Estimating waterfowl breeding pair and brood densities using distance sampling with uncrewed aerial systems

Wildlife management relies on effective methods to estimate population indices, which are crucial for understanding the status and trends of wildlife populations and the factors that influence their persistence. Biologists have conducted surveys of waterfowl since the 1950’s using a combination of aerial (fixed wing aircraft) and ground-based methods to account for imperfect detection. While some individuals may be missed by aerial observers, ground observers are assumed to detect all individuals. This allows for a visual correction factor to be applied to the initial aerial count. However, the assumption that ground-observers are detecting every individual is unlikely and can potentially lead to biased population estimates.
We conducted breeding waterfowl surveys in Wisconsin during 2022 and 2023 and recorded observations of duck pairs and broods using uncrewed aerial systems (UAS) equipped with thermal cameras, which have been shown to improve detection of many wildlife species. We fl..., , # Data from: Estimating waterfowl breeding pair and brood densities using distance sampling with uncrewed aerial systems

Dataset DOI: [10.5061/dryad.wh70rxx2m](https://doi.org/10.5061/dryad.wh70rxx2m)

## Description of the data and file structure

The pair and brood data files contain observational data collected during both years (2022 & 2023) of UAS surveys in Wisconsin. Each row in the file represents a single event and includes associated survey information. If multiple observations were made on a transect during a single survey, there will be one row per observation. A positive detection is indicated by a \"1\" in the \"Pairs\" or \"Broods\" column, while transects with no observations have a single row with a \"0\" in the \"Pairs\" or \"Broods\" column.

### Files and variables

#### File: BroodData.csv

**Description:** UAS brood observations during the 2022 and 2023 field seasons

##### Variables

* Effort: length of transect (km)
* Region.Label: survey ID
* Sample.Label: transect ID
* ob...,