Category: Post 8: Tables and Graphs

Some initial findings of my data collection in which the mosses occurring on different slope positions on several rock outcrops were identified and their percent cover classified are shown in Figure 1 (included in Assignment 5). Specifically, Figure 1 illustrates the number of plots on which each species occurs within each slope position, summarized across the 4 replicate plots.

I did not have any difficulty organizing, aggregating or summarizing my data, although I did have to manually calculate the number of plots in which each species occured based on my field data. I also had to separate out the results in order to see the results per slope position instead of just as an overall total.

I found it interesting that the three most abundant species overall (PYLSP, PLESCH, DICSCO) occurred on all slope positions and that these were the only species to occur on the west-facing slopes. I also found it interesting that of all of the other species that occurred, each of these occurred only in one slope position. These include RACCAN, which only occurs on the crest, HYPSUB and HETPRO, which only occur on the eastern slopes, and KINORE and RHYLOR, which only occur in the depressions.  This was only unexpected in that it does illustrate what I had predicted, which is a pleasant surprise. This confirms, at least initially, that I am on the right track in my investigation.

As a separate study, it could be interesting to specifically target the species of moss that were found only on one slope position elsewhere within the study area, and to collect data on the same environmental attributes (i.e. slope position, slope, aspect, substrate, overstorey and understorey cover) in these locations. Analysis of that data could reveal whether the occurrences of these species on a particular slope position are a trend or just isolated occurrences, and also to tease out which, if any, environmental attributes are correlated with the occurrences of these species.

I found it quite simple to organize and summarize my data as the results of my field data were not so complicated. I resorted to a table to display the length and aperture measurements on both Castle beach and Jetty beach. I also made a separate column for the length to aperture ratio for both beaches. Upon calculating all the ratios, I totalled the ratios for the exposed and sheltered shores and took a mean. One of my results at Castle beach was anomalous (n=8) since it had a large variance from the mean value of 1.58. Therefore, it does not fit the remaining values and I did not use it in my calculation. The lengths of the dog whelks are generally smaller and the aperture is larger at Castle beach, giving a smaller ratio. For instance, the highest value for the length to aperture ratio at Castle beach was 1.74 while the highest value at Jetty beach was 2.04. Similarly, the lowest value for the length to aperture ratio at Castle beach was 1.37, whereas the lowest value at Jetty beach was 1.42.

An interesting investigation involving dog whelks may be comparing the genetics of dog whelks on exposed and sheltered shores and trying to explain what differences in the genetic makeup cause the variation in shell size on the shores. This study can involve more shores and replicates. However, varying abiotic and biotic factors such as weather and predators may cause discrepancies in the results.

My project includes fish capture data (for Cultus Pygmy Sculpin; CPS) and corresponding dissolved oxygen levels.  The most challenging part of creating the graph was organizing the data into an appropriate format. I am analysing the data using Tableau Public (a free data visualization tool) which is very effective once the data is formatted.  I made a regression plot comparing CPS occurrence and dissolved oxygen concentrations. I removed the data points for minnow traps that did not yield any CPS captures, as they made the results confusing and I wanted to focus to be on where CPS are occurring as opposed to where they are not occurring.  CPS is a species at risk, so inherently it is not observed in many locations. The results were surprisingly clear.  Only 1 of the 169 captures occurred in dissolved oxygen levels of less than 7 mg/L, suggesting that CPS may have an aerobic threshold of around this level.  I find this a little surprising. I know that salmon (in general) have an aerobic threshold of around 5 mg/L. I expected CPS to be similar. Notably, this is not a huge dataset and further investigation is required before determining the aerobic threshold of CPS. This was, however, a great start.

The results of my field data were easy to summarize and visually represent in tables and graphs. The bar graph I submitted summarizes bird abundance (number of individuals) observed at the three different sites along the urban gradient representing different levels of urbanization. I predicted that bird abundance would follow a gradient with the lowest number of individuals observed in the urbanized area (Site 3) and the highest number of individuals observed in the natural area (Site 1) . When I initially graphed this data I found that the highest abundance was in fact at the most urban site. However, further examination of the data indicated that this was due to the large portion of observations (roughly 2/3) in the urban area that consisted of seagulls and crows. As a result, the graph I created displays the overall abundance along the urbanization gradient but highlights the proportion of each bird species at each site so that the underlying trend becomes apparent, which confirms my prediction

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For one of my figures, I utilized a graph to show the average density of Northwestern Crows in each of the selected survey points. The graph highlights that as you move along the gradient from no anthropogenic disturbance to a high disturbance rate there will be more crows. The outcome was exactly as I expected with a large number of crows present in urbanized areas. The graph was consistent with lots of other research regarding the same general premise that crows are very good at exploiting urbanized areas.

I found it challenging to present my data in a way that would be easy to understand without some additional text. However, it was good to get into Excel after not using it for a couple of years. As my final assignment comes together, I’m sure my visuals will change.

The outcome was not exactly what I was expecting, but with ongoing observations of the site, I have developed some additional ideas to explore. For instance, I thought there would be a steady increase in the response variable along the environmental gradient but there was some variance to it. During field observations, I noticed some microclimates in areas where stand density increased. Interesting stuff!

Here’s one of the figures I battled with Excel to make: