Département Ecologie aquatique

Département Ecologie aquatique

Le département Écologie aquatique de l'Eawag se compose de huit groupes de recherche et englobe un grand nombre de disciplines diverses de l'écologie et de la biologie de l'évolution, couvrant ainsi l'éventail complet depuis le niveau individuel jusqu'aux écosystèmes en passant par les associations. En savoir plus

News

Moule quagga: pronostic pour les lacs affectés

La moule quagga dans le Léman: la biomasse par mètre carré devrait passer ces 22 prochaines années du facteur 9 à 20 dans le lac de Constance (Photo: Eawag, Linda Haltiner).
16 novembre 2023 –

La comparaison de trois lacs suisses avec les grands lacs d’Amérique du Nord montre que la moule quagga se propage avec une dynamique similaire.

La comparaison de trois lacs suisses avec les grands lacs d’Amérique du Nord montre que la moule quagga se propage avec une dynamique similaire.
En savoir plus

Project Aquascope

Imagerie sous-marine en temps réel pour la surveillance du phytoplancton des lacs et du zooplancton (en Anglais)

Publications

Adde, A., Rey, P. L., Brun, P., Külling, N., Fopp, F., Altermatt, F., … Guisan, A. (2023). N-SDM: a high-performance computing pipeline for Nested Species Distribution Modelling. Ecography, 2023(6), e06540 (11 pp.). doi:10.1111/ecog.06540, Institutional Repository

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM.
Adde, A., Rey, P. L., Fopp, F., Petitpierre, B., Schweiger, A. K., Broennimann, O., … Guisan, A. (2023). Too many candidates: embedded covariate selection procedure for species distribution modelling with the covsel R package. Ecological Informatics, 75, 102080 (8 pp.). doi:10.1016/j.ecoinf.2023.102080, Institutional Repository

1. Selecting the best subset of covariates out of a panel of many candidates is a key and highly influential stage of the species distribution modelling process. Yet, there is currently no commonly accepted and widely adopted standard approach by which to perform this selection.
2. We introduce a two-step "embedded" covariate selection procedure aimed at optimizing the predictive ability and parsimony of species distribution models fitted in a context of high-dimensional candidate covariate space. The procedure combines a collinearity-filtering algorithm (Step A) with three model-specific embedded regularization techniques (Step B), including generalized linear model with elastic net regularization, generalized additive model with null-space penalization, and guided regularized random forest.
3. We evaluated the embedded covariate selection procedure through an example application aimed at modelling the habitat suitability of 50 species in Switzerland from a suite of 123 candidate covariates. We demonstrated the ability of the embedded covariate selection procedure to provide significantly more accurate species distribution models as compared to models obtained with alternative procedures. Model performance was independent of the characteristics of the species data, such as the number of occurrence records or their spatial distribution across the study area.
4. We implemented and streamlined our embedded covariate selection procedure in the covsel R package, paving the way for a ready-to-use, automated, covariate selection tool that was missing in the field of species distribution modelling. All the information required for installing and running the covsel R package is openly available on the GitHub repository https://github.com/N-SDM/covsel.
Alexander, J., Spaak, P., Möst, M., Straile, D., & Hetzenauer, H. (2023). Ökosystemforschung am Bodensee. Abschluss des Forschungsprojekts "Seewandel: Leben im Bodensee - gestern, heute und morgen". Aqua & Gas, 103(6), 54-59. , Institutional Repository

Während fünfeinhalb Jahren untersuchten Forschende aus den drei Bodenseeländern den Einfluss und die Wechselwirkung verschiedener Stressfaktoren wie Nährstoffänderungen, invasive Arten und Klimawandel auf das Ökosystem des Sees. Der aktuelle Wandel ist tiefgreifend und mit voraussichtlich weitreichenden Folgen für das komplexe Ökosystemgefüge und die Gewässernutzenden. Am 13. und 14. Juni 2023 schliesst das Projekt mit einem wissenschaftlichen Symposium und einer Informationsveranstaltung für die Praxis ab.

SNSF Fellowships Ambizione / Eccellenza / Prima

Informations pour les parties intéressées (anglais)

Eco seminaires

06.12.​2023,
18. - 19.01.​2024,
20.03.​2024,

Projet de recherche

Un projet de développement d'un concept de monitoring de la moule quagga et de soutien aux mesures de prévention et de protection en Suisse

Développement d'un concept de monitoring de la moule quagga et soutien aux mesures de prévention et de protection
The architecture of community structure, functional traits and trophic networks across blue-green ecosystems

Architecture of blue-green communities
We test how natural selection acts on quantitative immune defence traits and how ecological factors create variation in the form and strength of selection.

Natural selection on immune function
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Cyano Bloom

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