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Blog post 6: Data collection

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Create a blog post describing your field data collection activities. How many replicates did you sample? Have you had any problems implementing your sampling design? Have you noticed any ancillary patterns that make you reflect on your hypothesis?

 

I have implemented three data collection activities to recognize the healthiness of ecology depending on humans intervention rate.

First observation  was collecting the ratio of fresh vegetation per area. Fresh vegetation proportion was measured by the colour, green. In a quadrat (10cm*10cm), if the area is composed of greens more than 50%, it would be recorded as 1. If the area observed had green vegetation less than 50% of the area,  it was measured as 0. Each landscape, ornamental garden, ornamental steps, and preserved hill side, was measured 8 quadrats and it was measured with 5 replication. Since the area wasn’t that big, the quadrat selection might overlap easily. I had to use systematic sampling techniques. The area was divided into 5 area and quadrates were selected in subdivided area per visit. After one visit the other area was selected for data collection. In this way, overlapping of quadrat was avoided.

Second observation was collecting the number of vegetation species observed per quadrat (10cm*10cm). As above the area was divided in to five and subdivided area was observed each visit. 8 quadrats were observed per visit and was measured with 5 replication.

As going through both experiment 1 and 2, there should have been consideration of the amount of water the land received and type of soil the vegetation grows. They both affect highly in vegetation growth. The hill nearby the church isn’t actively managed by someone, the amount of water the landscape receives and type of soil they grow on might be different. On the next observation, observing this point is another important criteria.

The third observation was measuring the bird activity rate depending on landscape. Bird activity should be considered morning, afternoon and evening. The bird activity rate changes during the time of the day. Therefore, each visit per day must be three times; morning (9am-9:30am), afternoon and evening, with 10 replication. Each observation lasted 10 minutes and the number of birds flying around the landscape was measured.

Blog Post 5: Design Reflections

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I chose to do a Systematic sample by distance, and my sampling strategy worked quite well for collecting data. I had 3 transect lines along 3 different gradients 20 feet apart. Each transect was 50 feet long, with a point (one unit) selected every 10 feet to make up 5 points on each transect. Each point was a stake in the ground where I would measure the 7 closest vascular plants. The forest was quite accessible this time of year, since a lot of the plants had lost their leaves, it wasn’t too overgrown to walk through. It became problematic when I was trying to measure distance between some plants, especially in dense areas. I initially was going to take down data for 5 different plants at each point, but found 7 to be more useful due to the closeness of some plants, and more data to work with. Another difficulty I came across was the fact that a lot of the plants had lost leaves and made it tough to identify. I took a lot of images and spent quite a bit of time making sure they were the correct species.

The data was only surprising in that there were species here I did not know about. I knew ferns would be quite dominant, but didn’t realize that they would be prominent in a large portion of the selected points.

One thing I should consider, is that it may be of my interest to measure the density or circumference of each plant to get a greater understanding of how well they grow in each gradient. This would mean I would have to re-do the data collection, but I am not against the idea of narrowing down my study to a couple species. This might help focus my study and allow me narrow it down.

Blog post 4: Sampling Strategies

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Haphazard Sampling of an Area

Percent error of 2 most common: Red Maple 1.5%, White oak 7% (percent error is [(E-T)/T]*100

Percent error of 2 least common: White Ash 100%, Yellow Birch 100%

Sampling time: 24 hours 13 minutes

Random Sampling of an area

Percent error of 2 most common: Red Maple 9%, White Oak 1.3%

Percent error of 2 least common: White Ash 100%, Yellow Birch 662.5%

Sampling time: 26 hours 1 minute

Systematic Sampling of an Area

Percent error of 2 most common: Red Maple 1%, White oak 5%

Percent error of 2 least common: White ash 150%, Yellow Birch 100%

Sampling time: 26 hours 40 minutes

 

 

Haphazard has the least amount of time which is surprising considering there is not necessarily a technique used to save time due to how irregular it can be. This may be a certain instance where it took less time to complete a sample. The least abundant species have the worst percent error, which makes sense as there are less to find in an area and can easily be missed in a study. The most abundant species have the lowest percent area, because there are enough of them to create a more accurate set of data. The lowest overall percent error would be in Systematic Sampling which could be explained by the fact that entire sections may not be missed as they would be in random or haphazard. Different gradients are more likely to be covered with a systematic sample.

Post 6

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Background: I have decided to change my hypothesis and organism I am studying to make my sampling simpler. I have observed that deer fern are more present in some areas of Burnaby Mountain covered in forested than areas than areas that don’t have trees. To keep this simple I will categorize a sampling area as either shaded having most of the sky covered by the tree canopy, partially shaded having part of the sky shaded and non-shaded. These three zones represent differing levels of sunlight that is able to reach the ferns on the ground. My new hypothesis is that the deer ferns are more successful under lower lighting levels. As such my prediction is that I will find a greater abundance of ferns in shaded and partially shaded areas than non-shaded areas.

I chose three sites to conduct my sampling each had a non-shaded area, partially shaded area and shaded area. At each area I recorded 10 samples. For a total of 90 replicates. My sampling unit was 1m2. Which I had planned to measure the ground cover of the ferns; however, I found that most ferns covered the whole area. So instead of trying to measure ground cover I just recorded either the area being mostly covered by deer fern, grass, bare ground, tree or thorns. Of my three sites one of the non-shaded areas is managed to some extent because a gas line is below so they clear cut the area every few months. I think this has had the effect of reducing the amount of ferns that can grow because they are in competition with other plants that perform better under higher lighting conditions. The partially shaded zones had the most ferns with 15, shaded at 5 and no-shade at 6. This is what I had initially observed; however, I had expected to find more ferns in the shaded areas. I observed in the shaded areas underneath the trees (evergreens) that very little grew, the ground was mostly covered in pine needles. In the partially shaded areas the trees were mostly thin in diameter deciduous trees. One of the non-shaded areas had a high number of ferns which was the opposite of the other 2 non shaded areas that had only 1 fern each.

 

Blog Post 4: Sampling Strategies

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Systematic sampling (area):

Sampling time= 12 hrs 7 min

Hemlock= 637.5….Percentage error= (35.67%)

Red Maple= 116.7…Percentage error= (1.85%)

White Pine= 8.3…Percentage error= (1.19%)

Striped Maple= 12.5…Percentage error= (28.57%)

I was surprised when I found that the most abundant species was represented so inaccurately while the least abundant was very accurate. I believe this may have occurred because the quadrats followed a very specific gradient going south to north, therefore we miss out on the other species that may be more present to the east or to the west of our selected quadrats.

Random sampling (area):

Sampling time= 12 hrs 42 mins

Hemlock= 420.8….Percentage error= (10.45%)

Red Maple= 100…Percentage error= (15.91%)

White Pine= 16.7…Percentage error= (98.81%)

Striped Maple= 20.8…Percentage error= (18.96%)

Overall this was even more inaccurate than the systematic sampling, especially with the rarest species- White Pine. By chance, the program sampled double the proportions of White Pine than are actually in the forest which is surprising. The more abundant species were more accurately represented.

Haphazard sampling (area):

Sampling time= 12 hrs 46 mins

Hemlock= 420.8….Percentage error= (10.45%)

Red Maple= 104.2…Percentage error= (12.4%)

White Pine= 0…Percentage error= (100%)

Striped Maple= 12.5…Percentage error= (28.60%)

This was the most inaccurate of the sampling techniques with not a single White Pine being sampled. This does not surprise me as their actual representation is quite low and haphazardly choosing quadrats without attention to the different gradients could easily lead to this result. The species in abundance were more accurately represented which is what I would expect.

Conclusion:

The systematic sampling technique had the fastest sampling time and was the most accurate. As long as the entire sampling area had similar environmental factors such as sunlight exposure, space, soil type/quality, etc then I feel this would be the best technique to use. If the environment was more diverse, more sampling points would be necessary in order to correctly represent the gradients. Either way, an increase in sample points would have been beneficial as I think would always be the case, but then that is more time consuming.

Blog post Four:

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Community Sampling Exercise by Carmen Bell 

 Community: Snyder-Middleswarth Natural Area 

 

The three virtual sampling strategies used to assess species density for the Snyder-Middleswarth Natural Area included random, systematic and haphazard. Of the three, the area-based haphazard sampling method was the fastest at 12 hours, 12 minutes, likely because these are known representations of the larger area taken in a non-random manner. The longest duration was the area-based random or systematic method of 12 hours, 45 minutes. The difference in time between the two is not vast for the 24 plots, however, the difference may increase given more sample points.  

 

1. Area, random or systematic  2. Area, random or systematic  3. Area, haphazard 

   

12 hours, 35 minutes  12 hours, 45 minutes  12 hours, 12 minutes 

 

For the total area of the Snyder-Middleswarth Natural Area, 24 sample points were not enough to represent the diversity of the 200ha old-growth hemlock-yellow birch forest. In effect, each sample point represents 8.3 hectares (20.6 acres) over the steep terrain of a ravine created by the Swift Run River. As this is a virtual exercise, the representation can only be imagined. In a real case scenario, the accuracy represented in the sample points would depend, in part, on the variation of soil composition within the degrees of steepness. 

 

My assessment of the histograms from a perspective of relative species abundance, determined that the Eastern Hemlock and Sweet Birch be considered common, while the remaining species be considered rare. Within the context of biodiversity, “…, n individuals usually fit a hollow curve, such that most species are rare … and relatively few species are abundant” (McGill, et al., 2007). Each of the Yellow Birch, Chestnut Oak, Red Maple, Striped Maple and White Pine had a relatively hollow curve given the limited data. The Snyder-Middleswarth Natural Area is known as an old-growth hemlock-yellow birch forest. The lower density of yellow birch could be attributed to the larger stem size of an old growth tree. 

Considering the Eastern Hemlock and Sweet Birch as the most common species, the most accurate density reading was the Eastern Hemlock with a 10.6 percentage error between the known and sampled data. Considering the Yellow Birch, Chestnut Oak, Red Maple, Striped Maple and White Pine species as rare, the most accurate density reading lay with the Striped Maple at 14.3% error. I would like to point out that only 20 trees were sampled with a known density of 17.5. The Yellow Birch had a higher percentage of error at 30.2, however, the known density is 108.9 with 76.0 represented in sample data, demonstrating a stronger representation of the species.  

 

 

Eastern Hemlock

                 Actual   Data

Density  469.9  520.0 

 

520.0 – 469.9 / 469.9 x 100 = 10.6% percentage error 

 

Sweet Birch  

Actual   Data

Density  117.5  188.0 

 

188.0 – 117.5 / 117.5 x 100 = 60.0% percentage error 

 

 

Yellow Birch

Actual   Data

Density  108.9  76.0 

 

76.0 – 108.9 / 108.9 x 100 = 30.2% percentage error 

 

 

Chestnut Oak

Actual   Data

Density  87.5  36.0 

 

36.0 – 87.5 / 87.5 x 100 = 58.9% percentage error 

 

 

Red Maple  

Actual   Data

Density  118.9  152.0 

 

152.0 – 118.9 / 118.9 x 100 = 27.8% percentage error 

 

 

Striped Maple  

Actual   Data

Density  17.5  20.0 

 

20.0 – 17.5 / 17.5 x 100 = 14.3% percentage error 

 

 

White Pine

                 Actual   Data

Density  8.4  0.0 

 

0.0 – 8.4 / 8.4 x 100 = 100% percentage error 

 

Blog Post 3: Ongoing Field Observations

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Organism being studied: Alnus rubra (Red alder)

Gradient: The tree line of a forested ravine, the level top of of a slope before the shoreline, the shoreline itself which consists of boulders, small trees, mixed shrubs and perennials.

Alnus rubra is much more dominant along the shoreline and potentially non-existent along the tree line. This could be due to sunlight, a preference for more well-drained and less rich soil, or maybe Alnus rubra has a tolerance to the salt exposure (from the ocean) and has been able to outcompete other less tolerant species. It may also be that because its the only small tree species along the shore it’s much easier to spot than looking into a thick tree line.

The trees almost seem to have formed a natural spacing between individuals as well with none closer than roughly 20 meters. These specimens all seem quite mature considering the harsh environments in which they grow (taller than 20 feet with stems more than 8” in diameter). Even though these trees are well-spaced and have an abundance of flowering and fruiting bodies, I could not observe any seedlings or juvenile specimens in any part of the gradient.

Hypothesis: The mature Alnus rubra in this region, despite an abundance of flowers and fruit, can no longer reproduce in this location via seed.

Formal prediction: The number of Alnus rubra seedlings and/or juvenile specimens recorded will be very low or non-existent along the shoreline and at the tree line. Due to a change in some environmental factor (or factors) mature specimens have survived but their seed cannot germinate or the seedlings cannot survive.

Potential response variable: Continuous. The number of immature Alnus rubra along the gradient.

Potential predictor variable #1: Continuous. The number of mature, seed-bearing Alnus rubra along the gradient.

Other potential predictor variables:

Categorical- Soil type along gradient (sandy, loam, clay, etc)

Continuous- pH and/or nutrient composition along gradient.

scan_p205726_2020-01-06-06-52-58

Post 5: Design Reflections

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I found my sampling strategy difficult to implement in that the samples could be clumped close together. In order to eliminate this difficulty I could try adjusting the number of steps associated with the random number generated, for example, only taking 10+ steps from my starting points instead of 9 or less.

I also found the data gathered to be difficult to work with. Douglas Fir circumference and ambient temperature do not seem to provide much information on their own in such a short timeframe. In order to make this data easier to interpret over the duration of this study I would like to add quadrant aspect and the relative subject population within each quadrant.

 

Blog Post 1: Observations

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Time: 8:30AM on Dec 30 2019

Weather: Cloudy

Size and Location: Cristopherson Steps in Surrey, BC. The natural areas alongside the stairs as well as the roughly 2 km stretch of beach front at it’s base.

General description: The stairs begin from a suburban street and go steeply down a ravine, eventually over a railway track and to a beach. It’s quite rocky in this area and the shoreline is eroding from both the sea on one side and the regular removal of plants from the track area behind.

Designation: Cristopherson Steps is property of the City of Surrey but in the beach front area it is a mix of City of Surrey, BNSF Railway and potentially the provincial government because the tide comes very close to the area of interest.

Vegetation: Natural area alongside the stairs and down the ravine is mostly infested with ivy but there are also ferns, western red cedar, douglas firs. The beach front consists of deciduous shrubs and trees, and a mix of perennial and annual weeds.

3 intriguing questions:

  1. There are very young western red cedar trees no more than 6 feet in height that have germinated from seed presumably from the few larger specimens above (some are growing inside or along decaying tree stumps, therefore I don’t believe they have been planted). English ivy is already climbing these young trees and I wonder if western red cedar can survive without removing the ivy.
  1. Along the beachfront the shoreline in front of the railway tracks is built up with large boulders. The areas that have Rosa and Holodiscus discolor growing within the cracks of boulders seem to be less eroded than the areas without. Do these plants stabilize the area better than the other more eroded areas? 
  1. The heavily eroded portions of the shoreline are sparsely populated with what I believe is Digitalis. I could not find it present in any other areas except where the shoreline has recently collapsed and the soil conditions seem poor. Are the areas where the shoreline has recently collapsed from erosion providing this plant with the conditions it needs to germinate and then not be out-competed by other plant species?

Field notes 1