Abstract:

Contaminant data and predator-prey types from 1976 to 2021 were collected via a search of previously published literature for a meta-analysis study to determine how a variety of contaminants in freshwater and marine systems affect consumer-resource interactions in different parts of the world.

Suggested Citation:

Fodrie, F.J., S.L. Ziegler, and L.R. Clance. 2021. Investigating contaminant impacts on consumer-resource dynamics: a meta-analysis study of contaminant and predator-prey data from 1976 to 2021. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/1WXDC382

Purpose:

The dataset was developed to assess the positive or negative effects of aquatic contaminants on consumer resource interactions that may alter overall food web dynamics and ecosystem function. Contaminants were separated into major groups and their influence on consumer resource interactions were assessed across freshwater or marine systems, consumer trophic level, resource mobility (sessile or mobile).

Data Parameters and Units:

Headers include Reference: A short citation for the source publication that data were extracted in the format of author last name and year of publication; Year published: Year citation was published; figure.ID: Name of the figure or table from the source publication from which the data were extracted; lab.field: Description of the location where the study was conducted (lab or field); region: Broad geographic region where the study occurred; contaminant.type: Description of the general type of contaminant in the study (Acidification, Metals, Microplastics, Oil, Pesticides, PPCPs); fresh.marine.sys: The type of aquatic system where the study was conducted (fresh, marine); contaminent.name: The specific type of contaminant used in the study; predator.species.name: Consumer name at the species level; predator.species.taxa: The broad taxonomic group of the consumer; predator.trophic.level: Trophic level of the consumer (herbivore, primary consumer, secondary consumer); prey.species.name: Species name of prey/resource; prey.species.taxa: Broad taxonomic level of prey/resource; prey.trophic.level: Trophic level of prey/resource (0= primary producer, 1= herbivores and filter feeders, 2=primary consumer); prey.type: Descriptor of prey mobility (immobile, mobile); exposed: Description of organism exposed (consumer, resource, both); time.exposed: Time that the consumer or resource or both were exposed to the contaminant; time.exposed.units: Units for the amount of time exposed to the contaminant; time.exposed.days: Exposure time, expressed as days for direct comparison across studies; N.trials: Number of trials conducted; fig.lnRR: Calculated log response ratios (ln(experiment/control)) for each figure within a reference; paper.lnRR: The average log response ratio averaged across all figures from a single reference; s.dev: standard deviation of averaged paper lnRR values; s.e.: standard error of averaged paper lnRR values; Notes.

Methods:

We conducted a literature search using Google Scholar and ISI Web of Science from January 2018 to May 2018, April 2020 and May 2021 using keywords such as “consumer”, “predator”, “prey”, and “contaminants.” We also searched for literature using specific contaminants such as “No.2 fuel oil,” “mercury,” or “Pentachlorophenol (PCP).” Contaminants were grouped into six categories: (1) oil, (2) insecticides and pesticides, (3) microplastics, (4) OA, (5) metals (and ionic liquids) and (6) pharmaceuticals and personal care products (PPCP’s). For each experiment, we collected metadata information such as experimental setting (in situ vs. ex situ), consumer and resource scientific name, time exposed to contaminants, and contaminant type. We also recorded the number of trials conducted for each experiment. We then extracted the response of consumers to each contaminant and control treatment from tables or figures using the software Datatheif III.

Individual Files: