Yaamini’s Notebook: Larval Mortality Analysis

Do I have a story for my larval data?

The short version: Maybe not

The long version:

I’m preparing to write up my submission for the Journal of Shellfish Research’s Special Issue on Ocean Acidification. My paper will follow the general outline of the talk I gave at NSA 2018, so at least the difficult, conceptual thinking part is done! The only thing I have left is to figure out if it’s worth adding my larval data to the story. To do this, Steven suggested I first plot all of my data with error bars. I decided to go back a step and plot my data without error bars first. Calculating all of the standard deviations and manually adding error bars is annoying, and if there’s already a lot of overlap between my data points, I can get a good understanding for whether or not their mortality rates were different. Cue the R graphics

When analyzing my Day 0 count data, I saw a significant maternal effect from the pregametogenic pH treatment. The goal with these graphs is to see if the carryover effect persists into larval survival. In my R script, I visualized all of my data from the four different parental pH treatment families (i.e. everything besides the heat shock data). I plotted all of the data together, as well as the data from each family separately.

I know what you’re thinking. The axis labels and points are too small, the y-axis label is cutoff, and the colors aren’t high contrast enough. I was too lazy to adjust these aspects of my plots if I wasn’t sure if they would be used later on. Lazy, but efficient…?

all-dataFigure 1. All larval count data over the course of the experiment. The big takeaway here is that all of the colors are overlapping.

FLML

FLMAFigures 2-3. Larval count data from families with females exposed to low pH conditions. Males were exposed to either low pH (top) or ambient pH (bottom).

FAML

FAMAFigures 4-5. Larval count data from families with females exposed to ambient pH conditions. Males were exposed to either low pH (top) or ambient pH (bottom).

It may be useful to create a multipanel plot to look at the families side by side. I’ll see if this is necessary. There is evidence of human error when counting larvae (how did I count more larvae than I started with?!), but it seems to be day-dependent as opposed to treatment-dependent. It doesn’t look like there’s any significant difference in survival between treatments, which could also be an interesting point. While we saw a carryover effect when counting larval output, that same effect may not persist into larval survival. Sometimes a null result is still a cool result.

My next step is to channel my inner Mac and write the methods and results section of my paper (or, at least get a Google Doc and paper repository started first). I’ll include this section and if I or others feel it is not relevant, I’ll eliminate it.

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