Dataset: ASU Fragmentation – DIDSON Fish Z-Score

Name: Fish measurements sampled by Dual Frequency Identification Sonar (DIDSON) within Artificial Seagrass Units (ASU) in Back Sound, NC from July to September 2018
Published: 2023-03-10
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.891686.1

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

Temporal Extent: 2018-07 - 2018-09

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 (US Army Corps of Engineers, USACE-ERDC)

BCO-DMO Data Manager:

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

Version:

Version Date:

2023-03-10

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Fish measurements sampled by Dual Frequency Identification Sonar (DIDSON) within Artificial Seagrass Units (ASU) in Back Sound, NC from July to September 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-meter 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 (PID)	Date PID Issued
2023-03-10	Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)	10.26008/1912/bco-dmo.891686.1	2023-03-28

Methods & Sampling

This dataset represents fish measurements 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:
October fish lengths were excluded from this dataset, as we were interested in comparing z-scores across all three landscape locations (i.e., largest patch, near-patch, and inter-patch). DIDSON samples were only taken in the largest patch during October.

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.

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.

To examine potential reader bias in fish length measurements, we subset the data to only include frames on which the same fishes were observed by all three readers (i.e., all readers agreed on fish count). We used a linear mixed model with fish length as the response variable, reader identity as the fixed effect, and frame number as a random intercept. In this case, significant bias in fish measurement was observed among readers (χ²= 101.1, DF =2, P < 0.001), where some readers consistently measured fishes at smaller or larger sizes than other readers. Therefore, we took the mean fish length per frame and converted all mean fish lengths to z-scores.

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 "%" sybmol from data cells

Data Files

File
asufrag_didson_fish_zscore.csv (Comma Separated Values (.csv), 376.43 KB) MD5:60e8b653897024e63b5d8c2805718654 Primary data file for dataset 891686, Version 1.

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.

scale

Supplied Name: Ohaus H-5276

Supplied Description:

Instrument Type

Generic Name: scale

Acronym: balance

Community Identifier: http://vocab.nerc.ac.uk/collection/L05/current/LAB13/

Generic Description:

An instrument used to measure weight or mass.

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
Sample_ID	Month_Site ID_Class ID	unitless	sample
Frame_ID	Month_Site ID_Class ID_Frame	unitless	sample
L_z	Z-score of mean fish length of all fish in frame	unitless	image_analysis
Month	Month of sample round completion	unitless	month
Class	Focal sample location within ASU landscape (largest patch, near-patch, inter-patch)	unitless	site_descrip
Class_ID	Class abbreviation (LP = largest patch, NP = near-patch, IP = inter-patch)	unitless	Site_ID
Frame	Frame number	unitless	image_analysis
Reader	Initials of data processor	unitless	comment

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Dataset: ASU Fragmentation – DIDSON Fish Z-Score