Modeling potential habitats and predicting habitat connectivity for Leucanthemum vulgare Lam. in northwestern rangelands of Iran

• Published in: Environmental monitoring and assessment 2022-02, Vol.194 (2), p.109-109, Article 109
• Main Authors: Khanghah, Sahar Samadi, Moameri, Mehdi, Ghorbani, Ardavan, Mostafazadeh, Raoof, Biswas, Asim
• Format: Article
• Language: English
• Subjects: Acidic soils; Acidity; Atmospheric Protection/Air Quality Control/Air Pollution; Calcium; Circuits; Current density; Distribution; Earth and Environmental Science; Ecological effects; Ecology; Economics; Ecosystem; Ecotoxicology; Electrical conductivity; Electrical resistivity; Elevation; Entropy; Environment; Environmental Management; Environmental Monitoring; Environmental science; Global positioning systems; GPS; Habitat connectivity; Habitats; Hydroxyapatite; Introduced Species; Invasive plants; Invasive species; Iran; Leucanthemum; Leucanthemum vulgare; Magnesium; Maximum entropy; Modelling; Monitoring/Environmental Analysis; Neutralizing; Nonnative species; Organic carbon; Organic matter; Organic phosphorus; Phosphorus; Positioning systems; Potassium; Predictions; Random sampling; Rangelands; Sampling methods; Sand; Saturation; Sodium; Soil; Soil properties; Soil temperature; Soil texture; Statistical sampling; Structure-function relationships; Temperature; Texture
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-021-09716-5

Invasive plants can alter the function and structure of ecosystems resulting in social, economic, and ecological damage. Effective methods to reduce the dominance of invasive plants are needed. The present study was aimed at modeling the invasive species Leucanthemum vulgare Lam. in the rangelands of the Namin region in northwest Iran, as well as predicting the habitat connectivity of this species to detect areas with high habitat connectivity. Modeling of potential habitats was performed using logistic regression (LR) and maximum entropy (MaxEnt); the ensemble map which resulted from these was used to predict habitat connectivity using the electrical circuit method. Topography (elevation, slope, and aspect), climate (precipitation and temperature), and soil (acidity, electrical conductivity, soil texture, calcium, magnesium, sodium, phosphorus, potassium, organic carbon, organic matter, saturation percentage, and total neutralizing value) were used in this study. The presence and absence points of the L. vulgare were recorded using a stratified-random sampling method by means of a global positioning system. Soil samples were collected at a depth of 0 to 30 cm where L. vulgare was present and also where it was absent. According to the results, in LR, the variables of temperature, phosphorus, organic matter, and sand and in the MaxEnt, the variables of sand, total neutralizing value (TNV), and silt were the most influential factors on the distribution of L. vulgare . The appraisal of the MaxEnt performance and the precision of the model prediction were 0.97. The Kappa indices for the predicted map obtained from the LR and MaxEnt models were 0.80 and 0.73, respectively. The models’ evaluation indicated that both models were able to predict the distribution of L. vulgare habitats with a high level of accuracy; however, LR was more reliable. According to the LR prediction, 9.91% (10,556.25 ha) of the Namin region was attacked by L. vulgare . Connectivity assessment showed that the current density spread of L. vulgare continued from the northeast of the Namin region toward the southeast. On the other hand, the higher current density spread was demonstrated in the eastern region (rangelands adjacent to Fandoghlu forests), and other rangelands which are more threatened by the invasion of L. vulgare . Identifying sites exposed to invasive species can help implement programs to prevent invasive species from invading areas where management and prevention should be i