Improved analysis of lek count data using N‐mixture models

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 na€ ıve 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 na€ ıve 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. Ó

Dual‐frame lek surveys for estimating greater sage‐grouse populations

“…Unfortunately, it is unclear how our detection probability estimates compare to other greater sage‐grouse populations because of a lack of comparable data. McCaffery et al () used N‐mixture models to estimate per‐occasion detection probability of males over a 13‐year period in Montana, but these models have been discounted by some (Barker et al ) and our estimates of detection probability are for leks rather than males. Other studies have reported detectability of marked males on leks (Johnson and Rowland , Baumgardt , Blomberg et al , Fremgen et al ), mean annual lek attendance (Blomberg et al ), or daily lek attendance rates (Fremgen ) rather than detectability of leks.…”

“…However, this limitation creates challenges for establishing effective policies to manage and conserve populations at local scales, or in response to immediate concerns. Therefore, investigating the reliability of lek‐count data for monitoring changes in greater sage‐grouse populations, testing their underlying assumptions, and developing innovative methods to estimate population size are research priorities (Naugle and Walker ) and the focus of several recent studies (Baumgardt , Blomberg et al , McCaffery et al , Baumgardt et al , Fremgen et al ).…”

A generalized integrated population model to estimate greater sage‐grouse population dynamics

“…The use of the IPM smoothes the variation in population growth rate among years, and predictions for population growth rate are quite different between the naïve estimates and the IPM in certain years. The N-mixture estimates of abundance reduce some of the sampling variance associated with the lek counts (see Results;McCaffery et al 2016), and the addition of demographic information further reduces interannual variation in estimates of population growth. Sage-grouse biologists tend to use the mean number of males per lek over time as a trend metric rather than the absolute population size we report here, which standardizes counts against the number of leks counted that year (e.g., WAFWA 2008).…”

“…The use of N-mixture models partially addresses the issue of unequal counts on leks in space and time, because lek population sizes estimated from raw lek counts are corrected for the fact that not all leks are surveyed in all years and that not all leks are surveyed multiple times in each year (McCaffery et al 2016). However, we know that there are leks on the landscape that are not being monitored.…”

“…Rather than use these raw counts to estimate population size in our IPM, we used open N-mixture models (Royle 2004, 2009 to first estimate annual male population size using information from multiple interannual counts conducted at a subset of leks (McCaffery et al 2016). While yearling and adult females are also present at leks, they are much more cryptic and do not stay on the lek as long as males.…”