Data and code from: 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 (Chlamydotis
macqueenii). 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 (1,449 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 ~5,293 individuals (95 % CI: 3,778–7,473).
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.