Category: Post 9: Field Research Reflections

Blog Post 9: Field Research Reflections

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As mentioned in previous posts, I initially had a challenging time executing my research design, as my study site was washed out due to flooding. I found another site which ended up being a better selection, as my study object, polypore fungi, were far more numerous than at the original location. The process of collecting samples and studying the substrates was a very enjoyable experience, minus the unbelievable amount of bugs. (I feel for any ecologists conducting field work in northern Canada in the summer).

The correlation between my variables (quantity of bracket fungi and soil moisture) was weak based on the data I collected which was frustrating. I think all ecologists hope to discover a satisfyingly strong connection between their predictor and response variables, and I am no exception! This experience has taught me a lot about the challenges of conducting field research, and the necessity for rigorously assessing and accounting for a wide range of confounding variables. It was somewhat naive of  me to think that a correlation would be found simply by counting bracket quantity per tree and measuring the soil at the tree base. I see now that many other factors need to be considered in a multivariate analysis and that a much larger sample size should be collected for such a study.  Such a study would also benefit from being repeated over multiple seasons and years to assess for changes in polypore growth and soil moisture potentials.

This experience has given me a new appreciation for the complexities and frustrations of ecological research, as the nebulous interactions of biotic variables make ecology less straightforward than other scientific disciplines. Developing an ecological theory is a daunting task because it is very difficult to make any definitive conclusions when endless variables need to be considered and generalities cannot necessarily be made from one region to another.

Blog Post 9 – Field Research Reflections

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I had to change field experiments halfway through the project, which caused many problems. Originally, I was looking at the relationship between moss and cedar trees (Thuja plicata) but they didn’t have a strong enough correlation. This study would have been considering seedling establishment so I chose to go with a categorical response variable for the cedar trees, with the logic that if no seedlings establish, then there will be no trees on the site. When I changed the field experiment to the relationship between soil moisture and cedar trees, I already had the data on the cedar trees and didn’t have time to restart. If I were to do this project again, I would choose a continuous response variable, such as density or productivity (most likely density, as it is easier to measure). This change in design would improve the study by not having to use logistic regression and having more data to work with. 

Another issue I had with the design was that I didn’t have the tools to dig as far down as I would have liked to get the soil samples. My small shovel wasn’t very good for getting past large rocks or in some cases, I barely could dig at all as the bedrock was so close to the surface. I had to change my design so that I only dug down 10 cm deep or until I hit bedrock. If I were to do this study again, I would get some tools to be able to take samples at 2 meters deep as well as at the surface, to account for the tree’s root system. 

Engaging in ecology has certainly changed how I think about ecological theory and its development. There are so many variables that contribute to ecosystems, such as inter and intraspecific interactions, resource availability and disturbances. It is difficult to decide which factors are the most relevant for a study because one definitely does not have the time or resources to consider them all! Although developing theory is difficult in all sciences, I feel like these extra variables and the difficulties in an uncontrolled environment make developing theory in ecology even harder.

Blog Post 9: Field Research Reflections

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The field project aspect of this course certainly opened my eyes to the difficulties and complexities in both designing and undertaking an ecological study. As I have a very limited background in carrying out field studies, I did find the broad and self-directed nature of this project difficult, especially in terms of selecting a project I could carry out myself appropriately. There were certainly a lot of adjustments that had to be made in terms of my sampling design and variables to assess. 

My sampling of multiple quadrats within multiple transects was definitely time-consuming, and I also feel I chose a project with too many confounding variables which created an added difficulty and stress when putting my project and data together. Luckily I was able to find a fair amount of relevant research that aided in describing the results of my study.

Although this has been by far my weakest course thus far in my degree, I have certainly gained valuable skills in critically assessing scientific articles, and more importantly, gained a valuable appreciation and understanding for the amount of work involved in creating a scientifically sound and unbiased field study. Lastly, it was a great change of pace to have a fully hands-on aspect to an online course, which enabled a practical application of the theory being taught.

Blog Post 9: Field Research Reflections

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Creating an ecological study, collecting data, and turning the data into a scientific paper is a daunting task. Luckily the course was designed in a way that helps guide you along all of the necessary steps. In order to keep my research project fully objective I wanted to find a study subject with predictor and response variables that were clear and easy to measure. I chose to observe the internode length in wild roses (Rosa acicularis).

During initial data collection measurements of internode length were easily obtained as the stems were vertical and non-branched and the buds were small, yet clearly visible. Unfortunately by the time it was time for the major data collection, the buds had sprouted and had developed into small branches and leaves. This made measurement of internode length much more difficult as it was hard to place the tape measure in a position in which it ran parallel to the main stem and it was hard to see the measurements as the branches would obstruct the view of the tape measure. Although data collection was harder than anticipated, it was still possible to obtain all of the measurements necessary for this study.

Engaging in this study has revealed to me the amount of work required to derive even the most basic of ecological conclusions. The results of my study suggest that Rosa acicularis plants at the Queen Elizabeth Disc Golf Course in Vancouver, BC have optimized internode length and that the internode length is not significantly related to the height of the plant. In order to use this information for further research time consuming data collection would be required. To observe the impact of various environmental pressures on internode length in R. acicularis, plants growing in many different environments with varying levels of environmental pressures would be required.

The choice of sampling unit and how to randomize sampling was a foreign subject to me before this course. I have learned that is of upmost importance to select samples in an appropriate manor so that extrapolations can accurately represent the population as a whole. The exercise of conducting a research project has given me insight into how scientists derive ecological conclusions and has made interpretation of journal articles much as easier.

Post 9: Field Research Reflections

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This course and its field research project were novel, difficult, eye-opening, and mind-expanding. Having never taken an online university course before, the experience was new to me. Having been out of school (college) for >10years and launching into a 3rd year science course, I found the workload and expectations difficult – attributes I am grateful for, because it makes me feel that I have earned the credits. The course and project were eye-opening, because I got to see the sheer amount of work that scientists put into the field-components of their research. And the concepts – especially those of predictor and response variables and how they tie into the experimental/statistical designs – blew my mind.

In terms of the field-research component of this course, I’d like to expand on the eye-opening comment I just made. First of all I spent countless hours looking for patterns in the winter ecosystems around my home and the forests that I work in. Each pattern I found was not suitable for experimental analysis. I had to start over at one point when the instructor pointed out to me that my initial plan (assessing aspen ramet attributes [density, height, girth] as a response to distance from an abandoned beaver lodge) was fraught with sampling vs replication errors and bias, to which I am grateful for the reality check. During this time I read about experimental design for sampling vegetation, and I used this information to design the experiment I finally landed on, which seemed to work for me (though I likely would have chosen a different setup had I taken the time to read the literature at this point in the course).

I spent days creating quadrats out of wood as well as tent-like wooden structures to carry out my experiment of testing springtail snow-surface density (response variable) to covered and uncovered environments (predictor variable). Then I spent 5 days counting springtails at three specific times each day. The data set that I generated was time-consuming to analyze and I felt lost in the world of excel and the utter mystery of statistics, and the results were a tiny bar graph! Comparing this to the amount of data-gathering and intricate designs outlined in some of the papers we had to read for this course, I see how scientific research can be so all-consuming and difficult to perform. Hats off to scientists!

By the time my data was collected and my literature review began, I started to realize that my project could/should have been so much more. I should have done more replicates. I should have sampled in different habitats such as forests, wetlands, and wildfire-burnt openings. I should have measured temperature above the surface instead of on the surface. I should have identified each and every springtail individual to the species level. I should have factored in other weather-related variables as predictors such as cloud cover and barometric pressure. The list could go on.

All in all, the reality of studying ecology – its wonders, difficulties and intricacies – very much hit home thanks in large part to this field research project.

Post 9: Field Research Reflections

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Overall, my study went quite well, but of course not perfect. I ended up having quite different results than I had expected and found it difficult to find journal articles that supported my study. I do wish I had spread out my course load and did not have to rush my project at the end, as I think I could have done a better job. I did have to adjust my hypothesis while planning my study design but not at any point afterwards.

I really enjoyed this course. It did feel like a lot of work at times but I really appreciated having such a hands-on online course. It is often difficult to stay engaged during online courses, but the field research project made it a lot easier. I have a great appreciation for all of the work that ecologists do.

Blog Post 9: Field Research Reflections.

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Doing a field research was a great experience for me, not only as a student fascinated by Biological processes, but also as an individual who did not always pay much attention to the ecosystems around her. Some words that I could use to describe the overall experience include eye opening, intellectually challenging, and inquisitive.

During the process of designing the field experiment, I decided to use the systemic sampling strategy to help me avoid experimenter bias while choosing the samples. Initially, I thought I could randomly select the first bean plant, and then systemically select the next samples. However, later in the experiment I realised that the garden beds were not large enough for the samples to be spread out perfectly in fives (the random number generated using excel). Thus, I decided to use the same approach, but this time recording every third plant instead of the fifth.

Another change that I made while implementing the experiment was that I only collected data from 2 garden beds (locations) instead of 3, which were from the original plan. This was due to the inaccessibility of the garden bed because of the long fence around it, unfortunately I could not reach individual beans without making damage.

Finally, I can confidently say that engaging in the practice of ecology increased my appreciation for how ecological theory is developed. I learned that it starts as a simple process from observation, which grows over time as the experimenter finds certain patterns and organizations that sometimes represents significant processes in the surrounding communities, and even ecosystems.

Blog Post 9: Field Research Reflections

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I had originally envisioned random sampling across the entire park. In the end, it was much easier for me to implement my sampling via stratification. For one, the predictor variable I was working with (tree species composition) was fairly well divided into subsections. If I had relied on recording tree species composition for each individual sampling point, I would have had to employ a second sampling unit and a whole secondary methodology to determine which predictor class a given sample fell under. Given my relatively large sample size for the scope of the project (n= 60), it would have taken much longer to collect field data had this been my strategy.

QGIS was instrumental in automating my randomization. I had a few setbacks while trying to transfer data from QGIS to the limited software available for my GPS unit, but overall I think it was worthwhile to employ this strategy. I have used QGIS for a number of applications, including mapping species distributions using herbarium data, but never to implement sampling. It was nice to have an excuse to expand my GIS skillset.

One thing which was challenging about sampling was taking things from the digital realm to the field. From a satellite image or a shapefile it’s impossible to predict which areas will be too dense with brush to reach to sample or where there is standing water (although I didn’t run into the second problem in my data collection). I had a hard time trying not to incorporate subjectivity when I was forced to slightly move my sample site due to unforeseen obstacles. In the end, I decided to move 2m away in a random direction, but its hard to say how random that direction actually is when I have to consciously make the decision to choose a direction. It goes to show that even if you go into the field with fully randomized predetermined sample points, there is always some margin of human subjectivity that gets incorporated into your data.

Lastly, I definitely have a deepened understanding of the development of ecological theory. The pitfalls of trying to observe patterns in nature without accidentally incorporating your own bias toward patterning are prominent and hard to avoid. Like in all science, in ecological theory the importance of building upon previous knowledge and peer review is indispensable in rendering theoretical assertions universally applicable. Without multiple viewpoints, bias cannot be diminished to acceptable levels.

Reudink, Post 9: Field Research Reflections

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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 9: Field Research

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While conducting the field research on moles and their predators,  I learned a great deal more than I expected about research techniques and ecology.

It was necessary to change my design a few times, as I acquired feedback from my instructor. Primarily the adjustments included how to conduct the samples in an accurate manner, and how to gather data efficiently while ensuring replicates were conducted to limit the possibility of errors due to small sample sizes. In retrospect I may have complicated my project by selecting a predator and prey model rather than something more simplistic such as non moving organisms like grasses or lichen in microhabitats. Regardless the challenge (and fun) was to find a way to sample my communities accurately.

Participating in this course and engaging in these activities has given me an appreciation for ecology.  The rich complexities of how sampling, research, statistics, natural history, geography and even geology are brought to bear on a problem.

While I do not consider myself an ecologist, I have enjoyed the process and will likely look at the natural world differently now. The blessing in all of this has been that I have learned some basic tools on how to see how communities interact and have a new found appreciation for ecology.