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Thomas Claverie

Researcher at University of Montpellier

Publications -  39
Citations -  1541

Thomas Claverie is an academic researcher from University of Montpellier. The author has contributed to research in topics: Coral reef & Reef. The author has an hindex of 18, co-authored 31 publications receiving 1132 citations. Previous affiliations of Thomas Claverie include University of Massachusetts Amherst & University Marine Biological Station Millport.

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Journal ArticleDOI

Spatial flow and sedimentation patterns within patches of epibenthic structures: Combining field, flume and modelling experiments

TL;DR: In this article, the authors used a 3D hydrodynamic model to simulate the long-term sediment dynamics in the field and found that sedimentation was much larger in the high-density patches than the low-density ones.
Journal ArticleDOI

Global status and conservation potential of reef sharks

M. Aaron MacNeil, +131 more
- 22 Jul 2020 - 
TL;DR: The results reveal the profound impact that fishing has had on reef shark populations: no sharks on almost 20% of the surveyed reefs, and shark depletion was strongly related to socio-economic conditions such as the size and proximity of the nearest market, poor governance and the density of the human population.
Journal ArticleDOI

A morphospace for reef fishes: elongation is the dominant axis of body shape evolution.

TL;DR: It is found that evolutionary changes in body shape along an axis of elongation dominates diversification in reef fishes, suggesting a role for a range of developmental processes and functional consequences.
Journal ArticleDOI

A Deep learning method for accurate and fast identification of coral reef fishes in underwater images

TL;DR: Deep Learning methods can perform efficient fish identification on underwater images and offer promises to build-up new video-based protocols for monitoring fish biodiversity cheaply and effectively.
Book ChapterDOI

Coral reef fish detection and recognition in underwater videos by supervised machine learning : Comparison between Deep Learning and HOG+SVM methods

TL;DR: Two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos using a traditional two-step approach and a Deep Learning method based on Deep Learning.