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Record W4388806505 · doi:10.1016/j.dsr.2023.104198

Multiscale spatial patterns and environmental drivers of seamount and island slope megafaunal assemblages along the Mozambique channel

2023· article· en· W4388806505 on OpenAlexaff
Mélissa Hanafi‐Portier, Sarah Samadi, Laure Corbari, Marion Boulard, Elda Miramontes, Pierrick Penven, Boris Leroy, Thibault Napoléon, Stéphan Jorry, Karine Olu

Bibliographic record

VenueDeep Sea Research Part I Oceanographic Research Papers · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSeamountGeologyOceanographyMegafaunaBenthic zoneTransectContext (archaeology)VolcanoSpecies richnessGeographyPaleontology

Abstract

fetched live from OpenAlex

Seamounts are vulnerable ecosystems targeted by fishing and potentially by future mineral exploitation. Their abundance, widespread distribution, and heterogeneity of faunal and abiotic components require integrated studies at multiscale to describe spatial patterns and identify environmental drivers needed by conservation plans. There is also a lack of knowledge on seamount benthic ecosystems in some regions, such as the Indian Ocean. These gaps, in the context of Marine Protected Areas establishment in the region, have motivated the present study focusing on the Mozambique Channel Eparses islands and flat top seamounts, along a 10-degree latitude gradient. These structures are characterized by complex volcanic and carbonate geomorphologies at multiscale and are distributed along a highly dynamic turbulent ocean circulation area with large anticyclonic eddies. For the first time, we analysed, from seabed image transects obtained by towed-camera on four seamounts, and two volcanic islands - Bassas da India and Mayotte - external slopes, and from multiscale environmental data, how benthic communities respond to this high habitat heterogeneity at regional, and local scales. This study reveals high discrepancies of benthic megafauna richness, density, and beta diversity between seamounts and between slopes of the same islands. Moreover, at similar latitude, seamounts display higher densities than island slopes. The highest densities found on a seamount of the Glorieuses archipelago are explained by strong currents and flat homogeneous geomorphology. Except on this seamount, the beta diversity is high, despite the quite limited depth range explored (84–734 m) and is the highest on island slopes and Hall Bank, driven by the diversity and hardness of the substrate. Beta diversity is mainly due to taxa turnover, with high contribution of the habitat-forming sponges and cnidarians, together with a few mobile taxa. We identified from biogeographic network analysis 12 dominant faunal assemblages, displaying a patchy distribution, with variability in composition both between and within sites. Currents and primary productivity explain ∼15% of the observed assemblage structure along the channel, while geomorphology (km scale), topography (60–500 m scale) and substrate (60-m units) explain together 24% of the faunal spatial patterns. Analysis of spatial structures along island slopes detected some small (100–200 m), medium (∼1 km) and large scale (∼2–6 km) megabenthic community structures, partly explained by topography, substrate, depth, and slope. Despite limited taxonomic identifications for this poorly sampled area, this study reveals an outstanding heterogeneity of megabenthic assemblages at multispatial scales in the Mozambique Channel seamounts and island slopes, in response to the complex hydrography and geology of the area. Further characterization of environmental drivers with greater focus at local scales including hydrographic variables are therefore needed to improve predictions of suitable habitats of vulnerable marine ecosystems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.300
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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