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Blog Post 7: Theoretical Perspectives

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My research project focuses on dabbling duck habitat selection within Colony Farm Regional Park. I predicted that dabbling ducks (specifically mallard and wood duck) found within the constructed drainage channels that border the existing dyke network would select channels with increased emergent vegetation cover. I tested this hypothesis by assessing waterfowl abundance within drainage channels that contained varying amounts of emergent vegetation cover.

My research primarily focuses on waterfowl habitat selection and whether emergent vegetation cover within aquatic habitats affects habitat selection by waterfowl. Relevant literature indicates that emergent vegetation cover provides several benefits to dabbling ducks, including habitat complexity and reduced predation risk. These aspects will also be discussed as part of my research project. Three keywords that I could use to describe my research project are emergent vegetation, habitat selection and dabbling ducks.

Blog Post 6: Data Collection

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I used the point count method to assess the abundance of waterfowl (dabbling ducks) within selected drainage channels within Colony Farm Regional Park. Eight point count locations were chosen along the existing dike network in the park based on channel visibility and varying cover of emergent vegetation (yellow pond lily, Nuphar sp.). Each point count was considered one replicate (sample unit). Selected locations (8) were visited on 6 separate occasions (sampling events), with 48 total replicates sampled. Percent cover of yellow pond-lily was visually estimated within each channel section, to a maximum distance of 80 metres. Channel sections were strategically selected on the basis that they varied in percent cover of yellow pond-lily (from 0% to approximately 75% cover) to provide a representative sample. Drainage channels chosen were fairly uniform in terms of their topography, hydrology, surrounding vegetation, and overall width.

I spent five minutes at each point count location observing dabbling duck species, specifically mallard (Anas platyrhynchos) and wood duck (Aix sponsa), within the selected channel sections.  Total number, species, and approximate life stage of observed waterfowl within the channels were recorded. Other information I recorded included the start and end time of each point count, weather conditions, date, start and end time of each sampling event, and other notes including the presence of other wildlife and/or predators in the area. All point counts were collected between the hours of 1800 – 2000 hours (prior to sunset at 2030 hours), as this was the time of day that waterfowl within the park were observed to be the most active. Randomization was incorporated into the study by using a random number generator (between numbers 1 to 8) to decide the order in which point count locations were visited during each sampling event.

One problem that I encountered while carrying out my sampling design was the low number of dabbling ducks observed within the channels for the duration of the data collection. To combat this, I visited the site more frequently (i.e. sampled more replicates) to ensure that I would have adequate data to analyze later on.  If I were to collect data in the future, I may choose to do so in the fall months when waterfowl occur in greater numbers. A second issue that arose had to do with the site conditions. Connectivity between channel sections led me to realize that not all replicates would be independent of one another. After reviewing relevant literature related to point count methodology, point count locations are suggested to be located greater than or equal to 250 metres apart. Based on the limitations of the study area, and in order to reduce variables between selected drainage channel sections, spacing of the point count locations at this minimum distance was simply not possible. Despite this, the layout of the study area allowed for ample visibility, which prevented double counting of birds and reduced bias among point count locations.

Initial summation of the total number of waterfowl within each channel has lead me to believe that my hypothesis may be rejected. The numbers did not indicate a strong preference of heavily vegetated channels by waterfowl, versus those channels with sparse emergent vegetation cover. This will be subject to further analysis.

Post 7: Theoretical Perspectives

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The hypothesis for my study is that the moisture level of soil greatly affects the vegetation of that area. Therefore, I predict that location 2 will have the lowest percent moisture in the soil, followed by location 1 and finally location 3. My hypothesis touches on a few ecological processes. Those being species richness, water cycling, and variation in vegetation.

To summarize my study in three keywords, I would include soil moisture, vegetation variation, and vegetation success.

Post 3: Ongoing Field Observations

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For the ongoing field observations, I intend to document the creosote bushes of the Sonoran scrublands. The creosote bushes are one of the most frequent scrubland vegetations in this community.

I picked three 10m x 10m plots, 50m, 20m and 5 meters from a man-made oasis in Papago Park.

Area 3 (50m from water source):

Creosote plants are about about 1.5m evenly spaced from each other. The plants have grown to a height of about 2.2m tall on average. There are 46 individual plants in the plot with a wide band of separation around the outside of the plot where no plants are found. Each creosote plant is a pale green in colour with moderate foliage.

Area 2 (20m from water source):

The Creosote plants are about 1.1m evenly spaced from each other. The plants have grown to a height of about 2.4m tall on average. There are 61 individual plants in the plot with no band of separation around the outside of the plot. Each creosote plant is a pale green in colour with moderate foliage.

Area 1 (5m from water source):

The creosote plants are about 0.6m but unevenly spaced from each other. The plants have grown to a height of about 2.6m tall on average. There are 12 individual plants in the plot with wide bands of separation around each grouping of individuals. Each creosote plant is dark green in colour with heavy foliage.

My hypothesis is that creosote plants compete heavily with each other for soil moisture. I predict that creosote plants are adept at monopolizing soil moisture in a given area around themselves but either do not compete well with other plants at high soil moistures or prefer the rocky soil with better drainage farther from the water source.

A response variable: Creosote productivity (amount of mass per plot)

An explanatory variable: Soil moisture

Both variables would be continuous as they are prone to measurement and change.

Post 2: Sources of Scientific Information

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The article Ecology and population genetics of Sonoran Desert
Drosophila was published in the journal Molecular Ecology is a peer-reviewed academic research article.

The authors are professional researchers from the University of Arizona in the Ecology and Evolutionary Biology department.  The authors include their methods and results of their study in a satisfactory manner and expand on the cited knowledge when necessary. They provide dates of submission and extensive references and citations. All of their donations and contributors are acknowledged and made public in the acknowledgements section of their paper.

Citation:

Pfeiler, E., Markow, T. A. (2001). Ecology and population genetics of Sonoran Desert Drosophila. Molecular Ecology. 10, 1787 – 1791. Retrieved from http://labs.biology.ucsd.edu/markow/articles/EcologyandPopulationGenetics.pdf

Post 1: Observations

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The area I have decided to to observe is the Sonoran desert scrublands around Scottsdale in Arizona, USA. The area is quite large comprising about a quarter of the state (~74 000sqkm) although I will be observing a conservative amount of the area at around a square kilometer. The topography is flat, sandy scrubland with occasional stand-alone mountains jutting out of the ground. The vegetation is comprised of mostly succulents and small, shrub like trees. The density of this plant life is quite sparse, with large amounts of dust and sand between any two individual plants forms.

As I am visiting the area in winter, the weather and temperature are quite temperate. The day of observation saw partially cloudy skies and a max temperature of 17 degrees celcius. I spent the largest amount of time observing Papago Park at about noon to 3pm.

I am most interested in seeing whether vegetation cover improves closer to the man-made waterways that go through the park, whether the mountains themselves affect vegetation (by providing shade or alternative rocky substrate) and whether or not the mountain caves provide shelter for different kinds of vegetation or animal life relative to the open Sonoran scrublands.

Elevated view of the open scrublands.

The type of mountains that appear in the scrublands.

Man-made waterways that supply water to the region.

Post 6: Data Collection

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My data involved collecting soil samples from each of my three locations. I randomly took 5 soil samples from within a 1m x 1m quadrant in each area. The quadrant was placed in an area which contained the main vegetation present, however, because in the main vegetation in location 1 was fir trees the quadrant was placed directly beside them. I took the soil samples from the top soil, meaning first I scraped off the top layer known as the humus and collected from the layer underneath. All my samples were placed in plastic bags to prevent moisture loss and I brought them to a lab at my University. There I placed them in individual foil cups, removing any rocks or other debris and pounding out the clumps. I then weighed them and placed them in an oven between 105-107 degrees celsius for 24 hours. Afterwards, I weighed the samples again and calculated the percent moisture of each.

I haven’t had any problems implementing my sampling design. However, I have noticed that there are far more factors than I initially considered that could also play a role in determining the vegetation present. This realization is not going to change how I go about my research, but I will take more consideration into this factor when I am writing my results.

Post 5: Design Reflection

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For the initial data collection I used the random sampling strategy in a 1m x 1m area. I chose this technique not only because I thought it would be the most efficient but also, for the soil samples I didn’t want any bias involved as to where they were taken from. The only difficulty I can across was samples in the exact areas where for example a sagebrush plant or fir tree was. In this case I just took the soil sample directly beside the plant or tree. I will continue to use this technique to collect my final data.

On going field observations

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The last few weeks I have made repeated trips to my study area. This time of the year most trees, shrubs and grass vegetation is dormant with all the snow coverage in the park. One of the few organisms that stays around during the winter months is the black capped chickadee (Poecile atricapillus). They are very abundant in the area I am studying and based on initial observations I inquired to myself where these birds like to spend most of their time in the winter months. The city park I chose has noticeable environmental variances: A large grass fielded area when not covered in snow, grouped coniferous trees, and a denser mixed vegetative with coniferous trees and deciduous trees and small shrubs. (Please see attached photos below.) My hypothesis is that Poecile atricapillusspecies spend more time in realtivly dense vegetation environments to protect them from the elements and from larger birds that prey on them. My response variable would be the Poecile atricapillus.This would be a continuous variable. The predictor variable would be the vegetation environment type and this would be continuous as well. The experiment design would be regression. This experiment could provide valuable feed back to  city planners when developing parks as to what kinds of vegetation lay out is beneficial to these small non migratory birds that live in the urban environment.

 

 

 

Post 4: Sampling Strategies

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The virtual forest tutorial allowed for the testing of three different sampling strategies: systematic sampling, random sampling and haphazard sampling. It was determined that the fastest sampling time was for systematic sampling at 12 hours, 34 minutes. Followed by haphazard sampling and random sampling, 12 hours and 37 minutes, and 12 hours and 47 minutes, respectively.

The two most common tree species were the Eastern Hemlock and Red Maple. The most accurate of the three techniques for the Eastern Hemlock was systematic sampling with a 1.3% percent error, and the most accurate for the Red Maple was Haphazard with a percent error of 5.4%. The least effective sampling strategy for Eastern Hemlock was Haphazard with a percent error of 45.4%, and the least effective for Red Maple was random sampling with a percent error of 68.4%. Additionally, systematic sampling had a percent error or 26.0% for Red Maple, and random sampling had a percent error or 7.3% for Eastern Hemlock.

The two least common tree species were the White Pine and the Striped Maple. Systematic sampling was the best technique for White Pine and the worst technique for Striped Maple, with percent errors of 4.8% and 174.3%, respectively. The next best sampling strategy for White pine was haphazard sampling with a percent error of 98.8%, followed by random sampling with a percent error of 147.6%. For Striped Maple, the best sampling strategy was random sampling with a percent error of 18.9%, followed by haphazard sampling with a percent error of 66.9%.

Overall, the accuracy of the sampling got worse with lower species abundance as shown with the difference with the two groups above, the most common and least common species. For example with a larger species abundance (the most common species group), the percent error did not go above 68.4%. However, with a smaller species abundance (the least common species group), the percent error went as high as 174.3%. Overall, there is not one sampling strategy that clearly stands out as the most accurate, however, Systematic sampling is the closest, it has the two lowest percent errors for Eastern Hemlock and White Pine. It is hard to tell which is more accurate between random sampling and haphazard sampling as their percent errors are all fairly similar.