DOI: 10.5061/dryad.jdfn2z3gx
title: Prediction of the potentially suitable areas of Leonurus japonicus with the optimized MaxEnt model

description: Leonurus japonicus Houtt. is a traditional Chinese medicinal
 plant with high medicinal and edible value. Wild L. japonicus resources
 have been reduced dramatically in recent years. This study predicted the
 response of distribution range of L. japonicus to climate change
 in China, which provided the scientific basis for the conservation and
 utilization. In this study, 489 occurrence points of L. japonicus were
 selected based on GIS technology and spThin package. The default
 parameters of the Maxent model were adjusted by using ENMeva1 package of
 the R environment, and the optimized Maxent model was used to analyze the
 distribution of L. japonicus. When the feature combination in the
 model parameters is hing and the regularization multiplier is 1.5, the
 Maxent model has a higher degree of optimization. With the AUC of 0.830
 our model showed a good predictive performance The results showed that L.
 japonicus was widely distributed in the current period. The maximum
 temperature of the warmest month, the minimum temperature of the
 coldest month, the precipitation of the wettest month, the
 precipitation of the driest month and altitude were the main environmental
 factors affecting the distribution of L. japonicus. Under the three
 climate change scenarios, the suitable distribution area of L. japonicus
 will range-shift to high latitudes, indicating that the distribution of L.
 japonicus has a strong response to climate change. The regional change
 rate is the lowest under the SSP126-2090s scenario and the highest under
 the SSP585-2090s scenario.
description: Prediction of the potentially suitable areas of Leonurus japonicus
 habitability zones with maxent occurrence points：By sorting out the
 information of Leonurus japonicus specimens recorded in the Chinese
 Digital Herbarium (CVH, http://www.cvh.ac.cn/), and combining with the L.
 japonicus presence points in the Global Biodiversity Information Platform
 (GBIF, https://www.gbif.org/), the existing distribution positions of L.
 japonicus were preliminarily obtained, and then the corresponding latitude
 and longitude coordinates of each distribution point were obtained by
 Baidu coordinate system. All were used for modeling. environmental
 variables：Species’ ecoloical niches are affected by climate, topography,
 biology, and other factors. In consideration of the comprehensiveness and
 complexity of ecological factors, 34 environmental variables which could
 reflect species’ ecoloical niches were selected. The list included 19
 bioclimatic factors, 14 soil factors and a topographic factor
 (altitude).The current (1970–2000), 2050s (2041–2060), and 2090s
 (2081–2100) bioclimatic factor data used in this research were derived
 from the world climate database Worldclim (http://www.worldclim.Org), and
 the pixel size of the data was 2.5 arc-minutes (-5 km). The climate data
 of the 2050s and 2090s were obtained from the Beijing Climate
 Center-Climate System Model-Medium Resolution (BCC-CSM2-MR), one of the
 Coupled Model Inter-Comparison Project Phase 6 (CMIP6) datasets, which
 included three scenarios: sustainable development (SSP126), intermediate
 development (SSP245) and conventional development (SSP585). SSP scenarios
 have a high accuracy and separation rate and can integrate local
 development factors, and so are more convincing than CMIP5 data. The data
 of soil factors and topographic factors were obtained form the World Soil
 Database (HWSD) of the FAO (http://www.fao.org/faostat/en/#data), and the
 provincial national vector map were from China’s Ministry of Natural
 Resources (http://www.mnr.gov.cn/). The environmental variables is in
 ASCii format. ASCii can be viewed using standard GIS software such as:
 environmental variables\\climate\\50126\\bio1.asc Naming convention: Type
 Variables Description UNITS Bio1 Annual Mean Temperature ℃×10 Bioclimatic
 Bio2 Mean Diurnal Range ℃×10 Variables Bio3 Isothermality 1 Bio4
 Temperature Seasonality 1 Bio5 Max Temperatur ℃×10 Bio6 Min Temperature of
 Coldest Month ℃×10 Bio7 Temperature Annual Range  ℃×10 Bio8 Mean
 Temperature of Wettest Quarter ℃×10 Bio9 Mean Temperature of Driest
 Quarter ℃×10 Bio10 Mean Temperature of Warmest Quarter ℃×10 Bio11 Mean
 Temperature of Coldest Quarter ℃×10 Bio12 Annual Precipitation mm Bio13
 Precipitation of Wettest Month mm Bio14 Precipitation of Driest Month mm
 Bio15 Precipitation Seasonality 1 Bio16 Precipitation of Wettest Quarter
 mm Bio17 Precipitation of Driest Quarter mm Bio18 Precipitation of Warmest
 Quarter mm Bio19 Precipitation of Coldest Quarter mm T\_GRAVEL Topsoil
 Gravel Content %vol. Top Soil Variable T\_SAND Topsoil Sand Fraction % wt.
 T\_SILT Topsoil Silt Fraction % wt. T\_CLAY Topsoil Clay Fraction % wt.
 T\_USDA\_TEX\_CLASS Topsoil USDA Texture Classification name
 T\_REF\_BULK\_DENSITY Topsoil Reference Bulk Density kg/dm3 T\_OC Topsoil
 Organic Carbon % weight T\_PH\_H2O Topsoil pH (H2O) -log(H+) T\_CEC\_CLAY
 Topsoil CEC (clay) cmol/kg T\_CEC\_SOIL Topsoil CEC (soil) cmol/kg T\_BS
 Topsoil Base Saturation % T\_TEB Topsoil TEB cmol/kg T\_ESP Topsoil
 Sodicity (ESP) % T\_ECE Topsoil Salinity (Elco) dS/m Terrain ELEV
 Elevation m ENMeval package: To avoid overfitting due to the high
 complexity of the model constructed with the default parameters, which may
 cause the predicted distribution of the potential habitat of L. japonicus
 to deviate too much from the actual situation, this study used the ENMeval
 package in R 4.3.1, and adjusted the two most important parameters,
 namely, regularization multiplier (RM) and feature combination (FC), to
 improve the prediction accuracy of the model. CoordinateCleaner：The R
 software package ‘CoordinateCleaner’ was used to removing records without
 coordinate precision and suspected outliers. Based on the ‘subset’
 ‘clean\_coordinates’ operation in CoordinateCleaner, we obtained the
 results of bias corrections on the datasets. SpThin package: Spatial
 thinning of species occurrence records can help address problems
 associated with spatial sampling biases. Ideally, thinning removes the
 fewest records necessary to substantially reduce the effects of sampling
 bias, while simultaneously retaining the greatest amount of useful
 information. Spatial thinning can be done manually; however, this is
 prohibitively time consuming for large datasets. Using a randomization
 approach, the ‘thin’ function in the spThin R package returns a dataset
 with the maximum number of records for a given thinning distance, when run
 for sufficient iterations. geosphere package:The geosphere package in the
 R environment was used to calculate the centroid range shift distance of
 L. japonicus under different climate change scenarios. SDMTools：The
 package in R language was used to calculate the location of centroid in
 the suitable area of Leonurus japonicus under 6 different economic paths
 in the current and future periods. VIF package: The usdm package provides
 a set of functions to support dealing with problematic situations in
 species distribution modelling (e.g., multicollinearity, positional
 uncertainty).To detect whether predictor variables are subjected to
 multicollinearity, you may use vif (variance inflation factor) metric, and
 some methods implemeted in this package including vifstep or vifcor (a
 stepwise procedure to identify collinear variables).