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© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Johnson, M. D., Swaminathan, S. D., Nixon, E. N., Paul, V. J., & Altieri, A. H. Differential susceptibility of reef-building corals to deoxygenation reveals remarkable hypoxia tolerance. Scientific Reports, 11(1), (2021): 23168, https://doi.org/10.1038/s41598-021-01078-9., Ocean deoxygenation threatens the persistence of coastal ecosystems worldwide. Despite an increasing awareness that coastal deoxygenation impacts tropical habitats, there remains a paucity of empirical data on the effects of oxygen limitation on reef-building corals. To address this knowledge gap, we conducted laboratory experiments with ecologically important Caribbean corals Acropora cervicornis and Orbicella faveolata. We tested the effects of continuous exposure to conditions ranging from extreme deoxygenation to normoxia (~ 1.0 to 6.25 mg L−1 dissolved oxygen) on coral bleaching, photophysiology, and survival. Coral species demonstrated markedly different temporal resistance to deoxygenation, and within a species there were minimal genotype-specific treatment effects. Acropora cervicornis suffered tissue loss and mortality within a day of exposure to severe deoxygenation (~ 1.0 mg L−1), whereas O. faveolata remained unaffected after 11 days of continuous exposure to 1.0 mg L−1. Intermediate deoxygenation treatments (~ 2.25 mg L−1, ~ 4.25 mg L−1) elicited minimal responses in both species, indicating a low oxygen threshold for coral mortality and coral resilience to oxygen concentrations that are lethal for other marine organisms. These findings demonstrate the potential for variability in species-specific hypoxia thresholds, which has important implications for our ability to predict how coral reefs may be affected as ocean deoxygenation intensifies. With deoxygenation emerging as a critical threat to tropical habitats, there is an urgent need to incorporate deoxygenation into coral reef research, management, and action plans to facilitate better stewardship of coral reefs in an era of rapid environmental change., This research was funded by an award from the National Oceanic and Atmospheric Administration’s National Centers for Coastal Ocean Science Competitive Research Program to AA, MJ, and VP (NA18NOS4780170) through the University of Florida. MJ was funded by postdoctoral fellow awards from the Smithsonian Institution's Marine Global Earth Observatory (MarineGEO), the Smithsonian Marine Station, and Woods Hole Oceanographic Institution. This material is based on work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1842473. This is contribution 259 from the Coastal Hypoxia Research Program, 93 from the Smithsonian’s MarineGEO and Tennenbaum Marine Observatories Network, and 1167 from the Smithsonian Marine Station at Fort Pierce.

Background: Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data. Methods: We conducted focus groups with university students who had (n = 13) and had never (n = 14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data. Results: The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation. Conclusions: Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy.

Sheep scab is an important parasitic disease of livestock, particularly sheep, found worldwide. In Great Britain, its prevalence has grown steadily since reintroduction in 1973, despite attempted national control prior to 1992 and a number of industry-led interventions. Recent reports of resistance in the causative agent, the mite Psoroptes ovis, to macrocyclic lactones, increases importance of urgent coherent action. The aim of this thesis was to develop epidemiological and economic models for sheep scab in Great Britain that could provide tools able to identify better scab management strategies. A within-farm transmission model was developed, based on the classic SIR model used widely in epidemiology (Chapter 2). Model results show that 100 days after the introduction of one scab index case, around 80% of a flock are likely to be infected. Sensitivity analysis suggests that the transmission rate is the most important parameter to target in future interventions. The model was expanded into a metapopulation model, with transmission across Great Britain possible via neighbour-to-neighbour contact (Chapter 3). Farm clusters with high connectivity and transmissibility are identified, which could be targeted in future interventions. Model simulation results show that scab spreads rapidly when introduced into one of these clusters, however, it is then limited to the cluster edges, suggesting that scab is unlikely to spread across the whole of Great Britain by neighbour-to neighbour contact only and that long distance movements may be important future intervention targets. The within-farm model is revisited in Chapter 4 and an additional compartment is added for carriers of scab. The newly parameterised model produces output which is shown statistically to be from the same distribution as experimental data. These changes are carried into Chapter 5, where an alternative metapopulation model is presented. Approximate Bayesian Computation is used to fit t