Estimating the survival of unmarked young from repeated counts

Timothy P Lyons,Thomas J Benson,Kirk W Stodola

doi:10.2981/wlb.00529

Abstract

Estimating the survival of juveniles is important to the study of ecology and wildlife management. Methods to estimate survival from uniquely marked young are generally preferred but may be difficult to implement. Alternative methods to estimate juvenile survival based on counts of unmarked young with marked parents generally do not account for detection probability or encounter difficulty estimating survival when there are >5 offspring. We developed a hierarchical Bayesian model to estimate survival of unmarked offspring from known (marked) parents from a minimum of two counts on while accounting for imperfect detection. We simulated data to evaluate the performance of the model across a range of detection probabilities and sample sizes and to explore violations of some model assumptions. We then demonstrate the utility of this approach by estimating chick survival for a population of ring-necked pheasants Phasianus colchicus in east-central Illinois, USA. Mean error of parameter estimates decreased with increasing sample sizes and detection probability and was greater for covariate coefficients, compared to mean detection or survival probabilities. However, posterior distributions of mean survival and detection parameters were poorly estimated and had small effective sample sizes when the mean detection probability was ≤0.4 or the number of broods comprising the sample were <30. The model was relatively robust to violations of the model's closure assumption, with a <0.04 increase in bias of detection and survival probabilities when survival between repeated counts was <1. When applied to our data set of 38 pheasant broods, we were able to identify important temporal and environmental covariates affecting survival and detection. Mean detection probability was only 0.56. We believe the coupling of this model with an appropriate field sampling framework provides a useful and flexible approach that is time- and cost-efficient for estimating survival of unmarked young.

Highlights

Survival of juvenile offspring is an important, often overlooked and poorly studied component of population dynamics
To provide a possible alternative, we developed a hierarchical model and sampling framework that uses Bayesian Markov-chain Monte Carlo (MCMC) methods to estimate the survival of chicks prior to independence, while accounting for imperfect detection
We applied the model to estimate the 15–22-day survival probability of ring-necked pheasant Phasianus colchicus chicks in east-central Illinois

Summary

Introduction

Survival of juvenile offspring is an important, often overlooked and poorly studied component of population dynamics. The uncertainty in estimation arises because the number of potential combinations of survival and detection that can produce the observed data increases when there are a large number of young, leading to convergence issues using maximum likelihood estimation (Lukacs et al 2004) This method may be impractical for studies of many species, such as game birds or waterfowl, without many broods and visits (Lukacs et al 2004). If the counts are performed at approximately the same age for all broods, they can be used to estimate chick survival during a period (e.g. first X days or Y weeks) of interest (Riley et al 1998, Pollentier et al 2014, Davis et al 2016) Based on these assumptions, we simulated data across a range of plausible detection probabilities and violations of the closure assumption. Our purpose in developing the model was associated with research on pheasants, so we use terminology associated with gamebirds as a result

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