Research Product

Long-term records of biological data are extremely valuable for documenting ecosystem changes, for differentiating natural changes from those caused by humans, and for generating and analyzing testable hypotheses. Long-term sampling, however, is generally discouraged by a variety of institutional disincentives, so that today such records are uncommon. We discuss approaches for overcoming these disincentives through improved research planning and design, including clearer a priori definition of management and regulatory actions and information needs, more rigorous adherence to hypothesis formulation and testing, and proper spatial and temporal scaling in sampling. We distinguish between prospective study design, in which the foregoing elements are essential for cost-effectiveness, and retrospective analysis, which relies on reconstruction of long-term records from existing data sets. We demonstrate the great value of retrospective analysis of encountered data, and argue for renewed attention to archival of data sets with documented data quality, intercalibration and documentation of methodologies, and long-term storage of samples for future analysis. Such practices are essential to ensure the quality of long-term records that are reconstructed.