Progress Towards The Revised Goods Classification Tool

Biogeographical provinces delineated by the Global Open Oceans and Deep Seabed (GOODS; UNESCO, 2009) classification scheme can help safeguard marine biodiversity, support the ecosystem approach and marine spatial management including MPA network design (Rice et al., 2011). Although now adapted for A...

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Main Author: Lea-Anne Henry
Format: Conference Object
Language:unknown
Published: Zenodo 2017
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Online Access:https://dx.doi.org/10.5281/zenodo.571070
https://zenodo.org/record/571070
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spelling ftdatacite:10.5281/zenodo.571070 2023-05-15T17:32:02+02:00 Progress Towards The Revised Goods Classification Tool Lea-Anne Henry 2017 https://dx.doi.org/10.5281/zenodo.571070 https://zenodo.org/record/571070 unknown Zenodo Open Access Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess CC-BY Text Presentation article-journal ScholarlyArticle 2017 ftdatacite https://doi.org/10.5281/zenodo.571070 2021-11-05T12:55:41Z Biogeographical provinces delineated by the Global Open Oceans and Deep Seabed (GOODS; UNESCO, 2009) classification scheme can help safeguard marine biodiversity, support the ecosystem approach and marine spatial management including MPA network design (Rice et al., 2011). Although now adapted for ABNJ waters >800 m deep (Watling et al., 2013), GOODS lacks ocean-scale biogeographic data from VMEs (Watling et al., 2013). Without improved understanding of biogeography of the fauna that comprise VMEs, it is risky to assume that a protected or managed area is representative of other “similar” VMEs because biogeographically unique areas could be overlooked resulting in a loss of biodiversity. By Month 30 in ATLAS, WP3 aims to deliver a revised GOODS tool for the North Atlantic, focussing specifically on integrating data on VME indicator taxa and deep-sea fish fauna associated with VMEs. Over the last year, appropriately scaled environmental datasets that might resolve biogeographic provinces for the North Atlantic have been compiled from existing sources (see UNESCO, 2009; Watling et al., 2013) but also Esri’s latest geospatial EMU (Ecological Marine Unit) maps of NOAA’s 50-year World Ocean Atlas that clustered the ocean’s physiographic domain at ¼ x ¼ degree resolution resolved over depths. Also being considered is ATLAS’ latest VIKING20 output on MLD (mixed layer depth) and water mass spread. Next steps are to finalise and grid the environmental data in ArcGIS to identify clusters, then validate and refine these with actual VME biodiversity point data compiled in WP3 and species distribution models. Parallel to this revised GOODS activity are statistical modelling activities in WP3 to understand how the interplay between environmental, historic and intrinsic factors determine the biogeography of VME fauna: ATLAS preliminary findings suggest that the life history traits of VME associated fauna can significantly extend or limit biogeographic ranges beyond what was expected purely by environmental factors alone. Conference Object North Atlantic DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
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description Biogeographical provinces delineated by the Global Open Oceans and Deep Seabed (GOODS; UNESCO, 2009) classification scheme can help safeguard marine biodiversity, support the ecosystem approach and marine spatial management including MPA network design (Rice et al., 2011). Although now adapted for ABNJ waters >800 m deep (Watling et al., 2013), GOODS lacks ocean-scale biogeographic data from VMEs (Watling et al., 2013). Without improved understanding of biogeography of the fauna that comprise VMEs, it is risky to assume that a protected or managed area is representative of other “similar” VMEs because biogeographically unique areas could be overlooked resulting in a loss of biodiversity. By Month 30 in ATLAS, WP3 aims to deliver a revised GOODS tool for the North Atlantic, focussing specifically on integrating data on VME indicator taxa and deep-sea fish fauna associated with VMEs. Over the last year, appropriately scaled environmental datasets that might resolve biogeographic provinces for the North Atlantic have been compiled from existing sources (see UNESCO, 2009; Watling et al., 2013) but also Esri’s latest geospatial EMU (Ecological Marine Unit) maps of NOAA’s 50-year World Ocean Atlas that clustered the ocean’s physiographic domain at ¼ x ¼ degree resolution resolved over depths. Also being considered is ATLAS’ latest VIKING20 output on MLD (mixed layer depth) and water mass spread. Next steps are to finalise and grid the environmental data in ArcGIS to identify clusters, then validate and refine these with actual VME biodiversity point data compiled in WP3 and species distribution models. Parallel to this revised GOODS activity are statistical modelling activities in WP3 to understand how the interplay between environmental, historic and intrinsic factors determine the biogeography of VME fauna: ATLAS preliminary findings suggest that the life history traits of VME associated fauna can significantly extend or limit biogeographic ranges beyond what was expected purely by environmental factors alone.
format Conference Object
author Lea-Anne Henry
spellingShingle Lea-Anne Henry
Progress Towards The Revised Goods Classification Tool
author_facet Lea-Anne Henry
author_sort Lea-Anne Henry
title Progress Towards The Revised Goods Classification Tool
title_short Progress Towards The Revised Goods Classification Tool
title_full Progress Towards The Revised Goods Classification Tool
title_fullStr Progress Towards The Revised Goods Classification Tool
title_full_unstemmed Progress Towards The Revised Goods Classification Tool
title_sort progress towards the revised goods classification tool
publisher Zenodo
publishDate 2017
url https://dx.doi.org/10.5281/zenodo.571070
https://zenodo.org/record/571070
genre North Atlantic
genre_facet North Atlantic
op_rights Open Access
Creative Commons Attribution 4.0
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.5281/zenodo.571070
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