Saman Ghasemian Sorboni
Mehrdad Hadipour1*†
Sharareh Pourebrahim- 1Kharazmi University, Tehran, Iran
- 2Sunway University, Bandar Sunway, Malaysia
Potamon ibericum, a freshwater crab species, is highly sensitive to environmental changes, especially water temperature and flow regimes. This sensitivity makes it an excellent bioindicator for assessing the health and stability of freshwater ecosystems under climate change scenarios. However, its limited dispersal ability makes it vulnerable to habitat fragmentation and climate-induced range shifts. Therefore, predicting its future habitat suitability is crucial for early conservation planning. This study utilizes current and future (2060–2080) climate variables along with species distribution modeling (SMD) tools. By collecting presence records of the species from various datasets and published articles, we examined its potential distribution. The results indicated that temperature seasonality is the most significant factor influencing the species’ distribution. Additionally, with increasing climatic changes, the species’ altitude range shifts to higher elevations, averaging between 1,600 and 1,900 meters above sea level. We also assessed the degree of overlap between Iran’s protected areas and the current and future suitable habitats for the species. The findings revealed that the most important refuge is the Central Alborz Protected Area, which encompasses approximately 1,803 square kilometers of suitable habitat. However, future projections under the most severe climate scenarios suggest that less than one-fifth of the suitable habitat will remain within protected areas. In general, P. ibericum may face the risk of extinction and significant loss of suitable habitat in Iran due to extreme future climatic conditions. Protecting this sensitive and ecologically important species within freshwater ecosystems is vital, and immediate management actions are necessary to ensure its conservation.
1 Introduction
The effects of climate change are becoming increasingly clear and widespread, posing serious challenges for many plant and animal species. Freshwater ecosystems are especially vulnerable, and major climate shifts are expected before the end of this century (Döll and Zhang, 2010; Woodward et al., 2010; Dallas and Rivers-Moore, 2014; Prakash, 2021). These shifts are driven not only by rising temperatures but also by altered hydrological regimes, including changes in rainfall variability, drought frequency, evapotranspiration, and the timing of snowmelt (Döll and Zhang, 2010). Individual species, along with their interactions and habitats, are directly affected by these changes, making them central to understanding the wider impacts of climate change (Díaz et al., 2019). Climate change—defined as the gradual increase in Earth’s atmospheric and oceanic temperatures—poses a major threat to biodiversity, particularly for freshwater species that have limited ability to migrate (Habibullah et al., 2022; Hassan et al., 2020). These ecosystems provide vital services but are among the most at risk globally (Vári et al., 2022). In some regions, freshwater species may even face the risk of extinction (Woodward et al., 2010; Vörösmarty et al., 2010).
Species with limited ability to migrate are especially vulnerable to rising global temperatures and shifting precipitation patterns, which pose serious threats to the biodiversity of their ecosystems (Singh et al., 2021). This issue is particularly evident in biodiversity hotspots like Iran, where many species have already been negatively affected by climate change (Kafash et al., 2021). As global warming continues to alter habitats through changes in temperature, rainfall patterns, and extreme weather events (Sarkar, 2012), it becomes increasingly important to focus on these vulnerable species. Understanding their current status and predicting how their habitats may shift under different climate scenarios is essential for effective conservation planning.
Freshwater crabs play a crucial role in supporting the biodiversity of inland aquatic ecosystems by decomposing organic matter and contributing to nutrient cycling and detritivory. Among them, Potamon ibericum is a particularly important species in northern Iran, where it is the most widespread freshwater crab, primarily found in the provinces of Mazandaran, Gilan, and Golestan. First described by Olivier in 1804, it was the earliest recorded freshwater crab species in Iran (Farhadi and Harlıoğlu, 2018). As a true freshwater crab, P. ibericum completes its entire life cycle without any dependence on the marine environment (Cumberlidge, 2016). While freshwater crabs—often referred to as river crabs—are distributed throughout tropical and warm temperate regions (Yeo et al., 2008; Khatami and Valinasab, 2003), their ecological functions remain poorly understood (Dobson, 2004). The conservation status of P. ibericum is especially concerning; it is currently listed as Near Threatened (NT) on the IUCN Red List (Cumberlidge, 2008), highlighting its vulnerability to environmental change and the urgent need for focused research and conservation measures.
Species like P. ibericum, which have specific habitat requirements and limited dispersal ability, are particularly vulnerable to increasing temperatures and climate-induced habitat changes (Parvizi et al., 2018). Monitoring how such species respond to environmental changes, identifying where they can survive now and in the future, and implementing proactive conservation measures are essential steps to ensure their survival. Given the ecological importance and sensitivity of P. ibericum, understanding how its habitat may shift under future climate conditions is crucial. Species distribution modeling (SDM) provides a valuable tool for assessing the potential effects of climate change on species distributions (Elith and Leathwick, 2009; Miller, 2010; Naqibzadeh et al., 2022). These models analyze the relationship between species occurrences and environmental variables to predict where species are likely to occur under current and future conditions (Hijmans and Elith, 2013; Elith and Franklin, 2013). By integrating species presence data with climatic and environmental layers, SDMs can identify suitable habitats both now and under various future scenarios (Fois et al., 2018). Grounded in ecological theory, SDMs use statistical approaches to address critical conservation issues such as habitat suitability, biodiversity loss, and climate-driven range shifts (Sillero, 2011; Barbosa et al., 2012). In this study, we used the sdm package in R, developed by Naimi and Araújo (2016), to construct a distribution model for P. ibericum. Focusing on northern Iran, we applied the severe greenhouse gas emission scenario SSP5-8.5 to project future changes in habitat suitability, aiming to support early conservation planning for this Near Threatened species (Cumberlidge, 2008). The main objective of this research is to identify areas of current and future habitat suitability for P. ibericum and to evaluate the overlap of these habitats with Iran’s protected areas. This approach helps prioritize regions for conservation efforts under climate change scenarios.
2 Materials and methods
2.1 Study area
The study area includes the northern provinces of Iran, located between 35° and 40° N latitude and 45° and 58° E longitude, chosen due to the high population of P. ibericum in the region (Farhadi and Harlıoğlu, 2018) (Figure 1).
Figure 1. Current distribution of P. ibericum in Iran. Red dots indicate presence records of the species.
2.2 Occurrence data
Presence data for P. ibericum were compiled from three verified sources: 22 records from the GBIF database (GBIF Secretariat, 2023), 30 records from Parvizi et al. (2019), and three records from Ghasemian Sorboni et al. (2024). The dataset by Parvizi and colleagues originates from a master’s research project at the University of Tehran, where specimens were collected between 2002 and 2015 from 31 stations across the Caspian Basin and Central Desert and are preserved in the Museum of Animal Biology, University of Tehran, under catalogued voucher tags. The records by Ghasemian Sorboni et al. (2024) were based on recent field collections from Mazandaran Province, identified and verified by taxonomists at the same institution. All GBIF records were cross-checked for coordinate accuracy and taxonomic consistency. Duplicate points and records located within 1 km² were removed in RStudio to avoid spatial autocorrelation and sampling bias. The final dataset contained 55 unique, verified presence points. Because only presence data were available, pseudo-absence (background) points were generated using the bg argument in the sdmData function, with 100 randomly distributed background points (Barbet-Massin et al., 2012; Ghasemian Sorboni et al., 2024). It is important to note that the absence of records in some parts of the study area does not necessarily indicate true species absence but may reflect under-sampling or data gaps. This limitation introduces a degree of uncertainty to the model outputs, which should be considered.
2.3 Environmental variables and preparing the model
The bioclimatic variables used in the modeling were obtained from the WorldClim database for the time period 2060–2080 (centered on 2070) under the SSP5-8.5 climate change scenario (O’Neill et al 2017), along with historical climate data interpolated from meteorological records between 1970 and 2000 (Fick and Hijmans, 2017). The variables used in our study include the following: Annual Mean Temperature (bio1), Mean Diurnal Range (bio2), Isothermality (bio3), Temperature Seasonality (bio4), Maximum Temperature of the Warmest Month (bio5), Minimum Temperature of the Coldest Month (bio6), Temperature Annual Range (bio7), Mean Temperature of the Wettest Quarter (bio8), Mean Temperature of the Driest Quarter (bio9), Mean Temperature of Warmest Quarter (bio10), Mean Temperature of Coldest Quarter (bio11), Annual Precipitation (bio12), Precipitation of Wettest Month (bio13), Precipitation of Driest Month (bio14), Precipitation Seasonality (bio15), Precipitation of Wettest Quarter (bio16), Precipitation of Driest Quarter (bio17), Precipitation of Warmest Quarter (bio18), and Precipitation of Coldest Quarter (bio19). The bioclimatic data were sourced from the HadGEM3-GC31 (Roberts, 2017; Andrews et al., 2020) and MRI-ESM2 (Yukimoto et al., 2019) models at a spatial resolution of 1 km². These variables are created to provide biologically relevant factors for SDM, derived from monthly precipitation and temperature data.
Modeling was conducted in the R environment using the sdm package, employing an ensemble approach with four machine learning algorithms: Boosted Regression Trees (BRT; Elith et al., 2008), Random Forest (RF; Breiman, 2001), Maximum Entropy (Maxent; Phillips et al., 2006, 2017), and Support Vector Machine (SVM; Cortes and Vapnik, 1995). We used three replicates for each method, for a total of 15 replicates per model, using cross-validation (CV). For model evaluation and performance assessment, we considered mean area under the ROC (receiver operating characteristic) curve (AUC) and True Skill Statistic (TSS) metrics. We applied a minimum quality threshold of AUC > 0.7 and TSS > 0.6 to exclude low-performing models. Then, the ensemble function (Araújo and New, 2007) applied a weighted averaging approach, where higher-performing models were assigned greater influence. The opt = 2 argument used the threshold selection criterion ‘max(se + sp)’, while the power = 2 parameter further increased the weighting for models with superior performance, enhancing their impact on the ensemble output (Liu et al., 2013, Liu et al., 2015).
2.4 Variable selection and model evaluation
To avoid multicollinearity among the bioclimatic variables, we employed the vifstep function and the Pearson correlation method from the usdm R package (Naimi, 2023), setting a Variance Inflation Factor (VIF) threshold of less than 5. This step ensured that only variables with minimal collinearity were retained for use in the species distribution model.
Next, two key evaluation metrics—Area Under the Curve (AUC) and True Skill Statistic (TSS)—were used to assess the accuracy and predictive power of the models (Shabani et al., 2018). AUC values range from 0.5 (random prediction) to 1 (perfect prediction), with values between 0.7 and 0.8 considered good, 0.8 to 0.9 excellent, and above 0.9 outstanding (Fawcett, 2006). TSS ranges from −1 to 1, with 0 indicating no skill and 1 representing perfect accuracy (Allouche et al., 2006). These metrics allowed us to filter out models with low performance and ensured the reliability of the ensemble predictions.
3 Results
3.1 Variable importance and model performance
In this research, the AUC and TSS values indicated that the Maxent algorithm outperformed other modeling approaches, demonstrating higher reliability and performance (Table 1). The final bioclimatic variables selected for modeling included Isothermality (bio3), Temperature Seasonality (bio4), Mean Temperature of the Wettest Quarter (bio8), Mean Temperature of the Driest Quarter (bio9), Precipitation Seasonality (bio15), and Precipitation of the Coldest Quarter (bio19). Among the bioclimatic variables, Temperature Seasonality (bio4) contributed the most to the model (Figure 2), with the response curve revealing that the optimal range for Potamon ibericum is between 650 and 750 units, with a broader range extending from 650 to 1000 units. Following bio4, the Mean Temperature of the Driest Quarter (bio9) and Isothermality (bio3) ranked second and third, respectively. The favorable range for bio9 was identified as 15 °C to 25 °C, while for bio3, the most suitable values ranged between 27% and 36%, with 27% being the optimal condition for the species.
Table 1. Performance evaluation of models based on various indicators.
Figure 2. The relative variable importance for the model based on AUC and correlation (COR).
The results highlight the critical role of temperature-related variables in determining the distribution of P. ibericum, reflecting the species’ sensitivity to thermal fluctuations. The dominance of temperature seasonality (bio4) as the most influential factor suggests that this species thrives in regions with moderate temperature variability. The response curve for bio4 indicates that deviations outside the optimal range (650–750) may negatively affect habitat suitability. The influence of the Mean Temperature of the Driest Quarter (bio9) further underscores the species’ dependence on consistent water availability and temperature during drier periods. Similarly, the importance of Isothermality (bio3) implies that large diurnal temperature fluctuations may pose physiological stress on the species. These findings emphasize the need to consider temperature stability in habitat management plans and conservation strategies, especially under future climate change scenarios where temperature extremes are expected to increase.
3.2 Current and future habitat suitability of Potamon ibericum
Before generating the ensemble model, we assessed the outputs of individual algorithms—RF, Maxent, BRT, and SVM, to understand the variability and consensus among different modeling approaches (Figure 3). The final distribution map, generated using the ensemble function in the sdm package, reveals that over 85% of Mazandaran and Gilan provinces, along with the southern areas of Golestan province (within the Hyrcanian forests and Alborz slopes), are highly favorable habitats for the species. Additionally, nearly all protected areas in these three northern provinces of Iran overlap with the species’ presence and its suitable habitats (Figures 4, 5). At present, approximately 5,570 km2 of the species’ habitat overlaps with protected areas, with the Central Alborz (Alborz-e Markazi) protected area being the largest contributor, covering 1,803 km2. This area is, on average, four times larger than the second-largest protected area for this species, highlighting its critical importance in conserving the species’ habitat. The Central Alborz area features striking mountains, vast plains with medium to high vegetation density, Hyrcanian forests, and numerous rivers and springs, supporting a rich biodiversity (Talebi Otaghvar et al., 2022).
Figure 3. Habitat suitability for P. ibericum predicted by the single algorithm models.
Figure 4. The distribution map of P. ibericum for the current period and future projections based on two GCMs, showing the overlap of suitable areas with Iran’s protected areas.
Figure 5. Ensemble results of P. ibericum modeling for: (a) current distribution, and future projections for (b) MRI-ESM2 and (c) HadGEM3 models for the time period 2060–2080. Red areas represent the projected suitable habitats for the species.
When projecting the species’ future (2070) distribution under severe climate change, the results indicate significant habitat loss. Both the MRI-ESM2 and HadGEM3 models predict a loss of 90%–96% of the species’ habitat, a substantial reduction for a species with limited migratory capacity. By examining the overlap of future suitable habitats with protected areas, the projected coverage drops to less than 1,000 km2. The largest remaining protected area is the Yayghari Protected Area, with an optimal range of 130–200 km2, located along the border of East Azerbaijan and Ardabil provinces.
To facilitate easier analysis and clearer visualization of species distribution, the final results from all three models (current and future scenarios under two general circulation models) were converted into binary classifications of “suitable, 1” and “not suitable, 0” habitat conditions (Figures 4, 5).
These findings underscore the urgent need for targeted conservation measures to protect Potamon ibericum, especially as climate change is projected to drastically reduce its suitable habitat. The dramatic decline in habitat suitability—up to 96% by 2070—combined with the species’ limited ability to disperse, suggests that without intervention, P. ibericum may face serious population declines or even local extinctions. The Central Alborz Protected Area currently plays a critical role in supporting the species, but under future scenarios, much of this refuge may no longer remain suitable.
3.3 Elevation change
To investigate changes in the elevation range of the species’ presence, a digital elevation raster map corresponding to the study area was extracted from Google Earth Engine (Gorelick et al., 2017). The current model identifies potentially suitable areas spanning from –31 m to about 5000 m a.s.l.; however, this range reflects the full elevational extent of pixels classified as suitable, not the species’ observed elevational limits. Field observations indicate that P. ibericum currently occurs mostly below 1500 m, with a mean elevation of approximately 873 m. The broad elevational range predicted by the model reflects the influence of key climatic factors such as temperature seasonality, which determine the species’ climatic niche. Under future scenarios, increased temperature seasonality and altered rainfall patterns are likely to restrict suitable habitats to mid- and high-elevation zones, where cooler and more stable conditions prevail. Both the HadGEM3 and MRI-ESM2 projections under the SSP5-8.5 scenario suggest an upward shift of suitable climate conditions, with average elevations of 1600 m and 1900 m, respectively. The MRI-ESM2 model predicts this shift mainly toward the southern slopes of the Alborz Mountains in Mazandaran and Gilan provinces, while HadGEM3 indicates movement toward northwestern Gilan and parts of Ardabil Province (Table 2).
Table 2. Summary of elevation changes for P. ibericum suitable habitat under SSP5-8.5 climate scenario.
4 Discussion
Our modeling results indicate that Potamon ibericum is vulnerable to climate change, with a severe reduction in suitable habitat predicted over the next few decades. This projected habitat loss highlights the critical need for targeted conservation actions to protect this species while maintaining ecological balance. To address these concerns, a multifaceted conservation approach is needed. Key strategies include habitat preservation and restoration, establishment of climate-resilient protected areas (Bryndum-Buchholz et al., 2022; Gillingham et al., 2024), enhancing habitat connectivity (Correa Ayram et al., 2015), and implementing integrated water resource management to sustain critical freshwater systems (Bănăduc et al., 2022).
The results indicate that Temperature Seasonality, Mean Temperature of the Driest Quarter, and Isothermality are the primary climatic factors influencing the distribution of P. ibericum. Higher contributions from temperature seasonality (above 750 units) suggest that the species is particularly sensitive to variations in seasonal temperature, which can reduce habitat suitability. The species thrives in areas where the mean temperature of the driest quarter ranges from 15°C to 25°C, indicating that extreme temperatures during dry periods, whether hot or cold, negatively impact its survival (Charmantier, 1992). This pattern aligns with previous ecological studies showing that P. ibericum prefers relatively stable thermal environments (Parvizi et al., 2019). For isothermality, the species favors regions where daily temperature fluctuations are less extreme relative to seasonal changes.
The current optimal elevation range of P. ibericum is concentrated below 1500 meters above sea level, with an average elevation of approximately 873 meters. Although the model identified climatically suitable pixels between –31 and 5000 meters, these values represent the full elevation range of grid cells classified as suitable by the model, not the species’ observed elevational limits. These findings are consistent with broader ecological patterns showing that many species are shifting their ranges upward in response to rising temperatures (Santos et al., 2017; Vitasse et al., 2021). Importantly, our projections show an elevational shift of about 700 to 1,000 meters on average under both climate scenarios, with models predicting the species’ future habitat moving toward higher altitudes—especially the southern Alborz range and parts of Ardabil and Gilan provinces. The projected 90%–96% loss of suitable habitat under future scenarios is driven primarily by increased temperature seasonality and altered precipitation regimes, which reduce stream flow and elevate thermal stress in lowland rivers. Consequently, the species’ potential range is expected to contract toward cooler, mid- to high-elevation zones of the Alborz Mountains. This shift does not imply true colonization of extreme elevations but rather a reduction of viable lowland habitats and fragmentation of remaining populations. This upward movement will likely lead to a significant contraction of the species’ ecological niche, due to the limited availability of suitable habitats at higher elevations and the rugged terrain. Such elevational changes could expose P. ibericum to new environmental stressors, such as colder microclimates, reduced food availability, and increased interspecies competition. In addition, isolation in montane habitats may fragment populations and raise extinction risk. These projections reinforce the importance of elevation-sensitive conservation planning, including the development of altitudinal corridors to enable climate-tracking dispersal.
By assessing the overlap between Iran’s protected areas and the current and projected habitats of the species, it was found that the efficiency of these protected areas will decrease to one-fifth of their current effectiveness. Currently, the Central Alborz protected area, spanning Mazandaran, Tehran, and Alborz provinces, covers approximately 1,803 km2 of the most suitable habitat for the species. However, the modeling results suggest that the species could lose 90 to 96% of its habitat, and the effectiveness of the protected areas will diminish accordingly. Therefore, identifying regions where the species may still find suitable habitat in the future is crucial.
Freshwater crabs play an essential role in aquatic ecosystems and food chains (Rahman et al., 2008; Cumberlidge et al., 2009; Yousefi et al., 2022). Preserving this key species against climate change and other anthropogenic factors is vital. By recognizing areas within current protection zones that may remain suitable for the species in the future, effective management strategies can be developed to preserve and restore its habitat. The results indicate that the following areas will likely contain the most optimal future habitats: (1) Lisar Protected Area, (2) Sabalan Nature Monument, (3) Anzan Protected Area, (4) Yayghari Protected Area, (5) Gharchagheh Protected Area, (6) Sarani Protected Area, (7) Alborz Protected Area, (8) Gasht Rudkhan Protected Area, (9) Sarvelat & Javaher Dasht Protected Area, (10) Kiamaki Wildlife Refuge, and (11) Arasbaran Protected Area. These protected areas are visualized in Figure 4 to facilitate spatial understanding of their distribution relative to current and projected suitable habitats. As a result, future conservation strategies should prioritize climate-resilient regions, such as the Yayghari Protected Area, and incorporate proactive measures, including habitat connectivity, assisted migration, and the identification and preservation of microrefugia. These strategies are essential to buffer P. ibericum against climate-induced range contractions and to maintain the ecological integrity of freshwater ecosystems in northern Iran.
Given the limited dispersal ability of freshwater crabs and their dependence on stable aquatic environments, targeted conservation strategies—including habitat restoration, water resource management, and elevation-inclusive protection planning—are essential. Due to their inability to migrate naturally, P. ibericum is unlikely to reach future suitable habitats, particularly in areas beyond its current distribution. The species relies on a consistent and clean water supply, making it highly vulnerable to both climatic shifts and anthropogenic disruptions. Without immediate intervention, there is a genuine risk of local extinction in Iran. Therefore, this research provides crucial insights for guiding adaptive management and policy efforts aimed at ensuring the long-term survival of this ecologically important species. Conservation strategies should include the expansion and realignment of protected area boundaries to cover future suitable habitats and mitigate land-use pressures (Hermoso et al., 2016; Carroll and Ray, 2021). In addition, effective water resource management and addressing additional anthropogenic threats such as pollution, habitat alteration, and hydrological changes must be prioritized (Cumberlidge et al., 2009; van Rees et al., 2019). Finally, we recommend more in-depth field studies to support and refine long-term conservation planning.
5 Conclusion
This study highlights the significant vulnerability of P. ibericum to future climate change, with projections indicating a drastic reduction—up to 96%—in its suitable habitat by 2070 under the SSP5-8.5 scenario. Key climatic variables such as Temperature Seasonality, Mean Temperature of the Driest Quarter, and Isothermality are identified as major drivers shaping the species’ distribution. Our results also show a notable elevation shift, with the species moving toward higher altitudes in response to rising temperatures, further limiting its range due to topographical and ecological constraints.
The overlap analysis between suitable habitats and Iran’s current protected areas reveals a significant future decline in conservation coverage, dropping to less than one-fifth of present effectiveness. This emphasizes the urgent need to expand and adjust protected area boundaries to encompass predicted future habitats. Identifying priority areas such as Lisar, Yayghari, and Arasbaran offers practical guidance for long-term conservation efforts.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
Author contributions
SG: Formal analysis, Software, Writing – original draft, Methodology. MH: Writing – original draft, Visualization, Supervision, Conceptualization, Writing – review & editing. JC: Writing – review & editing, Funding acquisition, Methodology. SP: Writing – review & editing, Conceptualization, Writing – original draft.
Funding
The author(s) declare financial support was received for the research and/or publication of this article. We gratefully acknowledge Sunway University for providing funding for publication.
Acknowledgments
We thank Dr. Seyed Jalil Alavi for revising our R script and offering valuable feedback. We also extend our sincere appreciation to Prof. Alireza Sari and the Animal Biology Lab at the University of Tehran for their collaboration in verifying the current presence locations of the species in Mazandaran, Iran.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Correction note
This article has been corrected with minor changes. These changes do not impact the scientific content of the article.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Allouche O., Tsoar A., and Kadmon R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. doi: 10.1111/j.1365-2664.2006.01214.x
Andrews M. B., Ridley J. K., Wood R. A., Andrews T., Blockley E. W., Booth B., et al. (2020). Historical simulations with hadGEM3-GC3.1 for CMIP6. J. Adv. Modeling Earth Syst. 12, e2019MS001995. doi: 10.1029/2019MS001995
Araújo M. B. and New M. (2007). Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47. doi: 10.1016/j.tree.2006.09.010
Bănăduc D., Simić V., Cianfaglione K., Barinova S., Afanasyev S., Öktener A., et al. (2022). Freshwater as a sustainable resource and generator of secondary resources in the 21st century: stressors, threats, risks, management and protection strategies, and conservation approaches. Int. J. Environ. Res. Public Health 19, 16570. doi: 10.3390/ijerph192416570
Barbet-Massin M., Jiguet F., Albert C. H., and Thuiller W. (2012). Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338. doi: 10.1111/j.2041-210x.2011.00172.x
Barbosa M. A., Sillero N., Martínez-Freiría F., and Real R. (2012). Ecological niche models in Mediterranean herpetology: Past, present and future. Ecol. Model., 173–204. doi: 10.13140/2.1.3746.6560
Bryndum-Buchholz A., Boerder K., Stanley R. R. E., Hurley I., Boyce D. G., Dunmall K. M., et al. (2022). A climate-resilient marine conservation network for Canada. FACETS 7, 571–590. doi: 10.1139/facets-2021-0122
Carroll C. and Ray J. C. (2021). Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change. Global Change Biol. 27, 3395–3414. doi: 10.1111/gcb.15645
Charmantier G. (1992). Occurrence of fresh-water crabs, genus Potamon, in southern France. J. Crustacean Biol. 12, 620–626. doi: 10.1163/193724092X00102
Correa Ayram C. A., Mendoza M. E., Etter A., and Salicrup D. R. P. (2015). Habitat connectivity in biodiversity conservation. Prog. Phys. Geography: Earth Environ. 40, 7–37. doi: 10.1177/0309133315598713
Cortes C. and Vapnik V. (1995). Support-vector networks. Mach. Learn. 20, 273–297. doi: 10.1007/bf00994018
Cumberlidge N. (2008). Potamon ibericum (Switzerland: In The IUCN Red List of Threatened Species 2008). doi: 10.2305/IUCN.UK.2008.RLTS.T134681A3997379.en
Cumberlidge N. (2016). “Global diversity and conservation of freshwater crabs (Crustacea: decapoda: brachyura),” in A Global Overview of the Conservation of Freshwater Decapod Crustaceans. Eds. Kawai T. and Cumberlidge N. (Springer, Cham), 1–22. doi: 10.1007/978-3-319-42527-6_1
Cumberlidge N., Ng P. K. L., Yeo D. C. J., Magalhães C., Campos M. R., Alvarez F., et al. (2009). Freshwater crabs and the biodiversity crisis: Importance, threats, status, and conservation challenges. Biol. Conserv. 142, 1665–1673. doi: 10.1016/j.biocon.2009.02.038
Dallas H. F. and Rivers-Moore N. (2014). Ecological consequences of global climate change for freshwater ecosystems in South Africa. South Afr. J. Sci. 110. doi: 10.17159/sajs.2014/a0062
Díaz S., Settele J., Brondízio E., Ngo H. T., Guèze M., Agard Trinidad J., et al. (2019). “Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services,” in Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Bonn, Germany: IPBES Global Assessment Reporton Biodiversity and Ecosystem Services – Summary for Policymakers). doi: 10.5281/zenodo.3553579
Dobson M. (2004). Freshwater crabs in Africa. Freshw. Forum. 21, 3–26. Available online at: https://core.ac.uk/download/pdf/228600966.pdf (Accessed December 01, 2024).
Döll P. and Zhang J. (2010). Impact of climate change on freshwater ecosystems: A global-scale analysis of ecologically relevant river flow alterations. Hydrology Earth System Sci. 14, 783–799. doi: 10.5194/hess-14-783-2010
Elith J. and Franklin J. (2013). “Species distribution modeling,” in Encyclopedia of biodiversity, 2nd ed. Ed. Levin S. A. (United States: Elsevier Inc), 692–705. doi: 10.1016/B978-0-12-384719-5.00318-X
Elith J. and Leathwick J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecology Evolution Systematics 40, 677–697. doi: 10.1146/annurev.ecolsys.110308.120159
Elith J., Leathwick J. R., and Hastie T. (2008). A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813. doi: 10.1111/j.1365-2656.2008.01390.x
Farhadi A. and Harlıoğlu M. M. (2018). Distribution and diversity of freshwater crabs (Decapoda: Brachyura: Potamidae, Gecarcinucidae) in Iranian inland waters. Aquat. Sci. Eng. 33, 110–116. doi: 10.26650/ASE2018422064
Fawcett T. (2006). An introduction to ROC analysis. Pattern Recognition Lett. 27, 861–874. doi: 10.1016/j.patrec.2005.10.010
Fick S. E. and Hijmans R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatology 37, 4302–4315. doi: 10.1002/joc.5086
Fois M., Cuena-Lombraña A., Fenu G., and Bacchetta G. (2018). Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions. Ecol. Model. 385, 124–132. doi: 10.1016/j.ecolmodel.2018.07.018
GBIF Secretariat (2023). “Potamon ibericum (Marschall de Bieberstein 1809),” in GBIF Backbone Taxonomy (Checklist dataset). Copenhagen, Denmark. doi: 10.15468/39omei
Ghasemian Sorboni S., Hadipour M., and Ghasemian Sorboni N. (2024). Modeling the current suitable habitat range of the yellow-bellied gecko (Hemidactylus flaviviridis rüppell 1835) in Iran. Biol. Life Sci. Forum 39, 1. doi: 10.3390/blsf2024039001
Gillingham P. K., Britton J. R., Jones G., Miller-Rushing A., Stafford R., and Slater H. (2024). Climate change adaptation for biodiversity in protected areas: An overview of actions. Biol. Conserv. 289, 110375. doi: 10.1016/j.biocon.2023.110375
Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., and Moore R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. doi: 10.1016/j.rse.2017.06.031
Habibullah S., Din B. H., and Tan S. H. (2022). Impact of climate change on biodiversity loss: Global evidence. Environ. Sci. pollut. Res. 29, 1073–1086. doi: 10.1007/s11356-021-15702-8
Hassan B., Qadri H., Ali M. N., Khan N. A., and Yatoo A. M. (2020). Impact of climate change on freshwater ecosystem and its sustainable management. Fresh Water pollut. Dynamics Remediation, 105–121. doi: 10.1007/978-981-13-8277-2_7
Hermoso V., Abell R., Linke S., and Boon P. (2016). The role of protected areas for freshwater biodiversity conservation: challenges and opportunities in a rapidly changing world. Aquat. Conservation: Mar. Freshw. Ecosyst. 26, 3–11. doi: 10.1002/aqc.2681
Kafash A., Ashrafi S., and Yousefi M. (2021). Distribution of Pipistrellus pipistrellus (Schreber 1774) under the climate change: Identifying climate refugia and assessing protected areas effectiveness. J. Anim. Res. (Iranian J. Biology) 34, 44–56.
Khatami S. H. and Valinasab T. (2003). Species identification of freshwater crab in Jairoud River. Iranian Sci. Fisheries J. 12, 57–66.
Liu C., White M., and Newell G. (2013). Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789. doi: 10.1111/jbi.12058
Liu C., White M., and Newell G. (2015). On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348. doi: 10.1002/ece3.1878
Miller J. (2010). Species distribution modeling. Geogr. Compass 4, 490–509. doi: 10.1111/j.1749-8198.2010.00351.x
Naimi B. (2023). usdm: Uncertainty analysis for species distribution models (Version 2.1-7) (R package). doi: 10.32614/CRAN.package.usdm
Naimi B. and Araújo M. B. (2016). sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. doi: 10.1111/ecog.01881
Naqibzadeh A., Sarhangzadeh J., Sotoudeh A., and Jafari M. J. (2022). Habitat suitability modeling of Goitered gazelle (Gazella subgutturosa): A Maximum Entropy approach from Samelghan plain, Iran. Sci. Rep. Life Sci. 3, 11–28. doi: 10.5281/zenodo.7058808
O’Neill B. C., Kriegler E., Ebi K. L., Kemp-Benedict E., Riahi K., Rothman D. S., et al. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environ. Change 42, 169–180. doi: 10.1016/j.gloenvcha.2015.01.004
Parvizi E., Keikhosravi A., Naderloo R., Solhjouy-Fard S., Sheibak F., and Schubart C. D. (2019). Phylogeography of Potamon ibericum (Brachyura: Potamidae) identifies Quaternary glacial refugia within the Caucasus biodiversity hot spot. Ecol. Evol. 9, 4749–4759. doi: 10.1002/ece3.5078
Parvizi E., Naderloo R., Keikhosravi A., Solhjouy-Fard S., and Schubart C. D. (2018). Multiple Pleistocene refugia and repeated phylogeographic breaks in the southern Caspian Sea region: Insights from the freshwater crab Potamon ibericum. J. Biogeography 45, 1234–1245. doi: 10.1111/jbi.13195
Phillips S. J., Anderson R. P., Dudík M., Schapire R. E., and Blair M. E. (2017). Opening the black box: An open-source release of Maxent. Ecography 40, 887–893. doi: 10.1111/ecog.03049
Phillips S. J., Anderson R. P., and Schapire R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. doi: 10.1016/j.ecolmodel.2005.03.026
Prakash S. (2021). Impact of climate change on aquatic ecosystem and its biodiversity: An overview. Int. J. Biol. Innov. 3, 312–317. doi: 10.46505/IJBI.2021.3210
Rahman M. A., Rahman M. M., Ahmed A. T. A., Mollah A. R., and Hossain M. A. (2008). A survey on the diversity of freshwater crabs in some wetland ecosystems of Bangladesh. Intl. J. Sustain. Crop Prod. 3(4), 10–17.
Roberts M. (2017). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 HighResMIP (Earth System Grid Federation). doi: 10.22033/ESGF/CMIP6.1901
Santos M. J., Smith A. B., Thorne J. H., and Moritz C. (2017). ). The relative influence of change in habitat and climate on elevation range limits in small mammals in Yosemite National Park, California, U.S.A. Climate Change Responses 4. doi: 10.1186/s40665-017-0035-
Shabani F., Kumar L., and Ahmadi M. (2018). Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global J. Human-Social Sci. 18. Available online at: https://globaljournals.org/GJHSS_Volume18/2-Assessing-Accuracy-Methods.pdf (Accessed December 15, 2024).
Sillero N. (2011). What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol. Model. 222, 1343–1346. doi: 10.1016/j.ecolmodel.2011.01.018
Singh G., Singh A., Singh P., and Mishra V. K. (2021). “Impact of climate change on freshwater ecosystem,” in Water Conservation in the Era of Global Climate Change. Eds. Thokchom B., Qiu P., Singh P., and Iyer P. K. (The Netherlands: Elsevier), 73–98. doi: 10.1016/B978-0-12-820200-5.00017-8
Talebi Otaghvar Y., Najafi Alamdarlo H., Esmaili R., Asadi M. A., Mosavi S. H., Vakilpoor M. H., et al. (2022). Estimation of the monetary value of biodiversity in the Central Alborz Protected Area. Environ. Sci. pollut. Res. 29, 19553–19562. doi: 10.1007/s11356-021-17147-5
van Rees C. B., Cañizares J. R., Garcia G. M., and Reed J. M. (2019). Ecological stakeholder analogs as intermediaries between freshwater biodiversity conservation and sustainable water management. Environ. Policy Governance 29, 303–312. doi: 10.1002/eet.1856
Vári Á., Podschun S. A., Erős T., Hein T., Pataki B., Iojă I.-C., et al. (2022). Freshwater systems and ecosystem services: Challenges and chances for cross-fertilization of disciplines. Ambio 51, 135–151. doi: 10.1007/s13280-021-01556-4
Vitasse Y., Ursenbacher S., Klein G., Bohnenstengel T., Chittaro Y., Delestrade A., et al. (2021). Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Reviews/Biological Rev. Cambridge Philos. Soc. 96, 1816–1835. doi: 10.1111/brv.12727
Vörösmarty C. J., McIntyre P. B., Gessner M. O., Dudgeon D., Prusevich A., Green P., et al. (2010). Global threats to human water security and river biodiversity. Nature 467, 555–561. doi: 10.1038/nature09440
Woodward G., Perkins D. M., and Brown L. E. (2010). Climate change and freshwater ecosystems: impacts across multiple levels of organization. Philos. Trans. - R. Society. Biol. Sci. 365, 2093–2106. doi: 10.1098/rstb.2010.0055
Yeo D., Ng P., Cumberlidge N., Magalhães C., Daniels S., and Campos M. (2008). Global diversity of crabs (Crustacea: Decapoda: Brachyura) in freshwater. Hydrobiologia 595, 275–286. doi: 10.1007/s10750-007-9023-3
Yousefi M., Naderloo R., and Keikhosravi A. (2022). Freshwater crabs of the Near East: Increased extinction risk from climate change and underrepresented within protected areas. Global Ecol. Conserv. 38, e02266. doi: 10.1016/j.gecco.2022.e02266
Keywords: species distribution modeling, habitat loss, freshwater crab, SSP5-8.5 scenario, geographical distribution, species extinction risk
Citation: Ghasemian Sorboni S, Hadipour M, Chen JE and Pourebrahim S (2025) Evaluating climate change impacts on habitat suitability for Iberian freshwater crab “Potamon ibericum Bieberstein, 1808” in Iran. Front. Ecol. Evol. 13:1608518. doi: 10.3389/fevo.2025.1608518
Received: 09 April 2025; Accepted: 24 November 2025; Revised: 10 November 2025;
Published: 18 December 2025; Corrected: 09 January 2026.
Edited by:
Antonella Petrocelli, National Research Council (CNR), ItalyReviewed by:
Leroy Soria-Díaz, Universidad Autónoma de Tamaulipas, MexicoAlberto Basset, University of Salento, Italy
Copyright © 2025 Ghasemian Sorboni, Hadipour, Chen and Pourebrahim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mehrdad Hadipour, bWVocmRhZGhAc3Vud2F5LmVkdS5teQ==; Sharareh Pourebrahim, c2hhcmFyZWhwQHN1bndheS5lZHUubXk=
†Present addresses: Mehrdad Hadipour, Sunway Institute for Global Strategy and Competitiveness, Sunway University, Selangor, Malaysia
Saman Ghasemian Sorboni, Department of Land, Environment, Agriculture and Forestry (TeSAF), University of Padova, Padova, Italy