This dataset includes three-dimensional structural complexity metrics derived from Structure-from-Motion (SfM) photogrammetry for 60 Pocillopora spp. colonies in the Maatea Size Experiment on the north shore of Mo'orea, French Polynesia. Colonies were photographed at two time points (December 2019 and May 2021) and reconstructed as 3D mesh models using Agisoft Metashape Professional at 200,000-face resolution. Structural metrics include surface area, planar projected area, maximum and minimum height, height range, skeletal volume, and extent volume. Supplemental files include time-point-specific photogrammetry summaries.

Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
  • Problem Description
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

See the "Related Datasets" section for more data from this experiment and data from other experiments and surveys in this study. 

In particular, see closely related bco-dmo dataset https://www.bco-dmo.org/dataset/999432 which has CAFI community data and genetic metadata from this same Maatea Size Experiment.

Study Description (describes this and related datasets):

Two field experiments and one observational survey were performed investigating how cryptic invertebrate communities (CAFI - Coral Associated Fauna and Invertebrates) interact with and influence coral reef ecosystems in Mo'orea, French Polynesia (2019-2021). The research addresses how spatial configuration of coral habitat affects CAFI community assembly and the reciprocal effects of CAFI on coral health and growth.

The Maatea Size Experiment (the focus of this dataset) examined how coral colony size affects CAFI community composition and coral physiology using 60 Pocillopora colonies spanning a natural size gradient. The MRB Amount Experiment tested how coral habitat density affects CAFI colonization and community assembly using 54 Pocillopora colonies deployed in low, medium, and high density treatments on an experimental grid. The Mo'orea Survey characterized natural CAFI communities and their relationship to coral characteristics across 114 Pocillopora colonies at multiple reef sites around the island.

Data include: CAFI taxonomic identification and abundance measurements (17,073 individual organism records representing multiple phyla including Arthropoda, Mollusca, and Annelida), coral three-dimensional photogrammetry measurements (surface area, volume, height), coral physiology measurements (protein content, carbohydrate content, zooxanthellae density), fish community surveys, genetic sample metadata, and experimental treatment information. All measurements span multiple time points including pre-experiment baseline (December 2019) and post-experiment final sampling (May 2021).

Data were collected by SCUBA at backreef sites 2-10 meters depth. CAFI were extracted from coral colonies using anesthetic seawater, identified to the lowest taxonomic level possible, measured for body size, and counted. Coral morphometrics were quantified using underwater photogrammetry and Agisoft Metashape software. Coral tissue samples were analyzed for protein and carbohydrate content using standard biochemical assays, and zooxanthellae density was quantified using hemocytometer counts.

Study Experimental Design:
Two experiments and an observational survey were conducted: (1) Maatea Size Experiment examining effects of coral colony size on CAFI communities using 60 Pocillopora colonies spanning a natural size gradient, (2) MRB Amount Experiment testing effects of coral habitat density on CAFI colonization using 54 Pocillopora colonies in low, medium, and high density treatments arranged on a 9x6 experimental grid, and (3) Mo'orea Survey characterizing natural CAFI communities across 114 Pocillopora colonies at multiple reef sites.

​Project location description:

Mo'orea Island, French Polynesia. Data were collected at three primary study sites on backreef habitats at 2-10 meter depth: (1) Maatea site on the south shore east of Atiha Pass (17.60°S, 149.81°W), (2) MRB (Maharepa Research Base) site on the north shore near Maharepa township (17.48°S, 149.81°W), and (3) multiple survey sites distributed around the island including northern and western shores. All sites were located 30-300 meters from the reef crest in sand and coral rubble habitats dominated by Pocillopora corals. Field work was conducted from the UC Berkeley Richard B. Gump South Pacific Research Station.

​Acronyms:

MRB = Maharepa Research Base
CAFI = Coral Associated Fauna and Invertebrates
AFDW = Ash-Free Dry Weight
WoRMS = World Register of Marine Species (marinespecies.org)
AphiaID = Aphia ID (ID for taxonomic names at WoRMS)

Study Design:

A 3 x 2 factorial experiment was conducted at the Maatea reef site on the north shore of Mo'orea, French Polynesia (approximately 17°29'S, 149°50'W). Sixty Pocillopora spp. colonies were selected and categorized into three size classes based on branching morphology (thin, medium, thick). Within each class, colonies were randomly assigned to either a CAFI removal treatment (n=10 per class) or control treatment with CAFI present (n=10 per class).

CAFI Sampling:

Colonies were sampled at two time points: December 2019 (~6 months) and May 2021 (~18 months). Colonies were carefully extracted from the reef and transported in sealed containers to a field laboratory within 2 hours. Colonies were broken open systematically to expose internal branches and crevices. All visible invertebrates and resident fishes were collected, preserved in 95% ethanol, and identified to the finest possible taxonomic resolution using stereomicroscopes (Nikon SMZ745T) and taxonomic keys. Multiple expert taxonomists verified identifications for difficult groups (crustaceans, molluscs, polychaetes).

Fish Surveys:

Prior to colony extraction, visual fish surveys were conducted within a 1-meter radius around each coral colony. All fishes were identified to species, counted, and categorized by size class and residency behavior.

Physiological Sampling:

Tissue samples (~5 cm2) were collected from each colony prior to destructive sampling and frozen at -20°C. Samples were analyzed for protein content (Bradford assay at 595 nm), carbohydrate content (anthrone method at 630 nm), zooxanthellae density (hemocytometer cell counts), and ash-free dry weight (combustion at 450°C for 4 hours). All values normalized to coral surface area determined by the wax-dipping method.

Genetic Sampling:

Tissue samples were collected and preserved in 95% ethanol for genetic analysis. DNA extractions were attempted for Pocillopora haplotype identification.  Related dataset https://www.bco-dmo.org/dataset/999432 includes supplemental genetic samples metadata detailing which samples were collected and their intended use, but the sequencing results were not usable and no genetic data were included.

DATA CLEANING AND STANDARDIZATION:
Raw field data were transcribed from underwater data sheets and entered into digital spreadsheets. Taxonomic names were standardized using the World Register of Marine Species (WoRMS) database (accessed 2021-2023). WoRMS Aphia IDs were added for all taxa to ensure consistency and facilitate data integration. Size measurements recorded as text categories ("<5", "<1", "L", "M", "S") were retained in original field data columns, with separate numeric-only columns created for quantitative analyses (recorded as NA when non-numeric).

PHOTOGRAMMETRY PROCESSING:
Photogrammetry data were processed using Agisoft Metashape Professional (versions 1.6-1.7). Processing workflow: (1) photo alignment with high accuracy settings, (2) dense point cloud generation, (3) mesh construction at 200,000 face count for standardization, (4) texture mapping, (5) measurement extraction using polygon selection tools and volume measurement functions. Models were quality-checked for reconstruction artifacts, and problematic models were flagged in metadata. Scale bar measurements were used to verify spatial accuracy of reconstructions.

PHYSIOLOGICAL DATA PROCESSING:
Protein and carbohydrate concentrations were calculated from spectrophotometer absorbance values using standard curves generated from bovine serum albumin (protein) and glucose (carbohydrates) standards. Zooxanthellae densities were calculated from hemocytometer counts accounting for dilution factors and homogenate volumes. All measurements were normalized to coral surface area (determined from photogrammetry) and expressed per square centimeter.

STATISTICAL PROCESSING:
Data processing and quality control conducted in R (version 4.0+). Processing scripts documented data cleaning steps, outlier detection, and creation of derived variables. Missing data coded consistently as NA. Data files exported as CSV format with UTF-8 encoding.

VERSIONING:
* Note: The version number described here refers to the research group's internal versioning system used during data processing and quality control. It is independent of BCO-DMO's versioning system, which starts at version 1 and increments each time an updated version of this dataset is published at BCO-DMO.

Dataset version 2.2 reflects final quality control and BCO-DMO submission preparation. All processing steps were documented in repository README files. Original raw data files were preserved alongside processed versions.  Raw data are not provided as part of this dataset but were used for internal verification, and provenance tracking  (see Github repository https://github.com/stier-lab/moorea-cafi-data).

Missing Data Identifiers:
* The original data used NA as the encoding for no data (missing data identifier).
* In the BCO-DMO data system missing data identifiers are displayed according to the format of data you access. For example, in csv files it will be blank (null) values. In Matlab .mat files it will be NaN values. When viewing data online at BCO-DMO, the missing value will be shown as blank (null) values.

Data processing and import:
- Loaded CSV file "maatea_size_photogrammetry_summary_dec_2019_v1.csv" as table "dec_2019_summary"; applied metadata (descriptions, units, standard name IDs) to columns Chunk, Model, area_cm2, extent_volume_cm3, height_range_cm, max_height_cm, min_height_cm, model_type, surface_area_cm2, volume_cm3; set types for all columns (string or number as appropriate)
- Loaded Excel file "maatea_size_photogrammetry_summer_2019_v1.xlsx" (Sheet1) as table "summer_2019_setup"; applied metadata to 22 columns including coral_id, row, column, photogrammetry measurements, surface areas, volumes, scalebar errors, camera, and notes; set types (string, number) for all columns; columns row, column, total_photos, and photos_not_aligned were initially set as number due to Excel float storage, then rounded and converted to integer format to match original Excel display
- Loaded CSV file "metadata_for_genetic_samples.csv" as table "metadata_for_genetic_samples"; this table contains information about coral colony collection and field measurements
- Loaded CSV file "maatea_size_photogrammetry_2019_2021_v1.csv" as table "999444_v1_maatea-size-exp_photogrammetry"; applied metadata to columns including Chunk, Model, area_cm2, extent_volume_cm3, height_range_cm, max_height_cm, min_height_cm, model_type, surface_area_cm2, volume_cm3, and visit_date; set types (string, number, date) for all columns with visit_date parsed as "%Y-%m-%d"
- Extracted coral_id from Chunk column in "999444_v1_maatea-size-exp_photogrammetry" for consistency since related data have the coral_id as intependent column. Used a find/replace operation to extract the coral_id (e.g., extracting "FE-POC40" from "1-1 (FE-POC40)").
- Cleaned one outlier coral_id value that was due to inconsistent format in the Chunk column: removed unintended prefix "2-13_" from "2-13_FE-POC40", resulting in "FE-POC40". Other chunk values used parentheses except this one.
- Joined lat, long, and site columns from "metadata_for_genetic_samples" into "999444_v1_maatea-size-exp_photogrammetry" using coral_id as the key (half-outer join, first-value aggregation). Genetic sample metadata including coral colony information is in related bco-dmo dataset https://www.bco-dmo.org/dataset/999432 along with additional data and metadata from this experiment.
- Output four final tables: dec_2019_summary.csv, summer_2019_setup.csv, metadata_for_genetic_samples.csv, and 999444_v1_maatea-size-exp_photogrammetry.csv

Some coral colonies had incomplete photogrammetry models due to challenging underwater conditions (surge, turbidity) or complex colony morphology. Approximately 5-10% of models had minor reconstruction artifacts. Manual caliper measurements were used as validation checks against photogrammetry-derived metrics.

Coverage: Mo’orea, French Polynesia (17° 29.433'S, 149° 49.579'W)

NSF Award Abstract:
Nearshore habitats such as coral reefs, seagrass beds, and oyster reefs perform a number of services including reducing storm protection, nutrient cycling, and water purification. Many of these habitats have experienced widespread loss and fragmentation due to human activities. This loss threatens the services these ecosystems provide to humans as well as the extraordinary biodiversity of fishes and invertebrates that live within them. However, there is still a lot that is unknown about these habitats which makes it difficult to understand the likely impacts of habitat loss or the benefits of habitat restoration. This research focuses on habitat loss and fragmentation in coral reef ecosystems. The focus of the research is to understand how habitat loss and fragmentation affect the biodiversity of fish and crustaceans on coral reefs in the South Pacific. Because many creatures living within the coral offer important benefits to the coral such as defense from coral predators and removal of sediment, this research also seeks to better understand how changes in the biodiversity and abundance of fish and invertebrates associated with corals, affect the capacity of corals to withstand future impacts, such as sedimentation and outbreaks of coral-eating seastars. Understanding whether habitat loss alters the capacity of corals to withstand stress in an increasingly stressful world is critical to devise effective strategies to manage and protect coral reefs and the many services they provide to society. Furthermore, this research facilitates restoration efforts, enhance the scientific workforce through mentorship of a diverse group of undergraduates, graduate students and a postdoctoral fellow, and engage the public in both French Polynesia and the United States in scientific research and knowledge.

Many marine systems are characterized by habitat-forming foundation species, which harbor a diversity of occupants, and whose dynamics are thought to drive resilience of entire ecosystems As a result, there is widespread concern over the ongoing loss and fragmentation of biogenic habitats such as seagrass beds, oyster reefs, kelp forests, and coral reefs. Yet, without a more complete understanding of marine landscape ecology, we struggle to predict how the degradation or restoration of habitat alters ecosystem dynamics, function, and resilience. Most research in marine landscape ecology has focused on spatial patterns of occupant abundance and biodiversity; however, the causes and consequences of these patterns are rarely explored. An important but understudied consequence of variation in occupant density is that it may alter how occupants interact with their biogenic habitat. Because occupants can benefit biogenic habitat or harm biogenic habitat, changes in occupant density can affect habitat growth and survival. Consequently, habitat-driven variation in occupant density should feed back to alter habitat dynamics and the spatial patterning of the habitat. In summary, we are limited in our understanding of why patterns in landscape ecology exist, how these patterns alter the population dynamics and spatial patterns of the occupants as well as their habitat, and the implications of habitat degradation or restoration. The central objective of this proposal is to examine the causes and consequences of the nonlinear relationship between occupant abundance and the amount of biogenic habitat. Specifically, the investigators: (i) examine the habitat-based mechanisms that produce spatial variation in occupant density; (ii) quantify how habitat-driven occupant density feeds back to alter habitat growth and survival; and (iii) apply this knowledge to understand how bidirectional habitat-occupant interactions affect the long-term dynamics, create novel spatial patterns, and drive variation in how systems respond to and recover from disturbances.

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.