Document Type : Research paper
Authors
1
Department of Horticultural Science, School of Agriculture, Shiraz University, Shiraz, Iran, Department of Horticultural Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2
Department of Horticultural Science, School of Agriculture, Shiraz University, Shiraz, Iran
3
Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran
4
Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
5
Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater, USA
Abstract
Native turfgrasses and regionally adapted plants offer significant economic value while enriching genetic diversity. Habitat suitability modeling for these economically important species provides landscape managers with crucial tools for making informed decisions. These species are economically beneficial due to their roles in providing animal feed as forage, their use in landscape design as turf, erosion prevention, and their low water requirements, making them particularly valuable in areas facing water scarcity. In this study, species occurrence data were divided into training sets (75% of the total records) for model calibration and test sets (25% of the records) for evaluation. Accuracy was measured by the mean AUC values, with L. perenne achieving 0.94 and L. rigidum 0.84, indicating that the models performed better than random predictions. The model for L. perenne showed very good accuracy, while the model for L. rigidum
demonstrated good accuracy. The habitat suitability for L. perenne was strongly influenced by factors such as annual average precipitation, elevation, sand content, river proximity, and salinity levels. In contrast, L. rigidum’s potential distribution was primarily affected by land use, sand content, annual average precipitation, and pH. Notably, L. rigidum
demonstrated a wider range of suitability compared to L. perenne, indicating that it is more adaptable to regions where water is scarce or unevenly distributed, and where annual precipitation is low. In conclusion, L. rigidum showed greater resilience in areas with limited water resources, making it a better option for arid or semi-arid regions. These findings can help guide the selection of species for sustainable landscape management in different environmental conditions.
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