Prof. Pengjuan Zu’s group aims to scale up the mechanistic understanding of species interactions from species to community level by integrating information theory into eco-evolutionary theories. The work combines empirical and theoretical aspects, and aims to deliver novel insights on how to improve ecosystem stability and sustainability under global changes.
Alpine grasslands are species rich communities and provide valuable resources for alpine pollinators. However, global warming may strongly affect the diversity of plants and insects and their interaction networks. This project aims to study the patterns of chemical communications in plant-insect networks and develop a novel theoretical framework to tackle the roles of chemical information in mediating species interactions in ecological communities and how they may get affected by climate changes.
We use field sites of alpine grasslands at different elevations at the Calanda (Graubünden, Switzerland).
Fieldwork includes:
Agricultural ecosystems, or agroecosystems, dominate many landscapes around the globe, feeding the burgeoning human population but threatening the biodiversity and often referred as “green deserts". With the growing concerns about biodiversity loss and its potentially disastrous consequences on ecosystem functioning and stability, we are under an urgent call for innovations in agricultural sustainability. So far, we know that some agricultural practices (e.g., organic polyculture farms) are more sustainable compared to some others (e.g., conventional monoculture farms). However, most previous studies only demonstrated phenomenological patterns on how different management practices exhibit different levels of productivity, biodiversity, and stability, lacking a process-based understanding of why this happens.
The objective of the project is to understand how to achieve synergistic (win-win) agroecosystems of productivity and other ecosystem services in a changing world.
This interdisciplinary project combines chemical ecology, agriculture, ecological networks, and information theory. The findings from this work promise a comprehensive understanding of and effective innovations for agroecosystem sustainability that can assist stakeholders and guide farmers to design and manage farms that are more resilient to climate variability and changes