Climate Ready Estuaries - COAST in Action: 2012 Projects from Maine and New Hampshire

In summer 2011 the US EPA’s Climate Ready Estuaries program awarded funds to the Casco Bay Estuary Partnership (CBEP) in Portland, Maine, and the Piscataqua Region Estuaries Partnership (PREP) in coastal New Hampshire, to further develop and use COAST (COastal Adaptation to Sea level rise Tool) in their sea level rise adaptation planning processes. The New England Environmental Finance Center worked with municipal staff, elected officials, and other stakeholders to select specific locations, vulnerable assets, and adaptation actions to model using COAST. The EFC then collected the appropriate base data layers, ran the COAST simulations, and provided visual, numeric, and presentation-based products in support of the planning processes underway in both locations. These products helped galvanize support for the adaptation planning efforts. Through facilitated meetings they also led to stakeholders identifying specific action steps and begin to determine how to implement them

Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

OBJECTIVE: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival. DESIGN: Prospective multicentre observational cohort study. SETTING: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams). PARTICIPANTS: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services. MAIN OUTCOME MEASURES: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions. RESULTS: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival. CONCLUSIONS: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival

Potential for regional air pollution episodes in Colorado

November 1983.Includes bibliographical references.Sponsored by the National Science Foundation ATM-8015309

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Masters of the Art of Comman

A Substrate-Independent Benthic Sampler (SIBS) for Hard and Mixed-Bottom Marine Habitats: A Proof-of-Concept Study

Sea cage fish farms are increasingly situated over hard and mixed substrate habitats for production and waste-dispersion reasons; yet in many cases, these installations are not being effectively managed with respect to benthic impacts due to the lack of a practical sampling method. This study presents the first set of results from a newly developed Substrate Independent Benthic Sampler (SIBS) device that captures the unconsolidated organic and inorganic matter that overlies almost all substrates. The contents of the samples were analyzed using extracted environmental DNA (eDNA) followed by metabarcoding of the bacterial 16S rRNA gene. SIBS microbial assemblages reliably changed with proximity to farm and concurred with visual assessments of impact. Moreover, the approach appeared to be very sensitive with respect to the enrichment gradient, being able to discern influences at distances of 500–1500 m from the impact source. Other spatial differences, due to region and farm, were small in comparison, and the effect of the underlying substrate type was minor. The samples contained sufficient previously described bacterial bioindicator taxa from enriched sediments, such that a meaningful biotic index could be calculated, thereby placing them on a well-established benthic enrichment spectrum with established environmental thresholds. SIBS-derived bacterial data provide a powerful new approach for mapping spatial boundaries of farm effects irrespective of substrate type and topography. More importantly, the tool should also permit quantitative assessment of benthic enrichment levels irrespective of substrate type from depths of at least 100 m. It therefore has the potential to solve the hard-bottom problem that has until now prohibited effective environmental monitoring at mixed and hard-bottom locations.publishedVersio

Epifaunal habitat associations on mixed and hard bottom substrates in coastal waters of Northern Norway

Hard and mixed seafloor substrates are an important benthic habitat in coastal northern Norway and they are known to be colonized by relatively diverse communities of sessile epifauna. These assemblages are highly susceptible to physical damage and stresses imposed by organic material from industrial and municipal sources. However, despite increasing prevalence of stressors, the diversity and distribution of benthic substrates and biological communities in coastal Arctic and sub-Arctic regions remain poorly documented. In response, this study has characterized the composition of mixed and hard bottom substrates and associated sessile epifauna in fjords in Finnmark, northern Norway, using remote sensing and an innovation low-cost towed camera method. The study fjords supported a dense covering (0.1 to 0.68 individuals m–2) of sponge taxa common to deep-water ostur sponge habitats (Geodia sp., Mycale lingua, Polymastia sp., Phakellia ventilabrum, and Axinella infundibuliformis). In addition, aggregations of the soft coral (Duva florida), the tunicate (Ascidia sp.), the seastar (Ceramaster granularis) and anemone (Hormathia digitata) were prominent fauna. The small-scale spatial patterns of the epifaunal communities in this study were primarily influenced by the local hydrodynamic regime, depth, the topographical slope and the presence of hard bedrock substrates. This description of the composition, distribution and the identification of environmental drivers of epibenthic communities is valuable for the development of predictive habitat models to manage the benthic impact of multiple stressor on these ecological valuable and vulnerable Arctic habitats.publishedVersio

Beyond taxonomy: Validating functional inference approaches in the context of fish-farm impact assessments

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200 m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.publishedVersio