Understanding how ecological networks respond to environmental change requires perspectives that extend beyond contemporary observations. This theme uses paleoecological data and network modeling to reconstruct species interaction networks across deep time, allowing us to explore how networks assemble, persist, and reorganize under long-term environmental dynamics.
As part of my current postdoctoral research, I investigate how large-scale metawebs give rise to realized interaction networks and how hierarchical structure and scale influence extinction risk and system-level resilience. By integrating paleo data with probabilistic network frameworks, this work connects deep-time ecology with predictive network theory.
Scaling Paleo Metawebs to Realised Networks
This flagship project applies hierarchical network frameworks to paleoecological systems to reconstruct species interaction networks across deep time. By treating observed paleo networks as probabilistic realizations of a larger interaction metaweb, this work investigates how long-term environmental change shapes network assembly, persistence, and collapse.
A central goal is to understand how scale, uncertainty, and hierarchical structure influence extinction risk and system-level resilience over evolutionary timescales.
Network Resilience and Extinction Dynamics Across Time
Building on probabilistic and hierarchical network models, this project examines how network structure mediates species persistence and extinction under long-term environmental change.
By integrating paleo data with predictive network theory, this work links deep-time patterns to contemporary questions about resilience and biodiversity loss.
Bridging Paleo and Contemporary Network Ecology
This project explicitly connects paleoecological network reconstructions with modern ecological networks, allowing insights from deep time to inform prediction, uncertainty quantification, and theory in contemporary systems.