A thematic cost-benefit analysis of a marine protected area

The implementation of Marine Protected Areas (MPAs) is ultimately a social endeavour to sustain or improve human well-being via the conservation of marine ecosystems. The degree to which ecological gains are realised can depend upon how economic, ecological and social costs (negative impacts) and benefits (positive impacts) are included in the designation and management process. Without the support of key stakeholder groups whose user rights have been affected by the creation of an MPA, human impacts cannot be reduced. This study analyses a three year dataset to understand the themes associated with the economic, environmental and social costs and benefits of an MPA in Lyme Bay, United Kingdom (UK) following its establishment in 2008. Methodologically, the paper presents an ecosystem based management framework for analysing costs and benefits. Two hundred and forty one individuals were interviewed via questionnaire between 2008 and 2010 to determine perceptions and the level of support towards the MPA. Results reveal that despite the contentious manner in which this MPA was established, support for the MPA is strong amongst the majority of stakeholder groups. The level of support and the reasons given for support vary between stakeholder groups. Overall, the stakeholders perceive the social, economic and environmental benefits of the MPA to outweigh the perceived costs. There have been clear social costs of the MPA policy and these have been borne by mobile and static gear fishermen and charter boat operators. Local support for this MPA bodes well for the development of a network of MPAs around the UK coast under the United Kingdom Marine and Coastal Access Act 2009. However, this initial optimism is at risk if stakeholder expectation is not managed and the management vacuum is not filled.

The Importance of Regional, System-Wide and Local Spatial Scales in Structuring Temperate Estuarine Fish Communities

An extensive literature base worldwide demonstrates how spatial differences in estuarine fish assemblages are related to those in the environment at (bio)regional, estuary-wide or local (within-estuary) scales. Few studies, however, have examined all three scales, and those including more than one have often focused at the level of individual environmental variables rather than scales as a whole. This study has identified those spatial scales of environmental differences, across regional, estuary-wide and local levels, that are most important in structuring ichthyofaunal composition throughout south-western Australian estuaries. It is the first to adopt this approach for temperate microtidal waters. To achieve this, we have employed a novel approach to the BIOENV routine in PRIMER v6 and a modified global BEST test in an alpha version of PRIMER v7. A combination of all three scales best matched the pattern of ichthyofaunal differences across the study area (rho = 0.59; P = 0.001), with estuary-wide and regional scales accounting for about twice the variability of local scales. A shade plot analysis showed these broader-scale ichthyofaunal differences were driven by a greater diversity of marine and estuarine species in the permanently-open west coast estuaries and higher numbers of several small estuarine species in the periodically-open south coast estuaries. When interaction effects were explored, strong but contrasting influences of local environmental scales were revealed within each region and estuary type. A quantitative decision tree for predicting the fish fauna at any nearshore estuarine site in south-western Australia has also been produced. The estuarine management implications of the above findings are highlighted.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.