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Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon

Authors: T.R.R. Pearman; Katleen Robert; Alexander Callaway; Robert Hall; C. Lo Iacono; Veerle A.I. Huvenne;

Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon

Abstract

Submarine canyons are associated with increased biodiversity, including cold-water coral (CWC) colonies and reefs which are features of high conservation value that are under increasing anthropogenic pressure. Effective spatial management and conservation of these features requires accurate distribution maps and a deeper understanding of the processes that generate the observed distribution patterns. Predictive distribution modelling offers a powerful tool in the deep sea, where surveys are constrained by cost and technological capabilities. To date, predictive distribution modelling in canyons has focussed on integrating groundtruthed acoustically acquired datasets as proxies for environmental variables thought to influence faunal patterns. Physical oceanography is known to influence faunal patterns but has rarely been explicitly included in predictive distribution models of canyon fauna, thereby omitting key information required to adequately capture the species-environment relationships that form the basis of predictive distribution modelling. In this study, acoustic, oceanographic and biological datasets were integrated to undertake high-resolution predictions of benthic megafaunal diversity and CWC distribution within Whittard Canyon, North-East Atlantic. The main aim was to investigate which environmental variables best predict faunal patterns in canyons and to assess whether including oceanographic data improves predictive modelling. General additive models, random forests and boosted regression trees were used to build predictive maps for CWC occurrence, megafaunal abundance, species richness and biodiversity. To provide more robust predictions, ensemble techniques that summarise the variation in predictions and uncertainties between modelling approaches were applied to build final maps. Model performance improved with the inclusion of oceanographic data. Ensemble maps identified areas of elevated current speed that coincided with steep ridges and escarpment walls as the areas most likely to harbour CWCs and increased biodiversity, probably linked to local hydrodynamics interacting with topography to concentrate food resources. This study shows how incorporating oceanographic data into canyon models can broaden our understanding of processes generating faunal patterns and improve the mapping of features of conservation, supporting effective procedures for spatial ecosystem management

This work was funded by the NERC MAREMAP programme, the ERC CODEMAP project (Grant no 258482), the EC FP7 IP HERMIONE, DEFRA and the NERC CLASS programme (Grant No NE/R015953/1). Tabitha Pearman is a PhD student in the NERC-funded SPITFIRE Doctoral Training Programme (Grant number NE/L002531/1). Veerle Huvenne currently receives funding from the NERC CLASS programme and from the European Union’s Horizon2020 research and innovation programme iAtlantic project (grant agreement No 818123)

With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

17 pages, 8 figures, 4 tables, supplemental material https://doi.org/10.1016/j.pocean.2020.102338

Peer reviewed

Countries
Spain, United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Species distribution Biodiversity Submarine canyon Canyon geography geography.geographical_feature_category Oceanography Benthic zone Ecosystem management Environmental science Species richness Predictive modelling

Keywords

Aquatic Science, Species distribution models, Geology, Submarine canyons, Internal tide, Cold-water corals

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    visibility views 55
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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    41
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
41
Top 1%
Average
Top 1%
55
132
Funded by
UKRI| SPITFIRE - the Southampton Partnership for Innovative Training of Future Investigators Researching the Environment
Project
  • Funder: UK Research and Innovation (UKRI)
  • Project Code: NE/L002531/1
  • Funding stream: NERC
,
EC| iAtlantic
Project
iAtlantic
Integrated Assessment of Atlantic Marine Ecosystems in Space and Time
  • Funder: European Commission (EC)
  • Project Code: 818123
  • Funding stream: H2020 | RIA
Validated by funder | iis
,
EC| HABISS
Project
HABISS
Eco-hydrodynamics of cold water coral habitats across integrated spatial scales
  • Funder: European Commission (EC)
  • Project Code: 890815
  • Funding stream: H2020 | MSCA-IF-EF-CAR
Validated by funder | sysimport:crosswalk:repository
,
EC| CODEMAP
Project
CODEMAP
COmplex Deep-sea Environments: Mapping habitat heterogeneity As Proxy for biodiversity
  • Funder: European Commission (EC)
  • Project Code: 258482
  • Funding stream: FP7 | SP2 | ERC
sysimport:crosswalk:repository
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