Considering climate change in the development of marine protected areas

Marine ecosystems are facing a diverse range of threats, including climate change, prompting international efforts to safeguard marine biodiversity through the use of spatial management measures. Marine Protected Areas (MPAs) have been implemented as a conservation tool throughout the world, but their usefulness and effectiveness is strongly related to climate change. However, few MPA programmes have directly considered climate change in the design, management or monitoring of an MPA network. Under international obligations, EU, UK and national targets, Scotland has developed an MPA network that aims to protect marine biodiversity and contribute to the vision of a clean, healthy and productive marine environment. This is the first study to critically analyse the Scottish MPA process and highlight areas which may be improved upon in further iterations of the network in the context of climate change. Initially, a critical review of the Scottish MPA process considered how ecological principles for MPA network design were incorporated into the process, how stakeholder perceptions were considered and crucially what consideration was given to the influence of climate change on the eventual effectiveness of the network. The results indicated that to make a meaningful contribution to marine biodiversity protection for Europe the Scottish MPA network should: i) fully adopt best practice ecological principles ii) ensure effective protection and iii) explicitly consider climate change in the management, monitoring and future iterations of the network. However, this review also highlighted the difficulties of incorporating considerations of climate change into an already complex process. A series of international case studies from British Columbia, Canada; central California, USA; the Great Barrier Reef, Australia and the Hauraki Gulf, New Zealand, were then conducted to investigate perceptions of how climate change has been considered in the design, implementation, management and monitoring of MPAs. The key lessons from this study included: i) strictly protected marine reserves are considered essential for climate change resilience and will be necessary as scientific reference sites to understand climate change effects ii) adaptive management of MPA networks is important but hard to implement iii) strictly protected reserves managed as ecosystems are the best option for an uncertain future. This work provides new insights into the policy and practical challenges MPA managers face under climate change scenarios. Based on the Scottish and international studies, the need to facilitate clear communication between academics, policy makers and stakeholders was recognised in order to progress MPA policy delivery and to ensure decisions were jointly formed and acceptable. A Delphi technique was used to develop a series of recommendations for considering climate change in Scotland’s MPA process. The Delphi participant panel was selected for their knowledge of the Scottish MPA process and included stakeholders, policy makers and academics with expertise in MPA research. The results from the first round of the Delphi technique suggested that differing views of success would likely influence opinions regarding required management of MPAs, and in turn, the data requirements to support management action decisions. The second round of the Delphi technique explored this further and indicated that there was a fundamental dichotomy in panellists’ views of a successful MPA network depending upon whether they believed the MPAs should be strictly protected or allow for sustainable use. A third, focus group round of the Delphi Technique developed a feature-based management scenario matrix to aid in deciding upon management actions in light of changes occurring in the MPA network. This thesis highlights that if the Scottish MPA network is to fulfil objectives of conservation and restoration, the implications of climate change for the design, management and monitoring of the network must be considered. In particular, there needs to be a greater focus on: i) incorporating ecological principles that directly address climate change ii) effective protection that builds resilience of the marine and linked social environment iii) developing a focused, strong and adaptable monitoring framework iv) ensuring mechanisms for adaptive management.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.