{"refrec":{"BRefID":369224,"RR":"<b>Schertenleib, K.S.H.; Bruen, M.; Crowe, T.P.; O'Connor, N.E.</b> (2023). Linking environmental monitoring data and the predicted effects of climate change for urban coastal management: A case study of Dublin Bay. <i>J. Sea Res. 196</i>: 102442. <a href=\"https://dx.doi.org/10.1016/j.seares.2023.102442\" target=\"_blank\">https://dx.doi.org/10.1016/j.seares.2023.102442</a>","BEntID":366954,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>J. Sea Res. 196</i>: 102442. <a href=\"https://dx.doi.org/10.1016/j.seares.2023.102442\" target=\"_blank\">https://dx.doi.org/10.1016/j.seares.2023.102442</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":1,"TerrestrialFlag":0,"Authorstring":"Schertenleib, K.S.H.; Bruen, M.; Crowe, T.P.; O'Connor, N.E.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Schertenleib, K.S.H. <i>et al.</i>","Englishabstract":"Environmental managers of coastal regions must consider the combined effects of climate change and multiple other stressors simultaneously. While routine monitoring programmes exist, this information is usually summarised as a metric or index for ecological status classification and does not integrate the biological and environmental data in a format that is useful for managers. We present a framework using conditional inference tree analyses and Bayesian Network methodology that synthesises monitoring data, identifies links between environmental and biological variables, and predicts the effects of climate change for Dublin Bay, Ireland. The ecological quality status of phytoplankton biomass was usually high but degraded when silica became limiting. Sediment organic content was positively related to benthic invertebrate richness and the abundance of wading birds, although invertebrate communities were most indicative of pristine conditions when sediment organic content was low. 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