Expanding and Linking Protected Areas Network: A Case Study of GAP Analysis and Corridor Design for Brown Bears in Iran

doi:10.21203/rs.3.rs-4870240/v1

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Expanding and Linking Protected Areas Network: A Case Study of GAP Analysis and Corridor Design for Brown Bears in Iran

https://doi.org/10.21203/rs.3.rs-4870240/v1

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The brown bear is Iran's largest carnivore with a wide distribution range. However, the brown bear is listed as nationally endangered. Brown bears' habitats in Iran are very fragmented, especially at the local scale. Thus, researching the potentially suitable habitat areas in a human-dominated landscape like Lorestan province in Iran is vital to ensure the conservation of this species. In this study, we used a presence-only based maximum entropy (MaxEnt) approach to model the brown bear's habitat suitability. Then, we utilized the Gap Analysis to identify the gap between highly suitable habitat areas and the existing protected areas network. Furthermore, the potential habitat corridors were designed between some protected areas, and between a protected area and a no-hunting area to maintain connectivity between them. Our results showed that the most important variables impacting the distribution of brown bears were the distance from roads, annual precipitation, elevation, and distance from agricultural lands respectively. The results also demonstrated that only 7.4% of Lorestan province is considered a highly suitable habitat, with 36.07% remaining unprotected. In conclusion, this study can be a valuable tool for expanding the current protected and no-hunting areas and establishing new ones in the province and other similar regions.

Corridor Design

Habitat Suitability Modeling

Gap Analysis

MaxEnt

Protected Areas Network

Ursus arctos

Human activities have turned the landscapes into parts of residential areas, agricultural lands, and fragments isolated from the natural ecosystems of organisms (Bennett 2003). This habitat fragmentation process reduces habitats and the size of habitat blocks and isolates wildlife more (Austin 1996). This increases the risk of extinction due to inbreeding and random genetic and demographic factors (Wilcox and Murphy 1985; Mills and Smouse 1994). At the same time, protected areas (PAs), as measures to limit human activity and provide safe spaces for nature to thrive, have become one of the vital tools for conservation, and if effective, significantly alleviate human pressure and associated threats to biodiversity (Mazaris et al. 2013; Schulze et al. 2018). In addition, habitat corridors can increase the conservation value of protected areas and support connectivity (Convention on Biological Diversity 2010). Nevertheless, current protected areas are often unable to support feasible large carnivore populations globally due to their low densities, large area requirements, and high dispersal abilities (Hilty et al. 2012; Cushman et al. 2011; Oriol-Cotterill et al. 2015; Cushman et al. 2018; Rio-Maior et al. 2019; Macdonald et al. 2019) and habitat connectivity is now a key focus of almost all modern conservation planning around the world (Opdam et al. 2003). Thus, planning for large carnivore conservation requires assessing the effectiveness of the current protected areas network, prioritizing the establishment of new ones in strategic regions, and protecting dispersed populations that move between the network (Johansson et al. 2016; López-Bao et al. 2017; Crespin and Simonetti 2019; Kaszta et al. 2020a,b).

For this purpose, the Gap analysis is one of the tools used to enhance the PAs network by identifying gaps in the coverage of biodiversity and distinct environments (Ladle and Whittaker 2011; Liang and Li 2024). GAP analysis, a geographical approach to biodiversity conservation, involves identifying factors such as vegetation types and species that are underrepresented or missing in protected areas network and aims to identify and address these gaps (Scott et al. 1993). Additionally, corridors are designed to facilitate dispersal between populations, thereby improving the long-term viability of the population (Hofman et al. 2018). The core habitats and corridors of large carnivores are often difficult to determine due to their mainly secretive and nocturnal essence (Balme et al. 2009; Almasieh et al. 2019a), while species distribution models (SDMs) (Guisan and Zimmermann 2000) have helped researchers predict suitable habitats for them. Moreover, SDMs have been applied as input data to predict habitat corridors to facilitate gene flow and dispersal among key habitats to assist species management (McRae and Beier 2007; Farhadinia et al. 2015; Almasieh et al. 2019b).

The brown bears, as the large carnivores, in the Middle East, known as neglected populations, are endangered (Can and Togan 2004; Davison et al. 2011) and limited to small and in many cases isolated populations in Iran, Iraq, and Turkiye (Can and Togan 2004; Ridings 2006; Linnell et al. 2008). The brown bear is the biggest carnivore in Iran. They are critical in mountain ecosystems as an umbrella and flag species (Kolivand et al. 2013). The brown bear is listed as nationally endangered (EN) under criteria C1 (Yusefi et al. 2019). The habitats of brown bears in Iran include three regions: 1) the Hyrcanian forests on the northern slopes of the Alborz Mountains and the southern coast of the Caspian Sea (19000 Km²), 2) the Caucasus mountains in the northwest of the country (3500 Km²), 3) the Central Zagros Mountains in the west of Iran (5000 Km²) (Sathyakumar and Can 2007). Iran's Department of the Environment categorizes the species as a protected species. As a result, bear hunting is illegal in Iran (Nezami and Farhadinia 2011; Almasieh et al. 2019a).

So far, some research has been conducted on the habitat of the brown bear (e.g., Ataei et al. 2012; Farashi 2018; Almasieh et al. 2019a; Mohammadi et al. 2021), and the impact of climate change on brown bear distribution in Iran (Ara et al. 2022a), but few studies have been carried out in protected areas of Lorestan province (e.g., Ahmadipari et al. 2021). Since the species in these areas, like other wildlife species, need to be studied and planned for conservation, determining regions with suitable habitats for establishing new protected areas and habitat corridors seems essential. As a result, this study was carried out to model the brown bear's habitat suitability in order to predict the core habitats in the Lorestan province, western Iran. In addition, this research aimed to analyze Gaps between, the current protected areas network and “what ought to be” by using habitat suitability results. We also modeled the habitat corridors between the current protected and no-hunting areas. Eventually, we provided some suggestions for the future establishment of new and expansion of current protected areas.

Study area

The study area was Lorestan province in Iran. Lorestan province covers an area of 28,064 square kilometers and is situated in the western region of Iran and the Central Zagros Mountains. It covers 1.7% of the total area of the country. Lorestan, with an annual average of more than 600 mm of precipitation, is one of the rainiest provinces in Iran (Khosravi and Zeid Ali 2011). The most important habitats of the brown bears in the Zagros Mountains are located in Sefid Kooh, Oshtoran Kooh, and Ghali Kooh protected areas. These protected areas are core habitats for this species. Furthermore, the brown bear has the highest rate of reproduction in the Zagros Mountains and Oshtoran Kooh protected area compared to other habitats in Iran (Almasieh et al. 2019a; Ahmadipari et al. 2021) (Fig. 1).

Occurrence data collection

The occurrence data of the species (n = 116) were collected through various non-random methods, such as opportunistic direct observation, indirect signs of the species, such as fur and footprints, and documented human reports of damage caused by the brown bear, by the authors and personnel of Iran’s Department of Environment (DoE). All observations, documents, and signs were recorded by GPS (Ara et al. 2022a). Moreover, we used randomly located camera trap data, as proposed by Rowcliffe et al. (2008). We spatially filtered points prior to analysis based on the average home range for male brown bears in Iran (Moqanaki et al. 2018; Mohammadi et al. 2021) and similar habitats in Turkey (Ambarli et al. 2016) to reduce the occurrence points spatial autocorrelation, which can bias model predictions. Consequently, we used the Spatially Rarify Occurrence Data tool in the SDMtoolbox63 to place a 5 km radius circle around each point of presence to exclude proximal points (Brown 2014). Generally, we retained 76 points of presence in the final dataset used for habitat modeling.

Environmental variables

Five categories of variables based on literature reviews of the brown bear ecology were selected as the main variables in modeling habitat suitability for the species, including water resources, land cover, human disturbance, topography, and bioclimate (Almasieh et al. 2019a; Ahmadipari et al. 2021; Mohammadi et al. 2021) (Table 1). We obtained the elevation data, with a scale of 250 meters, from a digital elevation model (DEM) sourced from the website http://srtm.csi.cgiar.org. These data were acquired from the 90-meter resolution of the Shuttle Radar Topography Mission (https://earthexplorer.usgs.gov/). ArcGIS software version 10.8.2 (Esri 2021) (https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources) was used to build all environmental layers. The slope was calculated from DEM by using the spatial analyst tools. The forest and agriculture layer of the Lorestan province was extracted from a layer prepared by Iran's Forest, Range, and Wetland Management Organization (IFRWO 2010). This layer has classified the land cover and land use types into 12 categories (e.g., forests, range, agriculture, wetlands, and drylands). Furthermore, this research used variables like rivers, roads, cities, and villages (Almasieh et al. 2022). Because of the importance of water resources (Ansari and Ghoddousi 2018) we used distance from rivers as a predictor variable. The distance from roads was selected as a human disturbance variable because bears tend to avoid roads (Brody and Pelton 1989). Cities and villages (residential areas) were also chosen as another human disturbance variable. The Euclidean Distance tool was applied in ArcGIS software to generate the distance to the features layer. Out of 19 bioclimatic variables (Hijmans et al. 2005), its four main variables including, annual mean temperature (Bio1), temperature seasonality (Bio4), annual precipitation (Bio12), and precipitation seasonality (Bio15) (Almasieh et al. 2016a,b), downloaded from the https://www.worldclim.org/ site. Then we downscaled all bioclimatic variables proportional to other variables for modeling.

Pearson correlation coefficient between annual precipitation and annual mean temperature was more than 70% (Pearson ≤ 0.7). Because annual temperature has a greater ecological effect on brown bears, precipitation seasonality was excluded from the variables. There was also a correlation between elevation and annual mean temperature. Due to the mountainous nature of the habitat and the importance of elevation in the distribution of brown bears, the annual mean temperature was eliminated (Almasieh et al. 2016a,b) (Table 1).

Table 1

Details of the environmental variables.
Category	Variable	Source
Land Cover	Distance to agriculture (m) Distance to forest (m)	Iran's Forest, Range, and Wetland Management Organization (IFRWO)
Water Resources	Distance to river (m)
Human Disturbance	Distance to road (m) Distance to residential area (m)
Topography	Elevation (m) Slope (%)	SRTM
Bioclimate	Temperature seasonality (⁰C) Annual precipitation (mm)	WORLDCLIM database

Habitat suitability modeling

In this research, Maxent 3.4.3 software (Phillips et al. 2020) was used to determine the suitability of the brown bear habitat in Lorestan province. Maxent software compares presence points with pseudo-absence points to predict optimal habitat areas. This method requires only species presence data (Phillips and Dudík 2008). Also, determining which environmental variables are the essential factors in how the species is distributed is one of the most important strengths of this software (Almasieh et al. 2016a,b). In this modeling, 75% of the data were considered as training data and 25% as test data (Pearson et al. 2007; Ara et al. 2022b; Garmaeepour et al. 2024). Ten thousand pseudo-presence points were used as the best number with the highest prediction accuracy (Phillips and Dudík 2008). Modeling was also replicated 10 times through bootstrapping. The Area under the receiver operating curve (AUC) was applied as a scale to assess the model accuracy (Askaripour et al. 2022) and estimation capacity for training and test data. Moreover, the Jackknife test was employed to determine significant variables in species distribution.

GAP analysis

According to the obtained habitat suitability distribution map and the maps of the protected areas network in Lorestan province, the potentially suitable habitat outside these areas was determined to be the area of conservation vacancy by applying the GAP analysis in ArcGIS (Liang and Li 2024). To do so, we first classified the species distribution map into 1) highly suitable areas, 2) moderately suitable areas, and 3) Unsuitable areas. We also used the minimum predicted value According to Philip's study method (Phillips et al. 2006) by averaging 10 replication methods (Tesfamariam et al. 2022). Then, species Occurrence points, protected areas network map, and species distribution map were used in the GAP analysis. In fact, we used the overlay analysis tools of ArcGIS to overlay the habitat suitability map obtained from the MaxEnt results with the existing protected areas of Lorestan. Finally, The highly suitable areas placed outside the protected areas were deemed the conservation gap areas.

Habitat corridor modeling

We used the CorridorDesigner extension in ArcGIS 10.8.2 (Esri 2021) to model habitat corridors (Majka et al. 2007; Almasieh et al. 2016a,b) to design potential corridors between the existing protected areas to ensure habitat connectivity. The CorridorDesigner identifies the least-cost corridors between termini (start–end locations) (Majka et al. 2007). To do so, we used a habitat suitability map obtained from the Maxent result and the maps of the present protected areas network. We selected three out of four protected areas, namely Sefid Kooh, Oshtoran Kooh, and Ghali Kooh protected areas as the potential population patches because they are core habitats for this species (Almasieh et al. 2019a; Ahmadipari et al. 2021). We used the mean size of male brown bear home ranges in Turkey (83 km²) (Ambarli et al. 2016) and circles of a 5 km–radius for modeling the corridors in this region.

Model performance

The ROC analysis results showed that the average AUC value was 0.884 (on a scale of 0–1, where 1 indicates perfect discrimination of presence points from pseudo-absence points), suggesting that this model had high reliability in identifying and predicting the areas that are potentially suitable for the brown bear in the province (Fig. 2).

MaxEnt variable contribution

The results of the JackKnife analysis of habitat modeling illustrated that the essential factors in assessing the distribution of brown bears were the distance from roads, annual precipitation, elevation, and distance from agricultural lands, respectively, while the distance from the river was the least important in distribution modeling (Fig. 3).

According to the response curve of the environmental variables, the habitat suitability increases as the distance from roads increases to around 100–150 meters. Then, as the distance from the roads increases, the habitat suitability drastically decreases. Additionally, results showed that increasing annual precipitation up to approximately 300–350 mm enhances the habitat suitability for the brown bear. However, exceeding these levels results in a slight decrease in suitability. Afterward, the habitat suitability remains constant. Also, the suitable elevation range for the brown bear habitat was estimated to be between approximately 1500 and 2000 meters. The habitat suitability of this species rises with the increase of distance from agricultural lands and reaches its peak at approximately 20–60 meters. However, suitability drastically declines beyond these distances (Fig. 4, 5).

Gap analysis results

Gap analysis revealed that the extent of the protected areas network to conserve the brown bear in Lorestan province is medium, as it is currently limited to four protected areas and six no-hunting areas (Fig. 6). Other potential suitable areas for brown bears in this province remain unprotected. Lorestan province spans an approximate area of around 28000 Km², with ~ 7.4% (2080 Km²) deemed as highly suitable habitats, ~ 89.52% (25138 Km²) considered moderately suitable, and ~ 3.067% (861.4 Km²) identified as unsuitable habitats for brown bear (Table 2). Sefid Kooh, Oshtoran Kooh, Ghali Kooh, and Shadab Kooh protected areas cover nearly 987 Km² of the total highly suitable areas. Furthermore, Char Shakh, Garin, Hashtad Pahloo, Sarkan, Khorosan, and Poldokhtar no-hunting areas cover ~ 342 Km² (Table 3). In other words, these protected and no-hunting areas encompass 63.93% of the highly suitable habitats, leaving the remaining 36.07% unprotected.

Table 2

The extent of highly suitable, moderately suitable, and unsuitable habitats for the brown bear in Lorestan province.
Habitat suitability	Km²	~ %
Highly suitable areas	2080	7.4
Moderately suitable areas	25138	89.52
Unsuitable areas	861.4	3.067

Table 3

The extent of highly suitable and unsuitable habitats for the brown bear in protected areas (†) and no-hunting areas (⁕) of Lorestan province.
	Highly suitable areas (Km²)	Unsuitable areas (Km²)
^† Oshtoran Kooh	455.3776	0
^† Sefid Kooh	252.7466	0
^† Ghali Kooh	275.2349	0.3771633
^† Shadab Kooh	4.337378	0
^⁕ Char Shakh	11.17346	0
^⁕ Garin	312.7156	0
^⁕ Hashtad Pahloo	6.411777	0
^⁕ Sarkan	11.73921	0.04714541
^⁕ Khorosan	0.09429083	0
^⁕ Poldokhtar wetland	0	44.26954
	1329.83081583 (of the total)	44.69384871 (of the total)

Habitat corridor

We decided to design habitat corridors between the Sefid Kooh, Oshtoran Kooh, and Ghali Kooh protected areas, and the Garin no-hunting area because they cover large areas of highly suitable habitats and there are no links between them. We also suggested some highly suitable areas to add to the protected areas network or introduce as new protected areas (Fig. 7).

We modeled two habitat corridors between Oshtoran Kooh and Ghali Kooh protected areas (Corridor A and B), a habitat corridor between Oshtoran Kooh and Sefid Kooh protected areas (Corridor C), and two habitat corridors between Sefid Kooh protected area and Garin no-hunting area (Corridor D and E). The lengths of Corridor A and B were estimated at ~ 10.41 Km and ~ 8.98 Km. The length of Corridor C was calculated at ~ 74.64 Km. Furthermore, Corridor D and E lengths were measured at ~ 25.84 Km and ~ 23.32 Km. The mean density of roads and rivers in all habitat corridors were ~ 0.58 m/km² and ~ 0.24 m/km² respectively (Table 4). Furthermore, the annual mean precipitation in all habitat corridors is approximately 385 mm and the mean elevation is about 2125 meters.

Table 4

Properties of habitat corridores designed for the brown bear between Protected and no-hunting areas.
Corridor	Mean length (Km)	Mean width (Km)	Mean road density (m/km²)	Mean river density (m/km²)
Corridor A	10.41215	1.80206	1.601636432	0.140992566
Corridor B	8.98515	0.91256	1.062669469	0.435423648
Corridor C	74.64809	2.23891	0.075571924	0.222064613
Corridor D	25.83543	2.04051	0.046467702	0.132163854
Corridor E	23.3174	2.33961	0.115425176	0.27253771

Habitat suitability

Our study demonstrated that the most critical factors affecting the distribution of this species in the province were the distance from roads, annual precipitation, elevation, and distance from agricultural lands, respectively.

The findings indicated that the habitat suitability increases as the distance from roads increases to around 100–150 meters. Bears prefer to avoid roads due to the heavy traffic (Skuban et al. 2017). Highways and expressways are included among these types of roads. The results of a study conducted by Straka et al. (2012) indicated that brown bears' avoidance of roads is likely due to the negative impact on gene flow and the high fatality rate caused by collisions with vehicles. Brown bears tend to cross roads during the nighttime or early morning hours when there is minimal human presence and traffic (Graves et al. 2006; Graham et al. 2010). Therefore, increasing the distance from the road improves the habitat suitability. Nevertheless, we also found that the initial increase in suitability with distance from roads causes a drastic decrease in suitability at greater distances. Consequently, intermediate distances from roads may offer a balance of safety from human activity and resource access. Habitats too far from roads may lack vital resources and habitat structures required by brown bears explaining the observed patterns in habitat suitability in this study.

Our results also showed that annual precipitation affects brown bears’ habitat suitability. Precipitation plays an important role in determining the quality of brown bear habitat, ultimately influencing their distribution, behavior, and physiological adaptations (Su et al. 2018). Habitat suitability for the brown bear increases by increasing annual precipitation up to around 300–350 mm, and more than this amount causes a slight decrease in habitat suitability. A study conducted by Su et al. (2018) found that the increase in runoff and overflowing rivers may cause a decrease in the speed at which brown bears can find food. Thus, this could lead to a decrease in the suitability of their habitat.

The elevation was also important The brown bears’ habitat suitability in this study. It was calculated that the suitable elevation range for brown bear habitat falls between 1500 and 2000 meters. The reason for the importance of this variable is that elevation affects habitat suitability by influencing temperature and precipitation patterns (i.e., rain or snow) (Morovati et al. 2020). This species is more distributed in the high elevation due to human activity and disturbances reduction (Naves et al. 2003) triggering greater security (Almasieh et al. 2019a; Ahmadipari et al. 2021), safer food sources (Selva et al. 2017), and dense forest cover as food sources and shelter (Naves et al. 2003; Khosravi et al. 2022). Our study illustrated that the habitat suitability of brown bears in relation to agricultural lands follows a pattern of initial increase and then drastically decreases. There are reasons why brown bears avoid approaching these areas. Since agricultural lands are human-modified landscapes, brown bears generally prefer to stay away from human-populated areas (Graves et al. 2006; Graham et al. 2010) where the mortality risk is high because they are considered potential ecological traps (Steyaert et al. 2016). Therefore, the distance from the agricultural lands can increase habitat suitability up to a certain distance.

However, agricultural lands hold considerable appeal (Hellgren et al. 1991; Romain 1997) in the form of crops (Maddrey and Pelton 1995; Ditmer et al. 2018) for brown bears. As a result, areas further away from agricultural lands lead to a decrease in habitat suitability (Ditmer et al. 2018).

GAP analysis and habitat corridors

The results of the Conservation Gap Analysis showed that 36.07% of highly suitable habitats for brown bears remain unprotected. Indeed, the findings reveal that there are medium conservation gaps in this area for the conservation of this species compared to some studies on different species like Li et al. (2022) and Tian et al. (2022). Covering 63.93% of highly suitable areas by protected areas network strongly indicates that Iran's DoE has prioritized core habitats. However, addressing the remaining areas is crucial for a comprehensive and effective conservation strategy. Hence, proper design of protected areas and corridors should incorporate spatially explicit prioritization of both core areas and connectivity networks among them, which will ensure that protected areas functionally protect focal populations and enable dispersal among them, which facilitates demographic rescue and gene flow which are crucial for long-term species conservation (Ruiz-González et al. 2014; Fedorca et al. 2019). In addition, protecting a habitat large enough to sustain the life history traits of brown bears as an umbrella species will likely also protect the long-term persistence of many other species (Morales-González et al. 2020; Macdonald et al. 2020).

With the following proposals, these gaps can be minimized to ensure the long-term protection of the species. The Garin no-hunting area covers one of the core habitats of this species in the province. there is potential for it to be designated a higher category of protection like a protected area along with the adjacent area on its eastern border and Char Shakh no-hunting area. Another important habitat is located in the northwestern part of the Ghali Kooh protected area. This region is suggested to be added to the Ghali Kooh protected area because according to Li et al. (2021), increasing the size of protected areas may successfully protect species that highly rely on the characteristics of habitat. Since the brown bear has the highest rate of reproduction in the Oshtoran Kooh protected area based on Ahmadipari et al. (2021), areas on the north, west, and southeast of this area should be attached to this protected area. Two areas are also placed in the eastern and southwestern boundary of the Hashtad Pahloo no-hunting area. Other areas are too small and fragmented to be considered protected or no-hunting areas.

Although there are medium conservation gaps in the province, core habitats are totally fragmented, and ecological connectivity between protected and no-hunting areas is very low. Fragmentation can diminish the effectiveness of conservation efforts, so maintaining corridors is crucial for species movement and genetic exchange (McRae and Beier 2007; Farhadinia et al. 2015; Almasieh et al. 2019b). Thus, modeling habitat corridors is vital. The results of habitat corridors modeling illustrated that the designed corridors are entirely consistent with the results of the habitat suitability modeling, and this showed that the accuracy in designing corridors is very high, and they are the most suitable corridors for this species in the province. Most parts of the proposed corridors have moderate suitability due to the tremendous human disturbances. Therefore, the corridors designed in limited areas have a high suitability for the species.

While the mean road density of each corridor was very low, the main roads crossed the designed corridors. Hence, it is essential to prioritize road sections for improving connectivity and mitigating mortality risk. There are many techniques to do so like fencing and overpass structures (Kusak et al. 2009) to funnel dispersing the brown bear across the freeway in optimal locations for network linkage (Mohammadi et al. 2021). It is noteworthy that comparing the results of this study with the research carried out by Nayeri et al. (2022) illustrated that the corridors designed in this study are the best-proposed corridors for the brown bear in this region since the corridors are far from the hotspots of human-caused the mortality of brown bears in Iran.

Our study is the first attempt to introduce new areas for conservation and linking them in this region. This study displayed that Lorestan’s Department of Environment (DoE) should expand the existing protected areas, introduce new areas, and establish habitat corridors based on the results of this study to ensure the conservation of this species in the human-modified landscape. We also recommend that systematic genetic studies should be carried out to provide information about the movement of this species between these protected and no-hunting areas. Additionally, since our study was not aimed at drawing new protected areas' boundaries, we suggest that researchers, for their future studies, delineate appropriate protected area boundaries of the province that are ecologically sound. All in all, the results of this study could be used to improve the coverage of protected areas and the connectivity between them based on highly suitable areas which our analysis shows are relatively unprotected and disconnected.

Acknowledgments The authors would like to thank rangers and experts of the Lorestan provincial office of Iran’s Department of Environment who provided some occurrence points for this study. We also thank Zamin-e-Farda Environmental NGO, and Ms. Batoul Ghaffarikhah for providing some data.

Author contributions Conceptualization, S.R.A. and N.A.; methodology, S.R.A. and N.A.; validation, S.R.A. and N.A.; formal analysis, S.R.A. and N.A.; investigation, S.R.A. and N.A.; resources, S.R.A. and N.A.; data curation; S.R.A. and N.A.; writing—original draft preparation, S.R.A. and N.A.; writing—review and editing, S.R.A. and N.A.; visualization, S.R.A. and N.A.; supervision, S.R.A. and N.A.; project administration; S.R.A. and N.A. All authors have read and agreed to the published version of the manuscripts.

Funding Not applicable.

Data availability Data are available upon request.

Competing interests The authors declare no competing interests.

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Expanding and Linking Protected Areas Network: A Case Study of GAP Analysis and Corridor Design for Brown Bears in Iran

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Abstract

Figures

Introduction

Material and methods

Study area

Occurrence data collection

Environmental variables

Habitat suitability modeling

GAP analysis

Habitat corridor modeling

Results

Model performance

MaxEnt variable contribution

Gap analysis results

Habitat corridor

Discussion

Habitat suitability

GAP analysis and habitat corridors

Conclusions

Declarations

References

Additional Declarations

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