Blog Post 5

Methods:

3 transects (plots) on each study site were established at a length of 9.5m starting from the city sidewalk and running toward the house. Each transect has a 0.5m spacing on either side of the centre. The first transect centre is placed 0.5m from the edge (driveway) of the area being studied. The following transects are placed 1m away from the initial centreline of the previous transect to prevent overlap.

The total area sampled in each plot is 28.5m. Transects are labelled from A to F.

A, B, and C are located in the diverse site. D, E, and F are located on the grass lawn. They are labelled in order from South to North, with transect A at the most Southerly position.

Species recordings are plotted at the measured distance from the sidewalk along with a measured distance away from the transect line to provide an x,y coordinate of where the species was located and in what conditions.
A positive y location indicates a position South of the line and a negative y location indicates a position North of the line.
A further recording of whether or not the species was located in an area with vegetation or ground cover is made with a Y/N indication and a sub-indicator code for logs, rocks, and others. A recording without a sub indicator means that it was found on vegetation.

Transect F

 

Transect A

 

Transect E close-up

Results:

Number of species located in diverse area (Transects A,B,C):

Transect A: 11

Transect B: 2

Transect C: 1

Composition:

Spiders: 4

Beetles: 3

Caterpillars: 7

Location type (Transects A,B,C):

Total species in/on vegetation/cover: 10

Total species in non-vegetated/cover location: 4

Total: 14

 

Number of species located in grass area (Transects D,E,F):

Transect D: 0

Transect E: 0

Transect F: 5

Composition:

Ants: 5

Location type (Transects D,E,F):

Total species in/on vegetation/cover: 0

Total species in non-vegetated/cover location: 5

Total: 5

 

Changes in methodology:

The method I used to collect the data has so far been effective. Being able to sample the entire site has the advantage of eliminating many assumptions that would be necessary if I were performing randomized or representative samples of just a portion of the site.

This sampling strategy only works well because the site size is relatively small and it is possible to sample the entire area in a reasonable amount of time.

The challenge is to ensure that the transect line is placed in an exact location each time. For this, I used a tape measure and a length of string attached to a railroad spike. When the spike is placed in the ground, it is possible to accurately measure locations for the centreline of the transect without movement.

I will continue to sample this site in this manner so that my data remains consistent.

Blog post 4

Blog Post 4

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

Systematic: Sampling along a topographic gradient 4 hours, 5 minutes (20 minutes faster than haphazard)

This is from a distance based sampling strategy.

Percent error

Systematic: Sampling along a topographic gradient

Percent error common species (Eastern Hemlock): 12.9%

Percent error rare species (Striped Maple): 46.9%

Random: Distance, random or systematic

Percent error common species (Eastern Hemlock): 13.6%

Percent error rare species (Striped Maple): 58.9%

Haphazard or subjective sampling:

Percent error common species (Eastern Hemlock): 6.5%

Percent error rare species (Striped Maple): 100%

 

The most accurate sample strategy for common species was Haphazard or subjective sampling.

The most accurate sample strategy for rare species was Systematic: Sampling along a topographic gradient.

The accuracy for rare species declined over the sampling methods used.

The accuracy rate changed in relation to species abundance.  The less abundant a species was correlated with a greater increase in error of collecting a sample for that species.

24 sample points was not enough points to capture the number of species in this community. In the Haphazard sample, the error rate of 100% would miss this species entirely. The sample strategy that most captured this species still had an error rate of 46.9%, which greatly under-represents this species.  Adding more sample locations would reduce the error rate.

Blog Post 3

Blog Post 3

My hypothesis is that when considered in a small scale residential garden setting; insects, isopods, and arthropods increase in diversity and abundance when there is a diversity of plants and ground cover (large woody debris/rocks).

I predict that I will find a diverse range of insects, isopods, and arthropods in the diverse garden setting that are not present or are greatly reduced in the relatively uniform grass lawn setting, despite that the grass lawn setting contains more cover overall.  I expect to find this diversity in and immediately around the plants and ground cover, but not on the bare earth in-between the plants and ground cover.

A potential response variable will be an increased abundance and diversity of insects, isopods, and arthropods in the diverse cover setting.  I think that this will happen because the range of habitat and food source options are greater in the diverse setting and that this can support greater abundance and diversity in insect, isopod, and arthropod populations.

I have chosen to sample the plants, and ground cover (large woody debris/rocks) in the more diverse setting as one collective group (diversity).  I will also sample the bare earth in-between plants and ground cover in this setting (lack of diversity) to determine if any recorded diversity is isolated to the plants and ground cover within this setting.  The final sample will be the grass lawn (lack of diversity).

The response variable is continuous because it is a count.  The predictor variable is categorical, which is the type of cover.

There has been a long history in ecological literature which suggests that things which provide habitat or food (plant and ground cover diversity) correlates with diversity of the things that need or consume them (insect, isopod, and arthropod populations) (Hutchinson, G. 1959).

It is possible that plant diversity alone is not sufficient to determine a link between increased plant habitat or resources and increased insect diversity.  A bottom-up approach or an examination of various trophic levels in the environment suggest that things like nutrient availability, or plant pathogens, among others, could have cascading effects that influence things like population density and diversity in insect populations (Hunter, M., & Price, P. 1992).  It is also possible that the size of the area is too small to provide a meaningful effect.

For the purposes of this study, I will focus on using a generalized definition of plant diversity and ground cover which does not include nutrient availability, the presence of pathogens, water availability, etc., but rather focuses on the quantity, characteristics, size, and distribution of the plants and structures.

Observations:

April 24, 2018

1:30 pm

Weather: Sunny, 19 degrees

I have observed two funnel web structures on two separate pieces of wood that simulate a fallen tree branch and rotting stump and one caterpillar on a sword fern plant (diversity).  I have not observed anything in the lawn setting, despite there being more overall cover (lack of diversity).  I have also not observed anything in the bare earth patches between the diverse plants/ground cover.

 

Uniform grass cover

 

Funnel webs

 

Uniform setting vs. diverse setting

 

Caterpillar on sword fern

 

Field note tally

 

References:

Hunter, M., & Price, P. (1992). Playing Chutes and Ladders: Heterogeneity and the Relative Roles of Bottom-Up and Top-Down Forces in Natural Communities. Ecology, 73(3), 724-732.

Hutchinson, G. (1959). Homage to Santa Rosalia or Why Are There So Many Kinds of Animals? The American Naturalist, 93(870), 145-159.

Blog 9: Field Research

I very much enjoyed coming up with an experiment and then conducting it by myself. It was very challenging to come up with an idea since when I first started this class there was very heaving snow fall occurring so it was hard to really explore somewhere to find something that was interesting for me to want to further investigate.

It was challenging to set up the experiment where I had to make some modifications when mapping out the experiment guide lines so that it fit the rule of 10 and also limiting down on what I was wanting to examine since at first my idea was too broad. However, once  I had a clear outline for my experiment I had no troubles collecting data and was not too difficult interpreting the data.

By doing this experiment I have learn how much dedication and time is need when conducting ecological experiments where even for my experiment more data is needed to be collected over a larger time period to determine exactly what weather condition effects moss richness and if it changes during different seasons of the years. Where this also shows just how interconnected everything is in the environment and how ecology theories are important in understand some of these relationships found in nature.

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I chose to use a graph to display the data that I have collected. I used a line graph with two lines, one for grazing the 10 grazing replicates and one for the 10 non-grazing replicates, to show the average amounts of damage in percentage to each of the replicate areas.I chose to display it as an percentage of the overall area because I think it is easier for readers to understand and interpret. I think that the line graph I selected clearly displays the difference in damage between the two treatments and the overall trend. I did not have a difficult time organizing my data as I collected it in an excel spreadsheet so that it was quick and easy to enter equations and form graphs and tables from it. The data stayed with my predicted trend more or less, but I would be interested to explore the affect that the number of geese present grazing has on the percent of area damaged. When I first visited the site sometime ago there were only 2-3 geese grazing, but on the day I collected the 20 replicates there were 5. I’d assume that the increase in geese presence would also increase the % of area damaged.

Blog Post 9: Field Research Reflections

I found the process of carrying out this research project to be quite interesting and educational.  My single biggest challenge was coming up with a topic as there are so many interesting things to study!  My next biggest challenge was taking the theory of an experimental design and actually implementing it in the field.  I ended up changing my overall design a couple of times.  For those not following my research project, I had looked at the differences in the frequency of occurrence and cover of species of moss on different slope positions of rock outcrops.

I had originally thought that I would use separate rock outcrops as the replicates, but I only found 4 outcrops in the area that had similar enough attributes to be compared as replicates, which did not meet the rule of 10.  I therefore ended up collecting 10 samples in each slope position from among the different rock outcrops to serve as the replicates.

I had also originally intended to use a transect along which to locate evenly spaced plots within each rock outcrop, but I found that this restricted my sample locations too much given the variation in the orientation of the slope positions and it did not enable me to collect enough samples in the narrower slope positions (particularly the crests).  I therefore switched to a strategic randomized selection in which I sampled an equal number of randomly selected plot locations within each slope position, in order to get a sufficient sample size from each slope position.

This process certainly gave me an appreciation of the complexity of implementing ecological experiments, particularly in a field setting, where it is sometimes difficult (e.g. costly or time-consuming), if not impossible (e.g. conditions have changed or species have moved on), to repeat any data collection.  It is also very difficult to control for external environmental variables, which can interact with one another and vary across even small distances.

 

 

Blog Post 6: Data Collection

I have made several changes to my data collection strategy since last time. While I am still using a haphazard sampling method with the help of Google maps, and point counts for estimating Black-billed magpie abundance in relation to human presence in Edmonton’s Hermitage park, I have moved to a dependent double-observer approach. Also, I have reduced the time frame spent at each point count to 20 minutes. There was an additional one-minute settling time given at each point, and five minutes were given to walk from one point to another. Additionally, I have chosen to assess human presence solely by recording the number of pedestrians observed during given time frame. Doing so resulted, I believe, in less bias than recording various predefined “human traces”.  Moreover, this time, I chose to record separately: 1. No. of birds seen within the first 5 minutes, 2. No. of birds seen flying over. Doing so will give the reader an idea of whether the presence of observers might have attracted more birds after the first five minutes. Birds seen flying over are generally noted separately in current similar literature, because they cannot be recorded in standard density calculations (Gregory et al. 2004).

To increase the accuracy of my results, I have increased the number of point counts from five to nine which I sampled on two different days (March 24th and March 28th 2018).

As I will explain further in my final report, my literature review as well as previous comments on my past assignments has led me to make those changes. My intention was to minimize bias, standardize my sampling as much as possible, and increase the use of randomization/replication in my sampling strategy. Using a dependent double-observer approach made it much easier to implement my sampling design. Not only did it reduce observer bias, less coordination was needed than it was using different observers for each point count. The only problem encountered was that most of the time, the walking time took a bit less than five minutes, so the real “settling time” was more than a full minute at most points. It was hard to keep track of the exact time spent at each location for that reason.

Gregory, R. D., D. W. Gibbons, and P. F. Donalds. 2004. Bird census and survey techniques. Pages 17–55 in R. E. Green, W. J. Sutherland, and I. Newton. Bird ecology and conservation: a handbook of techniques. Oxford University Press, New York, New York, USA.

Blog 8: Summary Table

To summarize my findings for the richness of healthy living moss found on the trunks of trees I had to use a number scale to help average out my finds to show any difference in the moss richness found on the three tree top coverage categories. Once it was decided that a tree with hardly no moss present would be given a value of zero, a tree with some healthy moss present was 0.5, and a tree with high moss richness was 1.0 it was easy to summarize the data collected.

As shown in table 1 the partial tree top exposer category showed the highest average of moss richness, this went against the prediction that moss richness would be highest at the shelter tree top group. Though it was shown that the exposed tree top group did have the lowest moss richness where it was about half the amount of the other two tree categories which supports the prediction that the exposed tree top group would have the lowest moss richness. As the data was being collected over the two weeks it was observed that the partial tree top exposer group had increased the moss richness on the tree trunk the most noticeably. Where it is thought the partial tree top exposer will have the fastest rate of increased moss richness on the tree trunks when entering the spring season in British Columbia when compared to the other two tree groups. However, more data collected over a longer time period during the late winter and spring season is needed to test this prediction.

Tree Top Exposer Total Average Moss Richness
Exposed 0.125
Partial 0.288
Sheltered 0.250
Table 1: Total average of  healthy living moss found on the tree trunks of the three tree top exposer categories. Each tree top category contained ten replicates where date was collected four times at each site over the spanned of two weeks. A number value of 0.0, 0.5, and 1.0 was used to stand for no moss richness, some moss richness, and high moss richness found on each of the tree trunks respectively.

Blog Post 7: Theoretical Perspectives

The main ecological process in which my hypothesis touches upon is Herbivory. Herbivory is the consumption of plant material by animals, and herbivores are animals that are adapted to eat plants. Herbivory is a type of predation-prey interaction, which in this the case the Canada Geese is the predator and the Kentucky Bluegrass is the prey. Many plants, specifically weeds, in the park area have evolved defenses like chemicals and thorns to protect themselves from the predators, so the Canada Geese must be careful to pick the less defensive plants, such as the Kentucky Bluegrass. Another process which the hypothesis relates to is the reproductive (and evolutionary) fitness of the Kentucky Bluegrass species in the park area. The hypothesis speaks to the level of damage inflicted by Canada Geese on Kentucky Bluegrass in the grazing area relative to the level of damage in a non-grazing area. This shows how the Canada Geese grazing habits are affecting the fitness of the Kentucky Bluegrass. I have predicted that the study will show that overall the grazing habits of Canada Geese will cause more damage to the grass species than if there was no grazing at all. This damage will decrease the amount of grass in the area and therefore reduce the reproductive fitness of the Kentucky Bluegrass species. Three keywords that I would use to describe my research project would be:

  1.  Canada Goose Grazing Habits
  2. Herbivory Interaction of Canada Goose and Kentucky Bluegrass
  3. Managing Negative Impacts of Canada Geese

Blog Post 6: Data Collection

I have made several revisions to my sampling in the field. While the overall strategy will stay the same, haphazard with subjective location selection, I will be decreasing the size of my quadrats from 100cm x 100cm to 30cm x 30cm.This reduction will allow for quicker observation of the damaged area and give an overall clearer picture. In addition to the reduction of my quadrats I will now be looking at the damage to the Kentucky Bluegrass species instead of the Ryegrass species. With the turn from winter to spring, Kentucky Bluegrass has shown itself to be the dominant species in the grassy area. Also, I will now be taking 10 replicate samples, 5 grazing and 5 non-grazing, in order to satisfy the rule of ten. In satisfying the rule of ten I hope to achieve a more accurate representation of the ecological processes that are occurring. There were no major difficulties in implementing the original sampling design, but I believe that these changes will allow for a more streamline process. There is one ancillary pattern that may have an effect on the original hypothesis. This pattern is the increasing presence of the number of geese in the grazing areas with increasingly warm weather. It appears that on warm and sunny days the number of geese in the grazing area of the park will increase by anywhere from 2 to 8 geese. While this does not change the fact that the geese are damaging the Kentucky Bluegrass in the area, it is worth observing the damage done on days where geese numbers are higher versus the damage done on days where geese numbers are at 2 or 3.

 

 Figure 1: Warm Weather Brings an Increased Number of Geese to Feed at McMaster University.