Category: Post 5: Design Reflections

Blog Post 5: Design Reflections

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Initial data collection was successful. Point count locations were selected based on visibility within the drainage channels. Point count locations were also selected based on varying densities of emergent vegetation within the drainage channels in order to provide a representative sample. Point count duration at each point was 5 minutes. Emergent vegetation cover was visually estimated within the visible channel sections. Visibility was greater than 100 metres (m), however, I capped channel section observations to between 60 and 80 metres to increase both the accuracy of waterfowl identification and the overall precision of vegetation estimates.

Prior to my data collection visit on August 17th, I had decided to use a systematic sampling strategy where I overlaid the sitemap with a grid and used a random number generator to select sampling locations. This strategy was unsuccessful due to limited visibility at the randomly selected points. Randomly selected point count locations also did not take into account disturbance of birds within the channels. As the site is diked and exposure to human activity is common, it quickly became important to not only select vantage points that provided adequate visibility but also to select points where waterfowl would be less likely to be disrupted. This modification will improve the accuracy and reliability of my data.

The data collected was generally consistent with my hypothesis in that the total number of waterfowl observed increases with increasing emergent vegetation cover.  I did notice that the total number of waterfowl observed at all point count stations was fairly low. As a result, I will need to ensure that I incorporate additional point count stations and implement enough replicates in order to identify any relevant trends. Initial field data was collected during the hours just before sunset. I intend to collect data just after sunrise in order to determine when waterfowl activity is greatest.

Although the systematic sampling strategy did not pose any major difficulties, I need to consider modifying the strategy slightly to ensure that my replicates are independent of one another.  To ensure independence, I will select channel sections that are at least 100 m apart. This will also reduce the likelihood of counting waterfowl twice. Based on the layout of the survey area, any incoming individuals from nearby point count locations will be highly visible and will not be recorded.

I would also like to be more specific as to what constitutes “emergent vegetation”. Emergent vegetation, for the purposes of this study, will be restricted to yellow pond lily (Nuphar lutea), as it is the most dominant emergent aquatic species within the drainage channels and occurs at varying densities.  This modification will help me to generate a clear and specific research hypothesis and experimental design.

Blog 5: Design Reflections

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After collecting my initial field data, I noticed an issue with my methods. I counted the ducks 5 times in one day, at two hour intervals. I found that since ducks have the ability to walk and not stay stationary like plants, I had some trouble counting the ducks without error. I alleviated this problem for the most part by taking pictures of the areas and counting the ducks that way, but some could have been missed if they were in the bushes surrounding the water. I noticed that the ducks at the bridge preferred to be in the water around noon and 4pm but being in the shade or the sun didn’t seem to be a factor at these times even with the warmer water temperature, but at the other two study sites, there didn’t seem to be enough ducks to make a solid hypothesis, so I have decided that for my next data collection, I will count the ducks at 10am, 2pm, and 6pm to account for the changes in behaviour, and I will mainly focus my studies on the site of the moat with the bridge. I also believe that my initial hypothesis had too many variables, and the food source variable will be much harder to test for, so I decided to condense my hypothesis to just include water quality. In doing so, it made my study much easier to conduct. Overall, I do believe my systematic sampling method to work and though I found a few of my observations to be surprising at the time, looking back on them, they do make a lot of sense when I think about what I know about how ducks behave.

Design Reflections

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I conducted my research using a simple random sampling technique to sample how many bees were around different types of flowers. To limit my bias, I used a random number generator on my computer to come up with the number of steps to take (between 1-20 steps) and the compass bearings (between 0-360). I used a .5 meter squared quadrate to analyze how many bees were surrounding the flower of interest. I also used a point count sampling technique to observe how many bees were either on the flowers or an inch away from them for a time period of five minutes. I located 6 flower samples using the random sampling technique and measured the amount of bees surrounding them three times.

I had a couple difficulties implementing my sampling strategy. Some of the coordinates from the random number generator lead me to areas with no flowers. For example, the 3 steps and 213 degree compass bearing led me to the middle of the playground. Another difficulty that interfered with my data collection was disruption from children. Some kids playing around the park would approach the flowers I was observing which may have scarred the bees away. The data I collected did not surprise me in any way because it matched my hypothesis. Overall, I believe that my sampling technique works really well for my research. The one modification I will make to improve my research is to make my observations earlier in the morning to limit the disruption from children.

Blog post 5: Design Reflection

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While collecting my initial data I realized my hypothesis was not as specific as I wanted it to be. I had too many variables that would not be easy to measure within my question. I decided I was going to alter the wording of my hypothesis to make it more straight forward; ultimately so I could make my variables easily measurable. Instead of considering all the animal activity, I decided to focus specifically on the bird activity, and instead of how human activity affect the animals, which I found was a hard variable to measure, I chose to use how sheltered areas effected the bird’s activity. Since both of these new variables are measurable and more consistent, it will be a better way for me to get more reliable data.

Once I changed up my hypothesis and my response and predictor variables it was a lot easier for me to then set up how I would be going about this experiment. I decided since I was going to be working with mobile organisms that the point count station was the best sampling strategy to use. However, I did have a slight difficulty when it came to deciding if I should visit the same point count station at different times throughout 5 different days or if I should go to 5 different point count stations all within the same day. After a little trial and error, I decided it would be best if I combined the two options. I went through out 5 days and I had chosen 5 different point count stations. This way I could keep it consistent. I did go at approximately the same time every day so that the temperature would be roughly the same and I did go only in the morning, since bird activity is usually higher early on in the day.

Since I was so focused on consistency, I thought it would be a good idea to have my point stations spread out and this way I could incorporate some variety that would ultimately give me extra notes and potential observations.

After I had the strategy all figured out, I thought more about what I would be expecting. I predicted that with the more shelter and trees/shrubs around the more area for nests, perching and protection for the birds, therefore, more bird activity. Once I reviewed my data I found that this surprisingly wasn’t necessarily true. From what I had recorded, birds did in fact like the shelter but I found them most active at areas where there was shelter and open space (lawn area). I’m wondering if this has anything to do with the bird feeders nearby…  Only more time and conducted studies will tell.

I think this was an efficient way to collect data. The point count stations worked very well and I do think I had enough variety within my station, since they were spread out but still were ultimately random. One thing I’m still unsure of is if I want to continue taking into account the bird calls. I like the idea of recording bird calls because I know there may be more birds in the area than are in sight, but I also don’t want to be over counting or even double counting the same bird call.

Blog Post 5 for Katarina Duke

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The only real challenge that presented itself was finding a viable location within each creek to perform discharge measurements. The device used for obtaining the discharge measurements requires a fairly cobble-free creek bed, no obstructions to flow within the creek (i.e. logs), and a visually uniform flow (i.e. avoid switchbacks). This proved to be challenge for Durieu Creek and Legace Creek as they experience a great deal of sediment deposition. Durieu Creek also follows a steep terrain with many switchbacks causing many sections of the creek to be unsuitable for obtaining measurements. Selection of the creeks for the study was chosen systematically based on their hydraulic connection with the Miracle Valley aquifer. Locations for sampling along the creeks were conducted using random sample selection. I intend to collect data using the same technique as it is the method defined in the Manual of British Columbia Hydrometric Standards and Environment Canada’s “A Field Guide to Streamflow Measurement by Gauging and Metering.” I will also continue to use the SonoTek Flow Tracker 2 as it is widely used by water resource professionals and provides quality control warning messages to assist with data accuracy. The location of measurements will not be changed as a base point is required to determine changes in water depth. Also differences in creek bed composition (i.e. amount of cobbles) can affect flow measurements. By using the same locations and equipment the influence of confounding variables can be controlled.

 

 

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Blog Post 3: Ongoing Field Observations

Posted on July 4, 2018 by 

Blog Post 5: Design Reflections

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From observations gathered from the Alfred Howe Greenway, Port Moody, BC, in Blog Post 1 and Blog Post 3, when walking along from the south end of the trail (elevation: 118 m) to the north end of the trail (elevation: 50m), there appeared to be a change in pine tree density.

A stratified random sampling strategy was used to measure pine tree density along three points of an elevation gradient using the nearest individual method to select each sampling unit. Along each elevation category (A. 120-110 m, B. 90-80 m, C. 60-50 m) five sampling units were selected by generating a random compass bearing and number of paces using an app. From the randomly generated location, the distance from the nearest pine tree to its neighbour was measured.

Although using the nearest individual method to select the sampling unit was more efficient than formulating a coordinate grid overlaying a map of the area in order to select a specific sample quadrant, a few difficulties were encountered in implementing and interpreting data collected using the nearest individual method, stratified random sampling strategy. When interpreting the results of this particular sampling strategy, the pine tree density of a particular elevation category is measured as the average distance from one pine tree to the next, which would (1) record the upper limit of the pine tree density for that particular area, as there would be a disregard for spaces where there is a significant lack of pine trees. This would particularly effect data in areas where it was observed that pine trees were found in “patches” rather than having a more uniform distribution. Furthermore, when implementing the sampling strategy, due to the steep elevation gradient, (2) difficulties were encountered to generate a random bearing that would generate a sampling location within the desired elevation category. If the same technique is used for a future data collection, perhaps the range for the number of paces should be decreased.

The data collected (although having a sample size of less than 10 measurements for each elevation category) represented a linear decrease in the average pine tree density along the elevation gradient. Although this result supports an initial hypothesis of pine tree density decreasing along the elevation gradient, the difficulties encountered (mentioned above) in interpreting the data brings the accuracy of the result to question (ex. perhaps there might be an exponential decline).

It would be favourable to continue to build future research of the Alfred Howe Greenway around pine tree data collection, as they seem to be found along the entire trail, in contrast to other plant species that only appear at one point of the trail or are seasonal. Perhaps a modified approach of a point-centered quarter method will be used for measuring pine tree species density in order to attain more accurate pine tree density measurements. By using the point-centered method, the distance of each pine tree (with the average interpreted as the pine tree density for a particular elevation category) would be recorded as a measurement of the nearest individual from each quarter to the centre point of the quadrant. As a result, recordings of solely the upper density limits will be avoided.

For the second portion of this blog post I commented on kmcara’s Blog Post 3: Ongoing Observations .

EDIT: From researching for archives about the Alfred Howe Greenway on the Port Moody, BC, Government website, it was found that the north point of the trail encompasses an area formerly used as a landfill site from the 1950’s to 1982, in addition to being used for green waste up until 2002. (City of Port Moody, 2018; Payne, 2015)

This offers a clear explanation for the significantly contrasting abundance of pine trees (and overall species diversity) between the north point (formerly a landfill) and south point (historically forested area) of the trail.

As a result of this newly found information I will perhaps shift my area of study to observe any current impact the former landfill has on the surrounding ecosystem.

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.

Post 5: Design Reflections

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For my project, I am sampling from three different sites that contain varying amounts of water, with the sites being a bird and wildlife sanctuary, a meadow and park, and a residential condo complex. I want to determine the insect abundance and species composition between the sites, and also explore the hypothesis that the amount of available water in a given area determines the density of individuals and the abundance of species. Insects were caught on sticky traps consisting of a thick paper card and double sided tape and then placed in individual plastic bags. Using a magnifying glass, the number of identifiable species were counted, and the total number of individuals was counted. Five sticky traps were placed in each location, at a variety of heights so as to account for both flying and crawling insects. The only trouble I had with implementing my sampling method was the worry, as all three locations were regularly visited by humans, that others would remove the cards. So far, this situation has not occurred. The data I collected was surprising only in that I thought more insects, in general, would be present on the traps. When collecting data again, I decided to use more heavy duty traps with stickier adhesive in order to account for insects that are stronger and able to escape the trap. I also decided to increase the time that the traps were left out from 24 hours to 72 hours (~ 3 days).

Design Reflections (#5)

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The acquisition of the initial data in module 3 was relatively uncomplicated, though I think I could improve the way I collect the data in a few mays. First by refining the categories; variables such as percentage of exposed soil and potentially relevant attributes of other species present were not recorded, and the method I used to categorize the moisture regime was imprecise. I may make changes to my tables to better isolate the relevant variables, and I will likely modify my technique to collect my data to allow for better numbers and accuracy.

The technique I used was to overlay a map of my chosen area with a grid of squares, each measuring 20×20, and designating each square as either disturbed or undisturbed. I selected one of each in close proximity and I divided each of these squares into a grid of 400 1x1m squares from which 3 were randomly selected in the disturbed square and 2 in the undisturbed square. These 5 plots were my samples, and I recorded the light exposure, moisture regime, and the other plant species present as my predictor variables, and the number and the average height of rose bushes present as my response variables. Because undisturbed squares outnumber disturbed squares, by selecting an equal number of each to collect an equal number of samples from, I hope to reduce the number of samples necessary to see relevant trends in the data.
Changes I will make to this technique will include the number of samples I record and the way I select them. I will still use randomization, but instead of recording observations from 2 and 3 randomly selected replicates per square, I will likely record 2 transects of 20 plots per 20x20m square, providing more data and an equal number of observations from disturbed and undisturbed areas.