When is a knowledge gap filled in environmental monitoring?

Abstract

Monitoring is essential for environmental management, including filling knowledge gaps and improving predictive capabilities. Existing thinking around the required duration of monitoring is dominated by the perceived importance of long-term datasets. However, how long do we need to monitor to fill a knowledge gap? We use existing data on spawning of a native fish in the Goulburn River, south-east Australia, an existing flow-ecology model, and three techniques: (i) historical data evaluation (ii) bootstrapping analysis and (iii) synthetic data simulation, to examine the contribution of additional data to reducing model uncertainty. Results from the observed monitoring data appear to suggest that 4 years of data (2014-2017) is adequate, and that additional data provides limited additional understanding of the relationship between discharge and spawning. However, this result was contingent upon the particular sequence of 4 years of data that was observed. Bootstrapping the same dataset and simulating data both suggest 8 years might be necessary. With the case-study program having now collected 10 years of monitoring data, we contend that monitoring could be scaled back, with resources freed up shifting to addressing other knowledge gaps. This case study demonstrates that, when it comes to filling specific knowledge gaps, long-term data sets from monitoring are not always required, potentially allowing managers to reallocate monitoring effort to other areas of higher uncertainty. While different managers may draw this line at different points, using resampled or synthetic monitoring data to demonstrate the value of monitoring is a method that can be applied to other endpoints and other monitoring programs. This approach can serve as a useful tool to support evidence-based decision-making, ultimately contributing to more efficient environmental management.

Keywords: Adaptive monitoring; Environmental monitoring; Flow-ecology relationship; Golden perch spawning; Knowledge gaps.