Species density from Braun-Blanquet seagrass surveys on clusters of artificial reefs at the Abaco Islands, Bahamas in May of 2022

Website: https://www.bco-dmo.org/dataset/922248
Data Type: Other Field Results
Version: 1
Version Date: 2024-03-14

Project
» Using novel ecosystem-scale experiments to quantify drivers of reef productivity in a heavily impacted coastal ecosystem (Reef Production Drivers)
ContributorsAffiliationRole
Allgeier, JacobUniversity of MichiganPrincipal Investigator
Munsterman, KatrinaUniversity of MichiganStudent
York, Amber D.Woods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

Abstract
Species density from Braun-Blanquet seagrass surveys for artificial reef clusters at the Abaco Islands, Bahamas in May of 2022.The site (PN) was constructed in May 2021 in the waters north of Little Abaco Island. Three clusters of nine reefs were constructed at the site. Each cluster was separated by at least 150 m and were constructed at ~3 m depth.


Coverage

Location: Abaco Islands, The Bahamas
Spatial Extent: N:26.91192 E:-77.00752 S:26.341 W:-77.62688
Temporal Extent: 2022-05-14 - 2022-05-26

Dataset Description

See "Related Datasets" section for access to data and metadata from other datasets from the same surveys.


Methods & Sampling

At each distance from the reef a 1x1m quadrate was placed over the seagrass bed. The percent cover was then assessed for all species present (often for the macroalgae we were only able to identify to the genus-level). Braun-Blanquet survey method, density codes are as follows: 0.1 = single individual is present, 0.5 = multiple individuals are present but < 10%, 1 = 10-20% cover, 2= 20-40% cover, 3= 40-60% cover, 4= 60-80% cover, 5= 80-100% cover.


BCO-DMO Processing Description

* Sheet 1 in the submitted file BBSurveys2022_FinalNSF.xlsx (submitted to BCO-DMO 2024-04-18) was imported into the BCO-DMO data system for this dataset with values N/A as the missing data identifier.

* 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]

* Date converted to ISO 8601 format

* Note: These data include currently unaccepted synonyms of the accepted taxon names. The supplemental file fish_and_invert_species_list.csv which includes names and identifiers for both the names used in the dataset and the equivalent currently accepted names (as of 2024-02-26).

* Reef cluster site list added from information in related dataset file FishSurveys2022_FinalNSF.xlsx


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Data Files

File
922248_v1_bb-survery-seagrass-pn.csv
(Comma Separated Values (.csv), 26.87 KB)
MD5:b33cd83d9a33a5724ed4553166942dfa
Primary data file for dataset ID 922248, version 1

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Supplemental Files

File
Reef Cluster Site List
filename: reef_cluster_site_list.csv
(Comma Separated Values (.csv), 499 bytes)
MD5:c1c109da3d9dd8eec65fca35dcf3f838
Artificial reef cluster site list for fish and invertebrate surveys conducted in 2022. Two different sites: reefs with name PN# were constructed in May 2021, and CM# were constructed in May 2022. At each site three clusters of nine reefs were constructed. Each cluster was separated by at least 150 m and were constructed at ~3 m depth.

Column name, description, units:
reef_name, Reef cluster identifier
lat_dd, site latitude, decimal degrees
lon_dd, site longitude, decimal degrees
Construction_Month, Month of reef construction (format: %b, .e.g. "May")
Construction_Year, Year of reef construction (format: %Y, e.g. "2021")
Site_Description, Description of the site location and island.

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Related Datasets

IsRelatedTo
Allgeier, J., Munsterman, K. (2024) Fish data from fish and seagrass surveys on clusters of artificial reefs at the Abaco Islands, Bahamas in 2022. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-03-14 doi:10.26008/1912/bco-dmo.922228.1 [view at BCO-DMO]
Relationship Description: Datasets part of the same fish and seagrass surveys conducted in 2022 on artificial reef clusters in the Abaco Islands (created in 2021 and 2022).
Allgeier, J., Munsterman, K. (2024) Invertebrate data from fish and seagrass surveys on clusters of artificial reefs at the Abaco Islands, Bahamas in 2021 and 2022. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-03-14 doi:10.26008/1912/bco-dmo.922236.1 [view at BCO-DMO]
Relationship Description: Datasets part of the same fish and seagrass surveys conducted in 2022 on artificial reef clusters in the Abaco Islands (created in 2021 and 2022).
Allgeier, J., Munsterman, K. (2024) Seagrass blade height from fish and seagrass surveys on clusters of artificial reefs at the Abaco Islands, Bahamas in May of 2022. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-03-14 doi:10.26008/1912/bco-dmo.922242.1 [view at BCO-DMO]
Relationship Description: Datasets part of the same fish and seagrass surveys conducted in 2022 on artificial reef clusters in the Abaco Islands (created in 2021 and 2022).

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Parameters

ParameterDescriptionUnits
date

data survey was conducted

unitless
obs

observer

unitless
cluster

unique cluster ID

unitless
cluster_lat

latitude of cluster location

decimal degrees
cluster_lon

longitude of cluster location

decimal degrees
reef

unique reef ID

unitless
transect

Transect identifier (A, B, C, D)

unitless
distance

distance from the reef

meters (m)
Thalassia

Thalassia values are either 0.1 (single individual), 0.5 (< 0.1 proportion coverage), 1 (0.1-0.2), 2 (0.2-0.4), 3 (0.4-0.6), 4 (0.6-0.8), 5 (0.8-1.0). 

unitless
Syringodium

Proportion (0-1) of sample that was covered by category 'Syringodium'. See Methods and Sampling section for more details.

unitless
Penicillus

Proportion (0-1) of sample that was covered by category 'Penicillus'. See Methods and Sampling section for more details.

unitless
Halimeda

Proportion (0-1) of sample that was covered by category 'Halimeda'. See Methods and Sampling section for more details.

unitless
Laurencia

Proportion (0-1) of sample that was covered by category 'Laurencia'. See Methods and Sampling section for more details.

unitless
Rhiphocephalus

Proportion (0-1) of sample that was covered by category 'Rhiphocephalus'. See Methods and Sampling section for more details.

unitless
Udotea

Proportion (0-1) of sample that was covered by category 'Udotea'. See Methods and Sampling section for more details.

unitless
Sponge

Proportion (0-1) of sample that was covered by category 'Sponge'. See Methods and Sampling section for more details.

unitless
Avrainvillia

Proportion (0-1) of sample that was covered by category 'Avrainvillia'. See Methods and Sampling section for more details.

unitless
Bataphora

Proportion (0-1) of sample that was covered by category 'Bataphora'. See Methods and Sampling section for more details.

unitless
Acetabularia

Proportion (0-1) of sample that was covered by category 'Acetabularia'. See Methods and Sampling section for more details.

unitless
Dictyosphaeria

Proportion (0-1) of sample that was covered by category 'Dictyosphaeria'. See Methods and Sampling section for more details.

unitless
Valonia

Proportion (0-1) of sample that was covered by category 'Valonia'. See Methods and Sampling section for more details.

unitless
Jania

Proportion (0-1) of sample that was covered by category 'Jania'. See Methods and Sampling section for more details.

unitless
unknown_species_1

Proportion (0-1) of sample area that was covered by category 'unknown_species_1', a consistently identified species of which the classification is unknown. Described as "green stringy." See Methods and Sampling section for more details.  

unitless
unknown_species_2

Proportion (0-1) of sample area that was covered by category 'unknown_species_2', a consistently identified species of which the classification is unknown.  Described as "fuzzy green finger." See Methods and Sampling section for more details.  

unitless
unknown_species_3

Proportion (0-1) of sample area that was covered by category 'unknown_species_3', a consistently identified species of which the classification is unknown. Described as "soft brown stick." See Methods and Sampling section for more details.  

unitless
Heterosiphonia

Proportion (0-1) of sample that is covered by category 'Heterosiphonia'. See Methods and Sampling section for more details.

unitless
Porites

Proportion (0-1) of sample that is covered by category 'Porites'. See Methods and Sampling section for more details.

unitless
Halidule

Proportion (0-1) of sample that is covered by category 'Halidule'. See Methods and Sampling section for more details.

unitless
Turf

Proportion (0-1) of sample that is covered by category 'Turf'. See Methods and Sampling section for more details.

unitless
Cyanobacteria

Proportion (0-1) of sample that is covered by category 'Cyanobacteria'. See Methods and Sampling section for more details.

unitless
Caulerpa

Proportion (0-1) of sample that is covered by category 'Caulerpa'. See Methods and Sampling section for more details.

unitless
other

Proportion (0-1) of proportion of sample area that is covered by an organism that does not have its own column. See Methods and Sampling section for more details.

unitless
animals

description of the animal. The ID and the count of individual animals in the quadrat

unitless
notes

notes from datasheet, etc.

unitless

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Project Information

Using novel ecosystem-scale experiments to quantify drivers of reef productivity in a heavily impacted coastal ecosystem (Reef Production Drivers)

Coverage: Caribbean coastal ecosystems


NSF Award Abstract:
Tropical coastal marine ecosystems (e.g., coral reefs, seagrass beds, and mangroves) are among the most productive ecosystems in the world providing important services, such as fisheries, to millions of people. Despite this, they are also among the most impaired ecosystems, necessitating improved understanding of the mechanisms that underpin their productivity. This project seeks to understand the key factors that drive ecosystem production in a degraded coastal ecosystem in Haiti using artificial reefs. Past research has shown that artificial reefs have substantial potential to increase the number and diversity of plants and animals, but the extent to which this can be achieved at scales relevant to society remains unknown. This project is constructing clusters of artificial reefs to test how (1) spatial arrangement and (2) fishing pressure (fished/not fished) influence the productivity of seagrass, coral, and fish over the course of four years. The fishing treatment is being implemented through collaborations with local fishers whereby small-scale no-take zones are created around three of the six artificial reef clusters. A unique aspect of the research is that it capitalizes on the experimental design to simultaneously achieve an important conservation initiative, while testing ecological theory. Community engagement and outreach are integrated directly into the research and local fishers are being surveyed to assess the extent to which fishing occurred on any of the artificial reefs. This research represents a novel effort to integrate experimentation with cutting-edge community-based conservation initiatives in one of the most impoverished regions of the world. The project is improving strategies for conservation and reef management.

Identifying the factors that regulate the structure and function of ecosystems is a fundamental challenge for ecological theory and applied science. This challenge is often framed within the context of Top-Down (TD) versus Bottom-Up (BU) regulation, but the extent to which this framework can predict processes in complex, real-world ecosystems is not fully understood. It is now widely recognized that TD/BU factors do not act in isolation. For example, in many ecosystems, consumers contribute to both TD (via consumption) and BU (via excretion) pathways. Environmental factors, including human-induced change, can further alter the nature of these interactions. Quantifying the strength of TD and BU pathways and the extent to which they regulate the structure and function in highly dynamic ecosystems requires an experimental system that is sufficiently tractable that all its components can be quantified, while still being representative of real ecosystems. To address this challenge, this research project creates a unique ecosystem-scale artificial reef (AR) experiment in Haiti to test how two factors (AR structure, and fishing pressure) alter the strength of independent and interactive TD and BU pathways to regulate the structure and function of real-world reef ecosystems. Over the course of four years, the production of seagrass (surrounding the ARs), coral (transplanted onto the ARs), and fish (in and around the ARs) is being measured, providing a quantitative assessment of ecosystem-level production across the two treatments. Linear and structural equation models are used to measure the independent and interactive strengths TD and BU pathways, and to identify the suite of directional relationships between each trophic level that best predict overall ecosystem production. Harnessing the ability to use ecosystem-scale experiments and quantify production across all trophic levels in a highly complex, real-world system enables an unprecedented test of TD/BU theory.

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.



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Funding

Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)

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