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

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? I determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, I use simulation methods and examine 822 populations of vertebrate species. Here 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 simple rules of thumb. For both simulations and the time series data, 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 of sampling populations over long periods of time. I argue that statistical power needs to be considered in monitoring program design and evaluation. Short time series are likely under-powered and potentially misleading.