Volume 13, Issue 2 (10-2025)
Ecol Iran For 2025, 13(2): 12-27 |
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Hedayati Kaliji S, Hosseini S M, Alavi S J, Amiri M. (2025). Ensemble Modeling of the Potential Impacts of Climate Change on the Habitat Suitability of Chestnut-leaved Oak (Quercus castaneifolia C. A. Mey) in the Hyrcanian Ecoregion. Ecol Iran For. 13(2), 12-27. doi:10.61882/ifej.2025.563
URL: http://ifej.sanru.ac.ir/article-1-563-en.html
URL: http://ifej.sanru.ac.ir/article-1-563-en.html
1- Department of Forest Sciences and Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran
2- Department of Range and Watershed Management, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
2- Department of Range and Watershed Management, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract: (1019 Views)
Extended Abstract
Background: Climate change is currently considered a serious threat to many species and is recognized as one of the most important factors in the global biodiversity loss. Therefore, understanding how the spatial distribution and species composition are affected by climate change is very important in protecting natural ecosystems and achieving sustainable development. Species distribution models are the most widely used methods to predict the effect of climate change on plant species distribution changes. Considering the ecological, economic, and commercial values of the chestnut-leaved oak (Quercus castaneifolia C. A. Mey) in the Hyrcanian forests, this study aims to use different modeling algorithms to simulate suitable climatic ranges to determine the habitat suitability of Q. castaneifolia in the current climatic conditions and its potential changes in 2070 and 2100.
Methods: After determining the species presence using the statistical data of forestry projects in the north of Iran and the detailed plans of Golestan, Mazandaran and Gilan provinces, the bioclimatic variables of 1979-2013 were extracted from the CHELSA global database. The bioclimatic variables related to 2014-2019 were also produced in the Idrisi TerrSet software using raster images of monthly precipitation and monthly maximum and minimum temperatures available in the same database. Then, the weighted average of these two series of bioclimatic variables (1979-2019) was included in the modeling process. In addition, the elevation, slope, and solar-radiation aspect index (TRASP) as physiographic variables extracted from the Digital Elevation Model (DEM) were also used as inputs to the modeling process. After selecting the environmental variables with the Variance Inflation Factor (VIF), the relationship between the species occurrence data and the map of environmental variables was mathematically defined using the R statistical-programming software. Regression, machine learning, and classification modeling algorithms, including Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Linear Model (GLM), Multiple Adaptive Regression Spline (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF), were evaluated using the Biomod2 package. A unified framework, including six species distribution models, was used to reduce uncertainty. The Area under the Curve (AUC) index, True Skill Statistic (TSS), Sensitivity, and Specificity were used to evaluate the performance of the models. After determining the importance of the participating variables in the modeling with the VarImp function, the species response curves to the most important variables were drawn based on the outperformed individual model. The effect of climate change on species distribution was predicted using the MRI-ESM2-0 model of the sixth phase of climate change models (CMIP6) under two optimistic (SSP1-2.6) and pessimistic (SSP5-8.5) climate change scenarios over the near future (2041-2070) and distant future (2071-2100).
Results: Based on the evaluation criteria, the individual models had good performance and were considered to create an ensemble model. Among models, the ensemble model with TSS and AUC equal to 0.904 and 0.988, respectively, and then the RF model had the highest efficiency. Based on the contribution percentage values, precipitation of the driest month (Bio14), the slope, and precipitation seasonality (Bio15) had the largest contribution to the distribution of Q. castaneifolia and determining its habitat suitability, respectively. According to the ensemble model, the suitable habitat areas of the species in the current climatic conditions cover 60% of the Hyrcanian area. The produced maps show the high suitability of Q. castaneifolia in the western and central parts of the Hyrcanian region. According to the species response curves to the most important environmental variables, the precipitation of the driest month is at least 10 mm, the precipitation seasonality is less than 50 mm, and the average slope is 2-22% in suitable habitats. In both periods and under both climate change scenarios, there will be changes in the spatial distribution of Q. castaneifolia, and the most severe one would be a 5.29% loss in the species’ suitable climate range in 2100 under the pessimistic scenario (RCP8.5). By comparing the ensemble map of current habitat suitability and habitat suitability under the effect of climate change, it was predicted that the greatest change in habitat suitability will occur in the eastern and southern parts of the Hyrcanian region. Probably, the habitat of species will shift from lower latitudes or altitudes to higher latitudes with the increase in temperature.
Conclusion: Despite the difference in the nature of different modeling algorithms, the resulting predictions were almost similar for Q. castaneifolia. Meanwhile, the RF and GLM had the highest and the lowest accuracies, respectively, among the individual models. Only the RF model could have a performance equivalent to the average output of several modeling methods. The suitable habitat of Q. castaneifolia will decrease under the pessimistic scenario in both the future time frame and under the optimistic scenario until 2070. Examining the potential effects of climate change on the spatial distribution of this valuable plant species, as an important species of Hyrcanian forests, seems to be an essential tool for its planning, conservation, and management. Habitat suitability maps can be proposed as a basis for reforestation plans in threatened areas. Therefore, it seems necessary to prepare comprehensive conservation plans aiming to reduce the effects of climate change on this valuable species.
Background: Climate change is currently considered a serious threat to many species and is recognized as one of the most important factors in the global biodiversity loss. Therefore, understanding how the spatial distribution and species composition are affected by climate change is very important in protecting natural ecosystems and achieving sustainable development. Species distribution models are the most widely used methods to predict the effect of climate change on plant species distribution changes. Considering the ecological, economic, and commercial values of the chestnut-leaved oak (Quercus castaneifolia C. A. Mey) in the Hyrcanian forests, this study aims to use different modeling algorithms to simulate suitable climatic ranges to determine the habitat suitability of Q. castaneifolia in the current climatic conditions and its potential changes in 2070 and 2100.
Methods: After determining the species presence using the statistical data of forestry projects in the north of Iran and the detailed plans of Golestan, Mazandaran and Gilan provinces, the bioclimatic variables of 1979-2013 were extracted from the CHELSA global database. The bioclimatic variables related to 2014-2019 were also produced in the Idrisi TerrSet software using raster images of monthly precipitation and monthly maximum and minimum temperatures available in the same database. Then, the weighted average of these two series of bioclimatic variables (1979-2019) was included in the modeling process. In addition, the elevation, slope, and solar-radiation aspect index (TRASP) as physiographic variables extracted from the Digital Elevation Model (DEM) were also used as inputs to the modeling process. After selecting the environmental variables with the Variance Inflation Factor (VIF), the relationship between the species occurrence data and the map of environmental variables was mathematically defined using the R statistical-programming software. Regression, machine learning, and classification modeling algorithms, including Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Linear Model (GLM), Multiple Adaptive Regression Spline (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF), were evaluated using the Biomod2 package. A unified framework, including six species distribution models, was used to reduce uncertainty. The Area under the Curve (AUC) index, True Skill Statistic (TSS), Sensitivity, and Specificity were used to evaluate the performance of the models. After determining the importance of the participating variables in the modeling with the VarImp function, the species response curves to the most important variables were drawn based on the outperformed individual model. The effect of climate change on species distribution was predicted using the MRI-ESM2-0 model of the sixth phase of climate change models (CMIP6) under two optimistic (SSP1-2.6) and pessimistic (SSP5-8.5) climate change scenarios over the near future (2041-2070) and distant future (2071-2100).
Results: Based on the evaluation criteria, the individual models had good performance and were considered to create an ensemble model. Among models, the ensemble model with TSS and AUC equal to 0.904 and 0.988, respectively, and then the RF model had the highest efficiency. Based on the contribution percentage values, precipitation of the driest month (Bio14), the slope, and precipitation seasonality (Bio15) had the largest contribution to the distribution of Q. castaneifolia and determining its habitat suitability, respectively. According to the ensemble model, the suitable habitat areas of the species in the current climatic conditions cover 60% of the Hyrcanian area. The produced maps show the high suitability of Q. castaneifolia in the western and central parts of the Hyrcanian region. According to the species response curves to the most important environmental variables, the precipitation of the driest month is at least 10 mm, the precipitation seasonality is less than 50 mm, and the average slope is 2-22% in suitable habitats. In both periods and under both climate change scenarios, there will be changes in the spatial distribution of Q. castaneifolia, and the most severe one would be a 5.29% loss in the species’ suitable climate range in 2100 under the pessimistic scenario (RCP8.5). By comparing the ensemble map of current habitat suitability and habitat suitability under the effect of climate change, it was predicted that the greatest change in habitat suitability will occur in the eastern and southern parts of the Hyrcanian region. Probably, the habitat of species will shift from lower latitudes or altitudes to higher latitudes with the increase in temperature.
Conclusion: Despite the difference in the nature of different modeling algorithms, the resulting predictions were almost similar for Q. castaneifolia. Meanwhile, the RF and GLM had the highest and the lowest accuracies, respectively, among the individual models. Only the RF model could have a performance equivalent to the average output of several modeling methods. The suitable habitat of Q. castaneifolia will decrease under the pessimistic scenario in both the future time frame and under the optimistic scenario until 2070. Examining the potential effects of climate change on the spatial distribution of this valuable plant species, as an important species of Hyrcanian forests, seems to be an essential tool for its planning, conservation, and management. Habitat suitability maps can be proposed as a basis for reforestation plans in threatened areas. Therefore, it seems necessary to prepare comprehensive conservation plans aiming to reduce the effects of climate change on this valuable species.
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