Integrating Multiple‐Covariate Distance Sampling and Habitat Modeling to Inform Conservation of the Asian Houbara in Central Iran

Reliable estimates of abundance and habitat associations are critical for conserving low-density species such as the Asian houbara (<i>Chlamydotis macqueenii</i>). Despite its vulnerable global status, robust estimates of houbara population size and habitat requirements remain scarce across much of its range. We combined multiple-covariate distance sampling (MCDS) with habitat modeling (Random Forest, GAMs, and GLMs) to estimate density and identify habitat relationships of houbaras in central Iran. In spring 2022, 223 line-transect surveys (1449 km) covering a 10,000 km² area yielded 205 individuals across 67 detections. The best-supported MCDS model included fine gravel cover (positive) and vegetation height (negative) as detectability covariates, though their effects were weak. This model estimated a density of 0.53 individuals/km² (95% CI: 0.37-0.75), corresponding to ~5293 individuals (95% CI: 3778-7473). Estimates were nearly identical to those from the best conventional distance sampling (CDS) model, indicating that detectability covariates did not materially improve model accuracy. However, habitat models consistently identified fine gravel cover and vegetation height as the most influential predictors, underscoring their ecological relevance for habitat use. Results indicate an ongoing population decline relative to previous regional estimates, highlighting the need for continued monitoring. Integrating population estimation with habitat modeling provides a practical framework for improving conservation assessments of the Asian houbara and other ground-dwelling birds in open habitats. Conservation actions should prioritize the protection and management of suitable habitats, supported by standardized survey protocols that improve population assessments and inform management decisions.