Improved analysis of lek count data using N‐mixture models. Issue 6 (7th May 2016)

• Record Type: Journal Article
• Authors: McCaffery, Rebecca; Nowak, J. Joshua; Lukacs, Paul M.
• Is Part Of: Journal of wildlife management. Volume 80:Issue 6(2016)
• Page Start: 1011
• Page End: 1021
• Publication Date: 2016-05-07
• Subjects: abundance -- Bayesian model -- Centrocercus urophasianus -- greater sage‐grouse -- hierarchical model -- Montana -- population trend -- simulation; Wildlife management -- Periodicals; Zoology -- Periodicals; 333.954
• DOI: 10.1002/jwmg.21094
• Languages: English
• ISSNs: 0022-541X

Abstract

ABSTRACT: The greater sage‐grouse ( Centrocercus urophasianus ) is a species of conservation concern in western North America that is experiencing ongoing population declines due to habitat loss, energy development, disease, and other factors. It is therefore imperative to have robust estimates of population size and trends in this species across its range as part of monitoring, management, and conservation efforts. Greater sage‐grouse are typically monitored by conducting counts of males at breeding leks, but the relationship between this index and true population size is unknown. In an attempt to improve the analyses of this population index, we examined the potential of N ‐mixture models to evaluate population size, detection probability, and trend in greater sage‐grouse using lek count data collected over space and time. We used simulations to test how well the models recovered abundance and growth rate parameters with increasingly sparse count data. We found that the models correctly recovered parameters for scenarios with both constant and variable detection probability, even with up to 90% of the data missing, where 95% of simulations contained the true population growth rate parameter value within the 95% credible interval. We then applied the model to 13 years of lek count data from Montana, USA, collected at 2 spatial scales. Statewide, we determined that the population was decreasing by 7% per year on average over this time period, and that mean annual detection probability ranged from 0.20 to 0.76. In contrast, regressions of naïve counts over time showed a 9% annual decrease in population size with a confidence interval spanning 0. High interannual variability in detection probability demonstrates that naïve counts do not accurately measure interannual variability in population size, and may lead to misleading trends in population size over time. Although N‐ mixture models still have limitations, they are a promising approach for conducting robust analyses of population trends for species that aggregate at discrete breeding sites, even when datasets are sparse or uneven. © 2016 The Wildlife Society.