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. 2026 Apr 17;16:17930. doi: 10.1038/s41598-026-47602-7

Modelling habitat suitability and estimating protected areas coverage for Falco cherrug across its wintering habitats

Masoud Yousefi 1, Saeed Mohammadi 2,✉, Ali Khani 3, Mohamad Ali Adibi 4, Fahimeh Goudarzi 5, Bagher Nezami 6, Elham Abtin 7
PMCID: PMC13249813  PMID: 41998202

Abstract

The saker falcon (Falco cherrug) is an Endangered avian predator that faces multiple threats within its wintering habitats, particularly in Iran. In this study, we aimed to model habitat suitability across the species’ wintering range, identify the key environmental predictors of suitable habitat, and assess the extent to which these habitats are covered by protected areas in Iran. We applied an ensemble habitat suitability modeling approach (Generalised Boosting Models (GBM), Support Vector Machines (SVM), Maximum Entropy modelling (Maxent), and Random Forest (RF)) to predict the distribution of suitable wintering habitats for the saker falcon across Iran. Our results indicate that the most suitable wintering habitats are concentrated in northeastern and southern Iran, especially around the Persian Gulf and the Gulf of Oman. Annual precipitation with 41% contribution, NDVI with 21% contribution and distance from shrublands with 15% contribution emerged as the most influential predictors of habitat suitability. We also found that suitable habitats for the saker falcon are poorly represented within the current protected areas network. The suitable but unprotected habitats identified in this study may play a crucial role in the conservation of the species and should therefore be considered for inclusion within the protected areas system. Overall, our findings provide important insights for the conservation of this Endangered falcon, benefiting not only its wintering populations in Iran but also its global populations.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-47602-7.

Keywords: Raptors, Avian ecology, Avian habitat, Conservation, Iran

Subject terms: Ecology, Ecology, Zoology

Introduction

Global biodiversity is decreasing rapidly due to human activities worldwide1,2. Protected areas are designed to act as a shield against human activities that cause biodiversity loss, like habitat destruction, illegal hunting, overgrazing, pollution, overexploitation, human settlements and roads development, land use change and climate change1,2. But accelerating loss of biodiversity signals important conservation gaps highlighting an urgent need for new protected areas and/or redesigning existing protected areas particularly by consideration of rapid environmental changes3. Then we can expect protected areas to play a very crucial role in conservation of biodiversity and halt biodiversity loss4,5.

Birds providing important ecosystem services like pollination, pest control, seed dispersal, food provision, and ecotourism but still, they are among the most threatened groups of species globally6–9. Among the avian species raptors are the most threatened group and are rapidly decreasing in number and being lost from their natural habitats10–12. Raptors are particularly important because of their critical ecosystem services and they affect human health and well-being through disease mitigation, agricultural production and waste-disposal services12. Thus, it is essential to prioritise them for conservation efforts but despite their importance, popularity and conservation values, some species remained unknown hampering their evidence-based conservation planning10–12.

The saker falcon Falco cherrug, a medium-sized raptor, occurs across Eastern Europe, Russia, and Asia13. Populations of long-distance migratory saker falcon are declining13,14. It has been revealed that they are suffering severe devastation in their distribution range and are imperilled by habitat destruction and anthropogenic impacts such as a large number of trapping this bird due to the rising popularity of falconry, poisoning, collisions with power lines and increasing international trade of this raptor mainly by Middle East countries and huge annual deaths by electrocution on mid-voltage lines13,15,16. Moreover, changes in agricultural practices reduced prey availability. The species is listed as ‘Endangered’ globally by the International Union for Conservation of Nature (IUCN) because a population trend analysis indicates that it may be undergoing a very rapid decline13. Nevertheless, a large portion of breeding and wintering populations of saker falcon are dispersed over a vast range in deserts and semi-arid habitats of Iran13.

Habitat suitability models predict the likelihood of species presence at a spatial location using environmental variables, thus quantifying the environmental conditions that may lead to species occurrence17,18. Species Distribution Models (SDMs) have found many applications in avian ecology and conservation19. SDMs are used to model avian habitat suitability20–22, predict the impacts of future climate changes on birds23, identify avian hotspots and conservation priorities24–27, and paleo-distribution of bird species28. For example, Booms et al.29 used SDMs to project Gyrfalcon Falco rusticolus nest occurrence in distant ranges of Arctic Alaska with high precision of the projecting model. Panthi et al. (2021)30 applied SDMs to model the global distribution of Egyptian vulture (Neophron percnopterus) and its relationship to environmental variables during breeding and overwintering.

To help conservation of saker falcon in its wintering habitats, we aimed to develop habitat suitability models of the species across its wintering habitats in Iran, identify the most important predictors of the species’ suitable habitats and estimate protected areas coverage for its suitable habitats. Considering the poor development of protected areas within the arid ecosystem of Iran31, we expect low coverage of the suitable habitat of the species by the protected areas network. Based on repeated field observations indicating that saker falcons are frequently encountered in coastal regions during the winter season (A. Khani, personal observations), we hypothesized that coastal habitats may represent suitable wintering areas for the species.

Methods

Distribution data and environmental predictors

The saker falcon is considered an Endangered predator, and in addition listed a protected species in Iran, there is no systematic surveying and monitoring program has been performed in the country. Thus, distribution records of the species were obtained through fieldwork from October 2018 to February 2025 corresponding to the winter season in Iran. Field surveys were carried out by the authors, on foot and using vehicles, for monitoring the distribution of birds of Iran. In total, we included 193 distribution points in modelling.

To take into account environmental conditions for habitat suitability of the saker falcon, environmental variables (Table 1) related to climate, land cover, topography and human presence, which were known to be important on raptors ecology were used in the modelling process32–38. Bioclimatic variables were downloaded from CHELSA (Climatologies at high resolution for the earth’s land surface areas; http://chelsa-climate.org/). CHELSA contains high resolution (1 km2) climate data (time period 1979–2013) for the earth’s land surface areas currently hosted by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL39. Land cover layers and variables related to human presence were obtained from the Iranian Forests, Range and Watershed Management Organization (IFRWO) National Land Cover map and improved land cover map of Iran developed by Ghorbanian et al. (2020)40.

Table 1.

Environmental variables and their VIF values which were used in modeling habitat suitability of the saker falcon in Iran.

Variable type Variable name VIF
Climate Mean diurnal range (Bio2) 6.25
Isothermality (Bio3) 7.42
Temperature annual range (Bio7) 4.21
Mean temperature of driest quarter (Bio9) 3.62
Mean temperature of warmest quarter (Bio10) 2.89
Annual precipitation (Bio12) 2.49
Precipitation of driest quarter (Bio17) 4.33
Topography Slope 1.09
Vegetation Normalized Difference Vegetation Index (NDVI) 2.12
Distance to shrublands 1.38
Distance to croplands 3.17
Distance to rangelands 2.27
Anthropogenic Distance to human settlements 1.42
Distance to roads 2.01
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To quantify how the species distribution is affected by human presence we included layers of human settlements and roads as two anthropogenic variables. We included Normalized Difference Vegetation Index (NDVI) in the saker falcon model because it is an indicator of plant productivity that might influence resource availability for birds41,42. Vegetation is associated with higher prey availability and availability of nesting and perching sites for predators33–35. In addition, it can significantly improve the performance of SDMs43 and is proven to be useful in predicting bird species richness and distribution44,45. We obtained NDVI data at a spatial resolution of 1 km2 from the Copernicus Land Monitoring Service (CLMS) for winter 2020. Slope was calculated using the terrain function in the Raster package46, this function calculates slope as a function of elevation. The elevation layer for the study area was obtained from the Shuttle Radar Topography Mission (SRTM) elevation model47.

Environmental variables are sometimes highly correlated with one another, leading to multicollinearity in SDMs48. To avoid collinearity among variables, a variance inflation factor (VIF) was calculated for the variables using the ‘usdm’ package in R environment, and results showed that collinearity among variables is low (VIF < 10) (Table 1). The spatial resolution of environmental variables was 1 km2, so to avoid pseudo-replication, we only used the presence records of the species that were at least 1 km apart. Thirty-four records were removed and 159 were used in distribution modelling. All environmental variables were obtained and prepared at a spatial resolution of 1 km2 for the whole country.

Distribution modelling and model performance

Since different modelling methods can yield varying results, it is recommended to use an ensemble of several methods48,49. The saker falcon distribution model saker falcon and also determine the most important environmental predictors, we used the sdm package50 in the R environment. We applied an ensemble approach51 using four algorithms: Generalised Boosting Models (GBM), Support Vector Machines (SVM), Maximum Entropy modelling (Maxent), and Random Forest (RF). All four methods need background data (e.g., pseudo-absence points), so we generated a randomly drawn sample of 5,000 background points from the extent of the study area using the PresenceAbsence package52. We calibrated models using the 75% of occurrence points as training data, and evaluated the performance of models using the remaining 25% of data set as test data. To measure the performance of the models, we used two statistics: the true skills statistic (TSS) and area under the receiver operating characteristic curve (AUC)48.

Protected areas coverage of the suitable habitats

To estimate Iran’s protected areas coverage of suitable habitats of the saker falcon, the degree to which these habitats were protected was calculated through overlaying the protected areas with predicted suitable habitats for our study species. Hence, we used the Maximum test sensitivity plus the specificity threshold53, to convert the continuous suitability predictions into binary suitable/unsuitable maps. To classify the ensemble model, we used the mean threshold of the four models (GBM, SVM, Maxent and RF).

Results

Habitat suitability

The performance of prediction models evaluation showed that all models performed well (GBM (AUC: 0.86 and TSS: 0.72), SVM (AUC: 0.85 and TSS: 0.71), Maxent (AUC: 0.96 and TSS: 0.79), and RF (AUC: 0.92 and TSS: 0.77)) in predicting distribution of wintering populations of saker falcon in Iran. Habitat suitability model of saker falcon showed that the north-east and southern parts of Iran around the Persian Gulf and the Gulf of Oman are the most suitable habitats for the species (Figs. 1 and S1–S4). Results showed that according to the ensemble model, 351,151.5 km2 of the country was suitable for the species. Based on all models, annual precipitation (Bio12) with 41% contribution, NDVI with 21% contribution, and distance from shrublands with 15% contribution were the most important environmental predictors of the species distribution saker falcon (variables contributions averaged among the four models) (Fig. 2).

Fig. 1.

Fig. 1

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The habitat suitability map of the saker falcon in Iran. The model is developed using ensemble of four modelling algorithms: Generalised Boosting Models (GBM), Support Vector Machines (SVM), Maximum Entropy modelling (Maxent), and Random Forest (RF). Map was generated using QGIS 3.4.1 (http://www.qgis.org).

Fig. 2.

Fig. 2

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The importance of environmental variables for saker falcon. Mean diurnal range (Bio2), isothermality (Bio3), temperature annual range (Bio7), mean temperature of driest quarter (Bio9), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12) and precipitation of driest quarter (Bio17). Map was generated using QGIS 3.4.1 (http://www.qgis.org).

Protected areas coverage

Results of assessing protected areas coverage (Figs. 3 and S5–S8) of suitable habitats of saker falcon indicated that only 8.7–9.1% of the suitable habitats were represented within protected areas (Table 3). Among the models, RF showed the lowest overlap (8.7%) with protected areas, while GBM showed the highest overlap (9.1%).

Fig. 3.

Fig. 3

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Protected areas coverage for the suitable habitats of the saker falcon. Map was generated using QGIS 3.4.1 (http://www.qgis.org).

Table 3.

Protected areas coverage for the suitable habitats of the saker falcon.

Model Suitable habitats (km2) Protected areas coverage (km2)
GBM 431,282 39,246 (9.1%)
SVM 384,459 34,601 (9%)
Maxent 298,356 26,255 (8.8%)
RF 290,509 25,274 (8.7%)
Ensemble 351,151.5 31,252 (8.9%)
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Discussion

Populations of migratory birds can be affected by events occurring in different countries. A recent review of the state of the world’s raptors finds that 18% are threatened and 52% have declining population trends11,12. Thus, for successful conservation planning, it is crucial to consider raptors’ ecology and determine the factors that influence their distribution. In the present study, we modeled the habitat suitability of the saker falcon in Iran and determined the most important environmental predictors which effect on habitat suitability of the species during the winter season.

We found that annual precipitation, NDVI, and distance from shrublands are the most important environmental variables in predicting the distribution of the saker falcon in Iran. There is evidence that rainfall is an important component of weather that is related to the reproductive success of birds17,54–56. Precipitation is an important determinant of vegetation gross and habitat productivity, providing food for raptors18,57. This variable has also been identified as the most important factor in driving biodiversity distribution patterns in Iran58–61. Moreover, NDVI profitably predicted bird species distributions62 which is another important variable of the saker falcon’s occurrence. NDVI recognized as an indicator of productivity, which can influence prey availability for the species42.

Distance from shrublands was the second most important environmental factor shaping the species habitat suitability. This is probably because the species is physically adapted to hunting close to the ground in open terrain and generally lives across open landscapes such as desert edge, semi-desert, steppes, agricultural, and arid montane areas13. Interestingly, this perfectly matched our habitat suitability model in Iran.

Species distribution models are important tools to estimate protected areas of suitable habitats for species. We found that less than 10% of the suitable habitats of the species are covered by protected areas. Considering threats which this species faces in its wintering range and its global conservation status, more suitable habitats need to be conserved by the Iranian protected areas network. The total coverage of protected areas in Iran is around 10%, which is lower than the world average63. Iran’s protected areas’ effectiveness in conserving avian diversity was questioned recently in other studies25,64. In fact, only 8% of hotspots of threatened bird species are located inside protected areas, which means re-planning of protected areas in Iran is necessary64,65.

Iran is an important area for illegal falcon trappers and this species is suffering from population decline. The shores of the Persian Gulf and the Oman Sea were identified with high suitability for the saker falcon, but these areas are among the highest-risk regions for trapping and capturing the saker falcon. Since saker falcon trappers choose areas with good populations for setting their traps, the spatial overlap between high-risk regions and highly suitable habitats supports the performance of our habitat suitability model. Thus, we predict that other highly suitable areas identified for the saker falcon’s winter distribution are also likely to be at high-risk form trappers. For this reason, highly suitable habitats should be regularly monitored by conservationists to ensure that no illegal trapping is taking place at these habitats.

In this study, we modelled the habitat suitability of an Endangered avian predator across Iran and highlighted the poor representation of saker falcon’s suitable habitats within the country’s protected areas. Thus, it should be noted that the current network of protected areas in Iran is not sufficiently effective in supporting the species. This is evidenced by the high number of trappers and the continued smuggling of the species into adjacent counties. The species is facing several threats, including illegal trapping, climate change, land-cover change, and habitat destruction. Therefore, we recommend that future studies focus on these threats and provide effective conservation solutions.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (5.5MB, docx)

Acknowledgements

We would like to thank Andrew Dixon for reviewing a previous version of the manuscript.

Author contributions

M.Y. and S.M. conceived and designed this study. M.Y., S.M., A.Kh., MA.A., F.G., B.N and E.A. collected distribution data, M.Y. performed statistical analyses. M.Y. and S.M wrote the first draft of manuscript. M.Y., S.M., A.Kh., MA.A., F.G., B.N and E.A. read, revised, and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and publication of this article.

Data availability

All data used in this study are included in the main text and/or the Supplementary Materials, or are available in the references cited herein.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the anthropocene: The great acceleration. Anthropocene Rev.2, 81–98 (2015). [Google Scholar]
  • 2.Maxwell, S. L., Fuller, R. A., Brooks, T. M., & Watson, J. E. Biodiversity: The ravages of guns, nets and bulldozers. Nature536, 143–145 (2016). [DOI] [PubMed]
  • 3.Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett.8, 329–337 (2015). [Google Scholar]
  • 4.Nila, M. U. S., Beierkuhnlein, C., Jaeschke, A., Hoffmann, S. & Hossain, M. L. Predicting the effectiveness of protected areas of Natura 2000 under climate change. Ecol. Processes. 8, 13 (2019). [Google Scholar]
  • 5.Duncanson, L. et al. The effectiveness of global protected areas for climate change mitigation. Nat. Commun.14, 2908 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Gaston, K. J. et al. Population abundance and ecosystem service provision: The case of birds. BioScience68, 264–272 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Green, A. J. & Elmberg, J. Ecosystem services provided by waterbirds. Biol. Rev.89, 105–122 (2014). [DOI] [PubMed] [Google Scholar]
  • 8.Michel, N. L., Whelan, C. J. & Verutes, G. M. Ecosystem services provided by Neotropical birds. Condor: Ornithol. Appl.122, duaa022 (2020). [Google Scholar]
  • 9.Gaston, K. J. Birds and ecosystem services. Curr. Biol.32, R1163–R1166 (2022). [DOI] [PubMed] [Google Scholar]
  • 10.McClure, C. J. W. & Rolek, B. W. Relative conservation status of bird orders with special attention to raptors. Front. Ecol. Evolut.8–2020, (2020).
  • 11.McClure, C. J. W. et al. State of the world’s raptors: Distributions, threats, and conservation recommendations. Biol. Conserv.227, 390–402 (2018). [Google Scholar]
  • 12.O’Bryan, C. J. et al. Human impacts on the world’s raptors. Front. Ecol. Evolut.10, 624896 (2022).
  • 13.International, B. Species factsheet: Saker Falcon Falco cherrug.) (2021).
  • 14.Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv.131, 93–105 (2006). [Google Scholar]
  • 15.Stretesky, P. B., McKie, R. E., Lynch, M. J., Long, M. A. & Barrett, K. L. Where have all the falcons gone? Saker falcon (falco cherrug) exports in a global economy. Global Ecol. Conserv.13, e00372 (2018). [Google Scholar]
  • 16.Dixon, A., Maming, R., Gunga, A., Purev-Ochir, G. & Batbayar, N. The problem of raptor electrocution in Asia: Case studies from Mongolia and China. Bird. Conserv. Int.23, 520–529 (2013). [Google Scholar]
  • 17.Kassara, C. et al. Current and future suitability of wintering grounds for a long-distance migratory raptor. Sci. Rep.7, 8798 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yousefi, M., Mohammadi, S. & Kafash, A. Modeling global habitat suitability and environmental predictor of distribution of a Near Threatened avian scavenger at a high spatial resolution. Frontiers Ecol. Evolution11, 1112962 (2023).
  • 19.Engler, J. O. et al. Avian SDMs: Current state, challenges, and opportunities. J. Avian Biol.48, 1483–1504 (2017). [Google Scholar]
  • 20.Kassara, C., Fric, J. & Sfenthourakis, S. Distribution modelling of Eleonora’s Falcon Falco eleonorae Géné, 1839 occurrence in its wintering grounds: A niche-based approach with satellite telemetry data. Bird. Conserv. Int.24, 100–113 (2014). [Google Scholar]
  • 21.Asgharzadeh, M., Alesheikh, A. A. & Yousefi, M. Disentangling the impacts of climate and land cover changes on habitat suitability of common pheasant Phasianus colchicus along elevational gradients in Iran. Environ. Sci. Pollut. Res.30, 60958–60966 (2023). [DOI] [PubMed] [Google Scholar]
  • 22.Ashoori, A. et al. Habitat modeling of the common pheasant Phasianus colchicus (Galliformes: Phasianidae) in a highly modified landscape: application of species distribution models in the study of a poorly documented bird in Iran. Eur. Zoological J.85, 372–380 (2018). [Google Scholar]
  • 23.Reside, A. E., VanDerWal, J. & Kutt, A. S. Projected changes in distributions of Australian tropical savanna birds under climate change using three dispersal scenarios. Ecol. Evol.2, 705–718 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Costa, G. C., Nogueira, C., Machado, R. B. & Colli, G. R. Sampling bias and the use of ecological niche modeling in conservation planning: A field evaluation in a biodiversity hotspot. Biodivers. Conserv.19, 883–899 (2010). [Google Scholar]
  • 25.Moradi, S., Sheykhi Ilanloo, S., Kafash, A. & Yousefi, M. Identifying high-priority conservation areas for avian biodiversity using species distribution modeling. Ecol. Ind.97, 159–164 (2019). [Google Scholar]
  • 26.de Carvalho, D. L. et al. Delimiting priority areas for the conservation of endemic and threatened Neotropical birds using a niche-based gap analysis. PLOS ONE. 12, e0171838 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Yousefi, M. et al. Global scale high-resolution habitat suitability modeling of avifauna providing pollination service (sunbirds, Nectariniidae). Sci. Rep.15, 9489 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Smith, S. E., Gregory, R. D., Anderson, B. J. & Thomas, C. D. The past, present and potential future distributions of cold-adapted bird species. Divers. Distrib.19, 352–362 (2013). [Google Scholar]
  • 29.Booms, T. L., Huettmann, F. & Schempf, P. F. Gyrfalcon nest distribution in Alaska based on a predictive GIS model. Polar Biol.33, 347–358 (2010). [Google Scholar]
  • 30.Panthi, S., Pariyar, S. & Low, M. Factors influencing the global distribution of the endangered Egyptian vulture. Sci. Rep.11, 21901 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Darvishsefat, A. Atlas of Protected Areas of Iran (Department of Environment, 2008).
  • 32.Buechley, E. R. et al. Priority areas for vulture conservation in the Horn of Africa largely fall outside the protected area network. Bird. Conserv. Int.32, 188–205 (2022). [Google Scholar]
  • 33.Sutton, L. J. et al. Directing conservation action for the Critically Endangered Philippine Eagle to mitigate mining impacts and maximize indigenous land management. bioRxiv, 2025.2005.2019.654818 (2025).
  • 34.Sutton, L. J., Benjara, A., Rene de Roland, L-A., Thorstrom, R. & McClure, C. J. W. Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagle Eutriorchis astur. Bird. Conserv. Int.33, e48 (2023). [Google Scholar]
  • 35.Tellería, J. L. et al. Constraints on raptor distribution at the southwestern boundary of the Palaearctic: Implications for conservation. Biodivers. Conserv.28, 603–619 (2019). [Google Scholar]
  • 36.Panuccio, M., Agostini, N., Nelli, L., Andreou, G. & Xirouchachis, S. Factors shaping distribution and abundance of raptors wintering in two large Mediterranean islands. Community Ecol.20, 93–103 (2019). [Google Scholar]
  • 37.Bustamante, J. & Seoane, J. Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: Statistical models work better than existing maps. J. Biogeogr.31, 295–306 (2004). [Google Scholar]
  • 38.Jankowiak, L. et al. Diurnal raptor community wintering in an extensively used farmland. Ornis Fennica. 92, 76–86 (2015). [Google Scholar]
  • 39.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data. 4, 170122 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Ghorbanian, A. et al. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J. Photogramm. Remote Sens.167, 276–288 (2020). [Google Scholar]
  • 41.Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens.26, 4485–4498 (2005). [Google Scholar]
  • 42.Laube, I., Graham, C. H. & Böhning-Gaese, K. Niche availability in space and time: Migration in Sylvia warblers. J. Biogeogr.42, 1896–1906 (2015). [Google Scholar]
  • 43.Wen, L., Saintilan, N., Yang, X., Hunter, S. & Mawer, D. MODIS NDVI based metrics improve habitat suitability modelling in fragmented patchy floodplains. Remote Sens. Appl. Soc. Environ.1, 85–97 (2015). [Google Scholar]
  • 44.Gordo, O. Why are bird migration dates shifting? A review of weather and climate effects on avian migratory phenology. Climate Res.35, 37–58 (2007). [Google Scholar]
  • 45.Pettorelli, N. et al. The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Climate Res.46, 15–27 (2011). [Google Scholar]
  • 46.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.4–13 edn (2021).
  • 47.Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-Filled SRTM for the Globe Version 4. Available from the CGIAR-CSI SRTM 90m Database (2008).
  • 48.Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, 2017).
  • 49.Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv.5, eaat4858 (2015). [DOI] [PMC free article] [PubMed]
  • 50.Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography39, 368–375 (2016). [Google Scholar]
  • 51.Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol.22, 42–47 (2007). [DOI] [PubMed] [Google Scholar]
  • 52.Freeman, E. A. & Moisen, G. PresenceAbsence: An R package for presence absence analysis. J. Stat. Softw.23, 1–31 (2008). [Google Scholar]
  • 53.Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr.40, 778–789 (2013). [Google Scholar]
  • 54.Gordo, O., Brotons, L., Ferrer, X. & Comas, P. Do changes in climate patterns in wintering areas affect the timing of the spring arrival of trans-Saharan migrant birds? Glob. Change Biol.11, 12–21 (2005). [Google Scholar]
  • 55.Mellone, U., López-López, P., Limiñana, R. & Urios, V. Wintering habitats of Eleonora’s Falcons Falco eleonorae in Madagascar. Bird. Study. 59, 29–36 (2012). [Google Scholar]
  • 56.Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Soc. B Biol. Sci.278, 3437–3443 (2011). [DOI] [PMC free article] [PubMed]
  • 57.Sheykhi Ilanloo, S. et al. Applying opportunistic observations to model current and future suitability of the Kopet Dagh Mountains for a Near Threatened avian scavenger. Avian Biol. Res.14, 18–26 (2020). [Google Scholar]
  • 58.Yousefi, M. et al. Habitat suitability and impacts of climate change on the distribution of wintering population of Asian Houbara Bustard Chlamydotis macqueenii in Iran. Bird. Conserv. Int.27, 294–304 (2017). [Google Scholar]
  • 59.Yousefi, M., Mahmoudi, A., Kafash, A., Khani, A. & Kryštufek, B. Biogeography of rodents in Iran: Species richness, elevational distribution and their environmental correlates. Mammalia 86, 309–320 (2022).
  • 60.Yousefi, M., Jouladeh-Roudbar, A. & Kafash, A. Mapping endemic freshwater fish richness to identify high-priority areas for conservation: An ecoregion approach. Ecol. Evol.14, e10970 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Kafash, A., Ashrafi, S. & Yousefi, M. Biogeography of bats in Iran: Mapping and disentangling environmental and historical drivers of bat richness. J. Zoological Syst. Evolutionary Res.59, 1546–1556 (2021). [Google Scholar]
  • 62.St-Louis, V. et al. Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography32, 468–480 (2009). [Google Scholar]
  • 63.Kolahi, M., Sakai, T., Moriya, K. & Makhdoum, M. F. Challenges to the future development of Iran’s protected areas system. Environ. Manage.50, 750–765 (2012). [DOI] [PubMed] [Google Scholar]
  • 64.Farashi, A. & Shariati, M. Biodiversity hotspots and conservation gaps in Iran. J. Nat. Conserv.39, 37–57 (2017). [Google Scholar]
  • 65.Yousefi, M. Challenges and opportunities for biodiversity governance in Iran. Nat. Rev. Biodivers.1, 679–680 (2025). [Google Scholar]

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Supplementary Materials

Supplementary Material 1 (5.5MB, docx)

Data Availability Statement

All data used in this study are included in the main text and/or the Supplementary Materials, or are available in the references cited herein.

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