Predicting the distribution of suitable habitat of the poisonous weed Astragalus variabilis in China under current and future climate conditions

Abstract

Astragalus variabilis is a locoweed of northwest China that can seriously impede livestock development. However, it also plays various ecological roles, such as wind protection and sand fixation. Here, we used an optimized MaxEnt model to predict the distribution of suitable habitat of A. variabilis under current (1970–2000) conditions and future (2021–2080) climate change scenarios based on recent occurrence records. The most important environmental variables (suitability ranges in parentheses) affecting the distribution of A. variabilis were average maximum temperature of February (–2.12–5.34°C), followed by total precipitation of June (2.06–37.33 mm), and topsoil organic carbon (0.36–0.69%). The habitat suitability of A. variabilis was significantly correlated with the frequency of livestock poisoning (p < 0.05). Under current climate conditions, the suitable environment of A. variabilis was distributed in central and western Inner Mongolia, Ningxia, central and northwestern Gansu, central and northwestern Qinghai, and the four basins around the Tianshan Mountains in Xinjiang. Under future climate conditions, the suitable habitat of A. variabilis shifted to higher latitudes and altitudes. No previous studies have used niche models to predict the suitable environment of this species nor analyzed the relationship between the habitat suitability of poisonous plants and the frequency of animal poisoning. Our findings provide new insights that will aid the prevention of livestock animal poisoning and the control of poisonous plants, promote the development of the livestock husbandry industry, and provide basic information that will facilitate the maintenance of the ecological balance of grassland ecosystems.

Modeling of the potential geographical distribution of naked oat under climate change

“…The soil data were derived from the HWSD. 5 According to the relevant literature, the organic carbon content (t_oc) of the upper soil attribute in the soil variable and the organic carbon content (s_oc) and exchangeable sodium salt (s_esp) of the lower soil attribute were selected ( Huang et al., 2022 ; Shao et al., 2022 ). In order to avoid overfitting of the model, principal component analysis was used to screen the environmental variables with low correlation but high significance ( Fan et al., 2020 ).…”

Potentially suitable habitats of Daodi goji berry in China under climate change

“…The dataset contained 18 soil variables. Due to the lack of data on future soil layers, we assumed that future soil layers were consistent with the current ones over these short time frames ( Zhang et al., 2020a ; Huang et al., 2022 ). The current (averages for 1960 – 1990) and the future 2050s (averages for 2041 – 2060) and 2070s (averages for 2061 – 2080) climate data were obtained from the World Climate Database (WorldClim v1.4, 2.5-minute resolution, https://www.worldclim.org/ ) and converted to ASCII format using ArcGIS.…”

“…The maximum training sensitivity plus specificity logistic threshold (MTSPS) output by MaxEnt under current climatic conditions was employed to classify these ASCII files into unsuitable and suitable for the Daodi goji berry. This threshold is considered simple and effective in determining modeled species’ presence/absence maps ( Huang et al., 2022 ). Areas with suitability values greater than MTSPS were considered suitable for the species ( Dai et al., 2023 ).…”

Predicted Distribution of Locoweed Oxytropis glabra in China under Climate Change

“…The final predictions for the suitable habitat distribution of O. glabra were conducted using MaxEnt 3.4.4 [24], with the optimal combination of Feature Classes (FC) and Regularization Multipliers (RM) settings, consistent with previous studies [25,26].…”