Data from: Non-brooding northern bobwhite resource selection during breeding season in a managed longleaf pine ecosystem

Grayum, Jeffrey 1 ; Conner, Mike2; Crawford, Daniel2; Henshaw, Zach2; Iglay, Raymond1; Rectenwald, Justin3; Rutledge, Brandon2; Sisson, David2; Sisson, Clay3; Smith, Scott2; McConnell, Mark1

Published Apr 08, 2025 on Dryad. https://doi.org/10.5061/dryad.jdfn2z3m5

Abstract

Investigating resource selection patterns of declining species is critical to developing effective conservation strategies and mitigating negative population trends. We trapped and radio-collared northern bobwhite (Colinus virginianus) and recorded the location of each individual three times per week during the northern bobwhite breeding season (April – September) to infer resource selection by non-brooding adults. We defined 13 vegetation communities with varying fire histories and assessed their relative use via a distance-based logistic regression model within a Bayesian framework. We found reliable evidence that non-brooding northern bobwhite preferred supplemental feed lines, recently burned natural pine stands, one-year rough in shrub/scrub, and one-year rough in hardwood-pine stands. Conversely, we found they avoided one-year rough in natural pine stands, recently burned shrub/scrub, and recently burned hardwood-pine stands. Our data suggest that non-brooding bobwhite’s successional stage preference may vary between vegetation communities within the same study area and adds further evidence to the importance of fire and vegetation interspersion in bobwhite management. Results from our study may be applied to enhance habitat for non-brooding northern bobwhite and potentially increase adult survival, an important metric correlated with bobwhite population growth.

https://doi.org/10.5061/dryad.jdfn2z3m5

Description of the data and file structure

This dataset supports a study examining resource selection of non-brooding adult northern bobwhite (Colinus virginianus) during the breeding season (April–September) at a longleaf pine research site in Georgia, USA. We radio-collared bobwhite and collected locations three times per week to assess third-order selection across 13 defined vegetation communities with different fire histories.

We used a distance-based approach within a Bayesian logistic regression framework to evaluate relative habitat use.

File Description

Bobwhite Location Data
Summer2022Locs.xlsx

Summer2023Locs.xlsx

Each Excel spreadsheet contains location data for non-brooding bobwhite adults recorded during the 2022 and 2023 breeding seasons.

Columns include:

Column Description
title Unique tracking ID for each individual bobwhite
latitude Latitude of observed location (WGS 84)
longitude Longitude of observed location
northing UTM Northing (Zone 16N)
easting UTM Easting (Zone 16N)
band_numb Metal leg band number (unique bird ID)
date Date of the observation
status A = Alive, N = Nesting, B = Brooding, D = Dead

Spatial Data – Study Site Land Cover Shapefiles

The dataset includes shapefiles defining the 13 vegetation communities present at the study site during each year of data collection. These spatial data were used to calculate distances between individual bobwhite locations and vegetation types for inclusion in the resource selection models.

Shapefile sets:

2022_shapefile.*

2023_shapefile.*

Each shapefile consists of multiple files commonly used together in GIS:

Extension Purpose
.shp Stores geometry (polygons representing vegetation communities)
.dbf Stores attributes (e.g., vegetation type, burn history, unit ID)
.shx Index linking geometry and attributes
.prj Contains coordinate reference system info (NAD 1983 UTM Zone 16N)
.xml Metadata describing the shapefile
.sbn, .sbx Spatial indexing (used by some GIS programs for faster rendering)
.cpg Defines character encoding for attribute data
Each shapefile includes polygons representing land cover classifications used in the analysis:

Code/software

We analyzed data using R Studio Version 2023.06.1+524. The data is uploaded as an R Markdown (.rmd) file. Required libraries are included in the code. All coding done in R.