Blog Post 6: Data Collection

After tweaking some aspects of my study design, I returned to my study area 3 times over the past two weeks for some more data collection. I recorded data from 10 new replicates (based on the “rule of 10”) from the SE flank of Rainbow Mountain, as well as an additional 10 from the same are but at a lower elevation. I will not be including the data I collected a month ago for my “initial data collection assignment” in my final report (there are several reasons for this, including the fact that initially I was collecting my measurements too close to the ground, between 1 & 1.5m).

I have begun collecting my data from between 4-5m to mitigate possible confounding factors, including the slope angle near the base of the trees. This proved challenging at first, as making measurements higher up the tree was initially difficult to do with any degree of accuracy. I brought along a stepladder and a tape measure to assist with my measurements this time out, and after some practice I was able to devise a system for counting branches higher up the trees. I also used a different app to collect sunlight data to record in different units (watts per meter squared), which I think will provide a better representation of my predictor variable (“sunlight received”).

At the suggestion of professor Hebert, I also began taking measurements of the distance to the nearest neighbouring trees, as their presence may be a confounding variable in the growth of branches on the replicates being studied. In selecting the “nearest neighbour” I deemed only those trees that were 5m or taller to qualify, as any trees smaller than this would be unlikely to block sunlight from potentially reaching the replicates.

During my “initial observations” assignment, I was collecting on a day with some clouds, and their passing between taking measurements would create large inconsistencies in my light readings, even within the two sides of the same tree. In order to ensure the most uniform measurements of light, I collected on days with similar weather (clear, no clouds), and at the same time of day (12:00). (My first day of data collection took place at 14:00, so I returned at a later date to repeat the light measurements).

 Field note book measurements

I noticed several nuances during my data collection that complicated the process more than I initially anticipated: The first one being that trees don’t always grow perfectly vertical. They often grow at an angle, which can make placement of the light meter somewhat difficult. Secondly, the nature of light filtering through a forest means that a slight difference in where the meter is placed can have vast implications on the reading it generates (i.e. the difference of being directly in a sunbeam or in the shade can be a matter of only a few cm). And furthermore, the location of where light filters through changes constantly throughout the day. Being consistent with the location of the light meter and time of data collection, as well as trying to move quickly without allowing haste to affect the quality was all I could do to ensure uniformity of results.

The topography in the lower elevation study area varied somewhat from the upper one, as did the species that populated it. While the slope was fairly uniform at 850m, closer to the valley bottom at 610m there existed many rolling microfeatures (small knolls) that affected the ways the trees caught the light. I chose to continue with my randomized sampling method in both areas, however it was more difficult to come across the species I was studying (Pseudotsuga menzeisii) at the lower elevation area, and several times I would have to re-enter compass bearing and number of paces in order to find a replicate. This was not an issue at the higher elevation.

One ancillary pattern I noticed during my data collection was that it is not merely the number of branches that seems asymmetrical on the two sides of the trees, but also the length and foliage of branches as well. While the data collection seems to have strengthened my belief in the prediction that more branches grow on the downhill side of the trees, they also seem to be significantly longer as well as more likely to be covered in foliage. I did not notice this trend until well into my data collection, and did not take any measurements regarding branch length however, as it would be quite difficult to do at a height of 4-5 meters and I was unprepared to do so. It is an interesting pattern nevertheless and I will consider if there is a way I can return to incorporate it into the project going forward.

Example of a replicate with asymmetrical foliage

Data Collection

 

Ran into a few more issues this time around trying to implement my study. First I was mistaken in my original thought that NaCl would have an effect on the pH of a liquid. ( I have never taken chemistry although, that is not an excuse). So in order to test what I truly wanted to test which is the salinity I had to make some adjustments to my initial design.

I chose to collect samples on a day following a snowfall, I did this so that the roads and sidewalks would have been salted the day before and street/sidewalk clearing would have already taken place. I chose the starting point for my transects at random along the sidewalk in the residential area and the path by the lake by random computer generated number spanning 0 to 42 signifying the 42 meters along each path that I wanted to study (length of the path by the lake). From there I tested 6 units along the transect, each 2 meters apart.

In order to measure the salinity of the snow without expensive tools at my disposal I used a less precise/ yet still functional way to measure the salinity. I first collected snow samples along two transects (6 units each) both at the residential area and lake area. I used baking measuring cups to measure out 2 cups of snow at each sampling unit and put these into sealable glass jars. After collection I moved the jars inside and the snow melted. I then used Total Dissolved Salts (TDS) to measure how much salt there was in each sample. I evaporated the water from each sample and then collected the residue from each into a separate and labeled bag. I don’t have a scale and so I went to the local grocery store and used theirs in order to measure the grams of each sample. The findings followed my predictions with more total dissolved salts in the snow samples in the residential area.

This was extremely time-consuming! I had originally planned to do two transects at both locations but I decided that one would have to suffice.) The whole process took several days, as I had to ensure to wash away and dry out the pan after each sample to avoid left over residue from the previous sample.

I plan to do this after another snowfall in order to compare values.

snow sample
salt residue after TDS
TDS
Snow sample from the residential area. in this sample you could see blue road salt (after sidewalk clearing a lot of the salt ends up on the lawns).

Data Collection

Hypothesis:  Species of tree that is more abundant in the area will have a specific pattern of distribution throughout. I predict that more than one specie is dominant in the area and hence will have same distribution pattern.

I have gathered all the samples necessary to do my analysis of the hypothesis proposed. I have obtained five replicates of the three different sizes of quadrats from three different gradients at the Cranberry Flat which were done on the following days Sep 23, Sep 24, Sep 26, Sep 27, and Sep 28 2016. I didn’t have major difficulties on site other than being there longer than anticipated; I had initialled estimated only being there for about three days but it was five in total. This is a very busy park throughout the summer but fortunately since it was the end of the season the park attracted less people as fall began.

The locations chosen to sample remained the same, East of the parking lot, Middle of the and close to river bank. These spots were picked because they have lots of trees and vegetation to sample, also they are not really in the way where I would be interrupting any visitor; except the middle section of the park where a lot of the shrubs are but the busy season was over.

The weather wasn’t too bad a few days in the averaged about 13 degrees Celsius and last two were much warmer about 16 degrees Celsius just needed to have enough layers as the day could get windy. I executed my strategy as planned on site, it wasn’t difficult at all to identify the trees since there isn’t a great variety of species out there, which made my work go much faster when sampling, the vegetation on the ground was also easily identifiable.

A bit more time was required to do soil testing for sieve. I had labelled and bagged all my samples on site, before they were dried in the oven I did the hand test to identify texture after they were dried I proceeded to do my analysis to identify the grains as these were crushed and passed through the mesh.

As I began to spend more time on the location I started to see trends and began to understand more about the site.

Post 6: Data Collection

Create a blog post describing your field data collection activities. How many replicates did you sample? Have you had any problems implementing your sampling design? Have you noticed any ancillary patterns that make you reflect on your hypothesis?

Remember to check the “Categories” box for Post 6: Data Collection when you post.