Contributors | Affiliation | Role |
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Putnam, Hollie | University of Rhode Island (URI) | Co-Principal Investigator |
Strand, Emma | University of Rhode Island (URI) | Student |
Soenen, Karen | BCO-DMO Data Manager |
Photosynthetic Irradiance Curves: Prior to experimental exposures, 10 coral fragments (5 per species) were used to generate a photosynthesis-irradiance curve to determine saturating irradiance for assessing rates of photosynthesis. Fragments were exposed to 10 light levels: 0, 15, 30, 60, 91, 136, 227, 416, 529, and 756 µmol m-2 s-1 generated by two LED lights (Aqua Illumination Hydra FiftyTwo) hung above the incubation chambers (described below). Rates of oxygen consumption or evolution were extracted using curve fitting of a non-linear least squares fit for a non-rectangular hyperbola (NLLS; Marshall & Biscoe 1980, Heberling 2013) was used to identify PI curve characteristics of each species. This model is as follows: = фPAR+√(φPPFD + Pmax)2-4ΘφPAR Pmax2Θ-Rd . Theta was set at 0.6 for M. capitata and 0.64 for P. acuta. Photosynthesis-irradiance curves were performed four times throughout the experiment on fragments of both species in the HTHC treatment to determine that there were no significant changes in Ik that occured during bleaching, and thus no need to change light settings used to measure respiration and photosynthetic rates.
Fragments were placed in individual respiration chambers (~610mL), with individual temperature (Pt1000 temperature sensor, PreSens) and fiber-optic oxygen probes (Oxygen Dipping Probes DP-PSt7, accuracy = ± 0.05% O2, PreSens) connected to a 10-channel oxygen meter (OXY-10 ST, accuracy = ± 1.0 °C, resolution = 0.1 °C, PreSens), to evaluate photosynthesis under saturating light conditions, as determined by PI curves described above, and light enhanced dark respiration (LEDR; Edmunds and Davies 1988). The respirometry setup consisted of 10 chambers with stirbars. Samples were measured in a series of runs that consisted of eight fragments (n=4 per species and n=2 blank chambers) per run, and exposed to PAR irradiance of 590 ± 7.16 µmol photons m-2 s-1 for 15 minutes to assess photosynthetic rates. Immediately afterwards, these fragments were exposed to dark conditions (0 µmol photons m-2 s-1) for 20 minutes to assess LEDR. Following respirometry, fragments were set back in their respective tanks.
Raw photosynthetic (temperature corrected) data was thinned every 40 seconds and rates of oxygen flux were calculated by local linear regressions in LoLinR package (Olito et al. 2017) in µmol L-1 s-1. Rates were corrected to chamber volume including coral displacement. Rates of the blank chambers were subtracted from the rates of the coral fragments within each run to account for background photosynthesis or respiration due to phytoplankton, zooplankton, and bacteria. Rates of oxygen flux were normalized to surface area by the wax dipping technique (Veal et al. 2010) for final units of µmol cm-2 h-1. Net photosynthesis was calculated by subtracting the respiration rates of the dark runs from the gross photosynthetic rates of the light runs, to isolate photosynthetic rates of the symbionts from the light enhanced dark respiration (LEDR) of the coral holobiont.
File |
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heatwave_irradiance_all.csv (Comma Separated Values (.csv), 94.77 KB) MD5:338e4ce06ce1489ca89f1f76f9b7a6cc Primary data file for dataset ID 884537 |
PI_Curves filename: PI_curve_rates.zip (ZIP Archive (ZIP), 30.12 KB) MD5:7e285fae8d99020c59d3966891d1ad32 Individual .csv files of photosynthetic irradiance curves for dataset 884537 |
Parameter | Description | Units |
sampling_point | Record number | unitless |
Light_Level | Light level number on scale of 1-10 | unitless |
Fragment_ID | Fragment replicate ID number with species initials (PA, MC) indicated | unitless |
Intercept | Intercept value from PreSens | umol/L/second (micromol per liter per second) |
Aumol_L_sec | change in dissolved oxygen value | umol per liter per second |
Temp | Temperature at that time of umol.L.sec data point | degrees Celsius (°C) |
Date | Date of PI curve | units |
Sample_ID | Fragment.ID and Date | unitless |
Plug_Number | The individual coral fragment ID number | unitless |
Chamber_Channel | The chamber and channel ID number that was associated with that coral fragment during PI curve | unitless |
Position | Position in the PI curve set up associated with that chamber during that run | unitless |
Bin_ID | The bin in the PI curve set up associated with that chamber during that run | unitless |
Run | PI curve run number for that sampling date | unitless |
Species | Coral host species: Montipora capitata or Pocillopora acuta | unitless |
Fragment_Number | Fragment replicate ID number | unitless |
Sample_Type | Sample or Blank indication | unitless |
Temp_Cat | Temperature of calibration from PreSens | degrees Celsius (°C) |
Chamber_Vol_L | Volume in liters of the chamber that coral fragment was in | liters (L) |
Surf_Area_cm2 | Estimated surface area of that coral fragment | cm2 |
Start_time | Time that PI curve light level was started | unitless |
Stop_Time | Time that PI curve light level ended | unitless |
Light_Dark | Light or Dark indication | unitless |
Light_Value | PAR value for that light level | nm (nanometers) |
Notes | Notes taken during PI curve in the field | unitless |
QC | Notes taken during quality control | unitless |
sample_micromol_s | Calculated coral fragment's PI curve rate for that light level | umol per liter per second |
blank_micromol_s | The calculated blank chamber's PI curve rate for that light level | umol per liter per second |
corr_micromol_s | Calculated corrected value (sample value - blank value; sample.micromol.s - blank.micromol.s) | umol per liter per second |
micromol_cm2_s | Calculated corrected value normalized to surface area (corr.micromol.s/Surf.Area.cm2) | umol per liter per second |
micromol_cm2_h | Calculated corrected value | umol per liter per hour |
source_file | The name of the source ifle | unitless |
Dataset-specific Instrument Name | Apex cosine corrected PAR Sensor |
Generic Instrument Name | Biospherical PAR sensor |
Dataset-specific Description | Apex cosine corrected PAR Sensor to measure light |
Generic Instrument Description | Unspecified Biospherical PAR. An irradiance sensor, designed to measure Photosynthetically Active Radiation (PAR). |
Dataset-specific Instrument Name | |
Generic Instrument Name | LI-COR LI-192 PAR Sensor |
Dataset-specific Description | Li-Cor cosine corrected PAR sensor (LI-192) to measure light |
Generic Instrument Description | The LI-192 Underwater Quantum Sensor (UWQ) measures underwater or atmospheric Photon Flux Density (PPFD) (Photosynthetically Available Radiation from 360 degrees) using a Silicon Photodiode and glass filters encased in a waterproof housing. The LI-192 is cosine corrected and features corrosion resistant, rugged construction for use in freshwater or saltwater and pressures up to 800 psi (5500 kPa, 560 meters depth). Typical output is in um s-1 m-2. The LI-192 uses computer-tailored filter glass to achieve the desired quantum response. Calibration is traceable to NIST. The LI-192 serial numbers begin with UWQ-XXXXX. LI-COR has been producing Underwater Quantum Sensors since 1973.
These LI-192 sensors are typically listed as LI-192SA to designate the 2-pin connector on the base of the housing and require an Underwater Cable (LI-COR part number 2222UWB) to connect to the pins on the Sensor and connect to a data recording device.
The LI-192 differs from the LI-193 primarily in sensitivity and angular response.
193: Sensitivity: Typically 7 uA per 1000 umol s-1 m-2 in water. Azimuth: < ± 3% error over 360° at 90° from normal axis. Angular Response: < ± 4% error up to ± 90° from normal axis.
192: Sensitivity: Typically 4 uA per 1000 umol s-1 m-2 in water. Azimuth: < ± 1% error over 360° at 45° elevation. Cosine Correction: Optimized for underwater and atmospheric use.
(www.licor.com) |
Dataset-specific Instrument Name | Fiber-optic oxygen probes Oxygen Dipping Probes DP-PSt7 PreSens |
Generic Instrument Name | Oxygen Microelectrode Sensor |
Generic Instrument Description | Any microelectrode sensor that measures oxygen. |
Dataset-specific Instrument Name | |
Generic Instrument Name | PreSens OXY-10 Mini oxygen meter |
Generic Instrument Description | The OXY-10 mini is a precise multi-channel oxygen meter for up to 10 'in-house' sensors, simultaneously controlling and reading them. The meter is used with oxygen sensors based on a 2mm optical fibre. The meter is compatible with sensors that are type PSt3 which has a detection limit 15 ppb, 0 - 100% oxygen. |
Dataset-specific Instrument Name | Pt1000 temperature sensor, PreSens |
Generic Instrument Name | Water Temperature Sensor |
Generic Instrument Description | General term for an instrument that measures the temperature of the water with which it is in contact (thermometer). |
NSF Abstract:
The remarkable success of coral reefs is explained by interactions of the coral animal with its symbiotic microbiome that is comprised of photosynthetic algae and bacteria. This total organism, or "holobiont", enables high ecosystem biodiversity and productivity in coral reefs. These ecosystems are, however, under threat from a rapidly changing environment. This project aims to integrate information from the cellular to organismal level to identify key mechanisms of adaptation and acclimatization to environmental stress. Specific areas to be investigated include the role of symbionts and of epigenetics (molecular "marks" on coral DNA that regulate gene expression). These aspects will be studied in Hawaiian corals to determine whether they explain why some individuals are sensitive or resistant to environmental perturbation. Results from the proposed project will also provide significant genomic resources that will contribute to fundamental understanding of how complex biological systems generate emergent (i.e., unexpected) properties when faced with fluctuating environments. Broader impacts will extend beyond scientific advancements to include postdoctoral and student training in Science, Technology, Engineering and Mathematics (STEM). Data generated in the project will be used to train university students and do public outreach through live videos of experimental work, and short stop-action animations for topics such as symbiosis, genomics, epigenetics, inheritance, and adaptation. The research approaches and results will be shared with the public in Hawaii through the Hawaii Institute of Marine Biology education department and presentations at Hawaiian hotels, as well as at Rutgers University through its 4-H Rutgerscience Saturdays and 4-H Rutgers Summer Science Programs.
Symbiosis is a complex and ecologically integrated interaction between organisms that provides emergent properties key to their survival. Such is the case for the relationship between reef-building corals and their microbiome, a meta-organism, where nutritional and biogeochemical recycling provide the necessary benefits that fuel high reef productivity and calcification. The rapid warming and acidification of our oceans threatens this symbiosis. This project addresses how relatively stress resistant and stress sensitive corals react to the environmental perturbations of increased temperature and reduced pH. It utilizes transcriptomic, epigenetic, and microbial profiling approaches, to elucidate how corals respond to environmental challenges. In addition to this profiling, work by the BSF Israeli partner will implement powerful analytical techniques such as network theory to detect key transcriptional hubs in meta-organisms and quantify biological integration. This work will generate a stress gene inventory for two ecologically important coral species and a (epi)genome and microbiome level of understanding of how they respond to the physical environment. Acknowledgment of a role for epigenetic mechanisms in corals overturns the paradigm of hardwired genetic control and highlights the interplay of genetic and epigenetic variation that may result in emergent evolutionary and ecologically relevant properties with implications for the future of reefs. Furthermore, clarifying the joint contribution of the microbiome and host in response to abiotic change will provide an important model in metazoan host-microbiome biotic interactions.
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) |