Minimum time required to detect population trends: the need for long-term monitoring programs

  Center for Population Biology, University of California, Davis, Davis, California, The United States of America
DOI
10.7287/peerj.preprints.3168v4
Published
Accepted
Subject Areas
Aquaculture, Fisheries and Fish Science, Conservation Biology, Ecology, Natural Resource Management
Keywords
experimental design, ecological time series, statistical power, power analysis, monitoring, sampling design
Copyright
© 2018 White
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
White ER. 2018. Minimum time required to detect population trends: the need for long-term monitoring programs. PeerJ Preprints 6:e3168v4

Abstract

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

Author Comment

This version includes changes to the text and additional analyses in the supplemental material.

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