DNA metabarcoding unveils multiscale trophic variation in a widespread coastal opportunist

A thorough understanding of ecological networks relies on comprehensive information on trophic relationships among species. Since unpicking the diet of many organisms is unattainable using traditional morphology‐based approaches, the application of high‐throughput sequencing methods represents a rapid and powerful way forward. Here, we assessed the application of DNA metabarcoding with nearly universal primers for the mitochondrial marker cytochrome c oxidase I in defining the trophic ecology of adult brown shrimp, Crangon crangon, in six European estuaries. The exact trophic role of this abundant and widespread coastal benthic species is somewhat controversial, while information on geographical variation remains scant. Results revealed a highly opportunistic behaviour. Shrimp stomach contents contained hundreds of taxa (>1,000 molecular operational taxonomic units), of which 291 were identified as distinct species, belonging to 35 phyla. Only twenty ascertained species had a mean relative abundance of more than 0.5%. Predominant species included other abundant coastal and estuarine taxa, including the shore crab Carcinus maenas and the amphipod Corophium volutator. Jacobs’ selectivity index estimates based on DNA extracted from both shrimp stomachs and sediment samples were used to assess the shrimp's trophic niche indicating a generalist diet, dominated by crustaceans, polychaetes and fish. Spatial variation in diet composition, at regional and local scales, confirmed the highly flexible nature of this trophic opportunist. Furthermore, the detection of a prevalent, possibly endoparasitic fungus (Purpureocillium lilacinum) in the shrimp's stomach demonstrates the wide range of questions that can be addressed using metabarcoding, towards a more robust reconstruction of ecological networks

Simple models of the chemical field around swimming plankton

Background. Cervical cancer is the fourth most common cancer in women, and we recently reported human leukocyte antigen (HLA) alleles showing strong associations with cervical neoplasia risk and protection. HLA ligands are recognized by killer immunoglobulin-like receptors (KIRs) expressed on a range of immune cell subsets, governing their proinflammatory activity. We hypothesized that the inheritance of particular HLA-KIR combinations would increase cervical neoplasia risk. Methods. Here, we used HLA and KIR dosages imputed from single-nucleotide polymorphism genotype data from 2143 cervical neoplasia cases and 13 858 healthy controls of European decent. Results. The following 4 novel HLA alleles were identified in association with cervical neoplasia, owing to their linkage disequilibrium with known cervical neoplasia-associated HLA-DRB1 alleles: HLA-DRB3*9901 (odds ratio [OR], 1.24; P = 2.49 × 10−9), HLA-DRB5*0101 (OR, 1.29; P = 2.26 × 10−8), HLA-DRB5*9901 (OR, 0.77; P = 1.90 × 10−9), and HLA-DRB3*0301 (OR, 0.63; P = 4.06 × 10−5). We also found that homozygosity of HLA-C1 group alleles is a protective factor for human papillomavirus type 16 (HPV16)-related cervical neoplasia (C1/C1; OR, 0.79; P = .005). This protective association was restricted to carriers of either KIR2DL2 (OR, 0.67; P = .00045) or KIR2DS2 (OR, 0.69; P = .0006). Conclusions. Our findings suggest that HLA-C1 group alleles play a role in protecting against HPV16-related cervical neoplasia, mainly through a KIR-mediated mechanism

Defining the genetic susceptibility to cervical neoplasia - a genome-wide association study

Funding: MAB was funded by a National Health and Medical Research Council (Australia) Senior Principal Research Fellowship. Support was also received from the Australian Cancer Research Foundation. JL holds a Tier 1 Canada Research Chair in Human Genome Epidemiology. The Seattle study was supported by the following grants: NIH, National Cancer Institute grants P01CA042792 and R01CA112512. Cervical Health Study (from which the NSW component was obtained) was funded by NHMRC Grant 387701, and CCNSW core grant. The Montreal study was funded by the Canadian Institutes of Health Research (grant MOP-42532) and sample processing was funded by the Reseau FRQS SIDA-MI. The Swedish Research Council, the Swedish Foundation for Strategic Research, the ALF/LUA research grant in Gothenburg and Umeå, the Lundberg Foundation, the Torsten and Ragnar Soderberg’s Foundation, the Novo Nordisk Foundation, and the European Commission grant HEALTH-F2-2008-201865-GEFOS, BBMRI.se, the Swedish Society of Medicine, the KempeFoundation (JCK-1021), the Medical Faculty of Umeå University, the County Council of Vasterbotten (Spjutspetsanslag VLL:159:33-2007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer reviewedPublisher PDFPublisher PD

Dispersal Routes and Habitat Utilization of Juvenile Atlantic Bluefin Tuna, Thunnus thynnus, Tracked with Mini PSAT and Archival Tags

Between 2005 and 2009, we deployed 58 miniature pop-up satellite archival tags (PSAT) and 132 implanted archival tags on juvenile Atlantic bluefin tuna (age 2–5) in the northwest Atlantic Ocean. Data returned from these efforts (n = 26 PSATs, 1 archival tag) revealed their dispersal routes, horizontal and vertical movements and habitat utilization. All of the tagged bluefin tuna remained in the northwest Atlantic for the duration observed, and in summer months exhibited core-use of coastal seas extending from Maryland to Cape Cod, MA, (USA) out to the shelf break. Their winter distributions were more spatially disaggregated, ranging south to the South Atlantic Bight, northern Bahamas and Gulf Stream. Vertical habitat patterns showed that juvenile bluefin tuna mainly occupied shallow depths (mean  = 5–12 m, sd  = 15–23.7 m) and relatively warm water masses in summer (mean  = 17.9–20.9°C, sd  = 4.2–2.6°C) and had deeper and more variable depth patterns in winter (mean  = 41–58 m, sd  = 48.9–62.2 m). Our tagging results reveal annual dispersal patterns, behavior and oceanographic associations of juvenile Atlantic bluefin tuna that were only surmised in earlier studies. Fishery independent profiling from electronic tagging also provide spatially and temporally explicit information for evaluating dispersals rates, population structure and fisheries catch patterns

On the Variability of the Length Weight Relationship for Atlantic Bluefin Tuna, Thunnus thynnus (L.)

Following extensive review, a model of the Atlantic bluefin tuna (ABFT), Thunnus thynnus (L.), length–weight relationship for the eastern Atlantic and Mediterranean (RW = 0.0000188 SFL3.01247; Ec 1) is presented on the basis of samples of ABFT spawners, with an average value of index K = 2.03 ± 0.15SD, collected by the Atlantic traps of Portugal and Spain in the Strait of Gibraltar (1963; 1996–1998; 2000–2012), and a set of samples of juvenile fishes from ICCAT–GBYP (n = 707). The resulting model (Ec 1), together with the model used for the eastern stock assessment (RW = 0.000019607 SFL3.0092; Ec 2) and a recently adopted by ICCAT Standing Committee on Research and Statistics (SCRS) (RW = 0.0000315551 SFL2.898454; EAST) are analyzed in using a bi-variant sample [SFL (cm), RW (kg)] of 474 pairs of data with the aim of validating them and establishing which model(s) best fit the reality represented by the sample and, therefore, will have the greatest descriptive and predictive power. The result of the analysis indicates that the model EAST clearly underestimates the weight of spawning ABFT and that model Ec 2 overestimates it slightly, being model Ec 1 that best explains the data of the sample. The result of the classical statistical analysis is confirmed by means of the quantile regression technique, selecting the quantiles 5, 25, 50, 75, and 95%. Other fisheries and biological indicators also conclude that the model EAST gradually underestimates the weight of ABFT spawners (of 2–3 m) by 9–12.5 %, and does not meet the criterion that for RW = 725 kg (Wmax), SFL = 319.93 ± 11.3 cm (Lmax).Cort, JL.; Estruch Fuster, VD.; Neves Dos Santos, M.; Di Natale, A.; Abid, N.; De La Serna, JM. (2015). On the Variability of the Length Weight Relationship for Atlantic Bluefin Tuna, Thunnus thynnus (L.). Reviews in Fisheries Science & Aquaculture. 23(1):23-38. doi:10.1080/23308249.2015.1008625S2338231Aguado-Giménez, F., & García-García, B. (2005). Changes in some morphometric relationships in Atlantic bluefin tuna (Thunnus thynnus thynnus Linnaeus, 1758) as a result of fattening process. Aquaculture, 249(1-4), 303-309. doi:10.1016/j.aquaculture.2005.04.064Block, B. A., Teo, S. L. H., Walli, A., Boustany, A., Stokesbury, M. J. W., Farwell, C. J., … Williams, T. D. (2005). Electronic tagging and population structure of Atlantic bluefin tuna. Nature, 434(7037), 1121-1127. doi:10.1038/nature03463Chapman, E. W., Jørgensen, C., & Lutcavage, M. E. (2011). Atlantic bluefin tuna (Thunnus thynnus): a state-dependent energy allocation model for growth, maturation, and reproductive investment. Canadian Journal of Fisheries and Aquatic Sciences, 68(11), 1934-1951. doi:10.1139/f2011-109Cort, J. L., Arregui, I., Estruch, V. D., & Deguara, S. (2014). Validation of the Growth Equation Applicable to the Eastern Atlantic Bluefin Tuna,Thunnus thynnus(L.), UsingLmax, Tag-Recapture, and First Dorsal Spine Analysis. Reviews in Fisheries Science & Aquaculture, 22(3), 239-255. doi:10.1080/23308249.2014.931173Cort, J. L., Deguara, S., Galaz, T., Mèlich, B., Artetxe, I., Arregi, I., … Idrissi, M. (2013). Determination ofLmaxfor Atlantic Bluefin Tuna,Thunnus thynnus(L.), from Meta-Analysis of Published and Available Biometric Data. Reviews in Fisheries Science, 21(2), 181-212. doi:10.1080/10641262.2013.793284Fraser, K.Possessed. World Record Holder for Bluefin Tuna. Kingstown, Nova Scotia: T & S Office Essentials and printing, 243 pp. (2008).Fromentin, J.-M., & Powers, J. E. (2005). Atlantic bluefin tuna: population dynamics, ecology, fisheries and management. Fish and Fisheries, 6(4), 281-306. doi:10.1111/j.1467-2979.2005.00197.xHattour, A.Contribution a l’étude des Scombridés de Tunisie. Université de Tunis. Faculté des Sciences, 168 pp. (1979).Karakulak, S., Oray, I., Corriero, A., Deflorio, M., Santamaria, N., Desantis, S., & De Metrio, G. (2004). Evidence of a spawning area for the bluefin tuna (Thunnus thynnus L.) in the eastern Mediterranean. Journal of Applied Ichthyology, 20(4), 318-320. doi:10.1111/j.1439-0426.2004.00561.xKoenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33. doi:10.2307/1913643Koenker, R. (2005). Quantile Regression. doi:10.1017/cbo9780511754098Milatou, N., & Megalofonou, P. (2014). Age structure and growth of bluefin tuna (Thunnus thynnus, L.) in the capture-based aquaculture in the Mediterranean Sea. Aquaculture, 424-425, 35-44. doi:10.1016/j.aquaculture.2013.12.037Perçin, F., & Akyol, O. (2009). Lengthâ weight and lengthâ length relationships of the bluefin tuna,Thunnus thynnusL., in the Turkish part of the eastern Mediterranean Sea. Journal of Applied Ichthyology, 25(6), 782-784. doi:10.1111/j.1439-0426.2009.01288.xPercin, F., & Akyol, O. (2010). Some Morphometric Relationships in Fattened Bluefin Tuna, Thunnus thynnus L., from the Turkish Aegean Sea. Journal of Animal and Veterinary Advances, 9(11), 1684-1688. doi:10.3923/javaa.2010.1684.1688Rooker, J. R., Alvarado Bremer, J. R., Block, B. A., Dewar, H., de Metrio, G., Corriero, A., … Secor, D. H. (2007). Life History and Stock Structure of Atlantic Bluefin Tuna (Thunnus thynnus). Reviews in Fisheries Science, 15(4), 265-310. doi:10.1080/10641260701484135Sinovcic, G., Franicevic, M., Zorica, B., & Cikes-Kec, V. (2004). Length-weight and length-length relationships for 10 pelagic fish species from the Adriatic Sea (Croatia). Journal of Applied Ichthyology, 20(2), 156-158. doi:10.1046/j.1439-0426.2003.00519.xTičina, V., Grubišić, L., Šegvić Bubić, T., & Katavić, I. (2011). Biometric characteristics of small Atlantic bluefin tuna (Thunnus thynnus, Linnaeus, 1758) of Mediterranean Sea origin. Journal of Applied Ichthyology, 27(4), 971-976. doi:10.1111/j.1439-0426.2011.01752.

Background matching in the brown shrimp Crangon crangon : adaptive camouflage and behavioural-plasticity

A combination of burrowing behaviour and very efficient background matching makes the brown shrimp Crangon crangon almost invisible to potential predators and preys. This raises questions on how shrimp succeed in concealing themselves in the heterogeneous and dynamic estuarine habitats they inhabit and what type of environmental variables and behavioural factors affect their colour change abilities. Using a series of behavioural experiments, we show that the brown shrimp is capable of repeated fast colour adaptations (20% change in dark pigment cover within one hour) and that its background matching ability is mainly influenced by illumination and sediment colour. Novel insights are provided on the occurrence of non-adaptive (possibly stress) responses to background changes after long-time exposure to a constant background colour or during unfavourable conditions for burying. Shrimp showed high levels of intra- and inter-individual variation, demonstrating a complex balance between behavioural-plasticity and environmental adaptation. As such, the study of crustacean colour changes represents a valuable opportunity to investigate colour adaptations in dynamic habitats and can help us to identify the mayor environmental and behavioural factors influencing the evolution of animal background matching