was used to create figure 2, 3 and 4.
 1. Number of variables: 5 2. Number of cases/rows: 219 3. Variable List: *
 Salinity_Zones: Three salinity zones in Sundarban Mangrove Forest from
 where the data were collected: LSZ (Low Salinity Zone), MSZ (Medium
 Salinity Zone), HSZ (High Salinity Zone) * Species: Three tree species
 (Excoecaria agallocha, Xylocarpus mekongensis, Heritiera fomes), which
 were sampled from above mentioned salinity zones. * DH: The hydraulically
 weighted diameter (DH, µm) was calculated using a statistic that weights
 the vessel lumen size. * Potential_conductivity_(Kp): Potential
 conductivity (KP, kg m−1 MPa−1 s−1) for each individuals of three species
 were calculated based on hydraulic diameter (DH) and vessel density (VD)
 using Hagen–Poiseuille’s law. * Leaf_Specific_Conductivity_(KL):
 Leaf-specific conductivity (KL, kg m–1 MPa–1 s–1) was calculated as the
 ratio of potential conductivity (KP) to leaf area of each individual tree.
 * (t/b)^2: The vessel wall reinforcement (t/b)^2 was calculated as the
 ratio of the paired vessel wall thickness (t) and the diameter of the
 vessel closest to the hydraulic mean diameter. 4. Missing data codes:
 "NULL" 5. Specialized formats or other abbreviations used: None