Habitat suitability assessment of mouflon applying a comparative approach on expert-based and species distribution models
- 1Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran
- 2Department of Environment, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran
- 3Faculty of Forestry, University of British Columbia, Vancouver, Canada
Abstract
During the last decades, anthropogenic activities such as land use change and urbanization have caused significant biodiversity loss and ecosystem services disruption worldwide. Accurate identification and prioritization of suitable habitats, both within and beyond existing protected areas and ecological networks, require complementary habitat assessment models that evaluate species-specific suitability alongside habitat conservation priority. This study employed the maximum entropy (MaxEnt) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to assess the habitat of mouflon (Ovis gmelini) in central Iran. We specifically investigated how these models, with their distinct data inputs—MaxEnt relying on species occurrence and environmental variables, and InVEST utilizing land use/cover maps, and expert-based threat assessments—provide complementary insights for detecting and managing suitable habitats. Model performance was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve and the Boyce Index (BI). The spatial outputs were further analyzed using the Getis-Ord Gi* statistic to identify statistically significant hotspots. The results indicated that MaxEnt was highly effective in identifying currently occupied (realized) suitable habitats within protected areas (AUC = 0.937, BI = 0.965), reflecting areas that are already being used by mouflon populations, whereas InVEST identified broader extents of potential habitats, including areas that are ecological suitable, but are not consistently or currently utilized by the species. The spatial overlap between models was only 7.3% within protected areas, underscoring their complementary nature. Hotspot analysis revealed that while areas of high habitat quality and high suitability overlapped in some core habitats, their spatial distributions also showed distinct differences. For enhancing conservation networks under data-poor conditions, InVEST offers valuable guidance by highlighting priority zones based on habitat quality and degradation. However, the combined application of both models provides a robust framework for conservation planning, enabling the identification of core habitats, connectivity corridors, and degraded areas requiring restoration. These findings are critical for conserving wide-ranging ungulates like the mouflon and support the development of resilient ecological networks in the face of anthropogenic and climate change pressures.
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BibTeX
@article{rezvani2026habitat,
title = {{Habitat suitability assessment of mouflon applying a comparative approach on expert-based and species distribution models}},
author = {Azita Rezvani and Shekoufeh Nematollahi and Davoud Fadakar and Sedighe Abdollahi and Sima Fakheran},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0353063},
url = {https://doi.org/10.1371/journal.pone.0353063},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Habitat suitability assessment of mouflon applying a comparative approach on expert-based and species distribution models
AU - Azita Rezvani
AU - Shekoufeh Nematollahi
AU - Davoud Fadakar
AU - Sedighe Abdollahi
AU - Sima Fakheran
PY - 2026
DA - 2026/09/21
JO - PLOS One
DO - 10.1371/journal.pone.0353063
UR - https://doi.org/10.1371/journal.pone.0353063
AB - During the last decades, anthropogenic activities such as land use change and urbanization have caused significant biodiversity loss and ecosystem services disruption worldwide. Accurate identification and prioritization of suitable habitats, both within and beyond existing protected areas and ecological networks, require complementary habitat assessment models that evaluate species-specific suitability alongside habitat conservation priority. This study employed the maximum entropy (MaxEnt) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to assess the habitat of mouflon (Ovis gmelini) in central Iran. We specifically investigated how these models, with their distinct data inputs—MaxEnt relying on species occurrence and environmental variables, and InVEST utilizing land use/cover maps, and expert-based threat assessments—provide complementary insights for detecting and managing suitable habitats. Model performance was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve and the Boyce Index (BI). The spatial outputs were further analyzed using the Getis-Ord Gi* statistic to identify statistically significant hotspots. The results indicated that MaxEnt was highly effective in identifying currently occupied (realized) suitable habitats within protected areas (AUC = 0.937, BI = 0.965), reflecting areas that are already being used by mouflon populations, whereas InVEST identified broader extents of potential habitats, including areas that are ecological suitable, but are not consistently or currently utilized by the species. The spatial overlap between models was only 7.3% within protected areas, underscoring their complementary nature. Hotspot analysis revealed that while areas of high habitat quality and high suitability overlapped in some core habitats, their spatial distributions also showed distinct differences. For enhancing conservation networks under data-poor conditions, InVEST offers valuable guidance by highlighting priority zones based on habitat quality and degradation. However, the combined application of both models provides a robust framework for conservation planning, enabling the identification of core habitats, connectivity corridors, and degraded areas requiring restoration. These findings are critical for conserving wide-ranging ungulates like the mouflon and support the development of resilient ecological networks in the face of anthropogenic and climate change pressures.
SN - 1932-6203
ER -