DOI: 10.5061/dryad.v15dv4201
title: Early nest initiation and vegetation density enhance nest survival in Wild Turkeys

description: The theory of adaptive habitat selection suggests resource selection by
 animals should reflect underlying quality, such that individual selection
 confers an adaptive advantage via increased fitness. Using resource
 selection functions and nest survival models, we demonstrated that visual
 obstruction at the nest site was adaptively significant but timing of nest
 initiation had the greatest effect on nest survival for eastern wild
 turkeys (Meleagris gallopavo silvestris). Predation risk is a selective
 pressure, and if individuals can perceive predation risk, they may respond
 by altering selection of nest site characteristics based on prior
 experience. We evaluated patterns in nest site selection of 387 wild
 turkeys and consequences of selection on reproductive success across the
 southeastern United States from 2014-2019. We monitored 549 nest sites and
 found that nest initiation date had the strongest effect on daily nest
 survival rates, wherein adult females at our earliest nest initiation date
 were ~ 4 times more likely to successfully nest than females at our latest
 nest initiation dates. Selection of nest sites with greater visual
 obstruction also increased daily nest survival rates as females were 1.17
 (1.100 – 1.234; 95% CI) and 1.37 (1.258 – 1.486; 95% CI) times more likely
 to select sites for every 10 cm increase in visual obstruction and maximum
 vegetation height, respectively. Collectively, our results indicate that
 nest initiation date is likely the critical parameter driving wild turkey
 nest success, whereas vegetative conditions play a lesser role influencing
 nest success. Females nesting earlier may be in better body condition and
 show increased nest attentiveness, which may mediate nest success more
 than vegetation conditions around nest sites. Our work indicates that
 increasing the reproductive success of wild turkeys may hinge on females
 being able to nest as early as possible within the reproductive season.
description: We captured female turkeys using rocket nets from January–March
 2014–2019. We radio-tagged each female with a backpack-style,
 mortality-sensitive GPS transmitter with VHF capabilities (Biotrack Ltd.,
 Wareham, Dorset, UK; Guthrie et al. 2011). We programmed transmitters to
 record hourly locations from 0500-2000 and one nightly location at 2359
 for the life of the unit or until the unit was recovered (Cohen et al.
 2018). Turkey capture, handling, and marking procedures were approved by
 the Institutional Animal Care and Use Committee at the University of
 Georgia (Protocol No. A2014 06008Y1A0,
 A343701, and A2016 04-001-R1) and
 the Louisiana State University Agricultural Center (Protocol No.
 A2014-013, A2015-07, and A2018-13). We downloaded GPS locations from each
 female ≥ 1 time per week. We conducted vegetation surveys at each nest
 site at expected date of hatch regardless of nest fate
 (McConnell et al. 2017) and measured vegetation conditions
 immediately surrounding the nest (Yeldell et al. 2017a, Wood et al.
 2019). We used conditional logistic regression in a
 matched-pairs case-control design (Compton et al. 2002, Manly et al.
 2002) to estimate a resource selection function
 and explain patterns in nest site selection by female turkeys. Cases were
 the initiated nest site locations of individual female turkeys, and
 controls were random sites paired with the nest site locations.
 Paired-random sites were located 100-500 m away ( ) at a random direction from the nest site,
 and we sampled vegetation using identical methods described above for nest
 site locations. We used logistic exposure models in a
 Bayesian framework (Schmidt et al. 2010) to examine the effect of covariates
 influencing nest site selection on daily nest survival
 rates. We fit models using Markov Chain Monte Carlo
 (MCMC) methods implemented in JAGS (Plummer 2003) via program R (v4.0.3, R Core Team
 2020) and the <em>R2jags package</em>
 (Su and Yajima 2020). We used uninformative priors for all
 parameters in the model. We ran 3 independent Markov chains for 100,000
 iterations each, discarded the first 10,000 iterations as a burn-in
 period, and used a thinning rate of 5.
description: The csv data files can be opened in Microsoft Excel or similar
 program that reads csv files.