Ecosystem Forecasting Methodology Research

Our team develops tools for ecosystem management by adapting methods used by physicists for dealing with large complex systems.

A program of the Southwest Fisheries Science Center’s Fisheries Ecology Division.

The Ecosystem Forecasting Team develops robust tools for managing ecosystems, complex dynamical systems which are incompletely observed. There are always species that are not counted, differences among individuals within species that are not tracked, or changes in gene frequencies which are not measured, all of which contribute to ecosystem function.

Instead of filtering observations through assumptions, we try to let the observations of the ecosystem and how it has responded to previous management actions indicate where the system is going next. We use this information to help develop sustainable policies for conservation and management.

This involves developing new approaches to modeling complex systems, as well as making use of recent developments in other fields like nonlinear dynamics, physics, and machine learning. These tools often outperform more traditional approaches to ecological modeling. We have applied them to predicting recruitment in harvested fish populations, estimating state-dependent species interactions, and understanding synchrony and asynchrony in marine metapopulations.

Current research areas include Bayesian nonlinear forecasting, spatio-temporal delay embedding, and approximate dynamic programming.

Selected Publications

Therkildsen, Nina O., Aryn P. Wilder, David O. Conover, Stephan B. Munch, Hannes Baumann, and Stephen R. Palumbi.
2019. Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing. Science 365(6452):487-490.
https://doi.org/10.1126/science.aaw7271

Munch, Stephan B., Alfredo Giron-Nava, and George Sugihara.
2018. Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish and Fisheries 19(6):964-973.
https://doi.org/10.1111/faf.12304

Deyle, Ethan R., Robert M. May, Stephan B. Munch, and George Sugihara.
2016. Tracking and forecasting ecosystem interactions in real time. Proceedings of the Royal Society B 283(1822):20152258 (9 p.).
https://doi.org/10.1098/rspb.2015.2258

Boettiger, Carl, Marc Mangel, and Stephan Munch.
2015. Avoiding tipping points in fisheries management through Gaussian process dynamic programming. Proceedings of the Royal Society B 282(1801): art. 20141631 (9 p.).
https://doi.org/10.1098/rspb.2014.1631

Perretti, Charles T., Stephan B. Munch, and George Sugihara.
2013. Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data. Proceedings of the National Academy of Sciences of the United States of America 110(13):5253-5257.
https://doi.org/10.1073/pnas.1216076110

Salinas, Santiago, and Stephan B. Munch.
2012. Thermal legacies: transgenerational effects of temperature on growth in a vertebrate. Ecology Letters 15(2):159-163.
https://doi.org/10.1111/j.1461-0248.2011.01721.x

Our Team

Team Leader: Steve Munch

Last updated by Southwest Fisheries Science Center on November 13, 2020

Ecosystem Modeling