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Blog Post 7: Theoretical Perspectives

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My research project is on the frequency of western sword ferns in Mundy across three district zones (uplands,  transition, and swamp area) as a result of soil moisture. My hypothesis is that the frequency of western sword ferns in Mundy Park is significantly different in the uplands, upland transition, and swamp area due to changes in soil moisture. The ecological process behind my research is biological fitness as it relates to the survival and reproduction of ferns in different environmental conditions as well as zonation. Some underlying factors that may also be at play in this project include soil acidity, soil nutrition, and competition.

Three keywords that describe my research project: soil moisture, western sword fern, wetlands.

Blog Post 6: Data Collection

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The collection of data was relatively straightforward. I used the same technique that was used in the collection of my preliminary data therefore I had already tested this sampling method. Unlike the preliminary data, for this data collection, I also collected data on the soil moisture. The soil-moisture meter was somewhat challenging as there were lots of areas where there were numerous rocks and roots in the soil which made inserting the moisture probe difficult. I had also did not anticipate for any bears to be in the area and a very close encounter with a black bear, which was an intense experience. I did 14 replicates of my sample in each of the three sampling areas and I did not notice any ancillary patterns while collecting the data.

Blog Post 6: Data Collection (Robyn Reudink)

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My field data was collected on a weekly basis over a four-week timeframe from June 13th to July 4th 2021. This included measuring both the plant shoot height and the maximum basal diameter (mm) for each sunflower plant. There was a total of 12 sunflower plant replicates for each of the 2 study groups/ treatment levels. I didn’t encounter any problems implementing my sampling design.

During the field data collection (including on June 13, 20, 27 and July 4) sunflower plants H1, H2, and H3 were observed to be the smallest (both in shoot height and maximum basal diameter) of the plants grown in the high-water volume study group (note: sunflower plants H1, H2 and H3 were all grown/ located within the same pot). The exact reason for this pattern is unknown, however, it may be due to variation in the microsite and/ or microclimate at this specific plant-pot location. All of the other sunflower plant replicates that were grown in the high-water volume study group (H4-H12) were noted to be larger (both in shoot height and maximum basal diameter) than the sunflower plants grown in the low-water volume application study group (L1-L12).

Blog Post 5: Design Reflections (Robyn Reudink)

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The data I have collected for my experiment includes measuring each of the sunflower plants shoot height and the maximum basal diameter (mm). The systematic sampling strategy that I used allows for easy data collection and I plan to continue to use this approach. However, my three-sunflower plant/ sampling units that are located in each of the pots do not have enough distance between each other to be considered independent from one another. It would have been better if I had used individual plant pots for each of the sunflower plant replicates, so that they are considered independent from one another.

Blog Post 5: Design Reflections

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As part of my sampling strategy, I decided to use the simple random technique in which a random number generator drew numbers from 1-360 and 1-40. The numbers would then determine my compass coordinates. The technique was relatively straightforward and easy to use however, I would often find myself randomly selecting areas that were inaccessible to me. I overcame this barrier by simply redrawing numbers and sampling the ferns which were accessible. Although, instead of redrawing numbers, I could have saved some time by sampling ferns that were as close to the randomly selected coordinates as possible. I was not really surprised by the data I collected as it correlated with my initial hypothesis however, I did not expect there to be such a big difference in moisture levels between the larger growing ferns and the medium sized ferns. I had also expected there to be a slightly larger difference between the pH levels in each sample. This was not the case as each sample had a pH reading of about 8-7.5.

Moving forward, I will continue to use the random sampling technique with slight modifications. First, I will not redraw numbers when I encounter coordinates to areas which I cannot access. Instead, I will sample the closest fern and record how many paces it takes for me to get there. Another modification will be to wipe down the hydrometer after each use, which was something I had overlooked in my data collection. By wiping down the hydrometer I can ensure the hydrometer reading is accurate for the one specific fern. Lastly, I had received a comment on my last blog post to which someone had mentioned that there may be more nutrients in the middle of the forest than in the two other locations I had chosen for my field observations. This was something I had never taken into account and to address this issue, I will be limiting my samples to the middle of the forest instead of the entire forest so that I can limit the amount of confounding variables which may also be affecting fern growth.

Blog Post 7: Theoretical Perspectives

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For my research project, I observed how the presence of dogs might impact the time an American Robins (Turdus migratorius) spends foraging at Derby Reach Regional Park in Langley, BC. I observed the foraging time at two locations: in the dog park and the nearby meadow. I hypothesize that the amount of time a Robin spends foraging in the meadow will differ from the dog park. I predict that the length of time a Robin spends foraging in the meadow will be greater than in the dog park due to the greater number of dogs present in the dog park than in the meadow. The ecological process reflected from the study focuses on community ecology and the interactions between species.

Keywords: American Robins, dogs, foraging, and parklands. 

Blog Post 6: Data Collection

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The hypothesis for my research project is the length of time an American Robin (Turdus migratorius) spends foraging in the meadow will differ from in the dog park. For my field data collection, I used the Pont Count method to monitor the length of time at least one Robin spent foraging in the meadow and the dog park. Ten replicates were carried out over ten days. For each replicate, I visited the meadow and dog park for 30 minutes each while alternating each day which location was visited first, and the field visits were conducted during the hours of 5:00-7:00 PM. The data collection strategy was relatively simple. Sitting quietly at a picnic bench with binoculars and a stopwatch, I recorded the time when at least one Robin present or absent in the meadow or dog park. 

 

Considering the time of day, I was surprised by the number of Robins actively foraging and how easily they would return to each location once the area was vacant of dogs. It was also interesting to observe the behaviour between the Robins as one Robin appeared to be territorial over one particular tree and would chase any other Robins that would come near. Ultimately, it was interesting to see that the time a Robin spent foraging in the dog park was relatively close to the time spent foraging in the meadow.

Blog Post 4: Sampling Strategies

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  1. Which technique had the fastest estimated sampling time?

Systematic sampling had the fasted sampling time as each sample was taken in a linear transect. The estimated sampling time for the systematic technique was 12 hours 35 minutes while the estimated sampling time for the random and haphazard technique took 12 hours 38 minutes and 12 hours 40 minutes respectively. In comparison to the random and haphazard technique, the systematic technique was faster by only 3-5 minutes.

 

  1. Compare the percentage error of the different strategies for the two most common and two rarest species

The two most common species was the Eastern Hemlock and Sweet Birch.

% Error for Eastern hemlock

Systematic sampling: 20.0%

Random sampling: 13.1%

Haphazard sampling: 10.0%

% Error for sweet birch

Systematic sampling: 18.3%

Random sampling: 7.8%

Haphazard sampling: 7.8%

 

The two rarest species found were Striped maple and White Pine.

% Error for Striped Maple

Systematic sampling: 8.6%

Random sampling: 90.2%

Haphazard sampling: 66.7%

% Error for White Pine

Systematic sampling: 90.5%

Random sampling: 142.9%

Haphazard sampling: 48.8%

  1. Did the accuracy change with species abundance? Was one sampling strategy more accurate than another?

The accuracy did change with species abundance as the two most common species gave some of the lowest calculated percent errors. Haphazard sampling was the most accurate sampling strategy for Eastern hemlock, and Sweet birch with percent errors of 13.1% and 7.8% respectively. It is hard to tell which sampling strategy was more accurate for rare species as systematic sampling gave the lowest percent error for Striped Maple at 8.6% and haphazard sampling gave the lowest percent error for White Pine at 48.8%.

Blog Post 9 – Field Research Reflections

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I had to change field experiments halfway through the project, which caused many problems. Originally, I was looking at the relationship between moss and cedar trees (Thuja plicata) but they didn’t have a strong enough correlation. This study would have been considering seedling establishment so I chose to go with a categorical response variable for the cedar trees, with the logic that if no seedlings establish, then there will be no trees on the site. When I changed the field experiment to the relationship between soil moisture and cedar trees, I already had the data on the cedar trees and didn’t have time to restart. If I were to do this project again, I would choose a continuous response variable, such as density or productivity (most likely density, as it is easier to measure). This change in design would improve the study by not having to use logistic regression and having more data to work with. 

Another issue I had with the design was that I didn’t have the tools to dig as far down as I would have liked to get the soil samples. My small shovel wasn’t very good for getting past large rocks or in some cases, I barely could dig at all as the bedrock was so close to the surface. I had to change my design so that I only dug down 10 cm deep or until I hit bedrock. If I were to do this study again, I would get some tools to be able to take samples at 2 meters deep as well as at the surface, to account for the tree’s root system. 

Engaging in ecology has certainly changed how I think about ecological theory and its development. There are so many variables that contribute to ecosystems, such as inter and intraspecific interactions, resource availability and disturbances. It is difficult to decide which factors are the most relevant for a study because one definitely does not have the time or resources to consider them all! Although developing theory is difficult in all sciences, I feel like these extra variables and the difficulties in an uncontrolled environment make developing theory in ecology even harder.

Blog Post 8: Tables and Graphs

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For my research project, I am studying the correlation between soil moisture and polypore quantity on individual trees. I collected soil samples from the bases of 24 trees: 16 with polypore fungi, and eight without. For assignment 5, I submitted both a graph to depict the data for the polypore-infected trees and a table for the polypore-free trees. Creating the graph for the polypore infected trees was quite simple using Excel. I inserted the x and y values into a table and then converted them to a scatter plot. This process nicely displayed my data in a manner that is easy to interpret.  However, I had a more challenging time trying to create a graph for the replicates without polypores. I struggled with figuring out how to present the data since the response variable was different than the first set of replicates. Since the fungi quantity for all eight replicates is “0”, it did not make sense to present fungi quantity on the Y axis. I decided that the information I wanted to convey for these replicates was soil moisture and whether or not they were clustered near polypore-infected trees (a simple yes/no variable). Since I could not figure out how to best graph this, I opted to create a table for this set of replicates. I will still play around with the data to see how best to display it for the final report.

The data was surprising to me as no clear patterns emerged between soil moisture and bracket fungi quantity per tree. I was hoping to see a clear trend wherein the higher the moisture content in the soil, the greater number of visible brackets on a tree. Similarly soil moisture was just as variable for the trees without bracket fungi, as many trees in very moist environments did not have any fungi. This has given me a lot to consider for further research which I will discuss in my final report. Variables such as (but not limited to) polypore-infected tree density, slope, canopy cover, soil pH, proximity to the watercourse, diameter at breast height and age of the trees could also impact bracket fungi quantity, and would be worthy of further exploration.