Estimating linear temporal trends from aggregated environmental monitoring data |
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| Affiliation: | 1. Upper Midwest Environmental Sciences Center, US Geological Survey, La Crosse, WI, United States;2. Department of Mathematics and Statistics, University of Wisconsin – La Crosse, La Crosse, WI, United States;1. School of Labor Economics, Capital University of Economics and Business, Beijing 10070, China;2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada;3. Water Resources Research Institute, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;4. College of Urban and Environmental Science, Peking University, Beijing 100871, China;5. College of Environmental Economic, Shanxi University of Finance & Economics, Taiyuan 030006, China;1. Fujian Province Key Laboratory of Plant Virology, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou 350002, P.R.China;2. Huai''an Institute of Agricultural Sciences, Huai''an 223001, P.R.China;1. Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States;2. Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States;1. Museum of Vertebrate Zoology, Department of Integrative Biology, University of California, Berkeley, 3101 Valley Life Sciences Building, Berkeley, CA 94720, USA;2. Environmental Science Policy and Management, University of California, Berkeley, 130 Mulford Hall #3114, Berkeley CA 94720, USA;3. Pritzker Laboratory for Molecular Systematics and Evolution, The Field Museum, 1400 South Lake Shore Drive, Chicago IL 60605, USA;1. Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, Tartu 51014, Estonia;2. Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu 51005, Estonia;3. Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, Tartu 51014, Estonia |
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| Abstract: | 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. |
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| Keywords: | Aquatic invasive species Long term monitoring MARSS Mississippi River State-space model Trend estimation |
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