ENVS550, 2010 Vol 1:1-10 Journal of Fabricated Studies
You R. Name 1 & Fred G.R. Watson 2
Division of Science & Environmental Policy, California State University Monterey Bay, Seaside, CA, USA.
1
Completed all analysis and most of the writing.
2
Conceived the study and wrote the introduction.
<In general, follow these web guidelines throughout this document: http://sep.csumb.edu/wiki/index.php/Writing_guidelines >
<Delete all the red text as you go, so the document ends up perfectly clean with no color, no comments, etc.
Scoring will start at full points, with about 0.1% of total course score deducted for each point that is incorrect on statistical, technical or scientific grounds, and 0.05% for each failure to follow instructions. In general, if you made no fundamental errors, you should get an A score for the assignment; but you’d get more like a B if you made a fundamental error, like not using a p-value correctly, or inferring too much from a hypothesis test, or skipping a key requirement.>
Abstract
<Summarize the document. Don’t write anything that is not in the document. Use exactly one sentence per section, in the same order as the sections.
>
Introduction
Global biodiversity is in decline, largely because of the decline in the biodiversity of aquatic invertebrates in rangeland streams, which in turn is associated with low dissolved oxygen (DO) levels in these streams (Bitious,
2009). Fencing to keep cattle out of streams has been shown to improve DO in Canada (Miller et al. 2010).
In this study, we replicated Miller et al’s study in the
Central Coast region of California, to examine the generality of their conclusion. Specifically, we investigated whether or not fencing leads to increased
DO by constructing an experimental fenced exclosure around a portion of stream that was otherwise affected by low DO levels. We postulated that the exclosure would promote a biochemical environment that was less respiratory and more productive, thus yielding higher DO downstream at the point of outflow as compared to the point of inflow. We considered that evidence of higher DO would constitute support for this postulate.
Methods
Following Miller et al., we used a null hypotheses testing approach with:
H
0
: ….
<Write a null and alternative hypothesis. Use subscripts in the hypothesis name symbols. Define the hypothesis in terms of mathematical symbols. Use italics for variable names. Use spaces between elements of equations. where <define symbols and units here. Again, use italics for variable names (not units or numbers). Use subscripts where appropriate. Adopt standard conventions for choosing the letters of symbols, such as using greek letters for population parameters. For example: “where 
(m) was the mean height of students in the class.” >.
<State the details of your statistical test: significance level, sample size, one-sidedness if applicable, assumptions and how you checked them, pairing of data points if applicable, basis for inference (i.e. pvalue). Put your R code in the appendix, and refer to it from here. Put an illustrative example of how the t-test worked in the appendix, and refer to it from here. State
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ENVS550, 2010 Vol 1:1-10 Journal of Fabricated Studies that you used R, and cite it properly; type citation() in
R to see how to do this.>
<Add a short bogus paragraph here describing the study stream, the physical aspects of the experimental site you constructed, how you measured DO in the field, the season and frequency of sample, and the pairing of samples.>
Results
<State achieved sample size, report any missing data if applicable, plot several histograms, summarize data using means and standard errors; do these steps in terms of both the raw data and the paired differences; add proper captions to figures; number figures and refer to them from the text; provide the raw data in a table in the appendix and refer to it from here. Create plots in R, and paste them directly into this document.
Make sure the fonts are at least as large as this text.
Label axes properly. Do not include a title for the plot.
Do not include excessive margin space. Use the windows() and par(mai()) commands. Include a legend when necessary. The code for the plot below is: windows(3,3); par(mai=c(0.8,0.8,0.1,0.1)); plot( x=rnorm(10), y=rnorm(10), xlab="x", ylab="y", cex=2 ) >
Figure 1. <Write a caption that helps the read understand what they should be seeing in the plots. Don’t state the obvious (like “Graph of my data”). Also avoid stating things that are redundant with the main text. Avoid putting information in the caption that really should be in a legend on a plot. Use boldface 1-point smaller than the main text font.> Example: “Relationship between y and x, showing that y is apparently unrelated to x”.
<State statistical results in terms of p-values, sample size, results of any checks on the validity of assumptions, and if valid, acceptance or rejection of the null hypothesis.”
Discussion
< Discuss results in terms of what you can infer about the original postulate given the statistical evidence.
Discuss potential limitations of the work, such as serial autocorrelation in the data, lack of generality, and insufficient replication. Relate your results to previous published work. >
Conclusion
<Very briefly state what you can conclude from your results (primarily about the original postulate/s), and how they should be applied. >
References
Bitious, A. M. 2007. Biting off way more cud than can be chewed. J. Rumination , 3:1415-9265.
Miller, J., D. Chanasyk, T. Curtis, T. Entz, and W.
Willms. 2010. Influence of streambank fencing with a cattle crossing on riparian health and water quality of the Lower Little Bow River in Southern Alberta,
Canada. Agricultural Water Management , 97:247-258.
Appendix A – R code
<R code used to conduct the t-test in this paper, including comments. Use 8-point ‘Courier New’ font for code, with single-spaced lines (i.e. make it neat and compact). Only include the essential code that someone who knew R well would need to replicate your analysis.>
Appendix B – Raw data
<Put your raw data here in a complete and correctly formatted table, with a caption at the top (for tables).
To create your table, import your data into MS Excel, format the table in Excel, and then paste it here as a
“Picture – Enhanced Metafile”. Use a single horizontal line to separate headers and columns, and very few if any other lines; use bold for headers; include units.>
Appendix C – T-test explanation
<Add an R plot here illustrating how your t-test worked. It should show the PDF of the t-distribution
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ENVS550, 2010 Vol 1:1-10 fitted to your data under the null hypothesis, the location of your sample mean (a vertical line), shading to indicate the rejection region for the null hypothesis, and a legend or annotation text where appropriate.
Preface the plot with some brief text saying what it is; add a caption; and refer to the figure as a numbered figure from the text.>
Journal of Fabricated Studies
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