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Blog Post 9: Field Research Reflections

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I did not encounter any major issues with the implementation of my design and also did not make any changes to my design while I was in the process of collecting data. However, if I were to repeat this experiment, I would change some aspects of my design.

When performing the statistical analysis for each set of data related to each alternative hypothesis, the results show no statistically significant difference for both cases. Therefore, my null hypothesis for my first analysis cannot be rejected and my hypothesis has to be rejected. For the second analysis, my null hypothesis can be rejected and my hypothesis can be accepted.

First analysis:

H0 = The number of birds of the genus Larus spp. present in the intertidal zone does not vary in function of the tide level.

H1 = The number of birds of the genus Larus spp. present in the intertidal zone varies in function of the tide level.

Second analysis:

H0: The number of birds of other genus present in the intertidal zone varies in function of the tide level.

H1: The number of birds of other genus present in the intertidal zone does not vary in function of the tide level.

Based on my field observations, I predicted that there would be a significantly higher number of gulls present in the intertidal zone at low tide than at high tide. I believe that I wasn’t able to reject my null hypothesis because my sample size was too small (F=3.6078 ˂ Fcrit 4.4139 ; p = 0.074 ˃ 0.05). My p-value was close to being lower than 0.05. Hence, if I were to repeat this experiment, I would collect data on at least 30 occasions at low tide and 30 occasions at high tide. Furthermore, there seem to be significantly less gulls in the intertidal zone at low tide in the morning than in the afternoon. I would therefore divide my data collection further into low tides occurring in the morning and low tides occurring in the afternoon. This would allow me to determine if the time of the day also influences the presence of gulls in the intertidal zone at low tide.

Designing this experiment has allowed me to appreciate how much effort is put into this process. I now better understand the complexity of experimental design. I am also aware that every detail needs to be carefully thought and that small mistakes make a significant difference in the outcome.

Post 3: Ongoing Field Observations

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On my second visit to Pat Bay it was unfortunately quite rainy and dreary to be outside. The time was at 10:00am and no one was around.

I have decided to study a relationship between the honey bees (Apis) and the blackberry bushes (Rubus) that line the bank and up by the hill. The gradient starts at the road and goes to the paved path that people walk along. From there the gradient turns to grass, with no blackberry bushes in sight, and then down to another path near the water bank. The bushes habitat that area around the water bank and end at the rocks down to the water edge. I have come up with two questions for a hypothesis:

  1. Would temperature/weather conditions affect the productivity of honey bees?
  2. Would blackberry bushes near the busy road not be as successful as the bushes closer to the water’s edge farther away from the road (humans)?

For my hypothesis I decided to go with:

Temperature and weather conditions affect the productivity of bees  on blackberry bushes, ultimately affecting the final product (production of berries).

I predict that weather and temperature will affect the bees as they do not go out in colder weather and stigma’s on flowers do not stay receptive for long.

I have come up with a couple options for variables too that I have yet to decide which would be the best and most efficient to observe.

Response variable- # of bees in a time frame that are pollinating or amount of blackberries produced over the duration of the summer.

Predictor- weather/temperatures

Both variables are continuous.

 

Theoretical Perspectives on Measuring Branch Growth Frequency

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Being that my study aims to examine factors influencing the growth of tree branches, some pertinent areas of tree physiology may include photosynthesis, allometry and hormonal regulation. Naturally, these fields are all well documented in the scientific literature, and while I don’t expect to be breaking new ground in regards to vascular plant biology, I am excited to be broadening my personal knowledge base in this field, which I find more and more interesting the more research I do. I am quickly becoming appreciative of the fact that we are very privileged to live in a time where access to so much scientific knowledge is so readily available, in addition to rudimentary material for the budding student as well!

Even within the context of this course I am finding there to be some overlap with similar projects recently undertaken by fellow students. In particular, Doug’s study of insolation on species diversity has helped shed some light on the slope effect for me (pun intended).

While this blog post is supposedly on the theoretical perspectives of this project, I can’t help but ponder what possible practical angles it may hold as well. So far, the research I have done suggests that the measurement of biomass is a regular subject in the field of ecology, and is particularly of interest to the forestry and silviculture industries. The ability to manage biomass production is core to the practice silviculture and the better this process is understood the more effectively this process can be achieved.

Some tags that could be used to help identify this work could include Branch Growth, Sunlight, Pseudotsuga Menziesii and gymnosperm (one extra for good luck).

 

 

Final Reflection

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I was fortunate in that the implementation of my project went very well. I think that due to making a few visits to my location prior to beginning data collection I was able to design a feasible experiment and refine my methodology in order to prevent disappointment after my initial data collection.

As I begin to write up my paper, especially considering the limitations to my study, I have such a great appreciation for the thought process and time that has to go into ecological research and its experimental design. What I have really noticed is there are so many variables to consider, making it harder to fully trust your results, as there may be more at play than you are able to measure (given time and resources). However, even conducting a “simple” project, I can see the benefit my results could make to the field of ecology.

Graph

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Figure 1. Mean number of creeping juniper projections by location.
The graph generated for the purposes of this post (Figure 1) displays the results for my hypothesis that the number of creeping juniper projections differs between side of the stair case (South-East versus North-West). As the predictor variable is categorical, a bar graph was used to display the results. Location is found along the x-axis and mean number of projections is found on the y-axis. Mean calculations and the Mann-Whitney U test (for non-parametric data) were performed using SPSS v24. The graph was generated using Excel.

Despite there being a visual difference in bar height, the data did not support the results I had expected.There was no statistical difference between conditions (p = 0.25). Given the small sample size, I suspect that if I were to increase the number of replicates I would be able to detect a significant difference. I was limited in the number of samples I could take based on the amount of creeping juniper present and ensuring that my data were randomized by varying the distance between samples taken. This method allowed me to make observations along nearly the entire available creeping juniper. Future work should seek to sample multiple similar locations (e.g., slope, geographical area, similar abiotic features present).

Theoretical perspectives and underlying processes

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The man-made staircase that forms the foundation of my hypotheses is an abiotic factor that may influence the growth potential of creeping juniper. More specifically, because the staircase is embedded in the sand, and creeping juniper typically grows along sand, the absence of availability of a favourable environment may decrease plant size. Because my two conditions differ in the amount of sand present for creeping juniper to grow in/along, I am able to statistically test for this abiotic influence.

Keywords for this project would include: creeping juniper; abiotic environmental influences; growth limiters

Post 2: Hummingbirds, Bees, and Flowers

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a) The article I have chosen is “Bird-pollinated flowers in an evolutionary and molecular context” Journal of Experimental Botany, Vol. 59, No. 4, pp. 715–727, 2008, by Quentin Cronk* and Isidro Ojeda.

The link to the article is: https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/jxb/59/4/10.1093/jxb/ern009/2/ern009.pdf?Expires=1497250100&Signature=X5MYqRKanmgt1YJIc7X7nR3oWOYrSl76-maryxm5PVzdqipcA2Z9mgK7lcVQuIfXgV6WbwDt2g-6ZSTPDp1ikIt1AtymzigwIrUQ74a0kS7b67pksJYW9NeA5Nj5rWO5Bag1lxTlRbLcwy3CCcxh~vmNkIkSGSomq9hzd5E3Jb7YO24ELGDzZgt8j-qQ3ewAE3E4R-MWDoYV-oH1p-c7icHn3eq8t21L0g8OXU031EsXALTRr320b5sg-GPI-nmlqt5fzakrcwLaSKsWpmHnPmb38zRR~dLW7-phsh8bvkJrYEqTtzfbv1Yake~LqcLEmDaNVwfemRqYVSvafBpqyw__&Key-Pair-Id=APKAIUCZBIA4LVPAVW3Q

b) The article classification is an academic, peer-reviewed, review material

c) The authors are PhD students at UBC and research the evolution of bird pollination, which indicates a an academic article. The paper contains in text citations and includes a bibliography/ references.

The ‘Journal of Experimental Botany’  was peer reviewed.

A personal study was not done in order to write the paper. Although the paper does include an introduction, it does not include methods or results so it is not a research paper, and therefore is for review.

 

Post 1: Pat Bay Victoria BC

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I chose to do my Field Project at Pat Bay in Victoria BC, a short drive from my home. It is approximately half an acre located in Deep Cove, Victoria BC, just off the Pat Bay highway near the airport. Jagged rocks and grassy hills surround the bay, which also homes search and rescue boats and float planes. Vegetation includes many blackberry bushes, wildflowers that I have yet to identify, and arbutus trees covering a vast majority of the grasslands down to the water’s edge. Animals include a variety of birds such as sparrows, robins, and hummingbirds as well as honey bees, seagulls, geese, and the occasional seal passing through. A pathway separates the grassy hills from the water where quite often people will come to walk their dogs or entertain children.

 

My first visit was on June 10, 2017 at 18:36. The grass was damp, which indicated earlier rainfall, but at the time the sun was peaking out from behind white clouds dotting the blue sky. Pink and orange flowers poked up through the brush here and there, indicating that spring is upon us. A good indication of spring came from the honey bees buzzing about. Although only a few could be seen, their buzz was unmistakably heard through the soft sounds of the water’s edge. A hummingbird, which looked to be a juvenile, was spotted perched upon a tree branch, and sparrow bird chirp could be heard from within the bushes. No sign of aquatic animals, but it is a little too early for that.

My three questions are:

  1. How does the vegetation differ between the rocky beach of the bay and the grassy hills and why do they differ?
  2. What impact do the animals that habitat that space have on the vegetation?
  3. Does human interaction with the surroundings affect it in any way and if so, are the effects negative or positive?

 

Data Collection at Cranberry Flats

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I sampled 10 replicates on each side of the staircase. My sampling strategy was straight forward and easy to implement. One difficulty I had was that I intended to randomly measure three projections at each sampling site; however, at nine of 20 sampling sites, no projections met my inclusion criteria (at least 30cm in length). This means that I have no data for these sites. This is relevant for my hypothesis that there are more projections on one side of the stairs compared to the other. Unfortunately, this also means that I am not able to calculate an average length of projections for these sampling sites, decreasing the amount of data I have to analyze. This means that I am less likely to find a difference between conditions as differences are difficult to detect in small samples, unless the difference is large. I believe that my hypotheses are sound, although there are other variables that could explain any potential differences in groups (e.g., amount of sunlight received, ground moisture and run-off) which I will have to address in my study limitations. Patterns in these variables may be of interest; however, I am unable to measure them.

Blog Post 6: Data Collection

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After tweaking some aspects of my study design, I returned to my study area 3 times over the past two weeks for some more data collection. I recorded data from 10 new replicates (based on the “rule of 10”) from the SE flank of Rainbow Mountain, as well as an additional 10 from the same are but at a lower elevation. I will not be including the data I collected a month ago for my “initial data collection assignment” in my final report (there are several reasons for this, including the fact that initially I was collecting my measurements too close to the ground, between 1 & 1.5m).

I have begun collecting my data from between 4-5m to mitigate possible confounding factors, including the slope angle near the base of the trees. This proved challenging at first, as making measurements higher up the tree was initially difficult to do with any degree of accuracy. I brought along a stepladder and a tape measure to assist with my measurements this time out, and after some practice I was able to devise a system for counting branches higher up the trees. I also used a different app to collect sunlight data to record in different units (watts per meter squared), which I think will provide a better representation of my predictor variable (“sunlight received”).

At the suggestion of professor Hebert, I also began taking measurements of the distance to the nearest neighbouring trees, as their presence may be a confounding variable in the growth of branches on the replicates being studied. In selecting the “nearest neighbour” I deemed only those trees that were 5m or taller to qualify, as any trees smaller than this would be unlikely to block sunlight from potentially reaching the replicates.

During my “initial observations” assignment, I was collecting on a day with some clouds, and their passing between taking measurements would create large inconsistencies in my light readings, even within the two sides of the same tree. In order to ensure the most uniform measurements of light, I collected on days with similar weather (clear, no clouds), and at the same time of day (12:00). (My first day of data collection took place at 14:00, so I returned at a later date to repeat the light measurements).

 Field note book measurements

I noticed several nuances during my data collection that complicated the process more than I initially anticipated: The first one being that trees don’t always grow perfectly vertical. They often grow at an angle, which can make placement of the light meter somewhat difficult. Secondly, the nature of light filtering through a forest means that a slight difference in where the meter is placed can have vast implications on the reading it generates (i.e. the difference of being directly in a sunbeam or in the shade can be a matter of only a few cm). And furthermore, the location of where light filters through changes constantly throughout the day. Being consistent with the location of the light meter and time of data collection, as well as trying to move quickly without allowing haste to affect the quality was all I could do to ensure uniformity of results.

The topography in the lower elevation study area varied somewhat from the upper one, as did the species that populated it. While the slope was fairly uniform at 850m, closer to the valley bottom at 610m there existed many rolling microfeatures (small knolls) that affected the ways the trees caught the light. I chose to continue with my randomized sampling method in both areas, however it was more difficult to come across the species I was studying (Pseudotsuga menzeisii) at the lower elevation area, and several times I would have to re-enter compass bearing and number of paces in order to find a replicate. This was not an issue at the higher elevation.

One ancillary pattern I noticed during my data collection was that it is not merely the number of branches that seems asymmetrical on the two sides of the trees, but also the length and foliage of branches as well. While the data collection seems to have strengthened my belief in the prediction that more branches grow on the downhill side of the trees, they also seem to be significantly longer as well as more likely to be covered in foliage. I did not notice this trend until well into my data collection, and did not take any measurements regarding branch length however, as it would be quite difficult to do at a height of 4-5 meters and I was unprepared to do so. It is an interesting pattern nevertheless and I will consider if there is a way I can return to incorporate it into the project going forward.

Example of a replicate with asymmetrical foliage