Welcome! We seek to understand the structure and resilience of biological systems - often marine ecosystems - in theory and in data. We use a ~50/50 mix of ecology and math, so check out the sections below for an idea of what we do and the tabs above for the questions we like - and our linked publications.

Math perspective:
We explore how cycles, chaos, and phase shifts among attractors play out across networks - in particular, networks with heterogeneous nodes or dynamic interactions. For this, we use attractor reconstruction (aka Takens theorem) and Gaussian Processes (aka ‘deep’ machine learning) alongside classical approaches like ODEs, integrodifference equations, random matrices, and maximum likelihood. Foremost, we focus on finding novel dynamics and the simplest models that capture them.

Ecology perspective:
We look for the key ecological processes - warming, competition, animal behavior - that predominantly shape ecosystems and their resilience. To do this, we build simple models of competing ecological hypotheses, and then see which model (hypothesis) better explains patterns in nature. Fitting mechanistic models to field data, we also get estimates of ecological rates, interactions, and resilience “for free”. These are critical for management but notoriously hard to measure in the field.