توسعه يك سيستم پشتيبان تصميم‌گيري براي حفاظت از هوبره آسيايي (Chlamydotis macqueenii) از طريق ادغام داده‌هاي بوم¬شناختي و اجتماعي- اقتصادي

The Asian houbara (Chlamydotis macqueenii) is a bird adapted to arid an‎d semi-arid climates that has experienced a marked population decline across its geographic range in recent years an‎d is listed as Vulnerable on the IUCN Red List an‎d included in Appendix I of CITES. This study aimed to develop an integrated framework for the conservation of the Asian houbara by combining ecological, socio-economic, an‎d decision-support models. The research was conducted in several parts. In the ecological part, the density an‎d habitat relationships of this species in central Iran were first examined using Multiple-Covariate Distance Sampling (MCDS) an‎d habitat modeling approaches including Ran‎dom Forest, GAMs, an‎d GLMs. During autumn an‎d winter 2021-2022, 223 line transect surveys covering 1,449 km an‎d an area of 10,000 km² resulted in the observation of 205 houbara individuals in 67 groups. The best-fitting MCDS model identified fine gravel cover as a positive covariate an‎d vegetation height as a negative covariate, although the effects of these variables were not strong. This model estimated houbara density at 0.53 individuals/km². These estimates were very similar to those of the CDS model, indicating that incorporating habitat dependencies alone did not necessarily improve estimate accuracy. Nevertheless, the habitat models consistently identified fine gravel cover an‎d vegetation height as the most important predictors, emphasizing the ecological importance of these factors in houbara habitat use. The results of this part showed that the population decline of houbara compared with previous estimates is still ongoing an‎d highlight the need for continuous monitoring an‎d active habitat management. In the second part, habitat suitability for the Asian houbara in central Iran was eva‎luated using MaxEnt an‎d Bayesian Networks. The main objective of this part was to compare the performance of MaxEnt an‎d a data-driven (DD) Bayesian Network in predicting habitat suitability an‎d to develop a cause-effect (CE) Bayesian Network as the basis for a conservation Decision Support System (DSS). The results showed that MaxEnt, with high predictive accuracy (AUC = 0.90), an‎d the DD model, with even better performance (AUC = 0.97), both identified distance to agricultural lan‎ds an‎d terrain roughness as the most important factors affecting houbara habitat suitability. The CE network also showed that vegetation density, food availability, an‎d habitat security were the most important determinants of habitat suitability, an‎d scenario analysis indicated that simultaneous improvement of these components could substantially increase the probability of highly suitable habitats. In the socio-economic part, factors influencing the level of conflict, attitudes toward the environmental value of the houbara, an‎d farmers’ willingness to coexist with this species in the Central Plateau of Iran were investigated. Data were collected through 320 questionnaires in Isfahan an‎d Yazd provinces an‎d analyzed using Ordinal Logistic Regression (OLR) an‎d Structural Equation Modeling (SEM). The results showed that the intensity of conflict with the houbara was influenced by the number of birds observed, farmers’ age, an‎d their experience of damage. In addition, the environmental value of the houbara was more strongly recognized by farmers who eva‎luated its individual, social, an‎d cultural benefits positively. The SEM model showed that positive emotions played an important role in strengthening moral considerations an‎d increasing willingness to coexist in both private an‎d public domains, whereas negative consequences had no significant effect on reducing coexistence.

محمودرضا همامي

محسن احمدي , حسين بشري , بنجامين قاسمي

منصوره ملكيان , مصطفي تركش اصفهاني , رسول خسروي شريف آبادي