masoud salari; Fereydoon Sarmadian; Ali Salajegheh
Abstract
Wild sheep (Ovis orientalis) are a critical component of wildlife biodiversity in Iran and are categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in maintaining the integrity of rangeland ecosystems and contributes to ecological balance within their habitats. ...
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Wild sheep (Ovis orientalis) are a critical component of wildlife biodiversity in Iran and are categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in maintaining the integrity of rangeland ecosystems and contributes to ecological balance within their habitats. Variations in land characteristics (including climate, topography, soil, vegetation, hydrological factors, and land use) result in distinct habitat suitability classifications for this species. This study involved long-term observational research on wild sheep behavior over a decade, aiming to identify the most influential factors affecting habitat suitability and to generate a habitat suitability map using machine learning algorithms alongside the Analytical Hierarchy Process (AHP) in Khabr National Park. The findings indicate that the region has relatively high suitability for this species, with elevation, slope, vegetation cover, and proximity to water resources emerging as the most significant factors. Validation of the results using the kappa coefficient and the overall accuracy index confirms the high precision of the findings. This underscores the value of integrating machine learning models with AHP in habitat suitability assessments, aiding management in understanding the species’ ecological requirements and identifying priority conservation areas.
Hamidreza Keshtkar; Hassan Yeganeh; Omid Kavoosi
Abstract
Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six ...
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Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six predictive models: Artificial Neural Networks, Random Forest, Classification Tree Analysis, Surface Range Envelope, Generalized Boosting Machines, and Generalized Linear Models. To evaluate the interactions between topographic factors and other variables, two environmental datasets were quantified and used for model calibration. The first dataset includes eleven factors covering topographic, climatic, edaphic, and remote sensing variables. Meanwhile, the second dataset contains six factors, focusing on climatic, edaphic, and remote-sensing variables. Model accuracy was evaluated using the True Skill Statistic (TSS), the area under the curve of the Receiver Operating Characteristics (ROC), and the Accuracy Index. The evaluation indices indicate that the Generalized Boosting Machine (GBM) model predicted the ecological niche of F. gummosa more accurately than the other methods. Additionally, the results showed that removing topographical variables reduced the model accuracy by 11 to 25%. The slope, NDVI, wetness, and soil groups were found to be the most important factors in mapping potentially suitable habitats for the target plant. According to the results obtained from the GBM model, approximately 45% of the Ghorkhoud area is in excellent condition. This knowledge can aid in the selection of predictors for practical Species Distribution Model (SDM) applications and provide information on which modeling techniques are most useful for a group of species.
Fereshteh Babaei; Ataollah Ebrahimi; Ali Asghar Naghipour; Maryam Haidarian
Abstract
Climate change is one of the main determinants of plant species redistribution and biodiversity loss. This study aims to predict the impacts of climate change on the geographic distribution of Agropyron intermedium in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran. The presence points ...
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Climate change is one of the main determinants of plant species redistribution and biodiversity loss. This study aims to predict the impacts of climate change on the geographic distribution of Agropyron intermedium in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran. The presence points of studied species were recorded from our field surveys in the studied area. In this study, we used the ensemble predictions based on five species distribution models. The future projections were made for the year 2070 with three scenarios SSP126, SSP370, and SSP585, and two general circulation models GFDL-ESM4 and MRI-ESM2-0. According to the results, Random forest model and the generalized boosted model were recognized as the most reliable models for predicting species distribution. The most effective variables in the suitability of the A. intermedium species habitat were, respectively, elevation, Precipitation of wettest month, and slope. According to the finding, about 21.26% of the study area for A. intermedium species have had suitable habitats. The decline of suitable habitats of A. intermedium will be 36.06% to 63.20% under the GFDL-ESM4 general circulation model and 36.69% to 65.17% under MRI- ESM2 general circulation model due to climate change. The results also indicated that climate change will alter the range size of studied species and will probably shift to higher elevations in the future. The results of this study can be used to protect the habitat of the range plant species, as well as its rehabilitation and restoration.
