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Data Article
A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring
Sungsoo Yoon1,*orcid, Nguyen Duy Liem2orcid, Le Hoang Tu3orcid, Nguyen Kim Loi4orcid
GEO DATA 2024;6(3):150-158.
DOI: https://doi.org/10.22761/GD.2024.0022
Published online: September 30, 2024

1Associate Researcher, Ecological Information Team, National Institute of Ecology, 1210 Geumgang-ro, Maseo-myeon, Seocheon-gun, 33657 Chungcheongnam-do, South Korea

2Lecturer, Faculty of Environment and Natural Resources, Nong Lam University - Ho Chi Minh City, Linh Trung ward, Thu Duc city, 700000 Ho Chi Minh, Vietnam

3Researcher, Research Center for Climate Change, Nong Lam University - Ho Chi Minh City, Linh Trung ward, Thu Duc city, 700000 Ho Chi Minh, Vietnam

4Director, Research Center for Climate Change, Nong Lam University - Ho Chi Minh City, Linh Trung ward, Thu Duc city, 700000 Ho Chi Minh, Vietnam

Corresponding Author Sungsoo Yoon Tel: +82-41-950-5645 E-mail: yssfran@nie.re.kr
• Received: July 30, 2024   • Revised: August 30, 2024   • Accepted: September 23, 2024

Copyright © 2024 GeoAI Data Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.
Mangrove forests present one of the highest biological diversity and productive ecosystem in the globe by providing ecosystem services such as coastal area protection, carbon storage and habitat provision for various species (Alongi, 2014; Field et al., 1998; Marois and Mitsch, 2015; Nagelkerken et al., 2008). Mangrove ecosystems dominantly cover tropical and subtropical coastlines and are mostly found in Asia with the coverage of 56,900 km2 globally (Jia et al., 2023). In Southeast Asia, including Vietnam, efforts to restore and conserve mangroves are critical under the recognition of their importance in addressing climate change (Schmitt et al., 2013; Veettil et al., 2019). Although recent studies have published maps representing distributions of mangroves or drivers of mangrove forest change with 10 to 30 m resolution at the global scale (Bunting et al., 2022; Jia et al., 2023), comprehensive datasets, which integrate field surveys and remote sensing data to develop ecological models for the restoration and conservation of particular mangrove taxa are scarce.
This study aims to develop a dataset for supporting the building and projection of species distribution models (SDMs) for mangrove species in Vietnam. SDMs are statistical tools that generally use species presence data with environmental variables to predict suitable habitats or potential distributions of target species. We incorporate presence-only data from forest inventory map of Vietnam and predictors such as bioclimatic variables, soil properties and elevation data from open data sources. By enabling the accurate SDM construction and climate change scenario-based projections, this dataset will facilitate the development of effective conservation strategies and policy decisions aimed at preserving mangrove ecosystems in the face ongoing environmental challenges.
2.1 Study area
The study area encompassed Vietnam (Fig. 1), which has both tropical and temperate climate zone due to its long latitude and experiences typical monsoon season as of mainland Southeast Asia (Acharya and Bennett, 2021; Misra and DiNapoli, 2013). The annual average temperatures along the coast of Vietnam range from 22.7 to 27.6℃, and yearly receives between 1,500 and 2,500 mm of rainfall. The coastline features 114 estuaries and includes two major and fertile deltas: the Red River Delta in the North and the Mekong River Delta in the South. Vietnam is home to 113 mangrove species, comprising 42 true mangrove species and 71 mangrove associates (Phan and Hoang, 1993) The regional distribution of mangrove species varies, with most of mangrove species (69 and 34 species, respectively) found in the southern and northern regions where large mangrove forests are located.
2.2 Data collection, modification and integration
Mangrove occurrences were collected from 2013 to 2016 as part of national forest inventory map created by Forest Inventory and Planning Institute (FIPI) of Vietnam. The National Forest Inventory Monitoring (NFIM) of Vietnam is conducted every five years and collects mangrove data from circular sample plots, each with an area of 100 m2 and a radius of 5.64 m. In nationwide forested areas, 12 sample plots are assigned to each 1 km2 grid. The original mangrove occurrence data includes 53,676 points from seven genera of mangroves; however, we modified the data to represent the mangrove distribution in 1 km2 resolution grids, ensuring the same spatial extent and resolution of predictors as well as avoiding spatial autocorrelation in SDMs. Also, mangrove genera with fewer than 30 occurrences (n<30) were removed from the final dataset to ensure statistical robustness and reliability of the SDM-based analysis (Fig. 1).
We collected bioclimatic variables, soil properties and elevation data as environmental variables for developing SDMs and projecting SDM results. Historical and future climate data for 19 bioclimatic variables at 1 km resolution were obtained from WorldClim.org (WorldClim, 2024a; WorldClim, 2024b). Future climate data based on the GISS-E2-1-G global climate model (GCM) and four shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) cover two future periods: 2021-2040 and 2041-2060. We collected two soil properties soil organic carbon (SOC) and cation exchange capacity (CEC) based on the previous studies highlighting their significance impacts on the mangrove distributions (Donato et al,. 2011; Kusmana, 1991; Safe’i et al., 2021). The original source (ISRIC, 2024) of SOC and CEC provides values across six depth intervals (0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm). We calculated mean values of SOC and CEC to address the high correlation between the values at different depths and heterogeneous uncertainty associated with the original data. By using the geodata package (Hijmans et al., 2024) of R software (version 4.3.3; R Foundation, Vienna, Austria), digital elevation model (DEM) data were obtained from the Shuttle Radar Topography Mission with a 1 km resolution, which were aggregated from the 90 m resolution data.
The dataset is divided into prediction and projection data. The prediction data is a single table containing coordinate information for 652 occurrences of the genus Rhizophora and 49 occurrences of the genus Avicennia. The prediction data includes 19 bioclimatic variables from historical climate data, two soil property variables, and elevation data (DEM) as predictors. The projection data encompasses the entire territory of Vietnam, divided into 1 km2 grids and comprises eight tables corresponding to the four SSP scenarios and two different future periods. While the 19 bioclimatic variables are extracted from the future climate data modeled under SSP scenario, SOC, CEC, and DEM data were obtained from the original sources without applying SSP scenarios. To generate both the prediction and projection data, the original environmental variable data were aligned to the extent of the raster files of bioclimatic variables retrieved from WorldClim by using ArcGIS Pro (version 3.1.2; Esri, Redlands, CA, USA). However, many missing values are found in SOC, CEC, and DEM data. These gaps were filled using the k-nearest neighbor (kNN) algorithm of the kNN function from the R VIM package (Kowarik and Templ, 2016), which imputed missing data with a major value calculated from the nearest eight neighbor grids. The Mann-Whitney U test was conducted by using the wilcox.test function from the R stats package (R Core Team, 2023) to test the difference in environmental variables in the two mangrove genera. All analyses, including the kNN algorithm and Mann-Whitney U test, were conducted by using R software version 4.3.3.
When comparing the density distribution of environmental variables between two mangrove genera, Avicennia and Rhizophora, the prediction data, significant differences (Mann-Whitney U test, p<0.01) were observed in bio1 (annual mean temperature), bio3 (Isothermality), bio5 (maximum temperature of the warmest month), bio6 (minimum temperature of coldest month), bio8 (mean temperature of the wettest quarter), bio9 (mean temperature of the driest quarter), bio10 (mean temperature of the warmest quarter), bio11 (mean temperature of coldest quarter), bio12 (annual precipitation), bio14 (precipitation of driest month), bio17 (precipitation of driest quarter), bio18 (precipitation of warmest quarter), bio19 (precipitation of coldest quarter), DEM, and SOC (Fig. 2). Avicennia occurrences were found in areas with higher temperatures compared to the habitats where Rhizophora species were found. However, Avicennia species were located in areas with slightly lower precipitation, elevation, and SOC contents. On the other hand, CEC was not significantly different between occurrence sites of the two mangrove genera. In the projection data, an increasing trend of temperatures was generally observed as scenarios progressed from the sustainable future scenario (SSP1-2.6) to the fossil fuel-based development scenario (SSP5-8.5) (Table 2). Conversely, precipitation tends to decrease up to SSP3-7.0 but increases again in SSP5-8.5 in Vietnam. Mangrove occurrences sampled from NFIM in Vietnam are predominantly concentrated in low-lying areas compared to the national average elevation. Additionally, areas with mangrove occurrences showed the higher CEC and SOC values than the average values observed across the country (Table 3).
This study established a dataset, which is highly useful for SDMs for mangrove species in Vietnam, incorporating both prediction data for training SDMs and distribution forecasting using projection data. SDM researchers often rely on species occurrence data available from Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) to predict the potential range of target species based on the bioclimatic correlations (Beck et al., 2013). GBIF currently provides 2,820 and 354 coordinates for Rhizophora and Avicennia species, respectively (GBIF, 2024a; GBIF, 2024b); however, not only the lacking number (n<30) of unique locations per species cannot cover environmental range of mangroves, but GBIF data also do not follow the same nationwide sampling scheme as NFIM data used in this study. The Vietnam’s NFIM scheme has been regularly updated and made publicly available (Cuong, 2012); however, the data collected from this program has not been easily accessible in a useful format for further analysis. Furthermore, previous efforts have either opened the revised-NFIM data for limited geographic areas (Munionen et al., 2014) or focused on presenting results derived from the NFIM data (Paudyal et al., 2020) as well as discussion on possible methods for utilizing and integrating the NFIM data with other data sources (Eerikäinen et al., 2012). Unlike these studies, our research used nationwide, point-based field survey data that is genuinely open to the public. Although the number of Avicennia occurrence sites is significantly smaller compared to that of Rhizophora, distinct distributions across various predictors such as climate, soil proper-ties, and elevation between the two mangrove genera suggest that SDMs can capture specific ecological niches of each mangrove genus by using the prediction data presented in this study. Therefore, the dataset of this study integrates field surveys and remote sensing data, offering comprehensive data essential for accurate ecological modeling of mangrove restoration and conservation in contrast to recent studies that provide high-resolution global maps of mangrove distributions and change drivers (Bunting et al., 2022; Jia et al., 2023).
The kNN algorithm was used to impute missing values for aligning predictors, which means researchers should carefully select areas with potential data discrepancies by masking. For instance, in the case of soil data, many original values located in farmlands or water bodies were missing. The imputed missing values in the projection dataset were simply calculated using the values of nearest grids. Thus, when projecting actual SDM results, additional land use-based masking grids or polygons might be needed to refine the projection area according to the researcher’s objectives and realistic distribution areas of a target species. On the other hand, the projection data were generated to cover entire areas in Vietnam as much as possible, making them valuable for projecting SDMs developed for other species under future climate scenarios. This broad applicability ensures that the dataset can serve as a versatile tool for researchers aiming to predict the impacts of climate change on various species distributions across Vietnam. In conclusion, this dataset not only facilitates the development of niche-specific models for mangrove species but also supports broader ecological and environmental research. Future work should focus on refining the data imputation methods and incorporating more detailed land-use information to enhance the accuracy of SDMs.
Acknowledgements
The authors thank the researchers who providing data and participated in National Forest Inventory Monitoring of Vietnam.

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Funding Information

This work was supported by the Research for maintenance and application of EcoBank (2nd year) funded by National Institute of Ecology (grant number, NIE-B-2024-01).

Data Availability Statement

The data that support the findings of this study are openly available in EcoBank (https://nie-ecobank.kr/) at http://doi.or.kr/10.22756/GEO.20240000000871.

Fig. 1.
A map of study area with major sampling sites and revised mangrove occurrences.
GD-2024-0022f1.jpg
Fig. 2.
Density analysis results of standardized values of predictors with Mann-Whitney U test significance indicated by asterisks.
GD-2024-0022f2.jpg
Table 1.
A list of fields in the dataset
Field name Description Unit Data type
GNS Identification number of points - Character
LAT Latitude Decimal degrees Numeric
LON Longitude Decimal degrees Numeric
RFRNC Reference of occurrence data - Character
bio1 Annual mean temperature Numeric
bio2 Mean diurnal range (mean of monthly [maximun temperautre-minimum temperature]) °C Numeric
bio3 Isothermality (Bio2/Bio7) (×100) % Numeric
bio4 Temperature seasonality (standard deviation ×100) °C Numeric
bio5 Maximum temperature of warmest month °C Numeric
bio6 Minimum temperature of coldest month °C Numeric
bio7 Temperature annual range (Bio5-Bio6) °C Numeric
bio8 Mean temperature of wettest quarter °C Numeric
bio9 Mean temperature of driest quarter °C Numeric
bio10 Mean temperature of warmest quarter °C Numeric
bio11 Mean temperature of coldest quarter °C Numeric
bio12 Annual precipitation mm Numeric
bio13 Precipitation of wettest month mm Numeric
bio14 Precipitation of driest month mm Numeric
bio15 Precipitation seasonality (coefficient of variation) fraction Numeric
bio16 Precipitation of wettest quarter mm Numeric
bio17 Precipitation of driest quarter mm Numeric
bio18 Precipitation of warmest quarter mm Numeric
bio19 Precipitation of coldest quarter mm Numeric
CEC Cation exchange capacity mmol(c)/kg Numeric
SOC Soil organic carbon content in the fine earth fraction dg/kg Numeric
DEM Height above sea level m Numeric
Table 2.
The summary of bioclimatic variables for SSP scenarios from 2021 to 2060
Field name SSP1-2.6 (mean±SD)
SSP2-4.5 (mean±SD)
SSP3-7.0 (mean±SD)
SSP5-8.5 (mean±SD)
2021-2040 2041-2060 2021-2040 2041-2060 2021-2040 2041-2060 2021-2040 2041-2060
bio1 24.47±2.72 24.93±2.68 24.61±2.74 25.14±2.72 24.55±2.74 25.26±2.72 24.51±2.72 25.43±2.69
bio2 7.37±0.85 7.40±0.86 7.31±0.83 7.38±0.84 7.27±0.83 7.28±0.84 7.36±0.85 7.37±0.86
bio3 49.09±12.16 49.71±11.67 49.35±12.14 49.16±11.78 48.80±12.69 48.82±12.61 48.86±12.08 49.45±12.12
bio4 299.37±156.80 292.32±151.28 296.44±155.57 295.67±152.33 302.79±161.00 303.46±158.75 298.16±156.16 295.28±153.77
bio5 31.83±2.38 32.21±2.37 31.96±2.40 32.49±2.40 31.92±2.38 32.66±2.38 31.81±2.37 32.76±2.38
bio6 15.98±4.65 16.59±4.51 16.35±4.60 16.7±4.62 16.12±4.70 16.86±4.61 15.90±4.70 17.05±4.56
bio7 15.84±3.70 15.63±3.54 15.61±3.58 15.79±3.57 15.80±3.80 15.80±3.74 15.90±3.72 15.71±3.61
bio8 26.90±2.27 27.28±2.29 27.03±2.28 27.55±2.28 27.10±2.29 27.81±2.30 26.97±2.28 27.84±2.28
bio9 20.61±4.67 21.22±4.53 20.90±4.60 21.35±4.64 20.68±4.70 21.40±4.65 20.63±4.73 21.66±4.60
bio10 27.74±2.37 28.12±2.37 27.88±2.37 28.40±2.37 27.88±2.37 28.61±2.37 27.75±2.37 28.68±2.37
bio11 20.45±4.27 21.02±4.15 20.67±4.27 21.20±4.22 20.51±4.32 21.23±4.25 20.50±4.27 21.53±4.17
bio12 1,740.16±348.55 1,755.18±344.99 1,711.16±342.24 1,707.62±341.20 1,665.23±333.78 1,635.27±328.98 1,704.40±345.58 1,699.30±339.32
bio13 357.19±84.24 355.18±79.51 345.53±80.29 343.71±79.08 329.49±77.53 328.01±76.40 346.53±82.72 341.48±78.85
bio14 14.16±12.29 14.16±12.40 14.13±12.35 13.8±12.05 14.14±12.31 13.81±11.94 13.92±12.02 13.98±12.13
bio15 84.41±8.98 83.87±8.94 83.32±8.67 83.64±8.22 81.85±8.76 82.97±8.41 83.84±9.14 83.12±8.59
bio16 940.11±213.98 936.98±206.44 910.74±205.16 912.29±206.58 873.73±197.81 870.04±200.57 914.27±209.12 905.19±207.10
bio17 66.65±40.00 66.1±40.49 66.18±40.26 64.78±39.58 66.81±40.35 64.31±39.07 64.83±39.43 65.97±39.90
bio18 642.55±260.98 655.69±264.69 625.65±247.56 628.8±255.11 613.81±232.01 601.27±240.85 636.88±251.21 627.51±251.84
bio19 99.41±87.28 101.21±87.26 100.00±86.18 97.75±85.72 98.8±88.49 95.37±85.08 97.56±85.88 99.78±86.31

SSP, shared socioeconomic pathway; SD, standard deviation.

Table 3.
The summary of DEM and soil property variables in prediction and projection data
Field name Prediction (mean±SD) Prediction range (min, max) Projection (mean±SD) Projection range (min, max)
DEM 2.81±2.75 -3, 15 390.85±426.33 -30, 3,004
CEC 232.53±28.44 154.67, 315.00 177.62±25.13 108.17, 339.33
SOC 389.41±70.38 203.50, 636.66 210.25±71.40 83.17, 824.50

DEM, digital elevation model; SD, standard deviation; min, minimum; max, maximum.

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Meta Data for Dataset
Sort Field Subcategory#1 Subcategory#2
Essential *Title A dataset for species distribution modelling of mangroves in Vietnam: based on the national forest inventory monitoring
*DOI name http://doi.or.kr/10.22756/GEO.20240000000871
*Category Plant
*Temporal Coverage The prediction data:
- Mangrove occurrences: 2013-2016
- Historical climate: 1970-2000
- Soil property variables: 2017
- Elevation data (DEM): 2013
The projection data:
- Future climate: 2021–2040 and 2041–2060
*Spatial Coverage East: 109.4545,West: 102.1458, South: 8.570833, North: 23.3875
WGS84
*Personnel Name Sungsoo Yoon
Affiliation National Institute of Ecology
E-mail yssfran@nie.re.kr
*CC License CC BY-NC
Summary of Dataset The dataset includes genus-level scientific names of mangroves, occurrence coordinates, elevation, bioclimatic variables (covering the historical period from 1970 to 2000 and the future period from 2021 to 2060 using the GISS-E2-1-G GCM under four SSPs: 1-2.6, 2-4.5, 3-7.0, 5-8.5), and soil properties (SOC, CEC). The dataset is composed of one data table for prediction data and eight data tables for projection data.
Optional Project Research for maintenance and application of EcoBank (2nd year)
Instrument R software, ArcGIS Pro, Field Survey

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      A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring
      Image Image
      Fig. 1. A map of study area with major sampling sites and revised mangrove occurrences.
      Fig. 2. Density analysis results of standardized values of predictors with Mann-Whitney U test significance indicated by asterisks.
      A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring
      Field name Description Unit Data type
      GNS Identification number of points - Character
      LAT Latitude Decimal degrees Numeric
      LON Longitude Decimal degrees Numeric
      RFRNC Reference of occurrence data - Character
      bio1 Annual mean temperature Numeric
      bio2 Mean diurnal range (mean of monthly [maximun temperautre-minimum temperature]) °C Numeric
      bio3 Isothermality (Bio2/Bio7) (×100) % Numeric
      bio4 Temperature seasonality (standard deviation ×100) °C Numeric
      bio5 Maximum temperature of warmest month °C Numeric
      bio6 Minimum temperature of coldest month °C Numeric
      bio7 Temperature annual range (Bio5-Bio6) °C Numeric
      bio8 Mean temperature of wettest quarter °C Numeric
      bio9 Mean temperature of driest quarter °C Numeric
      bio10 Mean temperature of warmest quarter °C Numeric
      bio11 Mean temperature of coldest quarter °C Numeric
      bio12 Annual precipitation mm Numeric
      bio13 Precipitation of wettest month mm Numeric
      bio14 Precipitation of driest month mm Numeric
      bio15 Precipitation seasonality (coefficient of variation) fraction Numeric
      bio16 Precipitation of wettest quarter mm Numeric
      bio17 Precipitation of driest quarter mm Numeric
      bio18 Precipitation of warmest quarter mm Numeric
      bio19 Precipitation of coldest quarter mm Numeric
      CEC Cation exchange capacity mmol(c)/kg Numeric
      SOC Soil organic carbon content in the fine earth fraction dg/kg Numeric
      DEM Height above sea level m Numeric
      Field name SSP1-2.6 (mean±SD)
      SSP2-4.5 (mean±SD)
      SSP3-7.0 (mean±SD)
      SSP5-8.5 (mean±SD)
      2021-2040 2041-2060 2021-2040 2041-2060 2021-2040 2041-2060 2021-2040 2041-2060
      bio1 24.47±2.72 24.93±2.68 24.61±2.74 25.14±2.72 24.55±2.74 25.26±2.72 24.51±2.72 25.43±2.69
      bio2 7.37±0.85 7.40±0.86 7.31±0.83 7.38±0.84 7.27±0.83 7.28±0.84 7.36±0.85 7.37±0.86
      bio3 49.09±12.16 49.71±11.67 49.35±12.14 49.16±11.78 48.80±12.69 48.82±12.61 48.86±12.08 49.45±12.12
      bio4 299.37±156.80 292.32±151.28 296.44±155.57 295.67±152.33 302.79±161.00 303.46±158.75 298.16±156.16 295.28±153.77
      bio5 31.83±2.38 32.21±2.37 31.96±2.40 32.49±2.40 31.92±2.38 32.66±2.38 31.81±2.37 32.76±2.38
      bio6 15.98±4.65 16.59±4.51 16.35±4.60 16.7±4.62 16.12±4.70 16.86±4.61 15.90±4.70 17.05±4.56
      bio7 15.84±3.70 15.63±3.54 15.61±3.58 15.79±3.57 15.80±3.80 15.80±3.74 15.90±3.72 15.71±3.61
      bio8 26.90±2.27 27.28±2.29 27.03±2.28 27.55±2.28 27.10±2.29 27.81±2.30 26.97±2.28 27.84±2.28
      bio9 20.61±4.67 21.22±4.53 20.90±4.60 21.35±4.64 20.68±4.70 21.40±4.65 20.63±4.73 21.66±4.60
      bio10 27.74±2.37 28.12±2.37 27.88±2.37 28.40±2.37 27.88±2.37 28.61±2.37 27.75±2.37 28.68±2.37
      bio11 20.45±4.27 21.02±4.15 20.67±4.27 21.20±4.22 20.51±4.32 21.23±4.25 20.50±4.27 21.53±4.17
      bio12 1,740.16±348.55 1,755.18±344.99 1,711.16±342.24 1,707.62±341.20 1,665.23±333.78 1,635.27±328.98 1,704.40±345.58 1,699.30±339.32
      bio13 357.19±84.24 355.18±79.51 345.53±80.29 343.71±79.08 329.49±77.53 328.01±76.40 346.53±82.72 341.48±78.85
      bio14 14.16±12.29 14.16±12.40 14.13±12.35 13.8±12.05 14.14±12.31 13.81±11.94 13.92±12.02 13.98±12.13
      bio15 84.41±8.98 83.87±8.94 83.32±8.67 83.64±8.22 81.85±8.76 82.97±8.41 83.84±9.14 83.12±8.59
      bio16 940.11±213.98 936.98±206.44 910.74±205.16 912.29±206.58 873.73±197.81 870.04±200.57 914.27±209.12 905.19±207.10
      bio17 66.65±40.00 66.1±40.49 66.18±40.26 64.78±39.58 66.81±40.35 64.31±39.07 64.83±39.43 65.97±39.90
      bio18 642.55±260.98 655.69±264.69 625.65±247.56 628.8±255.11 613.81±232.01 601.27±240.85 636.88±251.21 627.51±251.84
      bio19 99.41±87.28 101.21±87.26 100.00±86.18 97.75±85.72 98.8±88.49 95.37±85.08 97.56±85.88 99.78±86.31
      Field name Prediction (mean±SD) Prediction range (min, max) Projection (mean±SD) Projection range (min, max)
      DEM 2.81±2.75 -3, 15 390.85±426.33 -30, 3,004
      CEC 232.53±28.44 154.67, 315.00 177.62±25.13 108.17, 339.33
      SOC 389.41±70.38 203.50, 636.66 210.25±71.40 83.17, 824.50
      Sort Field Subcategory#1 Subcategory#2
      Essential *Title A dataset for species distribution modelling of mangroves in Vietnam: based on the national forest inventory monitoring
      *DOI name http://doi.or.kr/10.22756/GEO.20240000000871
      *Category Plant
      *Temporal Coverage The prediction data:
      - Mangrove occurrences: 2013-2016
      - Historical climate: 1970-2000
      - Soil property variables: 2017
      - Elevation data (DEM): 2013
      The projection data:
      - Future climate: 2021–2040 and 2041–2060
      *Spatial Coverage East: 109.4545,West: 102.1458, South: 8.570833, North: 23.3875
      WGS84
      *Personnel Name Sungsoo Yoon
      Affiliation National Institute of Ecology
      E-mail yssfran@nie.re.kr
      *CC License CC BY-NC
      Summary of Dataset The dataset includes genus-level scientific names of mangroves, occurrence coordinates, elevation, bioclimatic variables (covering the historical period from 1970 to 2000 and the future period from 2021 to 2060 using the GISS-E2-1-G GCM under four SSPs: 1-2.6, 2-4.5, 3-7.0, 5-8.5), and soil properties (SOC, CEC). The dataset is composed of one data table for prediction data and eight data tables for projection data.
      Optional Project Research for maintenance and application of EcoBank (2nd year)
      Instrument R software, ArcGIS Pro, Field Survey
      Table 1. A list of fields in the dataset

      Table 2. The summary of bioclimatic variables for SSP scenarios from 2021 to 2060

      SSP, shared socioeconomic pathway; SD, standard deviation.

      Table 3. The summary of DEM and soil property variables in prediction and projection data

      DEM, digital elevation model; SD, standard deviation; min, minimum; max, maximum.


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