Dataset: Water flow data from oyster reciprocal transplant experiment conducted at two sites in an estuary in NE Florida between July 2019 and April 2020

Preliminary and in progressVersion 1 (2022-10-16)Dataset Type:Other Field ResultsDataset Type:experimental

Principal Investigator: David L. Kimbro (Northeastern University)

Co-Principal Investigator: J. Wilson White (Oregon State University)

BCO-DMO Data Manager: Dana Stuart Gerlach (Woods Hole Oceanographic Institution)


Project: Collaborative research: Quantifying the influence of nonconsumptive predator effects on prey population dynamics (Predatory NCEs and Scale)

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A reciprocal transplant experiment was conducted at two sites in an estuary in NE Florida, USA that encompassed different environmental (salinity, aerial exposure) and biotic (predators) stressors. Juvenile oysters were reciprocally transplanted within and between the two locations within the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR)--the Butler site at 29.77002 N, -81.2641 W, and the Pellicer site at 29.62923 N, -81.2144 W. At each location, the home and away oyster ‘demes’ were randomly assigned between a predator exclosure and a control treatment. 

To obtain site-specific information of water flow, a Nortek current profiler (Aquadopp Profiler 2MHz) was placed in a PVC stand and deployed in the water in front of the oyster reefs during the monthly oyster checks. The instrument was set with a blanking distance of 0.2m. The bin sizes were set 0.1m and 15 cells. The instrument recorded every minute except for two days (9/2/2018 and 9/4/2018) when the sampling interval was set to 30 seconds. The data was offloaded using AquaPro V1.37.08 and then converted to a Profile CSV. The .csv was uploaded into R and reorganized (see Processing Description). 


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Dataset: Tidal inundation results from oyster reciprocal transplant experiment
Kimbro, D. L., White, J. (2022) (DRAFT) Tidal inundation results from oyster reciprocal transplant experiment. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-16 http://lod.bco-dmo.org/id/dataset/882626
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Dataset: Water salinity and temperature data from oyster reciprocal transplant experiment
Kimbro, D. L., White, J. (2022) (DRAFT) Water salinity and temperature data from oyster reciprocal transplant experiment. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-16 http://lod.bco-dmo.org/id/dataset/882657
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Dataset: (DRAFT) Individual oyster results from reciprocal transplant experiment
Kimbro, D. L., White, J. (2022) Individual oyster results from an oyster reciprocal transplant experiment conducted at two sites in an estuary in NE Florida between August 2019 and May 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-14 http://lod.bco-dmo.org/id/dataset/880691
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Dataset: Predator size and abundance from oyster reciprocal transplant experiment
Kimbro, D. L., White, J. (2022) Predator size and abundance data from oyster reefs in a northeast Florida estuary collected between April and August 2019 as part of an oyster reciprocal transplant experiment. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-12-13 http://lod.bco-dmo.org/id/dataset/882641
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Dataset: Survival and growth results from oyster reciprocal transplant experiment
Kimbro, D. L., White, J. (2022) Survival and growth data from an oyster reciprocal transplant experiment conducted at two sites in an estuary in northeast Florida between August 2019 and May 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-16 http://lod.bco-dmo.org/id/dataset/882606

Related Publications

Results

Kimbro, D. L., White, J. W., Breef-Pilz, A., Peckham, N., Noble, A., & Chaney, C. (2022). Evidence for local adaptation of oysters to a within-estuary gradient in predation pressure weakens with ontogeny. Journal of Experimental Marine Biology and Ecology, 555, 151784. https://doi.org/10.1016/j.jembe.2022.151784
Software

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/