Utility of radio‐telemetry data for improving statistical population reconstruction

Abstract

Statistical population reconstruction using age‐at‐harvest and catch‐effort data has recently emerged as a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. Although there are clear benefits to incorporating radio‐telemetry data into reconstruction efforts, these data are costly and time‐consuming to collect. Managers that consider collecting these data alongside existing efforts could benefit from a comprehensive examination of how such benefits are influenced by the amount of radio‐telemetry data collected. Using a harvested population of American marten (Martes americana) in northeastern Minnesota, USA as a case study, we investigated the performance of population reconstruction using information on natural, harvest, or combined mortality derived from radio‐telemetry data collected over different numbers of years and with different numbers of animals collared each year. We simulated populations under a range of conditions and determined that incorporating radio‐telemetry data on natural and harvest mortality significantly improved model precision, and that each additional animal collared per year yielded a 0.50 ± 0.14% (SE) improvement in precision, whereas every additional year of radio‐telemetry data resulted in a 2.42 ± 0.70% improvement. Thus, including another year of radio‐telemetry resulted in similar gains in precision as including approximately 5 additional animals collared per year. In our applied marten example, incorporating radio‐telemetry data resulted in a significantly higher estimate of trapping vulnerability (0.20 vs. 0.058) and an overall smaller population size than reconstruction based solely on age‐at‐harvest and trapper effort data. These results illustrate the benefits of performing auxiliary studies, caution against relying on the results of population reconstruction based solely on age‐at‐harvest and hunter‐effort data, and demonstrate that improvements from incorporating radio‐telemetry data become evident even after as few as 2 years of data collection. © 2017 The Wildlife Society.

Comparing survey and multiple recruitment–mortality models to assess growth rates and population projections

“…An integrated population model using survey estimates combined with demographic data from the MNDNR studies may offer another approach to better understand trends in the population (Besbeas, Freeman, Morgan, & Catchpole, ). Reliance solely upon the survey to understand moose population dynamics will not be as informative or useful in the absence of demographic data gained from collaring studies, as has been demonstrated for fisher ( Pekania pennanti Erxleben 1777) and caribou ( Rangifer tarandus Linneaus 1758; Berg, Erb, Fieberg, & Forester, ; Murray et al, ; Serrouya et al, ). Collared animals are also needed to periodically recalibrate sightability models (Serrouya et al, ).…”

“…The adult mortality rate has generally been decreasing, but later estimates could be biased due to weakened animals being culled from the population through predation or health-related mortality and stronger animals surviving to later years of the study. A relationship between collared adult survival rates and population-wide assessments of winter nutritional restriction suggests the condition of the collared animals was representative of that of the free-ranging population in the earliest years of the study Berg, Erb, Fieberg, & Forester, 2017;Murray et al, 2006;Serrouya et al, 2017). Collared animals are also needed to periodically recalibrate sightability models (Serrouya et al, 2017).…”

Evaluation of ptarmigan management with a population reconstruction model

“…The AD model builder (Fournier ) has been used to fit many previous statistical population models (Broms et al , Fieberg et al ), but R, which has become widely used for statistical analysis, also has the capabilities to fit these models (Berg et al ). We found the maximum likelihood of the joint likelihoods using the nlminb function in the statistical software R (R Core Team ) and confidence intervals of the estimates using the profile likelihood method (Venzon and Moolgavkar ).…”

“…d and e are parameters to be estimated and describe the relation of survival to population density with a lag. these models (Berg et al 2017). We found the maximum likelihood of the joint likelihoods using the nlminb function in the statistical software R (R Core Team 2016) and confidence intervals of the estimates using the profile likelihood method (Venzon and Moolgavkar 1988).…”

“…Statistical population reconstruction models have been developed to estimate these demographic parameters simultaneously and obtain uncertainty in the estimates (Gove et al 2002, Skalski et al 2005). These models can use data from different sources such as age-at-harvest data along with auxiliary data such as radio-telemetry data (Fieberg et al 2010, Berg et al 2017 or index data (Skalski et al 2007, Broms et al 2010. Gove et al (2002) used statistical population models to estimate population size and other demographic parameters for elk (Cervus canadensis) and Skalski et al (2011) used them for the American marten (Martes americana).…”