Understanding the success and failure of oyster populations: The importance of sampled variables and sample timing

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dc.acquisition-srcDownloaded from-Web of Scienceen_US
dc.call-noen_US
dc.contract-noen_US
dc.contributor.authorSoniat TMen_US
dc.contributor.authorPowell ENen_US
dc.contributor.authorHofmann EEen_US
dc.contributor.authorKlinck JMen_US
dc.contributor.otherJournal of Shellfish Researchen_US
dc.date.accessioned2010-02-15T17:17:15Z
dc.date.available2010-02-15T17:17:15Z
dc.date.issued1998 Decen_US
dc.degreeen_US
dc.description1149-1165en_US
dc.description-otheren_US
dc.description.abstractOne of the primary obstacles to understanding why some oyster populations are successful and others are not is the complex interaction of environmental variables with oyster physiology and with such population variables as the rates of recruitment and juvenile mortality. A numerical model is useful in investigating how population structure originates out of this complexity. We have monitored a suite of environmental conditions over an environmental gradient to document the importance of short time-scale variations in such variables as food supply, turbidity, and salinity. Then, using a coupled oyster disease population dynamics model, we examine the need for short rime-scale monitoring. We evaluate the usefulness of several measures of food supply by comparing field observations and model simulations. Finally, we evaluate the ability of a model to reproduce field observations that derive from a complex interplay of environmental variables and address the problem of the time-history of populations. Our results stress the need to evaluate the complex interactions of environmental variables with a numerical model and, conversely, the need to evaluate the success of modeling against field observations of the results of complex processes. Model simulations of oyster populations only approached field observations when the environmental variables were measured weekly, rather than monthly. Oyster food supply was estimated from measures of total particulate organic matter, phytoplankton biomass estimated from chlorophyll a, and total labile organic matter estimated from a regression between chlorophyll a and total labile carbohydrate, lipid, and protein. Only the third measure provided simulations comparable to field observations. Model simulations also only approached field observations when a multiyear time series was used. The simulations show that the most recent year exerts the strongest influence on oyster population attributes, but that the longer time-history modulates the effect. The results emphasize that year-to-pear changes in environment contribute substantially to observed population attributes and that multiyear environmental time series are important in describing the time-history of relatively long-lived speciesen_US
dc.description.urihttp://gbic.tamug.edu/request.htmen_US
dc.historyen_US
dc.identifier.urihttp://hdl.handle.net/1969.3/23465
dc.latitudeen_US
dc.locationen_US
dc.longitudeen_US
dc.notesTimes Cited: 12ArticleEnglishSoniat, T. MNicholls State Univ, Dept Sci Biol, Thibodaux, LA 70310 USACited References Count: 40184RYC/O DR. SANDRA E. SHUMWAY, NATURAL SCIENCE DIVISION, SOUTHAMPTON COLLEGE, SOUTHAMPTON, NY 11968 USASOUTHAMPTONen_US
dc.placeen_US
dc.publisheren_US
dc.relation.ispartofseries51112.00en_US
dc.relation.urien_US
dc.scaleen_US
dc.seriesen_US
dc.subjectCrassostrea virginicaen_US
dc.subjectmodelingen_US
dc.subjectPerkinsus marinusen_US
dc.subjectpopulation dynamicsen_US
dc.subjectsestonen_US
dc.subjectPOSSIBLE AVAILABLE FOODen_US
dc.subjectCRASSOSTREA-VIRGINICAen_US
dc.subjectPERKINSUS-MARINUSen_US
dc.subjectSUSPENSION-FEEDERSen_US
dc.subjectGALVESTON BAYen_US
dc.subjectSHORT-TERMen_US
dc.subjectMANAGEMENTen_US
dc.subjectDYNAMICSen_US
dc.subjectTEXASen_US
dc.titleUnderstanding the success and failure of oyster populations: The importance of sampled variables and sample timingen_US
dc.typeJournalen_US
dc.universityen_US
dc.vol-issue17(4)en_US

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