Collecting samples in the field has been fairly easy, so long as the proper amount of time has been allocated to collect them. In total, 450 replicates were taken using a 0.25m2 quadrat. These samples were taken randomly and in equal numbers throughout the three zone types as outlined in my experimental design; canopied forest, uncanopied forest, and open grassland. A total of 9 sampling regions were sampled, with 50 samples taken from each. Three of these regions were in open grassland, three were canopied forest, and three were uncanopied forest (Figure 1).
No major issues were encountered when implementing my sampling design (other than many mosquitos!).
So far, it appears that the grassland samples have a higher occurrence of Knapweed than in either of the forest zones; this would support my hypothesis that access to sunlight affects the growth frequency of Knapweed. Statistical analysis has yet to be done on the data.
Figure 1. Satellite view of natural area; uncanopied area not accurately represented due to age of image (google maps). Rough sampling zones outlined; 50 random samples taken per sampled zone. Project total n= 450.
EDIT: A new data collection method was used following this blog post. 10 transects were sampled with 21 samples each, taken 10m apart along each transect (n=210). This new collection method cut the time needed to collect samples by a considerable margin. A 1m xx 0.5m quadrat was used to sample for presence/absence of Spotted Knapweed (Centaurea maculosa). See figure 2 for an updated design layout in the natural area to the South of residential Aberdeen, Kamloops.
Similarly, to the previous sampling method, grassland cover seems to have a higher frequency of Knapweed than either of the other two cover types. Data analysis still needs to be conducted to confirm the significance of this pattern.
Figure 2. ________. Natural area including walking trail south of residential area in Aberdeen, Kamloops. Coordinates for trail and trail head are 50° 38’ 1” N 120° 21’ 18” W. Elevation ranges from 860m at the bottom of transects (at trail level) to 935m at the top of transects. Transects are drawn in yellow, the walking path highlighted in red, and a blue length marker is included for reference; ten transects were sampled for presence or absence of Spotted Knapweed (Centaurea maculosa) and cover type (canopied forest, partially lit forest, open grassland) every 10m (total transect length 210m). Transect spacing was randomly chosen using a randomizer phone app. (total n=210).
The sampling strategy in the virtual forest tutorial that had the fasted estimated sampling time was haphazard sampling. For one of the two rarest species, White Pine, haphazard sampling had the most accurate results with a 1.2% error. The other rarest species, Striped Maple, has no accurate sampling strategy with this tutorial (all sampling strategies had >100% error).
For the most common species, Eastern Hemlock, systematic sampling narrowly beat out random sampling with 20% error (compared to 20.6% random sampling). For the second most common species, the Red Maple, systematic sampling had a much wider margin on accuracy with a 32.7% error (as opposed to 53% for both other strategies).
Species abundance did not seem to have a massive effect on accuracy; the lowest percent error was for a rare species. This leads me to believe more replicate samples should be done in a study like this for a better representative sampling.
Overall, it seems that a systematic sampling strategy had the lowest percent error for more species than the other two sampling strategies. Haphazard sampling yielded the lowest percent error for a few species and random samples did not produce the lowest percent error for any of the species in the tutorial.
The organism which I plan to study is the distribution of Spotted Knapweed, Centaurea biebersteinii.
The Knapweed plants have light purple flowers with thin and extending petals. The stalk is light-green, thin, and does not cover a lot of horizontal space. Each plant may have multiple flowers and stands 1 to 2 feet tall, generally speaking. The Knapweed plants seem to grow both in patches and also individually.
In each of the three stratum types, forested, forested with minimal/no cover, and open grassland, the physical features of each plant appeared similar. The distribution between areas is different, however. The Knapweed is virtually non-existent in the forested area; however, in areas of forest with no cover, Knapweed seems to grow in large patches as well as regularly by themselves. In the open grassland, Knapweed does not grow in patches, rather, it grows individually and often.
Processes that may play a part in this observed pattern could be access to sunlight, or competition with other species. Soil type is fairly uniform throughout this entire mountainside region, and precipitation is fairly rare in arid Kamloops this time of year.
Because of these observations, I hypothesize that Knapweed will grow more frequently in areas with more access to sunlight; if a stratum has more access to sunlight, then Knapweed will grow in greater frequency than in other stratums.
A response variable could be Knapweed frequency (continuous), or perhaps Knapweed density (continuous), and a explanatory variable could be access to sunlight (can be set up categorically or continuously).
I reviewed the article Late-Season Survey of Bumble Bees Along Canadian Highways of British Columbia and Yukon Territories. The article was found using TRU’s online database and the topic and abstract seemed interesting to me.
a) The source of this article is the Western North American Naturalist journal. (https://ezproxy.tru.ca/login?url=https://search-ebscohost-com.ezproxy.tru.ca/login.aspx?direct=true&db=a9h&AN=109305710&site=eds-live)
b) The article in an academic, peer-reviewed, research article.
c) This article is academic as it is written by experts in the field who are employed for ecological research groups and government agencies; in-text citations and a bibliography are present in the article. The article is peer-reviewed as evidenced by the inclusion of both ‘received’ and ‘accepted’ dates; googling the journal’s website shows that it is a peer-reviewed publication. Lastly, the article is a research article as data was collected by the authors and both a methods and a results section were included.
The field data that I have been collecting over the past few months has been duplicated 6 times and we are reaching the end of the Giant Hogweed life cycle for this season. Though noted in the assignment that I would duplicate this 2 times, I have found more time to duplicate the data collection. The sampling size being used is throughout the data collection period is randomly selected areas and then a 5-meter radius will be observed in the efforts of collecting information. These areas where chosen systematically random, by starting at the high disturbed area and working our way to the back of the property low disturbance area. Six sites where sampled 3 being in the high disturbance area and 3 being in the low disturbance area. The patterns I’ve noticed is that the Giant hogweed is growing along ‘trails’, road side, driveway, game trail through the tall grass, etc. And there are no Giant hogweed growing between the transition area (where the high and low disturbance change) and 0 plants growing in the low disturbance area. I believe this is due to such a high crown cover in the low disturbed area, making it very shaded.
During my data collection in the field, the systematic sampling strategy proved to be efficient at surveying the area. The few difficulties I encountered during the sampling did not damage the quality of my data in any way. First, the determination of transects was simple, but keeping that transect straight as I collected my subsamples across the field seemed to be a challenge. For the last three transects, I established three or four checkpoints along each transects in order to keep me straight. Having closer targets greatly improved the quality of my transects. Secondly, making my way along a transect turned out to be slightly more challenging than I expected. The vegetation got pretty dense in some portions of the field. I always managed to make my way through it but I had to push through some plants and small shrubs. Applying the quadrat down never was an issue. I would simply drop it over the vegetation of the area, however tall or dense that was.
The data was not surprising to me. These first samples even seem to play in favour of my initial hypothesis – more flowers appeared as I sampled away from the beach. One noticeable aspect of my data was that all types of flowers seemed to be displayed in clusters.
I think that my systematic approach to survey the site was the best option. The data collection was performed with minimal difficulties that were all overcame to maintain the essence of the systematic method. It eliminates the possibility of bias, and more samples will only add to the reliability of my data.
After the previous outing I decided to no longer spend any time sampling the back 15 acres of my property. This is because it is a heavily dense forest, therefore the crown cover is much to dense and cannot support the growing of Giant Hogweed as it is shade intolerant. That was the difficulty I found in my previous outings, too much time spent in the higher elevation and dense forest plots that would throw the results off of the findings. It is evident that they grow in disturbed sites and with lots of sun.
In the bottom portion of the property (disturbed site) I continued to use the random sampling method, as well as built a 1 m2 quadrant . Using a larger quadrant is appropriate for this study as the plants are much larger, so having a larger sampling plot will give a better representation of the 15 acre disturbed site. At each site I take 3 sample plots 5m apart in a random direction. This allows for more area being sampled.
This weekend I returned to my study sight to test out the data collection method I’d designed (outlined in Blog Post #3). Saturday was a bust due to poor weather conditions, but Sunday afternoon looked a lot better. I brought along the data collection tables I’d designed with the plan of collecting data on 3 individuals from each of my 4 species (Cormorant, Canada Goose, Franklin’s Gull and Mallard) for a total of 12 birds. For each bird, I recorded their behavior at 15 second intervals for a total of 5 minutes, noting the location of each behavior along my gradient (Shore→ Shallows → open water).
Replicate: individual birds
Response variables: behaviors (categorical)
Predictor variables: species (categorical), time of day (categorical: AM/Midday/PM), point on gradient (categorical)
Panoramic view of the large pond
A few limitations and problems I noticed when I got to my site and started collecting data:
I hadn’t planned HOW I was going to select individuals to study in order to avoid bias. Naturally, I was drawn to the most active birds who would be interesting to watch for 5 minute intervals. I was also drawn to the birds closets to my location on the pond.
I realized that my lofty goal of trying to record the behaviours of multiple individuals from 4 different species over 3 different daily time periods might have been a bit over-enthusiastic for this project. The Franklin’s Gulls, for example, DO NOT HOLD STILL! This species was frequently in flight, touching down for only brief periods. The range of their flight paths made it impossible to ensure I was watching the same individual over the course of 5 minutes.
I realized that the pond is actually quite a bit bigger than I realized when I needed to identify a Mallard from other similar looking duck species from a distance.
My observations led me on a full loop around the pond, stopping to collect data when I saw birds of interest. Again, this isn’t a very standardized procedure and could lead to bias when large groups catch my eye.
3x 5 minutes of behaviour observation is not a very significant period of time over the course of a 24 hour day. Will this be truly reflective of behaviour patterns?
The larger birds (Cormorants, Canada Goose) seemed to each have claimed specific territory around the pond. There were no observation sites that allowed me to view both species at the same time.
Sample data collection table for the 4 species of birds observed
Reflecting on my trial run this weekend, I’ve come up with a few modifications to my research project:
I plan to keep using the data tables I created as I found them easy to use and well laid out for the data I was collecting.
I’m going to narrow my focus from 4 species to 1, the Mallard. This species was found at many locations around the pond, and at all points along my gradient. They were present in the highest numbers as well, giving me plenty of subjects to sample from.
I’m going to use a randomized number generator (ie: 1-10) to select my subjects: I’ll count to the random number, starting from left to right across the pond, and collect data on that individual. This should eliminate bias in choosing subjects.
I’m going to select one observation point to work from, in order to prevent bias from wandering around looking for birds.
Now that I’m going to be observing 1 species instead of 4, I will increase my number of subjects sampled each visit from 3 to 5, and increase my observation time for each individual from 5 minutes to 10 minutes. Doubling my observation time should provide slightly better behavior data.
I’ve ordered a pair of binoculars off Amazon Prime, they’ll be here Wednesday! This should help me identify Mallards from other similar looking ducks and allow me to record data across the pond from a fixed location.
It appears Team Canada Goose has also claimed this bench for themselves…
Based on these modifications, my hypothesis requires some adjustment as well. I will keep the hypothesis that the water bird species studied will display increased levels of higher-energy activities (flight, feeding, etc) in dusk/dawn periods due to cooler temperatures, and increased display of lower energy activities (comfort, resting) mid-day when temperatures are higher.
Again, the null hypothesis would be that time of day has no effect on the time-activity budgets of water bird species.
Based on my research on Mallards thus far, I also suspect that typical behavior patterns will vary across my gradient, with resting/comfort behaviours being observed on land, feeding in the shallows, and locomotion/alert behavior taking place in open water. Mallards are considered “dabbling” ducks and feed by grazing on underwater plants indicating that I predict that I will see these behaviours most often in the portion of the gradient I have designated at “Shallows” (< 5 m from shore or visible plant matter appearing on/near the surface)
A view of the algae cover near the edges of the west side of the pond
The three sampling strategies used in the virtual forest were haphazard, random, and systematic. Haphazard was the fastest of the three methods (estimated time of 5 hours and 17 minutes), random being moderate in terms of time (estimated 5 hours and 44 minutes), and systematic being the slowest of the methods (estimated 14 hours and 33 minutes). It is safe to assume that haphazard sampling has the fastest sampling time due the limited travel between sample points.
The two most common species in my sampling scenario where the Red Maple and the Chestnut Oak. These two species accuracy (in terms of percent error) were greater than the species that were less commonly sampled.
While doing trying multiple sampling techniques, it was relevant that the use of random sampling posed the least amount of percent error, and this could be do to the fact that there is very little/no overlapping in the sampling plots.
The different sampling techniques did not have very much variation for me. The most common species was Red Maple and the rarest was Sweet Birch. The sampling error was similar in all three techniques but the time it took to do a random sampling was the shortest at 5.5 hours. The accuracy did however change with the abundance of each species, the more abundant the less accurate.
