R/FISH-100a

Strategic focus area:

Description:

OBJECTIVES: 1) Statistically describe the spatial pattern of oyster spatfall in relation to annual salinities and water temperatures by analyzing 70-y record of spatfall made at sentinel oyster bars; 2) Develop a predictive climate model that will allow forecasts to be made during late winter/early spring of salinity and temperature in different tributaries and Bay sections during the oysters reproductive period (June through August); 3) Use our climate model and statistically derived relationship between oyster spatfall and salinity and temperature to make spatial predictions of spatfall to guide that year's restoration activities; 4) Undertake a hindcasting analysis of a separate spatfall data set from the MD-DNR oyster repletion program to test the accuracy of our model forecasts; 5) Assess the value of our model to mangers by estimating the relative increase in oyster spat production that would have resulted had our model been used to guide cultch placement in past years. METHODOLOGY: Perform multivariate statistical analysis of 70-y record of oyster spatfall in Maryland's portion of Chesapeake Bay. Develop a synoptic climatological model to predict summer water quality conditions (salinity and temperature) for each year for which spatfall has been recorded. Develop a logistic regression model to predict spatfall patterns in the summer based on water quality conditions predicted from our synoptic climatological model using winter and spring climatological conditions. Train Co-PI Tarnowski within the MD-DNR Shellfish Program to input climatological data each year to make spatially explicit predictions of spatfall under that years prevailing weather conditions. RATIONALE: The magnitude of oyster spatfall varies spatially and between years. This variability is driven both by water quality conditions affecting the magnitude and success of larval production and also by variation in quantity and condition of cultch substrate (shell) for larval settlement. Restoration efforts in the months prior to oyster reproduction can include renovating moribund oyster bars by uncovering buried shell and placing fossil shell on bars. We will develop a model that will predict which locations, sufficiently far in advance (up to 3 months) under the likely prevailing weather conditions, have the greatest likelihood of receiving above average spatfall. Our model output will be provided to mangers and allow them to identify early in the year the locations where oyster bar rehabilitation will result in the greatest increase in oyster abundance.