Publications

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.