Evaluating the power of monitoring plot designs for detecting long-term trends in the numbers of common guillemots

1. In recent years concerns have been raised regarding the status of the common guillemot Uria aalge in the UK. Numbers have declined in several regions, highlighting the need for continued monitoring of this internationally important population. However, the extent to which the current monitoring scheme is capable of detecting declines and options for improving efficiency has received little attention. 2. We investigated the power of different monitoring design options for detecting long-term trends in abundance at a colony of guillemots. The ability to detect trends in abundance was reduced by the large temporal and spatial variability in colony attendance. Taking a linear mixed model approach, we obtained details on the sources and sizes of the variance components using count data collected from monitoring plots on the Isle of May, Scotland, and assessed how best to allocate sampling effort in the light of the count variability structure. 3. Our results indicated that trend detection will be improved by counting birds in more plots rather than by increasing the number of counts at existing plots. 4. The revisit pattern of counts at the monitoring plots during the seasonal counting period had little effect on trend detection power. However, given the practical issues associated with counting guillemots, alternative revisit patterns to the current approach are preferred. 5. For a fixed number of visits per plot, power is strongly influenced by the choice of revisit design if the day-to-day variation in colony attendance is increased. 6. Synthesis and applications. Aspects of the UK seabird monitoring scheme can be improved. Changes to the allocation of sampling effort and the plot-revisit pattern will improve both the statistical power to detect long-term trends and the efficiency of conducting the survey. We stress the importance of considering the structure and magnitude of the count variation in a power analysis because judicious design decisions depend on the relative magnitude of these variance components.