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Estimating linear temporal trends from aggregated environmental monitoring data

December 1, 2016

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Citation Information

Publication Year 2017
Title Estimating linear temporal trends from aggregated environmental monitoring data
DOI 10.1016/j.ecolind.2016.10.036
Authors Richard A. Erickson, Brian R. Gray, Eric A. Eager
Publication Type Article
Publication Subtype Journal Article
Series Title Ecological Indicators
Index ID 70178569
Record Source USGS Publications Warehouse
USGS Organization Upper Midwest Environmental Sciences Center