DOI: 10.5061/dryad.1jwstqk1f
title: Semi-arid African savanna habitat suitability for two Vachellia species

description: The habitat suitability of Vachellia stuhlmannii and Vachellia tortilis
 was assessed using Maximum Entropy model. Location data was collected and
 nineteen bioclimatic variables data for 1950 to 2000 downloaded from the
 WorldClim database. Soils were analyzed to investigate their influence on
 habitat suitability of the two species. MaxEnt effectively predicted
 current habitat suitability with an average test Area Under Curve value of
 0.936 for V. stuhlmannii and 0.689 for V. tortilis. Key influential
 variables were BIO-1 (Annual mean temperature) with 71.7% highest gain for
 V. stuhlmannii and BIO-17 (Precipitation of driest quarter) with 65.3%
 highest gain for V. tortilis. However, BIO-14 (Precipitation of driest
 month) exhibited limited influence in predicting habitat suitability for
 both species. V. stuhlmannii occupies twenty-nine hectares and V. tortilis
 covers 102 hectares. Both species predominantly occur on
 Prismacutanic/pedocutanic B-horizons. V. tortilis also thrive on Glenrosa
 and Mispah soils, which are crucial for diverse plant and animal life.
 These findings have practical implications for conservation efforts aimed
 at protecting both species. Identifying suitable habitats and preserving
 soil types is crucial as soil affects microclimate conditions and
 influences moisture retention. Overall, this study provides insights for
 the conservation of V. stuhlmannii and V. tortilis in the semi-arid
 African savanna.
description: Data
 collection Records of presence for <em>V. stuhlmannii
 </em>and<em> V. tortilis</em> were derived from
 Geographic Positioning System (GPS) locations’ data collected in June 2018
 (Nkosi et al., 2022a). Different bioclimatic variables are key biological
 variables that determine species’ environmental niches (Yi, Cheng, Yang,
 & Zhang., 2016). Historical climatic data of 19 bioclimatic
 variables (Table 1) were downloaded from the WorldClim database at
 approximately 1 km<sup>2</sup> (30 arc-seconds) spatial
 resolution (Fick & Hijmans, 2017) from the website:
 https://www.worldclim.org/. This is climatic data for 1970 – 2000, which was released in January 2020. The data include monthly climate data for minimum, mean and maximum temperatures, precipitation, solar radiation, wind speed, water vapour pressure and total precipitation (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005).  Data analysis To build the model for each of the two <em>Vachellia</em> species, partitions were created by randomly selecting 70% of the presence localities as training data with the remaining 30% as test data (Phillips et al., 2006). This resulted in eighty-two training localities and twenty-eight test localities for <em>V. stuhlmannii</em>, and<em> V. tortilis </em>with fifty-six training localities and nineteen test localities. The MaxEnt software version 3.4.4 was downloaded from https://biodiversityinformatics.amnh.org/open_source/maxent and applied in this study. The bioclimatic data were converted to “ASCII” file format in Quantum-GIS (QGIS Development Team, 2012) for compatibility with MaxEnt. The GPS location data of the two species (<em>V. stuhlmannii </em>and<em> V. tortilis</em>) were converted into “CSV” format and the converted file was used as input to MaxEnt. MaxEnt generated output results that predict the suitability of a habitat for each species using a scale of 0-1, where the lowest suitability areas are represented by zero, while the highest suitability areas are represented by 1 (Sharma et al., 2018). Response curves for each predictor variable were generated from MaxEnt. To assess the performance of the model, the metric used was the Area under the ROC (receiver operating characteristic) curve (AUC), which ranges from 0 to 1 and is a measure of the model’s effectiveness (Vanagas, 2004). According to Swets (1988), the closer the AUC value is to one, the better the model’s performance. MaxEnt also uses the jack-knife method to highlight the relative influence of each predictor variable (Khanum, Mumtaz & Kumar, 2013). To produce the potential habitat maps of the two species, the VLNR boundary map was overlaid on the grid file generated by MaxEnt in QGIS. Soil types (Mucina & Rutherford, 2006) were incorporated into the suitability maps to assess the habitat suitability of the two species in relation to soil characteristics. This is because soil type and structure can impact microclimate conditions by affecting moisture retention (Rost, Gerten, Hoff, Lucht, Falkenmark, & Rockström, 2009). The distribution of habitats could be influenced by the interplay between soil resources and herbivore impact (Skarpe et al., 2004). Moreover, other factors such as variations in soil types could potentially impact the differences observed in the attributes of woody vegetation (Gandiwa et al., 2011). Geology, topography, soil, and water are critical environmental factors that directly affect the structure and composition of vegetation at various scales, ranging from individual plants to landscape levels (Sankaran et al., 2005).
description: # MaxEnt Data
 [https://doi.org/10.5061/dryad.1jwstqk1f](https://doi.org/10.5061/dryad.1jwstqk1f) This dataset contains shapefiles of the two species' (Vachellia stuhlmannii and V. tortilis) locations data (GPS locations) wherein the attribute table, a row represents a unique data point, and each column corresponds to GPS coordinates. Bioclimatic (BIO1-Bio19) data is also included as raster files. Additionally, shapefiles for soil types as per Mucina & Rutherford, 2006's classification are included for the study area since soil analysis was performed to determine soil's influence on the spatial distribution of the two species. ## Description of the data and file structure The data are in formats compatible with MaxEnt: two species shapefiles and bioclimatic data converted into ASCII file format using GIS (QGIS was used in this case). Steps involved include dividing the dataset into two subsets: a training set (70%) and a test set (30%). Used this YouTube tutorial link for all other steps up to the output results: [https://youtu.be/Fv2Xx1TvM1s](https://youtu.be/Fv2Xx1TvM1s). Shapefiles for the soil classification of the study area included. ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. MaxEnt software version 3.4.4 was downloaded from: * [https://biodiversityinformatics.amnh.org/open_source/maxent](https://biodiversityinformatics.amnh.org/open_source/maxent) Bioclimatic data was derived from the following sources: * [https://www.worldclim.org/](https://www.worldclim.org/) ## Code/Software This is an optional, freeform section for describing any code in your submission and the software used to run it. Describe any scripts, code, or notebooks (e.g., R, Python, Mathematica, MatLab) as well as the software versions (including loaded packages) that you used to run those files. If your repository contains more than one file whose relationship to other scripts is not obvious, provide information about the workflow that you used to run those scripts and notebooks.