Linking environmental monitoring data and the predicted effects of climate change for urban coastal management: A case study of Dublin Bay
Schertenleib, K.S.H.; Bruen, M.; Crowe, T.P.; O'Connor, N.E. (2023). Linking environmental monitoring data and the predicted effects of climate change for urban coastal management: A case study of Dublin Bay. J. Sea Res. 196: 102442. https://dx.doi.org/10.1016/j.seares.2023.102442 In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101; e-ISSN 1873-1414, more |   |
| Keywords | Aquatic communities > Plankton > Phytoplankton
Biodiversity
Estuaries
Topographic features > Landforms > Coasts
Brackish water | | Author keywords | Bayesian network; Benthic invertebrates; Ctree; Decision making; Wading birds |
| Authors | | Top | - Schertenleib, K.S.H.
- Bruen, M.
- Crowe, T.P., more
- O'Connor, N.E.
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| Abstract | 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. Importantly, climate change simulations showed that the ecological status of Dublin Bay will decline in future, which highlights the importance of removing other stressors from the ecosystem. |
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