Contributors | Affiliation | Role |
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Fodrie, F. Joel | University of North Carolina at Chapel Hill (UNC-Chapel Hill-IMS) | Principal Investigator |
Yeager, Lauren | University of Texas - Marine Science Institute (UTMSI) | Co-Principal Investigator |
Lopazanski, Cori | University of North Carolina at Chapel Hill (UNC-Chapel Hill-IMS) | Scientist |
Poray, Abigail K. | University of North Carolina at Chapel Hill (UNC-Chapel Hill-IMS) | Scientist |
Yarnall, Amy | University of North Carolina at Chapel Hill (UNC-Chapel Hill-IMS) | Scientist, Contact |
Heyl, Taylor | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
To measure generalist consumption probabilities across landscapes, we conducted two squidpop consumption assays on 19-Oct and 1-Nov 2018 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). Consumption assays were conducted after Hurricane Florence disturbed our landscapes (see below), prior to the seasonal egress of nekton from local seagrass meadows (Baillie et al., 2015). Squidpops are 1-centimeter × 1-centimeter squares of dried squid mantle tied to 1-centimeter segments of monofilament (Duffy et al., 2015). Squidpops were secured to 60-centimeter long, 0.5-centimeter diameter, fiberglass stakes with attached floats for relocation. On each assay date, up to 10 squidpops were deployed within ASUs in each landscape, 1 meter apart and less than 0.5 meters from the ASU-matrix interface (the edge of ASU patches), to control for potentially different consumption probabilities between seagrass patch edges and interiors (Mahoney et al., 2018). The number of squidpops deployed in each landscape [mean of 9.2 ± 1.8 SD] depended upon the length of the available edge. Squidpop presence/absence was checked after 1 hour, 2 hours, and 3 hours to retrospectively assess the timeframe in which overall consumption probabilities allowed for the resolution of differences in consumption among sites (i.e., between one- and two-thirds of all bait consumed). This threshold was met after 1 hour, therefore we focus our results on these data. All absent squidpops were presumed eaten based on previous efforts that have demonstrated negligible spurious bait loss (Lefcheck et al., 2021).
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. We recalculated landscape parameters based on ASU-by-ASU checks made after Hurricane Florence. Holding the original landscape 234-square meter footprint constant, the percent cover and percolation probability of each landscape was recalculated from the remaining number of ASUs (excluding buried ASUs).
Known Issues:
Artificial seagrass landscapes were substantially altered by Hurricane Florence; therefore, landscape parameters were recalculated based on ASU-to-ASU checks. For the purposes of squidpop consumption analysis, buried ASUs were excluded from parameter calculations (as buried ASUs were not expected to influence above-ground fauna).
All data were entered electronically into an Excel spreadsheet.
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
File |
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asufrag_squidpopconsumptionprob.csv (Comma Separated Values (.csv), 9.69 KB) MD5:94dde592af493580cfe47d736a7014de Primary data file for dataset 891794. |
Parameter | Description | Units |
Site_ID | Artificial seagrass unit (ASU) landscape name (Percent cover value-Percolation probability value) | unitless |
Latitude | Latitude North (South is negative) of sampling site | decimal degrees |
Longitude | Longitude East (West is negative) of sampling site | decimal degrees |
Per_cov | Percent cover of ASUs in a 234 square meter landscape footprint (10, 22.5, 35, 47.5, 60) | percent (%) |
Frag | ASU landscape fragmentation per se indexed by percolation probability (0.1, 0.225, 0.35, 0.475, 0.59) | unitless |
Date | Date of squidpop assay | unitless |
Time_In | Time of squidpop assay deployment | unitless |
Time_Check | Time of squidpop assay check | unitless |
PostFlo_Per_Cov | ASU landscape percent cover after Hurricane Florence - including ASUs that are buried under sediment | percent (%) |
PostFlo_Frag | ASU landscape percolation probability after Hurricane Florence - including ASUs that are buried under sediment | unitless |
N | Number of squidpops deployed | unitless |
N_eaten | Number of squidpops eaten at time of check | unitless |
WaterTemp_C | Surface water temperature at time of minnow trap deployment | degrees C |
Sal_PSU | Surface salinity at time of minnow trap deployment | PSU |
Dataset-specific Instrument Name | ExTech 39240 |
Generic Instrument Name | digital thermometer |
Generic Instrument Description | An instrument that measures temperature digitally. |
Dataset-specific Instrument Name | |
Generic Instrument Name | minnow trap |
Generic Instrument Description | shore fishing gear |
Dataset-specific Instrument Name | VeeGee STX-3 |
Generic Instrument Name | Refractometer |
Generic Instrument Description | A refractometer is a laboratory or field device for the measurement of an index of refraction (refractometry). The index of refraction is calculated from Snell's law and can be calculated from the composition of the material using the Gladstone-Dale relation.
In optics the refractive index (or index of refraction) n of a substance (optical medium) is a dimensionless number that describes how light, or any other radiation, propagates through that medium. |
Amount and quality of habitat is thought to be of fundamental importance to maintaining coastal marine ecosystems. This research will use large-scale field experiments to help understand how and why fish populations respond to fragmentation of seagrass habitats. The question is complex because increased fragmentation in seagrass beds decreases the amount and also the configuration of the habitat (one patch splits into many, patches become further apart, the amount of edge increases, etc). Previous work by the investigators in natural seagrass meadows provided evidence that fragmentation interacts with amount of habitat to influence the community dynamics of fishes in coastal marine landscapes. Specifically, fragmentation had no effect when the habitat was large, but had a negative effect when habitat was smaller. In this study, the investigators will build artificial seagrass habitat to use in a series of manipulative field experiments at an ambitious scale. The results will provide new, more specific information about how coastal fish community dynamics are affected by changes in overall amount and fragmentation of seagrass habitat, in concert with factors such as disturbance, larval dispersal, and wave energy. The project will support two early-career investigators, inform habitat conservation strategies for coastal management, and provide training opportunities for graduate and undergraduate students. The investigators plan to target students from underrepresented groups for the research opportunities.
Building on previous research in seagrass environments, this research will conduct a series of field experiments approach at novel, yet relevant scales, to test how habitat area and fragmentation affect fish diversity and productivity. Specifically, 15 by 15-m seagrass beds will be created using artificial seagrass units (ASUs) that control for within-patch-level (~1-10 m2) factors such as shoot density and length. The investigators will employ ASUs to manipulate total habitat area and the degree of fragmentation within seagrass beds in a temperate estuary in North Carolina. In year one, response of the fishes that colonize these landscapes will be measured as abundance, biomass, community structure, as well as taxonomic and functional diversity. Targeted ASU removals will then follow to determine species-specific responses to habitat disturbance. In year two, the landscape array and sampling regime will be doubled, and half of the landscapes will be seeded with post-larval fish of low dispersal ability to test whether pre- or post-recruitment processes drive landscape-scale patterns. In year three, the role of wave exposure (a natural driver of seagrass fragmentation) in mediating fish community response to landscape configuration will be tested by deploying ASU meadows across low and high energy environments.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |