Analyzing 70 Years of Oyster Monitoring Data to Help Guide Oyster Restoration in Maryland
Principal Investigator:Roger I.E. Newell
Start/End Year:2011 to 2013
Institution:Horn Point Laboratory, University of Maryland Center for Environmental Science
Co-Principal investigator:David G. Kimmel, East Carolina University; Mitchell Tarnowski, Maryland Department of Natural Resources
Strategic focus area:Sustainable natural resources of coastal Maryland
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.
RECAP: Researchers have developed a model to predict which locations have the greatest likelihood of receiving above-average oyster spatfall (settling of larvae onto hard substrate), taking into account the effect of wet versus dry years. This model would allow managers to better focus restoration efforts by identifying the locations where oyster bar rehabilitation will result in the greatest increase in oyster abundance. The model also should be useful to commercial aquaculture operators seeking to maximize their production.
RELEVANCE: Management activities to rebuild the Chesapeake Bay eastern oyster population have been met with limited success. One reason is that the magnitude of oyster spatfall (settling and attachment of larvae to substrate) varies spatially and between years. This variability is driven both by water quality conditions affecting larval production and also by variation in quantity and condition of substrate (shell) for larval settlement. Restoration efforts can include renovating oyster bars by uncovering buried shell and placing fossil shell on bars. Researchers in this project have developed a model that can predict which locations have the greatest likelihood of receiving above-average spatfall under prevailing weather conditions. The model could allow natural-resource managers to better focus these restoration efforts.
RESPONSE: The principal investigators of this project are Roger I.E. Newell, University of Maryland Center for Environmental Science Horn Point Laboratory; David G. Kimmel, East Carolina University; and Mitchell Tarnowski, Maryland Department of Natural Resources. The researchers have used historical data, climate models, and the statistically derived relationship between oyster spatfall and salinity/temperature to make spatial predictions about spatfall.
RESULTS: The findings provided supporting evidence for a decision by the U.S. Army Corps of Engineers to locate a major, multiyear oyster restoration project in Harris Creek, a tributary of the Choptank River in Maryland. This project will serve as an important test case of oyster restoration methods in the Chesapeake Bay generally. The researchers' model indicated that weather patterns that produce wet conditions lead to lower salinity and to reduced juvenile oyster abundance; weather patterns that produce dry conditions resulted in higher salinity and higher juvenile oyster abundance. The results indicate that the model can predict regions of higher juvenile oyster abundance several months in advance.
Kimmel, DG; Tarnowski, M; Newell, RIE. 2014. The relationship between interannual climate variability and juvenile eastern oyster abundance at a regional scale in Chesapeake Bay. North American Journal of Fisheries Management 34(1):1-15. doi:10.1080/02755947.2013.830999. UM-SG-RS-2014-02.
Kimmel, DG; Tarnowski, M; Newell, RIE. 2012. Long-term (1939 to 2008) spatial patterns in juvenile Eastern oyster (Crassostrea virginica, Gmelin 1791) abundance in the Maryland portion of Chesapeake Bay. Journal of Shellfish Research 31(4):1023-1031. doi:10.2983/035.031.0414. UM-SG-RS-2012-13.