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Ecological forecasting and data assimilation in a data-rich era.

Publication ,  Journal Article
Luo, Y; Ogle, K; Tucker, C; Fei, S; Gao, C; LaDeau, S; Clark, JS; Schimel, DS
Published in: Ecological Applications : a Publication of the Ecological Society of America
July 2011

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.

Duke Scholars

Published In

Ecological Applications : a Publication of the Ecological Society of America

DOI

ISSN

1051-0761

Publication Date

July 2011

Volume

21

Issue

5

Start / End Page

1429 / 1442

Related Subject Headings

  • Time Factors
  • Models, Theoretical
  • Forecasting
  • Ecology
  • Ecology
  • Computer Simulation
  • 07 Agricultural and Veterinary Sciences
  • 06 Biological Sciences
  • 05 Environmental Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Luo, Y., Ogle, K., Tucker, C., Fei, S., Gao, C., LaDeau, S., … Schimel, D. S. (2011). Ecological forecasting and data assimilation in a data-rich era. Ecological Applications : A Publication of the Ecological Society of America, 21(5), 1429–1442. https://doi.org/10.1890/09-1275.1
Luo, Yiqi, Kiona Ogle, Colin Tucker, Shenfeng Fei, Chao Gao, Shannon LaDeau, James S. Clark, and David S. Schimel. “Ecological forecasting and data assimilation in a data-rich era.Ecological Applications : A Publication of the Ecological Society of America 21, no. 5 (July 2011): 1429–42. https://doi.org/10.1890/09-1275.1.
Luo Y, Ogle K, Tucker C, Fei S, Gao C, LaDeau S, et al. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications : a Publication of the Ecological Society of America. 2011 Jul;21(5):1429–42.
Luo, Yiqi, et al. “Ecological forecasting and data assimilation in a data-rich era.Ecological Applications : A Publication of the Ecological Society of America, vol. 21, no. 5, July 2011, pp. 1429–42. Epmc, doi:10.1890/09-1275.1.
Luo Y, Ogle K, Tucker C, Fei S, Gao C, LaDeau S, Clark JS, Schimel DS. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications : a Publication of the Ecological Society of America. 2011 Jul;21(5):1429–1442.
Journal cover image

Published In

Ecological Applications : a Publication of the Ecological Society of America

DOI

ISSN

1051-0761

Publication Date

July 2011

Volume

21

Issue

5

Start / End Page

1429 / 1442

Related Subject Headings

  • Time Factors
  • Models, Theoretical
  • Forecasting
  • Ecology
  • Ecology
  • Computer Simulation
  • 07 Agricultural and Veterinary Sciences
  • 06 Biological Sciences
  • 05 Environmental Sciences