The results of the three sampling strategies which are used are systematic, haphazard, and random. In this tutorial systematic/random and haphazard sampling are compaired.
The haphazard sampling had the fastest estimated sampling time because there was less travel time between the samples points.
When looking at the percent error, the overall accuracy of the species which were more common was greater than the species which were less common.
The systematic/random sampling strategy was the most accurate because the areas being analysed do not overlap.
I had a few difficulties with my random stratified sampling strategy. I was using a random number generator to get coordinates on where my quadrats should be for sampling. It took a long time to find coordinates in each strata that worked. It was also difficult to find the coverage of moss in each quadrat because I was counting the individual squares. The average time for one quadrat was approximately 30 minutes.
I was surprised to find moss in areas in an open field (0% canopy cover). It was surprising because the area gets direct sunlight the whole day, and horses are present in the field. I was also surprised to find moss in areas with almost full canopy cover, considering the ground was either covered in leaf debris or conifer debris (needles).
I plan to modify my approach in data collection. I’m going to use a piece of paper which is 10 cm x 10 cm. This will make the paper be 100 square cm and will be handy in counting moss abundance in the quadrat. The paper could be folded in half to represent 50 squares, in a quarter to represent 25 squares. This technique will make collecting data much easier. If the moss doesn’t completely cover the paper, I can subtract the squares moss isn’t present. This modification will improve the accuracy of data as well as shorten the sampling time for each quadrat.
Systematic, random, and haphazard sampling strategies were used in a virtual forest tutorial. The systematic sampling had the fastest sampling time (4 hours and 5 minutes), with haphazard being second (4 hours and 30 minutes), and random being the slowest (4 hours and 39 minutes). For the two most common species, Eastern Hemlock and Red Maple, the percentage errors varied. For Eastern Hemlock, the random sampling technique had the lowest percentage error (32.4%), haphazard (32.9%) with the second lowest, and systematic with the highest percentage error (37.5%). For Red Maple, haphazard sampling had the lowest percentage error (19.9%), random sampling had the second lowest (21.9%), and systematic with the highest percentage error (38.3%). The two most rare tree species, Striped Maple (40.0%) and White Pine (94.0%), percentage error was the highest for the systematic sampling strategy. Haphazard sampling had the lowest percentage error for Striped Maple (14.3%), and random sampling had the lowest for White Pine (8.3%). Random sampling for Striped Maple had the second lowest percentage error for Striped Maple (56.0%), and haphazard sampling for White Pine (78.6%). Accuracy decreased as species abundance decreased. the percentage error for the rare species ranged from 8.3-94.0% for White Pine, and 14.3-56.0% for Striped Maple. For the common species, the percentage error ranged from 32.4-37.5% for Eastern Hemlock, and 5.4-36.2% for Red Maple. Systematic sampling was the least accurate sampling strategy. Both random and haphazard sampling were approximately the same amount of accuracy. If all the seven species sampled were included, random was more accurate 43% of the time as well as haphazard, with systematic being accurate 14% of the time.
The organism I’m interested in studying is moss and its abundance in different levels of canopy cover. On April 12, 2019, I noticed that a majority of the moss in the area was in areas of shade or less canopy cover. In areas where canopy cover was more than 50%, there was moss on the trees, but little to none on the ground. In areas with less canopy cover, but still shade present, moss covered the ground. In open field, where there was 0% canopy cover, moss was present but slight.
high level of canopy cover, moss on trees, leaf debris on groundhigh level canopy cover, no moss evidenttreeline edge (less canopy cover), moss more abundant
I want to examine the abundance of moss covering the ground in three different areas where there are gradients of tree cover. Areas are divided into three different levels of canopy: greater than 50% canopy cover, less than 50% canopy cover (edge of treeline, open canopy), and areas with 0% canopy cover (open field). These ares provide a gradient of sun exposure to the moss. The underlying processes would be the balance between shade/canopy cover, and sunlight exposure. Moss need sun to photosynthesize, however, they prefer being out of direct sunlight. Too much sunlight can be drying to moss.
Nelson Bar Ranch
Hypothesis: The area with equal amounts of canopy cover/sunlight exposure, will support a higher abundance of moss. .
I predict moss will be the most abundant in areas with less than 50% canopy cover, compared to areas with more than 50% canopy cover, or 0% canopy cover.
A potential response variable would be the abundance of moss (% coverage/m^2) in each area, which would be a continuous variable. A potential predictor variable is the amount of canopy cover present which would be a continuous variable, however, I’ll be using it as a categorical variable.
This research project will focus mainly on the influence of seasonal temperatures on bird species. Other considerations would be the influence of human activity at the location of the study.
Overall the field research project was relatively straight forward. However, there will be many sources of error and factors which may affect the data that can be discussed in the final report. For example, if more bird species were studied this might have changed the trends. Or if the observations were made at a different time of year.
As a chemistry student, designing a non-lab based experiment was daunting at first however, it went along with only minor hiccups after the first few observations. It also took longer than I would like to come up with an idea for a project.
The data that was collected was separated into 4 different categories. The first three are the locations where the species observations were made (House of Learning, Science Building, House 9), and the fourth was a table of how many people were seen in each location over a span of an hour. For all four tables, the averages (means) were calculated and bar charts were used to summarize the data.
The tables generally supported the hypothesis however, further statistical analysis will help determine this.
My research project focuses on how the presence of humans effects the number of different species found on the Thompson Rivers University campus. In theory in locations where there is a higher number of humans present, there will be more species which are known to thrive in urbanized environments (such as crows, magpies and pigeons). I will also be discussing how the vegetation and building type affects the species distribution of the three location on campus which were studied.
Three keywords which can be used to describe my research project are species distribution, anthropogenic influences, and population density.
For the data collected near the House of Learning, 10 replicates were taken. For the data collected near the science building and house 9, 6 replicates were taken. I also made observations for the number of people around the three buildings. For this data collection, 3 replicated were taken. This was collected to confirm the theory that there will be more human traffic around the larger buildings.
Another pattern that I have noticed through my observations is that different vegetation is around the different observation points. This may influence the different species of birds which are more abundant in said locations.
Difficulties in executing the sampling strategy was mostly due to recognition. Since I’m not an ornithologist, I cannot say for certain that I did not count the same bird more than once.
The data that was collected was not overly surprising. I will continue collecting the data for the different birds using the same method however two locations were added, the science building and house 9. As well a couple of observations were made in each location to count the number of people to back up the predictions of which area has the most human traffic within a day.
