Long-term census data is necessary to better understand ecological processes and make management decisions. In addition, census data is often expensive, requiring a lot of people-hours or equipment. However, little work has actually addressed the length of time series required. In other words, when is a time series of census data long enough to address a question of interest? Here, I explore two approaches to address this question: simulated-based and an empirical approach. I specifically determine the minimum time series length (e.g. number of years) required to estimate significant increases or decreases in population abundance. Importantly, I examine the ability to detect a trend with a set level of statistical power, which is often neglected in ecological time series analyses. The simulation approach estimates the minimum time required to determine significant trends in abundance even before census data is collected. The empirical approach is complementary and determines if a previously collected time series is long enough to estimate trends in abundance.
Results/Conclusions
Using simple simulations, I demonstrate how the minimum time series length required increases with weaker trends in abundance and with higher variability in population size. In addition, I examine 868 populations of vertebrate species to determine the minimum time required to detect changes in their abundance. I found that 10-15 years of continuous monitoring are required in order to achieve a high level of statistical power. Similar to simulation-approaches, the minimum time required for field census data strongly depends on trend strength, population variability, and temporal autocorrelation. Perhaps surprisingly, the minimum time required did not correlate well with biological explanatory variables, like body size or generation length. These results point to the importance of sampling populations over long periods of time. Here I stress the need for verifying a study has enough statistical power to accurately determine long-term changes in population abundance. Most studies, especially those less than 10-15 years, are probably underpowered and potentially misleading.