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Post 4: Sampling Strategies

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Each technique took about the same amount of time sampling: systematic took 12.37H, random took 12.36H, and haphazard took 12.44H.

The most common species was Eastern Hemlock, next was Sweet Birch. Systematically sampling easter hemlock gave a 7.4% sample error and systematically sampling Sweet Birch gave 17.2%. Randomly sampling Eastern Hemlock gave 2.2% sample error and randomly sampling Sweet Birch gave 14.1%. Haphazardly sampling Eastern Hemlock gave 40% and 22% for Sweet Birch. Therefore the least amount of sample error came from the random sample and the most came from haphazardly sampling.

The lease common species was White Pine then Striped Maple was a bit less rare. Percent error of systematically testing White Pine was 70% and it was 100% for Striped Maple. Percent error of randomly sampling was 60% for White Pine and 100% for Striped Maple. Percent error for systematically sampling was 70% for White Pine and 120% for Striped Maple. None of these methods proved to be accurate enough to use in a proper data set. But randomly sampling had the least percent error for White Pine.

The more abundant the species it, the more accurate the sampling is. To increase accuracy, the number of samples taken can be increased.

Blog Post 3: Ongoing field Observations

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The biological attribute I am going to study is the number of birds in the yard. I am defining “in the yard” as: landing in the trees, bushes, on the fence, on the shed, or in the grass; not including birds that fly over without stopping.

The Environmental gradient I am using is the amount of precipitation: rain/no rain/length of rain. I have decided to use precipitation as a predictor of the number of birds because the termperature in Swift Current is fairly constant while during this season the rain is variable and will provide more variance throughout the test.

I have learned in biology classes that most small birds will wait out rain in some sort of shelter. Rain poses a risk of hypothermia. But smaller birds have smaller energy reserves and therefore can withstand less cold (and less rain) than larger birds can. But one problem of having a small energy reserve is that they can’t go too long without eating and therefore if it rains for a long period of time, the smaller birds will come out of their hiding places for some food. Therefore I predict that if I count birds in my yard immediately after it starts raining, that there will be very few to no small ones, but if it continues to rain then the numbers will eventually start to rise again. I will therefore need to establish the average number of birds that frequent the yard when it is not raining in order to compare the numbers in the variable conditions.

My response variable is number of birds in the yard. The predictor variable is if it is raining or not and how long it has been raining for and how much rain (heavy vs. light).

Blog Post 5: Design Reflections

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The collection of my initial data for my research project did prove to be a little challenging and I quickly realized some of the mistakes that I made. I was using a point count sample method in my location to count bird presence with ambient temperature as the predictor variable. However the sampling area was too large and therefore not the most effective way to sample. I used a grassed backyard area around 24 feet x 30 feet as the location which proved to be too confusing as I didn’t know whether to include birds on the fence. Also with birds flying in and through the area I wasn’t sure if I was double counting them. Therefore sometimes I counted them and sometimes I didn’t as I wasn’t sure if I had already. It was difficult to know whether the birds I was counting were ones that had already been counted. In hindsight I should have used a bird feeder on one of the trees and counted bird activity at the feeder.
Secondly, I also realized that my hypothesis was not detailed enough. My focus initially was hypothesizing that bird activity would be increased with warmer spring temperatures above 12.5 degrees C but I should have used a temperature range of 10 – 15 degrees C to hypothesize that temperatures outside of these ranges would have decreased bird activity because I needed to include temperatures both above and below the range as bird activity may be diminished in extreme temperatures on either end. I also should have done my testing in the morning when temperatures were cooler but due to time constraints I tested in the afternoon when temperatures were warmer and therefore I mostly had temperatures above my hypothesis. In hindsight I should have tested early in the morning when the temperatures weren’t as hot.
The results that surprised me were that the bird activity was strongest on the coolest day. I had hypothesized that birds prefer warmer weather but in fact based on the initial results they seem to prefer cooler weather.
When I test again, I plan to adjust my point count method by using the bird feeder. This will make it easier to count the birds, eliminate confusion and I feel it will be a more effective method. I also plan to conduct the research in the morning when temperatures will be cooler and slightly more variable (before the temperatures are at the height of the day). This will ensure I get enough days both above, below and within the range of my hypothesis. I also need to clarify my hypothesis before I complete the research project as it is too vague and doesn’t account for hot weather.

Post 4: Sampling Strategies

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I used the distance-based method for the Sampling Theory Using Virtual Forests Tutorial. Below is a table summarizing the comparison between the actual and estimated densities of the seven tree species in the Snyder-Middleswarth Natural Area as well as the percentage error using each (distance-based) sampling method. Also included in the table is the estimated sampling time for each method.

Based on the results above, the systematic method had the fastest estimated sampling time.

The two most common species are the eastern hemlock and red maple while the two rarest species are the striped maple and white pine. Between the three different strategies, percentage error is generally inversely related to species abundance. This is more prominently seen in the random sampling strategy.

When we only look at the most common and rarest tree species, the haphazard approach is the most accurate. However, when we consider all tree species, the systematic approach is the most accurate with a maximum percentage error of 57.7% versus the 100% error with the haphazard approach for the striped maple.

Overall, the systematic approach is best because of its relatively lower sampling time and for its relatively higher accuracy.

Blog Post 2: Sources of Scientific Information

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I chose:

Perry, G. H., & Verdu, P. (2017). Genomic perspectives on the history and evolutionary ecology of tropical rainforest occupation by humans. Quaternary International, 448, 150–157. https://doi-org.ezproxy.tru.ca/10.1016/j.quaint.2016.04.038

This is an academic, peer-reviewed paper.

The article is academic because it is written by professionals in their field. It has in-text citations and there are many papers referred to in the references section. The article is published in Quaternary International, which means that it is peer reviewed but it is a review not research material since it is lacking methods and results sections and has a topic instead of a research question it strives to answer.

Blog Post 1: Observations

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I have chosen my backyard in Swift Current Saskatchewan.

The area is approximately 37 m2 (~6m x 6m). It is a grassy backyard fenced in and has an ornamental shrub and a few trees in it.

I visited the site May 7, 2019 at 6:00 pm – it was 15°C with some clouds in the sky. It has been an uncharacteristically cold May and there were snow falls up until last weekend but it is finally warming up now.

I have observed:

  • Birds that are black with white on their wings and breast (Black-Billed Magpie?)
  • Some small brown and black birds (House Sparrow/House Finch/Dark-Eyed Junco?)
  • Small black squirrel

Some questions:

  1. Is this squirrel a frequent visitor of the yard? Does her visits depend on the time of day or weather?
  2. How many different types of small birds visit this yard? And how frequently do they visit the yard? Does this depend on time of day, weather, or temperature?
  3. Does the amount of rain effect the number of animals that visit the yard?

Blog Post 4: Sampling Methods

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In the virtual forest I chose to use Area Based sampling. No sampling method showed much efficiency over the other in terms of time spent.

The most accurate method overall was the random sampling method. For the most common species random sampling was the most accurate. For the least common species random sampling was somewhat accurate, although random sampling failed to return any samples of the least common tress, striped maple. Accuracy was better for the common species of trees due to lower percentage errors.

I would want to sample more areas in a random fashion to lower % error rates.

Tree Species Actual Density Area Systematic % error Area Random % error Area Haphazard % error
Most Common Eastern Hemlock 469.9 320.0 31.9 341.7 27.3 550.0 17.0
2nd Most Common Red Maple 118.9 84 29.4 137.5 15.6 162.5 36.7
2nd Least Common Chestnut Oak 87.5 36.0 58.9 58.3 33.4 41.7 52.3
Least Common Striped Maple 17.5 52.0 197 0 N/A 29.2 66.9
Time to Sample 12h37m 12h47m 12h31m

Blog Post 3: Ongoing Field Observations

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I have selected to study the pattern of trees adjacent to and away from pathways in Stanley Park. Three locations along the environmental gradient include survey plots with a diameter of 3m located adjacent to a pathway, 10m away from the pathway, and 20m away from the pathway.

I observed a more diverse mix of trees immediately adjacent to paths as well as an abundance and rich diversity of herbaceous plants including shrubs, ferns and grasses. Ten meters away from the pathway I observed less deciduous tree species and reduced incidence of shrubs and other understory plants. At 20m away I observed mostly mature evergreen species with a limited understory.

My hypothesis is that increasing distance from a site of forest disturbance, such as a pathway, is correlated with lower tree species diversity, lower tree density, and larger tree size. Based on this hypothesis I would expect to see larger cedar and hemlock trees further from a pathway due to the lack of disturbance. The disturbance of a pathway would allow for new species of plants to establish due to the availability of sun, runoff from pathways, and additional space. Further, the clearing of the edges of pathways would allow for continual colonization of new plants.

The response variable is the number (continuous), type (categorical) and size (categorical due to slotting trees into size brackets) of trees in a plot. The predictor variable is distance from a pathway (categorical due to 3 distance measurements being used).

10m From Trail

Adjacent to Trail

Notes1

Notes2

Notes3

20m From Trail

Blog post #4 Sampling strategies

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After trying out the sample methods tutorial I was able to gain some insight in to the pros and cons of each sample method. Haphazard sampling was the fastest method, but as I could see when comparing accuracy it was low preforming in that area. This makes sense since I chose my sections based on areas with high tree density. The one that took the longest was the systematic survey this seems accurate since I chose to check every 7th grid. This created extra work and more grids were surveyed then the others types.

 

The percent error of the two most common trees based on the three sampling strategies the random sampling was the most accurate, for the two most common trees, and the haphazard was the least accurate.As for the least populous trees only the random sampling actually accounted for both trees in the survey. The haphazard sample picked up neither, and systemic survey only found one of the two.

 

From the result over view it appears that the random sample survey did a more accurate job that the other two types. In terms of hours spent on surveys it was in the middle. This is important to consider when I am  looking at collecting my data for my project. More time invested in to systematic survey with a increased sample size my not give you the most accurate results. On the other hand random samples fall to chance and a large are of the survey grid could be missed. This is something to consider when looking at the randomly chosen quadrants and collecting your data.

 

Haphazard while convenient and more efficient and economical has to be critiqued  for a large bias in selecting quadrants or zones for surveying, or risk an invalid data set and wasted time.

Post 1 Observations

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I chose to observe a forest ecosystem that has recently been harvested.

20-04-2019 – 1200pm – 1430pm

Sunny and cool 12 degrees Celsius

Geographic Location – Hills, BC (UTM Ref: 462075 E 5553782N) West Kootenays

Gross Area – 53.8ha

Harvested Area – 34.1ha (Harvested in the fall of 2018)

Wildlife Retention Area – 10.3ha (Retention areas were noted to have high-value riparian areas)

Deferred Area – 7.6ha (small opening, immature forests) harvested in the year 2000. Planted in 2002.

Ecological Description

  • Biogeoclimatic Ecosystem Classification (BEC) – Interior Cedar Hemlock (ICHmw2) zone
  • A complex site with four site series
  • Mid-lower slope
  • Hummocky terrain
  • Slope range 3% – short pitches of 55%
  • Leading Vegetation – Too early in the season to identify any early serial species established.

Observations (Stations (1-1 -1-5) are associated with a KMZ file from Avenza Maps)

Questions:

  1. What native plant species will establish on the heavily disturbed soil from the rehabbed road (Hills 4200)?

  1. Numerous Pine Siskins were noted within the deferred area (12-year-old plantation). What habitat value or features do advanced plantations have? Where would birds nest in immature forests?
  2. After the mature timber harvested, what new limiting factors are introduced to the adjacent plantations and understory trees?