Minimum Sampling, Maximum Insight: Tracking Environmental Trends in a Tidal Estuary

Long-term environmental monitoring is essential for detecting ecological trends and managing dynamic systems. In estuarine environments, where monitoring is often constrained by cost and logistics, efficient resource allocation is key to sustaining effective programs. We developed a framework to optimize spatial and temporal sampling in the Great Bay Estuary (New Hampshire/Maine, USA), identifying the minimum number of years and sites needed to detect long-term trends. Using 23 years of data on five water quality parameters from 10 sites, we applied a resampling-based trend detection algorithm to estimate minimum sampling effort. Our results show that trend detectability varies by parameter and location, with each requiring different sampling durations. These thresholds also depend on the user-defined level of statistical power (e.g., 80% vs. 100%). For example, nitrogen trends were detectable with as few as five years of data, while dissolved oxygen required up to seven. Additionally, 8–9 sites were sufficient to achieve 80% statistical power, suggesting spatial redundancy at some locations. Variance partitioning revealed that autocorrelation, slope error, and data variability each influenced sampling effort for reliable trend detection. Parameters such as dissolved oxygen and water temperature—both highly autocorrelated—required longer time series, while those with lower slope precision, like nitrogen, were sensitive to measurement error. These findings underscore the value of adaptive monitoring designs that align sampling strategies with the statistical and ecological characteristics of individual parameters. Our approach provides a flexible, data-driven framework for refining estuarine monitoring programs, enabling managers to maintain ecological insight while optimizing resource use.