I visited my site for the third time on July 23rd, 2019 at 1511 hours to collect initial data for the small assignment submission. I chose the Stratified Random Sampling technique to select 5 plots that were then sampled for Common Fern Moss using 1m2 quadrats. My reasoning behind choosing Stratified Random Sampling is that my backyard is not homogeneous- it receives higher amounts of sunlight in specific areas and there also seems to be a reoccurring pattern in the locations where my dog urinates. I believe this technique of sampling would help avoid underrepresentation of the vegetation in my yard. I took 1m2quadrats comprised of a golf driver and a measuring stick and gave my best estimation of the percentage of cover of Common Fern Moss relative to the entire area of the quadrat. I then took the percent cover values of each replicate and calculated the average percent cover of Common Fern moss. This was done to make somewhat of a generalization about its abundance in my backyard. The response variable is percent coverage of the Common Fern moss relative to the quadrat area and the predictor variable is the absence or presence of grass. One difficulty I ran into during sampling was the tough decision on whether or not to include ‘Density’ in a table on my datasheet. Moss is a very tricky plant to measure or count individuals within a species, as the individual stems are hidden in the soil, small and can be closely surrounded by other stems. I originally visited the site thinking I could find a way to measure the abundance of the individuals using a count method. However, I came to the conclusion that the most efficient way to sample moss in this experiment was to use percent cover. The data received from this type of data collection showed that two plots closer to the South fence had a higher percent cover, and this did not come as a surprise to me as these areas receive less shade relative to the other plots, and are areas with bigger patches of dead grass. I plan to continue using this method of sampling as I continue working on my Field Research Project, however I hope to modify my approach by finding another measure of abundance that is time efficient and more accurate. By adding another common measure to the data collection process, I believe that my data will become more representative of my site and will allow for a more in-depth final report.
Category: Percy Hebert
Blog Post 3: Ongoing Field Observations
My second visit to my study site took place on July 17th, 2019. It had rained in the area about an hour and a half prior, leaving the ground quite moist. The sky was grey, and the temperature was 14 degrees Celsius. I have chosen to base my Field Study project off of the moss present in the dead patches of grass in shaded areas of my backyard. I am observing what I identified to be Common Fern moss (Thuidium delicatulum) across the gradient from healthy grass to dead patches of grass or areas of strictly soil. I will be looking specifically at the percent cover of species, structural integrity and abundance at different locations along this gradient. The response variable of my Field Study project is the percent cover of Fern moss. Due to the fact that this moss is only found in the shaded areas of my yard and strictly in the dead patches of grass where my dog urinates, one possible explanatory variable of this occurrence may be the lower pH soil composition in these areas causing low soil fertility and optimal growth conditions for this type of moss. These variables would be considered continuous data (measured on pH scale, percent coverage of moss at certain locations in the yard) as well as categorical data (presence or absence of species).
I chose the Southwest corner of my backyard for my first location. This area receives plenty of shade under the columnar aspen tree. The moss is thicker, and it seems to be growing along the transition between grass to no grass. It is very abundant in this location.
For the second location, I chose an area in between the Japanese lilac tree and the columnar aspen by the South fence. This area receives relatively the same amount of shade as location 1. The grass is lacking in this area and there are mainly patches of soil. I see rotten moss in this area, and there is a very small amount of healthy moss.
Location 3 is to the right of the Japanese lilac tree where the grass meets the border of the garden. There is barely any Fern moss in this area, however Pincushion moss (Leucobryum glaucum) is growing in the garden.
These observations have led me to the hypothesis that moss grows in specific levels of moisture, sunlight and acidity. When these factors exceed or fall short of the desired conditions, moss may react to these changes by rotting, stunting its growth or not growing altogether. A change in percent cover of Fern moss between three different locations in my yard may be a result of nonoptimal levels of these factors.
I predict that if my backyard lacked trees, a two-meter-high fence, and did not have burnt spots from my dog’s urine, the Fern moss would be less abundant, would have a low percent cover or be completely absent.
Blog Post #4
Sampling Theory Using Virtual Forests
I completed the Community Sampling Exercise on the Snyder-Middleswarth Natural Area and received the results from 3 different types of surveys to compare. The systematic-area method produced the shortest estimated sample time of 12 hours and 6 minutes. A quick calculation comparing the estimated density data with the actual data revealed the systematic-area method had a percent error of 28.7%, while random sampling had a percent error of 11.8% and haphazard sampling demonstrated the greatest accuracy rate at 11.6%. The percent error for the Striped Maple was 2.1%, and the White Pine was 14.4% both which are rare species. The common species were Easter Hemlock 6.2% and the Sweet Birch had a percent error of 5.4 %.
The most accurate way to measure both the common species and the rare species was the haphazard method. The accuracy declined with the rare species as some of the rare species were not detected by some of the sample strategies. This leads me to believe 24 sample points did not cover enough ground to accurately represent the rare species. While 24 was adequate to represent the common species, I would recommend increasing the sample points for greater accuracy of the rare species.
Blog Post 1: Observations
The area that I have selected to observe for my Field Study project is my backyard, located in southwest Calgary, near Stanley park. I chose this area because it will remain easily accessible throughout the span of this project. My backyard is approximately 54 m2, and is composed mainly of grass and fenced off garden areas on the North, East and West sides of the yard. There are alternating columnar aspen (Populus tremula ‘Erecta’)and bakeri spruce trees (Picea pungens ‘Bakeri’)on the east side of the fence, providing the majority of shade in the yard during the summer months. There is another columnar aspen as well as a Japanese lilac tree (Syringa reticulata) which also account for the shade in the yard during the day. Other plants featured in the gardens include: spirea (Spiraea), lemongrass (Cymbopogon)and pink and white rose bushes (Rosa).
I took observations on June 17th, 2019, in the midst of summer, from 5:11 pm to 6:00 pm. There was a bit of overcast with thunderclouds approaching from the North; the temperature was 23 degrees Celsius. During my visit, my first observation was the very prominent moss growth along the South and East sides of the yard, where the grass gets the most shade, and the patches where my dog chooses to urinate. This causes dead patches, where the moss is found. I noticed an area of what I believe to be rotten moss, and I am curious to figure out the cause of such a small affected area that is surrounded by live, healthy moss. Another interesting observation was a small area of grass where the blades of grass are partly white in colour. I discovered plenty of ants making their way up and down the columnar aspens, and upon further inspection, I noticed that these ants are burrowing holes into the bark. I also spotted many ants surrounding an ant hill underneath a rose bush close by.
These observations made during my first visit to this area have sparked a few questions that I wish to look into:
- Based off my observation of the selective moss growth on the South and East sides of the yard, I am wondering if shade, moisture, and bare soil/dead grass are the main factors contributing to the abundance of moss in these areas. I am also eager to figure out the cause of what I believe to be rotting moss in a small area near the fence.
- The ants crawling up and down the columnar aspen trees were something I’ve never seen before. Do these two organisms partake in a symbiotic relationship? If so, which type?
- I am curious to figure out the cause of the white blades of grass in a small area under the Japanese lilac tree. Could this be from an applied chemical or is this a natural occurrence?
Blog Post 4: Sampling Strategies
In the virtual forest tutorial, the three sampling techniques used to sample tree species in the Snyder-Middleswarth Natural Area were random/systematic sampling, random sampling and haphazard/subjective sampling. I found the technique with the fastest sampling time was the second exercise- random sampling. As opposed to the first and third exercises, the second exercise only required 24 locations to be sampled randomly, without any other restrictions. After reviewing the percentage errors for both the most common and rare species, it was found that the third exercise, haphazard/subjective sampling, was the most accurate technique, with a 3.75% error for Eastern Hemlock, and a 28.6% error for Striped Maple. The second most accurate technique was random/systematic sampling, with a 10.6% error and 52.4% error for Eastern Hemlock and White Pine, respectively. The random sampling technique had percentage errors of 14.7% for Eastern Hemlock and 54.8% for White Pine. These varying percentage errors indicate that with changing species abundance, the accuracy of each technique changes as well.
Blog Post 2: Sources of Scientific Information
For my second blog post, I chose an online source off of the ScienceDirect database. The article is titled, ‘Dung beetles and nutrient cycling in a dryland environment’, and it will be published to CATENA journal in August 2019.
https://www-sciencedirect-com.ezproxy.library.uvic.ca/science/article/pii/S0341816219301286#!
This online article fits the category of ‘Academic, peer-reviewed research material’. The tutorial in Module 1 on ‘how to evaluate sources of scientific information’ outlined the key differences between the four categories of information sources. The first step taken in classifying this source as academic material was the author’s expertise. The primary author, M. Belén Maldonado, is part of an Argentinian research collaboration, IADIZA, and has had a few of his research articles published to various science journals. This article includes in-text citations- an example on page 67, “Dung beetles, as well as termites, perform an important ecological function incorporating livestock dung to the soil and promoting pasture regeneration (Schowalter, 2016).” Also, at the bottom of the report, there is a properly formatted bibliography. My next step in this discrimination process was to figure out if this article had been reviewed by at least one referee before publication, and I found an initial revision of the manuscript was carried out by Silvina Verez. This narrowed the article down to a peer-reviewed source. My next inquiry was whether or not it included a results and methods section. A methods section, outlining the general procedure and instruments used, was present in the article under the Methods subtitle.
Blog Post 7: Theoretical Perspectives
The idea behind my research is that density/crowding of trees has a significant effect on new tree growth (annual budding) in addition to the overall biomass accumulated over years of growth. Each individual tree was planted the same year and are the same age. Also, they are all planted within the same acre with the same soil conditions. The only difference in their growing conditions is the distance that they were planted from one another, creating the crowding gradient. Soil and weather conditions and species interactions are all constant. The different levels of crowding between locations will theoretically create some variations in the soil nutrients and water available to each individual. Although I will not be testing those factors directly, I will be observing the subsequent results of their effects in the form of overall growth and biomass. Resource competition influencing growing capacity is the main underlying idea in my research. It should be stronger in the most crowded site (Location 1) compared to the least crowded site (Location 3) since there are more individuals competing for the same amount of resources.
Keywords: resource competition, density dependence, individual tree growth
Blog Post 5: Design Reflections
I used the area random sampling method. I built a 0.25m2 quadrat to determine bud density and measured the width of base branch growth for each replicate to gain an understanding of the effects of crowding on white spruces.The main difficulty that I encountered in my collection of data was in Location 1, the trees are so close together it is hard to walk between them. It was very hard to find my tagged replicates and carry my quadrat, measuring tape and field journal while fighting through the dense branches. Sometimes the outer branches of trees overlapped those beside them, making it harder for me to distinguish which buds belonged to the replicate I was sampling. The data was not surprising, it aligned with my hypothesis, which is that trees that are subject to crowding will be less productive than those that have ample space to themselves. The new growth bud density in Location 1 (most crowded) was on average lower than both that of Location 2 and 3 (least crowded). Despite my difficulties, I think this sampling strategy is the best one for my project. I still have to figure out how I am going to test the soil properties for each location (if I am even able to do so).
Post 9: Field Research Reflections
I had no idea what I was getting myself into when I started this course. I think Biology really teaches you how research works, atleast in the sciences. I knew what ecology was but now I have a more in-depth understanding. I don’t think that I would have done this online if I had known that it would have been such a deep dive.
The project itself was fine. I chose a topic that related to my experiences doing silviculture surveys so I didn’t have to figure a lot out. It was a good choice because there was much more to learn: writing a scientific paper, doing statistical analysis, literature reviews, etc. Not much about my methods changed. Though, my analysis did change as I realized that it was possible for me to do much more with the data than I originally envisioned. I also learned a lot, in general, about my topic of choice.
Next year I need to carry out a piece of research, which will be a major part of my degree (9 credits) and this project has helped me develop skills and knowledge that will assist in my research. It’s also helped me with my understanding of statistics. In terms of ecological theory, I find it a bit… theoretical. I’m interested in tangible, practical applications of knowledge. I’m glad there are people who are involved with ecological theory development but I’m going to be working on an applied basis.
Blog Post 4: Sampling Strategies
The first sampling technique I explored was area/haphazard. I sampled 27 quadrats, which was estimated to take 14 hours and 30 minutes. The percent error for the two most common species were 11.0% and 14.2% respectively. The two most rare species had percent errors of 62.6% and 79.7%. Accuracy changed drastically when abundance decreased and sample time is not optimal, therefore, this strategy is not the best choice for the Mohn Mills community.
The second method I tested was area/random. The most abundant species had percent errors of 9.2% and 14.5% while the two least abundant were 13.3% and 47.9%. I believe that accuracy only changed drastically due to an outlier. Otherwise, they might be very similar. Estimated sampling time for this method, also 27 quadrats, was 14 hours and 16 minutes. This is very similar to the first method’s sampling time.
The third method I looked at was distance/haphazard. The sampling time for 27 quadrats was only 5 hours and 15 minutes, making it much more reasonable than the area strategies. Percent error for the most common species was 13.2% and 13.2%, while the two rarest were 8.57% and 26.9%. Although the last percent error was higher than the others, these values are the most consistent out of all three sampling techniques. Along with the reasonable sampling time, this makes the distance/haphazard method the best choice for this community.
