Category: Post 5: Design Reflections

When I initially planned this experiment I thought I would be able to determine different shades of green present on the trees. I had planned to match the tree trunks to a color wheel and see different shades of green. After making my first few attempts at data collection I quickly realized that this method would not work. Different viewing angles changed the lighting on the trunk and that alone changed the color. It was also difficult to determine different shades depending on what was around the tree or even in the background. I decided this method was too subjective and changed from my continuous approach to a categorical one to make observations just on the presence or absence of green on the tree trunks. Some of the data I collected did surprise me. I did not expect to see green all the way around the trunk on some trees. I also didn’t expect to see it completely absent on others. I plan to continue to collect data based on the presence or absence of color. I feel it is the best method I have without access to more expensive and accurate ways of measuring the green layer. Ideally I would be able to cut out small samples of bark and measure the thickness of the chlorophyll layer to get a truly accurate measurement as well as see if it was present in the areas where it wasn’t visible to the human eye.

In module 3, I collected using point counting. I went out early in the morning and stayed for even increments of time at each plot. I found I got a good sense of surrounding wildlife and human activity but struggled to find the specimen I was looking for. I realized that this was further proof that my hypothesis is correct and thus persevere with my sampling strategy. I did some hiking following my samples to investigate the area further and found a sign stating dates the lake above the creek is stocked biannually and is about to be stocked in November 2020. I expect to find higher numbers of fry due to this and will resample to confirm.

While I do not feel the need to modify my approach, I do feel I should include human traffic in my official data as it was a significant finding and will show the overall impact on fish survival. I am also considering pollutants based on lack of aquatic plant life.

When I originally started gathering data, I tried keeping track of all the species that had percent coverage over 10%. In some of the quadrats there were 5 or 6 different species, so I have decided to only tabulate the most prevalent 2 or 3 species. From looking at all the quadrats the vegetation that seems to be the most common in all regions is the veiny meadow rue and cow parsnip.
When looking specifically at veiny meadow rue there seems to be a clear line moving horizontally across the hill that could define the floodplain. My hypothesis is that vegetation species and their percent cover will either decrease or increase in relation to their proximity to the stormwater pond. Vegetation such as cow parsnip will be more common regardless of distance from the stormwater pond as it is a more versatile species.
When selecting my quadrats I could have used a more methodical approach. Instead of blindly tossing a ball and then creating a quadrat around that I kind of “randomly” selected my quadrats based on what I thought would return the best quality of data that I could use. In that sense, the data presented may not accurately depict the actual percent coverage of vegetation in the 4 zones being studied.
I could have also increased the size or number of quadrats as larger species would naturally occupy more percent of a 1m x 1m quadrat and a larger quadrat or more quadrats would be able to more accurately display the percent coverage of smaller vegetation species.
These samples were not collected in the spring months, but I think it would have been interesting to see how vegetation would have responded to the melting and constant flooding that would be experienced. Maybe if there were larger amounts of snow that was melting then flooding would be more prevalent which could push vegetation boundaries up to higher elevations as opposed to a lighter winter where not as much snow would melt and vegetation boundaries may be at lower elevations.
It would be interesting to see these boundaries in relation to the amount of snow fall from the previous winter and see if there was some correlation between the two.

Prior to the arrival at Acadia Research Forest, I explained the protocols of surveying and safety measures to the forest guards. I built two different light traps for the two days and nights in the forest. The first trap had a 2.6 m× 1.6 m white sheet fixed between 2 tree trunks using the ropes. I suspended the 250 W vapour light, powered by the generator on the sheet, to attract the flying insects. I set up the second trap, 600 meters from the first trap, and it shed light on an 80-centimetre cylindrical white sheet employing a 40-centimetre actinic tube. In the day, I employed pitfall traps and aerial fruit traps to collect the ants, spiders, beetles, wasps, and butterflies frequently available in the forest.  I, however, only used the light traps to collect the data because this method had diversity and the highest abundance of the insects within the limited sampling time.  The light-trapping method is important compared to other techniques because it enables one to closely observe the live insects and interactions like competition and predation. It also offers an important chance of introducing some morphological traits typifying a different insect order.