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Assessing River Herring Migration Using Nonparametric Analysis

Author(s): Timothy Owen

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Summary:
In this lesson, users will play the role of a fisheries biologist by tackling a contemporary fisheries research problem by performing data normality tests, data transformations, nonparametric analysis, and data visualization in R Studio.

Description

In this lesson, users play the role of a fisheries biologist tasked with monitoring the run-strength of Atlantic River Herring in a tributary of the Chesapeake Bay. Users will learn how to visibly assess and present data by constructing plots with ggplot, test for data normality using a Shapiro-Wilk normality test, perform data transformations in R Studio, and conduct a nonparametric statistical analysis by using a Paired Samples Wilcoxon Signed Rank Test. Along the way, students will be exposed to contemporary fisheries science topics, and use their acquired skills to answer questions related to the ecological and quantitative concepts covered within the lesson.

To assist with this, users should download and review the associated components from this resource package. The creator of this lesson recommends downloading and reviewing the documents in the following order:

1.) RHerringBackground.mp4 - provides a high-level synopsis of the ecological backstory, quantitative lesson, and data sources for this resource.

2.) RHerringBackground_Instructor and/or RHerringBackground_Student - contains relevant ecological and methodological background information for the quantitative R Studio lesson.

3.) RHerringLesson.RMD + TrapDataV2.xlsx + DailyCounts_20.xlsxthis is the interactive quantitative lesson that can be executed using R Studio. These files need to be downloaded into the same folder. Please double-check that your downloaded file names match these.

4.) RHerringLesson.htmlthis is an alternative method of letting students experience the quantitative portion of the lesson – without needing to use R Studio.

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