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The theoretical basis of my project would be on how crows have adapted to anthropogenic effects. With most of the North Shore in Vancouver being developed, it’s interesting to look at the gradient from forested areas to fully developed areas and how crows utilize their new environment. One thing that my research focuses on is if there is a higher density of crows in the more developed area. Crows are very intelligent foragers, so the research is interested in if crows will utilize the easily accessible food. I believe that the crows will migrate to these more developed areas where the anthropogenic sources are the highest.

Keywords: Northwestern crow, urbanization, gradient analysis

Blog Post 8

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I found it challenging to present my data in a way that would be easy to understand without some additional text. However, it was good to get into Excel after not using it for a couple of years. As my final assignment comes together, I’m sure my visuals will change.

The outcome was not exactly what I was expecting, but with ongoing observations of the site, I have developed some additional ideas to explore. For instance, I thought there would be a steady increase in the response variable along the environmental gradient but there was some variance to it. During field observations, I noticed some microclimates in areas where stand density increased. Interesting stuff!

Here’s one of the figures I battled with Excel to make:

 

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For my field study, I’ve narrowed down my sampling unit down to 7 locations. These 7 locations were determined by observing the number of people and anthropogenic sources in Cates Park. I’ve categorized my anthropogenic sources from no disturbance which is the forested area in Cates Park all the way to a high disturbance which is a kids playground. My point counts of foraging crows have been conducted before 10 am and the survey plot is 50 meters. These points were selected from the 22-hectare park and each point was greater than 100 meters away from the previous point. So far I have visited the park four times and I plan on visiting it one more time.

So far I have implemented many changes to my study such as: categorizing the anthropogenic effect, focused more on foraging birds, and narrowed my sampling point down to 7 locations. I also made a change to my hypothesis which was too specific so with these changes my experiment has become a lot more manageable. With the gradient moving from no disturbances to disturbed areas there has been a trend emerging that supports my hypothesis.

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.

Blog Post 7

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The theoretical basis of my field project is that the response variable (stand density of maple trees) is influenced by a mature western cedar tree that dominates the site where observations are taking place. Ecological processes I may touch on include light availability, soil composition on western facing slopes in an ICH zone, influence of invasive groundcover that is present on the site, influence of litter as there is a thick blanket of maple leaves present on the site, impacts on the site from the rocks and boulders that are present, and the response of the maple trees to changes in canopy density as determined by the large cedar.

3 keywords or phrases that could be used to describe my project are: Acer glabrum, stand density, and forest ecology.

Blog 6

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November 20, 2017

In an effort to measure bird species presence and abundance along an urbanized gradient, surveys were completed within the Stanley park around the Lost Lagoon.

Site 1 – West side of Lost Lagoon, Site 2- South Side of Lost Lagoon, Site 3 – East Side of Lost Lagoon

4 replicate point count surveys in each of three areas listed above (2 replicate locations per area, on 2 different dates). I will complete at least one more day of data collection, with 2 more replicate point count surveys in each of the three areas over the next week. In addition, limiting the number of point count surveys to two per habitat has also made the surveys manageable as it still takes about 1 hour to complete all 6-point count surveys across the three areas on any given sampling day.

I calculated my explanatory variables for each area (approximately 300m x 300m area) as a whole using aerial photography to determine the percent cover of natural habitat (forest, wetland, etc…) and urbanized areas (buildings, roads, trails, etc…). I used a systematic sampling strategy to place my point count survey sites within each survey area randomly along the road or trail that runs through them. A random number generator provided the first survey point location in each site, while the second survey point was systematically placed 200m away to maintain the minimum distance required for independence between sites. At each point count survey, all birds seen and heard within a 50m radius of the observation point were recorded during a 5-minute period.

Bird abundance has been quite highest in site 2, site 1, and then site 3; in that order. Site 1 does have large flocks of Canadian geese swimming through it which will definitely elevate the overall abundance numbers for that site, whereas the other two sites have smaller abundances by individual species but more species overall.

blog 5

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My time collecting data at Lost Lagoon, Stanley Park, I had no difficulties in implementing my systematic sampling strategy. I focus the study on examining bird species presence and record other species that I have observed.  I’ve noticed a pattern of abundance at three different sites along an urbanized gradient.

Revised hypothesis: Bird species presence and abundance is impacted on how close their natural habitat is located near an urbanized city.

Revised predications are:

  1. Bird species richness will be highest in areas closer to the city (south side of lagoon)
  2. Bird species evenness will be highest in areas with the highest percent cover of natural habitat (west side of lagoon).
  3. Bird species richness/evenness will be lowest in areas near the main highway/road because of lack of food (east side of lagoon).

My response variable will remain as bird species presence and abundance (continuous variable), and my explanatory variable of percent cover (categorical variable) of natural habitat vs. urbanized areas (roads, buildings, trails) at each site as a whole will also remain the same.

In addition, the followings changes will be made in the experimental design in regards to the way data is collected:

  1. Initial sampling took place around 1:00pm and overall lots of activity and species present.
  2. My new sampling strategy includes two survey sites (point count surveys) in each of the three habitats along the gradient. During the replicate exercise, I expanded the number of point count surveys in one of the sites to five. While it would be ideal to maintain this type of replication it is too time consuming to perform five replicate surveys in each of the three sites. As a result, I will perform two replicates per site, and will conduct surveys at each site on at least three different days.

During the next blog I will go into more detail on the sampling strategy, sample unit, how these changes have impacted the data collection, and any ancillary patterns that appear in the data to support the new hypothesis and predictions.