Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

This dataset contains Imaging Pulse Amplitude Modulator (IPAM) Fluorometer data collected during various Acropora cervicornis treatment experiments. The data include photosynthetic maximum yield (mY), maximum electron transport rate (mETR), the irradiance at which maximum ETR is reached (mPAR), and the initial slope of the ETR curve (alpha). The experiments were conducted in tanks at Summerland Key, Florida (24.6616,-81.4538) between 2016-07-12 and 2016-09-09 with corals from a nursery located near Looe Key Reef (24.5636, -81.2786). 

The tank numbers listed in this dataset are the same tanks included in the following datasets:

Acropora cervicornis buoyant weight
Acropora cervicornis calcification rates
Acropora cervicornis photosynthesis and respiration rates

Corals from different treatment scenarios were IPAMed every other week for the duration of the two-month long experiment. All fragments were subjected to a light curve where the initial pulse represents the max Yield after dark acclimation and the subsequent electron transport rate (ETR) values are recorded after the corals were exposed to increasing light intensities over time. 

Data Processing: 
We derived four key parameters from the IPAM photophysiology data set: maximum yield (mY), maximum electron transport rate (mETR), the irradiance at which maximum ETR is reached (mPAR), and the initial slope of the ETR curve (alpha). Only mY was transformed, using a skewed and heavy-tailed LambertWxN back-transform to induce normality, though the lower and upper tails still remain heavier and lighter than the normal distribution respectively. 
PAM dataset parameters: Maximum electron transport rate (mETR) was estimated using a linear search algorithm that used the slopes between adjacent (PAR, ETR) data points to determine the first local critical point (maximum or plateau). The algorithm first searches for three data points between which the slope changes sign. If no sign change is detected, the algorithm then looks for largest set of two data points between which the slope is less than a user defined threshold (default is 0.1). In the former case, the middle point is chosen as the (mPAR, mETR) coordinate, while in the later case the second endpoint is used. If neither search yields a solution, the threshold is increased by 0.01 and the two linear searches are repeated, until a solution is found. The slope alpha is calculated with a similar algorithm that searches all PAR levels x such that min(PAR) = 55 < x < mPAR for pairs of points between which the ratio of the local slope to the initial slope (slope between (0,0) and first non-zero datapoint) is less than a user defined threshold (default is 0.1). If not solution is found, the threshold is increased by 0.01 and the search is repeated. Once a pair of points i and i+1 have been identified, all data points with PAR levels less than that of point i are used to calculate the slope.  If the threshold increases to greater than 0.25, the algorithm terminates by returning the slope of all points with PAR < mPAR. Both algorithms return NA when given zero vectors and are both implemented in R. 

BCO-DMO Data Manager Processing Notes:
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions
* alpha values rounded to three decimal places

NSF Award Abstract:
Caribbean staghorn coral was one of the most common corals within reefs of the Florida Keys several decades ago. Over the last 40 years disease, bleaching, overfishing and habitat degradation caused a 95% reduction of the population. Staghorn coral is now listed as threatened under the U.S. Endangered Species Act of 1973. Within the past few years, millions of dollars have been invested for the purpose of restoring the population of staghorn coral within Florida and the U.S. Virgin Islands. Significant effort has been placed on maintaining and propagating corals of known genotypes within coral nurseries for the purpose of outplanting. However, little is known about the individual genotypes that are currently being outplanted from nurseries onto coral reefs. Are the genotypes being used for outplanting resilient enough to survive the three major stressors affecting the population in the Florida Keys: disease, high water temperatures, and ocean acidification? The research within the present study will be the first step in answering this critically important question. The funded project will additionally develop a research-based afterschool program with K-12 students in the Florida Keys and U.S. Virgin Islands that emphasizes an inquiry-based curriculum, STEM research activities, and peer-to-peer mentoring. The information from the present study will help scientists predict the likelihood of species persistence within the lower Florida Keys under future climate-change and ocean-acidification scenarios. Results of this research will also help guide restoration efforts throughout Florida and the Caribbean, and lead to more informative, science-based restoration activities.

Acropora cervicornis dominated shallow-water reefs within the Florida Keys for at least the last half a million years, but the population has recently declined due to multiple stressors. Understanding the current population level of resilience to three major threats - disease outbreaks, high water temperatures, and ocean acidification conditions - is critical for the preservation of this threatened species. Results from the present study will answer the primary research question: will representative genotypes from the lower Florida Keys provide enough phenotypic variation for this threatened species to survive in the future? The present proposal will couple controlled laboratory challenge experiments with field data and modeling applications, and collaborate with local educators to fulfill five objectives: 1) identify A. cervicornis genotypes resistant to disease, 2) identify A. cervicornis genotypes resilient to high water temperature and ocean acidification conditions, 3) quantify how high water temperature and ocean acidification conditions impact disease dynamics on A. cervicornis; 4) determine tradeoffs in life-history traits because of resilience factors; and 5) apply a trait-based model, which will predict genotypic structure of a population under different environmental scenarios.