Publications

1. The FishErIes Size and functional TYpe model (FEISTY) is a mechanistic ecosystem model that fully integrates ecosystem structure across trophic levels through functional types.
2. We present an R package that enables users to run simulations ranging from a 0D chemostat to full global scales. The library is written in Fortran90 with an R interface and provides a web application for visual exploration.
3. We present and compare results from four core configurations across a range of depths, productivity and fishing levels, and we assess the convergence of solutions as the number of size classes is increased.
4. The model has historically been coupled to biogeochemical models of mesozooplankton and detritus production, but it can also be applied in a stand-alone version. We demonstrate the library to set up and simulate fish communities under varying productivity of mesozooplankton and benthos, and top-down forcing from fishing.
5. We outline three strategies for coupling FEISTY with biogeochemical model output and discuss future directions and open issues.

In the Atlantic Arctic, bowhead whales (Balaena mysticetus) were nearly exterminated by European whalers between the seventeenth and nineteenth centuries. The collapse of the East Greenland–Svalbard–Barents Sea population, from an estimated 50 000 to a few hundred individuals, drastically reduced predation on mesozooplankton. Here, we tested the hypothesis that this event strongly favoured the demography of the little auk (Alle alle), a zooplanktivorous feeder competitor of bowhead whales and the most abundant seabird in the Arctic. To estimate the effect of bowhead whaling on little auk abundance, we modelled the trophic niche overlap between the two species using deterministic simulations of mesozooplankton spatial distribution. We estimated that bowhead whaling could have led to a 70% increase in northeast Atlantic Arctic little auk populations, from 2.8 to 4.8 million breeding pairs. While corresponding to a major population increase, this is far less than predicted by previous studies. Our study illustrates how a trophic shift can result from the near extirpation of a marine megafauna species, and the methodological framework we developed opens up new opportunities for marine trophic modelling.

Robust projections of future trends in global fish biomass, production and catches are needed for informed fisheries policy in a changing climate. Trust in future projections, however, relies on establishing that models can accurately simulate past relationships between exploitation rates and ecosystem states. In addition, historical simulations are important to describe how the oceans have changed due to fishing. Here we use fisheries catch, catch-only assessment models and effort data to estimate regional fishing exploitation levels, defined as the fishing mortality relative to fishing mortality at maximum sustainable yield (F/FMSY). These estimates are given for large pelagic, forage and demersal fish types across all large marine ecosystems and the high seas between 1961 and 2004; and with a ‘ramp-up’ between 1841 and 1960. We find that global exploitation rates for large pelagic and demersal fish consistently exceed those for forage fish and peak in the late 1980s. We use the rates to globally simulate historical fishing patterns in a mechanistic fish community model. The modeled catch aligns with the reconstructed catch, both for total catch and catch distribution by functional type. Simulations show a clear deviation from an unfished model state, with a 25% reduction in biomass in large pelagic and demersal fish in shelf regions in recent years and a 50% increase in forage fish, primarily due to reduced predation. The simulations can set a baseline for assessing the effect of climate change relative to fishing. The results highlight the influential role of fishing as a primary driver of global fish community dynamics.

Planetary boundaries represent thresholds in major Earth system processes that are sensitive to human activity and control global-scale habitability and stability. These processes are interconnected such that movement of one planetary boundary process can alter the likelihood of crossing other boundaries. Here we argue that the observed deoxygenation of the Earth’s freshwater and marine ecosystems represents an additional planetary boundary process that is critical to the integrity of Earth’s ecological and social systems, and both regulates and responds to ongoing changes in other planetary boundary processes. Research on the rapid and ongoing deoxygenation of Earth’s aquatic habitats indicates that relevant, critical oxygen thresholds are being approached at rates comparable to other planetary boundary processes. Concerted global monitoring, research and policy efforts are needed to address the challenges brought on by rapid deoxygenation, and the expansion of the planetary boundaries framework to include deoxygenation as a boundary helps to focus those efforts.

Plastic pollution in marine environments is of global concern, yet its distribution within seagrasses remains poorly understood. We explore the efficiency of Posidonia oceanica in trapping microplastics (MPs) across various components (leaves, rhizomes, sediment), examine their potential transfer through the food web and assess their dispersal using advanced modelling techniques. Field surveys confirm that P. oceanica traps MPs across all components, with the often-overlooked rhizomes accumulating over twice as many MPs (0.2 ± 0.41 items/rhizome) as leaves (0.08 ± 0.28 items/leaf). MP abundance is lower in vegetated sediments than in the adjacent unvegetated seabed (15 ± 1.9 vs. 49 items kg−1 dry weight, respectively). While individual meadow’s substrates exhibit low MP levels, the overall concentration increases substantially when accounting for its multi-dimensional structure. Species-specific traits, such as leaf height, and local hydrodynamic processes are likely influencing MPs spatiotemporal distribution. The elevated risk of MPs ingestion by seagrass-associated grazers cannot be confirmed, but further investigation is necessary. This study highlights the effectiveness of a holistic approach in assessing MP pollution within seagrass ecosystems, emphasizing its importance as the way forward.

Despite considerable efforts to improve scientific understanding and risk management, governments and businesses remain insufficiently prepared to confront large oil spills considered to be the so called ‘high-impact low-probability’ disasters. To alleviate this problem, we focus on the historical HAVEN oil spill (off the Port of Genoa, 1991) recognized not only as the largest shipwreck in the European waters, but also as one of the worst oil pollution cases in the Med. We reconstruct this spill with the Lagrangian oil spill model MEDSLIK-II forced by the to-date high resolution meteo-oceanographic datasets. Moreover, we run the HAVEN oil spill scenario stochastically sampling virtual spills randomly in space and time. The results are presented as the pollution hazard indices in probabilistic terms, which is supposed to be a representative indicator of future accidents. The highest indices are found in the Alboran Sea, Algerian and Liguro-Provençal subbasins, and in the center of the Ionian Sea. Conversely, the southern part of the Ionian, the areas east of Sardinia and west of Corsica, the Gulf of Lion, the northern Adriatic, and north-eastern Aegean Sea do not reveal high hazards.

Inversion models, in the context of oceanography, relate the observed ocean color to the concentrations of the different biogeochemical components present in the water of the ocean. However, building accurate inversion models can be quite complex due to the many factors that can influence the observed ocean color, such as variations in the composition or the optical properties of biogeochemical products. Here we assess the feasibility of the inversion approach, by implementing the three-stream light inversion model in a one-dimensional water column configuration, represented at the BOUSSOLE site in the northwestern Mediterranean Sea. Moreover, we provide a comprehensive sensitivity analysis of the model’s skill by perturbing the parameters of the bio-optical properties and phytoplankton physiology. Analysis of the inversion indicates that the model is able to reconstruct the variability of the optical constituents. Results indicate that chlorophyll-a and coloured dissolved organic matter play a major role in light modulation. The sensitivity analysis shows that the parameterization of the ratio of chlorophyll-a to carbon is important for the performance of the inversion model. A coherent inversion model, as presented, can be used as an observational operator to assimilate remote sensing reflectance.

plasticparcels is a python package for simulating the transport and dispersion of plastics
in the ocean. The tool is based on v3.0.3 of the parcels computational Lagrangian ocean
analysis framework (Delandmeter & van Sebille, 2019; Lange & van Sebille, 2017), providing a
modular and customisable collection of methods, notebooks, and tutorials for advecting virtual
plastic particles with a wide range of physical properties. The tool applies a collection of
physical processes to the virtual particles, such as Stokes drift, wind-induced drift, biofouling,
and turbulent mixing, via custom particle behaviour programmed in the form of Kernels. In
addition to the fine-scale physics parameterisations, plasticparcels provides global particle
initialisation maps that represent best estimates for plastic pollution emissions along coastlines
(Jambeck et al., 2015), from river sources (Meijer et al., 2021), and in the open-ocean from
fishing-related activities (Kroodsma et al., 2018), as well as a current best estimate of buoyant
plastic concentrations globally (Kaandorp et al., 2023). We envisage plasticparcels as a
tool for easy-to-run plastic dispersal simulations; as well as for rapid prototyping, development,
and testing of new fine-scale physics parameterisations.

Squid differ from fish by their high growth rate, short life span, and feeding behavior. Their fast life strategy is thought to impose a high predation pressure on zooplankton, fish, and other squid preys, and a rapid transfer of energy to upper trophic levels of marine food webs. However, there is a lack of understanding of how squid’s fast life cycle affects the food-web structure, which is needed to project squid biomass across marine regions under shifting climatic conditions. Here, we examine the role of squid on community metabolism and biomass by collecting data on squid somatic growth and incorporating squid in a size- and trait-based fish community model. We show that squid have a 5 times higher average somatic growth rate than fish. Due to their high food demands, squid are constrained to regions of high pelagic secondary production. The presence of squid in these systems is associated with a reduction in total consumer biomass. This decline is caused by an increase in community-level respiration losses associated with squid. Our results indicate that squid might have a large impact on ecosystem structure even at relatively low standing stock biomass. Consequently, the recent proliferation of squid in ecosystems around the world is likely to have significant ecological and socio-economic impacts.

The northwestern shelf of the Black Sea has been affected by eutrophication and bottom hypoxia since the sixties. Consequently, the macrozoobenthos has suffered a well-established decline in biodiversity. However, the nature of the current benthic communities remains questionable. From 1995 to 2017, we compiled species and abiotic data for 138 sites over the shelf. Through an appropriate multivariate analytical approach, we identified benthic community changes solely due to organic pollution variations. Our results show signs of recovery with an increase in biodiversity and proportion of species vulnerable to organic enrichment. These changes were related to a decrease in riverine loads and subsequent eutrophication. However, some long-lived species typical of the area still did not exhibit noticeable recovery, which suggests that either the recovery process has not yet been achieved or some environmental conditions are still not met to warrant a sea floor ecosystem state substantially healthy.

Data assimilation (DA) in marine and freshwater systems combines numerical models and observations to deliver the best possible characterization of a waterbody’s physical and biogeochemical state. DA underpins the widely used 3D ocean state reanalyses and forecasts produced operationally by, e.g., the Copernicus Marine Service. The use of DA in natural waters is an active field of research, but testing new developments in realistic setting can be challenging as operational DA systems are demanding in terms of computational resources and technical skill. There is a need for test beds that are sufficiently realistic but also efficient to run and easy to operate. Here, we present the Ensemble and Assimilation Tool (EAT), a flexible and extensible software package that enables data assimilation of physical and biogeochemical variables in a one-dimensional water column. EAT builds on established open-source components for hydrodynamics (GOTM), biogeochemistry (FABM), and data assimilation (PDAF). It is easy to install and operate and is flexible through support for user-written plugins. EAT is well suited to explore and advance the state of the art in DA in natural waters thanks to its support for (1) strongly and weakly coupled data assimilation, (2) observations describing any prognostic and diagnostic element of the physical–biogeochemical model, and (3) the estimation of biogeochemical parameters. Its range of capabilities is demonstrated with three applications: ensemble-based coupled physical–biogeochemical assimilation, the use of variational methods (3D-Var) to assimilate sea surface chlorophyll, and the estimation of biogeochemical parameters.

The super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called “Hybrid covariance super-resolution data assimilation” (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources.

Satellite observations provide a global or near-global coverage of the World Ocean. They are however affected by clouds (among others), which severely reduce their spatial coverage. Different methods have been proposed in the literature to reconstruct missing data in satellite observations. For many applications of satellite observations, it has been increasingly important to accurately reflect the underlying uncertainty of the reconstructed observations. In this paper, we investigate the use of a denoising diffusion model to reconstruct missing observations. Such methods can naturally provide an ensemble of reconstructions where each member is spatially coherent with the scales of variability and with the available data. Rather than providing a single reconstruction, an ensemble of possible reconstructions can be computed, and the ensemble spread reflects the underlying uncertainty. We show how this method can be trained from a collection of satellite data without requiring a prior interpolation of missing data and without resorting to data from a numerical model. The reconstruction method is tested with chlorophyll a concentration from the Ocean and Land Colour Instrument (OLCI) sensor (aboard the satellites Sentinel-3A and Sentinel-3B) on a small area of the Black Sea and compared with the neural network DINCAE (Data-INterpolating Convolutional Auto-Encoder). The spatial scales of the reconstructed data are assessed via a variogram, and the accuracy and statistical validity of the reconstructed ensemble are quantified using the continuous ranked probability score and its decomposition into reliability, resolution, and uncertainty.

Seagrass meadows represent a key marine ecosystem owing to the significant biodiversity they host. Protection actions are often implemented without considering connectivity between habitats. In this article, we project and prioritise Mediterranean seagrass habitats (Posidonia oceanica and Cymodocea nodosa) based on their potential as sources/retention and stepping stones for dispersal propagules of the associated biotic communities. We use this information to identify gaps in the protection of highly ranked habitats.

The importance of the response-effect trait dichotomy in marine benthic ecology has garnered recent attention. Response traits, characterising species responses to environmental variations, have been a dominant focus in the development of ecological indicators for ecosystem health assessment. In contrast, effect traits, expressing effects of organism activities on the ecosystem, still do not benefit from an equal interest in spite of the complementary facet that they provide to complete our understanding of functional diversity and ecosystem vulnerability. In this study, we explore the consequences of disturbance by bottom trawl fisheries on benthic effect trait composition.

The ways living forms develop in the biosphere are the same everywhere: growing and surviving for an ultimate reproductive success. In this achievement, organisms need to cope with various environmental constraints, but they have found solutions over evolutionary time by combining differently life history traits. Studying these adaptation processes has been in the heart of functional ecology, with a growing research endeavour in the marine benthos, particularly well suited given its presence in habitats of highly variable spatio-temporal dynamics. The marine benthos is subject to a particularly appealing research interest as, next to its diversity of life cycles, it ensures crucial ecosystem functions. This has led to numerous compilations of biological trait data sets in which very different functional information can be found. In recent years, trait-based benthic ecology has been strongly fostered by functional diversity assessments (Weigel et al., 2016; Breine et al., 2018; Llanos et al., 2020; Murillo et al., 2020; Sutton et al., 2020; Dreujou et al., 2021; Zhulay et al., 2021; Gusmao et al., 2022; Robinson et al., 2022; Festjens et al., 2023). Nowadays, benthic ecologists dispose of sophisticated analytical tools that can process various sets of traits to generate functional diversity indices (FD). However, FD assessments have been done in various contexts with mixed types of traits, often without specifying the theoretical links between traits and FD, which brings the meaning of FD subject to debate. In this opinion piece, I point out important issues regarding FD assessment in the marine benthos in the context of ecosystem functioning.

Projections of the range expansions of marine species are critical if we are to anticipate and mitigate the impacts of climate change on marine ecosystems. However, most projections do not assess the level of uncertainty of future changes, which brings their usefulness for scenario planning and ecosystem management into question. For the overall climate system, these uncertainties take three forms: scenario uncertainty, climate model uncertainty and internal climate variability. Critically, internal variability, a measure of how natural variability affects future climate projections, has largely been ignored in ecological studies. Here we use an ensemble modelling approach for the non-native Pacific oyster in Europe to understand the impact of these uncertainties. Future Pacific oyster recruitment was projected using a model that relates recruitment to cumulative and instantaneous heat exposure. Model projections were carried out for four climate change scenarios: SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5. In each scenario an ensemble of over twenty climate models was used. The impact of internal variability in climate models was assessed by using five climate models which were available with multiple pre-industrial starting points. We find that model uncertainty within SSP1 2.6 is higher than the differences between SSP1 2.6 and SSP 4.5, but it is unclear if overall scenario uncertainty is greater than climate model uncertainty due to its subjective nature. Comparisons of scenario projections indicate that future recruitment areas of Pacific oysters under the SSP5 8.5 scenario could be more than twice as high as in the low emissions SSP1 2.6 scenario. Importantly, the ensemble showed that near-term changes in Pacific oysters are highly uncertain due to internal variability, which is of a similar magnitude to climate model uncertainty on a 20-year timescale. Our results show that it is critical to think about the future in terms of potential scenarios and not individual projections.

We use complex network theory to better represent and understand the ecosystem connectivity in a shelf-sea environment. The baseline data used for the analysis are obtained from a state-of-the art coupled marine physics-biogeochemistry model simulating the North-West European Shelf (NWES). The complex network built on model outputs is used to identify the functional types of variables behind the biogeochemistry dynamics, suggesting how to simplify our understanding of the complex web of interactions within the shelf-sea ecosystem. We demonstrate that complex networks can be also used to understand spatial ecosystem connectivity, both identifying the (geographically varying) connectivity length scales and the clusters of spatial locations that are connected. These clusters indicate geographic regions where there is a substantial flow of information between the degrees of freedom within the ecosystem, while information exchange across the boundaries of these regions is limited. The results of this study help to understand how natural, or anthropogenic, perturbations propagate through the shelf-sea ecosystem, and can be used in multiple future applications such as stochastic noise modelling, data assimilation, or machine learning.

Chromophoric dissolved organic matter (CDOM) significantly contributes to the non-water absorption budget in the Mediterranean Sea. The absorption coefficient of CDOM, a_CDOM(λ), is measurable in situ and can be retrieved remotely, although ocean-colour algorithms do not distinguish it from the absorption of detritus. These observations can be used as indicators for the concentration of other relevant biogeochemical variables in the ocean, e.g. dissolved organic carbon. However, our ability to model the biogeochemical processes that determine CDOM concentrations is still limited. Here we propose a novel parameterization of the CDOM cycle that accounts for the interplay between the light- and nutrient-dependent dynamics of local CDOM production and degradation, as well as its vertical transport. The parameterization is included in a one-dimensional (1D) configuration of the Biogeochemical Flux Model (BFM), which is here coupled to the General Ocean Turbulence Model (GOTM) through the Framework for Aquatic Biogeochemical Models (FABM). Here the BFM is augmented with a bio-optical component that resolves spectrally the underwater light transmission. We run this new GOTM-(FABM)-BFM configuration to simulate the seasonal a_CDOM(λ) cycle at the deep-water site of the Bouée pour l’acquisition de Séries Optiques à Long Terme (BOUSSOLE) project in the northwestern Mediterranean Sea. Our results show that accounting for both nutrient and light dependence of CDOM production improves the simulation of the seasonal and vertical dynamics of a_CDOM(λ), including a subsurface maximum that forms in spring and progressively intensifies in summer. Furthermore, the model consistently reproduces the higher-than-average concentrations of CDOM per unit chlorophyll concentration observed at BOUSSOLE. The configuration, outputs, and sensitivity analyses from this 1D model application will be instrumental for future applications of BFM to the entire Mediterranean Sea in a three-dimensional configuration.

Earth System Models (ESMs) are the primary tool for understanding the impacts of global change and several ESMs are updated on a regular basis to provide more reliable scenarios of the future. However, the confrontation of ESMs outputs to observations reveals biases that are important to correct, especially for impact applications where the absolute scale of the environmental variable is as relevant as its trends. In addition, regional impact studies need fine scale projections to devise strategic planning and management measures. Statistical downscaling provides a fast way to produce regional ocean forcing from ESMs and can additionally produce bias-corrected outputs, which are necessary for impact applications driven by or fitted to observed data, like many ecological models. Statistical downscaling can make use of different parametric distributions depending on the variables used, and generalized regression can provide a flexible approach for this purpose. We propose a multi-model approach based on non-parametric generalized regression and a suite of indicators to select a robust statistical downscaling model that can be used for projection of future scenarios. The empirical cumulative distribution of the variables to downscale is modeled, ensuring that not only the mean but also the variance and quantiles (including the minima and maxima) are properly represented, improving the prediction of extreme events and taking into account spatial autocorrelation. The approach presented here is applied to two contrasted regional case studies, the Bay of Biscay-Celtic Sea ecosystem and the Northern Peru Current ecosystem, using the Sea Surface Temperature from the IPSL-CM5A-LR ESM. The results showed that a multimodel selection approach is appropriate as individual model performance is case specific.