Quantitative argument for long-term ecological monitoring

Marine ecology progress series

, Adrienne Lee¹  , David Dannecker¹  , Gerald M. Pao²  Alfredo Giron-Nava³  , Chase C. James³  , Bethany Kolody³  , Hao Ye³  , Andrew F. Johnson³  , Maitreyi Nagarkar³  , David G. Johns³ 

[1] Division of Biological Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA;Salk Institute for Biological Studies, 10010 N Torrey Pines Road, La Jolla, CA 92037, USA;Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

关键词: Long-term monitoring;    Predictability;    Nonlinearity;    Time series;    Population dynamics;    Ecological data;   

DOI  :  10.3354/meps12149

学科分类：海洋学与技术

来源: Inter-Research

【 摘 要 】

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.

【 授权许可】

Unknown