Project description
A closer look at the mutualistic networks in a changing world
In the face of the biodiversity crisis, ecological networks are undergoing a profound reorganisation known as ‘interaction rewiring’. This reshuffling poses a threat to nature’s vital functions and services to humanity. In this context, the MSCA-funded ECONET project aims to predict the rewiring of mutualistic networks on a global scale. Through a fusion of empirical data and cutting-edge machine learning techniques, ECONET aims to unravel the complex web of interactions by quantifying species’ rewiring potential and modelling the effects of global change scenarios. Spearheading this mission is a team of experts, committed to equipping biodiversity management with spatially-informed insights and innovative conservation strategies, all while aligning with the EU’s environmental vision.
Objective
One of the main consequences of the biodiversity crisis is the reorganization of ecological networks. This reorganization, usually termed ‘interaction rewiring’, can drastically alter nature’s functions and services to humanity. Therefore, predicting interaction rewiring is paramount to predicting the future structure, functioning and stability of the ecosystems. Until now this has not been done because quantifying and predicting rewiring into the future requires extensive data on species traits and interactions and computationally efficient and ecologically relevant analytical tools. The main objectives of ECONET are to quantify the potential of rewiring in mutualistic networks globally and to predict how mutualistic networks rewire due to global change (climate change, land cover change, human-driven extinctions). For this, I will use empirical data of pollination and seed dispersal networks to predict probabilities of all pairwise interactions in metawebs (network covering all possible pairwise interactions) with machine learning. I will quantify the species' rewiring potential as their interaction niche breadths in the metawebs. Then, I will construct scenarios of compositional change in local networks caused by global change and identify rewired interactions under different scenarios. I will also assess the stability and functionality of rewired networks under different scenarios. The main outcome of ECONET will be spatially-explicit knowledge of global change consequences on mutualistic networks. ECONET will provide practical and novel guidance to biodiversity management and conservation strategies, including nature-based solutions embedded in the EU’s environmental agenda. As a key part of ECONET, I will receive essential training from world-leading experts in the fields of ecological machine learning and scenario development at an outstanding centre. This will allow me to establish an independent and distinct research agenda in global change ecology.
Fields of science
- natural sciencesearth and related environmental sciencessoil sciencesland-based treatment
- natural sciencesbiological sciencesecologyecosystems
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
8000 Aarhus C
Denmark