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Blog Post 5: Design Reflections

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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

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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

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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

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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

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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

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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

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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

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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

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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%)

Reudink, Post 3: Ongoing Field Observations

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1.

My study will be examining the incidence of White Poplar trees (Populus alba) relative to their proximity to the perimeter of their forested area. I noticed two different environmental gradients in my observations: (i) a north-south gradient and (ii) an east-west gradient. The north-south gradient is more pronounced, as the southern zone is abundant with coniferous trees while the north zone is absent of coniferous trees and contains various deciduous species (Figure 1). Since the distribution of trees close to houses has likely been manipulated by past owners, it would be difficult to be sure that my measurements are associated with ecological differences rather than anthropological differences (e.g., conifers would be attractive to plant near one’s home as they give more coverage/privacy in the winter months). For that reason, I will focus on the east-west gradient in the deciduous zone that I have identified. I have separated the deciduous zone into three subzones: (i) east zone, (ii) central zone, and (iii) west zone (Figure 2). The downslope from east to west is less than 10°.

Figures 1 and 2

2.

Observations:

East zone: Abundant with various deciduous species that are native to Manitoba. White Poplar trees are more abundant on the north end of this zone. There were several large Manitoba Maple trees (Acer negundo L.) and Scrub Oak trees (Quercus macrocarpa Michx.). Coniferous trees were sparsely found in the southern region but decreased in abundance as I moved north, to the point of being absent at the north end. I could not reliably assess soil moisture becasue I had no equipment to do so and the snow had been melting over all of these areas over the last two days.

Central zone: Most diverse location and seemingly symmetrically distributed tree species. This zone, relative to the other zones, had a lower abundance of White Poplar that increases in abundance northwards. No conifers in the north end.

West zone: Appears to be the zone that is most abundant with White Poplars. Some conifers can be found on the south end, while none are found on the north end. The trees on the west perimeter seem to be uniformly slanted towards the west. The trees are approximately 5-10m from the adjacent dike which is currently dry. The overall

3.

There are many factors that could be contributing to this gradient. Soil composition comes to mind as a potential contributor to the incidence of tree species. Sun exposure also probably plays a role. There seems to be much greater diversity and competition in the central zone, so the White Poplars may be competing with other tree species for sunlight. I believe that sun exposure would be a better “predictor” for White Poplars than soil composition, as White Poplars are hardy trees that tolerate an array of soil conditions. Furthermore, soil composition and White Poplar concentration may have a bidirectional relationship, whereas sun exposure and White Poplar concentration likely has a unidirectional relationship. Therefore, my proposed hypothesis is as follows.

Hypothesis: Sun exposure influences the incidence of White Poplar trees.

Prediction: The incidence of White Poplar trees will be greatest in locations of high sun exposure, like the east and west perimeters.

4.

Predictor (explanatory) variable: Duration of sun exposure (continuous)

Response (outcome) variable: Incidence of White Poplar trees (continuous)

A regression analysis would be sufficient for an experimental design (statistical analysis), as both variables are continuous.