Ocean Acidification Policy: Applying the Lessons of Washington to California and Beyond

This Article aims to distill the lessons of Washington’s experience with ocean acidification (OA) policy and apply them to the political framework that exists in California. More generally, this Article evaluates the political landscape in which OA policy is taking shape along the west coast of the United States and highlights elements of a political and policy strategy that would build current momentum on OA in California and elsewhere into a larger, more sustained policy infrastructure capable of addressing coastal issues of environmental resilience and water quality in the context of global change. It concludes by identifying some ways in which OA policy might benefit from action on—and constituencies for—the multiple interacting drivers of environmental chang

Ten Ways States Can Combat Ocean Acidification (and Why They Should)

The ocean is becoming more acidic worldwide as a result of increasing atmospheric concentrations of carbon dioxide (“CO2”) and other pollutants. This fundamental change is likely to have substantial ecological and economic consequences globally. In this Article, we provide a toolbox for understanding and addressing the drivers of ocean acidification. We begin with an overview of the relevant science, highlighting known causes of chemical change in the coastal ocean. Because of the difficulties associated with controlling diffuse atmospheric pollutants such as CO2, we then focus on controlling smaller-scale agents of acidification, discussing ten legal and policy tools that state government agencies can use to mitigate the problem. This bottom-up approach does not solve the global CO2 problem, but instead offers a more immediate means of addressing the challenges of a rapidly changing ocean. States have ample legal authority to address many of the causes of ocean acidification; what remains is to implement that authority to safeguard our iconic coastal resources. Republished with permission from 37 Harv. Envtl. L. Rev. 57 (2013)

Narratives can motivate environmental action : the Whiskey Creek ocean acidification story

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ambio 43 (2014): 592-599, doi:10.1007/s13280-013-0442-2.Even when environmental data quantify the risks and benefits of delayed responses to rapid anthropogenic change, institutions rarely respond promptly. We propose that narratives complementing environmental datasets can motivate responsive environmental policy. To explore this idea, we relate a case study in which a narrative of economic loss due to regionally rapid ocean acidification—an anthropogenic change—helped connect knowledge with action. We pose three hypotheses to explain why narratives might be particularly effective in linking science to environmental policy, drawing from the literature of economics, environmental policy, and cognitive psychology. It seems that yet-untold narratives may hold similar potential for strengthening the feedback between environmental data and policy and motivating regional responses to other environmental problems.2015-09-0

Environmental DNA reveals patterns of biological invasion in an inland sea

Non-native species have the potential to cause ecological and economic harm to coastal and estuarine ecosystems. Understanding which habitat types are most vulnerable to biological invasions, where invasions originate, and the vectors by which they arrive can help direct limited resources to prevent or mitigate ecological and socio-economic harm. Information about the occurrence of non-native species can help guide interventions at all stages of invasion, from first introduction, to naturalization and invasion. However, monitoring at relevant scales requires considerable investment of time, resources, and taxonomic expertise. Environmental DNA (eDNA) metabarcoding methods sample coastal ecosystems at broad spatial and temporal scales to augment established monitoring methods. We use COI mtDNA eDNA sampling to survey a diverse assemblage of species across distinct habitats in the Salish Sea in Washington State, USA, and classify each as non-native, native, or indeterminate in origin. The non-native species detected include both well-documented invaders and species not previously reported within the Salish Sea. We find a non-native assemblage dominated by shellfish and algae with native ranges in the temperate western Pacific, and find more-retentive estuarine habitats to be invaded at far higher levels than better-flushed rocky shores. Furthermore, we find an increase in invasion level with higher water temperatures in spring and summer across habitat types. This analysis contributes to a growing understanding of the biotic and abiotic factors that influence invasion level, and underscores the utility of eDNA surveys to monitor biological invasions and to better understand the factors that drive these invasionsThe authors received no specific funding for this wor

Using atmospheric trajectories to model the isotopic composition of rainfall in central Kenya

Publisher’s version made available under a Creative Commons license.The isotopic composition of rainfall (δ2H and δ18O) is an important tracer in studies of the ecohydrology, plant physiology, climate and biogeochemistry of past and present ecosystems. The overall continental and global patterns in precipitation isotopic composition are fairly well described by condensation temperature and Rayleigh fractionation during rainout. However, these processes do not fully explain the isotopic variability in the tropics, where intra-storm and meso-scale dynamics may dominate. Here we explore the use of atmospheric back-trajectory modeling and associated meteorological variables to explain the large variability observed in the isotopic composition of individual rain events at the study site in central Kenya. Individual rain event samples collected at the study site (n = 41) range from −51‰ to 31‰ for δ2H and the corresponding monthly values (rain volume-weighted) range from −15‰ to 15‰. Using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we map back-trajectories for all individual rain hours occurring at a research station in central Kenya from March 2010 through February 2012 (n = 544). A multiple linear regression analysis demonstrates that a large amount of variation in the isotopic composition of rainfall can be explained by two variables readily obtained from the HYSPLIT model: (1) solar radiation along the trajectory for 48 hours prior to the event, and (2) distance covered over land. We compare the measurements and regression model results to the isotopic composition expected from simple Rayleigh distillation along each trajectory. The empirical relationship described here has applications across temporal scales. For example, it could be used to help predict short-term changes in the isotopic composition of plant-available water in the absence of event-scale sampling. One can also reconstruct monthly, seasonal and annual weighted mean precipitation isotope signatures for a single location based only on hourly rainfall data and HYSPLIT model results. At the study site in East Africa, the annual weighted mean δ2H from measured and modeled values are −7.6‰ and −7.4‰, respectively, compared to −18‰ predicted for the study site by the Online Isotopes in Precipitation Calculator

Feedback of patient-reported outcomes to healthcare professionals for comparing health service performance: a scoping review

Objective: Patient-reported outcomes (PROs) provide self-reported patient assessments of their quality of life, daily functioning, and symptom severity after experiencing an illness and having contact with the health system. Feeding back summarised PROs data, aggregated at the health-service level, to healthcare professionals may inform clinical practice and quality improvement efforts. However, little is known about the best methods for providing these summarised data in a way that is meaningful for this audience. Therefore, the aim of this scoping review was to summarise the emerging approaches to PROs data for &lsquo;service-level&rsquo; feedback to healthcare professionals. Setting: Healthcare professionals receiving PROs data feedback at the health-service level. Data sources: Databases selected for the search were Embase, Ovid Medline, Scopus, Web of Science and targeted web searching. The main search terms included: &lsquo;patient-reported outcome measures&rsquo;, &lsquo;patient-reported outcomes&rsquo;, &lsquo;patient-centred care&rsquo;, &lsquo;value-based care&rsquo;, &lsquo;quality improvement&rsquo; and &lsquo;feedback&rsquo;. Studies included were those that were published in English between January 2009 and June 2019. Primary and secondary outcome measures: Data were extracted on the feedback methods of PROs to patients or healthcare providers. A standardised template was used to extract information from included documents and academic publications. Risk of bias was assessed using Joanna Briggs Institute Levels of Evidence for Effectiveness. Results: Overall, 3480 articles were identified after de-duplication. Of these, 19 academic publications and 22 documents from the grey literature were included in the final review. Guiding principles for data display methods and graphical formats were identified. Seven major factors that may influence PRO data interpretation and use by healthcare professionals were also identified. Conclusion: While a single best format or approach to feedback PROs data to healthcare professionals was not identified, numerous guiding principles emerged to inform the field.</jats:sec

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference

Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations. Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all model simulations to train a neural surrogate for the likelihood function. However, the performance of SNL under model misspecification is unreliable and can result in overconfident posteriors centred around an inaccurate parameter estimate. In this paper, we propose a novel SNL method, which through the incorporation of additional adjustment parameters, is robust to model misspecification and capable of identifying features of the data that the model is not able to recover. We demonstrate the efficacy of our approach through several illustrative examples, where our method gives more accurate point estimates and uncertainty quantification than SNL

Reactive oxygen species induce virus-independent MAVS-oligomerization in systemic lupus erythematosus

The increased expression of genes induced by type I interferon (IFN) is characteristic of viral infections and systemic lupus erythematosus (SLE). We showed that mitochondrial antiviral signaling (MAVS) protein, which normally forms a complex with retinoic acid gene I (RIG-I)–like helicases during viral infection, was activated by oxidative stress independently of RIG-I helicases. We found that chemically generated oxidative stress stimulated the formation of MAVS oligomers, which led to mitochondrial hyperpolarization and decreased adenosine triphosphate production and spare respiratory capacity, responses that were not observed in similarly treated cells lacking MAVS. Peripheral blood lymphocytes of SLE patients also showed spontaneous MAVS oligomerization that correlated with the increased secretion of type I IFN and mitochondrial oxidative stress. Furthermore, inhibition of mitochondrial reactive oxygen species (ROS) by the mitochondria-targeted antioxidant MitoQ prevented MAVS oligomerization and type I IFN production. ROS-dependent MAVS oligomerization and type I IFN production were reduced in cells expressing the MAVS-C79F variant, which occurs in 30% of sub-Saharan Africans and is linked with reduced type I IFN secretion and milder disease in SLE patients. Patients expressing the MAVS-C79F variant also had reduced amounts of oligomerized MAVS in their plasma compared to healthy controls. Together, our findings suggest that oxidative stress–induced MAVS oligomerization in SLE patients may contribute to the type I IFN signature that is characteristic of this syndrome