Blog Post 3 – initial observations

  1. I plan to study the elm, ash, and maple trees during the change of seasons (fall to winter).
  2. I want to see how fast the trees lose their leaves in the following locations at Quarry Park:
    1. by the pond- this location has an abundance of water as well as many trees surrounding the perimeter.
    2. in the “forest” – this location has a lot of surrounding trees and limestone in the area. It also has hiking trails so it can see a lot of human activity from time to time, especially in the summer.
    3. in the parking lot of the park – this location still has trees, however the number is a lot smaller as it is mainly used for parking and has a heritage building located in the middle. This location will see a lot of human interaction and cars throughout the year.
  3. Hypothesis: a few studies have indicated that pollution and areas that have less direct sunlight cause trees to lose their leaves faster during the changing of the seasons.

Prediction: Due to the increase in human interaction, the buildings, and fossil fuels (gasoline from cars), the trees in the parking lot will lose their leaves at a faster rate than those in the forest and by the pond.

4. Response Variable = elm, ash, and maple trees

Predictor Variable = pollution (cars and people) and degree of sunlight

observation 1 – post 3

 

2 thoughts to “Blog Post 3 – initial observations”

  1. I think the overall idea can work for your study. As I noted before if you get a high wind event this could influence your results and your sampling will depend on the leaves staying around until you can start sampling.
    One thing to consider is that your predictor variable is likely generally associated with human disturbance but you won’t be able to say if it is specifically pollution. Soils are likely different and overall there could be less abundant resources. Why is sunlight different in the parking lot? Based on some of this feedback you may have to rework your prediction and your predictor variable. Mainly what you call your predictor variable. It sounds like level of development / ecosystem type….

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