Category: Robyn Reudink

Reudink, Post 9: Field Research Reflections

TRU Open Learning 1 Comment

Creating a field experiment, carrying it out, analyzing the results, and then interpreting them in a scientific report was an informative experience. Since I have done my entire degree online, I have learned a lot about how different discoveries were scientifically validated but I had not previously had the opportunity to experience this process for myself. I had difficulties in conceiving a good design, initially; however, having a “field expert” on-call, there was always a solution to my issues. One of the largest changes I made was in my sampling design. I went from considering a randomized square plot design to a systematically selected circle plot design. The systematic selection ensured all of my plots were far enough from each other to be independent, while the circle plotting was just plain convenient (i.e., stand in the middle of the circle plot and measure whether specimens are within the radius of the circle).

I have two regrets after completing my study. Firstly, I wish I had the ability to wait for better weather before gathering my data. I am quite certain that the snowy conditions confounded my results. Secondly, I would have liked to fit my data to a model and see whether my correlations were statistically significant. I tried an ANOVA regression and a linear regression; however, the sample size was so small that p values were above 0.6… If I had better statistical know-how, I’m sure I could have found a better model to fit my data to and more accurately measure significance.

Engaging in my own ecological enquiries gives me a deeper appreciation for the work and time that goes into the research that contributes to ecological theory. Just like catching the right camera shot in nature documentaries, collecting good data for ecological science is time-consuming and difficult. This process has also given me an increased sense of curiosity and wonder while I navigate through nature. Who knew science is right around my back door!

Post 2: Sources of Scientific Information

TRU Open Learning Leave a comment

Source of Scientific Information:

  • Vye SR, Dickens S, Adams L, et al. Patterns of abundance across geographical ranges as a predictor for responses to climate change: Evidence from UK rocky shores. Divers Distrib. 2020;26:1357–1365. https://doi. org/10.1111/ddi.13118

 

Type of information:

  • It can be classified as Academic, peer-reviewed research material.

 

Evidence to support classification:

  • The paper is written by expert authors that come from different institutions with scientific backgrounds (Bangor University, Newcastle University, University of Liverpool…etc). It also includes in-text citations and has references with all the sources used.
  • The paper was reviewed by three anonymous reviewers that reviewed the manuscript.
  • The paper contains a methods and results section which shows that the researchers conducted field research by collecting data, verifying it, and using statistical analysis to reflect on their results.

 

Reudink, Post 8: Tables and Graphs

TRU Open Learning Leave a comment

For my project I wanted to see if (a) Populus alba density was correlated with soil moisture content and (b) if P. alba density was different between my measured transects. I compiled my data into a table and also made a few figures. The table was made simply with excel and then I imported the excel sheet into R as a .csv file to make a few figures. I have never been great at using R or excel, but with help of a few YouTube videos and forums I was able to figure out the input required to make the graphs I wanted. My biggest difficulty was ensuring that all of the labels on my graphs were correct.

The outcome of my data was surprising because there was (a) a NEGATIVE correlation between P. alba density and soil moisture content and (b) There was a linear increase in P. alba density from the east transect to the west. This is most surprising because there is a dyke on the west side, so one would think that the soil moisture would be highest closer to the dyke (westward), but the results showed the opposite. This strangeness prompted me to further investigate whether sampling error was a large contributor to this. I am not well-versed in using R to fit data to a model, but I know what normal distributions are supposed to look like on histograms, so I mapped my data onto histograms and analyzed for normality. None was found in my soil samples, so I’m chalking this up to sampling error and would recommend further investigation at a drier time of the year to detect how soil moisture is related to P. alba density. I have attached a word doc of all my figures and tables I will be using for my final report.

Figures and tables for final report

Reudink, Post 7: Theoretical Perspectives

TRU Open Learning 1 Comment

My project is investigating the gradient of increasing Populus alba density seen in a forested area and if that is associated with soil moisture content. There is an empty dyke near the west perimeter of the study area and P. alba density increases linearly westward, so I’m testing to see if the same gradient can be seen in the soil moisture content. My hypothesis is that P. alba density is associated with soil moisture. This is investigating a form of population ecology that is primarily concerned with the ecological process of how abiotic factors can affect a population. If no correlation is found between soil moisture and P. alba density, then I would consider other abiotic factors as explanatory factors such as soil pH, nitrogen content, sunlight exposure, etc. Biotic factors could also be considered to affect this population such as interspecies competition or the incidence of infectious disease.

Keywords: Populus alba, soil moisture, density gradient

Blog Post 2: Sources of Scientific Information

TRU Open Learning 1 Comment

A) say what the source is (and/or link to it),

 The source is Scott, Virgil E., Keith E. Evans, David R. Patton, and Charles P. Stone. Cavity-Nesting Birds of North American Forests. Washington: U.S. Government Printing Office, 1977. Print. Ser. 511.

The book is available at http://www.gutenberg.org/files/49172/49172-h/49172-h.htm.

 B) classify it into one of the four types of information discussed in the tutorial.

 I have classified the source as non peer-reviewed academic material.

 C) provide documentation to support your classification.

The source is non peer-reviewed academic material based on the following observations:

  • The authors’ institutional affiliations indicate they have been paid to do the research.
  • There are in-text citations.
  • The information source contains a references section.
  • The book is a government document and does not include an accepted date. However, the reviewers are mentioned, but they are not anonymous. After reviewing the publication’s editorial policies, the book was not peer-reviewed before publication (https://www.govinfo.gov/about/policies).
  • The article does not include a field or laboratory study (i.e. missing methods and results sections).

 

Blog Post 1: Observations

TRU Open Learning 2 Comments

The area that I observed was Edgewater Bar, located in Derby Reach Regional Park in Langley, BC (10 N 527496 5450356). The site includes walking trails, a dog park, picnic tables, and fishing along the Fraser River. I arrived at the site at 12:50 pm on Sunday, April 25th. The weather was overcast with slight rain, and the temperature was 11°C. The study area was approximately 400m2 and consisted of grassland, forest, and the bank of the Fraser River.

Among the grasses were dandelions randomly distributed throughout the landscape, with blackberry bushes and trees in the background on a slight hill surrounding the park’s perimeter. I noted that the dandelions seemed to have established more abundantly on the flat meadow than the surrounding hills. I wondered how the slope might affect the establishment of the dandelions.

As I made my way along the trail, I observed cherry blossoms in bloom with white flowers amongst other trees, including Sitka Spruce (Picea sitchensis), Grand Fir (Abies grandis), Western Redcedar (Thuja plicata), and Lawson’s Cypress (Chamaecyparis lawsoniana). I spotted two American Robins (Turdus migratorius) foraging for earthworms on the grass, then retreating onto a tree branch once they gobbled up their meal. The Robins did not appear to be phased as people walked by with their dogs. As dogs frequent the area, I wondered how dog barking might affect the courtship behaviour of the robins.

As I travelled northward, I entered the picnic area. The area consisted of grasses and flowering plants, including Creeping Buttercups (Ranunculus repens), Meadow Buttercup (Ranunculus acris), Greater Plantain (Plantago major), and Ribwort Plantain (Plantago lanceolata). Other trees present in the area included Japanese Maple (Acer palmatum), Red-berried Elders (Sambucus racemose), and a Paper Birch (Betula papyrifera) with a Hoof Fungus (Fomes fomentarius) growing on it. I wondered if tree clearing for the picnic area might contribute to the succession of invasive plant species.

As I approached the Fraser River, I could see filter fabric topped with gravel which capped the natural clay ground of the river bank. There was a fisherman upstream to the right who had just caught a small fish and dogs splashing downstream to the left. Looking back up from the river, I could see grasses growing amongst Western Sword Ferns (Polystichum munitum) and Creeping Snowberries (Symphoricarpos mollis). Throughout the observations, I could hear sounds of birds chirping, dogs barking, and people talking.

Link to images: https://drive.google.com/drive/folders/1q2c8m_LmNkOEtyvapqD9d15cPJy7CUMl?usp=sharing

Blog Post 8: Tables and Graphs

TRU Open Learning 1 Comment

For the small assignment on tables and graphs, I made a table comparing the gravimetric soil water content of sites with and without cedar trees. To do this, I used basic statistical analysis, including the minimum and maximum value, mean, median, and standard deviation.

I would have liked to put this data in a graph, as I think that would have displayed the data better. However, upon a preliminary search of how to do logistic regression graphs, I quickly realized that this was far out of my current level of comprehension. This produced the difficulty of having to put everything that would be represented in this type of graph in a table. I don’t think I fully achieved this, as I ended up leaving out all the separate data that I collected to keep the table simple and easy to read. I am currently brainstorming other methods that will fully represent the data for my final.

The outcome was what I expected, although not to the same magnitude. The mean for the results from sites with cedar trees was 52% compared to 41% for sites without cedar trees. I was expecting the soil to be more moist in the sites with cedar trees by about 20% as opposed to 11%. Foolishly forgetting that I live on the “Wet Coast”, I was also expecting both values to be lower overall.

It was interesting that the data from the sites with cedar trees had a fairly higher standard deviation than the ones without cedar trees. This may mean that other factors are affecting the soil moisture on sites with cedar than that aren’t affecting the sites without cedar trees. These other factors could be further explored in the future by doing a study focused on sites with cedar trees.

Blog Post 5: Design Reflections

TRU Open Learning 1 Comment

For my initial data collection, I implemented stratified random sampling using Google Earth and QGIS. I created polygons based on Google Earth satellite images for each predictor zone, exported them as a KML file and used QGIS to generate random points within one of the polygons to collect sample replicates. I then exported these points as a GPX file, put them on my GPS and located them in the park to take samples. I used discrete classes to represent percent coverage as outlined in the sampling design tutorial, ranging from 1-6.

I think my method for generating worked fairly well, but I fear that my areas my be too small to justify stratification. I’m also unsure if statification is the best approach, given that the basis of the stratification is also the predictor variable (dominant tree species as an indirect measure of soil moisture). The zones are fairly distinct in the park, but there are some interspersed wetter/drier sites, leading me to think that perhaps I should use a non-stratified approach and just record the predictor variable with each individual sample. The number of random points which land in one of the smaller zones (arbutus/ garry oak, alder) may be smaller, but since frequency of predictor variable is not a measure of concern it may be ok if I have more samples from the doug-fir/grand fir zone.

Blog Post 9: Field Research Reflections

TRU Open Learning Leave a comment

My research project was to examine the expansion of a stand of Trembling Aspen Populus tremuloides into a field at Campbell Valley Park in southwestern BC. When I initially chose this site for my project, it was summer, and all the plants had their full suite of foliage. I also observed many small Aspen shoots in the field which led me to hypothesize that the stand was expanding into the field. However, I did not start my final sampling until winter, and I observed very minimal shoots in the field and there was no foliage in the forest. The lack of foliage changed the patterns that I saw in the Aspen Stand from the summer, I observed more smaller (younger) trees dispersed further into the stand. In my original design, I wanted to sample 3 sizes of Aspen trees, those over 10cm diameter at breast height, between 2cm and 10cm and under 2cm. I was trying to capture the new Aspen trees or shoots with the smallest size. Given the fact that there were almost no shoots visible during my winter sampling, I chose to reduce this to the two larger sizes. Although I had some difficulty with this field research project and gathering of data, I have enjoyed being able to use what I have learned in this course in a practical way.

Blog Post 4: Sampling Strategies

TRU Open Learning Leave a comment

I was surprised to see that all three sampling techniques showed marginal differences in time required to sample. Perhaps it was the way I performed the exercise, but systematic sampling (12h36m) was barely faster than random sampling (12h40m), which was also minutely faster than haphazard sampling (12h42m). I’m not sure how the simulation calculates estimated sampling time, but intuitively it seems like haphazard sampling should be the fastest method.

In terms of percent error, systematic sampling yielded the worst results for a common species (eastern hemlock, 15.4% sweet birch, 17%) and haphazard sampling yielded the worth results for rare species (striped maple, 200% yellow pine, 160%). Random sampling yielded the most accurate results for both common (7.7% and 6.5% error) and rare species (0% and 25% error).

I imagine in reality that haphazard sampling should be the fastest technique, with consistently inaccurate results for rare species and potentially accurate results for common species, that systematic sampling would be the second fastest technique, with marginally accurate results for both common and rare species assuming that environmental gradients are crossed, and that random sampling is consistently the most accurate but takes the longest.