Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Daneshgah Street, Ardabil, 56199 13131, Iran.
Sci Rep. 2024 Jul 13;14(1):16185. doi: 10.1038/s41598-024-61073-8.
The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0-30 and 30-60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.
本研究旨在评估栖息地适宜性模型的预测精度，确定伊朗中部亚兹德省牧场中 Dorema ammoniacum 的潜在分布范围及其栖息地需求。确定了三个栖息地：Bafgh、Mehriz 和 Nadoushan，并在每个栖息地采用随机系统方法进行采样。在 350 米长的 18 个样带上，每隔相等距离设立了 10 个样方（共 10 个）。采集了 36 个剖面的 0-30 和 30-60 厘米两个深度的土壤样本，并在实验室进行了测量。制作了海拔、坡度和方位图，并从附近的气象站收集了气候信息。使用逻辑回归（LR）、最大熵（MaxEnt）和人工神经网络（ANN）对物种的栖息地进行预测建模。使用 Kappa 系数和曲线下面积（AUC）评估预测图的准确性。LR 模型用于预测 Mehriz（K=0.67）和 Nadoushan（K=0.56）栖息地中研究物种的栖息地，被认为是良好的。MaxEnt 模型预测 Bafgh 和 Mehriz 栖息地中选定物种的栖息地分布为极好（K=0.89，AUC=0.76，K=0.89，AUC=0.98），而在 Nadoushan 栖息地为非常好（K=0.78，AUC=0.85）。然而，ANN 模型预测 Bafgh 和 Nadoushan 栖息地为极好，Mehriz 栖息地为非常好（K=0.87，K=0.90，K=0.63）。总体而言，为了保护 D. ammoniacum 物种，应优先考虑在亚兹德省其他地区开发其栖息地，并在保护计划中优先考虑研究中的栖息地。
