Abstract
The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale-dependent, space-time correlated, and governed by nonlinear relations between organisms and their environment. This disparity in complexity between ecosystem models and data has created a large gap in ecology between model and data-driven approaches. Here, we explore data assimilation (DA) with the Ensemble Kalman filter to fuse a two-predator-two-prey model with abundance data from a 2600+ day experiment of a plankton community. We analyse how frequently we must assimilate measured abundances to predict accurately population dynamics, and benchmark our population model's forecast horizon against a simple null model. Results demonstrate that DA enhances the predictability and forecast horizon of complex community dynamics.
| Original language | English |
|---|---|
| Pages (from-to) | 93-103 |
| Number of pages | 11 |
| Journal | Ecology Letters |
| Volume | 21 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2018 |
| Externally published | Yes |
Funding
We appreciate the comments of the two anonymous reviewers and the Editor, which have greatly enhanced the quality of our manuscript. The first and last author greatly appreciate the support from UC-Lab Fees Research Program Award 237285 and the UCI Environment Institute, and the fourth author acknowledges support from NSF grant 1638577. We thank R. Heerkloss for the mesocosm data set, which is available in Appendix S1 of Benincà et al. (). The SODA methodology can be obtained from the last author, ([email protected]), upon request. We appreciate the comments of the two anonymous reviewers and the Editor, which have greatly enhanced the quality of our manuscript. The first and last author greatly appreciate the support from UC-Lab Fees Research Program Award 237285 and the UCI Environment Institute, and the fourth author acknowledges support from NSF grant 1638577. We thank R. Heerkloss for the mesocosm data set, which is available in Appendix S1 of Benincà et al. (2009). The SODA methodology can be obtained from the last author, (jasper@ uci.edu), upon request.
| Funders | Funder number |
|---|---|
| UCI Environment Institute | |
| National Science Foundation | 1638577 |
| National Sleep Foundation |
Keywords
- Data assimilation
- ecological models
- ecosystems
- food webs
- forecast horizons
- plankton
- predator-prey