Environmental decision support system development for natural distribution prediction of Festuca ovina in restoration of degraded lands

• Published in: Land degradation & development Vol. 34; no. 18; pp. 5713 - 5732
• Main Authors: Saffariha, Maryam, Jahani, Ali, Roche, Leslie M., Hosseinnejad, Zeinab
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.12.2023; Wiley Subscription Services, Inc
• Subjects: Anthropogenic factors; Decision making; Decision support systems; Ecological conditions; Ecology; Electrical conductivity; Electrical resistivity; environmental decision support system; Festuca ovina; Geographical distribution; Grasses; Human influences; Invasive species; Land degradation; Land management; Learning algorithms; Machine learning; Managers; multilayer perceptron; Multilayer perceptrons; plant distribution; Plant species; Plants (botany); Prediction models; Radial basis function; Range management; Rangelands; Restoration; Sensitivity analysis; Soil depth; Soil properties; Soil texture; Soils; support vector machine; Support vector machines; Texture
• ISSN: 1085-3278; 1099-145X
• DOI: 10.1002/ldr.4872

Anthropogenic activities, species invasions, and ecological factors are driving rapid changes in rangeland ecosystems. For ensuring the richness and sustainability of plant species habitats, there is a pressing need for a reliable prediction model that can accurately forecast and map species distribution under varying ecological conditions. We aimed to compare the performance of three widely used machine learning methods: Multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) in predicting the distribution of Festuca ovina in mountainous protected rangelands. We conducted our investigation by analyzing the distribution of F. ovina in 305 randomly selected plant sample plots. In each plot, we recorded 10 ecological variables. Three machine learning models were developed to predict the likelihood of F. ovina distribution. Our results demonstrated that the RBF model had a higher number of misclassifications (11 samples) compared to MLP and SVM models (10 samples), indicating that MLP and SVM models were more accurate for distribution modeling. Additionally, MLP showed a higher R‐squared value (0.87) compared to SVM (0.85), suggesting that MLP was the most accurate model for restoring degraded lands. Hence, we developed the Festuca ovina Distribution Model (FODM) using the MLP model. Sensitivity analyses revealed that soil texture, soil depth, electrical conductivity (EC), pH, and vegetation density significantly influenced F. ovina distribution, with respective sensitivity coefficients of 0.48, 0.47, 0.45, 0.41, and 0.41. Based on the finalized FODM, we designed an Environmental Decision Support System (EDSS) tool to assist rangeland managers in mapping F. ovina distribution. By applying the EDSS tool, we demonstrated its practicality in using the FODM for effective decision‐making and land management. The EDSS tool serves as a valuable resource for rangeland managers, enabling them to make informed decisions regarding F. ovina restoration and effectively use the predictive capabilities of the FODM in real‐world applications.