I decided to use tables to represent my data. Initially, I did find it a bit difficult to organize the data that I collected and calculated especially since I collected data for the distance of the closest black spruce tree to the centre point in each quadrant and the DBH. I decided to make a table that shows the distances in each of the quadrants and then another table to show the DBH measurements. I then made a table to summarize all of the means such as the mean frequency, mean distance, mean area, mean density, and mean basal area. I had tables to summarize the calculations for the ANOVA analysis. The last table I had is to summarize the relative values such as relative importance value. The data expected as it supported my hypothesis since the dominance (relative importance) of black spruce species increased with elevation.
Category: Post 8: Tables and Graphs
Blog Post 8
Blog Post 8: Tables and Graphs
Create a blog post discussing your table or graph. Did you have any difficulties organizing, aggregating or summarizing your data? Was the outcome as you expected? Did your data reveal anything unexpected or give you any ideas for further exploration?
As my data set was small there was not a need for any graphs such as a boxplot or scatter plots. I used pie charts and bar graphs to show the relative frequency and relative densities calculated in my study- to show an overall picture of what tree patterns were on each substrate. I did have difficulty with the calculations at first as they all seemed foreign to me. At the end of the calculations, I had a bunch of numbers that I was overwhelmed with, trying to figure out how I could tell the story I was trying to with all of the decimal places. I found myself wanting to make graphs more complicated then they needed to be. To look for data that may show something more then the simple story I was telling ( jack pine are in higher frequency and density amongst bedrock). Stepping back from trying to compare myself to the reports we have been reading in class, and looking at what my project actually was – a simple observational study helped give me some clarity. This helped in simply doing the calculations and making the bar graphs without complicating the paper with unneeded graphs and tables. Though I do hope that I represented the data to its fullest. I could have potentially done more with the DBH data.
The outcome of the experiment was what I expected. It would be interesting to measure distances between jack pine populations on different bedrock areas to see if there is a preferred spacing to avoid competition with each other.
Blog Post 8: Tables and Graphs.
The graph above shows a comparison of the average alkalinity from four water samples collected at each of the three locations on McArthur Island in Kamloops, B.C. This is a sample as I have not yet figured out how to properly insert the standard deviation bars but it will be added on as a graph element for the final report.
From my graph, it is quite obvious to see that the pond had much higher alkalinity which ducks tend to like, as stated in most of the research I read through. Though my observations and data collections show that very few ducks tended to be in the pond compared to the other two locations. I hypothesized that ducks prefer the moat by the bridge over the pond or the moat entrance to the Thompson River and from my duck counts over the 4 days of my collection, the hypothesis was supported. Though, since the water is close to neutral pH and is lower in alkalinity, and so far, the portion of my study that compared the number of ducks in the shade versus the sun doesn’t seem to show a pattern, it seems to suggest that there are other reasons why the ducks prefer the bridge area.
Blog Post 8 – Tables and Graphs
Organizing my data was surprisingly easy to do. I focused on gathering data that was exactly relevant to my predictions. That proved to be very useful as it truly was a breeze to put together. The outcome was fairly similar to what I expected. I did expect there to be more moss in the areas with more sunlight. So that was accurate. Perhaps I should actually add that as a proper prediction in my final report. I have been slightly confused whether a variable should be used as a prediction in the introduction as well, or if that is completely different that should be used in the discussion only. It will all come together nicely though. Evidently, that is the purpose of these blogs.
My data definitely gave me ideas of further exploration. I have limited time, resources and knowledge, so it would absolutely be interesting to gather soil samples to calculate the moisture, for example. I was going to calculate soil moisture, however, winter creeped up on me faster than expected. The soil is frozen now. It would be interesting to see how moisture can affect the directional growth of moss. It could be good to see if moss grows in the same places and trees each year. Of course, it would be perfect to go much more in depth to see the relationship between which moss grows on which tree to see if that affects anything. It would be good to do some kind of wildlife survey, see if there are any moss-eating creatures that perhaps prefer moss on the south side of trees, leaving the northern side lush of moss. There are so many interesting research ideas that could grow from this introductory study that examines an urban legend.
Blog Post 8
The graph shown above represents the average amount of moisture in soil samples obtained from hilltops and valleys in Valleyview Nature Park. The biggest difficulty I had was figuring out how to create different standard deviation bars on Excel. I decided to organize this data in a figure for ease of interpretation; the rest of my data is summarized in a table not included in this submission.
The average soil moisture content in hilltop samples was 21.95% +/- 1.03%. The average soil moisture content in valley samples was 27.20% +/- 2.22%. Due to limitations of laboratory access and weather, I had to collect the soil samples approximately seven hours after precipitation. Therefore, moisture levels were much higher than I initially anticipated. That being said, my results appear to support what I expected in that soil moisture content was higher in valleys than hilltops.
Blog Post 8
I did have a bit of problems trying to create a single graph that showed all the information that I wanted it to show. I ended up breaking it up into graphs for each plot.
There was nothing unexpected that I encountered with this project but for future exploration that I would have liked to look at would be to see if there is any grazing in the area that may have happened or the introduction of a biological control to this area to help minimize the growth of the thistle.
Blog Post 8: Tables and Graphs
When it came to creating my graph for my final results of my field data I really wanted to represent my data in a way that was very straight forward and clear. This was important for me to do because I wanted the “reader” to be able to look at my graph and quickly understand the results that were concluded. Because I did 5 different point count stations that all had varying degrees of urbanization I wasn’t able to show this data through a line graph. So, instead I decided the best way for me to present the data was through a simple but very useful bar graph. The final data was not exactly what I had predicted it to be. I had predicted that I would find the greatest number of birds using an area that had the most shelter (no urbanization) compared to areas with little shelter (fully urbanized) or areas with intermediate amounts of shelter (intermediately urbanized). This did not actually end up being the case. Instead the data suggest that the largest number of birds using an area were the intermediately urbanized areas. These were the areas where there was about half forest area and half urbanized areas (lawn/house area). Because these areas had some urbanization, there were bird feeds and even bird baths present. This seems to be a favorable circumstance for the birds; they are provided shelter through the trees, as well as food and water from the provided baths and feeders.
Another thing that caught my eye when examining the data was the surprisingly high amount of birds using the area that was just lawn space. This is the only area that has complete urbanization. No trees, all man-made shelter areas and lots of bird feeders around. This area had almost the same number of birds using the area as did the areas of all forest. Once again though, I’m curious if this is due to the large amount of bird feeders that area around the completely urbanized area.
One other thing that I did wonder about when looking at my final data was if the smokey weather had anything to do with the lack of birds I observed under those conditions. The days of heavy smoke from the wildfires was on the same day that I went to the point count station 1 and 2. At location 1, had observed 3 fewer birds from the previous times I visited the site and at location 2, I once again observed 3 fewer birds than previous times I had visited. This will be something I take into consideration when composing my final project.
Blog Post 8: Tables and Graphs
Soil samples and percent of frond yellowing on Polystichum munitum and Pteridium aquilinum from randomly selected samples were collected within the surrounding area of the Alfred Howe Greenway trail. Soil samples were later measured for their pH, nitrogen, phosphorous, and potash content using the Rapitest 1601 Soil Test Kit. The following graph summarises collected field data for topsoil samples, illustrating soil type (sandy, loam, clay) and nitrogen content in relation to where the sample was collected along the length of the trail:
Some difficulties were encountered when aggregating data at some soil sample locations, with (1) underground root systems preventing the ability to collect the soil sample from the required depth (4”) or (2) inability to reach the location, such as the case in the presence of very dense vegetation or a steep change in slope elevation. In the case of these difficulties, the nearest accessible soil sample was collected.
No major difficulties were encountered when performing soil tests, however, due to the waiting periods required in waiting for the soil to settle (30 min. to 24 hours) and for colour to develop when conducting the sample test (10 min. for nitrogen, phosphorus, and potash) (Photo 1), soil testing was conducted on several separate days. However, all soil tests were conducted during the day, ensuring sufficient daylight was available for measuring each sample.
Photo 1: (Left) clear solution of soil sample L1S2 after soil settling (60 minutes). (Right) Nitrogen colour determination (N3 = sufficient) of soil sample L1S2 at 10 minute mark.
When the final results for the nitrogen tests were summarised (Fig. 1), it was surprising to observe that nitrogen levels were lower on average in the remediated landfill area in contrast to the historically forested area, since it was predicted that any landfill waste still buried in the ground could induce biogas release, primarily in the form of methane and carbon dioxide, significantly altering the soil quality from that of a natural habitat, allowing nitrifying bacteria to flourish, leading to higher nitrogen levels (Isaka et. al., 2007).
A landfill cover is reported to be encompassing at least a portion of the remediated landfill, with its aboveground edge observed in a previous blog post entry (Blog Post 6, Photo 2). Perhaps the landfill cover is preventing soil contaminated with landfill content from permeating to the collected topsoil, leading to the recorded low levels of nitrogen (Kightley et. al., 1995) . An alternative explanation could be that denitrification is actually occurring at greater rates in the landfill area, resulting in lower nitrogen levels (Burton and Watson-Craik, 1998).
Tables & Graphs
I created a bar graph for small assignment 5 that represented my data on the number of bees that frequented different flower species. I choose to use a a bar graph because figures tend to present data more clearly. I did not have any problems with aggregating or summarizing my information on the graph. The only difficulty I encountered with the bar graph was formatting it online. The description ‘number of bees’ for the y axis was written vertically and I could not get it it to be horizontal. The results from the graph was as expected and supported my hypothesis, the number of bees increase around colourful flowers and decrease around white flowers.
Blog 8: Box Plot of Leaf Length
I knew after designing the experiment that I would be creating a box plot due to the comparison between a continuous and a categorical variable. While a strip plot would also have been acceptable, the box plot easily presents several other pieces of information that are harder to incorporate in a strip plot: upper and lower quartiles, minimum, and maximum values in addition to the mean value. Therefore not only does a box plot show you the mean values and the range of data, but it also allows one to visually appreciate the variance in within each data group. As discussed in post 6, I revised my experimental design slightly to aggregate samples from nearby trees into unified sampling areas. The reason for the revision is that there was no way to guarantee the leaves that had fallen beneath a given tree were grown on that particular plant. I opted therefore to group sampled leaves into a “habitat areas” that included the sampling area and a control area.
Figure 1. The effects of airborne particulate pollution on premature leaf abscission were estimated based on the leaf length of dropped leaves of Prunus spp. in two areas: near a roadway and construction site with active digging (Particulate-Exposed) and approximately 70 meters East of this location (Control). Mean leaf length between Particulate-Exposed (=50.5 mm) and Control (=54.8mm) are shown in bold horizontal lines. Upper and lower bounds of the box represent the 75th and 25th quartile values, respectively. Upper and lower “whiskers” represent maximum and minimum data points, respectively. (Figure created in R Studio V1.1.414)
