Minimum time required to detect population trends: the need for long-term monitoring programs
- 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
- © 2017 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
- 2017. Minimum time required to detect population trends: the need for long-term monitoring programs. PeerJ Preprints 5:e3168v1 https://doi.org/10.7287/peerj.preprints.3168v1
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. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.
Author Comment
This manuscript is in preparation for submission at Biological Conservation.
Supplemental Information
Supplementary text, figures, and tables
We provide an expanded methods sections, additional figures, minimum time calculations for determining exponential growth, simulations with a more complicated population model, minimum time calculations for determining long-term growth rates, and the use of generalized additive models to identify population trends. All code and data can be found at https://github.com/erwhite1/time-series-project