MEPS 572:269-274 (2017)  -  DOI: https://doi.org/10.3354/meps12149

NOTE
Quantitative argument for long-term ecological monitoring

Alfredo Giron-Nava1, Chase C. James1, Andrew F. Johnson1, David Dannecker2, Bethany Kolody1, Adrienne Lee2, Maitreyi Nagarkar1, Gerald M. Pao3, Hao Ye1, David G. Johns4, George Sugihara1,*

1Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
2Division of Biological Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
3Salk Institute for Biological Studies, 10010 N Torrey Pines Road, La Jolla, CA 92037, USA
4Sir Alister Hardy Foundation for Ocean Science, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK
*Corresponding author:

ABSTRACT: Although it seems obvious that with more data, the predictive capacity of ecological models should improve, a way to demonstrate this fundamental result has not been so obvious. In particular, when the standard models themselves are inadequate (von Bertalanffy, extended Ricker etc.) no additional data will improve performance. By using time series from the Sir Alister Hardy Foundation for Ocean Science Continuous Plankton Recorder, we demonstrate that long-term observations reveal both the prevalence of nonlinear processes in species abundances and an improvement in out-of-sample predictability as the number of observations increase. The empirical results presented here quantitatively demonstrate the importance of long-term temporal data collection programs for improving ecosystem models and forecasts, and to better support environmental management actions.


KEY WORDS: Long-term monitoring · Predictability · Nonlinearity · Time series · Population dynamics · Ecological data


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Cite this article as: Giron-Nava A, James CC, Johnson AF, Dannecker D and others (2017) Quantitative argument for long-term ecological monitoring. Mar Ecol Prog Ser 572:269-274. https://doi.org/10.3354/meps12149

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