An integrated modeling approach to estimating Gunnison sage‐grouse population dynamics: combining index and demographic data

Amy J Davis,Michael L Phillips,Mevin B Hooten,Paul F Doherty

doi:10.1002/ece3.1290

Amy J Davis, Michael L Phillips + Show 2 more

Open Access

https://doi.org/10.1002/ece3.1290

Copy DOI

Abstract

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large-scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short-term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate (λ) for Gunnison sage-grouse (Centrocercus minimus). The long-term population index data available for Gunnison sage-grouse are annual (years 1953–2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage-grouse to be variable and slightly declining over the past 16 years.

Highlights

Information is frequently sparse for rare and declining species (Beissinger and McCullough 2002) and is often poor quality or has little inferential value (Engler et al 2004; McKelvey et al 2008)
We demonstrate the utility of this approach by modeling GUSG population growth rate
We modeled the growth rate from the demographic data as log-normally distributed with a mean representing the relationship with the log of the lek count growth rate

Summary

Introduction

Information is frequently sparse for rare and declining species (Beissinger and McCullough 2002) and is often poor quality or has little inferential value (Engler et al 2004; McKelvey et al 2008). Large-scale count surveys, such as lek counts, often generate large amounts of data, but those data may be of questionable value (Walsh et al 2004). For many species, the most extensive information available is from these types of surveys (Sauer et al 1994). These data are typically analyzed separately from long-term monitoring data, but uncertainty and possible bias can exist in these analyses especially if the study is not long enough to capture the range of annual variability present in the system (Bierzychudek 1999). Recent work has focused on using intensive, short-term demographic data to bolster information inherent in long-running, index data with integrated modeling approaches (e.g., Catchpole et al 1998; Abadi et al 2010b)

Methods

Results

Conclusion