W-4,5-12 Resolving Uncertainties in Gulf Sturgeon Mortality Rates

Wednesday, August 22, 2012: 11:00 AM
Meeting Room 4,5 (RiverCentre)
Merrill Rudd , University of Florida, Gainesville, FL
Robert Ahrens , School of Forest Resources and Conservation, University of Florida, Gainesville, FL
William Pine III , Wildlife Ecology and Conservation, University of Florida, Gainesville, FL
Stephania K. Bolden , Protected Resources, NOAA Fisheries Southeast Regional Office, St. Petersburg, FL
Standardized tagging programs are vital for estimating mortality rates of long-lived fish over a large spatial range and temporal scale. Previous estimates of natural mortality for Gulf sturgeon vary across the seven critical habitat rivers in the Gulf of Mexico, and have led to diverging predicted population trajectories in the stock assessment. Varying natural mortality rates come from non-cohesive mark-recapture tagging studies using either PIT tags or acoustic tags, resulting in low capture probability and the inability to distinguish between mortality and emigration to other rivers. Mark-recapture studies consistently estimate higher total mortality than using life history parameters to estimate natural mortality, indicating that significant anthropogenic or fishing mortality is acting on Gulf sturgeon that we previously assumed negligible due to Gulf sturgeon’s threatened status. Federal agencies implemented a standardized acoustic telemetry tagging program for all seven rivers in 2010 to estimate natural mortality and movement rates over a five year period. This study simulated Gulf sturgeon population dynamics to assess the ability of a multistate model to estimate true survival rates, mimicking tag deployment of the standardized telemetry tagging program over a five-year period. We found that the model will be able to estimate non-biased survival, capture probability, and transition probabilities over five years. This study presents a robust analytical framework to estimate vital mortality rates for a long-lived, threatened species that can be applied to assess future monitoring programs.