Dataset: ASU Fragmentation – DIDSON Fish CPUE

Name: Fish densities sampled by Dual Frequency Identification Sonar (DIDSON) within Artificial Seagrass Units (ASU) in Back Sound, NC from June to October 2018
Published: 2023-03-13
Keywords: artificial seagrass units, catch per unit effort, contiguous versus patchy, density, benthopelagic fish, percolation probability, acoustic imaging, DIDSON, biota, oceans

Cite This Dataset

DOI:10.26008/1912/bco-dmo.891779.1

Spatial Extent: N:34.707 E:-76.589 S:34.701 W:-76.603

Temporal Extent: 2018-06-29 - 2018-10-22

Project:

Collaborative Research: Habitat fragmentation effects on fish diversity at landscape scales: experimental tests of multiple mechanisms (Habitat Fragmentation)

Principal Investigator:

F. Joel Fodrie (University of North Carolina at Chapel Hill, UNC-Chapel Hill-IMS)

Co-Principal Investigator:

Lauren Yeager (University of Texas - Marine Science Institute, UTMSI)

Scientist:

James Morley (University of North Carolina at Chapel Hill, UNC-Chapel Hill-IMS)

Amy Yarnall (University of North Carolina at Chapel Hill, UNC-Chapel Hill-IMS)

Contact:

Amy Yarnall (University of North Carolina at Chapel Hill, UNC-Chapel Hill-IMS)

BCO-DMO Data Manager:

Taylor Heyl (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2023-03-13

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Fish densities sampled by Dual Frequency Identification Sonar (DIDSON) within Artificial Seagrass Units (ASU) in Back Sound, NC from June to October 2018

Abstract:

To parse the ecological effects of habitat area and patchiness on faunal community structure and dynamics of estuarine nekton, we employed artificial seagrass unit (ASU) landscapes at a scale relevant to habitat fidelity of common fish and macroinvertebrates in our temperate study system, Back Sound, NC. These ASU landscapes were designed along orthogonal axes of artificial seagrass area (i.e., percent cover of each landscape = 10-60 percent) and fragmentation per se (i.e., percolation probability; 0.1-0.59) to delineate their independent and interactive effects on seagrass fish communities. To examine potential differences among faunal responses to habitat configuration within structured habitat (i.e., artificial seagrass) versus matrix habitat (i.e., sand/mudflat) within the borders of the landscape footprint, fish densities (catch per unit effort; CPUE) were sampled by Dual Frequency Identification Sonar (DIDSON) at three locations within each landscape from June to October 2018. Fish densities were sampled within the largest ASU patch of each landscape (“largest patch”) and at two locations within the matrix: 1-m away from the largest patch ("near-patch") and bisecting the largest interpatch distance ("interpatch"). Interpatch samples were not taken in landscapes with 0.59 percolation probability, as they only had one patch. DIDSON samples were collected by Drs. F. Joel Fodrie, James W. Morley, and Amy H. Yarnall for the Estuarine Ecology Laboratory of the University of North Carolina at Chapel Hill’s Institute of Marine Sciences.

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Archival Copy

Version Date	Archive	Persistent Identifier	Date Assigned
2023-03-13	Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)	10.26008/1912/bco-dmo.891779.1	2023-03-28

Methods & Sampling

This dataset represents fish densities (catch per unit effort; CPUE) sampled at three locations within each landscape on Oscar Shoal and an adjacent unnamed shoal in Back Sound, NC, USA (34°42′20" N to 34°41′60" N, 76°36′ 15" W to 76°35′17" W) during the summer of 2018. Benthopelagic fishes were sampled with Dual Frequency Identification Sonar (DIDSON). DIDSON allowed us to reliably detect fishes regardless of water visibility (compared to traditional video cameras) and diversify the size range of organisms we were able to sample. However, DIDSON did not generally allow for species identification.

DIDSON recordings (hereafter “samples”, n = 166), each 1 minute in length, were taken in each landscape's largest patch in July, September, and October, and in each matrix location (i.e., interpatch, near-patch) in July and September. The sampling field of view ranged from 2 meters to 6.5 meters from the DIDSON, and artificial seagrass unit (ASU) and mudflat habitats could be easily distinguished in samples. DIDSON samples were standardized by positioning the sample targets at a range of 4.5 meters in the center of the viewing field. The DIDSON was mounted to a kayak, held stationary by a person on the opposite side of the sampling field, and was operated at a consistent depth below the surface (approximately 10 centimeters) and oriented to include most of the water column and also the substrate.

Known Issues:
The study area and artificial landscapes were directly impacted by Hurricane Florence during 13-16 Sept 2018. Despite ASU re-enforcements made prior to Florence's landfall (i.e., additional lawn staples and cable ties), our landscapes experienced substantial disturbance akin to natural seagrasses in the vicinity, in many cases completely removing or burying ASUs which altered the landscape percent cover and fragmentation per se parameters. Holding the original landscape 234 square meter footprint constant, post-Florence landscape percent cover and percolation probabilities were recalculated both including and excluding ASUs that were fully buried under sediment. DIDSON samples were taken both before and after Florence. Due to considerable landscape parameter alterations over this timeframe and potentially confounding disturbance influences, caution should be taken in examining post-Florence fish densities.

Data Processing Description

Using DIDSON software (V5.26.06), fishes were counted and measured on 10 randomly selected frames from each 1-minute sample. Random frame selection was constrained such that all frames were spaced a minimum of 25 frames apart to improve sub-sample independence. Fish counts for all 10 frames per sample were summed (i.e., number per sample) for further statistical analysis. This method does not differentiate between fishes that are maintaining position within the area versus transiting through, but represents a cumulative total of fish presences. In addition to the 10 frames per sample used to estimate fish presence, readers also selected and processed the frame with the maximum number of fish present (MaxN). All observe total fish lengths (in centimeters) were measured. If no fish were observed on a given frame, this is denoted by a length measurement of zero.

To account for potential reader bias in fish counts and length measurements (in centimeters), some samples were analyzed by two (n = 18) or three (n = 15) readers (reading identical sample frames). Readers counted statistically similar numbers of fishes per sample (paired two-sample t-test, P > 0.05), ruling out significant reader bias on fish counts.

Data output text files from DIDSON software were reformatted, reduced to relevant information, and output as a master Excel file using R statistical software (v3.6.2).

BCO-DMO Processing Description:
- Missing data identifier ‘NA’ replaced with blank (BCO-DMO's default missing data identifier)
- Added "Latitude" and "Longitude" columns and rounded to three decimal places
- Removed "%" symbol from data cells

Data Files

File
asufrag_didsonfishcpue.csv (Comma Separated Values (.csv), 471.02 KB) MD5:ebc3c70e6cdc0e89299abd899c973ecd Primary data file for dataset 891779.

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-1635950

Deployments

None available yet

Instruments

Dual Frequency Identification Sonar (DIDSON 3000m)

Supplied Description:

Instrument Type

Generic Name: Dual Frequency Identification Sonar (DIDSON 3000m)

Acronym: DIDSON 3000

Generic Description:

The Sound Metrics DIDSON 3000 (Dual-Frequency Identification Sonar) multibeam imaging sonar is an acoustic camera that provides almost video-quality images in turbid or dark water where optical systems are ineffective. DIDSON uses acoustic lenses to focus beams and form an acoustic image on the transducer array. DIDSON forms images differently than an optical camera. DIDSON sends out short acoustic pulses in 48 or 96 acoustic beams. These beams are very narrow in the horizontal dimension (0.3° to 0.8°) and wide in the vertical dimension (14°). The beams are adjacent to each other and together form a field-of-view 29° horizontal and 14° vertical. The Didson 3000 is rated to a depth of 3000 meters. It has both Detection and Identification modes. The max frame rate (window length dependent) is 4-21 frames per second.

Parameters

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

Supplied Name	Supplied description	Supplied Units	Standard Name
Site_ID	Artificial seagrass unit (ASU) landscape name (Percent cover value-Percolation probability value)	unitless	Site_ID
Latitude	Latitude North (South is negative) of sampling site	decimal degrees	lat
Longitude	Longitude East (West is negative) of sampling site	decimal degrees	lon
Per_cov	Percent cover of ASUs in 234 square meter landscape footprint (10, 22.5, 35, 47.5, 60)	percent (%)	percent_cover
Frag	ASU landscape fragmentation per se indexed by percolation probability (0.1, 0.225, 0.35, 0.475, 0.59)	unitless	site_descrip
MaxN	Indicates whether this frame has the max number of fish observed in the sample (y/n)	unitless	image_analysis
Frames_sampled	Sample size of frames per recording (10 for MaxN = n; 1 for Maxfish = y)	unitless	image_analysis
Frame	Frame number	unitless	image_analysis
FrameSample	Frame number assigned to fish length (within +/- 5 frames of "Frame" to find best view for measuring the fish length)	unitless	image_analysis
L_cm	Total length of fish (L_cm = 0 indicates no fish were observed)	centimeters (cm)	length
Time	Time of fish observation by DIDSON Format: %H:%M:%S	unitless	time
Date	Date of fish observation DIDSON Format: %Y-%m-%d	unitless	date
Month	Month of sample round completion	unitless	month
Class	Focal sample location within ASU landscape (largest patch, near-patch, inter-patch)	unitless	site_descrip
Reader	Initials of data processor	unitless	comment

Database

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Dataset: ASU Fragmentation – DIDSON Fish CPUE