Using maintenance records from a long-term sensor monitoring network to evaluate the relationship between maintenance schedule and data quality.

Sensor-based environmental monitoring networks are beginning to provide the large-scale, long-term data required to address important fundamental and applied questions in ecology. However, the data quality from deployed sensors can be difficult and costly to ensure. In this study, we use maintenance records from the 12-year history of Louisiana's Coastwide Reference Monitoring System (CRMS) to assess the relationship between various dimensions of data quality and the frequency of field visits to the sensors. We use hierarchical Bayesian models to estimate the probability of missing data, the probability that a corrective offset of the sensor is required, and the magnitude of required offsets for water elevation and salinity data. We compared these estimates to predetermined risk thresholds to the help identify maintenance schedules that balanced the efficient use of labor resources without sacrificing data quality. We found that the relationship between data quality and increasing maintenance interval varied across metrics. Additionally, for most metrics, the maintenance interval when the metric's credible interval and risk threshold intersected varied throughout the year and with wetland type. These results suggest that complex maintenance schedules, in which field visits vary in frequency throughout the year and with environmental context, are likely to provide the best tradeoff between labor cost and data quality. This analysis demonstrates that quantitative assessment of maintenance records can positively impact the sustainability of long-term data collection projects by helping identify new potential efficiencies in monitoring program management.