Delivering on seafood traceability under the new U.S. import monitoring program

The United States is the world’s largest fish importer. Recent reports, however, indicate that 25–30% of wild-caught seafood imported into the US is illegally caught, heightening concerns over the country’s significant role in driving Illegal, Unreported, and Unregulated (IUU) fishing. In January 2017, NOAA enacted the Seafood Import Monitoring Program in an effort to combat IUU fishing through mandating improved seafood traceability requirements. This program requires reporting of fisheries data from harvest to arrival at the US border. Given the role of the US as a major global importer of seafood, this regulation could be a transformative action on fisheries worldwide if implementation includes two key components—(1) applying best available and most appropriate technologies and (2) building monitoring and enforcement capacity among trading nations. This paper provides insightful commentary on the potential for this US policy to lead by example and improve an essential natural resource that over a billion people worldwide depend on for nutrition and livelihoods

Genomic signatures of host-associated divergence and adaptation in a coral-eating snail, Coralliophila violacea (Kiener, 1836).

The fluid nature of the ocean, combined with planktonic dispersal of marine larvae, lowers physical barriers to gene flow. However, divergence can still occur despite gene flow if strong selection acts on populations occupying different ecological niches. Here, we examined the population genomics of an ectoparasitic snail, Coralliophila violacea (Kiener 1836), that specializes on Porites corals in the Indo-Pacific. Previous genetic analyses revealed two sympatric lineages associated with different coral hosts. In this study, we examined the mechanisms promoting and maintaining the snails' adaptation to their coral hosts. Genome-wide single nucleotide polymorphism (SNP) data from type II restriction site-associated DNA (2b-RAD) sequencing revealed two differentiated clusters of C. violacea that were largely concordant with coral host, consistent with previous genetic results. However, the presence of some admixed genotypes indicates gene flow from one lineage to the other. Combined, these results suggest that differentiation between host-associated lineages of C. violacea is occurring in the face of ongoing gene flow, requiring strong selection. Indeed, 2.7% of all SNP loci were outlier loci (73/2,718), indicative of divergence with gene flow, driven by adaptation of each C. violacea lineage to their specific coral hosts

Does the social equitability of community and incentive based conservation interventions in non-OECD countries, affect human well-being? A systematic review protocol

Background: An increasing number of conservation interventions aim to reduce their negative impacts on vulnerable people and to provide incentives aimed at improving overall human well-being. Community and incentive based conservation interventions have had variable rates of success in producing well-being outcomes, yet it is unclear why. Researchers have hypothesised that socially equitable conservation interventions will improve their likelihood of success. However, for community and incentive based interventions, there is a lack of evidence synthesis for the effect that social equity has on human well-being outcomes. Using this protocol, we will undertake a systematic review of relevant literature with the aim of using existing knowledge to address this gap. Methods: This protocol outlines the methodology we will use to examine the research question: Does the social equitability of community and incentive based conservation interventions in non-OECD countries, affect human well-being? We will conduct a systematic review of available studies, using articles that measure the effect of social equity, defined as the absence of avoidable and unfair, cost and benefit distributions between socially stratifying factors. To make this process efficient, and in order to prevent replication, we will utilize and update a literature search, and sub-set of data, collected in a previous systematic map that assessed the quantity and strength of evidence to support the effects conservation interventions have on human wellbeing. We will critically appraise each study we identify and capture the degree to which interventions integrated social equity within project participation and outcomes. Where integrated, we will determine if studies record or describe the effect that social equity had on human well-being. We have developed a conceptual framework that describes the expected effect of social equity, in order to capture and understand these effects. To understand the strength of relationships in our framework, and where data availability allows, we will undertake and combine a series of qualitative and quantitative data syntheses. By undertaking this study, we intend to understand how social equity considerations, specifically within community and incentive based conservation interventions, can affect human well-being. A better understanding of these features will inform conservation practitioners and researchers on the extent to which they ought to incorporate social equity into interventions in order to promote human well-being

Software support for environmental evidence synthesis

Ecological research is central to efforts to ensure the provision of critical societal needs such as clean water, carbon abatement, and to avert the loss of biodiversity. The amount of research published on these subjects has increased enormously in recent ears, yet this research is not always used to improve environmental management or policy4. This ‘research-implementation gap’ is sustained by many factors including low access to scientific research outside of academia, a lack of flexible decision-making structures to incorporate new information, and mismatches between management and scientific priorities. A key step towards bridging the research-implementation gap, however, is to gather insights from the entire body of available evidence to ensure that scientific advice is as consistent and accurate as possible. This requires evidence synthesis; work by individuals or teams that take scientific outputs (articles and reports) and use them to understand the effectiveness of an intervention in a range of contexts. Consequently, applied synthesis has become indispensable to the application of scientific information to socio-ecological problems

The CEEDER database of evidence reviews: An open-access evidence service for researchers and decision-makers

Evidence-informed decision-making aims to deliver effective actions informed by the best available evidence. Given the large quantity of primary literature, and time constraints faced by policy-makers and practitioners, well-conducted evidence reviews can provide a valuable resource to support decision-making. However, previous research suggests that some evidence reviews may not be sufficiently reliable to inform decisions in the environmental sector due to low standards of conduct and reporting. While some evidence reviews are of high reliability, there is currently no way for policy-makers and practitioners to quickly and easily find them among the many lower reliability ones. Alongside this lack of transparency, there is little incentive or support for review authors, editors and peer-reviewers to improve reliability. To address these issues, we introduce a new online, freely available and first-of-its-kind evidence service: the Collaboration for Environmental Evidence Database of Evidence Reviews (CEEDER: www.environmentalevidence.org/ceeder). CEEDER aims to transform communication of evidence review reliability to researchers, policy-makers and practitioners through independent assessment of key aspects of the conduct, reporting and data limitations of available evidence reviews claiming to assess environmental impacts or the effectiveness of interventions relevant to policy and practice. At the same time, CEEDER will provide support to improve the standards of future evidence reviews and support evidence translation and knowledge mobilisation to help inform environmental decision-making

Strengthen causal models for better conservation outcomes for human well-being.

BACKGROUND: Understanding how the conservation of nature can lead to improvement in human conditions is a research area with significant growth and attention. Progress towards effective conservation requires understanding mechanisms for achieving impact within complex social-ecological systems. Causal models are useful tools for defining plausible pathways from conservation actions to impacts on nature and people. Evaluating the potential of different strategies for delivering co-benefits for nature and people will require the use and testing of clear causal models that explicitly define the logic and assumptions behind cause and effect relationships. OBJECTIVES AND METHODS: In this study, we outline criteria for credible causal models and systematically evaluated their use in a broad base of literature (~1,000 peer-reviewed and grey literature articles from a published systematic evidence map) on links between nature-based conservation actions and human well-being impacts. RESULTS: Out of 1,027 publications identified, only ~20% of articles used any type of causal models to guide their work, and only 14 total articles fulfilled all criteria for credibility. Articles rarely tested the validity of models with empirical data. IMPLICATIONS: Not using causal models risks poorly defined strategies, misunderstanding of potential mechanisms for affecting change, inefficient use of resources, and focusing on implausible efforts for achieving sustainability

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

BACKGROUND: Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported. RESULTS: In the first step of our bioinformatics pipeline, we estimated the pairwise synergistic effects of SNPs on bladder cancer risk in both GWAS data sets using Multifactor Dimensionality Reduction (MDR) machine learning method that is designed specifically for this purpose. Statistical significance was assessed using a 1000-fold permutation test. Each single SNP was assigned a p-value based on its strongest pairwise association. Each SNP was then mapped to one or more genes using a window of 500 kb upstream and downstream from each gene boundary. This window was chosen to capture as many regulatory variants as possible. Using Exploratory Visual Analysis (EVA), we then carried out a gene set enrichment analysis at the gene level to identify those genes with an overabundance of significant SNPs relative to the size of their mapped regions. Each gene was assigned to a biological functional group defined by Gene Ontology (GO). We next used EVA to evaluate the overabundance of significant genes in biological functional groups. Our study yielded one GO category, carboxy-lysase activity (GO:0016831), that was significant in analyses from both GWAS data sets. Interestingly, only the gamma-glutamyl carboxylase (GGCX) gene from this GO group was significant in both the detection and replication data, highlighting the complexity of the pathway-level effects on risk. The GGCX gene is expressed in the bladder, but has not been previously associated with bladder cancer in univariate GWAS. However, there is some experimental evidence that carboxy-lysase activity might play a role in cancer and that genes in this pathway should be explored as drug targets. This study provides a genetic basis for that observation. CONCLUSIONS: Our machine learning analysis of genetic associations in two GWAS for bladder cancer identified numerous associations with pairs of SNPs. Gene set enrichment analysis found aggregation of risk-associated SNPs in genes and significant genes in GO functional groups. This study supports a role for decarboxylase protein complexes in bladder cancer susceptibility. Previous research has implicated decarboxylases in bladder cancer etiology; however, the genes that we found to be significant in the detection and replication data are not known to have direct influence on bladder cancer, suggesting some novel hypotheses. This study highlights the need for a complex systems approach to the genetic and genomic analysis of common diseases such as cancer