Easton R White, Christie A. Bahlai

Long-term monitoring programs are a fundamental part of both understanding system dynamics and informing management decisions. However, monitoring programs not always designed to consider statistical power, site selection, or the full costs and benefits of monitoring. Further, data from monitoring programs with different goals and protocols are now being combined for comparative analyses. Key considerations can be incorporated into the optimal design of a management program with simulations and experiments. Here, we advocate for the expanded use of a third approach: non-random sampling of previously-collected data. This approach conducts experiments with available data to understand the consequences of different monitoring approaches. We first illustrate non-random sampling in the context of monitoring programs to assess species trends. We then apply the approach to a pair of additional, more general case studies to show the versatility of conducting experiments with previously-collected data. Non-random sampling of previously-collected data is underutilized, but has the potential to improve monitoring programs. We show that this approach is useful in monitoring species trends, understanding fisheries and agriculture, as well as other areas. When combined with data on the cost of monitoring, this approach can also be used to assess the value of information gained from monitoring.