The Statistical Power of Two Butterfly Monitoring Schemes to Detect Trends

Author
van Strien Arco J.
van de Pavert R
Moss D.
Yates T. J.
van Swaay Chris A. M.
Vos P.
Abstract

<ol>
<li>Monitoring schemes for butterflies in the United Kingdom and the Netherlands are aimed at the detection of long-term trends. It is useful to examine the power of these schemes to detect trends in a given period of time.</li>
<li>The approach was based on an ANOVA-model, and the trends were compared with the random population changes from one year to another. Several assumptions were made for simplicity's sake: autocorrelation in the data was ignored and only linear trends in log<sub>10</sub> (N + 1) transformed data were examined.</li>
<li>The relevant variance components to examine were the year-to-year variances and year-by-site variances. These were estimated from the time series of the British Butterfly Monitoring Scheme. Year-to-year variances appeared to be higher in northern Britain than in other regions. In addition, variance components were related to the voltinism of species.</li>
<li>Power assessment was based on the estimates of variance components and on the number of sampling sites. In the British scheme, for 37 out of 51 species studied a decrease of 50% or less is detectable with a power of 80% within a 20-year period. In the Dutch scheme such a decrease is detectable for 29 out of 47 species.</li>
<li>Because the schemes lack power for a number of species, several strategies are discussed to enhance power. For species present at less than 25 sites, it is most effective to increase the number of sampling sites where they are present, if that is possible in practice. But for species that are present at more than 50 sites, a further increase hardly improves the power. For these species, it is more efficient to adjust the data for weather conditions than to increase the number of sites.</li>
<li>The assumptions we made hardly affect the results for common species. But for rare species the results are more or less questionable. To get better estimates of the power, methods to assess power for monitoring schemes need to be developed that treat count data as discrete random variables.</li>
</ol>

Year of Publication
1997
Journal
Journal of Applied Ecology
Volume
34
Issue
3
Number of Pages
817-828
Date Published
June 1, 1997
ISBN Number
0021-8901
URL
DOI
10.2307/2404926
Short Title
Journal of Applied Ecology