Recent Posts

Blog post 3: Ongoing Field Observations

TRU Open Learning 1 Comment
  1. Identify the organism or biological attribute that you plan to study.

 

The study I am conducting will focus on the abundance of Boxelder Maple tree species and the relationship between their increase in abundance as the distance towards the riverbank decreases.

 

  1. Use your field journal to document observations of your organism or biological attribute along an environmental gradient. Choose at least three locations along the gradient and observe and record any changes in the distribution, abundance, or character of your object of study.

 

Locations for study

 

  • Location one: “Riparian Area”

This are is located N/E using the parking lot as a reference point. It is a riparian area along the riverbank of the Red River with a sidewalk on the outskirts measured at 353.18 m. There is a very high abundance of Boxelder Maple trees beside the sidewalk and as I walked my way towards the river I noticed a few other tree species with low abundance such as Buroak and Arborvitae tree. Alongside the riverbank, there also seemed to be a high abundance of Boxelder Maple trees.

 

  • Location 2: “mini-forest”

I chose this area since it is the furthest area away from the Red River lake. I saw some White Spruce trees, a high abundance of Jack Pine trees, a low density of Boxelder Maple trees.

 

  • Location 3: “Forest next to River” but further away from the Riverbank when compared to location 1

As I walked from the off-leash dog area towards the direction of the river, there was a high abundance of White Spruce trees, some White Willow trees and as I got closer to the river there was an increased abundance of Boxelder Maple trees.

 

  1. Think about underlying processes that may cause any patterns that you have observed. Postulate one hypothesis and make one formal prediction based on that hypothesis. Your hypothesis may include the environmental gradient; however, if you come up with a hypothesis that you want to pursue within one part of the gradient or one site, that is acceptable as well.

 

Boxelder Maple trees dominate the area near the river. There are many reasons that can cause boxelder Maple trees to dominate the area near the river. One potential reason could be that it could be a natural area and since it is distant from other tree species such as Jack Pine trees it could have not been disturbed. In other areas of the park, Jack Pine trees dominate the area which could mean that it is an invasive species. Another potential reason could be their need for water. Boxelder Maple trees might require a higher amount of water in order to survive which is why they are found near the river and since trees have deep roots, they could be using the river as an additional source of water. The soil near the riverbank is the same level of dryness; therefore, soil moisture is not likely to be a potential reason for their abundance.

 

Hypothesis: Boxelder Maple trees is higher in abundance in areas near the river than areas further away from the river.

 

Prediction:

Boxelder Maple trees are more likely to be found in areas near the river than areas further away from the river.

Boxelder Maple trees require a higher concentration of water in order to survive.

 

  1. Based on your hypothesis and prediction, list one potential response variable and one potential explanatory variable and whether they would be categorical or continuous. Use the experimental design tutorial to help you with this.

 

Response Variable: Abundance of Boxelder Maple trees (Continuous measure).

 

Explanatory Variable: Distance from the Red River lake (Continuous measure).

 

Since both variables are continuous, this suggests that this study would require regression analysis for its experimental design.

 

Blog Post 7: Theoretical Perspectives

TRU Open Learning Leave a comment

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

TRU Open Learning Leave a comment

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)

TRU Open Learning 1 Comment

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)

TRU Open Learning 1 Comment

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

TRU Open Learning Leave a comment

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

TRU Open Learning 1 Comment

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

TRU Open Learning 1 Comment

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

TRU Open Learning Leave a comment
  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

TRU Open Learning 1 Comment

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.