Contributors | Affiliation | Role |
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Kimbro, David L. | Northeastern University | Principal Investigator |
Stallings, Christopher D. | University of South Florida (USF) | Co-Principal Investigator |
White, J. Wilson | Oregon State University (OSU) | Co-Principal Investigator |
Copley, Nancy | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
The status of juvenile oysters from a caged and non-caged recruitment experiments using two cohorts from Apalachicola Bay and Ocholckonee Bay stock. Reported data include spat density, the numbers of live recruits and 'gapers' on a spat or substrate, 2013-2016 and 2019.
Site Selection- From Hanley et. al 2019, this was a multi-step process that first involved using ArcGIS to partition the bay’s oyster reefs (commercial and non-commercial) into six zones. Zone assignment was based on the reef’s relative distance by water from the river input (near, mid, far) as well as a reef’s east-west orientation to the river (East Apalachicola and West Apalachicola). Next, we randomly selected three reefs out of all possible reefs (including the experimental reefs) within each zone.
Juvenile Experiment 2013-2016: Juvenile oysters were produced from parental broodstocks at a hatchery in Jupiter, Florida (Research Aquaculture, Inc.). To establish each cohort, we collected 25 adult oysters (shell length >75 mm) from 3–5 reefs of each estuary in July 2014 and shipped them to Research Aquaculture, Inc. At the hatchery, adult oysters and their offspring were held under identical conditions in separate flow-through seawater systems to prevent cross contamination between the two bays. The broodstock from each site was manually spawned on the same day. The larvae were held until they settled (~3 weeks) and then moved to a nursery facility at the hatchery under flow-through seawater conditions with standard food concentrations. In August 2014, the two cohorts were transferred to a common flow-through facility at the Florida State University Coastal and Marine Lab. These individuals were reared in a common environment and represented juvenile oyster cohorts of the same age that had experienced identical conditions. Each experimental unit consisted of juvenile oysters (mean shell length 8 mm) that were attached to a ceramic tile (13 cm × 13 cm) using a marine epoxy, and these tiles were affixed to concrete pavers in a vertical position. Tiles of initial densities of 3 or 12 juvenile oysters in 2013 and in 2014 and 2015, 2, 6, and 10 juvenile oysters, respectively. Juvenile oyster tiles were attached to the three unoccupied posts (experimental units) and then randomly assigned among the experimental treatments of cage, cage-control, and control treatment. When each experiment ended, the control and cage-control were removed, but the cage treatment was left on the reefs to continue to generate data on oyster growth through larger adult sizes.
Juvenile Experiment 2019: Juvenile oysters were collected from Easthole(#12) (29.6803, -84.8696). The oysters had settled on to pieces of rock rubble at the site. We separated the rubble into three piles: 1 spat, 2 spat or 3+ spat. We attached the rubble to bird netting using marine epoxy and then attached them to caging material (Industrial Netting). The treatments consisted of a full cage (10”X10”X10”), a cage control: a cage with one panel missing, and a control: a (10” X 10”) panel. The number of ruble pieces inside each treatment varied between 3-5 pieces for a low density, medium density and high-density treatment. Each treatment was attached to a crab trap which had been modified so all the openings were covered with Industrial Netting so fish or other organisms would not get trapped inside. The treatments were cable tied to the outside of the crab trap. Each trap was made up of one of each treatment with the same density of rubble. In each of the three zones in the bay (ABE1, ABE2 and ABE3) we deployed 5 traps along a 15m rope. A week later we came to count the number of surviving oysters. We repeated this again in September 2019 and December 2019.
BCO-DMO Processing Notes:
- data submitted in Excel file "Apalachicola_Data_2013-2019_ABP_4.xlsx" sheet "Juv.Expt.Recruitment" extracted to csv
- modified parameter names to conform with BCO-DMO naming conventions
- replaced all forms of 'NA' with 'nd' for no data
- re-formatted date from m/d/yyyy to yyyy-mm-dd
- changed commas in the notes column to semicolons
- sorted columns: {reef_type}{estuary}{region}{distance}{date}{combined_round}
File |
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juv_expt_recruitment.csv (Comma Separated Values (.csv), 4.97 MB) MD5:910449aeb89187898e631c8862e22f74 Primary data file for dataset ID 821793 |
Parameter | Description | Units |
reef_type | subtidal or intertidal | unitless |
estuary | apalachicola or ochlockonee | unitless |
region | east or west | unitless |
distance | distance from the river: zone 1 or 2 or 3. 1 is the closest and 3 is the furthest from freshwater input | unitless |
date | date experiment was checked | unitless |
round | original experiment round number | unitless |
round_subround | original experiment round number plus the time within the round (so round 1 time 2 becomes 1.2) | unitless |
combined_round | round.subround plus an identifier (AJ = Apalachicola Juvenile Experiment; OJ = Ochlockonee Juvenile Experiment; AP=Apalachicola Pedator Experiment) | unitless |
days | number of days since deployed | days |
weeks | number of weeks since deployed | weeks |
field_site | named field site within region | unitless |
reef | specific reef within the field site | unitless |
reef_name | name of reef | unitless |
latitude | latitude; north is positive | decimal degrees |
longitude | longitude; east is positive | decimal degrees |
treatment | cage; cage control; or control | unitless |
plot_id | each cage; cage control; or control was assigned a unique id/in 2019 the meter the treatment was placed at | unitless |
recruit_substrate | whether the spat is from the juvenile spat experiment or the rubble experiment | unitless |
substrate_density | number of items in a plot: low; medium; high | unitless |
spat_density | number of spat in a unit | spat |
rubble_id | identifier of rubble substrate | unitless |
spat_id | identifier of the deployed oyster | unitless |
size_mm | size of the deployed oyster | millimeters |
status | status of the deployed oyster; l=live; m=everything missing; z=only epoxy; g=gaper; h=half shell; hp=half shell predation; gp=gaper predation | unitless |
live_recruit_count | number of live recruits on a spat or piece of rubble | oysters |
gaper_recruit_count | number of gapers on a spat or a piece of rubble | oysters |
notes | notes and comments | unitless |
Dataset-specific Instrument Name | YSI |
Generic Instrument Name | Water Quality Multiprobe |
Dataset-specific Description | Used a YSI probe to get dissolved oxygen, temperature, salinity and pH at the surface and at the bottom. |
Generic Instrument Description | An instrument which measures multiple water quality parameters based on the sensor configuration. |
NSF Award Abstract:
Ecosystems can exhibit "tipping points" whereby an environmental disturbance pushes an ecosystem into an altered state from which it does not recover, even when the environment normalizes. This may have happened to valuable oyster reefs in Northwest Florida in 2012, when drought and low river flow allowed predators of oysters to flourish and consume nearly all the oysters. Despite subsequent years of normal rainfall and river flow, oysters have not recovered, suggesting the ecosystem may have crossed a tipping point. However, the timing and magnitude of the disturbance from Hurricane Michael (2018) may have pushed the ecosystem back towards its original, healthy state. In this project, investigators make field observations to gauge how predators and oysters are responding to Hurricane Michael and conduct lab experiments to test how predators and oysters respond to hurricane rainfall conditions. Additionally, they use mathematical models to predict whether effects observed in the field and lab could lead to a shift back past the tipping point. This is a rare opportunity to study how oyster ecosystems can shift back from altered to healthy states. However, a rapid response is essential before seasonal changes in the weather and bay obscure hurricane impacts. This research has several broader impacts. First, it will expand the ecological theory of tipping points. Second, it can support the management of the Apalachicola Bay oyster fishery, such as insight into the likely success of restoration efforts. The team coordinates with the Apalachicola National Estuarine Research Reserve to this end. Finally, research outputs are incorporated into ongoing public education and training efforts.
Ecosystems can rapidly shift from their original, high-value state to a new, degraded one. Such shifts have been observed in many ecosystems, but it is sometimes difficult to identify the mechanisms that mediate the shift beyond a "tipping point" and - to a greater extent - those that could mediate a shift back to the original state. Improving our understanding and predictive capability of tipping points depends on identifying the mechanisms that underlie bi-directional system shifts. In 2012, the oyster reefs of Apalachicola Bay, FL abruptly shifted into an oyster-less state when prolonged drought and low river flow allowed marine oyster predators to flourish. Despite subsequent years of normal rainfall and flow, there has not been a return shift, suggesting this ecosystem may have entered an alternate stable state. The hypothesis of this work is that in 2018 Hurricane Michael provided a sufficient disturbance to shift the system back into the attracting basin for its original state (prior observations support this prediction). This project couples field observations and lab experiments with population modeling to test whether and how Hurricane Michael initiated a reversal shift. A rapid response is essential before seasonal variability in this ecosystem obscures hurricane effects. The proposal's intellectual merit is based on its ability to address a central goal in ecology: identifying and predicting ecosystem tipping points. Combining empirical observations and models is a promising approach to advance this goal, but has not been widely applied in the field, mainly because researchers are not in place at the time of a shift. Hurricane Michael provides a unique opportunity to address this knowledge gap.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |