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Blog Post 5: Design Reflections

My initial data collection was after the transect sampling strategy. I marked out my transects and separated the transects into 9 quadrants. In each quadrant I marked the presence of flora. As the examples have previously shown, one is to count the amount of each flora within each transect, but for the purpose of my research, I was merely interested if there is a correlation between the flora that grows further away from the dyke water and the consistency of the soil.

Some difficult aspects I observed throughout my collection was the difficulty in discerning the different types of Poaceae (Grasses). Many seemed quite similar and as the grass within my area was cut short, it was difficult to make out each feature appropriately. I also observed plenty of dried, brown and dead grasses. In my notes, I make note of the dead grass, but unfortunately am not able to tell what type of grass it is. When I return later for more samples, I will attempt to see if there is new growth. Another observation I made was that when one looks closer, the grass has other flora mixed as well. I observed Taraxacum officinale (common dandelion) and Brachythecium frigidum (golden short-capsuled moss).

I believe I will continue with the transect method. It allows me to properly lay out an organized method of measuring instead of randomly sampling. I believe it suits my research study appropriately.

Blog Post 5: Design Reflections

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

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 #7: Theoretical Perspectives

In reviewing the theoretical perspectives of my project, I have had to combine observational activities with literature review to gain an understanding of the behaviour within my species of study. My study is looking at the presence of snow fleas (springtails in the order Collembola) on the surface of the snow in response to open-sky vs. shaded situations. I observed the way they jumped around above the surface but also the way they were able to disappear into the snow and presumably move about within the snow column. Although they weren’t evident in large numbers during my data collection period, I have witnessed them in extraordinary numbers peppering the snow at warmer times throughout the winter months. As Hagvar (among others) note, different circumstances may account for these large-number events including the need for migratory dispersal in temporary or patchy habitat environments, or just changes in soil conditions during periods of melt, such as inundation of water on the surface of the soil. Being able to be mobile on the surface of the snow is a great advantage for organisms less than 1mm in size in any terrestrial landscape, but is especially useful for migration over bodies of water or rivers, which springtails have been observed to do. The ecological processes my hypothesis is based on concern both a springtail’s need for cover as a means of hiding from predators, and the need for having a view of the sun as a navigational tool in migratory circumstances during the winter.

Keywords:  Snow fleas, dispersal, sunshine

References:

Hagvar, S. 2000. Navigation and behaviour of four Collembola species migrating on the snow surface. Pedobiologia 44: 221-233. https://doi.org/10.1078/S0031-4056(04)70042-6

Blog Post 4: Sampling Strategies

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.

Blog Post 3: Ongoing Field Observations

I have decided to look at the effect of site moisture on the abundance of Hedera helix.

I am interested in studying the effect of invasive species on the abundance of native species but had a hard time finding an observable gradient between the two categories of plants. Upon observation of the ivy in the area, I began to notice a potential link between site moisture and proliferation of ivy. Since the presence of ivy can almost always be attributed to a reduction in native ground cover species, I decided to narrow down my observations to simply abundance of ivy. While I could have compared species all of the ground cover species in a given quadrat, including other invasives like Daphne laureola and Ilex aquafolium, H. helix is having a markedly more destructive effect on native species abundance.

I looked at three different moisture gradients, using tree species as a proxy for soil moisture in lieu of specialized equipment. I’ve classified the three different points on the gradient as zones:

Douglas-fir/ grand fir zone.
-characterized by heavy shade and mesic soil. The highest elevation of the three zones.

Arbutus/ Garry oak/ douglas-fir zone.

-Mesic-dry/ approaching xeric. Along the edge of the river, roughly 2m above the water level. I imagine the soil near the surface is quite dry, and the tree species composition is indicative of such.

Red alder zone.

-hydric/ probably seasonally mesic. Ground is visibly saturated and has been for many months. The only tree species that are able to grow here are red alder, with a few doug-fir on the margins where the soil moisture is starting to drop off.

 

I hypothesize that soil moisture levels affect the ability of H. helix to proliferate and out-compete native ground cover. I predict that abundance of H. helix will decrease with decreasing site moisture levels, and native species abundance will be higher on drier sites.

A response variable would be % ground cover ivy. This is a continuous variable.

An explanatory variable would be site moisture (determined by tree species composition). Since I have designated three “categories” by tree species composition, this variable is discrete.

Percy Herbert, Post 3: Ongoing Field Observations

For my research study I am deciding to focus on vegetative bud formation on wild rose plants. I have observed that taller rose plants appear to have long stems with no vegetative buds forming until the upper portion of the plant. The density of the vegetative buds at the upper regions of the plants appear to be consistent regardless of the height of the plant and how long the barren stem is below the buds.

More specifically, I will be measuring the distance from the tip of rose plants to the first, third, fifth, tenth, and lowest bud on the stem. I will take measurements from many individual plants, each of which will be measured to determine the height of the plants. I will take measurement from non-branched plants ranging from under 50 centimeters to over 2 meters. I will then try to determine if there any observable trends relating the distance from plant tip to vegetative buds to the height of the plant.

My hypothesis for this study is: For wild rose plants in Queen Elizabeth Park, there is an optimal distance from the tip of the plant to vegetative buds, regardless of plant height.

My prediction: Once rose plants reach a certain height the lower section of the stem remains bare. The density of vegetative buds will be the same in the upper regions of short and tall rose plants.

The response variable: distance from tip of plant to the first, third, fifth, tenth, and lowest vegetative bud on the stem. (continuous)

The predictor variable: height of the plant (continuous). In my study I will trying to prove that the height of the plant is not the most important factor in determining the location of the vegetative buds on rose plants.

A regression study would be appropriate for this study as both the response and predictor variables are continuous.

Percy Herbert, Post 2: Sources of Scientific Information

The source of Ecological information that I will write about in this post is an article about Seamounts called, The Ecology of Seamounts: Structure, Function, and Human Impacts.

Here is a link to the article:  https://www.annualreviews.org/doi/full/10.1146/annurev-marine-120308-081109#_i2

This article is definitely considered to be academic material as it fulfills the three basic requirements.

The article is written by experts in the field whose credentials are listed under their names. The authors have affiliations with various Universities and other organizations.

The article also includes in-text citations and a list of literature cited at the bottom.

This article has been published in the peer-reviewed academic journal, Annual Review of Marine Science. Every article published in this journal must pass through a peer-review process to become published.

No new research results are presented in this article. There are no methods or results sections in the article as there is no research conducted.

Instead, the article summarizes major findings in the field into a concise overview of the state of research in the field. This article is academic, peer-reviewed review material.

 

Blog Post 5: Design Reflections

Initial data was collected at Mission Creek Regional Park on March 21, 2021. Systematic plots (400m^2) were used spanning from the mission creek riparian bank to the uplands crest, 100m total distance with plots alternating 20m along the transect. The number of pine and total number of trees were counted in each plot, with pine diameters measured and the average recorded.

Several difficulties were noted when implementing the sampling strategy. The first being physical constraints due to the terrain and floor vegetation, the transect was difficult to pace and plots challenging to confine. Another difficulty arose with plot size; counting and measuring individual trees became tedious. The collected data was surprising, with the number of Ponderosa pine remaining consistent along the transect though a gradient was suspected.

Moving forward I plan to collect data using plots of a smaller area to ease sampling constraints and modifying my approach to include adjacent area(s) of similar site characteristics to the study. I think this modification will improve research by increasing the data pool and providing a method of comparison.

Reudink, Post 4: Sampling Strategies

Which technique had the fastest estimated sampling time?

The systematic sampling technique had the fastest sampling time where 25 samples took 12 hours, 36 minutes.

Compare the percentage error of the different strategies for the two most common and two rarest species.

(most common to least common)

Systematic:

Eastern Hemlock (520.0-469.9)/469.9 * 100 = 10.7%

Sweet Birch (144.0-117.5)/117.5 * 100 = 22.6%

Striped Maple (44.0-17.5)/17.5 * 100 = 151.4%

White Pine (8.0-8.4)/8.4 * 100 = 4.8%

Mean percent error from above calculations = 47.4%

Random:

Eastern Hemlock (520.8-469.9)/469.9 * 100 = 10.8%

Sweet Birch (154.2-117.5)/117.5 * 100 = 31.2%

Striped Maple (41.7-17.5)/17.5 * 100 = 138.2%

White Pine (8.3-8.4)/8.4 * 100 = 1.2%

Mean percent error from above calculations = 45.4%

Haphazard:

Eastern Hemlock (504.0-469.9)/469.9 * 100 = 7.3%

Sweet Birch (140.0-117.5)/117.5 * 100 = 19.1%

Striped Maple (36.0-17.5)/17.5 * 100 = 105.7%

White Pine (4.0-8.4)/8.4 * 100 = 52.4%

Mean percent error from above calculations = 46.1%

Did the accuracy change with species abundance?

For the most part, yes. The most abundant tree, Eastern Hemlock, had a percent error ranging from 7.3-10.8% with a mean percent error of 9.6% across sample techniques. This was the most accurate mean percent error among all tree species. Interestingly though, the least abundant tree, White Pine, did not elicit the least accurate percent error across sampling techniques (PE = 19.5%). The least accurately measured tree species across sampling techniques wass the Striped Maple (PE = 131.8%).

Was one sampling strategy more accurate than another?

Based off the mean percent error of the two most abundant and two least abundant species, the random sampling strategy was the most accurate (mean PE = 45.4%)