Category: Percy Hebert

Post 1: Observations

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I have chosen to conduct my research in an area of Stanley Park in the City of Vancouver. I visited the site in late April as deciduous trees were unfurling their leaves on a sunny day when the temperature was about 15 degrees.

Stanley Park is a large urban park that is part of the downtown peninsula. It is just over 4 square kilometres in size and is bounded on 3 sides by water and joined to the rest of the City by an isthmus. Several small lakes dot the park, including lost lagoon, which was formerly an estuary. From its inception the park was intended to preserve the coastal rainforest found in this part of coastal BC and to this day it provides myself and many others with the opportunity to experience nature in the heart of the City. The park is mostly forested and is classified as Coastal Western Hemlock (CWH) under the biogeoclimatic zones of BC. However, there is considerable development within the park to facilitate recreation and transportation, including pathways, roads, restaurants, a major highway, the Stanley Park seawall, and cultural institutions.

Overall the topography of the park is that of gentle rises with the northern portion rising high above Burrard inlet. Several beaches surround the parks edge and form marine transition zones. The forest is mostly comprised of evergreens typical of the CWH zone and others that have been planted including western hemlock (Tsuga heterophylla), western red cedar (Thuja plicata), Douglas fir (Pseudotsuga menziesii), grand fir (Abies grandis), and sitka spruce (Picea sitchensis); deciduous trees include bigleaf maple (Acer macrophyllum), cottonwood (Populus trichocarpa), wild cherry (Prunus avium), choke cherry (Prunus virginiana), and red alder (Alnus rubra); shrubs, ferns, other herbaceous perennials, and moss are also present.

Even though the park has been considerably managed for over a decade, one can tell from moving through the wilder areas that species composition and concentration changes, especially if one walks off designated trails and into the forest. Direct human management in this park is definitely a factor affecting the composition and concentration of tree species; this can be seen by noticing numerous stumps. I also think that distance from developed areas of the park, whether they be boundaries between manicured park areas, paths, roads, and clearings to tracts of seemingly untouched forest away from these edges influence species composition and concentration.

Three questions that are of interest to me include:

  1. Does tree species composition and concentration in the park change as a function of distance from forest edges and forest interiors?
  2. Does tree species composition and concentration in the park change as a function of edge type (seaside, roadside, pathway, etc.)?
  3. Do forest edges allow for more growth of herbaceous plants and shrubs versus forest interiors?
Stanley Park Map
Cathedral Trail
Cathedral Trail Initial Notes

Post 3: Ongoing Field Observations: Courtenay Estuary/ K’Omosks Estuary

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Posted on April 22, 2019, by caudia

Cathy Audia

April 16, 2019

 

Upon my first visit to the Courtenay Estuary I noticed the reed type plants in the tidal flats appeared to be dead.  Moving upwards toward the shoreline the percentage of the healthy reeds increased in number. As my first visit was early spring, I began to wonder if the reeds die off and grow back seasonally, or if there was another factor.  Perhaps too much moisture had caused the reeds to parish.   For this natural experiment the biological attribute I will study are the reed type plants. I went back to the estuary one week later and found no change to report.  The weather was overcast, the temperature was 12 degrees Celsius and the time was 14:20.

I divided the estuary into 3 zones.

Zone 1: Tidal flats consisting mostly of muddy sand, seagrass, seaweed, and reeds, would be completely covered in water with each high-tide.

Zone 2:  Marshy area between the tidal flats and the bank, this area consists of more of the same vegetation as the tidal flats as well as some other types of plants, rocks, and logs. This area would be covered by the water during some high-tides.

Zone 3: The sloped sandy bank consists of some the same types of plants found in zone 2 but the plants appear much healthier. This area would rarely be covered in water.

Hypothesis: The reeds will grow back as spring progresses with the greatest abundance of healthy reeds located in zone 2.

Prediction: The reeds will not thrive in zone 1 due to too much moisture, conversely, they will not thrive in zone 3 due to too little moisture. The slope of the gradient is the factor that reduces each zone’s contact with water.

The reed-like plants are the response variable and the amount of moisture in the sand is the predictor variable.  The response variable will be measured with a continuous scale as the sliding scale will allow me to be more specific conveying the health of the plants.

Post Five: Design Reflections: Cates Park

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My sampling strategy had a few difficulties, and therefore I decided to attempt another, hoping to redeem my first effort.

The first sampling strategy used a transect with alternating quadrats. Using my roommate’s measuring tape was the first challenge, since it only had imperial measurements, so I had to convert data into centimetres. I’m grateful I had a willing assistant who could help lay the measuring tape along the necessary gradients. The data collected was surprising as it revealed low numbers, and I realized that my next similar attempt should be on a more grand scale. I will need to be creative with data collection along points that are steep or heavily forested. One other difficulty was creating a data sheet template that would work for my purposes. I improvised and moved the data to a new spreadsheet that was more organized.

The second set of data collected was haphazard and distance based, and I believe, more successful. Five trees were selected haphazardly for ease of access in this forested region. These were the centre point where I measured neighbouring species. Again, the tape measure was not an ideal tool, and I benefited from having someone to assist. After the data collection, I realized I should have created a map, image or layout of where each tree was situated in relation to the midpoint. This data was predicted but I’m looking forward to more sampling.

I will likely continue to collect data with the second approach, and add another kind of sampling strategy to assist in the bigger picture of my hypothesis. By adding varied sampling techniques, replicates and variables, I will likely be able to prove or disprove my prediction and hypothesis. Modifications to data collection will also include appropriate measuring techniques and recruiting more volunteers!

 

Post Four: Sampling Strategies

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In the virtual forest I chose to use Distance Based sampling.

Systematic sampling was the most efficient in terms of time spent sampling, but only by about 15 and 30 minutes respectively.

The actual densities varied widely with my estimated data from sampling. Haphazard sampling was the most accurate sampling strategy for common species, with an average error margin of 12.5%. The most accurate for rare species was Systematic sampling, with an average error margin of only 2.3%. My results showed that accuracy was better for more common species, which was not surprising. Although not recorded in the table below, I noticed that systematic sampling showed closer results for the species that were neither common nor rare. Because of wide ranges in error margins, it would be ideal to sample more than 24 points for a more accurate estimate.

 

Tree Species Actual Density Distance Systematic % error Distance Random % error Distance Haphazard % error
Most Common Eastern Hemlock 469.9 516.5 9.9 368.2 21.6 399.3 15
2nd Most Common Sweet Birch 117.5 42.3 64 108.3 7.8 105.9 9.9
Least Common White Pine 8.4 8.5 1.2 21.7 158 0 100
2nd Least Common Striped Maple 17.5 16.9 3.4 32.5 85.7 8.1 53.7
Estimated Time 4h15m 4h29m 4h44m

Post 1: Observations

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Post 1: Observations

Posted on April 10, 2019 by caudia

Cathy Audia

April 8, 2019

This study is being conducted at the Courtenay River Estuary which is designated as a City of Courtenay Park. The site is being visited on April 8, 2019 at 1830 hours.  The weather is overcast with little wind and the temperature is 9 degrees centigrade. The estuary is located approximately one kilometer from downtown Courtenay.  The Estuary is the area where the Courtenay River flows into the Pacific Ocean sitting at 49.68 degrees North and 124.99 degrees West. The shoreline of the estuary is approximately 1 km across with the depth changing from as little as 5 meters wide to 500 meters during low tide.

There is a bank from the edge of the beach up to the grassy plateau sloping at approximately 10% grade.  The estuary is made up of several different types of vegetation. Seagrasses and seaweed are found in the muddy flats with increasing amounts of seagrass closer to shore and decreasing amounts of seaweed closer to shore. The shoreline consists of a mixture driftwood, rocks, seagrasses, and various plants that appear to be more typical to be found on dry land than under water. There is a sprinkling of deciduous trees along the top of the bank.  It appears most of the native trees have been removed to create a paved walkway and a small airport which is located steps from the estuary.

Some of the flowers and flowering shrubs along the shoreline have flowers blossoming at different rates. As it is early spring, and vegetation started growing within the past 3 weeks.  The flowers that receive unobstructed sun are further along in their flowering cycle.

I pose the following 3 questions;

  1. I observe many of the reed-like plants have died in the tidal flats. Leading me to question did they die due to poor growing conditions, or do they die off seasonally. I discovered some of the same reed-like plants on the shore edge that appear healthy. I will continue to observe their growth as spring progresses.
  2. I also note an interesting pattern in the sand. It is circular shaped with greyish sand that was noticeably different then the surrounding beige sand.  The circle has 4 an imprint in it with arms similar to a starfish shape and in the center is a hole.  I suspect there must be some type of shellfish living underneath this hole. I will continue to look for clues to the source of this pattern.
  3. I also notice there are only 2 seagulls on the estuary side and 6 ducks in the small inlet. As the estuary is known as a good place to view a variety bird I wonder if I will see if more birds as the season changes.  

Blog Post 9

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Looking back on this research project it did not turn out exactly as I was hoping it would, but the experience of dealing with the complexity of implementing the research in this field did give me a large appreciation for all the effort that has to go into any study in the field of ecology. Lab work in general you get a fair share of hurdles to jump over but there’s so many confounding variables and factors to take into account when working in the field. I wish I’d had more of the tools I’ve read about for testing soil pH, like the onsite probes you can use in the field rather than having to make solutions and test it with a meter in the lab. That said having changed the ratio of the soil water solutions I was testing and sieving out the organic mater in the samples did at least allow me to collect reliable data where I was more sure about the readings being the true values, but if I’d had one of the probes I could have taken more samples.

Blog Post 8

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I did not have too much difficulty organizing my table or graph. For the table I had sample #, pH as read off a meter, pH as found off pH strips, and the presence or absence of underbrush as the different columns. For the graph of my data I graphed the presence of underbrush as teh dependent variable against the soil pH as read off the pH meter. The graph felt a little funky for me since the presence of underbrush is a categorical variable. I assigned 1 to be presence of underbrush and 0 to be absence of underbrush so the data looked like a Bernoulli distribution to me. When a best fit line was placed in on the scatter plot there appeared to be a slight negative relationship, but when the means of each of the two sites were calculated they were so close together only differing by a value of 0.066 pH that without being able to evaluate the data using p-values, since I can’t assume normal distribution, I have to accept the null hypothesis. This outcome is unexpected as I went in thinking that soil pH was probably an influencing factor on the distribution of the underbrush, but there are definitely other factors that could be the cause of this pattern. If I were to explore this topic further I would look into those other factors like interspecies competition, shading, soil type, and soil moisture.

Blog post 7

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The theoretical basis my research is based on is all the theories that relate to soil pH influences on environmental conditions. The acidity or basicity of a soil affects the presence of nutrients, the toxicity of heavy metals and the microbial community living in the soil. All three of those can affect the health and growth of plants, and the capability of plants to live at the different levels of these creates niches. With this in mind its easy to see how soil pH could be the factor causing different species to live in the two patches of ground I was investigating.

Keywords: Soil pH, growth pattern, ICHdw1 zone

Blog Post 6

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For my second set of data collection I used the same 5 replicates of samples from each site, but used slightly different techniques to more thoroughly assess the soil pH. I used a 1:1 ratio of dirt to water and tap water rather than distilled water for the samples I was testing under the pH meter. It seemed to improve the accuracy of the meter, as it could actually detect the pH with more ions present. The readings took much less time to stabilize at a steady pH reading with much less drift. With the use of pH strips, I was able to double check the results. The two methods gave the same results which seemed to be a uniform data distributed around 6.2 pH throughout both sites. This data plus the lack of any ancillary patterns makes me think that perhaps there is a different underlying cause for the pattern of underbrush presence in the Gibson park. I will report this data and continue with this topic for my paper but in my discussion, I will focus on what other underlying causes could be creating this pattern if not soil pH.

Post 5: Design Reflections

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Kevin Ostapowich
April 1, 2019

My study is looking at the distribution of birch within a mixed forest.  Using QGIS, I overlayed a 30m x 30m grid over my study area and haphazardly chose 5 sites to count individual trees.  I then went into the field, using GPS to find the sites, and counted trees of all species within the plots.  I didn’t have too much trouble implementing this procedure but I noticed that the different tree species tend to be grouped in clusters which may have skewed the results.  For example, one of my sample plots was almost entirely composed of pine while the surrounding forest was deciduous.  Another  plot was almost entirely aspen with no birch but immediately surrounding the sample area was a lot of birch.  The plots that I counted may not be entirely representative of the forest composition.

There are two solutions that I can see:  choose a larger grid size to encompass a greater diversity of tree species, or sample more locations.  For consistency and to keep sampling manageable, I plan on continuing with the method that I have already used and to increase the number of sampling sites.