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Post 9: Field Research

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My field research project is finally complete. Implementation of the experiment mostly went well although I did have some trouble in some areas due to the terrain. Running a transect through a blackberry thicket was somewhat of a challenge. After a few cuts and scrapes, and more then a fair share of curse words, I was able to get the data I set out to get but it wasn’t easy. As far as changes from my initial design there were only a few which involved narrowing my study area from my probably too ambitious initial plans. My transects became smaller due to some slopes I could not climb, and I had to constrict the overall study area because of brush that was thicker then the blackberry which gave me trouble. Better preliminary research could have avoided these problems and even could have changed the whole nature of my experiment, which turned out to prove my hypothesis wrong.

I have to admit that going into this course I understood many of the concepts of ecology but not the processes in which it’s studied, so engaging in this field study has been a great asset and has changed my understanding of the subjects involved. This course has definitely led to a greater appreciation of ecology as a science, especially since much of its study can’t be done in a lab and involves being out in the field, getting dirty, pushing through blackberry thickets!

Blog Post 5 – Design Reflections

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I did have some difficulty with implementing my sampling strategy in the field. I had previously selected several locations on a map of the area found from google maps that when I was onsite, I would measure and 2×2 foot square and assess the area for mosses as well as record the temperature. On site, I used my cell phone with the google maps app to find the locations I had previously selected. Even though I had visited the site before, I did not have extensive knowledge of the terrain and soon realized that some of the locations I had selected were on private property or very difficult to get to in order to survey them. For the data I did collect, I took pictures of the specimen with my cell phone – that automatically geotagged them – and used iNaturalist to identify them before completely filling in my field data sheet. I was also overwhelmed by the number of species present that I was not comfortable identifying, so I think that I should choose a few types to collect data on instead of trying to document everything I see. I definitely need to modify my approach since I did not get nearly enough data points to make any inferences due to the poorly selected locations on inaccessible terrain, and my lack of confidence identifying species. This data was collected a few months ago now, and due to the recent health environment I have left the city I had started the study in, and have decided that I will be continuing and using data collected by observers on iNaturalist. This way, I will still be able to collect species and location data without being present in Victoria, B.C.I will also be able to use exact coordinates of the locations I surveyed which may be helpful when it comes to displaying the data later on in the process of this project. 

Blog Post 4 – Sampling Strategies

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For this blog post, I used an online community sampling exercise to sample Mohn Mill. I used three techniques, systematic sampling, random sampling, and haphazard sampling. The most efficient sampling technique was random sampling, taking approximately 11 hours and 51 minutes in comparison to the other techniques taking over 12 hours. The two most common species were the Red Maple and the White Oak, and the two rarest species were the White Ash and Yellow Birch. The percentages are listed below for comparison. The accuracy of the tests varied widely between the common and rare species, the common species having errors as low as 1.33%, and the rare species having errors as high as 1037.5%, the accuracy declining significantly with the rare species. In general, random sampling method had the lowest percent error for both common and rare species, excluding the White Ash. The most accurate of the common species was the random sampling of the Red Maple, with percent error of 1.33%. The most accurate of the rare species was significantly worse, from all sample methods of the Yellow Birch with a percent error of 100% across the board. I think 24 sample points is enough to capture the number of species in this density, but it would not hurt to have more data to further confirm conclusions made. I think that 24 sample points is not enough to accurately estimate the abundance of these species, as the percent error for the rare species was astronomical in comparison to that of the common species and more data is needed to capture more accurate numbers for the rare species. 

RM random- 8%

ROM syst – 1.33%

RM hap – 7.12%

 

WO ran – 34.33%

WO syst – 46.44%

WO hap – 39.87%

YB ran – 100%

YB syst 

TB hap

WA ran 100%

WA syst 1037.5%

WA hap – 937.5%

Blog Post 3 – Ongoing Field Observations

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As of February, 2020

 

For my field research project, I have decided to study plants from the phylum Bryophyta

 

While visiting Mount Tolmie, I definitely noticed the amount of rocky faces as well as the incline, which is steep at times. While hiking this incline, I noticed that the types of plants seemed to change with elevation, forming a transitional zone. I noticed especially that there was a great variety in the types of moss present in the area, and seemed to change with respect to the elevation on the mountain. With the help of iNaturalist.org, I identified the following species from the phylum bryophyta that I observed on Mt. Tolmie. 

 

  1. Broom Moss  (Dicranum scoparium)
  2. Wooly Fringe-moss (Racomitrium lanuginosum)
  3. Cat’s Tail Moss (Isothecium stoloniferum)
  4. Hedwigia ciliata 
  5. Orthotrichum lyellii 

 

I hypothesize that on Mt. Tolmie, the density and diversity of bryophytes will be affected by an increase in elevation. I predict that the density and diversity of bryophytes on Mt. Tolmie will decrease as the elevation increases, moving along the gradient. I think that this change in elevation will cause more exposure to the elements, in a more hostile environment I think there will be a decrease in temperature, increased wind speed/exposure, decreased humidity and decreased soil nutrients associated with this increase in elevation. So, I predict that there will be a decrease in the number of moss plants seen and the diversity of the moss plants as I ascend the mountain. I predict that there will be an abundance of mosses near the base of the mountain, and the top will have very sparse populations. This hypothesis will be evaluated by the effect of the elevation (predictor value) on the abundance of mosses in each quadrat I study (response variable). I plan to gather several sets of data on these two variables along the gradient, which I expect to present a trend in abundance with elevation. Because the response and predictor variables are both continuous, I will use a regression study for my experiment. 

Post 5: Design Reflections

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I had a lot of trouble implementing my initial sampling strategy. Originally, I intended to systematically observe forb density and richness (0.5m by 0.5m quadrats) along transects extending down the slope from the uplands, through the riparian zone, and ending at the South Saskatchewan River. However, the varying steepness of the study site would not allow me to descend straight down the hill in many locations. This forced me to navigate to the subsequent plots from various angles and paths (in the interest of preserving the integrity of the transects). Knowing the amount of time that this would take when scaling my replications up to statistically valid levels, I opted to change my sampling method to a haphazard one while out in the field. I would select an appropriate location (which was, obviously, subjective) and lay the quadrat down before examining the forbs too closely at that location (in an attempt to mitigate some of my bias).

Something that I found surprising in my data was how a close examination of the forbs at each quadrat revealed how low in abundance they could be. I found myself, often times, looking at shrubs and saplings (of which I am excluding from my study). When this occurred: I would choose a location to sample based on it containing high abundance of broad-leaf foliage, begin examining the species, learn that they were mostly shrubs (such as Alemanchier alnifolia, or Rosa acicularis), then have to move on to the next quadrat without having any data related to my study of forb density. Having this preliminary data is useful because it does indicate that, when moving forward with my formal data collection for the study, I will need to ensure I have a high level of replications in order to capture the forb diversity in the area.

I do not intend on continuing the sampling strategies I implemented for module 3. I am planning on moving towards a simple random approach to laying down quadrats throughout the region. While transects are a good approach to this site (in theory), I believe that they are too difficult to implement in the study area. In addition, I would like to ensure that I am controlling my own bias and ensuring that the statistical analyses I would like to use are not compromised. Therefore, I, having had some more time to think about it, will be randomizing the coordinates for my replication locations. I acknowledge that randomizing individual quadrats will have the same navigational challenges as transects. However, I also believe that generating coordinates to unnavigable quadrats, needing to discard those points, and generate new coordinates is more favorable than breaking the integrity of systematic placements of quadrats in a transect (or severely restricting the locations that I can chose to generate unbroken transects).

Post 8: Tables and Graphs

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Making a table with my data was pretty straight forward. I added a totals section to my fieldwork tables and counted the number of species present in each quadrant. Transferring this information to a table was easy enough from there. After analyzing the data, made easier by line graphs of each individual transect, it was revealed to me that the pattern I was looking for was almost non existent. There were a couple of transects I sampled that seemed to support my initial observations and consequently my hypothesis that distance from the creek affects species diversity (After reading the textbook I realized I actually meant “species richness”). The majority of the transects sampled however had no discernible pattern between them. This goes to show the importance of sample size and repetition in scientific method. As far as further exploration, I would still be interested in what in what effect the creek has on species richness, but in order to find this out I would need to rethink the whole experiment and start from scratch. Perhaps requiring some comparison of near the creek vs not near the creek samples, and somehow controlling for other factors like slope, disturbance, sunlight, etc.

Blog Post 7: Theoretical Perspectives

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My hypothesis touches on the effect of a stream on species richness primarily, but could actually be more related to slope or competition for resources, which may also play a factor. At this point, it appears my hypothesis is wrong however, and I believe that species richness along the stream has more to do with patch dynamics and competition for other resources other water, which I originally thought would have a stronger impact. My research will be focused on how competition for resources creates known patch dynamics along a stream, or similar waterway, in an attempt to understand the less then uniform patterns I’m observing.

Keywords might be Patch Dynamics, Species Richness, Stream-side vegetation

Blog Post 6: Data Collection

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Today I began my data collection activities for my project at along D’Herbomez Creek in Heritage Park. I’ve sampled 6 out of 10 transects, each with 10-15 quadrants. In this I’ve come across  a few challenges. For one, I say 10-15 quadrants because while I intended to sample 15 quadrants per transect, some steep slopes have prevented this from occurring based on my sampling model. I’m continuing with 15 where possible but the final analysis may be of 10 to eliminate the incomplete samples from the data set. I’m finding so far that 10 should be enough to disprove my hypothesis regardless. Another challenge I didn’t foresee and perhaps should have, is the thickness of the brush in places. My initial observations saw lots of good sampling areas, but my method of randomization has sent me straight through some thickets of blackberry and other shrubs. I’ve managed but it’s definitely not the same as sampling an open field.  As far as my hypothesis, it seems to have already been disproven based on the patterns (or lack of) that I am seeing thus far. The patterns I initially observed visually, and to a lesser degree experimentally in a previous activity, don’t seem to be holding up when other, randomly chosen sites are selected. This is somewhat disappointing, but even a false hypothesis adds to our understanding.

Post 4: Sampling Strategies

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In the virtual forest tutorial, a systematic sampling method, a simple randomized sampling method, and a haphazard sampling method were used to determine the frequency of seven tree species. The systematic sampling method involved randomly selecting a point along the southern margin of the study area and running a transect, straight north, through the five topographical regions (Southern Ridge Top, North Facing Slope, Bottomland, South-Facing Slope, and Northern Ridge Top). Samples were then taken from 24 quadrats (alternating between the eastern side and western side of the transect) until the northern margin was reached. The simple randomized sampling method involved generating 24 random locations to collect data. Finally, my haphazard method of sample collection involved attempting to space the quadrats in such a way that they maximized the distance between each other and the edge of the study area.

Based on the estimated sampling times, the haphazard method proved to be the fastest method (12:17 hrs) of sampling, and the simple random sampling method ended up being the slowest (12:45 hrs). However, I think it is worth noting that the systematic sampling method was, anecdotally, the fastest to conduct in the simulation and it seems logical that it should be considerably faster that either of the other two methods. This is because it covered much less walking distance than the random and haphazard method.

The two most common species in the study area were eastern hemlock and red maple. For eastern hemlock, the haphazard sampling method yielded a 6.9% error, the systematic sampling method yielded a 13.2% error, and the random sampling method yielded a 26.4% error. For red maple, the haphazard sampling method yielded a 17.0% error, the systematic sampling method yielded a 5.1% error, and the random sampling method yielded a 5.9% error.   In both cases, the systematic method was more accurate than the random method, and the haphazard varied from being the best and the worst method.

The two most rare species in the study area were striped maple and white pine. For striped maple, the haphazard sampling method yielded a 100% error, the systematic sampling method yielded a 31.4% error, and the random sampling method yielded a 42% error. For white pine, the haphazard sampling method yielded a 98% error, the systematic sampling method yielded a 185% error, and the random sampling method yielded a 49% error.   The systematic sampling method was most accurate for the striped maple; however, the random method wasn’t far off. In the case of the white pine, the systematic sampling method was extremely inaccurate and the random method was the most accurate. The haphazard method was extremely inaccurate in both cases.

Overall, the haphazard method out-performed the other methods for four out of the seven species. However, it was extremely inaccurate with determining the frequency of rare species and red maple. The inconsistent percent error values of the haphazard method lead me to believe that this method has value; however, it is a risky sampling strategy. I believe that the success from my haphazard approach is likely derived from traits that it took from a stratified method. By choosing points that were relatively far away from each other, I, incidentally, chose a similar amount of points in each region (Southern Ridge Top, North Facing Slope, Bottomland, South-Facing Slope, and Northern Ridge Top). Similarly, the systematic method performed well in most cases but had a lot of challenge with the rare species. Therefore, even though the random sampling method only outperformed both other methods in one case, it was the most consistent for determining the frequency of common and rare species.

Post 3: Ongoing Field Observations

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Organism Studied: Alnus rubra (red alder)

Environmental Gradient: The environmental gradient of the study area is the rise in elevation from the shoreline to the railway and the corresponding changes in soil type, drainage and exposure to flood disturbance. Alnus rubra appears to dominate the lower elevations while coniferous species dominate the higher elevations.

I have selected 4 sites along a 100 metre stretch of shoreline. Some parts of the shoreline are very steep and rocky, with limited vegetation. I consequently selected 4 sites that had a more gradual slope, and thus had sufficient vegetation to demonstrate a response to elevation, in regards to species type, abundance and maturity.

Site 1:
Roughly one quarter of the site is a low lying flat area, within 1 metre in vertical elevation from the waterline. The soil is soft, dense and deep, with a layer of leaf and stick detritus completely covering it. It appears that only species present here is alnus rubra, with many young plants covering the area as well as 5 mature trees over approximately 8 metres tall. There are two western red cedars and one western hemlock between 6 and 8 metres tall at the top of the slope, approximately 5 metres above the waterline.

Site 2:
Young alnus rubra plants are growing densely in the area below 2 metres in vertical elevation from the shoreline. The area has deep moist soil covered in leaf and stick matter. The slope rises steeply over large stone boulders. Above 2 metres in elevation, several mature western red cedars (6-12 metres) grow in loose sandy soil on the boulders. At around 5 metres in elevation, several Douglas firs and western red cedars (all over 5m tall), and some young western red cedars are present.

Site 3:
Site 3 rises and dips in several areas, and is mostly lower than 3 metres above the water level. The southern half of the site has a rocky surface with a thin layer of course soil that rises from the shoreline for 5 metres before sloping downwards to an area of thicker moist soils. The higher rocky ground has a several mature (5-15m tall) western red cedars and Douglas fir trees. The northern side of the site is lower lying, covered in grasses, mosses or dead leaf matter, with soft deep, moist soils. There are many smaller red alder plants and 5 mature red alder trees over 6 metres tall.

Site 4:
Most of site 4 is less than 1 metre above the lake water level. These areas have deep moist soils covered either by grasses or leaf detritus. There are many young alnus rubra growing in these low lying areas and 5 mature alnus rubra trees over 5 metres in height. At the top of the slope, approximately 5 metres above the water level are some young western red cedar trees.

In all four sites the low-lying areas appear to be dominated by alnus rubra. These areas are mostly occupied by mosses, grasses and young alnus rubra, and the soils are deep, spongy and moist. The low areas are generally flat, and the land only rises where there are rock formations, which suggests to me that these areas are flood plains that have come about from erosion of softer parts of the shoreline. Walking further north along the shoreline I observed a grass and young red alder covered area beside a creek that was now submerged due to the increased creek flow from spring snow melts. This helped support my idea that these areas are likely subject to flood inundation. The vast majority of the alnus rubra in the low lying areas are young plants less than 50cm tall, which could be related to the frequency of flood disturbances, and red alder possibly being a colonizing species. The rocky, more elevated areas seemed to be dominated by mature conifers. Their age indicates that the area may not have not been subject to a significant flood disturbance for a long time, and the fact that there are no young conifer species at lower elevations might suggest that alnus rubra colonizes these areas before conifers do following floods, they out compete conifers there, or they are more resistant to flood disturbances so conifers are less likely to survive a flood.

I hence made the hypothesis that:
Alnus rubra will be the dominant tree species in flood prone areas of the Nita Lake shoreline.

Formal prediction:
Alnus rubra will be the most common tree species in areas of the riparian zone less than 2 metres in vertical elevation from the current waterline.

Predictor variable: elevation (continuous)

Response variable: abundance of tree species, age/size of trees (continuous)