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Blog post 7

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The theoretical basis my research is based on is all the theories that relate to soil pH influences on environmental conditions. The acidity or basicity of a soil affects the presence of nutrients, the toxicity of heavy metals and the microbial community living in the soil. All three of those can affect the health and growth of plants, and the capability of plants to live at the different levels of these creates niches. With this in mind its easy to see how soil pH could be the factor causing different species to live in the two patches of ground I was investigating.

Keywords: Soil pH, growth pattern, ICHdw1 zone

Blog Post 2: Sources of Scientific Information

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The article titled ‘Birds in New York State Have Altered Their Migration Timing and Are Experiencing Different Thermal Regimes While Breeding or on Stopover from 2010 to 2015’ was published in the International Journal of Zoology on September 19, 2017. The authors, from the biology department of Colgate University, were Natalie Pudalov, Sydney Ziatek and Ana Gabriela Jimenez.

This article is an academic piece because it is written by experts in the field (they represent Colgate University), it includes in text citations and a bibliography.

The article states that it was received on May 5, revised on June 26, accepted on July 11 and published on September 19 of that year. This suggests that the article is peer-reviewed as it went through a revision process.

The article lists a materials and methods section and a results section detailing the research that was conducted. This article is therefore a research piece.

As a result, this article constitutes an academic, peer-reviewed research material.

Reference:

Pudalov, N, Ziatek, S and Jimenez, A. (2017). Birds in New York State Have Altered Their Migration Timing and Are Experiencing Different Thermal Regimes While Breeding or on Stopover from 2010 to 2015. International Journal of Zoology, vol. 2017, Article ID 2142075. Retrieved from https://doi.org/10.1155/2017/2142075

Blog Post 1: Observations

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The backyard setting I observed is located on half an acre in the suburban community of Chatham, New Jersey. It is made up of a combination of manicured lawn, trees, shrubs and bushes, hundreds of natural trees, shrubs and bushes, mulch, and an unmanicured forested area with deadfall and decayed leaves. Many of the natural trees, consisting of what I believe are oak, birch, pine and dogwood, provide extensive ground cover and shade and are home to various squirrels, chipmunks and birds. I would like to learn more about these different species of trees.

Separating the manicured lawn and mulch area with the natural forested area is a stream with a small foot-bridge over top. At the time of this observation the stream was mostly dried up. It was muddy and wet in the bottom with a lot of dead leaves but was not freely flowing. A large oak tree sits in the middle of the manicured lawn area.

I first observed this setting on a spring afternoon, April 2nd at around 2:00 pm. It was partly cloudy but the sun was still shining in the backyard and the temperature was around 18 degrees celcius. I observed many branches and twigs on the ground that had fallen from the trees above in the previous days, likely from windy and rainy weather as well as just natural deadfall. I wonder about the health of the trees as there seems to be a lot of deadfall in this small area. Is this natural or are there potential issues?

In the forested area, some natural daisies and tulips had started to bloom but were not quite flowering yet. A magnolia tree on the manicured lawn was just starting to bud. The ground was slightly damp on the SW portion of the lawn although the rest was dry. Birds could be heard chirping in the trees above and a few squirrels could be seen running between the south fence and the large oak tree in the backyard. I observed a few different species of birds and am interested in identifying the types. Some may have been blue jays and others may have been cardinals. Small birds, potentially sparrows or robins, were pecking on the lawn although my movements startled them and they took off.filename-1 (13)

Blog Post 9: Field Research Reflections

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I was in a bit of rush to record my field data before autumn erased the data I was trying to capture and realized that I was about 10 samples short for a valid bivariate regression analysis so unfortunately, I have to add a caveat to results.  Like many science inquiries, this raised more questions for me than it answered.  The ecological conditions in the forest are more complex than a simple walk through the park suggests.  I cannot walk my dog through this portion of the forest anymore without thinking about the forest dynamics all around me.

Blog Post 8: Tables and Graphs

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The correlation between the data appears to be borderline and requires more in-depth analysis.  I expected a stronger positive correlation.  It is interesting to note the cluster that took place with the light intensity readings.  I am just guessing but this could be due to canopy openings are not as variable as they appear to the eye and form discrete opening sizes.  The one outlier defies explanation at this time.

 

 

Blog Post 7: Theoretical Perspectives

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It is common knowledge that plants need light, however, I am interested to know how strong the correlation is to the amount of available light and the proliferation of vegetation on the forest floor when the canopy has undergone a disturbance.  Additionally, the presence of pedestrian traffic through the study area ensures that there is continuous disturbance of the vegetation in the area of the footpath.  People routinely stray off-path, dogs run off-leash, and herbivores such as deer eat young vegetation as it develops.  These conditions offer benefits to invasive species as they can be found along any popular forest footpath in the area.  These factors probably have a major role in determining the overall composition of the ecological communities, but each community is primarily dependent on available light for photosynthesis.  Therefore, I believe that studying the relationship between light levels and amount of vegetation would provide an interesting insight into the revegetation in continuously disturbed areas.

Project Keywords:

Light-levels, vegetation ground cover, Human disturbance

Blog Post 6: Data Collection

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My study transect followed a footpath through the park forest.  The segment of the path in the study area is roughly 200m so I decided that 20 stations at approximately 10m intervals would provide a good sample.  After recording weather conditions and time/date, I followed the following procedure at each interval.

  1. Note interval station in Excel.
  2. Record relative light level in Lux (3x)
  3. Measure horizontal distance from footpath to significant change in elevation (not part of the study but I thought it might be interesting to collect this data)
  4. Measure max height of vegetation 1m from path (again not intended to be part of the study)
  5. Photograph the vegetation in the area perpendicular to the footpath.

Additionally, I took ambient light readings in urban areas, open areas and under full forest canopy to establish data boundaries.

The sampling strategy was efficient and didn’t cause any problems.  I was able to take the light reading in quick succession so that the ambient light would be fairly consistent and not changed due to time-of-day variation.  The photographic data collection was not as time-sensitive because of the colour and luminosity calibration which I included in each image.

Although plant height was extraneous to this study, I determined that there was some correlation between light levels and plant height but I have not completed the image analysis to comment on the central thesis of this study.

Blog Post 6

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For my second set of data collection I used the same 5 replicates of samples from each site, but used slightly different techniques to more thoroughly assess the soil pH. I used a 1:1 ratio of dirt to water and tap water rather than distilled water for the samples I was testing under the pH meter. It seemed to improve the accuracy of the meter, as it could actually detect the pH with more ions present. The readings took much less time to stabilize at a steady pH reading with much less drift. With the use of pH strips, I was able to double check the results. The two methods gave the same results which seemed to be a uniform data distributed around 6.2 pH throughout both sites. This data plus the lack of any ancillary patterns makes me think that perhaps there is a different underlying cause for the pattern of underbrush presence in the Gibson park. I will report this data and continue with this topic for my paper but in my discussion, I will focus on what other underlying causes could be creating this pattern if not soil pH.

Blog Post 4: Sampling Strategies

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I used the distance-based sampling methods to measure tree species abundance in the tutorial. Table 1 below illustrates the data gathered.

 

Table 1 shows that the estimated times to perform the studies were somewhat similar, but that the fastest method to sample 24 plots was the systematic approach by approximately 20 minutes.

The comparison of measurements of abundance showed that the most precise technique for the most abundant species of tree (Eastern Hemlock) was the haphazard technique. With a % error of only 3.21, it surpasses the random sampling method in accuracy by less than 3%. Both the random and haphazard techniques were far more accurate than the systematic method, which lend a terrible % error of 40.99%. As for the second most abundant species of tree in the area (Red Maple), the best sampling method was systematic, with a % error of 24.05.

For the rarest species of tree (White Pine), the most accurate sampling method was found to be the systematic approach. With a relatively high 53.57% error, it is far more accurate than the two other methods, who have both surpassed 100% error. The abundance of the second least abundant tree (Striped Maple) was very accurately measured by the haphazard method. It scored a 6.86% error, comparatively to 64.57% and 157.71% for random and systematic.

Generally speaking, the accuracy of all three methods seemed to have diminished as species abundance became lower. Except for a few odd data points, the majority of % error was inversely proportional to the actual species abundance, no matter the sampling strategy.

Finally, the average % error of the haphazard method (28.96%) was lower than for random and systematic methods (36.46% and 53.39%). Therefore, I conclude that the distance-based haphazard method is the most accurate for sampling abundance in a forest.

Post 5: Design Reflections

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Kevin Ostapowich
April 1, 2019

My study is looking at the distribution of birch within a mixed forest.  Using QGIS, I overlayed a 30m x 30m grid over my study area and haphazardly chose 5 sites to count individual trees.  I then went into the field, using GPS to find the sites, and counted trees of all species within the plots.  I didn’t have too much trouble implementing this procedure but I noticed that the different tree species tend to be grouped in clusters which may have skewed the results.  For example, one of my sample plots was almost entirely composed of pine while the surrounding forest was deciduous.  Another  plot was almost entirely aspen with no birch but immediately surrounding the sample area was a lot of birch.  The plots that I counted may not be entirely representative of the forest composition.

There are two solutions that I can see:  choose a larger grid size to encompass a greater diversity of tree species, or sample more locations.  For consistency and to keep sampling manageable, I plan on continuing with the method that I have already used and to increase the number of sampling sites.