Emergent system-level phenomena across the scales of life
This work focuses on what the patterns in the interactions of a system's variables say about the kind of system it is. In these studies I focus on classifying different kinds of emergence, trying to understand how patterns at the level of an entire system relate to, for example, taxonomic or phenomenological classifications, and the multivariate interactions that comprise them.
Langendorf, R. E., & Burgess, M. G. (2021). Empirically classifying network mechanisms. Scientific Reports. 11:20501.
Lyubchich, V., Gel, Y., Kilbourne, K. H., Miller, T. J., Newlands, N. K., & Smith, A. (Eds.). (2020). Evaluating Climate Change Impacts. CRC Press.
Langendorf, R. E., & Doak, D. F. (2019). Can community structure causally determine dynamics of constituent species? A test using a host-parasite community. The American Naturalist, 194(3), E66-E80.
Langendorf, R. E., & Goldberg, D. S. (2019). Aligning statistical dynamics captures biological network functioning. arXiv preprint arXiv:1912.12551.
Inferring interactions
These studies ask how individual variables within a system interact with each other. Is variable 1 the cause of variable 2, or does causality flow the other way? Also, what rules govern how variable 1 interacts with variable 2? These questions are about system discovery, recognizing that most of the data we collect on the natural world are the abundances of variables, not how they interact, which we must instead infer.
Langendorf, R. E., Lyubchich, V., Testa, J. M., & Zhang, Q. (2021). Inferring Controls on Dissolved Oxygen Criterion Attainment in the Chesapeake Bay. ACS ES&T Water. 1(8), 1665-1675.
Langendorf, R. E., Biosciences, A., Azofeifa, J. G., Basken, J. M., & Lai, M. B. (2020). Two day time series of nascent RNA levels explains TF regulation of the MAPK pathway.
Langendorf, R., Basken, J., Lai, M., & Azofeifa, J. (2019). Abstract LB-B15: New causal drivers of estrogen signaling revealed by dense time series of nascent RNA transcription.
Langendorf, R. E. (2018). Understanding interspecific causation in multi-species systems (Doctoral dissertation, Ch. 4, University of Colorado at Boulder).
Complex system management
This research uses mathematics and stochastic simulation to test management practices in complex ecosystems. Much of this work focuses on contextualizing proposed solutions with their biological and economic tradeoffs.
Burgess, M. G., Becker, S. L., Langendorf, R. E., Fredston, A., & Brooks, C. M. (2023). Climate change scenarios in fisheries and aquatic conservation research. ICES Journal of Marine Science, fsad045.
Hegwood, M., Langendorf, R. E., & Burgess, M. G. (2020) Why win-wins are rare in complex environmental management.
Doak, D. F., Waddle, E., Langendorf, R. E., Louthan, A. M., Isabelle Chardon, N., Dibner, R. R., Keinath, D. A., Lombardi, E., Steenbock, C., Shriver, R. K., Linares, C, Garcia, M. G., Funk, W. C., Fitzpatrick, S. W., Morris, W. F., & Peterson, M. L. (2021). A critical comparison of integral projection and matrix projection models for demographic analysis. Ecological Monographs.
Drake, M. D., Salerno, J., Langendorf, R. E., Cassidy, L., Gaughan, A. E., Stevens, F. R., Pricope, N. G., & Hartter, J. (2021). Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia. Conservation Science and Practice, e345.
Far away forecasts
Can we know the future by studying the past? Are mechanistic explanations better at forecasting novel situations than pattern propagating ML approaches? These studies improve the ways we make predictions about novel environments, species introductions/extirpations, drugs, and economic scenarios in out-of-sample places and times.
Burgess, M. G., Langendorf, R. E., Moyer, J. D., Dancer, A., Hughes, B. B., & Tilman, D. (2023). Multidecadal dynamics project slow 21st-century economic growth and income convergence. Communications Earth & Environment, 4(1), 220.
Burgess, M. G., Langendorf, R. E., Ippolito, T., & Pielke Jr, R. (2020). Optimistically biased economic growth forecasts and negatively skewed annual variation. SocArXiv. July, 9.
