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Blogpost 1: Observations

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I visited a natural area behind my house at 7:45 in the evening until about 8:35. The temperature was 22°C and the weather was sunny with light breezes.

The natural area is owned by the city and is sandwiched between residential area and agricultural land reserve. It is situated on the side of a north-facing mountain with residential are being at a lower elevation and agricultural land reserve occupying the upper portion of the mountain. The size of this area which I observed is very roughly 6 hectares (measured using google maps).

There are three distinct areas which I identified during this observation period; Forested, Forested with minimal/no canopy, and open grassland.

The dominant species of the forested area are Pinegrass covering the vast majority of the ground and Douglas Fir trees occupying most of the canopy. Numerous other species were also observed throughout the area. The dominating species in the open grassland was what appeared to be crested wheatgrass.

Some questions which I had while walking throughout the area were:

-Why does very little grow in the ravines (see attached photos)?

-How does the proximity to a ravine affect species richness?

-What influences the formation of large patches of medium-sized shrubs?

-How does the proximity to the residential area affect species productivity/ species richness?

Google maps view of visited area

Blog Post 6: Data Collection

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I collected my data on August 2nd during the afternoon. It was a very warm and stormy day here in Florida. I managed to find a 2-3h window between two rainfalls to go collect my data on the field.

In order to thoroughly sample my site, I decided to double the number of samples or transects from my initial data collection in module 3. I then collected the same number of subsamples (or quadrats) per transects (10) but on 10 samples instead of 5. To do so, I had to collect my samples every 8 metres along the width of the field instead of every 16m.

Considering that I had collected my pre-experimental data from module 3 a few months ago, I decided to collect 10 new samples instead of only adding the 5 new ones to my already collected data. I feared that flower abundance might have changed with time and so I resampled everything.

The previous data collection that was made a few months ago greatly improved and facilitated my afternoon of August 2nd. The difficulties I had with keeping my transects straight were eliminated by the simple trick I elaborated months ago. Before starting my sampling, I would spot 2 or 3 checkpoints along the transect to keep it straight. I believe this simple adjustment helped me maintain a greater quality of samples.

I did not observe any new patterns during this exercise. The data collected seems fairly similar to the set collected months ago and so my observations and comments were the same.

Blog Post 6 – Data Collection

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The field data that I have been collecting over the past few months has been duplicated 6 times and we are reaching the end of the Giant Hogweed life cycle for this season. Though noted in the assignment that I would duplicate this 2 times, I have found more time to duplicate the data collection.  The sampling size being used is throughout the data collection period is randomly selected areas and then a 5-meter radius will be observed in the efforts of collecting information. These areas where chosen systematically random, by starting at the high disturbed area and working our way to the back of the property low disturbance area. Six sites where sampled 3 being in the high disturbance area and 3 being in the low disturbance area. The patterns I’ve noticed is that the Giant hogweed is growing along ‘trails’, road side, driveway, game trail through the tall grass, etc. And there are no Giant hogweed growing between the transition area (where the high and low disturbance change) and 0 plants growing in the low disturbance area. I believe this is due to such a high crown cover in the low disturbed area, making it very shaded.

Post 8

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I would have preferred to display my data in a graph but because there was weak evidence for my hypothesis I felt it would be easier to interpret my data using a table.

I did struggle to collect the correct data for my hypothesis.  After starting the literature review I was able to better understand why some western redcedar trees sunscald after they had been exposed to full sun, but others are able to thrive in full sun.  Western redcedar is not shade requiring but rather shade tolerant, meaning they have adapted to both full sun and shade conditions.  If I had understood the evolutionary fitness process of being able to develop two morphologically and physiologically different leaves (Shade leaves and Sun leaves) I would have set up my plots to study different replicates.

However, the slight increase in light conditions has shown some damage to the foliage on the trees that were adapted to sun exposure which will correspond to my hypothesis.

I would like to further research the ability for shade leaves to recover or adapt to sun exposure.  To date, I have not been able to find any literature specifically related to western redcedar’s ability to recover from sunscalding.  Or does the tree just shed the damaged shade leaves and it’s new growth develop as sun leaves?

Blog Post 4: Sampling Strategies

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Sampling Using Virtual Forests (Mohn Mill – Sampling by Area)

Compare the results. Which technique is the most efficient in terms of time spent sampling? Compare the actual densities with the estimated (data) densities from your sampling. Calculate the percentage error for the different sampling techniques for both common and rare species. What was the most accurate sampling strategy for common species? What was the most accurate for rare species? Did the accuracy stay the same or decline for rare species? Was 24 a sufficient number of sample points to capture the number of species in this community? Was it enough sample points to accurately estimate the abundance of these species?

 

  • Which technique is the most efficient in terms of time spent sampling?

 

Time Spent Sampling:

Area Haphazard (t= 12:45, m=765)

Area Random (t=12:09, m=729)

Area Systematic (t=12:44, m=764)

In terms of time spent sampling area, random sampling was the most efficient by approximately 9.5% compared to both haphazard and systematic sampling which differed by only 1 minute.

 

  • Calculate the percentage error for the different sampling techniques for both common and rare species:

 

Red Maple Density

Actual (403.7); Area Haphazard Data (337.5) – Percentage Error= 16.4%

Actual (403.7); Area Random (425.0) – Percentage Error= 5.3%

Actual (403.7); Area Systematic Data (445.8) – Percentage Error= 10.4%

White Oak Density

Actual (74.5); Area Haphazard Data (41.7) – Percentage Error= 44%

Actual (74.5); Area Random Data (54.2) – Percentage Error= 27.24%

Actual (74.5); Area Systematic Data (125.0) – Percentage Error= 67.78%

Chestnut Oak Density

Actual (82.9); Area Haphazard Data (54.2) – Percentage Error= 34.62%

Actual (82.9); Area Random Data (91.7) – Percentage Error= 10.62%

Actual (82.9); Area Systematic Data (41.7) – Percentage Error= 49.69%

 

Witch Hazel Density

Actual (142.4); Area Haphazard Data (191.7) – Percentage Error= 34.62%

Actual (142.4); Area Random Data (166.7) – Percentage Error= 17.06%

Actual (142.4); Area Systematic Data (150.0) – Percentage Error= 5.33%

Red/Black Oak Density

Actual (46.7); Area Haphazard Data (29.2) – Percentage Error= 37.47%

Actual (46.7); Area Random Data (58.3) – Percentage Error= 24.84%

Actual (46.7); Area Systematic Data (0.0) – Percentage Error= 100%

Eastern Hemlock Density

Actual (45.6); Area Haphazard Data (8.3) – Percentage Error= 81.8%

Actual (45.6); Area Random Data (62.5) – Percentage Error= 37.06%

Actual (45.6); Area Systematic Data (41.7) – Percentage Error= 8.55%

Black Tupelo Density

Actual (35.5); Area Haphazard Data (33.3) – Percentage Error= 6.2%

Actual (35.5); Area Random Data (0.0) – Percentage Error= 100%

Actual (35.5); Area Systematic Data (33.3) – Percentage Error= 6.2%

White Pine Density

Actual (12.8); Area Haphazard Data (4.2) – Percentage Error= 67.2%

Actual (12.8); Area Random Data (12.5) – Percentage Error= 2.34%

Actual (12.8); Area Systematic Data (12.5) – Percentage Error= 2.34%

Downy Juneberry Density

Actual (9.9); Area Haphazard Data (8.3) – Percentage Error= 16.16%

Actual (9.9); Area Random Data (12.5) – Percentage Error= 26.26%

Actual (9.9); Area Systematic Data (16.7) – Percentage Error= 68.68%

Striped Maple Density

Actual (13.6); Area Haphazard Data (0.0) – Percentage Error= 100%

Actual (13.6); Area Random Data (4.2) – Percentage Error= 69.12%

Actual (13.6); Area Systematic Data (0.0) – Percentage Error= 100%

 

Hawthorn Density

Actual (4.5); Area Haphazard Data (0.0) – Percentage Error= 100%

Actual (4.5); Area Random Data (0.0) – Percentage Error= 100%

Actual (4.5); Area Systematic Data (4.2) – Percentage Error= 6.6%

Black Cherry Density

Actual (1.5); Area Haphazard Data (0.0) – Percentage Error= 100%

Actual (1.5); Area Random Data (0.0) – Percentage Error= 100%

Actual (1.5); Area Systematic Data (0.0) – Percentage Error=100%

Sweet Birch Density

Actual (1.2); Area Haphazard Data (0.0) – Percentage Error= 100%

Actual (1.2); Area Random Data (4.0) – Percentage Error= 233%

Actual (1.2); Area Systematic Data (0.0) – Percentage Error= 100%

American Basswood Density

Actual (1.5); Area Haphazard (0.0) – Percentage Error= 100%

Actual (1.5); Area Random Data (0.0) – Percentage Error= 100%

Actual (1.5); Area Systematic Data (0.0) – Percentage Error= 100%

Yellow Birch Density

Actual (0.8); Area Haphazard Data (0.0) – Percentage Error= 100%

Actual (0.8); Area Random Data (4.2) – Percentage Error= 425%

Actual (0.8); Area Systematic Data (16.7) – Percentage Error= 1987.5%

White Ash Density

Actual (0.8); Area Haphazard Data (0.0) – Percentage Error= (0.0-0.8)/0.8*100= 100%

Actual (0.8); Area Random Data (0.0) – Percentage Error= (0.0-0.8)/0.8*100= 100%

Actual (0.8); Area Systematic Data (0.0) – Percentage Error= (0.0-0.8)/0.8*100= 100%

 

  • What was the most accurate sampling strategy for common species?

The most accurate sampling strategy for common species overall tended to be random sampling.

 

  • What was the most accurate for rare species?

All the sampling methods were relatively poor for rare species, either missing them entirely or grossly overrepresenting them.  For example, both White Ash and American Basswood were missed entirely regardless of sampling method with consistent percent error of 100%.  Similarly, a mixture of missing entirely and grossly overestimating density was observed for both Yellow Birch and Sweet Birch, regardless of sampling method.

 

  • Did the accuracy stay the same or decline for rare species?

Accuracy for rare species declined drastically in almost all cases.

 

  • Was 24 a sufficient number of sample points to capture the number of species in this community?

 

Overall no, 24 was not a enough sample points to capture the number of species in this community.  Based on the analyses, using the sampling methods employed here with 24 sample points, I would have entirely missed White Ash, American Basswood and Black Cherry.

 

  • Was it enough sample points to accurately estimate the abundance of these species?

Overall no, some species were very accurately measured with some sampling methods with 24 sample points while others were very inaccurately measured.

 

 

 

 

Blog Post 6!

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The field data that I have been collecting I was only able to have 5 duplicates because of the zone of the intertidal zone at McNeil Bay did not allow for anymore.The sampling design (transect with a randomly located sample plot) I chose works well because of the elevation in the study area. The quadrats were chosen systematically at random. I started with the top pool and always made sure the next pool was 2 steps away. The patterns that I have been able to notice are the richness in the low tide pools due to the better living environment (stays wet) and more cover there too. Another pattern would be the size of the tide pools depicting the species diversity since I’m only recording what is in the quadrat but there are many species outside of it.

Blog Post 5!

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The collection of data in Module 3 went very well. A difficulty that I encountered was finding tide pools at the high tide region since most of them at McNeil Bay are at sea level. There were many more at the low tide region but I was only able to randomly select 5 from down below and had them spaced apart by at least 1 meter (2 steps). The water in the tide pools further down was quiet murky so it was hard to see what was inside all of the pools. Since I was measuring cover for the algae, I collected that data first and then looked for the individual species that could have been hiding underneath. The data that I collected was not overly surprising because I did see more biotic species (fish) in the pools closer to the ocean. I did however notice more seaweed cover at lower levels which I thought I would find more of higher up. I will collect the data in the same way for my final collection because I did not encounter any serious difficulties. I could improve the data collection by being more precise with the the sampling technique using transects along a measuring tape. This will improve the accuracy of pools that are in different elevation gradients.

Blog Post 3: Ongoing Field Observations

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The organism that I plan to study is the bullfrog (Lithobates catesbeianus) present at the Champlain Lake south wetland/swamp site chosen for observation. I had taken note that the stationary presence of a Lithobates catesbeianus in stagnant water appeared to be dependent on its location featuring sub-merged aquatic vegetation. None of the seven total observed Lithobates catesbeianus were present in an area of water that had no aquatic vegetation beneath the organism.

The three locations I have chosen to observe the organism of interest is the interior small channel flowing south, the main open body, and the lake mouth channel running into the wetland body (see photo for location references).

  1. Location 1: Interior small channel (south flowing)
    The water in the wetland had dropped approximately 1 foot, leaving the channel to be very shallow and featured minimal room for aquatic vegetation. The channel was very stagnant and I did not observe any bullfrogs (Lithobates catebeianus) present in this area. I assume their absence in this channel was due to low water levels causing restriction for travel along the channel.
  2. Location 2: Main Open Body
    The main open body of the wetland also had signs of lower water levels along the shoreline, exposing previously submerged rocks and some dried up aquatic vegetation. This location had the majority of Lithobates catebeianus present and featured ample aquatic vegetation beneath each organism. I noted in my field journal the surrounding areas lacking visible aquatic vegetation, also lacked the presence of a Lithobates catebeianus. The water was very stagnant in this location as well.
  3. Location 3: Lake Mouth Channel (running southbound into wetland body)
    The lake mouth channel had a few sections of submerged aquatic vegetation and featured one smaller Lithobates catebeianus on the interior side of the channel. This channel had slight/low flow entering southbound into the wetland body and I predict the lack of Lithobates catebeianus presence throughout the lake mouth channel location is due to minimal aquatic vegetation coverage and difference in water velocity.

I postulate that the presence of a stationary Lithobates catebeianus is dependent on ample (>50%) coverage of submerged aquatic vegetation beneath the organism. Based on the postulate hypothesis, one potential response variable could be the Lithobates catesbeianus and one potential explanatory variable being the percentage of submerged aquatic vegetation coverage. The response variable would be categorical in this case, being the presence/absence of the Lithobates catesbeianus, and the predictor variable would be continuous, being the percentage of aquatic vegetation coverage. I expect the Lithobates catebeianus is stationary in water with ample aquatic vegetation beneath the organism to evade other predators present in the wetland.

I had a few images to accompany this post, however, are too large to include.

Overview of the Locations discussed

The bullfrog present next to the dock. Only present in areas that featured submerged aquatic vegetation

 

Blog Post 3!

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I plan to study the tidal pools at McNeil Bay in Victoria BC. The organisms that live in these pools are both biotic and abiotic. Examples are barnacles, fish, brown and green algae. The abundance of these organisms varies depending on the zones of the pool from high tide to low tide. The three locations I chose to observe the changes in were low tide, intermediate tide and high tide. Below is some notes that I recorded on the locations:

  1. Low tide: Greater biodiversity, the organisms here do not need to be well adapted to drying out and extreme temperatures. Organisms such as anemones, brown seaweed crabs and fish live here.
  2. Intermediate tide: At this point there is a mixture of both species in the low and high tide but mostly it looks like there are a few invertebrates (chiton) but lots of seaweed.
  3. High tide: Organism here have to survive drying out, currents and wave action. A larger abundance of barnacles, seaweed with only a few invertebrates can be observed.

These patterns could be due to the very different environments that they live in varying with elevation. I hypothesize that low tide zones will have greater abundance than high tide for (species) due to the less harsh conditions for it to live in. I predict larger biotic organisms are only in low tidal zones. Based on my hypothesis one potential response variable would be the total number of each species in the tidal pools which is categorical. A continuous variable would be the percentage of cover of each pool with seaweed. One explanatory (predictor) variable would be the unique environment the tide pool provides for the species. This variable would be categorical since there are 3 distinct locations of the pools.

I am not very good at drawing so I only sketched a few things but mostly I have chosen to take pictures and document my field observations all electronically.

Image from the CRD showing the intertidal zones.

 

3 intertidal zone sketches. Note Mid-High and Low Tide order.

Blog Post 7: Theoretical Perspectives

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My research touches on biotic and abiotic factors, physiological stress, chemical stressors, and the stress-exposure-response model (SER model). Biotic factors are brought up in my experiment because my research examines the influence a living organism has on another. My dog has a direct influence on the grass because the urea from her urine becomes toxic and creates dead spots in my yard. Urea, combined with shade and moisture, all play the role of abiotic factors that influence the condition of Common Fern Moss (Thuidium delicatulum). When these nonliving agencies reach a suboptimal level, Thuidium delicatulumfaces physiological stress through restricted growth or rotting. Urea has a similar effect on the grass in my yard. Increased exposure of urea causes toxicity to the grass and kills it. These cause-effect relationships mentioned above allude to the SER model (stress-exposure-response). The SER model demonstrates the biological or ecological changes as a result of the change in intensity of environmental stressors.

 

Three keywords that I could use to describe my research project are: chemical stressors, percent coverage, and abiotic factors.