Rewiring of ecological networks under global change

ended

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.