Environmental DNA
- L’ADN environnemental, fascinante technique pour découvrir des espèces marines, Magali Reinert (Reporterre)
- Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification (mathon-et-al-2021)
- Uso de ADN Ambiental en la Evaluación de la Diversidad Funcional y Filogenética de los Peces (marquez-et-al-2021)
- Environmental DNA metabarcoding reveals and unpacks a biodiversity conservation paradox in Mediterranean marine reserves (boulanger 2021)
- GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding (marques-et-al-2021)
- Ecological indicators based on quantitative eDNA metabarcoding: the case of marine reserves (Sanchez et al. 2022)
- Global Ecology and Biogeography – The distribution of coastal fish eDNA sequences in the Anthropocene (mathon et al 2023)
- An environmental DNA assay for the detection of Critically Endangered angel sharks (Squatina spp.) – , , , , , et al. (2023)
Posidonia seagrass beds
- Underwater acoustic positioning systems as tool for Posidonia oceanica beds survey (Descamp et al., 2005)
- Ecological status assessment using P. oceanica (PREI) (Gobert et al., 2009)
- Acoustic telemetry for monitoring P. oceanica (Descamp et al., 2010)
- A biotic index using P. oceanica (BiPo) (Lopez Y Royo et al., 2010)
- An Ecosystem-Based Approach to Assess the Status of a Mediterranean Ecosystem (EBQI) (Personnic et al., 2014)
- Impact of 85 years of coastal development on seagrass beds (Holon et al., 2015)
- Posidonia ecosystem services economical value (Campagne et al., 2015)
- Monitoring Marine Habitats With Photogrammetry (Marre et al., 2019)
- Impact des pressions anthropiques et de l’environnement sur les herbiers de Posidonia oceanica en Méditerranée française (Houngnandan, 2020)
- Développement de la photogrammétrie et d’analyses d’images pour l’étude et le suivi d’habitats marins (Marre, 2020)
- Fine-scale automatic mapping of living Posidonia oceanica seagrass beds with underwater photogrammetry (Marre et al., 2020)
- Préservation des posidonies: les ressorts d’une collaboration efficace (Deter et al., 2022)
- Earth observation for ecosystem accounting: spatially explicit national seagrass extent and carbon stock in Kenya, Tanzania, Mozambique and Madagascar (Traganos et al., 2022)
- Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean Sea) (Rende et al 2022).
- Early signals of Posidonia oceanica meadows recovery in a contexte of wastewater tratment improvements, (Bockel et al 2024).
Anthropogenic pressures
• Impact of 85 years of coastal development on seagrass beds (Holon et al., 2015a)
• Fine-Scale Cartography of Human Impacts along French Mediterranean Coasts (Holon et al., 2015b)
• Interactions entre écosystèmes marins et pressions anthropiques (Holon 2015)
• Boat anchoring pressure on coastal seabed using AIS data (Deter et al., 2017)
• A predictive model based on coastal anthropogenic pressures (Holon et al., 2018)
• Anchoring pressure and the effectiveness of new management measures quantified using AIS data and a mobile application. Marine Pollution Bulletin (Bockel T, Marre G, Delaruelle G, et al, 2023)
Coralligenous reefs
• A Visual Basic program for the determination of coral and substrate coverage (Kohler & Gill 2006)
• New multidimensional functional diversity indices (Villéger et al., 2008)
• An index based on coralligenous assemblages : CAI (Deter et al., 2012)
• A photographic method detects depth gradient in coralligenous (Deter et al., 2012)
• Mapping biodiversity in three-dimensions challenges marine conservation strategies (Doxa et al., 2015)
• Fine-Scale Cartography of Human Impacts along French Mediterranean Coasts (Holon et al., 2015)
• Interactions entre écosystèmes marins et pressions anthropiques (Holon 2015)
• Taxonomic and functional diversity increase the aesthetic value of coralligenous reefs (Tribot 2016)
• Esthétique et Biodiversité des écosystèmes sous-marins (Tribot 2017).
• Integrating the aesthetic value of landscapes and diversity (Tribot et al., 2018)
• Deep convolutional neural networks to monitor coralligenous (Marre et al., 2020).
