COS 145-8
Spatiotemporal variability in Adélie demographic rates from opportunistic point count data

Friday, August 14, 2015: 10:30 AM
338, Baltimore Convention Center
Heather Lynch, Ecology & Evolution Department, Stony Brook University, Stony Brook, NY
Elise Zipkin, Department of Integrative Biology, Michigan State University, East Lansing, MI
Michael Schaub, Swiss Ornithological Institute, Sempach, Switzerland

Mark-recapture data provide the most direct estimates of demographic rates such as age-specific survivorship, but such intensive data are expensive and logistically complicated to collect. While point count data from opportunistic vessel based surveys and aggregated data from published short-term studies have been used to infer regional patterns of population change, such data have not been used to estimate demographic rates such as survivorship and reproduction. Integrating extensive data (e.g., point counts over large spatial scales) with intensive data (e.g., mark-recapture of individuals) is a technical challenge of broad interest to a variety of wildlife studies. We use a state-space model to estimate demographic rates for Adélie penguins at 18 sites in 4 regions on the Antarctic Peninsula using point counts of nests and chicks obtained by opportunistic sampling over the 36 year period 1979/80 to 2014/15. 


Using a Bayesian framework and informative priors derived from published mark-recapture data, we obtained site- and year-specific estimates of survival and reproduction for all the locations for which point counts were available. These parameter estimates are comparable to those achieved using a more traditional mark-recapture study but provide additional spatial information on demographic rates that would be impossible to collect using mark-recapture alone. We find that the use of demographic rates from mark-recapture studies to construct an informative Bayesian prior does facilitate the estimation of demographic transition rates in state-space models with age classes that are unavailable for survey, but that the results can be quite sensitive to the details of that ‘prior’ information. As expected, estimates of demographic rates improve for longer time series, emphasizing the importance of long-term monitoring. Our model provides a mechanism for using point count data to obtain estimates of demographic rates over larger spatial and temporal scales and at lower cost than mark-recapture methods, and naturally permit the integration of more intensive data streams where available.