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. Little work has actually addressed the length of time series required. In other words, when is a population time series long enough to address a question of interest? Here, I explore this question using both simulations 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. I then examine 822 populations of vertebrate species. I show that on average 15.9 years of continuous monitoring are required in order to achieve a high level of statistical power. However, there is a wide distribution around this average, casting doubt on common, simple rules of thumb. The minimum time required depends on trend strength, population variability, and temporal autocorrelation. However, there were no life-history traits (e.g. generation length) that were predictive of the minimum time required. These results point to the importance long-term monitoring. I argue that statistical power needs to be considered more in monitoring programs. Short time series are likely under-powered and potentially misleading.