Internship topic : Benthic Macrofauna – Integrating and Linking Benthic Monitoring with Water Column and Planktonic Compartment Monitoring within National Coastal Observing Systems: An Application to the Central English Channel.
Funding : ILICO Research Infrastructure
Internship supervision:
Francis ORVAIN (1/3) MERSEA « Marine Ecosystems and oRganisms reSEArch lab », – UR 7482, Caen – Inter SNO, modélisation
Jean-Philippe Pézy (1/3) : M2C « Morphodynamique Continentale et Côtière », – UMR 6143, Caen – SNO BenthOBS
Tania Hernandes-Farinas (1/3) : IFREMER LERN, Port-en-Bessin – SNO PHYTOBS et SOMLIT
The internship will take place at CREC (the Luc-sur-Mer Marine Station), using the station’s computing resources (computer, docking station, server, and virtual machines equipped with RStudio and MATLAB). Francis Orvain will provide day-to-day supervision, with additional co-supervision from Jean-Philippe Pezy and Tania Hernandez-Farinas.
Internship stipend: €4.50 per hour, based on 35 hours per week over 18 weeks, for a total of €2,835.
Duration : 4,5 months
Application deadline : 16 November 2026 contact : Francis.orvain@unicaen.fr (02 31 56 71 32)
General scientific background
– Internship objectives :
This internship aims to improve our understanding of how macrozoobenthic communities respond to hydrological variables by integrating benthic data with long-term phytoplankton community time series in the context of climate warming and at a broad spatial scale (coastal region). To achieve this objective, this collaborative project will analyse the long-term time series generated by the BenthOBS National Observation Service (SNO) in the central English Channel and compare the observed trends with those recorded by the SOMLIT, PHYTOBS-network and COAST-HF National Observation Services.
Following data preparation (harmonisation and pre-processing), the project will explore multivariate statistical methods using a shared analytical framework. These methods will first be used to interpret biological community dynamics at the local scale and subsequently at a broader spatial scale, in order to identify consistent or divergent patterns of change. The study will focus on integrating the four time series (BenthOBS, SOMLIT, PHYTOBS-network and COAST-HF) across the central English Channel, covering two benthic monitoring stations (PECTOW39 and SMILE), two SOMLIT stations, two PHYTOBS stations and one COAST-HF station. Additional data from the Water Framework Directive (WFD) monitoring programme will be incorporated into the BenthOBS dataset to obtain a time series spanning more than 15 years (Cabourg, PECTOW39).
The overarching objective is to bring together and strengthen the monitoring of benthic communities within the BenthOBS National Observation Service, thereby improving our understanding of benthic community variability in response to abiotic and biotic drivers. The project will also foster collaboration between benthic and plankton specialists across the Normandy-based partners involved in the four aforementioned National Observation Services.
This project will contribute to improving the mathematical modelling of planktonic and benthic species occurrence as a function of hydrological descriptors, with the aim of identifying shifts in ecological regimes that may affect different trophic levels across broad spatial scales (Greiner et al., 2023; Kröncke et al., 2019). Ultimately, it seeks to improve our understanding of how marine species may adapt to environmental change or migrate along latitudinal gradients in response to multiple pressures associated with global change and human activities, including resource exploitation, eutrophication and habitat modification.
Methods and resources
The internship will focus on the central English Channel dataset, which will serve as a training dataset. The intern will also develop scripts and algorithms capable of scaling analyses from the local level to broader geographical scales (the French coastline and mainland France). This work will contribute to a collaborative effort to harmonise analytical approaches with national partners, who will conduct similar analyses in parallel across other coastal regions (eastern English Channel, western English Channel, Iroise Sea, Atlantic coast and Mediterranean Sea). These activities will be undertaken through additional Master’s-level internships supervised by regional partners within the EC2CO Bentholink project (currently under evaluation).
The intern will use docking stations and virtual machines to process data using computing resources with substantial storage capacity and RAM. Shared digital tools will be available through virtual machines, including GIS software, RStudio with packages for multivariate analyses, accessible databases and MATLAB.
Datasets
LeThe internship work will be dedicated to the interpretation of BenthOBS data in the central English Channel, particularly focusing on 2 series (monitored at a semi-annual frequency since 2020), and to supplementing the data for one of the two stations (PECTOW 39) with historical WFD data DCE (<2020) available on QUADRIGE.
The aim of this project is to pool statistical approaches by combining the site scale in the Central English Channel. The proposed actions will be organized into 3 subtasks, covering the calculation of macrozoobenthic biodiversity indicators, the description of environmental forcing factors, and multivariate statistical analyses.
●Among the indicators of taxonomic and functional benthic biodiversity, the following will be calculated:
● Classical indicators such as species richness, the Shannon indices, and Pielou’s evenness.
● Indicators relating to sensitivity to eutrophication phenomena associated with organic enrichment (Borja et al., 2000; Chauvel et al., 2026), such as the AMBI, BEQI-FR, MUMARINEX, or BOPA indices.
● More recent functional diversity indicators based on biological traits (Bacouillard, 2020; Van Paemelen et al., 2026).
● Calculations of metabolic bioindicators (MSR), relating to bioenergetic concepts, such as standard metabolic rate (Brey, 2010) to estimate respiration rates, as well as overall activity indices (Cozzoli et al., 2018; Lehuen et al., 2024) that are sensitive to temperature and its latitudinal variations (Shokri et al., 2022).
To better describe the environmental forcing factors, the following will be incorporated:
● Granulometric variables, systematically measured in the BenthOBS series to characterize the dependence of communities on substrate types (Bacouillard et al., 2020; Chauvel et al., 2024).
● Data from the SOMLIT, PHYTOBS-network, and COAST-HF SNOs, which will be analysed to describe the forcing factors recorded in the water column using a harmonized approach to extract all hydrological factors (Lheureux et al., 2021, 2023).
● Daily satellite data (source: ODATIS, with a resolution of 300 × 300 m), which will help refine the description of water temperature and colour (suspended particulate matter, chlorophyll) as complementary information.
● A wind analysis will be carried out to add a variable relating to wave conditions (data from METEO-France and/or Copernicus CM-EMS).
● Data on variations in river discharge and concentrations from the network of rivers and streams (Eaufrance) to better establish links with river flow regimes.
● Climate indicators likely to explain winter regimes in the Central English Channel (AMO, NAO, AO; Lheureux et al., 2023; Kröncke et al., 2019).
Desired candidate profile (keywords)
Keywords: Macrofauna, benthos, oceanography, phytoplankton, benthic–pelagic coupling, hydrology, sediment, functioning of coastal ecosystems, spatial scale, time series, statistics, global warming and global change, biodiversity.
Students from various Master’s programmes will be contacted, including Master’s programmes in Marine Science and Oceanography, as well as in Ecology and the Master’s programme in Environmental Sciences, specialising in Environmental Management and Coastal Ecology, in the broadest sense, and particularly those related to modelling and data analysis.
The required skills include data processing and interpretation in marine ecology and oceanology.
Additional information about the internship
Data processing
The statistical methods to be used include:
●Dynamic Linear Models (DLMs), as well as other relevant methods, will be used to better identify temporal trends in long-term plankton monitoring time series (SOMLIT, PHYTOBS-network) and continental time series (coastal river discharge). These models can help detect abrupt shifts, longer-term changes (climate-driven trends), and phenological changes by removing oscillatory phenomena, thereby providing a better understanding of their impact on macrozoobenthos (Hernández-Fariñas et al., 2014).
● Multivariate analyses (e.g. WGCOIA, ETA, or DFA), prioritising those that best identify ecological trajectories over long time scales and enable the comparison of time series of different types, such as benthic and pelagic series (Djeghri et al., 2026; Kröncke et al., 2019; Sturbois et al., 2023; Toumi et al., 2023). Spatial and temporal groupings will be explored to better identify the potential impact of ecological tipping points and converging trends.
● In the event of a shift in dominance, statistical methods based on species interaction networks will be applied to identify competitive interactions and species replacements associated with global change (Blanchet et al., 2020).
● Quantile regression analyses will be fitted using models applied to populations of dominant benthic species observed in these time series, in order to explore ecological niche models based on environmental factors describing substrate characteristics (Chauvel et al., 2024) or the water column (Lehuen et al., 2025). Non-linear quantile regression methods (e.g. the normal distribution) will also be tested. These complementary approaches will be guided by the development of optimised and harmonised scripts during a cross-disciplinary workshop (the EC2CO BENTHOLINK project, currently under evaluation, and/or within the framework of the IR ILICO at the end of the year).
Planned tools
The intern will have access to all the IT resources necessary for the successful completion of this internship (docking station, virtual machine with high storage and memory capacity, office software, RStudio and MATLAB on the virtual machines) and will benefit from optimised co-supervision to interpret the time series, with support from the various experts for each component.
References
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