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Blog Post 7: Theoretical Perspectives

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My research project aims to validate a pattern I observed, that Common snowberry (Symphoricarpos albus) distribution is limited to environments with less than 20% slope gradient, or that common snowberry distribution diminishes as slope gradient percentage increases. During my initial field observations, I started noticing that plant species occurred in one environmental gradient, but not in the other. My first observation was that the dominant tree species would differ between environmental gradients. For example, the riparian area was dominated by black cottonwood (Populus trichocarpa), whereas the upland area was dominated by ponderosa pine (Pinus ponderosa). When I started making observations about the shrub and herb layer I noticed similar patterns, where some species were present in one area, but not in the other.

I chose to focus on common snowberry distribution and started asking questions that may explain why common snowberry was present in the riparian area, but not in the upland area. I questioned slope gradient percentage as a potential indicator of water availability, slope aspect as an indicator of sun exposure, soil type, soil moisture and surrounding topography. When I relate this back to ecological processes, I want to focus my research project on abiotic factors and the physical environment including local topography, the hydrological cycle and the energy cycle. In summary, my research aims to explore that the physical environment (topography, slope, aspect etc.) is an indicator of species occurrence and ecological communities.

Three keys words I would associate with my research project include, ecosystem indicators, ecological communities and physical environment.

Post 9: Field Research Reflections

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I really struggled with this course overall. I had a difficult time creating a hypothesis. I had a hard time making sure that my hypothesis and study was even tangentially related to ecology as I have absolutely no background in ecology at all. I was challenged to trust my results as I felt that it would take a much longer study period to really and truly make valid conclusions. The main issue that came up with implementing my field study was being available during the same time period every day. There were a few days that were missed due to work schedule changes that had me travelling out of the city or my parents forcing me to attend Thanksgiving dinner. I was lucky in that I was able to recruit an assistant (my husband who is contractually obligated to help me out, as stated in our vows) to attend a few days for me while I was away at a conference for 5 days. Overall, were I to attempt this study again, I would like to set up a remote monitoring system that would allow for 24 hour tracking for a full year to really gather a full sense of the variables that affect the populations of the park. I have so much appreciation for anyone who practices ecology as their knowledge and commitment far exceeds my own.

Post 8: Tables and Graphs

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This was my favorite part of the entire course. I had so much fun making these graphs. I was expecting a higher correlation to be shown when measuring the people per dog ratio in relation to the subjective weather and conditions. I made a few more graphs than I probably needed to, but as I said, I was really enjoying myself.

Post 7: Theoretical Perspectives

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The theoretical basis for my research is focused on abiotic weather factors that affect the species diversity at Mill Lake park. My research will touch on the mutualism between humans and domestic dogs. It explores park usage and environmental city planning.

The keywords that are most suited to my work are: Canis familiaris, Weather, Walking, Urban parks, and Mutualism

Post 6: Data Collection

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I have been attending Mill Lake Park nearly every day between 17:30-17:45 to gather weather and population data. So far, I have attended 37 days since September 10th having missed 4 days in that period. The main difficulty has been ensuring that I was available during the time period every day. I’ve noticed significant changes in the populations with subjective declines in weather conditions. I predicted that fewer people would visit the park on days of inclement weather, but I had not initially anticipated that those who did visit on the poor weather days would be predominantly accompanying a dog. Upon further reflection, it made sense to me that those who had to walk their dog would be less inclined to remain inside out of obligation to their pet.

Post 5: Design Reflections

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My sampling strategy was overall pretty easy to implement. The main difficulty that I ran in to was trying to be at the park every day during the same time period. There were a few days that were missed due to traffic conditions on the drive home when I was transferred to a different location for work. There were also a few days were I was at the World Sleep Symposium where my husband went to the park on my behalf but there were some inconsistencies with his measurements compared to mine that I suspect are user error differences.

One difficulty that I’m foreseeing if I continue to collect data is the upcoming time change as well as sunset rapidly descending upon my collection time. Currently I’m measuring from 17:30-17:45 every day. Sunset is currently at 18:08 and gets a few minutes earlier each day. I anticipate that the darkness will drastically change my collection numbers and I’m unsure if I should continue a 24 hr cycle of measurement after the time change and measure at 16:30-16:45 or if I should stay with the accepted clock time. These may not come up as I anticipate that I will stop collecting data before the time change, as far as it pertains to this report, though I may continue on my own accord out of sheer curiosity.

Post 4: Sampling Strategies

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For the virtual sampling exercise, I sampled from the Snyder-Middleswarth Natural Area. I utilized random, systematic, and haphazard sampling strategies. Of these, the systematic was the most time efficient clocking in at 4hrs and 5 minutes, though the haphazard was a close second at 4 hrs 27 minutes compared to the random strategy which took 12 hours and 34 minutes.

There was no method that I found to be particularly accurate in its estimation. I calculated error rates for each tree species rather than just the 2 most common and 2 rarest in an attempt to narrow down the “best” method but was still left with few answers.
My error calculations showed:
Eastern Hemlock (actual density 469.9) – Random – 3.3%; Systematic – 22.2%; Haphazard – 3.6%
Sweet Birch (actual density 117.5) – Random – 31.2%; Systematic – 11.7%; Haphazard – 60.9%
Yellow Birch (actual density 108.9) – Random – 57.9%; Systematic – 55.0%; Haphazard – 9.6%
Chestnut Oak (actual density 87.5) – Random – 52.3%; Systematic – 14.3%; Haphazard – 47.0%
Red Maple (actual density 118.9) – Random – 33.1%; Systematic – 5.3%; Haphazard – 8.1%
Striped Maple (actual density 17.5) – Random – 90.3%; Systematic – 60.6%; Haphazard – 47.4%
White Pine (actual density 8.4) – Random – 1.2%; Systematic – 123.8%; Haphazard – 100%
All in all, the accuracy was not great across the board so I’d generally recommend Systematic or Haphazard sampling strategies based solely on time savings compared to the Random sampling strategy.

Post 3: Ongoing Field Observations

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The organisms I intend to study are predominantly people and domestic dogs. I’ve set myself up alongside a paved walking path next to the lake. I’ve been measuring weather conditions, including rain volume, temperature, pressure, light, humidity, and subjective observations (example: sunny, overcast, rainy, etc). I’ve given a consistent 15 minute period each day where I count the numbers of birds, people, pets, and any other animals that pass through or into the area during that period.

During my period of establishing the best means to complete this study, I attempted several locations as well as times of the day. For location, I attempted a spot near the playground, a spot along the walking path, and a spot near a floating bridge. Ultimately, from these spots I opted to stay in the spot along the walking path as I overheard a couple discussing how they didn’t like going over the bridge during the rain as it made their dog slip due to the texture of the wood when wet and I found that a lot of children near the park were very interested in discussing what I was doing with all of my weather measuring tools which I worried would make some of their parents apprehensive about frequenting the park.
The other gradient I explored was time period. I wanted to be consistent about the time I visited each day to avoid variability. I explored 07:00-07:15; 14:30-14:45; and 17:30-17:45. I ultimately opted for the 17:30-17:45 period as I personally did not want to wake up early enough to attend the 07:00 time slot, and work would prevent me from attending the 14:30 time period outside of weekends. Whilst attempting a few of these time slots, I noticed the variation not only in the number of people who came through my field area, but also the percentage of those who were walking dogs. The morning time seemed to be predominantly dog walkers and joggers (many of whom were with dogs also). The afternoon had almost no dog at all, but many seniors who were strolling through the park. The evening had a mix of both. This led to my official hypothesis and prediction.
I believe that more people will visit the park during periods of dry weather than during periods of inclement weather; but also that those who visit during the periods of inclement weather will be a higher percentage of those accompanying dogs than those without dogs when compared to the percentages during the drier/warmer days.
My predictor variable will be the weather conditions and my response variable will be the number of people and pets travelling through the park. These variables are continuous, not categorical.

Blog Post 6: Data Collection

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On November 10, 2019 between 10:45 AM and 4:00 PM I collected my field data at Kalamalka Lake Provincial Park along Cosens Bay Trail, Vernon BC. I sampled my three sites, Site 1 Eastern Area, Site 2 Riparian Area and Site 3 Upland Area. On the day of my field collection the temperature was approximately 12 degrees Celsius and throughout the day the weather varied from sunny, to some sun with cloudy conditions.

I sampled Site 1 Eastern Area first between 11:00 AM and 12.35 PM. I sampled Site 3 Upland Area second between 1:15 PM and 2:15 PM. I sampled Site 2 Riparian Area third between 2:25 PM and 3:20 PM. At each site I sampled 10 quadrats, my quadrat was 1.5 m by 1.5 m that I built using PVC pipe and PVC elbows. The area of each quadrat represents 2.25 m2. I sampled a total of 30 quadrats. Within each quadrat I recorded the time, weather conditions, number of stems of snowberry, snowberry cover class (1-6), upland slope (%), slope aspect, soil moisture, light, pH and recorded the other vegetation observed within each quadrat. The number of stems of snowberry within each quadrat will be processed to represent the density of snowberry. The cover class of snowberry represents the percentage cover of snowberry within each quadrat. I used a clinometer for the upland slope (%), a compass to record the slope aspect and a soil moisture meter to record the soil moisture, light and pH within each quadrat.

During my field collection, I had to adjust the area I sampled at Site 2 Riparian Area due to the dense vegetation and presence of poison ivy. The riparian area was long enough that I was able to move my sampling location south and still had enough space to implement my sampling design. Before I went into the field, I printed off field collection sheets, field maps and grid coordinates to make it more efficient to locate each quadrat in the field which I found very helpful. I timed my field collection when it was not raining, and before the first snowfall. One issue I found during my field collection was that most of the snowberry had lost it’s leaves and berries, so I had to spend more time at each quadrat identifying the snowberry stems. Ideally, I would have collected my field data before the leaves had fallen to make identification more efficient.

On review of my data collection, I found that snowberry was more abundant in Site 1 Eastern Area, where I expected snowberry to be more abundant in Site 2 Riparian Area. I also found the soil moisture in Site 1 Eastern Area was lower than Site 3 Upland Area where there was no snowberry observed. This observation was not what I predicted, I predicted that dry soils would not support snowberry distribution. My hypothesis, that snowberry distribution is determined by slope gradient percentage is supported by my field collection. I predicted that snowberry will be present in areas where slope is less than 20%. My field collection supports this, however the slope gradient percentage that I recorded ranged from 3% to 19% and then 35% to 47% where snowberry was present between 3% to 19% and not present between 35% to 47%. Ideally one of my sites would have a slope gradient percentage somewhere between 19% and 35% which would support or falsify my hypothesis.

I also observed that the soil depth in Site 1 Eastern Area and Site 2 Riparian Area was deeper than the soil in Site 2 Upland Area. I observed this when I inserted the soil moisture meter within each quadrat. I had not included soil depth as a variable that may contribute to snowberry distribution, therefore I will be considering this as a variable moving forward.

I may consider adding additional sites along Cosens Bay Trail to strengthen my data set.

Blog Post 9: Field Research Reflections

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Blog Post 9: Field Research Reflections

November 11, 2019

 

Throughout the course I analyzed abundant plant species in my back yard in Christchurch NZ. I created and followed through with the hypothesis that Hydrocotyle heteromeria, an invasive water species, is in fact limited by the moisture content in the soil. The soil on the South side of the lawn where the Hydrocotlyle was found contained high levels of moisture in comparison to the Northern side. The idea that one side of the lawn had higher moisture levels than the other is what led me to construct my hypothesis. I did however change my field study design. I was planning to analyze one single plot over a gradient, but I needed a complex computer program to analyze a design that contained one categorical and one numerical variable. I decided to create two plots, one which had ‘wet’ soil and one which had ‘dry’ soil. This change allowed me to create two variables which were both categorical and could therefore be analyzed with a tabular method using a statistical Chi-test. Since I changed my design it allowed me to add the completed data to my research paper instead of writing in my results “what I could have done” to analyze the data. I was glad that I could completely follow through with the results to determine if the variables were independent of one another.

I did however find it difficult to get enough research articles for my annotation. There was not a lot of relevant literature on my specific Hydrocotyle species. I did find a book which said that all Hydrocotlye species can be grouped into the same category because their biology was all extremely similar.

This course has been very worthwhile. It has given me practical knowledge to create and design research papers and field studies. It has also taught me various aspects on reading research papers which pertains to all courses and how to scrutinize research paper results to determine if the findings are credible. This course has allowed me to appreciate the dedication it takes to create large scale research studies.