Prediction of the potentially suitable areas of Ligularia virgaurea and Ligularia sagitta on the Qinghai–Tibet Plateau based on future climate change using the MaxEnt model

Abstract

Ligularia virgaurea and Ligularia sagitta are two species of poisonous plants with strong invasiveness in natural grasslands in China that have caused considerable harm to animal husbandry and the ecological environment. However, little is known about their suitable habitats and the key environmental factors affecting their distribution. Although some studies have reported the distributions of poisonous plants on the Qinghai–Tibet Plateau (QTP) and predicted their potential distributions at local scales in some regions under climate change, there have been few studies on the widespread distributions of L. virgaurea and L. sagitta. In this study, we recorded 276 and 118 occurrence points of L. virgaurea and L. sagitta on the QTP using GPS, and then used the MaxEnt model to predict the distribution of suitable habitats. Results showed that (1) under current climate conditions, L. virgaurea and L. sagitta are mainly distributed in southern Gansu, eastern Qinghai, northwestern Sichuan, eastern Tibet, and southwestern Yunnan, accounting for approximately 34.9% and 39.8% of the total area of the QTP, respectively; (2) the main environmental variables affecting the distribution of suitable habitats for L. virgaurea and L. sagitta are the Human Footprint Index (52.8%, 42.2%), elevation (11%, 4.4%), soil total nitrogen (18.9%, 4.2%), and precipitation seasonality (5.1%, 7.3%); and (3) in the future, in the 2050s and 2070s, the area of habitat of intermediate suitability for L. virgaurea will spread considerably in northwest Sichuan, while that of high suitability for L. sagitta will spread to eastern Tibet and western Sichuan.

Prediction of suitable areas and division of key monitoring zones for Solidago canadensis in Guizhou Province, China

“…The MaxEnt model has been widely used in the prediction of economy, crops, wild protected plants, invasive plants and in other suitable areas [31,32,[46][47][48]. In this study, MaxEnt combined with ArcGIS and ZONATION software was used to predict suitable areas for S canadensis in Guizhou Province and determine its invasion risk areas.…”

“…Elevation data were downloaded from the Geospatial Data Cloud (http://www.gscloud.cn) at a spatial resolution of 30m. Based on the elevation data, slope and slope direction were generated using the ArcToolbox surface analysis tool in ArcGIS [32]. Soil factor data were obtained from the World Soil Database (HWSD V1.2, http://www.Fao.org), with fields beginning with T_ indicating upper soil properties (0-30 cm) and fields beginning with S_ for lower soil properties (30-100 cm).…”

Allelopathy and potential allelochemicals of Ligularia sagitta as an invasive plant

“…sagitta currently exists in alpine grasslands in western Chinese grasslands as a invasive poisonous weeds, impacting on local biodiversity the development of grassland husbandry. 59 Continuous mowing and replanting of grasses are effective restoration methods on grassland restoration. 4 The continued presence of L. sagitta can impede the ability of native grassland vegetation to recover, and continuous mowing and biomass removal reduces competitive and undesirable species, 60 therefore, in the restoration of grasslands degraded by L. sagitta , removing as much as possible of L. sagitta plants.…”

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions

“…The BCC-CSM2-MR climate model under the 6th International Coupled Model Intercomparison Program (CMIP6) climate model was selected for the future climate variables, using data from 2021 to 2040 under four shared socio-economic pathways (SSPs) scenarios: SSP126 (low GHG emissions: carbon dioxide emissions fall to net zero around 2075); SSP245 (intermediate GHG emissions: carbon dioxide emissions will remain at current levels until 2050 and then decline, but will not reach net zero by 2100); SSP370 (high GHG emissions: carbon dioxide emissions will double by 2100); and SSP585 (very high GHG emissions: carbon dioxide emissions will triple by 2075). BCC-CSM2-MR significantly improved the simulation of the climate distribution of mean annual precipitation in China compared to CMIP5 from the previous generation [57]. The land surface temperature and normalized difference vegetation index (NDVI) were obtained from MODIS (https://modis.gsfc.nasa.gov/, accessed on 27 November 2023), using average values from 2013 to 2022 with a spatial resolution of 1 km.…”