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 s..., , # Data and code from: Integrating multiple-covariate distance sampling and habitat modeling to inform conservation of the Asian houbara in central Iran

Dataset DOI: [10.5061/dryad.hmgqnk9z4](https://doi.org/10.5061/dryad.hmgqnk9z4)

## Description of the data and file structure

This supplementary dataset, integral to the associated research article, contains all data necessary for understanding and reproducing the analyses conducted in this study on the distribution, density, and habitat associations of the Asian houbara. The dataset includes transect‐based survey records, detection and distance‐sampling data, environmental and microhabitat measurements, and derived variables used in population estimation and predictive modeling. These data support analyses of density estimation and evaluation of environmental drivers using multiple statistical and machine‐learning approaches. The Excel files provided in this repository contain both raw and processed datasets required for interpretati...,