Introduction

Human activities have significantly impacted over 75% of terrestrial ecosystems (Ellis et al. 2010; Sanderson et al. 2002). These alterations, referred to as human footprints, serve as indicators of human activities within the environment (Toews et al. 2017). Through a multi-criteria evaluation, Rahimi and Dong (2022) investigated the impacts of infrastructures like roads, railways, cities, industries, mines, and agriculture on Iran’s protected areas. The findings revealed that, on average, 5.1–30.3% of the extent of protected areas is affected by the adverse impacts of human footprints. Notably, irrigated farming and roads emerge as the activities with the most detrimental effects on protected areas in Iran (Rahimi and Dong 2022; Rahimi et al. 2024b). In another study, Karimi and Jones (2020) showed that 12.8% of the area of national parks, 12% of the area of wildlife refuges, and 28% of the extent of protected areas in Iran are affected by the negative effects of human footprints.

Iran is divided into three phytogeographical regions (Talebi et al. 2014): the Euxino-Hyrcanian region, the Saharo-Sindian region, and the Irano-Turanian region. Additionally, ecologists have further classified Iran’s forests into three ecological zones—the Caspian or Hyrcanian zone, the Khalijo-Omanian zone, and the Iranian-Turanian zone. These zones are then subdivided into the Zagros mountainous zone and the central plateau zone. Approximately 81% of Iran’s land area is occupied by diverse natural features, including forests, deserts, rangeland, and bushes (Talebi et al. 2014). Notably, rangelands cover nearly half of the country’s land area, with a considerable portion characterized as poor quality. Deserts encompass around 20% of Iran’s territory, primarily located in the central, eastern, and southeastern regions, predominantly within the Irano-Turanian ecological region (Rahimi et al. 2021b). Unfortunately, biodiversity in Iran faces significant challenges, with iconic carnivore species such as the Caspian tiger (Panthera tigris virgata) and the Asiatic lion (Panthera leo persica) having been hunted to extinction by the early 1960s. The Asiatic cheetah (Acinonyx jubatus venaticus), one of the two remaining large cat species in Iran alongside the Persian leopard (Panthera pardus saxicolor) due to human intervention (Rahimi and Dong 2023), is classified as critically endangered according to the internationally recognized Red List of Threatened Species (Jowkar et al. 2016).

Iran is currently grappling with unprecedented climate-related challenges, including the desiccation of wetlands (Rahimi et al. 2023) and rivers, dust storms, record-setting temperatures, prolonged droughts, and increased occurrences of floods. Vaghefi et al. (2019) predicted that, compared to the 1980–2004 period, Iran is anticipated to undergo more prolonged episodes of extreme maximum temperatures in its southern regions during 2025–2049. In a review study, Yousefi et al. (2019) examined published papers focusing on the repercussions of climate change on Iran’s biodiversity since 2014. The analysis encompassed 37 species, revealing that 30 of them are projected to experience a reduction in their distribution ranges. The overall findings underscore the predominantly adverse impact of climate change on 81% of the studied species, posing a significant challenge to biodiversity conservation efforts in Iran.

Several studies have determined biodiversity hotspots and assessed the potential impacts of climate change on various species in Iran. While some of these studies have focused on smaller scales (Abolmaali et al. 2018; Ardestani and Ghahfarrokhi 2021; Ashrafzadeh et al. 2019a, b, 2022), others have examined these effects at the national level (Ebrahimi et al. 2021; Mohammadi et al. 2019b). For example, Kazemi et al. (2023) focused on 24 terrestrial venomous snake species in Iran, and their findings indicated that the hotspots for venomous snake species are predominantly situated in the western to southwestern and north-to-northeastern regions of Iran. Mohammadi et al. (2019a) employed MaxEnt models to forecast the potential shifts in the distributions of 537 birds in response to climate change in 2070 in Iran. The outcomes of the climate vulnerability assessment revealed that approximately 40% of bird species in Iran exhibit high susceptibility to climate-induced changes. Ashrafzadeh et al. (2022) also conducted a modeling study to assess the potential impact of climate change on brown bear distribution and habitat connectivity, projecting outcomes for the years 2050 and 2070. They found a decline in the current species’ range, covering 6749.8 km2 (40.8% of the landscape), ranging from 10% (2050) to 45% (2070) for this species.

Despite considerable research on the effects of climate change on species range shift in Iran, there has been limited focus on understanding these changes within protected areas. Apart from a handful of exceptions, most studies have only assessed potential shifts in species distribution at localized scales. Consequently, there remains a significant gap in our understanding of how climate change-induced threats may impact protected areas across Iran. For example, Ebrahimi et al. (2021) evaluated the impact of climate change on 16 endangered mammal species and the spatial coverage of protected areas, under two future climate scenarios for the year 2070. They showed that, under both scenarios, the coverage of the protected area network is expected to expand for six mammal species. We advocate for further research to elucidate and quantify the impacts of climate change on protected areas, particularly by considering a broader array of species. Therefore, our study aims to investigate these effects on a diverse range of fauna and flora, including 401 bird species, 156 plant species, 72 reptile species, and 19 mammal species in Iran.

Methods

Study area

Iran is recognized as a connecting link between oriental regions and African zoogeographical zones (Mittermeier et al. 2011). The presence of the Zagros and Alborz mountains has resulted in diverse climatic conditions across the country. These variations in climate and topographical elevation contribute significantly to Iran’s rich biodiversity, encompassing 169 protected areas, 46 wildlife refuges, and 31 national parks, spanning a total area of 177,137.7 km2. Iran boasts an impressive array of biodiversity, with around 8,000 plant species, 535 bird species, 197 mammals, 227 reptile species, and 21 amphibian species (Farashi et al. 2017; Yusefi et al. 2019). This study specifically concentrates on terrestrial protected areas, comprising 15 national parks, 27 wildlife refuges, and 125 other protected areas (Fig. 1).

Fig. 1
figure 1

The location of national parks, wildlife refuges, and protected areas in Iran

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Occurrence data

The number of occurrence data points required for species distribution modeling (SDM) is influenced by various factors, including model complexity, data quality, species traits, research objectives, and scale (Bean et al. 2012; Rahimi et al. 2021a). While there isn’t a strict rule, general guidelines suggest that a larger sample size is usually preferable because it provides more robust information for modeling. A common recommendation is to aim for a minimum of 30 to 50 presence points, although this requirement can vary significantly (Bean et al. 2012; Stockwell and Peterson 2002). In our study, we set the minimum requirement at 50 data points per continent to enhance model precision. It is important to note that the field of SDMs encompasses a wide range of studies with varying quantities of occurrence data, from as few as 10 points (Williams et al. 2009) to several thousand data points (Orr et al. 2021).

In our current study, we utilized presence data for all taxa obtained from the Global Biodiversity Information Facility (GBIF) website (www.gbif.org) and considered the minimum data point as 50. This involved downloading occurrence data for 394 birds, 157 plants, 72 reptiles, and 20 mammals, with a focus on often overlooked small mammal species (Table 1). Table 1 displays the list of bird, plant, mammal, and reptile orders and families, along with the corresponding number of species in the study. For birds, the most represented order is Passeriformes with 183 species, followed by Charadriiformes with 65 species. Plant orders include Poales with 34 species and Fabales with 31 species. Among reptiles, Lacertidae family leads with 22 species, while Gekkonidae and Agamidae follow closely with 16 and 15 species, respectively. In the mammal category, Rodentia stands out with 12 species, and Carnivora, Artiodactyla, and Lagomorpha each have fewer species, with 4, 3, and 2 respectively. This table provides a comprehensive overview of the biodiversity distribution across different orders and families within our study.

Table 1 The various bird, plant, mammal, and reptile orders and families along with the number of species
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Spatial filtering

To ensure data accuracy, a meticulous examination of presence points is required, aiming to eliminate duplicate records and those positioned beyond the study area boundary. The “rgbif"v3.8.0 package, a convenient R v4.2.3 interface to access biodiversity data from the Global Biodiversity Information Facility (GBIF), played a key role in data manipulation. This package (Chamberlain et al. 2022) enables users to query and retrieve biodiversity data directly within the R environment. Leveraging the rgbif v3.8.0 package, data filtering was executed, excluding records located outside of Iran to maintain relevance to the terrestrial research question under investigation. To counteract potential biases stemming from clustered or over-represented areas in the presence of records, a technique known as spatial thinning can be employed (Aiello-Lammens et al. 2015).

Three methods can be used in the “flexsdm"v1.3.4 R package (Velazco et al. 2022) to define the minimum distance between occurrence data (de Andrade et al. 2020): (1)‘moran’ determines the threshold as the minimum distance which minimizes the spatial autocorrelation in occurrence data; (2)‘cellsize’ (de Andrade et al. 2020; Rodríguez-Aguilar et al. 2023) filters occurrences based on the spatial resolution of predictors, and in addition that minimum distance can be adjusted to represent a larger grid cell size that are multiples of the original predictors; finally, (3) ‘determined’ requires a user-defined minimum distance in kilometers (Aiello-Lammens et al. 2015; Brown and Carnaval 2019; de Andrade et al. 2020; Velazco et al. 2022). We implemented various spatial thinning approaches to refine the analysis of species distributions. Specifically, we applied a 5 km buffer for plants and rodents, a 10 km buffer for reptiles, a 15 km buffer for Passeriformes, and a 25 km buffer for large carnivores and birds. These spatial thinning distances were chosen to effectively capture the spatial dynamics and ecological requirements of each taxonomic group, ensuring a more accurate representation of their distribution patterns under the influence of climate change scenarios.

Background data

Although most SDMs traditionally use both presence and absence data, obtaining absence data is a complex and time-consuming task. Hence, many studies opt for background points or pseudo-absence points as alternatives (Rahimi et al. 2021a; Valavi et al. 2020). The flexsdm v1.3.4 package offers three methods for allocating background points: (1) the ‘random’ method, which randomly samples background points within the study area; (2) the ‘thickening’ method, introduced by Vollering et al. (2019), which selects background points that are geographically biased towards species presences based on the superposition of buffers around these presences; and (3) the ‘biased’ method, which samples background points using a raster layer representing the sampling effort of occurrences across the study area (Elith et al. 2010; Phillips et al. 2009).

However, Barbet-Massin et al. (2012) recommend using a large number of pseudo-absences (e.g., 10,000) with equal weighting for presences and absences when employing regression techniques such as generalized linear models (GLM) and generalized additive models (GAM). They suggest averaging several runs (e.g., 10) with fewer pseudo-absences (e.g., 100), with equal weighting for presences and absences, for methods like multiple adaptive regression splines (MARS) and discriminant analyses. For classification techniques such as boosted regression trees (BRT), classification trees, and random forest, they advise using the same number of pseudo-absences as available presences, averaging several runs if there are few pseudo-absences. Velazco et al. (2022) considered the number of pseudo-absences and background points 10 times the number of presences found in each partition, respectively. Generally, the number of background points should be large enough to comprehensively sample and represent all environments in the region of interest. Having a very large number of background samples ensures better representation. Therefore, in this study, we generated 5000 background data points for each species under study.

Environmental variables

Considering the crucial role of climatic factors in species distribution, especially in Iran (Rahimi et al. 2024a), we focused exclusively on climatic variables to determine species distribution and assess the impacts of climate change. The WorldClim database offers Bioclimatic data comprising 11 temperature variables and 8 precipitation variables, each with a spatial resolution of approximately 4 km. Given the high correlation among these 19 variables, it is not advisable to use all of them in species distribution modeling. To address this issue, the “usdm”v2.1.7 package (Naimi 2017) was employed. Through a stepwise procedure based on the Variance Inflation Factor (VIF), highly correlated variables were systematically excluded. The retained variables for the modeling process include Isothermality (Bio 3), Temperature Seasonality (Bio 4), Mean Temperature of Wettest Quarter (Bio 8), Mean Temperature of Driest Quarter (Bio 9), Precipitation of Driest Month (Bio 14), Precipitation Seasonality (Bio 15), Precipitation of Warmest Quarter (Bio 18), and Precipitation of Coldest Quarter (Bio 19).

Model selecting

Modeling a species’ distribution under current and future conditions can yield different results depending on the species distribution model (SDM) used (Hao et al. 2019). Since no single technique universally outperforms the others, an ensemble forecast framework is recommended (Buisson et al. 2010; Hao et al. 2019, 2020; Valavi et al. 2022). This framework considers the central trend of multiple SDMs using various methods (Marmion et al., 2009) and is now widely adopted among species distribution modelers (Valavi et al. 2022). The flexsdm v1.3.4 package offers an ensemble approach to reduce model uncertainty and obtain a consensus prediction (Araújo and New 2007). The function “fit_ensemble” allows users to perform and validate an ensemble based on different modeling methods. The ability to validate an ensemble model is a notable feature of this package. In our ensemble model fitting, we selected the MaxEnt, GAM, and GLM algorithms for their complementary strengths in capturing species-environment relationships (Kaky et al. 2020; Rahimi and Jung 2024). The available ensemble methods include: (a) the average of different models’ suitability and (b) a weighted average based on model performance (Valavi et al. 2022). In this study, we utilized the weighted average based on the model performance method.

Binary thresholding

To measure the overlap between protected areas and the habitat suitability of species, it is common practice to convert continuous suitability maps into binary maps with values of 0 and 1. Various methods have been proposed for this conversion. For instance, Zhang et al. (2019) Zhang recommend using methods like Maximum True Skill Statistic (TSS) (MaxTSS). The MaxTSS threshold selects the suitability value that produces the highest True Skill Statistic (TSS) value to generate binary maps (Allouche et al. 2006). In this study, we tested all seven thresholding methods available in the flexsdm v1.3.4 package. After visual assessment, we selected the “max_jaccard” method (Leroy et al. 2018) for binary thresholding of continuous maps. This method utilizes the Jaccard index, which measures the similarity between observed and predicted presences. By finding the threshold that maximizes the Jaccard index, the max_jaccard method ensures the highest agreement between actual occurrences and model predictions. This approach provides a balanced and effective way to classify suitability maps, enhancing the discrimination capacity and interpretability of the results in ecological studies (Chung et al. 2019).

Overprediction

At times, there is a requirement for models to accurately estimate ranges that closely resemble realized distributions (Peterson and Soberón 2012). Models that overestimate this range can lead to misleading outcomes and misdirect conservation evaluations (Velazco et al. 2020). flexsdm v1.3.4 offers nine constraining methods adapted from the msdm package code (Mendes et al. 2020). These methods can be categorized into those that generate predictor variables used in model fitting (msdm_priori) and those where constraints are derived from the interaction between observed species presences and suitability patterns (msdm_posteriori). The second set of methods confines the generated species suitability patterns by incorporating estimates of site accessibility, which are not included as predictors during model fitting (de Andrade et al. 2020). In this study, we employed the ‘obr’ method (Occurrences Based Restriction). This method operates under the assumption that suitable patches intercepting species occurrences (l) are more likely to contribute to species distributions compared to suitable patches that do not intercept any occurrences (k). The method calculates the distance from all k patches to the nearest l patch and then removes k patches that exceed a species-specific distance threshold from the SDMs models (Velazco et al. 2022).

Estimating climate change effects

To estimate future climate changes, future distribution scenarios for the year 2070 were projected using climate change trajectories. Specifically, the Shared Socio-economic Pathway (SSP) 245 trajectory was utilized, which simulates CO2 emissions remaining relatively constant until 2050 before declining, although not reaching net zero by 2100. Additionally, the SSP585 scenario, representing a more severe climate change scenario, was employed. This scenario projects a threefold increase in CO2 emissions by 2075 and a temperature rise of 4.4 degrees Celsius by 2070 (O’Neill et al. 2016).

Percentage of change = ((number of cells in the future under the suitable class) - (number of cells at present under the suitable class)) / (number of cells at present under the suitable class)) * 100.

To assess the degree of overlap between protected areas and species distribution, we employed binary maps of species and we quantified how much of each protected area overlaps with raster cells indicating species distribution, providing a measure of spatial overlap between protected areas (polygons) and species distribution (raster data).

Model assessment

To assess the efficacy of various models, we employed three key performance metrics, namely the inverse mean absolute error (IMAE), the area under the ROC curve (AUC), and the Boyce Statistic (BOYCE), utilizing the flexsdm v1.3.4 package (Velazco et al. 2022). AUC values falling within the 0.7 to 0.9 range are considered acceptable, with values surpassing 0.9 indicating excellent discrimination capacity and highly precise predictions. Elevated IMAE and Boyce Index values are indicative of heightened model discrimination capacity. The evaluation of model performance was conducted through 5-fold cross-validation (Fielding and Bell 1997).

Results

Model assessment

Table 2 presents the model validation metrics, including mean values of the Area Under the Curve (AUC), Boyce Index (BOYCE), and Inverse Mean Absolute Error (IMAE), obtained through the implementation of the ensemble combination for different taxonomic groups such as Birds, Plants, Reptiles, and Mammals. The mean values are provided alongside their standard deviations in parentheses. For Birds, the AUC is reported as 0.82, indicating a good level of discrimination, while the BOYCE Index is 0.92 signifying a favorable agreement between observed and predicted suitability. The mean IMAE value for Birds is 0.93 reflecting the discrimination capacity of the model predictions. Similarly, Plants exhibit an AUC of 0.76, a BOYCE Index of 0.95, and an IMAE of 0.94, suggesting reliable model performance. Reptiles demonstrate an AUC of 0.83, a BOYCE Index of 0.93, and an IMAE of 0.95, indicating a high level of discrimination capacity and agreement in predictions. Lastly, Mammals show an AUC of 0.74, a BOYCE Index of 0.95, and an IMAE of 0.94, suggesting consistent and accurate model outcomes for this taxonomic group.

Table 2 Model validation metrics including AUC, BOYCE, and IMAE generated using the ensemble combination for species under study. The standard deviations are presented in parentheses
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Table 3 presents the average percentage of changes in the area of the high suitability class under the SSP245 scenario in Iran for various species categories. The “No. Species” column indicates the number of species whose distribution range will either increase or decrease under climate change scenarios. For birds, the distribution range of approximately 179 species is expected to increase, with an average increase of 412%, while around 215 species are projected to experience a decrease, averaging at -54.5%. In terms of plants, 21 species are anticipated to see an increase in the distribution range by an average of 31.6%, while the distribution range of 136 species is predicted to decrease by an average of -59.1%. Similarly, for reptiles and mammals, the number of species expected to experience an increase in distribution range is 30 and 7, respectively, with average increases of 281.6% and 46.3%. Conversely, the distribution range of 42 reptile species and 13 mammal species is expected to decrease, with average decreases of -65.8% and − 65.9%, respectively.

Table 4 showcases the average percentage of changes in the area of the high suitability class under the SSP585 scenario. For birds, approximately 174 species are projected to experience an increase, with an average increase of 412%, while around 220 species are expected to see a decrease, averaging at -57.1%. Regarding plants, 16 species are anticipated to have an increased distribution range by an average of 40.3%, while the distribution range of 141 species is predicted to decrease by an average of -68.8%. Similarly, for reptiles and mammals, the number of species expected to experience an increase in distribution range is 24 and 5, respectively, with average increases of 279.1% and 147.3%. Conversely, the distribution range of 48 reptile species and 15 mammal species is expected to decrease, with average decreases of -66.6% and − 71.8%, respectively.

Table 3 The average percentage of changes in the area of the high suitability class under the SSP245 scenario in 2070 in Iran. The No. Species column (increase/decrease) represents the number of species whose distribution range will change under climate change scenarios. The numbers in parentheses also show the standard deviations
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Table 4 The average percentage of changes in the area of the high suitability class under the SSSP585 scenario in 2070 in Iran. The No. Species column (increase/decrease) represents the number of species whose distribution range will change under climate change scenarios. The numbers in parentheses also show the standard deviations
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Table 5 presents the average coverage percentage of changes in the area of the high suitability class under two different scenarios (SSP245 and SSP585) projected for the year 2070. For national parks, the current percentage coverage for birds, plants, reptiles, and mammals is 32.2%, 24.2%, 18.4%, and 17.7% respectively. Under the SSP245 scenario, these percentages decrease to 25.7%, 15.3%, 8.6%, and 12.3%, and under the SSP585 scenario, they further decline to 19.9%, 13.2%, 6.8%, and 10%, respectively. In Wildlife refuges, the current coverage percentages are lower, with birds at 9.7%, plants at 10.9%, reptiles at 12.9%, and mammals at 7.4%. These percentages decrease across all species under both scenarios. Similarly, protected areas show declines in coverage percentages across all species under the SSP245 and SSP585 scenarios compared to the current percentages. Notably, Birds have the highest current coverage across national parks and protected areas, while mammals have the lowest across national parks and wildlife refuges, indicating potential vulnerabilities in mammal habitats across the areas.

Table 5 The average coverage (%) of changes in the area of the high suitability class under SSP245 and SSP585 scenario in 2070 in three types of protected areas in Iran
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Figure 2 illustrates the climate suitability for four bird species, Grus grus (Common crane), plant species Asperugo procumbens (madwort), mammal species Mus musculus (house mouse), and reptile species Eumeces schneiderii (Schneider’s skink), under the current conditions and projected future scenario of SSP585. These maps depict potential changes in suitability driven by climate change, particularly notable for Eumeces schneiderii, where its future distribution is anticipated to undergo a significant decrease.

Fig. 2
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Climate suitability maps of (A) Grus grus (Common crane), (B) Asperugo procumbens (madwort) (C) Mus musculus (house mouse), (D) Eumeces schneiderii (Schneider’s skink) at present and predicted for 2070 according to the SSP85 climate change scenario

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Discussion

This research investigated the anticipated impacts of climate change on the future distribution patterns of 394 bird species across 23 orders, 157 plant species from 23 orders, 72 reptile species from 11 families, and 20 mammal species from 4 orders. Additionally, we examined the overlap between the binary distribution maps of these species and protected areas in Iran. Our findings indicate a substantial difference in the number of taxa expanding their distribution; however, they may experience mostly a reduction in their distribution ranges within Iran under the SSP245 and SSP585 climate change scenarios by 2070. Comparing the results of the SSP245 and SSP585 scenarios, there was not a notable variation in the expected changes in distribution range across different species categories, with most species likely experiencing a decrease in their future distribution. Under the SSP585 scenario, the magnitude of changes in the distribution range appears to be more substantial, with a higher number of species showing a decrease across all categories. This suggests a potentially greater impact of climate change under the SSP585 scenario compared to SSP245, leading to more significant shifts in species distribution ranges in Iran.

We also examined the current intersection between species distributions and protected areas, finding that this overlap generally ranges between 7.4% (mammals in wildlife refuges) as the minimum and 32.2% (birds in national parks) as the maximum. This indicates a limited overlap between our analyzed species and protected areas. One reason for the low overlap of birds with wildlife refuges is that most of these refuges are situated in the arid regions of Iran. As demonstrated in Table 1, some bird orders, such as Charadriiformes, Anseriformes, and Pelecaniformes, are aquatic and inhabit areas far from these refuges. This limited overlap highlights a potential gap in the effectiveness of current protected areas in conserving diverse taxa. The findings suggest that protected areas, as they are currently designated, may not adequately cover the habitats required by many species, especially those dependent on aquatic environments or those located in more mesic regions. It underscores the importance of re-evaluating and possibly expanding protected areas to ensure they encompass a broader range of habitats, particularly those that are underrepresented, to enhance the conservation of Iran’s biodiversity amidst the challenges posed by climate change.

Using species distribution modeling (SDM) Farashi and Shariati (2017) modeled 18 mammal species, 26 bird species, and 7 reptile species in Iran to identify biodiversity hotspots. The findings revealed that 24% of Iran could be classified as biodiversity hotspots, with 10% falling under protection. The results demonstrated the considerable potential for large portions of Iran to be regarded as biodiversity hotspots. To identify hotspots for Iranian terrestrial mammal species listed in the IUCN red list, Farashi et al. (2017) demonstrated that biodiversity hotspots for threatened mammal species are concentrated in the northern, western, and central regions of Iran, particularly along the Zagros and Alborz mountain ranges. Consequently, habitats for threatened mammal species are confined to smaller portions of Iran, constituting approximately 27% of the country. While the majority of climate change studies focusing on species distribution in Iran indicate a general decline in distribution for many species (Yousefi et al. 2019), some studies report no significant changes in distribution. For example, Mohammadi et al. (2019a) analyzed the present potential distribution of Blanford’s Jerboa (Jaculus blanfordi) and the Arabian Jerboa (Jaculus loftusi), both belonging to the order Mammalia and family Rodentia, within the geographical boundaries of Iran. Their findings revealed that the potential distributions of these two jerboa species are unlikely to be significantly influenced by climate change.

Conclusion

In summary, this study provided a comprehensive exploration into how climate change is projected to affect the distribution patterns of 643 species in Iran, spanning birds, plants, reptiles, and mammals. By examining two climate change scenarios (SSP245 and SSP585) for the year 2070, we gained insights into the potential shifts in distribution ranges for different taxa. Notably, while birds showed a higher proportion of species with expected increases in distribution range, other groups like plants, reptiles, and mammals are anticipated to experience reductions in their distribution ranges under both scenarios, with the SSP585 scenario indicating more pronounced changes. Our analysis also sheds light on the current overlap between species distributions and protected areas in Iran. Interestingly, we found that this overlap is generally low, suggesting a limited alignment between the habitats of analyzed species and designated protected areas. The observed low overlap of species with national parks, wildlife refuges, and other protected areas highlights a significant concern for biodiversity conservation in Iran. This limited overlap indicates that the current national parks, wildlife refuges, and protected areas network may not effectively cover the diverse range of habitats necessary for many species, particularly those inhabiting less represented environments such as aquatic or mesic regions. Given the ongoing challenges posed by climate change, it is crucial to reassess the placement and design of protected areas to better accommodate the full spectrum of habitats required by various species. Re-evaluating and potentially expanding protected areas will help address these gaps and improve habitat coverage, thereby enhancing the overall conservation effectiveness.