Phylogenetics of Hydroidolina (Hydrozoa: Cnidaria)

Hydroidolina is a group of hydrozoans that includes Anthoathecata, Leptothecata and Siphonophorae. Previous phylogenetic analyses show strong support for Hydroidolina monophyly, but the relationships between and within its subgroups remain uncertain. In an effort to further clarify hydroidolinan relationships, we performed phylogenetic analyses on 97 hydroidolinan taxa, using DNA sequences from partial mitochondrial 16S rDNA, nearly complete nuclear 18S rDNA and nearly complete nuclear 28S rDNA. Our findings are consistent with previous analyses that support monophyly of Siphonophorae and Leptothecata and do not support monophyly of Anthoathecata nor its component subgroups, Filifera and Capitata. Instead, within Anthoathecata, we find support for four separate filiferan clades and two separate capitate clades (Aplanulata and Capitata sensu stricto). Our data however, lack any substantive support for discerning relationships between these eight distinct hydroidolinan clade

Phylogenetics of Hydroidolina (Hydrozoa: Cnidaria)

This is the published version, also available here: http://dx.doi.org/10.1017/S0025315408002257.Hydroidolina is a group of hydrozoans that includes Anthoathecata, Leptothecata and Siphonophorae. Previous phylogenetic analyses show strong support for Hydroidolina monophyly, but the relationships between and within its subgroups remain uncertain. In an effort to further clarify hydroidolinan relationships, we performed phylogenetic analyses on 97 hydroidolinan taxa, using DNA sequences from partial mitochondrial 16S rDNA, nearly complete nuclear 18S rDNA and nearly complete nuclear 28S rDNA. Our findings are consistent with previous analyses that support monophyly of Siphonophorae and Leptothecata and do not support monophyly of Anthoathecata nor its component subgroups, Filifera and Capitata. Instead, within Anthoathecata, we find support for four separate filiferan clades and two separate capitate clades (Aplanulata and Capitata sensu stricto). Our data however, lack any substantive support for discerning relationships between these eight distinct hydroidolinan clades

Refining fisheries advice with stock-specific ecosystem information

Although frequently suggested as a goal for ecosystem-based fisheries management, incorporating ecosystem information into fisheries stock assessments has proven challenging. The uncertainty of input data, coupled with the structural uncertainty of complex multi-species models, currently makes the use of absolute values from such models contentious for short-term single-species fisheries management advice. Here, we propose a different approach where the standard assessment methodologies can be enhanced using ecosystem model derived information. Using a case study of the Irish Sea, we illustrate how stock-specific ecosystem indicators can be used to set an ecosystem-based fishing mortality reference point (FECO) within the “Pretty Good Yield” ranges for fishing mortality which form the present precautionary approach adopted in Europe by the International Council for the Exploration of the Sea (ICES). We propose that this new target, FECO, can be used to scale fishing mortality down when the ecosystem conditions for the stock are poor and up when conditions are good. This approach provides a streamlined quantitative way of incorporating ecosystem information into catch advice and provides an opportunity to operationalize ecosystem models and empirical indicators, while retaining the integrity of current assessment models and the FMSY-based advice process.publishedVersio

The accumulation of microplastic pollution in a commercially important fishing ground.

Publication history: Accepted - 3 March 2022; Published online - 10 March 2022The Irish Sea is an important area for Norway Lobster Nephrops norvegicus fisheries, which are the most valuable fishing resource in the UK. Norway lobster are known to ingest microplastic pollution present in the sediment and have displayed reduced body mass when exposed to microplastic pollution. Here, we identified microplastic pollution in the Irish Sea fishing grounds through analysis of 24 sediment samples from four sites of differing proximity to the Western Irish Sea Gyre in both 2016 and 2019. We used µFTIR spectroscopy to identify seven polymer types, and a total of 77 microplastics consisting of fibres and fragments. The mean microplastics per gram of sediment ranged from 0.13 to 0.49 and 0 to 1.17 MP/g in 2016 and 2019, respectively. There were no differences in the microplastic counts across years, and there was no correlation of microplastic counts with proximity to the Western Irish Sea Gyre. Considering the consistently high microplastic abundance found in the Irish Sea, and the propensity of N. norvegicus to ingest and be negatively impacted by them, we suggest microplastic pollution levels in the Irish Sea may have adverse impacts on N. norvegicus and negative implications for fishery sustainability in the future.EMC is supported by the Department for Agriculture, Environment and Rural Affairs Northern Ireland. NHJ is supported by an Envision Doctoral Training Programme Scholarship funded by the UK National Environment Research Council (NERC). EMC gratefully thanks Dave Williams and Hazel Clark for their technical assistance, Prof Jochen H. E. Koop for facilitating the µFTIR analysis at the Federal Institute of Hydrology, BfG, Koblenz, Germany, and Dr Jason Kirby for facilitating the microplastic analysis at Liverpool John Moores University

Refining Fisheries Advice With Stock-Specific Ecosystem Information

Publication history: Accepted - 17 March 2021; Published - 9 April 2021.Although frequently suggested as a goal for ecosystem-based fisheries management, incorporating ecosystem information into fisheries stock assessments has proven challenging. The uncertainty of input data, coupled with the structural uncertainty of complex multi-species models, currently makes the use of absolute values from such models contentious for short-term single-species fisheries management advice. Here, we propose a different approach where the standard assessment methodologies can be enhanced using ecosystem model derived information. Using a case study of the Irish Sea, we illustrate how stock-specific ecosystem indicators can be used to set an ecosystem-based fishing mortality reference point (FECO) within the “Pretty Good Yield” ranges for fishing mortality which form the present precautionary approach adopted in Europe by the International Council for the Exploration of the Sea (ICES). We propose that this new target, FECO, can be used to scale fishing mortality down when the ecosystem conditions for the stock are poor and up when conditions are good. This approach provides a streamlined quantitative way of incorporating ecosystem information into catch advice and provides an opportunity to operationalize ecosystem models and empirical indicators, while retaining the integrity of current assessment models and the FMSY -based advice process.This project (Grant-Aid Agreement No. CF/16/08) was carried out with the support of the Marine Institute and funded under the Marine Research Sub-programme by the Irish Government

International Bottom Trawl Survey Working Group (IBTSWG). ICES Scientific Reports, 04:65

The International Bottom Trawl Survey Working Group (IBTSWG) coordinates fishery-independent bottom trawl surveys in the ICES area in the Northeast Atlantic and the North Sea. These long-term monitoring surveys provide data for stock assessments and facilitate examina-tion of changes in fish distribution and relative abundance. The group also promotes the stand-ardization of fishing gears and methods as well as survey coordination. This report summarizes the national contributions in 2021–2022 and plans for the 2022–2023 surveys coordinated by IBTSWG

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Working group on ecosystem assessment of Western European shelf seas (WGEAWESS)

The ICES Working Group on Ecosystem Assessment of Western European Shelf Seas (WGEA-WESS) aims to provide high quality science in support to holistic, adaptive, evidence-based man-agement in the Celtic seas, Bay of Biscay and Iberian coast regions. The group works towards developing integrated ecosystem assessments for both the (i) Celtic Seas and (ii) Bay of Biscay and Iberian Coast which are summarized in the Ecosystem Overviews (EOs) advice products that were recently updated. Integrated Trend Analysis (ITA) were performed for multiple sub-ecoregions and used to develop an understanding of ecosystem responses to pressures at varying spatial scales. Ecosystem models (primarily Ecopath with Ecosim; EwE) were developed and identified for fisheries and spatial management advice. The updated Celtic Seas EO represents a large step forward for EOs, with the inclusion of novel sections on climate change, foodweb and productivity, the first application of the new guidelines for building the conceptual diagram, inclusion of socio-economic indicators, and progress made toward complying with the Transparent Assessment Framework (TAF). We highlight ongoing issues relevant to the development and communication of EO conceptual diagrams. A common methodology using dynamic factor analysis (DFA) was used to perform ITA in a comparable way for seven subregions. This was supported by the design and compilation of the first standardized cross-regional dataset. A comparison of the main trends evidenced among subregions over the period 1993–2020 was conducted and will be published soon. A list of available and developing EWE models for the region was also generated. Here, we re-port on the advances in temporal and spatial ecosystem modelling, such as their capacity to model the impacts of sector activities (e.g. renewables and fisheries) and quantify foodweb indi-cators. We also reflect on model quality assessment with the key run of the Irish sea EwE model. The group highlighted the hurdles and gaps in current models in support of EBM, such as the choice of a relevant functional, spatial, and temporal scales and the impacts of model structure on our capacity to draw comparisons from models of different regions. The group aims to ad-dress these issues in coming years and identify routes for ecosystem model derived information into ICES advice.info:eu-repo/semantics/publishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Workshop on Raising Data using the RDBES and TAF (WKRDBESRaiseTAF; outputs from 2022 meeting)

41 páginasThe Workshop on Raising Data using the RDBES and TAF (WKRDBES-Raise&TAF) met online (26–30 of September 2022) to evaluate the use of the Regional Database and Estimation System (RDBES) format to reproduce the 2022 InterCatch input and output, identifying a Transparent Assessment Framework (TAF) structure to organize the intermediate steps and to propose standardized output formats. The main outcomes of WKRDBES-Raise&TAF were: · RDBES provides sufficient support for current national estimation protocols. However, some minor issues were reported that hampered an exact reproduction of the estimates. Therefore, adaptations of the data model should not be excluded completely. · All the input to stock assessment that InterCatch currently provides, could be reproduced. The participants started from the current stock extracts that can be downloaded from InterCatch. · A workflow was proposed with a national TAF repository for each country, a stock estimation repository and a stock assessment repository. The intermediate output of those repositories will be stored in an ‘intermediate output database’ and depending on the user role, you will get access to the relevant stages in this workflow. · The following requirements for the standard output formats were defined: they cannot be more restrictive than the InterCatch input and output format; they should present measures of uncertainty and sample sizes (for national estimates) and should have a configurable domain definition (for national estimates). Despite those successful outcomes, the current plan for transition to an operational system was concluded to be too optimistic. WKRDBES-Raise&TAF therefore recommends to the Working Group on Governance of the Regional Database and Estimation System (WGRDBESGOV) to revise the roadmap and allow RDBES to be in a test phase also for 2023. WKRDBES-Raise&TAF felt the need to test the proposed workflow on a small scale and therefore recommends to the WGRDBESGOV to arrange a workshop where two stocks (pok.27.3a46 (Saithe (Pollachius virens) in Subareas 4, 6 and Division 3.a (North Sea, Rockall and West of Scotland, Skagerrak and Kattegat) and wit.27.3a47d (Witch (Glyptocephalus cynoglossus) in Subarea 4 and Divisions 3.a and 7.d (North Sea, Skagerrak and Kattegat, eastern English Channel)) will be set up to go through the whole flow.Peer reviewe