Translating crustacean biological responses from CO2 bubbling experiments into population-level predictions
Many studies of animal responses to ocean acidification focus on uniformly conditioned age cohorts that lack complexities typically found in wild populations. These studies have become the primary data source for predicting higher level ecological effects, but the roles of intraspecific interactions in re-shaping biological, demographic and evolutionary responses are not commonly considered. To explore this problem, I assessed responses in the mysid Americamysis bahia to bubbling of CO2-enriched and un-enriched air into the seawater supply in flow-through aquariums. I conducted one experiment using isolated age cohorts and a separate experiment using intact populations. The seawater supply was continuously input from Narragansett Bay (Rhode Island, USA). The 28-day cohort study was maintained without resource or spatial limitations, whereas the 5-month population study consisted of stage-structured populations that were allowed to self-regulate. These differences are common features of experiments and were intentionally retained to demonstrate the effect of methodological approaches on perceptions of effect mechanisms. The CO2 treatment reduced neonate abundance in the cohort experiment (24% reduction due to a mean pH difference of −0.27) but not in the population experiment, where effects were small and were strongest for adult and stage 1 survival (3% change due to a mean pH difference of −0.25). I also found evidence of competition in the population experiment, further complicating relationships with cohort experiments. These results point to limitations of standard cohort tests. Such experiments should be complimented by studies of intact populations where responses may be affected by evolution, acclimation, and competition.
KeywordsCarbon dioxide Cohort Demography Ocean acidification pH
I am grateful to Ruth Gutjahr-Gobell and Doranne Borsay Horowitz for their assistance during the mysid experiments. Harriet Booth, Adam Pimenta and Brenda Rashleigh provided helpful comments on an earlier version of this manuscript. This manuscript was submitted with tracking number ORD-010744 by the Atlantic Ecology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency (EPA). Although the research described in this article was funded by EPA, it has not been subjected to agency review and does not necessarily reflect the views of the agency.
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference. Springer, New YorkGoogle Scholar
- Caswell H (2001) Matrix population models: construction, analysis and interpretation. Sinauer Associates, SunderlandGoogle Scholar
- Core Team R (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Gaylord B, Kroeker KJ, Sunday JM, Anderson KM, Barry JP, Brown NE, Connell SD, Dupont S, Fabricius KE, Hall-Spencer JM, Klinger T, Milazzo M, Munday PL, Russell BD, Sanford E, Schreiber SJ, Thiyagarajan V, Vaughan MLH, Widdicombe S, Harley CDG (2015) Ocean acidification through the lens of ecological theory. Ecology 96:3–15CrossRefPubMedGoogle Scholar
- Heard RW, Price WW, Knott DM, King RA, Allen DM (2006) A taxonomic guide to the mysids of the South Atlantic Bight. NOAA Professional Paper NMFS 4Google Scholar
- Jumars PA (2007) Habitat coupling by mid-latitude, subtidal, marine mysids: import-subsidised omnivores. Oceanogr Mar Biol 45:89–138Google Scholar
- Kuhn A, Bengtson DA, Simpson KL (1991) Increased reproduction by mysids (Mysidopsis bahia) fed with enriched Artemia spp. nauplii. Am Fish Soc Symp 9:192–199Google Scholar
- Morris WF, Doak DF (2002) Quantitative conservation biology: Theory and practice of population viability analysis. Sinauer Associates, SunderlandGoogle Scholar
- R Core Team (2014) R: a language and environment for statistical computing, version 3.1.2. R Foundation for Statistical Computing, ViennaGoogle Scholar
- SAS Institute (2008) The SAS System for Windows, version 9.2. SAS Institute, Inc., CaryGoogle Scholar
- Stearns SC (1992) The evolution of life histories. Oxford University Press, OxfordGoogle Scholar
- US EPA (1985) Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. US Environmental Protection Agency. National Technical Information Service, Springfield. http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/upload/85guidelines.pdf. Accessed 1 July 2016