Category: Nancy Elliot

Blog Post #5 – Design Reflections

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This weekend I returned to my study sight to test out the data collection method I’d designed (outlined in Blog Post #3).  Saturday was a bust due to poor weather conditions, but Sunday afternoon looked a lot better.  I brought along the data collection tables I’d designed with the plan of collecting data on 3 individuals from each of my 4 species (Cormorant, Canada Goose, Franklin’s Gull and Mallard) for a total of 12 birds.  For each bird, I recorded their behavior at 15 second intervals for a total of 5 minutes, noting the location of each behavior along my gradient (Shore→ Shallows → open water).

Replicate: individual birds

Response variables: behaviors (categorical)

Predictor variables: species (categorical), time of day (categorical: AM/Midday/PM), point on gradient (categorical)

Panoramic view of the large pond

A few limitations and problems I noticed when I got to my site and started collecting data:

  • I hadn’t planned HOW I was going to select individuals to study in order to avoid bias.  Naturally, I was drawn to the most active birds who would be interesting to watch for 5 minute intervals.  I was also drawn to the birds closets to my location on the pond.
  • I realized that my lofty goal of trying to record the behaviours of multiple individuals from 4 different species over 3 different daily time periods might have been a bit over-enthusiastic for this project. The Franklin’s Gulls, for example, DO NOT HOLD STILL!  This species was frequently in flight, touching down for only brief periods.  The range of their flight paths made it impossible to ensure I was watching the same individual over the course of 5 minutes.
  • I realized that the pond is actually quite a bit bigger than I realized when I needed to identify a Mallard from other similar looking duck species from a distance.
  • My observations led me on a full loop around the pond, stopping to collect data when I saw birds of interest.  Again, this isn’t a very standardized procedure and could lead to bias when large groups catch my eye.
  • 3x 5 minutes of behaviour observation is not a very significant period of time over the course of a 24 hour day. Will  this be truly reflective of behaviour patterns?
  • The larger birds (Cormorants, Canada Goose) seemed to each have claimed specific territory around the pond.  There were no observation sites that allowed me to view both species at the same time.
Sample data collection table for the 4 species of birds observed

Reflecting on my trial run this weekend, I’ve come up with a few modifications to my research project:

  1. I plan to keep using the data tables I created as I found them easy to use and well laid out for the data I was collecting.
  2. I’m going to narrow my focus from 4 species to 1, the Mallard.  This species was found at many locations around the pond, and at all points along my gradient.  They were present in the highest numbers as well, giving me plenty of subjects to sample from.
  3. I’m going to use a randomized number generator (ie: 1-10)  to select my subjects: I’ll count to the random number, starting from left to right across the pond, and collect data on that individual. This should eliminate bias in choosing subjects.
  4. I’m going to select one observation point to work from, in order to prevent bias from wandering around looking for birds.
  5. Now that I’m going to be observing 1 species instead of 4, I will increase my number of subjects sampled each visit from 3 to 5, and increase my observation time for each individual from 5 minutes to 10 minutes. Doubling my observation time should provide slightly better behavior data.
  6. I’ve ordered a pair of binoculars off Amazon Prime, they’ll be here Wednesday!  This should help me identify Mallards from other similar looking ducks and allow me to record data across the pond from a fixed location.

 

It appears Team Canada Goose has also claimed this bench for themselves…

Based on these modifications, my hypothesis requires some adjustment as well.  I will keep the hypothesis that the water bird species studied will display increased levels of higher-energy activities (flight, feeding, etc) in dusk/dawn periods due to cooler temperatures, and increased display of lower energy activities (comfort, resting) mid-day when temperatures are higher.

Again, the null hypothesis would be that time of day has no effect on the time-activity budgets of water bird species.

Based on my research on Mallards thus far, I also suspect that typical behavior patterns will vary across my gradient, with resting/comfort behaviours being observed on land, feeding in the shallows, and locomotion/alert behavior taking place in open water. Mallards are considered “dabbling” ducks and feed by grazing on underwater plants indicating that I predict that I will see these behaviours most often in the portion of the gradient I have designated at “Shallows” (< 5 m from shore or visible plant matter appearing on/near the surface)

 

A view of the algae cover near the edges of the west side of the pond

Post 5

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I used the haphazard / area sampling strategy.  The only difficulty I had was finding enough replicates.  I had to return to the site and establish two more plots to ensure that I had 10 replicates for each environmental gradient. I choose the haphazard strategy because I felt if I used random or systematic plot location that I may not have found enough representative Cedar trees within a reasonable amount of plot locations.  My only concern with haphazard is the potential for introducing bias into the research.

I will continue to use the established plots but will begin collecting additional information specifically for each tree.  Every Cedar tree within my plots has been marked and numbered.

To better test my hypothesis and if had the time to set up a long-term research project I think I would design this experiment to be manipulative in a controlled environment.  I think a manipulative experiment could help determine the level of resistance with better accuracy.

Blog Post 8: Tables and Graphs

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I chose to create a graph to represent the data I collected. It was beneficial that we had created a graph/table for our hypothesis as I learned from that what information I needed to include and I also learned some important lessons about the type of graph to use based on my study design. I had done my hypothesis graph incorrectly so I needed to do some research to figure out the type of graph that I should use. My response variable (birds) is a categorical variable and my predictor variable (temperature) is a continuous variable. Therefore my study design is a logistic regression. Initially I had created a line chart. However, a line chart would suggest that the response variable was continuous. I did some research and in the end I decided that a point graph (called a scatter plot in some journals) was the best way to chart my observations and results. I also had trouble with the legend and knowing what information should be included there. I tried to create it so that if an individual read the chart without knowing any information about my research study they would be able to decipher the results.  I think a table would have given more information than I was able to include in the graph such as the times of day that the observations were collected or information on other variables such as cloud cover and wind. However, I chose to create a graph because the visual information included in a figure can often tell a story that words cannot. My results surprised me and suggest that my hypothesis is not true, or rather that the null hypothesis may be correct. I would be interested in doing further research on the subject of bird activity with weather and expand it beyond temperature alone. How much of an affect is cloud cover on bird activity or humidity? Also, do smaller birds have different tolerances to weather than larger birds? Lastly, is bird activity affected by a combination of variables that include temperature but also precipitation, wind, cloud cover, humidity and time of day?

Blog Post #3 – Ongoing Field Observations

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  1. Identify the organism or biological attribute that you plan to study.

After visiting my study site several times over the last week, I am eager to focus my research project on bird species in some way.  Over the last couple days, my ideas for study subjects have been wide ranging. Amongst some of my ideas:

  • attempting to sample the abundance of all individual bird species I encounter  at various points in the park (an unrealistic idea for someone with little to no working knowledge of bird identification, especially by song)
  • Measuring the abundance of bird species at different times of day, to see when activity is highest. (an appealing idea, but again with little knowledge of various bird species this study would likely have a large bias due to my own inaccuracies)
  • I then thought about grouping birds into groups (ie: songbirds, water birds, birds of prey) and sampling throughout the park at various times of day.  (Better, as I’m confident I can accurately tell the difference between these 3 groups, however the studies I looked at still put considerable emphasis on using bird songs to count species that have low visibility.)

 

I finally decided that water bird observations would be the direction I took as there are fewer species and each is fairly easy to accurately identify, even for an inexperienced bird watcher like myself.  They are also highly visible given their propensity for water and shore-based activities, so the need to identify based on song is eliminated. I will focus my observations on the 2 ponds in the park as I have not observed water birds outside of the immediate area of the ponds.

I knew I wanted to look at behavior patterns throughout the course of the day to see if I could discern any differences.  Once I thought more about bird behavior, I realized I would need to find a way to quantify these activities in a way that I could then interpret as data.  My admittedly limited experience in ecology prior to this course led me on a clumsy search through the library resources where I eventually stumbled upon the term “Time-activity budgets”.  This describes perfectly what I was hoping to sample and I’ve found several good papers describing techniques that would be feasible for the scope of this project.

I finally settled on measuring the time-activity budgets of 4 common waterfowl species at 3 different times of day (dawn, midday, dusk)

      • Mallard
      • Canada Goose
      • Franklins Gull
      • Spotted Sandpiper

Note: This species list is still subject to change as I had not taken note of specific species abundance of waterfowl during my previous visits. I plan to use the 4 most common species present in the park and will finalize my list during a trial data collection period this weekend!

  1. Use your field journal to document observations of your organism or biological attribute along an environmental gradient. Choose at least three locations along the gradient and observe and record any changes in the distribution, abundance, or character of your object of study.
    • I’ve noticed that some species (ie: the Gulls) spend a lot of time on the shore while others (ie: Mallards and Canadian Geese) are often found swimming in the open water. Therefore, the gradient I am using in my observations:  Shoreline (land) → shallows (estimated < 5 m from shore or visible foliage above waterline) →  open water
    • Sample Times: Dawn/midday/Dusk
    • Using the “Rule of 10” suggestion from the tutorials, I plan to collect data at my site on 10 different days (10 replicates).
      • 10 days x 3 times of day = 30 total sample periods
    • I plan to sample 3 individuals from each species at each visit
      • 4 bird species x 3 individuals/species = 12 individual birds/period x 30 sample periods = 360 individual birds analyzed.
    • I will be recording bird activities in a categorical nature (ie: Feeding, Resting, Comfort care, Locomotion, etc) every 15 seconds for 5 minutes, for each subject analyzed.
      • 5 minutes/bird, recording behavior every 15 seconds = 20 data points/bird
      • Each sample period: record data for 1-3 members (depending on abundance, goal=3) of each target species = 4-12 birds x 5 minutes each = 20-60 minutes= 80-240 data points/sample period
      • 10 days of sampling at 3 periods/day = 30 total sample periods = 2400-7200 data points collected
  2. Think about the underlying processes that may cause any patterns that you have observed. Postulate one hypothesis and make one formal prediction based on that hypothesis. Your hypothesis may include the environmental gradient; however, if you come up with a hypothesis that you want to pursue within one part of the gradient or one site, that is acceptable as well.

I predict that the water bird species studied will display increased levels of higher-energy activities (flight, feeding, etc) in dusk/dawn periods due to cooler temperatures, and increased display of lower energy activities (comfort, resting) mid-day when temperatures are higher.

The null hypothesis would be that time of day has no effect on the time-activity budgets of water bird species.

In studying 4 different species, I also hope to detect differences in activity patterns between them. I predict that the larger species (ie: Canada Geese) will spend more time feeding than their smaller comparators due to the increased energy demands required by larger organisms.

The null hypothesis here would be that the size of bird has no effect on behavior patterns throughout the day.

 

  1. Based on your hypothesis and prediction, list one potential response variable and one potential explanatory variable and whether they would be categorical or continuous. Use the experimental design tutorial to help you with this.
    • Response variable: behavior patterns (categorical)
    • Explanatory variable: time of day (categorical)

Based on the tutorial on experimental design, my study would be classified as a tabular design.

 

 

Sample data collection table that I have designed and will test this weekend:

L=land S=Shallows O= Open water

 

Blog Post 1 – Observations

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The area I have chosen to observe is the Chinook Rotary Nature Park in Calgary, Alberta. Once a gravel pit, this 40-acre park in the southeast corner of the city was is now part of Fish Creek Provincial Park and contains engineered wetlands that help filter run-off water from surrounding areas before it enters the Bow River.

Chinook Rotary Nature Park (Google Maps)

The park is located along the eastern bank of the Bow River.  It is flanked to the east by a steep incline, with a residential neighborhood at the top. The north end of the park runs alongside Highway 22x, while the southern park land continues as natural land beyond park boundaries. The land within the park consists of a combination of flat fields and small rolling hills.

Small pond with green algae visible

The focal point of the park are the 2 man-made ponds, a small one at the north end of the park and a larger one to the south. The ponds are connected by a narrow straight, however their waters are separated from each other by a small dam-like structure.    There is a walking path around both ponds, and a small bridge across the connecting point of the two water bodies. The majority of land within the park is covered by long grasses with patches of small shrubs.  There are deciduous trees, in both small groups and individually, found intermittently around the park as well.

I first visited this site today, June 2.  The weather was approximately 22 C, the sun was out and there was minimal cloud cover. The air had a smokey haze due to wild fires north of Edmonton.  I noted that there wasn’t heavy human presence in the park, with only 2 small groups seen during my observations.

Three questions that came to mind while I was in the park:

  1. I noted that the smaller pond had more green algae on its surface compared to the larger pond. I wondered if this was because the small pond was closer to the highway, and would therefore get more run-off from the roads in the winter, potentially changing the water quality in some way? Alternatively, I thought perhaps this pond water was more stagnant than the bigger pond, which could be favorable for this type of growth.
  2. I noted a lot more birds, both in density and number of different species, present in the larger pond. What is it about the larger pond that is more desirable to these birds?
  3. I noticed small minnow-sized fish in the deeper waters of the large pond, as viewed from the bridge between the 2 ponds. This being a man-made wetland, I wondered if these were human-introduced species (ie: stocked fish) vs natural fish (perhaps from the nearby Bow River that made their way upstream during high water levels or flooding). Since the larger pond was where all the birds were congregating, I wondered if the smaller pond had fish as well, or could this be a reason they were all drawn to the large pond?

Post 4

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Tree Species Actual Density Distance Systematic Data Distance Random Data Distance Haphazard Data
Eastern Hemlock

(Most Common) 

 

469.9

  

308.6

 % Error

 

34.3

  

395.7

 % Error

 

15.8

  

427.4

 % Error

 

9.0
Red Maple 118.9 105.8  

11.0

 45.7  

61.6

 63.7  

46.4
Sweet Birch 117.5 167.5  

42.5

 60.9  

48.1

 127.3  

8.3
Yellow Birch 108.9 114.6  

5.2

 159.8  

46.7

 127.3  

16.9
Chestnut Oak 87.5 141.1  

61.3

 68.5  

21.7

 90.9  

3.9
Striped Maple 17.5 8.8  

49.7

 0.0  

100

 27.3  

56.0
White Pine

(Most Rare)

8.4 0.0  

100

 0.0  

100

 9.1  

8.3
Survey Time 4 hours, 5 min 4 hours, 44 min 4 hours, 11 min

All three sampling strategies appear to have little difference in time.  However, the Random and Haphazard sampling strategies should take longer overall if travel time between plots is considered.

The most accurate sampling strategy for the most common and most rare species was found using the Haphazard sampling strategy and the sampling error for both were similar.

The sampling error greatly increased from the most common to the rarest species in both the random and systematic sampling strategies.  However, no real pattern was observed.

The number of plots was sufficient in capturing the number of species in the community but to improve the accuracy of the data more plots should be added.

Blog Post 7: Theoretical Perspectives

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My project deals with the effect temperature has on bird activity. This type of research is important to the field of ecology because with the threat of global warming, the effect this has on bird species will be extremely important to understand. As average temperatures across the globe increase, bird habitat including breeding grounds, food and resource availability will be affected. Some birds such as woodpeckers are keystone species and therefore threats to their environment could wreak havoc on other dependent species including some birds such as wrens and sparrows who use the holes created by woodpeckers as nesting habitat. If temperature does not have an effect on bird activity, such as foraging, it may suggest that certain species of birds are better able to adapt to changes in climate. By understanding the effect that increases in global temperature have on some species of birds, ecologists and other scientists will be better equipped to predict the snowball effect that climate change will bring universally.

Blog Post #4 – Sampling Strategies

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For the Virtual Forests tutorial, I chose to use the area-based methods for my 3 samples. The fastest technique for sampling was the systematic technique along a topographic gradient with a time for 12 hours and 36 minutes.  What surprised me about the results was that the random and haphazard techniques, each taking 13 hours and 14 minutes, did not take much more time than the systematic approach.

The two most common species I found in my samples were the Eastern Hemlock and Sweet Birch.

Systematic Random Haphazard
Actual Density Measured Density Percentage error (%) Measured Density Percentage error (%) Measured Density Percentage error (%)
Eastern Hemlock 469.9 388.0 17.4 304.0 35.3 436.0 7.2
Sweet Birch 117.5 72.0 38.7 96.0 18.3 112.0 4.7

Analysis of the data collected for the 2 most common species indicates that the haphazard method of sampling was the most accurate strategy, with both common species having percentage errors in the single digits.

 

Systematic Random Haphazard
Actual Density Measured Density Percentage error (%) Measured Density Percentage error (%) Measured Density Percentage error (%)
Striped Maple 17.5 28.0 60 16.0 8.6 12.0 31.4
White Pine 8.4 0.0 100 0.0 100 4.0 52.4

Analysis of data collected for the 2 most rare species shows that all 3 sampling methods provided very inaccurate results. The second rarest species, the Striped Maple, was well sampled in the random method with a percentage error of only 8.6%. However, the rarest species, the White Pine was not found at all using this method. As species abundance decreased, percentage error of sampling using all 3 methods decreased.

 

Overall, 24 plots does not appear to be enough to get an accurate representation of species density across the range of species in the geographical area. I would predict that increasing the number of plots would increase the accuracy of all 3 sampling techniques.

To test this theory, I repeated both the haphazard and systematic techniques using 50 plots instead of 24. I found the same number of species (7) as before, however the haphazard method now yielded percentage errors of 0.02% for the most common species (Eastern Hemlock) and 19% for the rarest species (White pine). The systematic method now yielded a percentage error of 3% for the most common, and 19% for the rarest species.  I conclude, based on this observation, that more sampling plots, regardless of method, yield more accurate results.

Blog Post 2: Sources of Scientific Information

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I chose to review an article entitled “Spatial and Temporal Variation of Coyote (Canis latrans) Diet in Calgary, Alberta”, published in the journal Cities and the Environment.  I found this article online via Google Scholar. I was drawn to it in part because authors used Calgary’s Fish Creek Provincial Park in their research, a location I have chosen to observe for my research project.  I was also interested in the topic because I frequently see coyotes in the grasslands near my property and must keep in mind the safety of my pets when walking in the area.

This paper can be classified as peer-reviewed academic research material. Details of this statement can be broken down as follows:

  1. Peer-reviewed: Authors thank the three anonymous reviewers who provided feedback on their manuscript.
  2. Academic: Both authors are affiliated with the University of Calgary. An online search of their names confirms that both are accomplished researchers in the field of ecology.  In addition, the paper contains both in-text citations and a bibliography.  I noted that there seems to be some blank lines in their bibliography, however I’m not sure if this is an omission by authors or simply a formatting problem when downloading the paper.
  3. Research material: Authors are reporting on results of a field study. They include comprehensive information in the “Methods” and “Results” section that would enable readers to replicate their research if desired.

The article can be found at https://digitalcommons.lmu.edu/cate/vol4/iss1/8/

Citation: Lukasik, Victoria M. and Alexander, Shelley M. (2012) “Spatial and Temporal Variation of Coyote (Canis latrans) Diet in Calgary, Alberta,” Cities and the Environment (CATE): Vol. 4: Iss. 1, Article 8.

Blog Post 6: Data Collection

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I have started collecting data for my research project on the effects of temperature on bird activity. So far I have collected 4 replicates. I am using a point count method to count the number of birds in my sampling location while noting the temperature during each observation. I am using a bird feeder to count the number of birds at the feeder during each 10 minute observation. Some initial difficulties I had were the competition the birds had with the bird feeder with other species, specifically with squirrels. Within 24 hours of hanging the bird feeder, the squirrels had eaten nearly the entire feeder so I had to start again and buy a squirrel proof bird feeder. Another issue I have had is that it is tricky getting temperatures within my range. (I hypothesized that bird activity is highest between 10-15 celcius). Of all the replicates I have done, they have all been above my temperature range. I’m worried I won’t have enough ranges of temperature to fully test my hypothesis and perhaps I should have hypothesized a higher temperature range. In New Jersey, where I live the average low in May is 12 celcius and 17 celcius in June. Therefore, it is unlikely that I will get temperature ranges below my hypothesis of 10-15 degrees and it may even be tricky to get temperature ranges within my hypothesis.

I have a feeling that other variables are going to affect the results of the study too. In particular, wind and cloud cover seem to also affect the abundance of birds in my observation area. At this point I don’t know what variable seems to have the most effect on bird abundance: temperature, wind, cloud cover or precipitation. My hypothesis deals specifically with temperature but I have a feeling that cloud cover plays a significant role. While I am not testing these other variables, I have been taking note of them as I believe they may have an effect on the overall results.