Category: Percy Hebert

Post #1: observations

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Post #1: Observations at Beacon Hill Park

 

I will be conducting my field research at Beacon Hill Park, a designated city park in Victoria, BC. When Victoria was settled in 1843, Beacon Hill park was a completely natural area. Beacon Hill Park was reserved as a park by the city of Victoria in 1858. Although some of the park has experienced human alterations through the planting of flower beds, construction of a water park and petting zoo, much of the park still features its natural ecosystem.  Most notably, the park protects a native Garry Oak ecosystem. The park features several ecosystems, including grasslands, forested areas and a man-made lake.  The highest elevation of the park is approximately 40m above sea level.

Date of observations: January 4 2018

Time of day: 4:15pm

Weather: 4˚C, wind 14km/h from the northeast. Clear skies. Sun was just about to set at 4:32pm.

Area of park: ~0.62 km2

Seasonality: winter

 

For my field study, I decided to select 3 locations to make observations. Each location features a different ecosystem.

Figure 1: Aerial map of Beacon Hill Park

 

Location 1: Forest near the “world’s tallest totem pole”

 

GPS coordinates:

48.408699, -123.358520

The forested area near the “world’s tallest totem pole” features a network of human footpaths through a forest of deciduous and coniferous trees. This location had the most-dense vegetation and the most evidence of decomposition.  Many plants surrounded the trees including bushes that had lost their leaves, brown, tall grasses and short green grass. One of the bushes had white berries, leading me to believe it was a snowberry (Symphoricarpos albus).  I found evidence of herbivory in dry, brown leaves that covered the forest floor.  One small bird was spotted during my observations. Lichen was present on tree trunks. Garbage from humans was also present.

 

Figure 2: Vegetation along footpath

 

Figure 3: Snowberry bush Symphoricarpos albus

 

Figure 4. Possible evidence of herbivory

 

Location 2: Top of Beacon Hill

 

GPS coordinates: 48.410488, -123.364813

 

The top of Beacon Hill has the highest elevation at the park, at 40m above sea level. The area includes an exposed, grassy region surrounded by what appeared to be Garry Oak (Quercus garryana) trees and arbutus (arbutus unedo) trees.   The trees were bare of leaves, but the shed leaves had accumulated on the ground below. Some trees retained brown, dry leaves and these leaves showed evidence of herbivory by an insect.  I noticed a Himalayan blackberry bush (Rubus armeniacus), which is a rampant invasive species all around Victoria.  Similar “wheat” looking grasses were observed to the grasses at location 1.  As well, the bush with the white “berries” (Symphoricarpos albus) was present.

 

Figure 5: Garry Oak

 

Figure 6: Arbutus tree

Location #3: Man-made stream ecosystem

 

GPS coordinates:

48.414201, -123.365927

 

The final location I will consider is a man-made stream ecosystem on the western edge of the park.  Although most of the plants here were likely planted by humans, this area had the most biodiverse foliage.  I observed the following vegetation:

-ferns

-coniferous tree with very soft needles. This tree was the tallest of the trees I observed anywhere in the park and had the widest diameter.

-deciduous tree

-leafy ground plant

-a large shrub close to the stream’s edge

-moss on rocks

 

Ducks were heard, but not seen.  The ground was covered in a brown “mulch”.  The stream flowed at a medium rate into a relatively large man-made lake.

 

 

Figure 7: Man-made stream

 

Figure 8: soft needles

 

Potential research questions:

 

  • What impact does elevation have on number of species, growth rate of vegetation and microclimate?
  • How do bird species respond to man-made structures, such as the man-made stream ecosystem, in comparison to natural wetland ecosystems?
  • Which location experiences the most insect herbivory and why?

 

 

Post 7: Theoretical perspectives

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Since my project is fairly basic and I have no biology background, I will not be directly investigating underlying causes of the pattern I am studying. That seems above my skill level. However, I can look at the existing literature and discuss possible causes to investigate in a hypothetical future project. Some might include: moisture and soil drainage; competition with the other plants in the area; nutrient distribution in soil; and human disturbance (there has been quite a bit of construction in the area recently). From observation, I am drawn to soil drainage being the biggest factor, but I admit that I will need to do quite a bit more reading to be able to defend that position.

I have not definitively identified the plant I am studying, but I believe that it is one of two closely related species, either crested wheatgrass (gropyron cristatum) or desert wheatgrass (A. desertorum). I will hopefully be able to find literature investigating factors that affect those species ability to grow, and can make connections between them and my own data.

Keywords: crested wheatgrass, water stress, soil drainage

Post 6: Data Collection

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I am one third of the way through my data collection, and my biggest issue with sampling has been the tedium of it (I need to dig through the mud and snow to be able to see the gaps between bunches). I have collected ten replicates so far.

I have noticed that although the three large troughs have very abrupt and distinct borders and different vegetation, smaller troughs between hills spread throughout the field tend to have the same grass as the rest of the field. I have only sampled from one so far, and it was slightly less dense than the hills, but it remains to be seen if that pattern holds elsewhere.

Post 5: Design Reflections

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For background, I am looking at the density of a particular “bunchgrass” in a field on hilltops, slopes, and troughs, to see if there is any correlation.

For this initial sampling, I used simple random sampling. I made a grid over a map of my field in Photoshop, and randomly generated Cartesian coordinates, throwing out pairs that fell on the highway or residential areas adjacent. I took these locations and found their coordinates on Google Maps. I then made a 2ft x 2ft quadrat out of PVC piping I had around and used that to count the number of plants per square at the locations I generated.

I would have liked to have used stratified sampling, in order to get an even distribution of the three types of terrain I am looking at. However, since hills and slopes are not easy to see on a map, with the exception of a few very large troughs, I found this to be infeasible.

I intend to continue using this method. However, in order to obtain enough of each terrain type, I intend to throw out coordinates of terrains for which I have ten samples already. This means that I will need to generate more locations than I will actually use, but it will save me time in the field and ensure that I do not end up with, say, 20 hilltops and 2 slopes in my final data.

My data is about on par with what I would have guessed. There tended to be fewer or no plants in troughs (0-3). There does not yet appear to be any difference between hilltops and slopes (5-6).

Post 4: Sampling

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Well, to begin with, 24 samples is not nearly enough to get an accurate picture of this region.

Name Density (actual) Density (systematic) % error Density (random) % error Density (haphazard) % error
Eastern Hemlock 469.9 444.0 5.5% 675.0 44% 279.2 41%
Sweet Birch 117.5 176.0 50% 129.2 10% 83.3 29%
Striped Maple 17.5 8.0 54% 8.3 52% 12.5 29%
White Pine 8.4 4.0 52% 0.0 100% 12.5 49%

Haphazard had the fastest estimated sample time (12:31), but only marginally (systematic was 12:35 and random was 12:42).

Which technique would generally be most accurate for the most common species is inconclusive, as percentage error varied wildly in this sampling. The most common tree, Eastern Hemlock, had 5.5% error in systematic, but that shot up to 50% for the next most common, Sweet Birch. Similarly, Eastern Hemlock had 44% error for random sampling, but Sweet Birch had only 10%.

For the two most rare species, haphazard sampling outperformed the other two techniques, but was still fairly inaccurate.

Across all seven plants, the error margins were all over the place. Haphazard sampling was the only technique consistently falling under 50% error, falling between 1.3% and 49%, with a mean across all seven plants of 25%. Random sampling was the least consistent, giving between 1.1% and 100% error, with a mean of 49%. Systematic sampling varied between 5.5% and 54%, with a mean of 35%. There was no discernible correlation between density and error in any of the three.

Based on this, it would seem that haphazard is the most reliably accurate method, but I am deeply skeptical due to the small sample size and high variability in all three. I am inclined to think that I just got a rotten data set this time around.

Post 3: Ongoing Field Observations

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Yellow indicates low density of “bunchgrass” in low-lying area.

I intend to study the yellow “bunchgrass” which is nearly omnipresent in the field I have been visiting. During my most recent field observations, I found that in troughs, other plants dominated and the bunchgrass was barely present. I observed this is the areas marked in yellow on the map. N.B. Google Maps has apparently not updated this area in a few years.

I would guess that the “bunchgrass” grows better on light slopes and at the top of hills because moisture collects in the low-lying regions, and the plant is better suited to dryer soil.

Hypothesis: The density of yellow “bunchgrass” correlates to location on hills (i.e., peaks, troughs, or slopes).

Prediction: The density will be significantly lower in troughs.

Response variable: density of yellow “bunchgrass”, continuous.

Explanatory variable: location on hill (peak, trough, or slope), categorical.

Field notes
Example of difference between hills and troughs.

Blog Post 5 – Design Reflections

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My initial field data was measuring the diameters of sagebrush (Artemisia tridentata) along an elevation gradient. I had hypothesized that sagebrush diameters decreased as elevation increased and so far the data does not seem to falsify this. The data I have collected is not surprising. My sampling strategy was systematic sampling as I had to follow the elevation gradient up along certain paths that were suitable for walking on. The only minor difficulty I had was the elevation measuring app I had on my phone did not seem extremely accurate so I had to confirm the elevations on a computer program afterwards. Now that I know the proper elevations for the park it will be easier to continue sampling the rest of my data.

Post 2: Sources of Scientific Information

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I have selected for this post a journal article from researchers at the Nereus Program, an interdisciplinary ocean research partnership based out of UBC:

http://www.sciencedirect.com/science/article/pii/S0308597X17301409
Rebecca G. Asch, William W.L. Cheung, Gabriel Reygondeau, Future marine ecosystem drivers, biodiversity, and fisheries maximum catch potential in Pacific Island countries and territories under climate change, In Marine Policy, 2017, ISSN 0308-597X, https://doi.org/10.1016/j.marpol.2017.08.015.

The authors of this paper all hold PhDs in ecology-related fields and are oceans researchers at major universities (in addition to the info in the article byline, their academic history is available here: http://www.nereusprogram.org/about/fellows/). The paper is chock-full of citations and has a bibliography. Therefore, I can safely conclude that this is an academic document.
This paper was published in Marine Policy. On inspection of the journal’s website, they employ double-blind peer review.

The authors include sections on their methods and results, which indicates that this is research-based. The entire paper went way over my head, and threw around words like “synthesize” and “overview”, so I may well be wrong.

Post 1: Merritt Boogaloo

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Map
South Entrance (roundabout): 50°06’14.9″N 120°45’54.0″W

I chose to study a field near my home in Merritt. On this particular trip, I stuck to the east side of the main path, marked in yellow. I went on October 30th from about 2-4pm. It was a clear, sunny day (no clouds whatsoever). It has since snowed heavily, so this field will be a puddle next time I visit.

The entire field is flat, roughly 500m^2, and surrounded to the south and west by housing, to the east by the main road into town. There are a few human-made paths, visible in the map. The only animal signs I found were dog scat and bird calls, and I encountered no insects. The soil is moist and seems to be clay. There was quite a bit of litter at the human entrances, and housing construction at the west entrance.

The field is dominated by a long, yellow grass growing in bunches, with spiky pods (?) at the tips (which will probably take a month to remove from my socks). There are a few sparse coniferous trees and other plants in localized areas. These included:

  • A localized, 15m^2 patch of a wheat-like grass and burrs in the north (just northeast of the tree by the path)
  • Moss under the main grass in the southeast
  • A dead or dying leafy plant interspersed sparsely in the main grass
  • Red thornbushes on the slope up the the highway
  • Some reddish low plants (called “underbrush” in my notes) on a small northern section of the main path
  • A plant which resembled to me a round hairbrush along the edge of the northern section of the path

Possible Questions

  1. What are human impacts on the area (e.g. walking paths, recent construction, pollution) and how do they affect the local plantlife?
  2. What differentiates the small areas where the yellow grass does not grow as much from the rest of the field
  3. What are common factors (e.g. sunlight, slope, types of soil, etc) in areas where this yellow grass grows throughout the Nicola Valley?

Journal Pages

Additional Photos

The main yellow grass dominating the field.
Example of the “bunches” the yellow grass grows in.
Thornbushes growing on the slope to the highway
Example of the “bunches” the yellow grass grows in.
“Hairbrush” plant, found on north part of path.
“Underbrush”, found on north part of path.
Detail of “underbrush” plant.
Red berries found on thorn bushes near highway.
Burrs in wheaty stretch, referred to in journal as “thistle”.
“Wheat” grass, found in ~15m^2 area in the north.

Blog Post 4, Sampling Strategies

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The 3 sampling strategies learnt in the virtual forests tutorial are systematic, random, and haphazard sampling. Throughout the virtual forest tutorial I learned which were the most efficient, fastest, and most accurate. I sampled the Snyder-Middleswarth Natural Area using area sampling technique rather than the distance sampling technique.

 

Systematic Sampling –

A combination of both random and haphazard sampling. It is easier than random but has less of a bias than haphazard. Quadrats were chosen in a specific pattern across the location, usually a gradient. On the virtual forest tutorial, systematic was slower than haphazard, but faster than random sampling. However it did have a high percent error as follows:

Eastern Hemlock – 14.0%

Red Maple – 5.4%

Striped Maple – 138.3%

White Pine – 147.6%

 

Random Sampling –

Done by labelling quadrats and choosing the numbered quadrats at random, every quadrat has an equal chance at getting chosen. This takes the longest time to do to ensure everything is chosen at random. Random sampling did have the lowest percent error, making it the most accurate way of sampling.

Eastern Hemlock – 6.4%

Red Maple – 5.4%

Striped Maple – 66.8%

White Pine – 100.0%

 

Haphazard Sampling –

Done by choosing areas that have samples which are readily available, and taking samples from the different variations in your testing area. Haphazard samples are never random but always available, and therefore also have a high percent error.

Eastern Hemlock – 23.3%

Red Maple – 43.65%

Striped Maple – 4.6%

White Pine – 1.2%

 

Sampling speed:

Haphazard > Systematic > Random

 

Accuracy in 2 most common species:

Random > Systematic > Haphazard

 

Accuracy in 2 most rare species:

Haphazard > Random > Systematic

 

The accuracy did change with abundance of the species. Random sampling was the most accurate for common species, and haphazard was the most accurate for rare species. Overall, if time allows, random sampling would be the most accurate.