Dataset: Edmunds et al. 2024 Oecologia: Flow Speeds

Name: Flow speed on the north shore of Moorea, French from 2007 to 2021
Published: 2024-01-23
Keywords: Scleractinia, Coral reef, recruitment, oceans

Cite This Dataset

DOI:10.26008/1912/bco-dmo.918306.1

Spatial Extent: N:-17.475083 E:-149.8101 S:-17.4819 W:-149.84833

Moorea, French Polynesia

Temporal Extent: 2007-01-01 - 2021-01-01

Project:

Moorea Coral Reef Long-Term Ecological Research site (MCR LTER)

Program:

Long Term Ecological Research network (LTER)

Principal Investigator:

Peter J. Edmunds (California State University Northridge, CSUN)

Scientist:

Scott Burgess (Florida State University, FSU)

Stephane Maritorena (University of California-Santa Barbara, UCSB-ERI)

Version:

Version Date:

2024-01-23

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Flow speed on the north shore of Moorea, French from 2007 to 2021

Abstract:

Data Abstract: These data describe bottom and surface flow speed on the north shore of Moorea from 2007 to 2021. Results paper abstract, Edmunds et al. (2024, doi:10.1007/s00442-024-05517-y) : * [See "Related Datasets" section for access to related datasets discussed here] Understanding population dynamics is a long-standing objective of ecology, but the need for progress in this area has become urgent. For coral reefs, achieving this objective is impeded by a lack of information on settlement versus post-settlement events in determining recruitment and population size. Declines in coral abundance are often inferred to be associated with reduced densities of recruits, which could arise from mechanisms occurring at larval settlement, or throughout post-settlement stages. This study uses annual measurements from 2008 to 2021 of coral cover, the density of coral settlers (S), the density of small corals (SC), and environmental conditions, to evaluate the roles of settlement versus post-settlement events in determining rates of coral recruitment and changes in coral cover at Moorea, French Polynesia. Coral cover, S, SC, and the SC:S ratio (a proxy for post-settlement success), and environmental conditions, were used in generalized additive models (GAMs) to show that: (a) coral cover was more strongly related to SC and SC:S than S, and (b) SC:S was highest when preceded by cool seawater, low concentrations of Chlorophyll a, and low flow speeds, and S showed evidence of declining with elevated temperature. Together, these results suggest that changes in coral cover in Moorea are more strongly influenced by post-settlement events than settlement. The key to understanding coral community resilience may lie in elucidating the factors attenuating the bottleneck between settlers and small corals.

Expand/Collapse All

Archival Copy

Version Date	Archive	Persistent Identifier	Date Assigned
2024-01-23	Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)	10.26008/1912/bco-dmo.918306.1	2024-02-28

Methods & Sampling

The ecological methods are described in detail in Edmunds et al. (2024, doi:10.1007/s00442-024-05517-y), and are briefly summarized below.

The study utilized the time series of the Moorea Coral Reef LTER, as they relate to coral community dynamics on the north shore fore reef. Annual measurements of coral cover, the density of coral settlers, and the density of small corals were used together with records of the environmental conditions to which they were exposed. Analyses focused on 2008–2021, which captured the final years of the last population outbreak of the crown of thorns (COTs) sea star, the coral population recovery that took place between 2010 and 2019, and coral mortality attributed to bleaching in 2019. Biological data came from two sites (LTER1 and LTER2) that are ~ 3 km apart, with environmental data from the same or similar sites (temperature), one of the two sites (flow at LTER1), or from 4.5 km resolution remote sensing data (Chlorophyll a).

Bottom seawater flow (m s-1) was recorded at 15-m depth at LTER1 using an Acoustic Doppler Current Profiler (RDI Workhorse Sentinel) sampling every 20 minutes and averaged by month.

* See "Related Datasets" section for access to related dataset pages which include dataset-specific methodology.

Data Processing Description

Data was recorded at 20 minutes and averaged by month. Missing data values were interpolated with month averages across years for each depth.

BCO-DMO Processing

* Sheet "MCR_LTER_FOR01_PhysoData_Monthl" of file "Flow Speeds 2008-2022.xlsx" was imported into the BCO-DMO data system with values "nd" as missing data values.
** Missing data values are displayed differently based on the file format you download. They are blank in csv files, "NaN" in MatLab files, etc.
* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores. Can not start with a number]
* dataset references to results publication Edmunds et al 2023 changed to 2024 since that was the year assoicated with the DOI after final publication. Edmunds et al. (2024, doi:10.1007/s00442-024-05517-y)

Data Files

File
918306_v1_flow-speeds.csv (Comma Separated Values (.csv), 3.15 KB) MD5:bcfe1e17bdbe79100b957d3fad00e908 Primary data file for dataset ID 918306, version 1

Supplemental Files

File

Site list
filename: site_locations.csv

(Comma Separated Values (.csv), 215 bytes)
MD5:d13ffaef5e5725529594f401de6a97cc

Site location list in Moorea (LTER0,LTER1,LTER2) for datasets related to Edmunds et al. (2024, doi:10.1007/s00442-024-05517-y) and Edmunds et al. (2020, doi:10.1093/icesjms/fsaa015).  

Columns:

location, geolocation name

site, site identifier

lat_dd, site latitude, decimal degrees

lon_dd, site longitude, decimal degrees

lat_deg_decmin, site latitude, degrees decimal minutes

lon_deg_decmin, site longitude, degrees decimal minutes

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-2224354

Deployments

None available yet

Instruments

Acoustic Doppler Current Profiler

Supplied Name: RDI Workhorse Sentinel

Supplied Description:

Bottom seawater flow (m s-1) was recorded at 15-m depth at LTER1 using an Acoustic Doppler Current Profiler (RDI Workhorse Sentinel) sampling every 20 minutes and averaged by month.

Instrument Type

Generic Name: Acoustic Doppler Current Profiler

Acronym: ADCP

Community Identifier: http://vocab.nerc.ac.uk/collection/L05/current/115/

Generic Description:

The ADCP measures water currents with sound, using a principle of sound waves called the Doppler effect. A sound wave has a higher frequency, or pitch, when it moves to you than when it moves away. You hear the Doppler effect in action when a car speeds past with a characteristic building of sound that fades when the car passes. The ADCP works by transmitting "pings" of sound at a constant frequency into the water. (The pings are so highly pitched that humans and even dolphins can't hear them.) As the sound waves travel, they ricochet off particles suspended in the moving water, and reflect back to the instrument. Due to the Doppler effect, sound waves bounced back from a particle moving away from the profiler have a slightly lowered frequency when they return. Particles moving toward the instrument send back higher frequency waves. The difference in frequency between the waves the profiler sends out and the waves it receives is called the Doppler shift. The instrument uses this shift to calculate how fast the particle and the water around it are moving. Sound waves that hit particles far from the profiler take longer to come back than waves that strike close by. By measuring the time it takes for the waves to bounce back and the Doppler shift, the profiler can measure current speed at many different depths with each series of pings. (More from WHOI instruments listing).

Parameters

Date and time formats are described in python strftime() syntax.

Supplied Name	Supplied description	Supplied Units	Standard Name
Year	Year of measurement	unitless	year
Month	Month of measurement, 1 = Jan, 2 = Feb, etc.	unitless	month
Bottom_flow	Bottom flow speed (m/s)	meters per second (m/s)	flow_rate
Surface_flow	Surface flow speed (m/s)	meters per second (m/s)	flow_rate

Database

Contribute Data

Dataset: Edmunds et al. 2024 Oecologia: Flow Speeds