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Blog Post 3: Ongoing Field Observations

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The biological attribute I have decided to study is the diversity of grasses in Wakamow Valley. I noticed that the preserved ecological zone of the valley had a wide variety of grasses compared to Conor Park and Tatawaw Park.

Wakamow Valley has quite a few distinct ecosystems/areas. The gradient I am using to observe in this project includes Tatawaw Park, an old wild animal park that was abandoned and closed down in the 80s. The old enclosures still stand with their concrete pads, and the old asphalt roads and parking lots are used for walking paths, frequented by bikers, hikers and their dogs. The second area is Conor Park, a well-maintained natural area often used for outdoor weddings, BBQ’s and walking. It also includes a large playground and parking area, but still has significant natural habitat intact. The third area of the gradient is the Kingsway Park Ecological Zone. This area is virtually untouched by humans anywhere other than the natural dirt walking path. The variation in grass diversity between these three distinct areas could be do to this gradient of increasing fragmentation. It could be that with increasing fragmentation, a decrease in biodiversity of grasses occurs. Based on this information, it can be assumed that Kingsway Park is the least fragmented area, Conor Park is moderately fragmented, and Tatawaw Park is significantly fragmented.

An alternative explanation is the heavy presence of uniform lawn grass in developed areas because of their anthropogenic selection for their aesthetic purposes and accessibility. To account for this possible confounding factor, clearly sodded areas should be excluded from the study.

Hypothesis: Grass biodiversity decreases with increased fragmentation of land.

Prediction: Fragmentation of habitat will negatively correlate with grass species diversity.

Response variable: diversity of grass species, continuous

Predictor variable: fragmentation level, continuous

Experimental design: regression

Blog Post 8: Graphs

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25 March 2020

Shannon Myles

 

I was having trouble compiling all of my data within only one graph. It seemed as I had too much information to include in the graph. I then decided to combine 4 graphs (A, B, C, D) into my one Figure. By having those four graphs separated but put together in one figure, it was very easy to compare them all. The proximity and arrangement allowed for an easy and quick assessment of all four data that is essentially the same experiment but for four different species. I found that it illustrated well the differences between species. Some basic technical difficulties were met when I was trying to combine all four graphs and axis titles into one figure. I ended up combining them all through PowerPoint into one image that I then introduced into my word document. This technique facilitated the whole process by unifying all my elements.

My data put into graphs actually showed me that there was an increase in flower abundance as one gets away from the beach as I hypothesized. Though, I had not observed the fact that abundance declined within the last two or three subsamples along my transects. This new discovery would definitely be worth studying to understand if perhaps another gradient or ecotone is present as the field gets closer to the highway.

Post 2: Sources of Scientific Information

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On my shelf sits a book titled Environmental Science and Theology in Dialogue written by Russell A. Butkus and Steven A. Kolmes, two professors of the University of Portland, as part of a series titled “Theology in Dialogue.” This book is used as a textbook for classes at the University of Portland, and combined human ecology and theology to teach students to examine the impact they have on the world through a theological lens. Dr. Butkus is an environmental theologian, while Dr. Kolmes serves as the Environmental Studies Department chair, and has served on many governmental advisory committees, all that to say that both are experts in the field. The book includes footnotes and citations all cited at the end, and was reviewed by Anne Clifford. Given these facts and the book’s lack of lab or field study, I conclude that this source of information is classified as Academic, peer-reviewed review material, as outlined in the Tutorial on Evaluating Sources of Scientific Information.

Citation:

Butkus, R. A., & Kolmes, S. A. (2011). Environmental science and theology in dialogue. Maryknoll, NY: Orbis Books.

Blog Post 8: Tables and Graphs

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At first I had difficulty keeping my field notes, data tables, and field photos organized. Once I decided that I was going to break the study regions up into the sections of trail not adjacent Beaver Lake (‘exterior’ group) and the section of trail with the south side exposed to Beaver Lake (‘Beaver Lake’ group) I remade my field data tables, and collected the data in a much more organized way. After each sampling day, I returned from the field and entered the presence-absence data into an Excel table, so the observations were fresh in my mind. When taking photos, I took a photo of the field sheet (showing the replicate number) so I could organize and refer to my photos accurately for desktop confirmations. I performed desktop lichen group confirmations in 25% of samples collected, as a quality control step in lichen identifications. I had planned before data collection how I was going to aggregate the data into groups.

I arbitrarily chose 25% as the threshold to indicate ‘dominant’ lichen groups. I was surprised to find the distribution of lichen groups were somewhat similar between Cupressaceae and Pinaceae tree families. Interestingly, Pinaceae appeared to have a darker green version (variant or species) within the Cladonia genus, whereas the Cupressaceae were associated with a pale-green (less crinkled edges) species within the Cladonia genus. Also, Pinaceae in the exterior group had the least proportion of visible podetia (development stage of the Cladonia sp.). I am going to review the available information in the primary literature, related to the factors that play a role in the development of lichen podetia.

Post 7: Theoretical Perspectives

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According to previous research, epiphytic lichens associate differently depending on the type and age of the tree, as well as bark characteristics. There are additional variables within the microclimate, and larger local environment that influence lichen associations. My hypothesis focuses on differences between tree family (and therefore bark characteristics) and differences in microclimate caused by the proximity to a wetland/lake area. An additional ecological process my research may touch on is the different developmental stages of the fruticose lichen (Cladonia sp.). According to the field data collected this far, it appears that a fruticose lichen that resembles Cladonia sp. differs in developmental stage by the presence and size of the podetia. Preliminary findings suggest the developmental stage differs between trees, potentially due to differences in microclimate, tree family, or even due to tree size. Further collection of additional replicates and statistical analyses will help test these findings.

Three keywords I could use to describe my research project include; epiphytes, distribution, structure

Blog Post 8 – Tables and Graphs

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Blog Post 8 – 19/03/20

Overall the organization of my table went smoothly. There were some initial mistakes made as I tried to correctly tabulate the data; however, I proofread my table and worked hard to ensure that I correctly calculated all the values necessary to complete the information in the table. Aside from the initial minor calculation problems, the organization, aggregation, and summary of my data went well and there were no further difficulties. The outcomes from arranging this table were slightly unexpected. Early on in my data collection I had noticed that white birch  trees were found in higher amounts closer to the central pond. This was confirmed in the table and white birch had the highest distribution in pond land. Surprisingly, white spruce had an relatively even distribution over both central park land and edge land. This was an unexpected result and I will have to look into research previously done that examines the growth abilities of white spruce in a variety of soil conditions. The anticipated result of the experiment was confirmed in the aspen poplar species which had the highest distribution in central park land. This unexpected information from both white birch and white spruce species confirms that I need to study literature investigating ideal soil moisture conditions for growth of both of these trees before discussing my results in my final report.                                                                                                                                                                                                                                                                                                             

Blog Post 6. Data Collection

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Last weekend I collected a second round of samples from a Beaver Lake trail. In total, I sampled 10 replicates on 15 March 2020. The only problems I have faced so far with implementing my sampling design is identifying to the lichen genus level in the field. I have resolved this by taking photos in the field for desktop confirmations as well as written presence/absence notes for each replicate. I have also started grouping lichen observations by structural categories reported in the literature (i.e., crustose, squamulose, fruticose etc.)

There are also several sampling problems I have resolved. At first I was having difficulties identifying individual trees species in the field. I am now identifying trees to the family level, which is much easier. There is also scientific rationale for identifying trees at the family level when studying lichen because trees within the same family possess very similar bark. Since tree bark is the substrate used by epiphytic lichen, grouping replicates at the tree family level is appropriate.

I have noticed an ancillary pattern that has made me reflect on my hypothesis. I have noticed replicates along the periphery of Beaver Lake that appear to have more fruticose lichen types, and those identified as Cladonia sp. appear to have developed podetia (i.e., the fruiting body of the lichen). By comparison, the replicates sampled from the surrounding area that are buffered forest on both sides of the trail appear to have less fruticose lichen types and less have visible podetia.

Blog post 2: Sources of Scientific Information

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I found an interesting study cited here:
Couch, C. S., Burns, J. H. R., Liu, G., Steward, K., Gutlay, T. N., Kenyon, J., Eakin, C. M., & Kosaki, R. K. (2017). Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE, 12(9), 1–27. https://doi-org.ezproxy.tru.ca/10.1371/journal.pone.0185121

This is academic peer-reviewed research material.

I came to this decision because the authors are professionals in their field and are associated with The University of Hawaii, the US Fish and Wildlife Service, The Pacific Islands Fisheries Science Center, The National Oceanic and Atmospheric Association, and Global Science and Technology Inc. The first two authors mentioned have PhD’s in the relevant field of study and are associated with the University of Hawaii. The rest vary, with one author mentioned (Kanoelani Steward) being a student of the Marine Science Program at the University of Hawaii.

Courtney S. Couch1,2*, John H.R. Burns1, Gang Liu3,4, Kanoelani Steward5, Tiffany Nicole Gutlay1, Jean Kenyon6, C. Mark Eakin7, Randall K. Kosaki8

1. Hawai‘i Institute of Marine Biology, Kāne‘ohe, Hawai‘i, United States of America,
2. Ecosystem Sciences Division Pacific Islands Fisheries Science Center, NOAA Honolulu, Hawai‘i United States of America,
3. Coral Reef Watch,NOAA/NESDIS/STAR,College Park,Maryland, United States of America,
4. Global Science & Technology Inc. Greenbelt, Maryland, United States of America,
5. Marine Science Program University of Hawai‘I at Hilo,Hilo Hawai‘i,United States of America,
6. U.S.Fish and Wildlife Service,Honolulu, Hawai‘i,United States of America,
7. Coral Reef Watch,NOAA/NESDIS/STAR,College Park,Maryland, United States of America,
8. NOAA Papahānaumokuākea Marine National Monument, Honolulu,Hawai‘i,United States of America

There is also in-text citations that hyperlink to the cited material, which is also all present in the References section. For example:

“Coral bleaching involves the breakdown of the symbiosis between a coral and its endosymbionts (Symbiodinium spp.) in response to environmental stress (such as anomalous changes in temperature[1–3], salinity[4], sedimentation[5], and/or light[6], resulting in the expulsion of the algae[7].”

It is peer reviewed because it explicitly states in the Acknowledgements that it was reviewed by a named woman and three unnamed reviewers.  Shown here:

“Thank you to Eileen Nalley and three anonymous reviewers who provided feedback that greatly improved the quality of this manuscript.”

And finally it is research material because it is clearly a research project with a Methods and Results section including accompanying graphs, stats, and interpretation of the data in reference to the hypothesis.

Blog Post 4: Sampling Strategies

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In the virtual forest tutorial, I chose all area-based methods. The fastest sampling time was when using the systematic technique. It took an estimated time to sample of 12 hours and 4 minutes, compared to 12 hours 42 minutes for the randomized, and 12 hours 27 minutes for haphazard.

The most common species was the eastern hemlock. Below are my calculations for percentage error of this species.

Systematic: PE (495.8-469.9)/469.9*100=5.5%        Most accurate

Random: PE (680.8-469.9)/469.9*100=44.9%

Haphazard: PE (704.2-469.9)/469.9*100=49.9%

The least common species was the white pine. Below are my calculations for PE of this species.

Systematic: PE (8.3-8.4)/8.4*100=1.2%              Most accurate

Random: PE (8.3-8.4)/8.4*100=1.2%                  Most accurate

Haphazard: PE (4.2-8.4)/8.4*100=50%

It seems that calculating rare species is more accurate, but only when using random or systematic sampling. Haphazard sampling was not accurate in either species. For abundant species, systematic seems to be the only accurate sampling method.

The actual data compared to the estimated data left significant percentage errors in most cases for all species in the middle. Data was most accurate at the top and bottom, or most common and least common. I suggest more than 24 data samples would be needed to eliminate this.

 

 

Blog Post 7 – Theoretical Perspectives

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Blog Post 7 – 23/02/20

There are several ecological processes that underpin my research project. My project is primarily focused on the distribution and abundance of common trees in relation to soil moisture; however, there are many other factors that may contribute to this topic. Such factors include nutrient soil temperature and pH, availability of nutrients, as well as permafrost cycles and species climatic resistance. All of these factors can be seen as ecological processes that impact the research I am doing. Fluctuations in soil temperature and pH, while not explicitly measured in my experiment, are conditions that impact the quality of the soil and may explain, in addition to soil moisture, why trees are not found in bare stretches of the bark and conversely, why they are found in such high quantities in other locations. Along with those two factors, the nutrient cycles of various locations in the park surely must also have some impact on the presence or absence of trees in those areas. Numerous nutrient cycles and exchanges are constantly occurring regularly in the park. Seasonal changes to such cycles in the early lives of the trees may have influenced their growth potential. Permafrost cycles are another ecological process that likely impacted the growth potential of the trees as well as their abundance and distribution. It should also be noted that the climatic resistance of the different species likely impacted their growth in certain areas over others. I believe that three keywords that could be used to describe my research project are park sampling, soil moisture content, and tree distribution. Three other keywords could simply be the common names of the trees I am studying, those being white spruce, aspen poplar, and white birch.