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Blog Post 6: Data Collection

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I had been collecting data March 26 to March 28 on the number of ducks in the three locations (Land, Shallow water, Deep water) and found there to be somewhat more ducks in the deep water. I had collected a total of 5 samples. Sample 2 and 3 were on the same day at different times. Sample 4 and 5 were also on the same day at different times. During these sampling periods, I noticed something important: the ducks appear to change preference throughout the day. This data is simplified and shown in Table 1 below. Therefore,  I had collected 4 more days of data at three points in time during the week of April 2nd. This data has yet to be analyzed – but this had changed my initial hypothesis. I had originally hypothesized that the ducks prefer to be in the deep water at all times but I hypothesize that they prefer the shallow water during certain light levels.

Table 1. The average number of ducks seen in each location throughout the day for each sampling trial. Each trial shows the mean number of ducks in the location during the sampling hour. The Average number is shown in the last row.

Trial Land Shallow Deep
1 0.0 10.3 8.6
2 0.2 2.9 9.4
3 0.0 6.0 6.5
4 0.4 3.9 10.3
5 0.0 6.6 6.8
0.1 6.0 8.3

Blog Post 4: Sampling Strategies

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After using the sampling techniques I have found that the fastest sampling technique was the systematic sampling technique with it taking 12 hours and 39 minutes. The percent error is shown in Table 1. Both haphazard and random sampling lacked finding the striped maple, while the systematic approach still had a very large percent error. It appeared that the random sampling overall had a lower percent error than the other two techniques; however, with 24 samples, the percent error for all plants is still statistically significant. This is interesting as I wouldn’t have expected the percent errors to be so high with this many samples. The two more abundant species have a lower percent error than the two less abundant species. Therefore, abundance did appear to influence the percent error.

Table 1. Haphazard, Random and Systematic sampling percent error in regards to density. The (*) indicates no species found in the trials for said technique.

Haphazard Random Systematic
Eastern Hemlock 37.92296233 34.77336 11.51309
Sweet Birch 64.5106383 39.74468 11.48936
White Pine 114.2857143 4.571429 151.4286
Striped Maple 100* 100* 185.7143

Blog 7: Theoretical Perspectives

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This research project on moss richness on the tree trunks with different levels of tree top exposer touches on understanding the affects sunlight, rainfall, to snowfall has on moss richness in Canada. Where if there is direct exposer to the weather conditions how would this effect the richness of moss found growing on the trunks of trees that were observed at the end of British Columbia winter and the beginning of spring were the temperatures ranged from 5-15 C and during the 2 weeks the experiment was conducted rainfall, snowfall, and clear skies had all occurred at the location.

 

key words: moss richness, weather conditions, canopy coverage

Blog Post 5: Design Reflections

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My data collection from March 26 until 28 had shown some interesting results. I collected the location of the two duck species Anas platyrhynchos Aythya americana within the Gambles Pond at different times. The layout for my collection locations is shown below:

I collected data of for which regions the ducks were in with 5-minute intervals for an hour. The difficulties I faced were with differentiating ducks at low lighting. This was mediated by using binoculars – but still was difficult at lower light levels in the evening. I was surprised how often ducks entered the shallow water in the evening versus the earlier day. As a result, I wish to collect data at 3 times periods a day to see if there are any differences among the three periods. I think that this approach should be good for collecting the data.

Blog Post 5: Design Reflections

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There were no major difficulties in implementing my sampling strategy in the field. Using the quadrat method, I subjectively chose 5 different locations, 2 grazing and 3 non-grazing, in order to observe the damaged caused to Ryegrass by geese feeding habits. I laid out a 100cm x 100cm grid and estimated the amount of damaged area by measuring it and estimating area. The only difficulty experienced was that getting an exact measurement of the damaged area is tough as it is not always in a square shape. However, I do not expect this to be a major detriment to the effectiveness of the experiment as the estimates will be fairly accurate and it appears that the data still shows accurate trends. The data trends were close to expected, but the total area of damaged grass was slightly higher than I originally expected. I plan on continuing to collect data using this technique as it is effective as well as time efficient. Overall, I think the sampling strategy was highly effective.

 

Quadrat from observation of non-grazed Ryegrass

Blog Post 4: Sampling Strategies

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The technique with the fastest sampling time was the random sampling method. The Red Maple and the Eastern Hemlock were the two most common species and the Striped Maple and White Pine were the two rarest. The percentage error for these with the different sampling techniques are as follows;

 

  Systematic Random Subjective
Eastern Hemlock 11.5% 21.7% 10.8%
Red Maple 4.1% 13.4% 30%
Striped Maple ?? 347% 42.9%
White Pine 47.5% ?? ??

?? denotes the PE was unable to be calculated because the species was not found in sampling.

 

The accuracy of the sampling appeared to increase with increased species abundance. The rare species had much higher PE values than the common species and some were unable to be calculated at all because the species was not found using certain sampling techniques.  The Random and Systematic sampling techniques were on average about 4-5% more accurate than the Subjective/Haphazard method of sampling for common species. However, when calculating rare species the Systematic and Haphazard method were much more accurate than the Random method, but due to the lack of species abundance and data it is difficult to read too much into this.

Blog Post 3

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The organisms which I plan on studying are Canadian Geese, B. Canadensis, and Ryegrass, L. perenne. I will be observing effects that the Canadian Gooses grazing habits have on the growth and success of the Ryegrass. I have chosen to study this at the location near McMaster University, as it is nearby my home and is easily accessible. I believe that the feeding habits of Canada Geese in the park area greatly damages the growth and success of the Ryegrass, as they feed specifically on the roots. I predict that the areas in which the geese feed will have much higher percentages of bare spots and damaged grass due to the grazing behaviour. The potential response variable in this study is the Ryegrass and the explanatory variable is the geese feeding behaviours. The response variable is continuous in nature as it will be measured in m2 and then converted to a percentage of the area of the different test sectors. The predictor/explanatory variable is categorical in nature as it depends on the presence or absence of the geese feeding in the control areas. Given this information the experiment will use a ANOVA design with a one-way layout that compares the health of the grass in grazing and non-grazing areas.

Blog Post 9: Field Research Reflections

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To summarize my experiment, I took samples of Achillea millefolium around the interior of British Columbia at different elevations and counted their flowers to see if there seemed to be a relationship between elevation and fitness. I completely changed my study goals when a brutal winter hit Merritt and the bunchgrass I was investigating was literally frozen solid.

To be tactful, my research was not well thought out. To be blunt, my research was garbage. I started with two sites in Chilliwack, 170 km from my home, then took samples at three sites in the Thompson-Okanagan, much closer to home, once the snow had melted enough. This may not have been a great idea, simply because there are significant differences in climate and plant life in those areas that I was not equipped to control. Were I to repeat this experiment, I would stick to one biogeoclimatic zone.

As I read through the literature related to my plant, I realized that I did not account for numerous confounding factors in my design. The best I could do by that point was point out the glaring flaws in my design in the final paper, as I am in no position to go traipsing through the snow again to recollect all of my data. I also figured out that I really need to brush up on statistics (it’s been a few years for me, and I am certain that I made errors in interpreting my results).

I will say that I found the initial field observations highly enjoyable. As a computer scientist, I have had almost no exposure to ecology or field techniques, but it was a wonderful experience to take control of my own learning and pay attention to the fine details of my local natural community.

Overall, the entire process was a valuable learning experience. Even if my design was not a complete success, I was able to identify where there existed weaknesses by looking at others’ experimental designs with a critical eye.

Blog post 9

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My field research project on Sagebrush definitely increased my appreciation for the ecological importance of this ecosystem I’ve been taking for granted. Kamloops is full of dry, brownish hills in the summer that can be mistaken for drab. When you look closer, you see an amazing amount of diversity and persistence through incredibly difficult growing conditions. Late last summer we didn’t receive any precipitation for over a month. However, these huge bushes sustain themselves and then flower at the beginning of fall. It’s amazing.

I had to make changes when I was carrying out the study, including changing the sampling method from quadrats to linear transects. This made the data collection a lot simpler. In terms of the redoing the study, I would do it a lot differently next time. I would sample at a different time of year and include more transects at different places in the creek. I would also obtain a degree of incline along the hillsides to have more control over extraneous variables like flat spots. I think, ultimately, it would be nice to have had more measures to help control aspects of my study.

Ecological theory development is way more complex than I originally thought. There are so many variables that interact to create what we see around us. This semester I had the opportunity to do another project on the human microbiome and I found the ecology of this system to be fascinating. By designing my own study with the brush bushes and doing research on the topic, I gained a better understanding all of the variables impacting what had once seemed to be a simple question of depth to water.

Sources of Scientific Information (Post #2)

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The source I have chosen is an online pdf about the use of aquatic macrophyte Azolla pinnata as a biosorbent to remediate ecosystems damaged by the waste products produced by the oil and petroleum refining industry. The link can be found here:

http://www.ijaprr.com/download/1440396380.pdf

This document meets all criteria for academic peer reviewed research material. It was written by an expert in her field: Dr. Punita S. Parikh is an associate professor at the Maharaja Sayajirao University of Baroda. She has 25 years experience researching plant ecology, pollution ecology, and climate change. To be published in this journal, a document requires review by referees. More information on this process at this organisation can be found below:

www.ijaprr.com/

This study uses its own data. Numerous in-text citations can be found, and all relevant sections, including methods, results, references, and data tables from experiments, are present. As such, this document is an academic peer reviewed research paper.