College of Geographical Sciences, Xinjiang University, Urumqi, China.
Key Laboratory of Oasis Ecology of Ministry of Education, Xinjiang University, Urumqi, China.
PeerJ. 2022 May 3;10:e13337. doi: 10.7717/peerj.13337. eCollection 2022.
Rudolph and its variant ( var. (Klotzsch) Tsoong) are alpine plants and traditional Chinese medicines with important medicinal value, and future climate changes may have an adverse impact on their geographic distribution. The maximum entropy (MAXENT) model has the outstanding ability to predict the potential distribution region of species under climate change. Therefore, given the importance of the parameter settings of feature classes (FCs) and the regularization multiplier (RM) of the MAXENT model and the importance of add indicators to evaluate model performance, we used ENMeval to improve the MAXENT niche model and conducted an in-depth study on the potential distributions of these two alpine medicinal plants. We adjusted the parameters of FC and RM in the MAXENT model, evaluated the adjusted MAXENT model using six indicators, determined the most important ecogeographical factors (EGFs) that affect the potential distributions of these plants, and compared their current potential distributions between the adjusted model and the default model. The adjusted model performed better; thus, we used the improved MAXENT model to predict their future potential distributions. The model predicted that Rudolph and its variant ( var. (Klotzsch) Tsoong) would move northward and showed a decrease in extent under future climate scenarios. This result is important to predict their potential distribution regions under changing climate scenarios to develop effective long-term resource conservation and management plans for these species.
瑞香及其变种（Klotzsch）松是高山植物和传统中药，具有重要的药用价值，未来的气候变化可能会对其地理分布产生不利影响。最大熵（MAXENT）模型具有预测物种在气候变化下潜在分布区域的出色能力。因此，鉴于特征类（FC）和 MAXENT 模型的正则化乘数（RM）的参数设置的重要性，以及添加指标以评估模型性能的重要性，我们使用 ENMeval 来改进 MAXENT 生态位模型，并对这两种高山药用植物的潜在分布进行了深入研究。我们调整了 MAXENT 模型中的 FC 和 RM 参数，使用六个指标评估调整后的 MAXENT 模型，确定了影响这些植物潜在分布的最重要生态地理因素（EGFs），并比较了调整后模型和默认模型中它们的当前潜在分布。调整后的模型表现更好；因此，我们使用改进的 MAXENT 模型来预测它们未来的潜在分布。该模型预测瑞香及其变种（Klotzsch）松将向北迁移，在未来气候情景下分布范围将减少。这一结果对于预测这些物种在气候变化情景下的潜在分布区域，制定有效的长期资源保护和管理计划非常重要。
