Category: Nancy Elliot

Blog Post 4- Sampling Strategies

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Blog Post 4:

For this sampling exercise, I studied the 154-ha Mohn Mill area located within Pennsylvania at elevations that range between approximately 420 to 570 m ASL. Steeply dipping slopes make up the topography of the area and sandy loams cover the slopes. During the sampling exercise, I used three techniques that included systematic, random, and haphazard sampling. The results from the three sampling methods are as follows and show percent error data from the two most common and most rare trees.

Using random sampling methods Red Maple (RM) and Witch Hazel (WH) came up as the most dominant species. After 20 quadrats were sampled percent error for each showed errors of 1.98% and 26%, and a total time of 10 hours and 33 minutes was taken to sample. The two most rare species were black cherry (BC) and American basswood each having errors of 566% and 566% respectively.

Using haphazard sampling methods RM and WH came up as the most dominant species. After 20 quadrats were sampled percent error for each showed an error of 14.14% and 49%, and a total time of 10 hours and 34 minutes was taken to sample. The two most rare species were downy juneberry (DJ) and BC each having errors of 201% and 233% respectively

Using systematic sampling methods RM and WH came up as the most dominant species. 24 quadrats were sampled and systematically spaced 50 quadrats from one another. Percent error for each showed an error of 4.21% and 37%, and a total time of 12 hours and 22 minutes was taken to sample. The two most rare species were DJ and white pine and white pine each having errors of 57% and 67% respectively

Observing the results shows that RM and WH are the most common species. Out of the three methods random sampling produced the least percent error (1.98%, 26%), and I suggest that this is the most accurate method. RM appeared to have the lowest percent error in all three methods (1.98%, 14.14%, 4.21%). This could be an artifact from the actual data and more current analysis may have to be undertaken. Another observation includes percent error increasing with diminishing abundance of species with BC (566%, 233%) and DJ (201%, 57%) having the largest error and least recorded abundance. This concludes that accuracy in all three methods increases with the availability of species to sample and random sampling was the most accurate method for sampling. Moreover, systematic sampling took the longest to complete and haphazard and random took similar amounts of time.

Blog Post 5 – Design Reflections

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I did have some difficulty with implementing my sampling strategy in the field. I had previously selected several locations on a map of the area found from google maps that when I was onsite, I would measure and 2×2 foot square and assess the area for mosses as well as record the temperature. On site, I used my cell phone with the google maps app to find the locations I had previously selected. Even though I had visited the site before, I did not have extensive knowledge of the terrain and soon realized that some of the locations I had selected were on private property or very difficult to get to in order to survey them. For the data I did collect, I took pictures of the specimen with my cell phone – that automatically geotagged them – and used iNaturalist to identify them before completely filling in my field data sheet. I was also overwhelmed by the number of species present that I was not comfortable identifying, so I think that I should choose a few types to collect data on instead of trying to document everything I see. I definitely need to modify my approach since I did not get nearly enough data points to make any inferences due to the poorly selected locations on inaccessible terrain, and my lack of confidence identifying species. This data was collected a few months ago now, and due to the recent health environment I have left the city I had started the study in, and have decided that I will be continuing and using data collected by observers on iNaturalist. This way, I will still be able to collect species and location data without being present in Victoria, B.C.I will also be able to use exact coordinates of the locations I surveyed which may be helpful when it comes to displaying the data later on in the process of this project. 

Blog Post 4 – Sampling Strategies

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For this blog post, I used an online community sampling exercise to sample Mohn Mill. I used three techniques, systematic sampling, random sampling, and haphazard sampling. The most efficient sampling technique was random sampling, taking approximately 11 hours and 51 minutes in comparison to the other techniques taking over 12 hours. The two most common species were the Red Maple and the White Oak, and the two rarest species were the White Ash and Yellow Birch. The percentages are listed below for comparison. The accuracy of the tests varied widely between the common and rare species, the common species having errors as low as 1.33%, and the rare species having errors as high as 1037.5%, the accuracy declining significantly with the rare species. In general, random sampling method had the lowest percent error for both common and rare species, excluding the White Ash. The most accurate of the common species was the random sampling of the Red Maple, with percent error of 1.33%. The most accurate of the rare species was significantly worse, from all sample methods of the Yellow Birch with a percent error of 100% across the board. I think 24 sample points is enough to capture the number of species in this density, but it would not hurt to have more data to further confirm conclusions made. I think that 24 sample points is not enough to accurately estimate the abundance of these species, as the percent error for the rare species was astronomical in comparison to that of the common species and more data is needed to capture more accurate numbers for the rare species. 

RM random- 8%

ROM syst – 1.33%

RM hap – 7.12%

 

WO ran – 34.33%

WO syst – 46.44%

WO hap – 39.87%

YB ran – 100%

YB syst 

TB hap

WA ran 100%

WA syst 1037.5%

WA hap – 937.5%

Blog Post 3 – Ongoing Field Observations

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As of February, 2020

 

For my field research project, I have decided to study plants from the phylum Bryophyta

 

While visiting Mount Tolmie, I definitely noticed the amount of rocky faces as well as the incline, which is steep at times. While hiking this incline, I noticed that the types of plants seemed to change with elevation, forming a transitional zone. I noticed especially that there was a great variety in the types of moss present in the area, and seemed to change with respect to the elevation on the mountain. With the help of iNaturalist.org, I identified the following species from the phylum bryophyta that I observed on Mt. Tolmie. 

 

  1. Broom Moss  (Dicranum scoparium)
  2. Wooly Fringe-moss (Racomitrium lanuginosum)
  3. Cat’s Tail Moss (Isothecium stoloniferum)
  4. Hedwigia ciliata 
  5. Orthotrichum lyellii 

 

I hypothesize that on Mt. Tolmie, the density and diversity of bryophytes will be affected by an increase in elevation. I predict that the density and diversity of bryophytes on Mt. Tolmie will decrease as the elevation increases, moving along the gradient. I think that this change in elevation will cause more exposure to the elements, in a more hostile environment I think there will be a decrease in temperature, increased wind speed/exposure, decreased humidity and decreased soil nutrients associated with this increase in elevation. So, I predict that there will be a decrease in the number of moss plants seen and the diversity of the moss plants as I ascend the mountain. I predict that there will be an abundance of mosses near the base of the mountain, and the top will have very sparse populations. This hypothesis will be evaluated by the effect of the elevation (predictor value) on the abundance of mosses in each quadrat I study (response variable). I plan to gather several sets of data on these two variables along the gradient, which I expect to present a trend in abundance with elevation. Because the response and predictor variables are both continuous, I will use a regression study for my experiment. 

Blog Post 9: Field Research Reflections

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Create a final blog post that reflects on your field research. You both designed a field experiment and then carried it out. Did you have any issues with the implementation or have to make any changes to your design? Has engaging in the practice of ecology altered your appreciation for how ecological theory is developed?

My appreciation for the detail and depth in ecology has been wildly expanded. I performed the simplest of experiments and even then, the amount of hours, work, thought and literature review accompanying it surprised me. I have definitely found a new appreciation for ecologists. My implementation of design was simple from the beginning, but as I was sampling I kept thinking of more and more factors that should have been considered and further experimentation that would need to be performed to have my research make any sense or be of accuracy.

Blog Post 8: Tables and Graphs

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Create a blog post discussing your table or graph. Did you have any difficulties organizing, aggregating or summarizing your data? Was the outcome as you expected? Did your data reveal anything unexpected or give you any ideas for further exploration?

My data was very simple so it was easy to put into a table. I also think that it is very easy to interpret in table form. The outcome was slightly unexpected since I was expecting to falsify my hypothesis, and ended up proving it instead. Further exploration into species richness is of course needed since my understanding and experiment are both extremely simple, but I think it was a good start to a topic not seen very frequently in the literature.

Blog Post 9: Field Research Reflections

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Completing my field research and searching through scientific papers for more data confirmed and reinforced what I had just suspected to be the case when it came to completing ‘proper’ science, especially in the outdoor environment rather than in a more controlled environment. My four major takeaways were:

  1. Truly attempting to account for all potential variables in a dynamic environment rather than a lab setting is very time consuming! I found the comments from this blog and my instructor to be very helpful in finding holes that would render my observational results less reliable as well as learning from other field studies I read. In my opinion, from my experience with this small trial in an ecological field study, communicating with other members of the scientific community would be critical in producing good work; sharing information, giving constructive criticism, and accepting criticism.
  2. This field study really helped me understand just how much a research project in the field is a constant work in progress, continually revisiting and adjusting the hypothesis as new variables were recognized or pointed out to me. I believe this may be a taste of what the term “Physics envy” means. It is especially apparent when I think of all the potential anthropogenic influences that may be unaccounted for. I wonder where is the line between time/money accounting for every little variable and it being ‘good enough’ to be accepted as evidence?
  3. The actual physical terrain made me adjust my methods twice. I had to account for the ocean tide so I could actually reach my sample locations, in some areas the slope was too steep to traverse, and in others it was too thick with vegetation to access. I also felt this was a great example of a study that could not be done in any season except the dormant one, as once everything buds out, it would be very difficult to find any Alnus rubra seedlings among the blackberry and rose.
  4. More samples would be better! I’m taking a statistics course as well and feel that the combination of of the two courses made it clear that more samples=more accurate data. Unfortunately due to working full-time and other commitments (not to mention everything is budding out now) I did not generate more replicates to do so.

This project made it very clear to me just how much time (including the researchers personal time!) and money can be required to produce good scientific studies within the field of ecology. This is necessary in order generate honest and reliable evidence that ecological areas need to be preserved, protected, and reestablished where possible, especially in an age where everything is scrutinized so intensively.

The sooner I can get involved the better!

Blog Post 8: Tables and Graphs

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I had no difficulties in organizing, aggregating or summarizing my data. The outcome was not what I expected. It revealed that there was little correlation between Douglas fir tree abundance and average tree circumference. However, the relationship between tree abundance and aspect of growth was what I expected. The western aspect had the most Douglas fir trees, while the eastern aspect had the least. My data was collected only over the winter season, I would like to explore any changes that may occur in the abundance/circumference and abundance/growth aspect relationships as seasons and weather change.

Blog Post 9 – Field Research Reflections

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Blog Post 9 – 03/04/20

Throughout the duration of this course I designed a field experiment that had the objective of determining if soil moisture levels impacted abundance and distribution of three tree species (white spruce (Picea glauca), aspen poplar (Populus tremuloides), and white birch (Betula papyrufera)) in Kinsmen Park. I used a Stratified Random Sampling design to carry out this experiment and overall I felt, upon completion, that the design I chose was well suited to this type of study. Incorporating randomization into the study through selecting various locations to sample from within the designated strata allowed for bias to be completely eliminated. Furthermore, the randomization allowed me to come across some unexpected results that caused me to pause and really reflect upon my hypothesis, the patterns I saw, and the predictions that arose. There were no issues implementing the design. Some days were colder than others, making the ground slightly difficult to sample from, but overall the implementation of the stratified random sampling design went well. Engaging personally in the practice of ecology has certainly deepened and enhanced my appreciation for how ecological theory is developed. My study design was relatively simple and took place over a smaller area. It looked at very simple variables and factors and their impacts on each other. Through conducting my study as well as reviewing previous research for my annotated bibliography, I gained a much deeper appreciation for ecological studies that span years over vast distances and look at multiple variables. The effort that goes into planning and executing these types of studies is immense and the work these researchers are doing to advance ecology is incredibly important. I can’t help but marvel at their dedication to their research, to their field, and to ecology as a whole. Taking this course and performing a field experiment has allowed me to step into the shoes of a ecologist, albeit in a much smaller way, and has allowed me to develop a deepened sense of respect for how ecological theory is developed. 

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