Biogeographical Patterns in the Deep North Atlantic: a Revision of the GOODS Classification System

Open Access

Abstract

Poster presentation at ATLAS 3rd General Assembly. Understanding marine biogeography and, in particular, vulnerable marine ecosystems (VMEs) will lead to better ocean governance in a future ocean challenged by rapid rates of climate change and the exploitation of living and non-living resources in the deep ocean. Most of the deep-seabed and VMEs, however, lie in areas beyond national jurisdiction (ABNJ), where the study of VME biogeography has received far less attention and where there is very limited governance. Biogeographic classifications have been used to date to analyse patterns of marine biodiversity and advancing knowledge of evolutionary and ecosystem processes (Rice et al., 2011). These classifications can also assist governments in designing management tools such as marine protected areas. The Global Open Oceans and Deep Seabed (GOODS) biogeographic classification system (UNESCO, 2009; Watling et al., 2013) was developed to provide technical support to planning and policy decisions related to open ocean and deep-seabed areas. GOODS divides the deep ocean into pelagic and benthic biogeographic provinces based on biological data such as primary production, and a range of environmental variables. The classification is based entirely on physical proxies, presumed to reflect species biogeography. Physical-proxy based schemes are available now for managers and they are based on data that are more easily compiled and updated. Thus, a main purpose of my thesis is to validate GOODS using species data and refine where necessary to overcome three limitations of GOODS to delineate biogeographic provinces in the deep ocean. Firstly, GOODS has not been validated for complex habitats formed by VME indicator taxa, which underpins the need of testing the biogeography of VME indicator species. Secondly, it does not account for projected future climate change scenarios, and thus is currently only a static product. Finally, it represents a high-level classification system, with both pronounced heterogeneity and a ...