Dataset: Marine and Freshwater Energy Density Data Integrated and Organized by Taxonomy from Previous Research Sources (1961 through June 2024) Discovered by a Literature Review

ValidatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.948253.1Version 1 (2025-01-10)Dataset Type:Synthesis

Principal Investigator: Nathan T. Hermann (University of New Hampshire)

Co-Principal Investigator: Nathan B. Furey (University of New Hampshire)

Co-Principal Investigator: Mark J. Wuenschel (Northeast Fisheries Science Center - Woods Hole)

BCO-DMO Data Manager: Dana Stuart Gerlach (Woods Hole Oceanographic Institution)


Project: Nutritional ecology of climate change: Impacts on Northwest Atlantic fishes (NECC)


Abstract

Energy is the currency of exchange within ecosystems which defines the strength and influence of interactions, particularly between predator and prey. The ability to estimate the productivity of an ecosystem is, therefore, dependent upon the estimation of consumer diet contents and their energetic quality. To estimate growth, reproduction, and, ultimately, survival of individuals, measures of prey quality for predators are essential both at the individual level and for scaling to ecosystem-wide ...

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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.


Related Datasets

Related Research

Dataset: https://doi.org/10.1038/s41597-023-02689-9.
Gleiber, M. R., Hardy, N. A., Roote, Z., Krug-MacLeod, A. M., Morganson, C. J., Tandy, Z., George, I., Matuch, C., Brookson, C. B., Daly, E. A., Portner, E. J., Choy, C. A., Crowder, L. B., & Green, S. J. (2024). The Pelagic Species Trait Database, an open data resource to support trait-based ocean research. In Scientific Data (Vol. 11, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-023-02689-9
Related Research

Dataset: https://doi.org/10.5066/F7KH0KBK
U.S. Geological Survey. (2013). Integrated Taxonomic Information System (ITIS). U.S. Geological Survey. https://doi.org/10.5066/F7KH0KBK

Related Publications

Results

Hermann, N.T., Wuenschel, M.J. and Furey, N.B. (in review). Marine and Freshwater Organism Energy Densities Integrated Across Previous Sources. Ecology.
Methods

Sherman, K. (1991). The Large Marine Ecosystem Concept: Research and Management Strategy for Living Marine Resources. Ecological Applications, 1(4), 349–360. Portico. https://doi.org/10.2307/1941896
Methods

Sherman, K. (2014). Adaptive management institutions at the regional level: The case of Large Marine Ecosystems. Ocean & Coastal Management, 90, 38–49. https://doi.org/10.1016/j.ocecoaman.2013.06.008
Software

R Core Team (2023). R: A language and environment for statistical computing. R v4.3.2. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/