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Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs
Author(s) -
Massoud Elias C.,
Huisman Jef,
Benincà Elisa,
Dietze Michael C.,
Bouten Willem,
Vrugt Jasper A.
Publication year - 2018
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.12876
Subject(s) - predictability , data assimilation , ecology , chaotic , assimilation (phonology) , environmental science , statistical physics , biology , meteorology , geography , mathematics , computer science , statistics , physics , linguistics , philosophy , artificial intelligence
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.