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Blog post 3: ongoing field observations

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By using the observations made on the field and the online plant database of the website https://www.wildflower.org/, I was able to identify the four plants of the site as the following:

 

Crotalaria Juncea L. or Sun hemp, the two-lobed yellow flowers.

Helianthus debilis or Beach sunflower, the flowers with yellow petals and a black or yellow center of stamens.

Hepatica nobilis acuta sharp-lobed hepatica, the small white flowers with three-lobed green leaves.

Richardia brasiliensis small 6 petal purple flowers. They have long pointy green leaves.

 

During my observations on the field, the concept of ecotone or transitional zone is what really hit me first. The abundance of flowers seemed to be proportional to the distance a certain patch of grass was from the beach. In other words, it seemed like the farther away I walked from the beach, the more flowers were scattered on the ground around me. It also seemed like the two types of yellow flowers were much less abundant all throughout the field. Though surprisingly, those bigger flowers appeared much closer to the beach than the majority of the smaller white or purple flowers.

My hypothesis for this study will be that the natural step-cline creates a gradient in flower abundance that increases proportionally to its distance from the beach. The effect of the natural step-cline that is the beach, in this case, could be on nutrients in the soil or dryness of the soil. As observed, the soil in the area is very sandy, which is probably a result of its proximity to the beach. Very sandy and dry soil cannot support much plant life. Hence why the beach is one of the only places in the world where grass can not grow. So, my prediction for this research is that more flowers will appear as I walk away from the beach with my quadrat. No flowers should be observed in the first few meters from the beach as the soil will still be too dry and sandy. But, as I move towards the mainland, I predict that a few flowers will first appear and that abundance will increase after.

The hypothesis I will test will be evaluated by the effect of the predictor variable (the distance of the quadrat from the beach) on the response variable (the abundance or number of flowers in the quadrat). By repeatedly gathering data on those two variables along the gradient, I’m hoping I will discover a trend in abundance variation. Considering that both the response and predictor variables will be continuous data, a regression design study will be used.

Post Three: Ongoing Field Observations: Cates Park

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The organism I would like to study is Tsuga heterophyllum and their growth distribution associated with nurse logs, a biological attribute.

After observing nurse logs throughout Cates Park / Whey-Ah-Wichen during previous visits, noting that Tsuga heterophyllum are the predominate trees to grow out of long-ago logged Thuja plicata, I grew curious to their habits, growing regions, need for sunlight or idea soil conditions to thrive. Because Cates Park is situated on a point that has varying degrees of sunlight and wind, I chose four gradients to observe distribution and abundance of local species: the west and east sides of the park, and both through the canopy on trail, and on the beach for the marine-terrestrial interface.

  1. Southest side of Cates Park, from west path to pier, along beach
  2. Northest side of park, from west path to pier, along trail
  3. Southeast side of Cates Park, from stairs to small point, along beach
  4. Northeast side of park, from stairs to small point, along trail

Southwest

Northwest

Southeast

Northeast
Tsuga heterophyllum

(Western hemlock)

4

19

2 on nurse log

4

21

3 on nurse log
Thuja plicata

(Western red cedar)

18

21

6

5

1 on nurse log
Picea sitchensis

(Sitka spruce)

1

0

0

0
Pseudotsuga menziesii

(Douglas fir)

0

3

0

1
Unidentified deciduous tree (either Alnus rubra (Red alder), Populus trichocarpa (Black cottonwood) or Acer macrophyllum (Broadleaf maple))

10

0

37

35

Hypothesis: Western Hemlock (Tsuga heterophyllum) are more common in cleared forest areas because they are better suited to such disturbances.

Prediction: If Tsuga heterophyllum trees are better suited to take advantage of open forest canopy following a disturbance, they will grow more frequently in areas that have experienced harvesting

The predictor variable is are the amount of canopy cover and the type of substrate, either nurse log or forest floor. These are both categorical variabilities.  The response variable is also categorical, as the relative abundance of hemlocks on nurse logs compared with forest plots.

This natural experimental design is a Tabular study. Sample units will be an equal number of haphazardly selected nurse logs, in order to reach their location, and simple randomly selected forest plots. Nurse logs and forest plots will analyzed in each region of the park: both east and west of the park’s central point, and north and south to account for the gradient away from the ocean.

Post 4: Sampling Strategies

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Kevin Ostapowich

March 27, 2019

The sampling methods I chose were distance-based.  See the table below for a summary of the results:

Sampling techniques results

The method with the least amount of error is the Haphazard method.  The Random method takes the longest to sample and the Systematic takes the shortest amount of time to sample.

The error is lower for the most common species (eastern hemlock, sweet birch, red maple) across all methods compared to the two rarest species (white pine and striped maple) which have very high errors across all methods.  The more abundant the species, the more accurate any sampling method is.  Given the results from this trial I would probably choose the Haphazard method as it has the lowest errors (in this study) and enables the user more flexibility in choosing sample sites.

Post 8: Table & Graphs

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The data I collected was the number of trees with and without ivy in a 5 m radius. Each block, A through E, had five replicates. It was difficult deciding what sort of statistics to conduct on the data. I wanted to keep it straightforward and focused on the abundance of ivy on trees within the radius and then broaden out to look at each block as a whole. I believe looking at the relative frequency of the presence/absence of ivy will achieve this. Since the blocks vary in size it could potentially be helpful to standardize the frequency somehow between the blocks.

 

Table 2

Summary of data collected. The first letter delineates the block and the following number the replicate.
Point Radius (5m) # of Trees with Ivy # of Trees without Ivy Total # of Trees in Radius Relative Frequency with Ivy Relative Frequency without Ivy
A.1 7 4 11 0.6364 0.3636
A.2 1 5 6 0.1667 0.8333
A.3 2 3 5 0.4000 0.6000
A.4 5 1 6 0.8333 0.1667
A.5 5 5 10 0.5000 0.5000
ATotal 20 18 38 0.5263 0.4737
B.1 2 9 11 0.1818 0.8182
B.2 0 8 8 0.0000 1.0000
B.3 1 3 4 0.2500 0.7500
B.4 1 5 6 0.1667 0.8333
B.5 4 4 8 0.5000 0.5000
BTotal 8 29 37 0.2162 0.7838
C.1 5 3 8 0.6250 0.3750
C.2 0 1 1 0.0000 1.0000
C.3 4 3 7 0.5714 0.4286
C.4 1 5 6 0.1667 0.8333
C.5 1 4 5 0.2000 0.8000
CTotal 11 16 27 0.4074 0.5926
D.1 2 4 6 0.3333 0.6667
D.2 1 5 6 0.1667 0.8333
D.3 4 4 8 0.5000 0.5000
D.4 2 4 6 0.3333 0.6667
D.5 5 3 8 0.6250 0.3750
DTotal 14 20 34 0.4118 0.5882
E.1 1 4 5 0.2000 0.8000
E.2 0 3 3 0.0000 1.0000
E.3 3 5 8 0.3750 0.6250
E.4 1 5 6 0.1667 0.8333
E.5 5 2 7 0.7143 0.2857
ETotal 10 19 29 0.3448 0.6552
BlockTotal 63 102 165 0.3818 0.6182

 

 

Blog Post 4: Sampling Strategies

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The systematic sampling technique had the fastest estimated time to sample of 10 hours and 27 minutes. The haphazard sampling technique has the slowest estimated time to sample of 10 hours and 44 minutes. The random sampling technique had an estimated time to sample of 10 hours and 42 minutes. I can conclude that the accuracy decreased completely with a decrease in species abundancy. I would say that random and haphazard are equally accurate and systematic is a little less accurate than those.

 

Percentage Error – Most Common Species:

Random: 4.6% for Red Maple and 20.8% for White Oak

Haphazard: 4.0% for Red Maple and 34.2% for White Oak

Systematic: 5.3% for Red Maple and 14.1% for White Oak

 

Percentage Error – Least Common Species:

Random: 100% for Yellow Birch and White Ash

Haphazard: 100% for Yellow Birch and 525% for White Ash

Systematic: 100% for Yellow Birch and White Ash

Post 6: Data Collection

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My study area within Colliery Dam Park is composed of five blocks (A, B, C, D, and E) fragmented by paths. Within each block I sampled five replicates for a total of 25. Each replicate was a point radius of 5 m in which I determined how many trees did or did not have English ivy growing on them.

Since the blocks are different sizes, I was unsure if larger blocks should have had more replicates to accurately scale the replicates to block area. Additionally, the varying block size made it so that I had to generate new random numbers to determine replicate locations. For small blocks I would walk too far out of the study area.

Blocks C and D had the most similar distribution while Blocks A and B had the most dissimilar distribution of trees with vs without ivy. This is interesting because both sets of blocks are located next to one another.

 

Field observations and data.

 

Post 3: Ongoing Field Observations

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The species that I plan to study is Paper Birch (Betula papyrifera) and its distribution across elevation, aspect, and slope changes in the study area.

Site 1 – Lowest site in study area, located at toe of slope.  Elevation is 570m above sea level.  Slope is 0-5%.  This site is the only site in the study area that I have observed large cottonwood trees.  The forest composition is roughly 50% poplar, 30% birch, and 20% coniferous.  There are a few willows.

Site 2 – Mid slope in study area.  Elevation is 600m above sea level. Slope is 25-30%.  The forest composition is roughly 65% poplar, 30% birch, 5% coniferous.  There are more willows and low shrubs on this site than the other two sites.

Site 3 – Upper extents of study area.  Elevation is 640m above sea level.  Slope is 20-25%.  The forest composition is roughly 60% poplar, 20% Birch, 20% coniferous.  The forest is more widely spaced than the other two sites.  There are shrubs/willows in the openings.

All forest compositions are quick, visual estimates of the immediate surrounding forest.

There is snow on the ground in all locations, varying from 30 to 60cm in depth.

Observation sites in study area

My hypothesis is that birch tree populations will vary with changes in elevation, slope, and aspect.  My prediction is that the most populated sites will be the lowest in elevation, most northerly aspects, and low to moderately sloped hillsides.

The response variable is the density of paper birch.  This is a continuous variable.

The predictor variables are the slope, elevation, and aspect and are also continuous variables.  These are likely linked to water availability for the birch.  I am not able to measure the soil moisture content directly so I will rely on slope, elevation, and aspect as proxies to infer the soil moisture content of the soils.  I will also record the other types of vegetation that are growing in the sample sites to infer moisture conditions.  This is a categorical variable.

Field notes

Blog Post 5: Design Reflections

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In designing a sampling strategy, time was of the essence.  I began the course in October when the vegetation had full foliage but I knew that within the next week or two, fall would begin, and the plants would begin to lose their foliage or die off for the winter.  I was able to go out for a couple of successive days to observe the environment and decide on a topic and strategy.  In deciding to investigate light levels with vegetation coverage, I realized that the most prudent approach would be to quickly obtain a photographic record of the study area and analyze the vegetation cover from the photographs rather than studying the plants in-situ.  This approach utilizes computer data processing tools with methodologies widely used in Remote Sensing studies and utilizing recognized academic software, giving it credence for a scientific inquiry.

I selected the starting point of the path (where it branched off another path segment) and recorded data at approximately 10m intervals.  Capturing the data and photos at each interval went quickly so rather than random sampling, I collected data samples along the entire transect and I can either use the complete data set or randomly select points along the transect and query the data collection at these points.  Again, a measure of expediency was directing my work because I realized that if there was a data deficit, I would not be able to collect the data again during the course of this study.

I would have preferred to construct an apparatus to ensure consistent height and angles for the camera but I compensated for this by using a highly coloured measuring stick in each photo where the spatial integrity can be confirmed.  Additionally, since the same colours are available in each photo, I can calibrate the images’ colour balance and luminosity in much the same manner as NASA used with the Mars rover images.

I would have preferred to use a laboratory-grade light meter whose calibration could be independently certified.  However, this was not possible due to the cost of such equipment, so I calibrated my meter statistically to ensure that its tolerances were of an acceptable level for the study.  Also, I am not actually interested in the specific Lux values from the meter, but rather the relative light readings from one station to another.  As such, it was only to necessary to confirm the light meter’s response relatively across the spectrum and not to specific laboratory standards.  In other words, if the same level of light gives the same results on separate readings, I am able to consider the instrument calibrated for the purposes of this study.

I did the image analysis later at my home using the ImageJ scientific image analysis software.  Ideally, I would have liked to write a macro to bulk process and analyze the images automatically but the complexity of this task was too much for the scope of this project.  Therefore, I used personal judgement in some of the image processing which unfortunately could introduce bias or error but I endeavoured to follow the level of precision in the analysis and I believe that any discrepancies in analyses are statistically insignificant and do not affect the overall analysis.

Post 3: Ongoing Field Observations

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After doing some initial observations at General Brock Park, I have decided to study the dandelions (Taraxacum) growing there. I noticed that the dandelions tended to grow more abundantly and in greater density towards the perimeter of the field, especially towards the north and east sides. Although the dandelions can be observed to be growing throughout the field, it is sparser towards the centre of the field as well as on the south and west sides where a playground, benches, and street hockey court can be found nearby. I also noticed that the dandelions in this field were shorter compared to ones found on the lawns of nearby residents.

Dandelion growth on northwest perimeter of General Brock Park

 

From these observations, a hypothesis I have come up with is:
The abundance of dandelions found at General Brock Park is influenced by the proximity to areas of the park frequented by humans.

One response variable is the abundance of dandelions while one explanatory (predictor) variable is the frequency of human presence in certain areas of the park. The response variable is continuous while the explanatory (predictor) variable is categorical.

Post 5: Design Reflection

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There were some difficulties in implementing your sampling strategy. The difficulties mainly surrounded what parameters to record when examining the absence or presence of English Ivy. Such difficulties were if the type of tree or what side the ivy was growing on should be recorded. I wanted to keep the sampling strategy straightforward without generalizing.

I plan on continuing using the same data collection technique as it is straightforward and efficient to using randomly selected point-radii to examine the absence or presence of ivy.