Table of Contents

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

Original sources utilized variable sampling techniques. Our approach for integrating across these sources began by identifying sources containing energy density data for aquatic organisms. First, we conducted a literature search on Web of Science using the search string: “("energy densit*" OR "energy content*" OR "energy equivalen*") AND (marine OR Atlantic).”  The 745 results were rough filtered by a check of the abstract to identify whether they followed the correct topic, with potential sources being inspected more closely for containing data on energy density as a per weight unit (~2/3 sources following the first filter had suitable data). Appropriate conversions were made as necessary with a particular emphasis on conducting a conversion to energy density per gram wet weight as the focal measure of interest within the database. All sources which contained one or more taxon with an energy density measure were included within the database. Additional metadata about the record was gleaned from the source as available including the location and method of capture, storage and analytical technique, and number of replicate samples. Locations of estuarine and marine coastal data were also categorized by large marine ecosystem (LME; Sherman 1991, 2014); open ocean locations were defined by the ocean body; locations of freshwater data were categorized by continent.  Additional sources were sought out through a snowball method by which references for relevant papers on energetics which did not contain measures of energy density but cited sources of data were searched, of note as a source for other original records was the Pelagic Traits Database (Gleiber et al., 2024). Efforts to locate original sources of energy density data were always taken, but in some instances a review paper was included due to complications with accessibility or reference obscurity. The use of data from reviews may result in a “double counting” of individuals when both an original source and review include them in their average. Finally, grey literature was included through a haphazard search and through communications with experts.

Taxonomic classification for each record began with that reported by the original source, primarily a Genus species name but occasionally at broader grades such as Family and Order. Taxonomy for all records was retrieved 26-Apr-2024, from the Integrated Taxonomic Information Service (ITIS), www.itis.gov, CC0, https://doi.org/10.5066/F7KH0KBK. Species names from original records were changed to agree with valid ITIS names as of this date. 

Data were acquired from the original sources as they were published and available online or in print. They were then transferred by copy-paste automation whenever possible and manually in all other instances. The accurate entry of all data was checked by the principal investigator following each source being entered in completion. Missing data represents instances when a variable was not provided by the original source, often due to variable study designs or methodologies, e.g., Bomb Calorimetry studies do not collect data on percent of body mass that was lipid. Errors in values were identified by visualizing (for numeric variables) or tabulating (for character variables) each variable independently. The dataset was thoroughly examined by the principal investigator and original creator of the database, but was checked by the remaining members of the team for any anomalies. Additional independent review of an early draft was provided by S. Gaichas.

Data processing used a simple R script (preyEDs_processing.R) to input data files of both raw record and summarized averages to the central database. Quality assurance steps are performed for confirming data completeness. The script also includes the creation of a summary figure for the raw data.

Code is operated with R version 4.3.2 "Eye Holes". 
See Supplemental Files section for processing script "preyEDs_processing.R"

- Imported data from source file "preyEDs_integrated_12_24.csv" into the BCO-DMO data system.
- Missing data identifiers of NA, N/A, and "N/A" were replaced with blanks.

NSF Award Abstract:

Warming oceans are changing the distributions of fish populations worldwide. However, observed shifts in distribution differ from one species to the next, which lead to changes in the marine community and the biological interactions. Altered predator-prey relationships could force a switch in diet, which might influence growth and affect a fish’s ability to persist in the environment. This project aims to predict how fish behaviors contribute to responses of species, populations, and ecosystems to continued environmental change. The study is focused on the distribution and diets of fishes in the Northwest Atlantic Ocean, one of the most rapidly warming marine systems on the planet. An existing long-term data set (1973 – present) forms the basis for a retrospective analysis of how fish populations and their diets have changed over the past five decades. The model developed from these data is testing how observed changes in distribution and diet are related to species-specific behaviors and movements. This information is incorporated into predictions of the nature and quality of fish diets in the year 2055 using different climate projections. The broader impacts are focused on broadening participation in STEM careers, which includes training of students and a post-doc. To advance the recruitment and education of future scientists, project results are being integrated into the Gulf of Maine Research Institute’s LabVenture program, which serves 10,000 elementary students in Maine annually. In parallel, the project is partnering with the Seacoast Science Center in New Hampshire to develop and test educational modules. Research findings are being communicated to fisheries managers locally and nationally and are contributing to science-based resource management.

Increased water temperatures impact the energetics of individual organisms directly by increasing metabolism and indirectly by altering overlap with prey as a result of taxon-specific shifts in population distributions. Species-specific shifts in spatial distributions and diets could mediate or exacerbate the metabolic consequences of warming waters. Furthermore, food web structure, and any temporal shifts in its composition, could affect ecosystem stability. Behavioral flexibility in diet and space use could confer resilience of individual species to climate change, but empirical evidence is lacking. This project combines spatial statistics, multivariate analyses, and food web models to understand how warming waters impact individuals, species interactions, and community stability, as well as identify taxon-specific behaviors that could confer resilience. This project is conducting retrospective analyses to quantify both decadal-scale shifts in fish distributions and diet, and species-level flexibility in diet and movements. From these analyses, spatially explicit predator-prey interactions are being projected into the future (2055) using three climate change scenarios to predict novel interactions and changes in diet for important predatory fishes. Linkages between behavior and resilience to warming waters are being tested by comparing the energetic consequences of diet shifts between behaviorally flexible and inflexible species. Quantifying interaction strengths among food web components is providing insight into how flexibility of individual taxa affects broader food web structure and how community stability is maintained.

This project is jointly funded by the Biological Oceanography Program and the Established Program to Stimulate Competitive Research (EPSCoR).

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.