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Blog Post 4: Sampling Strategies

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Three sampling strategies, inclusive of systematic, random, and haphazard, were used in the virtual sampling tutorial. The technique with the fastest estimated sampling time was systematic sampling (12 hours and 31 minutes).

Eastern hemlock and sweet birch were the two most common species in this tutorial. Systematic sampling yielded the lowest percent error for eastern hemlock (1.98%), however, percent error for sweet birch was lowest when the haphazard sampling technique was applied (17%).

Eastern Hemlock: Systematic = 1.98% error, Random = 32.3% error, Haphazard = 10.8% error

Sweet Birch: Systematic = 36.17% error, Random = 46.21% error, Haphazard = 17% error

Overall, when comparing percent error results for the two most common species, haphazard sampling had the lowest overall average percent error (13.9%), compared to systematic sampling (19.1%) and random sampling (39.3%).

The random sampling strategy proved to be the most accurate technique for the two rarest species: striped maple and white pine. Percent error for striped maple using the random sampling technique was still quite high, despite having the lowest error of all the techniques applied.

Striped maple: Systematic = 76% error, Random = 50.9% error, Haphazard = 114% error

White pine: Systematic = 100% error, Random = 2.4% error, Haphazard = 100% error

Random sampling, on average, was the most accurate technique when used to sample the rarest species (26.7% error), as compared to systematic sampling (88% error) and haphazard sampling (107% error).

Overall, greater species abundance led to greater accuracy.

Post 5: Design Reflections

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For my study, I hypothesize that Sagebrush (Artemisia tridentate) will be more abundant on hill tops versus valleys. I did not have any difficulties with my sampling strategy. The data that I collected was surprising to me; the south side of the hill tops had little to no mature sagebrush, only juveniles that were less that 20 cm tall. I was expecting a more even distribution with more mature sagebrush on the south side. I did not yet gather data for the valleys. For next time, I will continue to use the same sampling strategy but I will change my approach slightly.  In the future, I will use rope or something similar in order to show the exact edges of the quadrant. In order to avoid bias, I will use a predetermined amount of space between each quadrant and measure accurately. I will also obtain soil samples in order to determine the soil moisture content.

Blog Post 5: Design Reflections

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Hello Class & Professor Elliot,

During the collection of initial field data in Module 3, I found the most difficult part was trying to design a sampling unit that would accurately represent the area I was trying to study. Once I had devised a plan to span an environmental gradient on both sides of Jack Creek, I found it was relatively easy to put together a sampling method. The difficulties in implementing the sample unit happened more on the ground when specific points I wanted to measure either had nothing to sample or weren’t easy to access on foot. The data I collected was surprising (in the context). I tested soil moisture to see what would happen and it was uniform, even as I got closer to the creek. I only took measurements on one side of the creek for my initial data so I am interested to see the differences on the eastern side. I plan to use the same technique for my larger data collection, however, I will need to modify what I am sampling as discussed below.

Previously, I was counting all vegetation present and I did not take diameter breast height (DBH) measurements for large woody vegetation. To date, I have only used desktop review to analyze the gradient or metres above sea-level (MASL). For my larger field data collection, I will use the compass and elevation reader on my smartphone to collect this data at each replicate point. I think calculating DBH and aspect will be most important to my study. By understanding the amount of aspect this will potentially show any underlying processes in microclimates.

For the second part of this blog post, I have decided to comment on M. Myles recent post on their recent field observations, as our study designs are similar.

Blog Post 3: Ongoing Field Observations

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The organism(s) I plan to study for my field research project include waterfowl and their allied species. During my subsequent field visits, I have observed different species of waterfowl (e.g., wood duck, mallard) utilizing the smaller waterways of the park where emergent vegetation is present.  Underlying processes that may cause this pattern include the use of the emergent vegetation by waterfowl for foraging purposes and for protection from predators.

Although there are multiple environmental gradients within the park, I have only observed waterfowl within specific aquatic habitats, inclusive of the drainage ditches and one pond/marsh area. This could be due to habitat preferences of individual species, life stage, foraging potential and presence of predators. Waterfowl may occur within the old field habitat adjacent to the drainage ditches and marsh area, however, abundance and height of grasses within the park at this time of year greatly reduce visibility. As such, I will only be using the visible waterways as potential study areas.

Based on these observations, my initial hypothesis is that waterfowl prefer to use aquatic habitats where emergent vegetation cover is present.  I predict that relative abundance of waterfowl will increase where emergent vegetation is present and decrease in areas where emergent vegetation is absent. A potential explanatory variable is percent cover of emergent vegetation (continuous). A potential response variable is waterfowl abundance (categorical).

Field Notes Blog Post 3

Blog Post 4: Sampling Strategies

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Three sampling methods were used in gathering data from the Snyder-Middleswarth Natural Area in the virtual forest tutorial: systematic sampling, random sampling, and haphazard sampling.

The systematic sampling method had the fastest estimated sampling time of 12 hours and 34 minutes. In contrast, the random sampling method had an estimated sampling time of 12 hours and 44 minutes. The haphazard sampling method had the longest estimated sampling time of 12 hours and 59 minutes.

The percentage error of the two most common and two rarest species for each sampling method are summarised below:

As observed from Table 1.4, on average, when the percentage errors of the two most common with the two rarest species in each sampling method are compared, it can be observed that accuracy increases with greater species abundance.

Furthermore, the systematic sampling method was more accurate on average (21.1%) than the random sampling method (30.78%) or the haphazard method (37.1%).

This result was surprising, having previously assumed that a method employing more randomisation in sample selection would have a smaller percentage error in its data collection. Perhaps because the species density distribution did not vary considerably along the y-coordinate plane, the samples collected using the systematic sampling method were (on average) more representative of actual data than the random sampling method (which had selected areas at random to sample, regardless of the evident topographic gradient).

It would be interesting to observe whether the relative accuracies of the sampling strategies would change if a much larger sample size was tested. Additionally, if would be interesting to observe whether a stratified random sampling method would have greater accuracy than the systematic sampling method for the Snyder-Middleswarth Natural Area.

 

Blog 4: Sampling Strategies

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The virtual forest tutorial had three different strategies to collect data which are systematic sampling, random sampling, and haphazard sampling. The strategy that had the quickest estimated time was systematic sampling with a time of 12 hours and 37 minutes. Not far behind though was random sampling at 12 hours and 45 minutes, and in last with the slowest time of 12 hours and 58 minutes was haphazard sampling.

The 2 most common tree species were the Sweet Birch and the Eastern Hemlock, and the systematic sampling technique proved to be the most accurate for them. Random sampling also did okay and so did haphazard, but that was only for the Sweet Birch, whereas for the Eastern Hemlock the error was much higher.

Systematic 

E. Hemlock – 17.4% error,  S. Birch- 15.7% error

Random

E. Hemlock- 21% error, S. Birch- 26.2% error

Haphazard

E. Hemlock- 47.4% error, S. Birch 22.3% error

When it came to the two least common species, White Pine and Striped Maple, the technique that seemed to be most accurate overall was random sampling, with an exception being 15.1% error using the haphazard method for Striped Maple.

Systematic

W. Pine- 131.7% error, S. Maple- 129.9%

Random

W. Pine- 49% error, S. Maple- 17.4%

Haphazard 

W. Pine- 175.8% error, S. Maple- 15.1%

I found that was the abundance of the species decreased, the percentage error increased.

Blog Post 4: Sampling Strategies (Percy)

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The technique that had the fastest estimated sampling time:

Random/systematic sampling of area (12 hours, 33 minutes) as opposed to 12 hours, 36 minutes and 12 hours, 40 minutes for the other sampling techniques.

Percent error:

Most common species include the Eastern Hemlock; Random/Systematic 14.25%, Haphazard 9.52%

Sweet Birch; Random/Systematic 24.68%, Haphazard 20.83%

Most rare species include the Striped Maple; As these were predicted to not be present in these samples, the percent error is negative.

White Pine; (above). The accuracy of this data corresponds to the amount of species within the given area, as the more species, the more accurate the results. The sampling strategy that seemed most appropriate for this experiment would be Systematic sampling of a given area as it was much more accurate and time-efficient.

Blog Post 2: Sources of Scientific Information

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The source of ecological information I have chosen for this blog entry is a journal article titled:

“Assessing the effect of emergent vegetation in a surface-flow constructed wetland on eutrophication reversion and biodiversity enhancement”

This journal article is an example of academic, peer-reviewed research material for the following reasons:

Academic Source:

  • Written by experts in the field

  • Includes in-text citations

  • Contains a bibliography

Peer-reviewed Material:

  • Includes a revision submission date and acceptance date

  • Credit is given to anonymous reviewers in the Acknowledgements section of the article.

Research Article:

  • Methods and Results sections within the article indicate that original research was conducted.

Blog post 9

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The design of my field research was relatively simple, which led to some paranoia about missing a key concept because of the simplicity.  However, given the intended use of the data and the relatively small sample site, I am confident that the design was appropriate for the question.

One feature of the field research that turned out to be unnecessary was the detailed locational data that I collected.  For each sample collected, I noted the exact location where it was found along the transect and in the plot.  This information proved to be of little use later on.  Collecting this data was probably the most time consuming aspect of the data collection, so designing a similar study in the future that eliminates that component would be ideal.

I was also interested to see how much human error or bias can be introduced into study designs and I have read other studies with a critical eye to this point.  It is difficult to design a study which does not introduce some kind of bias, whether it is related to financial constraints, areas of interest, knowledge, or any number of other potential biases and errors that are lurking.  This illustrates the importance of having many studies of a topic showing an observed effect before we can say with much confidence that there is a genuine observation being made that reflects the hypothesis.  I can now appreciate just how difficult it is to design a study in a way that reduces error and bias.  And even when that is done well, it is still important to note that there are some biases and errors which cannot be completely eliminated in such complex systems with the various constraints to doing research with limited time and funding.

Blog post 8

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The table I created was relatively easy to compile.  The greatest challenge was in choosing what not to include.

I had collected location data on my sample subject of invertebrates, which I thought to include in a table or figure in some manner.  Including this data would have added unnecessary complexity to the table or figure and the reader would be unlikely to gain any meaningful insights from knowing that a species was recorded 3m down a transect and 6cm to the East.

In graphical form, the data makes a visual statement that species were most often found in a non-vegetated location.  I had expected species to be found most often in vegetation, so the visual representation is particularly stark to me. Also with the invertebrate abundance being more or less equal between sites, it is a visual representation which I find interesting.