Abstract
Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6–12.4%) compared to variability between stations in a single day (CV range 2.4–88.6%), or variability over time for a single station (CV range 0.4–110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.
Highlights
Ongoing marine environmental monitoring programs, following the European Water Framework Directive, are designed to detect long-term trends over several years and to classify the environmental state into one of five ecological quality classes, ranging from bad to high quality (Heiskanen et al 2004; Carstensen 2007)
Marine environmental monitoring programs usually include only very few stations which are supposed to be representative of large areas
A Shapiro-Wilk test indicated a normal distribution of the time-integrated averages for the 21 stations included, and the average CV% for the six variables ranged from 0.55 to 12.36% (PAR extinction coefficient)
Summary
Ongoing marine environmental monitoring programs, following the European Water Framework Directive, are designed to detect long-term trends over several years and to classify the environmental state into one of five ecological quality classes, ranging from bad to high quality (Heiskanen et al 2004; Carstensen 2007). Ongoing monitoring programs usually only include a small fraction per year of this number, e.g., one per month, which would require more than 100 years to achieve sufficient data for a corresponding classification. We show in the present paper that the sampling program and statistical procedures can be feasible when the goal is to only get an estimate of the annual or seasonal average of a variable, without an evaluation of within-year fluctuations. Such a program will be suitable for comparing the environmental condition between single years. This is highly needed in environmental research, where there is a common problem to find logic explanations for changes in for example primary production (e.g., Rydberg et al 2006), zooplankton abundance (e.g., Moller et al 2015), and recruitment of fish (e.g., Pecuchet et al 2015) between consecutive years in the ecosystem
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
