Habitats have dramatically destructed the world wide. However a growing trend is emerging for restoring habitatats. One of the most effective approaches to revitalize them is to restore the conditions that have been lost. Studies indicate the high probability of the local extinction of Maral (Cervus elaphus maral) in the current habitats of Gilan due to severe habitat destruction. The current study, therefore, aimed to introduce the application of habitat suitability and resistances surfaces, connectivity paths and corridors to analyze and monitor changes in habitats as the starting point for their reconstruction. Based on the results, 35% reduction in suitable habitats, 34% increase in the number of patches, 89% in edge density, and 53% reduction in the average size of patches, indicated that the occurrence of habitat loss and fragmentation had severely disrupted the integrity of Maral habitats. The increase in the cost-weight distance to the Euclidean distance ratio and land cover change of paths and corridors showed the rise of habitat resistance and the reduction of landscape permeability. Results, therefore, represented a clear picture of the changes in habitat suitability and connectivity of Maral which could play an effective role in the restoration process.

In recent years, the destruction of biodiversity in Iran and worldwide has been increasingly attributed to the alarming effects of climate change. To effectively address the negative consequences on living organisms, the utilization of modeling methods is essential. This study focuses on the habitat and distribution of an unidentified species of black cobra in the southern and southwestern regions of Iran. By considering habitat variables and current/future climate conditions, specifically under two scenarios of future climate change (2040 and 2100) - mild (SSP126) and severe (SSP585) - the study employs the maximum entropy method with six climate models for modeling using Rstudio software, covering 64 present points and 18 environmental layers. The results revealed a predicted expansion of the black cobra's potential habitat beyond the previously documented areas in Iran. Furthermore, a comparison between future and current models demonstrates an estimated increase in the species' habitat by 14.27% and 18.58% under the mild and severe scenarios respectively, by 2040, compared to present conditions. However, by 2100, the favorable habitat for the black cobra is projected to decline to 12.11% and 12.19% under the mild and severe scenarios respectively. The findings underscore the influential factors in the distribution of the black cobra, with the human footprint emerging as the most significant. Climatic variables also play a crucial role in determining the habitat suitability for this species, as rising temperatures and reduced precipitation affect its desirability. In conclusion, the expanding range of suitable habitats for the black cobra until 2040 is expected to result in population growth and an escalation of conflicts between this species and residents of the south and southwest regions of Iran. However, if global warming continues to worsen until 2100, it will yield a grave impact, jeopardizing the availability of favorable habitats and putting the black cobra, like many other species, at a heightened risk of extinction.

Introduction: Biodiversity loss is a global threat to humanity. To address these challenges, international environmental organizations have adopted specific strategic goals and plans within the Kunming-Montreal Global Biodiversity Framework to understand how biodiversity changes over time and to identify the factors influencing these changes. Biodiversity modeling tools, particularly habitat suitability models (species distribution models), play a critical role in this effort. Despite significant advances in modeling techniques and the growing availability of spatial data, current models face serious limitations in accurately predicting biodiversity status and changes, hindering the development of an effective framework for monitoring biodiversity over time. One of the most significant practical limitations of these models is the inconsistency of recorded data in terms of species presence frequency and spatial extent across different time periods. This inconsistency limits the development of spatiotemporal models necessary for understanding species distribution dynamics over time. The objective of this study is to propose a solution to overcome the inconsistencies in biodiversity data over time and to develop a computational process for spatiotemporal habitat suitability modeling. Subsequently, spatiotemporal models were employed to quantify changes in species distribution.Materials and Methods: In this study, a spatiotemporal biodiversity model was developed using presence data of the Roan antelope (Hippotragus equinus). Long-term species presence data spanning from 1901 to 2020 were sourced from the global biodiversity database GBIF to develop the spatiotemporal models. Additionally, species time series data (abundance data) from the LPI and BioTime databases were used to validate the assessment of biodiversity changes. Climatic data were extracted from the CRU TS database, which was used to generate 19 annual environmental layers. After data cleaning and preparation, and selecting appropriate climatic variables by testing for multicollinearity, the time series data were integrated into a single data table or data pool. In this approach, species presence data for each year were linked to the corresponding climatic data for that year and location. To improve model efficiency and reduce uncertainty, 10 common machine learning algorithms were selected to calibrate the spatiotemporal models. After model validation, spatial distribution predictions for each year were obtained by combining predictions from different models using weighted averaging (ensemble), resulting in a 120-year time series of species distribution predictions. Next, the Sen’s slope estimator function was used to calculate the trend of habitat suitability changes over 120 years for each pixel.Results and Discussion: The results of model validation demonstrated that all modeling approaches performed exceptionally well, with AUC values ranging from 0.926 to 0.996, indicating high predictive accuracy. Analysis of the biodiversity trend maps over time revealed a gradual decline in the probability of species presence in southern latitudes. In contrast, an increase in presence probability was observed in the central African belt, suggesting shifts in species distribution patterns. Further validation of the results was carried out using time series data on species distribution and abundance from BioTime and LPI sources. This validation showed that the model accurately matched real data in 88% and 84% of the cases where habitat suitability had decreased. These findings confirm the high accuracy of the model in predicting both species distribution and changes over time. This strong correlation between model predictions and actual data underscores the effectiveness of the proposed spatiotemporal models in capturing and reflecting real-world biodiversity trends.Conclusion: The proposed solution in this study not only enables spatiotemporal modeling for analyzing species distribution patterns and their changes but also improves the accuracy of ecological niche quantification, enhancing spatial distribution predictions and reducing uncertainty in assessments. This approach addresses temporal data inconsistency challenges by increasing sample size and coverage, allowing optimal use of all available records. This study emphasizes the importance of the temporal dimension in species distribution models, particularly in regions with significant climatic changes, and can assist managers in making conservation decisions aligned with sustainable development goals, biodiversity conservation, and the KM-GBF global framework.

Climate change has significant impacts on living organisms and the environment. Therefore, it is important to predict and assess its impacts in order to reduce vulnerability and also to confront to climate change. Water resources will be the first resources to be affected by climate change and the rivers are considered as vital ecosystems in this situation. So assessing the impacts of the climate change on animal and plant species status in the rivers can provide a projection of the ecosystem.This study attempted to evaluate the effect of climate change on one of the southern Alborz water systems, Kordan River, and to estimate the changes in the aquatic Habitat Suitability Index (HIS) along a two-kilometer reach of the river. In this regard the future climate change in the region was first projected using HadCM3 general circulation model in three 30-year periods of 2011-2040, 2041-2070, and 2071-2099 considering A2 and B1 scenarios. Also the SWAT model was used to simulate effects of climate change on the river flow and the water temperature. Results showed that the changes in temperature and precipitation would have a decreasing effect on the river flow and the water temperature during the future periods; the average flow would decrease from 3.3 cms in the base period to 2.66 and 2.8 cms in A2 and B1 scenarios, respectively. Also it is indicated that the climate change has a significant impact on habitat suitability index for Oxynemacheilus bergianus.Assessing the rational distribution curve would also declare a 20 to 25 decrease in the HSI equaling 0.4 to 0.6 in the period of 2071 to 2099.

Aims: This research examined climate change’s impact on the Eurasian otter’s habitat (Lutra lutra) in Khuzestan Province based on habitat modeling in R regarding climate scenarios and the MRI-ESM2-0 general circulation model. Materials & Methods: 72 points were recorded, and ten climatic and environmental variables were used as inputs for the models. The ROC curve, TSS, and Kappa coefficient were used to assess model accuracy using three different methods. Findings: In the ROC model, AUC 0.7–0.8 indicates a suitable model, AUC 0.8–0.9 indicates a robust model, and AUC > 0.9 indicates a powerful model. In the TSS model,> 0.75 indicates excellent diagnostic power, 0.4–0.75 indicates good, and < 0.4 indicates weak diagnostic power. The Kappa coefficient (0.39–0.98) shows good prediction accuracy. The RF and GBM were the best for determining the habitat of the Eurasian otter in Khuzestan Province. River distance, BIO1, and BIO3 had the most significant role in habitat suitability. A total of 9176.185 km² of Khuzestan Province was identified as a suitable habitat. The prediction of the species’ distribution changes based on SSP126, SSP370, and SSP585 showed that this species’ habitat would decrease until 2070. Conclusion: Climate change significantly affects the distribution of the Eurasian otter. Similar to other studies on animal and plant species, it leads to habitat reduction and alterations in habitat ranges.

Carbon sequestration is one of the ways to reduce the amount of carbon dioxide in the atmosphere and thereby reduce the negative consequences of climate change. In this research, the initial evaluation of the amount of carbon reserves in Halocnemum strobilaceum habitat in Mighan Arak desert and determining the validity of Rothamsted Carbon Model (RothC) as well as investigating future two climate scenarios (Absence and onset of climate change) in estimating the changes in soil organic carbon stock were done. Sampling was done in the form of random-systematic design using 48 points in the topsoil. To evaluate the efficiency of the model, the coefficient of explanation (R2), correlation coefficient (r), root mean square error (RMSE) and also the efficiency index of the model implementation (PE) were used. The results showed that the highest and lowest amount of soil organic carbon in the habitats of this plant for each of the above scenarios, respectively, with the values of 19/1687, 20/0824, 20/0802 corresponding to the habitat of this plant in the west of Miqan desert and 9/7525, 10/2211, 10/22 tons per hectare in the south of Meyqan desert. Also, the results showed the reduction of all active carbon reserves, such as the reservoirs of degradable plant material, resistant plant material, microbial biomass, and soil humus organic matter equivalent to 14/167, 16/421, 13/976 and 1/91% respectively. It will decrease compared to the conditions of non-occurrence of climate change. Considering the fragile state of the Miqan desert ecosystem and other playas of Iran, if the economic value (the economic value of each ton of carbon is at least 50 US dollars) and the environmental value of carbon deposition (reducing the effects of global warming, etc.) add to the sum of the benefits and services of these ecosystems is done, the need to protect these ecosystems becomes more obvious.

Today, climate change and habitat loss are the biggest threats to wildlife. Therefore, accurate information on ecology and habitat requirements conserve species from these changes and identifying the most important factors to attract species and the development of habitat suitability maps can be considered a species protection process. After leopards and cheetahs, Caracal is the third biggest member of the cat family (Felidae) in Iran that has a key role in controlling of rodent populations and its habitat is mostly in arid areas. Accordingly, the aim of this study is to consider the effects of climate change on Caracal habitats and the distribution of the species under two climate scenarios RCP2.6 and RCP8.5 in the period of 2061 to 2080 in Iran by using the maximum entropy method. In this study, four groups of environmental variables are used: climat, topography, land cover, and land use. The results showed that distance from the conservation network, distance from sand dunes, and distance from dense forest areas had the greatest impact on the selection of suitable habitat for the Caracal at the present time and for the future time, the variables of mean temperature of warmest quarter and elevation had the highest importance on the distribution of Caracal. In addition, the study of Caracal's habitat suitability maps revealed that these species currently occupy only 13.2% of Iran, which have only 48.2% overlap with the current conservation network. While, in the future, the desired habitat rate of the species under the scenarios of RCP2.6 and RCP8.5 will be reached 30.9 and 27.4, respectively, and the amount of overlap with the current protected network will be reduced to about 66%, and the amount of overlap will have arrived at 17.8%.