Showing 3 results for Maxent
Volume 0, Issue 0 (1-2024)
Abstract
This study was conducted to assess the habitat suitability of Carissa carandas in India is crucial for its sustainable integration into agriculture under changing climatic conditions. This study utilized Maximum Entropy (MaxEnt) modelling to evaluate the species’ distribution across current and future scenarios (2050 and 2070) under four Representative Concentration Pathways (RCPs: 2.6, 4.5, 6.0, and 8.5). Results indicated that temperature-related variables, particularly the Minimum Temperature of the Coldest Month (MiTCM, contributing 46.8% in 2070 RCP 2.6) and Isothermality (contributing up to 35.2% in 2070 RCP 8.5), are the dominant climatic drivers. Land use and land cover (LULC) factors such as urbanization (49.8%), total cultivated land (28.1%), and grassland (9.0%) significantly influence habitat suitability. Under current conditions, optimal habitat spans 4,588 km², decreasing by 38.95% under LULC scenarios. Projected habitat changes indicate a 2.04% gain under 2070 RCP 2.6 but an 11.06% decline under 2050 RCP 2.6. Southern and western regions, including Karnataka, Tamil Nadu, Maharashtra, and Gujarat, exhibit high suitability, habitat fragmentation is projected in northern and western India due to climate change and land use modifications. These findings underscore the need for proactive conservation planning and climate-adaptive agricultural strategies to optimize the cultivation of C. carandas. Policymakers and stakeholders should focus on preserving suitable regions while mitigating urbanization-induced habitat loss. Furthermore, integrating underutilized crops into climate-resilient agriculture can enhance biodiversity, improve food security, and support sustainable farming practices in the face of climate change.
Mona Ghorbanian, Azadeh Karimi-Malati, Mahdi Jalaeian, Mahmood Fazeli Sangani,
Volume 9, Issue 4 (12-2023)
Abstract
Risk assessment is utilized to prioritize preventive measures based on the probability of dispersal success of pests. A main part of the risk assessment procedure is to determine the effects of environmental variables on the current and potential geographical distributions. In the present study, the spatial distribution of the Mediterranean pine engraver, Orthotomicus erosus (Wollaston), was mapped and predicted using MaxEnt. Presence records of O. erosus (north, northeast, west and centre of Iran), environmental and topographic variables, with the lowest correlations among themselves and the highest effects on the pest distribution were used. A total of 76 presence records of O. erosus were collected. The results of the distribution prediction modelling revealed that the northern part of Iran and the areas along the Zagros are the most suitable habitats for this species. Examining environmental variable importance on the distribution of O. erosus showed that the variables related to temperature and precipitation had more contribution in the MaxEnt model, respectively than the altitude. Furthermore, the high accuracy of the model (0.928) indicated that the MaxEnt had an acceptable performance for the prediction of O. erosus distribution. These findings would provide primary and critical information about the potential distribution of O. erosus in Iran, which could be effective for the stable population regulation of this destructive pest.
Volume 12, Issue 1 (4-2024)
Abstract
Aims: This study modeled sensitive areas to dust storms in Isfahan province, which is sensitive to successive droughts, and dust storms because of its climatic condition, and proximity to the desert, using meteorological codes related to dust, AOD values, and Maximum Entropy model (MaxEnt).
Materials & methods: 200 occurrence points of dust were determined using dust meteorological codes and AOD values of MODIS sensor, Terra satellite, (2011-2022). Ten parameters including temperature, rainfall, albedo, altitude, slope, land use, enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized difference salinity index (NDSI), and frequency percentage of erosive wind seed were considered dust-predictive factors. Finally, the MaxEnt model was utilized for modeling dust susceptibility. The performance of the model was specified using the AUC value and the importance of each influential factor was identified utilizing the Jackknife test.
Findings: The findings indicated that areas susceptible to dust are mainly bare lands, salt lands, and poor rangeland located mostly in the north, northeast to parts of the east and southeast of the Province, and also the central parts towards the southwest of Isfahan Province. According to the results, the MaxEnt model, with AUC=0.72, had a good efficiency in modeling susceptible areas to dust storms in Isfahan Province.
Conclusion: The major conclusion of this study is that the MaxEnt model had good performance in mapping susceptible areas to dust in Isfahan Province. The results of this research can be useful for decision-makers in identifying the areas prone to dust storms.
