Updated Marine Mammal Distribution and Abundance Estimates in British Columbia

Information relating to the distribution and abundance of species is critical for effective conservation and management. For many species, including cetacean species of conservation concern, abundance estimates are lacking, out of date and/or highly uncertain. Systematic, line-transect marine mammal surveys were conducted in British Columbia’s (BC) coastal waters over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007). In total, 10,057km of transects were surveyed in an 83,547km2 study area. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. The summer abundance estimates (with lower and upper 95% confidence intervals; all DSM method unless otherwise stated), assuming certain trackline detection (underestimates true population size) were: harbour porpoise (Phocoena phocoena) 8,091 (4,885–13,401); Dall’s porpoise (Phocoenoides dalli) 5,303 (4,638–6,064); Pacific white-sided dolphin (Lagenorhynchus obliquidens) 22,160 (16,522–29,721); humpback whale (Megaptera novaeangliae) 1,092 (993–1,200); fin whale (Balaenoptera physalus) 329 (274–395); killer whale (all ecotypes; Orcinus orca), 371 (222–621); common minke whale (B. acutorostrata) 522 (295–927); harbour seal (total; Phoca vitulina) 24,916 (19,666–31,569); Steller sea lion (total; Eumetopias jubatus) 4,037 (1,100–14,815); and northern elephant seal (CDS method; Mirounga angustirostris) 65 (35–121). Abundance estimates are provided on a stratum-specific basis with additional estimates provided for Steller sea lions and harbour seals that were ‘hauled out’ and ‘in water’. This analysis updates previous estimates by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters

Characterizing Fishing Effort and Spatial Extent of Coastal Fisheries

Biodiverse coastal zones are often areas of intense fishing pressure due to the high relative density of fishing capacity in these nearshore regions. Although overcapacity is one of the central challenges to fisheries sustainability in coastal zones, accurate estimates of fishing pressure in coastal zones are limited, hampering the assessment of the direct and collateral impacts (e.g., habitat degradation, bycatch) of fishing. We compiled a comprehensive database of fishing effort metrics and the corresponding spatial limits of fisheries and used a spatial analysis program (FEET) to map fishing effort density (measured as boat-meters per km2) in the coastal zones of six ocean regions. We also considered the utility of a number of socioeconomic variables as indicators of fishing pressure at the national level; fishing density increased as a function of population size and decreased as a function of coastline length. Our mapping exercise points to intra and interregional ‘hotspots’ of coastal fishing pressure. The significant and intuitive relationships we found between fishing density and population size and coastline length may help with coarse regional characterizations of fishing pressure. However, spatially-delimited fishing effort data are needed to accurately map fishing hotspots, i.e., areas of intense fishing activity. We suggest that estimates of fishing effort, not just target catch or yield, serve as a necessary measure of fishing activity, which is a key link to evaluating sustainability and environmental impacts of coastal fisheries

An Integrated Microfluidic Device for Monitoring Changes in Nitric Oxide Production in Single T-Lymphocyte (Jurkat) Cells

A considerable amount of attention has been focused on the analysis of single cells in an effort to better understand cell heterogeneity in cancer and neurodegenerative diseases. Although microfluidic devices have several advantages for single cell analysis, few papers have actually demonstrated the ability of these devices to monitor chemical changes in perturbed biological systems. In this paper, a new microfluidic channel manifold is described that integrates cell transport, lysis, injection, electrophoretic separation, and fluorescence detection into a single device, making it possible to analyze individual cells at a rate of 10 cells/min in an automated fashion. The system was employed to measure nitric oxide (NO) production in single T-lymphocytes (Jurkat cells) using a fluorescent marker, 4-amino-5-methylamino-2',7'-difluorofluorescein diacetate (DAF-FM DA). The cells were also labeled with 6-carboxyfluorescein diacetate (6-CFDA) as an internal standard. The NO production by control cells was compared to that of cells stimulated using lipopolysaccharide (LPS), which is known to cause the expression of inducible nitric oxide synthase (iNOS) in immune-type cells. Statistical analysis of the resulting electropherograms from a population of cells indicated a twofold increase in NO production in the induced cells. These results compare nicely to a recently published bulk cell analysis of NO

The relationship among oceanography, prey fields, and beaked whale foraging habitat in the Tongue of the Ocean

This article is distributed under the terms of the Creative Commons CC0 public domain dedication. The definitive version was published in PLoS One 6 (2011): e19269, doi:10.1371/journal.pone.0019269.Beaked whales, specifically Blainville's (Mesoplodon densirostris) and Cuvier's (Ziphius cavirostris), are known to feed in the Tongue of the Ocean, Bahamas. These whales can be reliably detected and often localized within the Atlantic Undersea Test and Evaluation Center (AUTEC) acoustic sensor system. The AUTEC range is a regularly spaced bottom mounted hydrophone array covering >350 nm2 providing a valuable network to record anthropogenic noise and marine mammal vocalizations. Assessments of the potential risks of noise exposure to beaked whales have historically occurred in the absence of information about the physical and biological environments in which these animals are distributed. In the fall of 2008, we used a downward looking 38 kHz SIMRAD EK60 echosounder to measure prey scattering layers concurrent with fine scale turbulence measurements from an autonomous turbulence profiler. Using an 8 km, 4-leaf clover sampling pattern, we completed a total of 7.5 repeat surveys with concurrently measured physical and biological oceanographic parameters, so as to examine the spatiotemporal scales and relationships among turbulence levels, biological scattering layers, and beaked whale foraging activity. We found a strong correlation among increased prey density and ocean vertical structure relative to increased click densities. Understanding the habitats of these whales and their utilization patterns will improve future models of beaked whale habitat as well as allowing more comprehensive assessments of exposure risk to anthropogenic sound.The data collection and analysis was funded by the Office of Naval Research as N00014-08-1-1162

Translating Marine Animal Tracking Data into Conservation Policy and Management

There have been efforts around the globe to track individuals of many marine species and assess their movements and distribution with the putative goal of supporting their conservation and management. Determining whether, and how, tracking data have been successfully applied to address real-world conservation issues is however difficult. Here, we compile a broad range of case studies from diverse marine taxa to show how tracking data have helped inform conservation policy and management, including reductions in fisheries bycatch and vessel strikes, and the design and administration of marine protected areas and important habitats. Using these examples, we highlight pathways through which the past and future investment in collecting animal tracking data might be better used to achieve tangible conservation benefits

SNAGA, TEORIJA I PRAKSA (Kraft, Theorie und Praxis)

We have developed a global biogeographic classification of the mesopelagic zone to reflect the regional scales over which the ocean interior varies in terms of biodiversity and function. An integrated approach was necessary, as global gaps in information and variable sampling methods preclude strictly statistical approaches. A panel combining expertise in oceanography, geospatial mapping, and deep-sea biology convened to collate expert opinion on the distributional patterns of pelagic fauna relative to environmental proxies (temperature, salinity, and dissolved oxygen at mesopelagic depths). An iterative Delphi Method integrating additional biological and physical data was used to classify biogeographic ecoregions and to identify the location of ecoregion boundaries or inter-regions gradients. We define 33 global mesopelagic ecoregions. Of these, 20 are oceanic while 13 are ‘distant neritic.’ While each is driven by a complex of controlling factors, the putative primary driver of each ecoregion was identified. While work remains to be done to produce a comprehensive and robust mesopelagic biogeography (i.e., reflecting temporal variation), we believe that the classification set forth in this study will prove to be a useful and timely input to policy planning and management for conservation of deep-pelagic marine resources. In particular, it gives an indication of the spatial scale at which faunal communities are expected to be broadly similar in composition, and hence can inform application of ecosystem-based management approaches, marine spatial planning and the distribution and spacing of networks of representative protected areas

Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.Fil: Kot, Connie Y.. University of Duke; Estados UnidosFil: Åkesson, Susanne. Lund University; SueciaFil: Alfaro Shigueto, Joanna. Universidad Cientifica del Sur; Perú. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Amorocho Llanos, Diego Fernando. Research Center for Environmental Management and Development; ColombiaFil: Antonopoulou, Marina. Emirates Wildlife Society-world Wide Fund For Nature; Emiratos Arabes UnidosFil: Balazs, George H.. Noaa Fisheries Service; Estados UnidosFil: Baverstock, Warren R.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Blumenthal, Janice M.. Cayman Islands Government; Islas CaimánFil: Broderick, Annette C.. University of Exeter; Reino UnidoFil: Bruno, Ignacio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Canbolat, Ali Fuat. Hacettepe Üniversitesi; Turquía. Ecological Research Society; TurquíaFil: Casale, Paolo. Università degli Studi di Pisa; ItaliaFil: Cejudo, Daniel. Universidad de Las Palmas de Gran Canaria; EspañaFil: Coyne, Michael S.. Seaturtle.org; Estados UnidosFil: Curtice, Corrie. University of Duke; Estados UnidosFil: DeLand, Sarah. University of Duke; Estados UnidosFil: DiMatteo, Andrew. CheloniData; Estados UnidosFil: Dodge, Kara. New England Aquarium; Estados UnidosFil: Dunn, Daniel C.. University of Queensland; Australia. The University of Queensland; Australia. University of Duke; Estados UnidosFil: Esteban, Nicole. Swansea University; Reino UnidoFil: Formia, Angela. Wildlife Conservation Society; Estados UnidosFil: Fuentes, Mariana M. P. B.. Florida State University; Estados UnidosFil: Fujioka, Ei. University of Duke; Estados UnidosFil: Garnier, Julie. The Zoological Society of London; Reino UnidoFil: Godfrey, Matthew H.. North Carolina Wildlife Resources Commission; Estados UnidosFil: Godley, Brendan J.. University of Exeter; Reino UnidoFil: González Carman, Victoria. Instituto National de Investigación y Desarrollo Pesquero; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Harrison, Autumn Lynn. Smithsonian Institution; Estados UnidosFil: Hart, Catherine E.. Grupo Tortuguero de las Californias A.C; México. Investigacion, Capacitacion y Soluciones Ambientales y Sociales A.C; MéxicoFil: Hawkes, Lucy A.. University of Exeter; Reino UnidoFil: Hays, Graeme C.. Deakin University; AustraliaFil: Hill, Nicholas. The Zoological Society of London; Reino UnidoFil: Hochscheid, Sandra. Stazione Zoologica Anton Dohrn; ItaliaFil: Kaska, Yakup. Dekamer—Sea Turtle Rescue Center; Turquía. Pamukkale Üniversitesi; TurquíaFil: Levy, Yaniv. University Of Haifa; Israel. Israel Nature And Parks Authority; IsraelFil: Ley Quiñónez, César P.. Instituto Politécnico Nacional; MéxicoFil: Lockhart, Gwen G.. Virginia Aquarium Marine Science Foundation; Estados Unidos. Naval Facilities Engineering Command; Estados UnidosFil: López-Mendilaharsu, Milagros. Projeto TAMAR; BrasilFil: Luschi, Paolo. Università degli Studi di Pisa; ItaliaFil: Mangel, Jeffrey C.. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Margaritoulis, Dimitris. Archelon; GreciaFil: Maxwell, Sara M.. University of Washington; Estados UnidosFil: McClellan, Catherine M.. University of Duke; Estados UnidosFil: Metcalfe, Kristian. University of Exeter; Reino UnidoFil: Mingozzi, Antonio. Università Della Calabria; ItaliaFil: Moncada, Felix G.. Centro de Investigaciones Pesqueras; CubaFil: Nichols, Wallace J.. California Academy Of Sciences; Estados Unidos. Center For The Blue Economy And International Environmental Policy Program; Estados UnidosFil: Parker, Denise M.. Noaa Fisheries Service; Estados UnidosFil: Patel, Samir H.. Coonamessett Farm Foundation; Estados Unidos. Drexel University; Estados UnidosFil: Pilcher, Nicolas J.. Marine Research Foundation; MalasiaFil: Poulin, Sarah. University of Duke; Estados UnidosFil: Read, Andrew J.. Duke University Marine Laboratory; Estados UnidosFil: Rees, ALan F.. University of Exeter; Reino Unido. Archelon; GreciaFil: Robinson, David P.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Robinson, Nathan J.. Fundación Oceanogràfic; EspañaFil: Sandoval-Lugo, Alejandra G.. Instituto Politécnico Nacional; MéxicoFil: Schofield, Gail. Queen Mary University of London; Reino UnidoFil: Seminoff, Jeffrey A.. Noaa National Marine Fisheries Service Southwest Regional Office; Estados UnidosFil: Seney, Erin E.. University Of Central Florida; Estados UnidosFil: Snape, Robin T. E.. University of Exeter; Reino UnidoFil: Sözbilen, Dogan. Dekamer—sea Turtle Rescue Center; Turquía. Pamukkale University; TurquíaFil: Tomás, Jesús. Institut Cavanilles de Biodiversitat I Biologia Evolutiva; EspañaFil: Varo Cruz, Nuria. Universidad de Las Palmas de Gran Canaria; España. Ads Biodiversidad; España. Instituto Canario de Ciencias Marinas; EspañaFil: Wallace, Bryan P.. University of Duke; Estados Unidos. Ecolibrium, Inc.; Estados UnidosFil: Wildermann, Natalie E.. Texas A&M University; Estados UnidosFil: Witt, Matthew J.. University of Exeter; Reino UnidoFil: Zavala Norzagaray, Alan A.. Instituto politecnico nacional; MéxicoFil: Halpin, Patrick N.. University of Duke; Estados Unido

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice