I completed my field data collection over the span of two days from 12:00-14:00 at Creekside Park. Throughout the past month, I have been visiting the site and thinking about my research project, notably the hypothesis and study design. My original hypothesis, “the abundance of bees increase around vibrant colourful flowers and decrease surrounding pale white flowers”,had some subjective terminology that needed clarification. The words ‘vibrant’, ’abundance’ and ‘pale had to either be removed or changed into an objective form. I switched ‘abundance’ with ‘number’, and removed ‘pale’ and ‘vibrant’. I ended up changing my hypothesis to “the number of bees increase around colourful flowers (purple, red, and yellow), and decrease around white flowers”.
I used a random sampling technique to look atbees around 10 flower samples in the park. I have not experienced many problems with the implementation of my study. The accuracy of my study has improved by implementing my sampling on weekdays, a time frame where their are less people to disturb the bees and flowers.
I quickly realized when visiting each of my sample spots on the Goldfinger Potentilla shrub that I did not account for my severe dislike of spiders when crawling in and around the bushes as well as my apparent lack of coordination when measuring the height of my quadrat and attempting to count. It took some practice but I managed to get it done.
I was surprised when I compared the three groups together. I expected there to be much more flowers in each sampling plot in group B then I did in either groups A or C. While overall there was an increase in the number of flowers in group B (which was not surprising) I still anticipated a greater over all difference.
To continue my project I think I will keep up with my initial sampling technique. I found it to be challenging at first but found that it helped me to eliminate any bias towards my three groups.
Overall, the process of this field project has been a learning experience to say the least. From choosing a project that was interesting yet hard to test (mayflies in Northern Ontario), to creating my own traps for my new project and them being taken away from pedestrians, I would say that I have learned more than I thought I would. The first change I had to make was changing the entire project itself, then I thought I would create my own minnow traps to save some money, realized those didn’t work (and were taken out of the water by pedestrians), and then finally getting the help I needed by meeting Joe the fisherman and getting tips and real minnow traps for the crayfish. Throughout this entire process, I have gone from being really excited to see what I caught to very upset and frustrated when realizing it wasn’t working. This process allowed me to really open my eyes to ecological studies and experiments as I have gained a true appreciation for the patience, concentration, and time that goes into a ecological field study. I have also realized that although there is evidence of a particular process occurring in an area, these processes can be influenced by the environmental conditions, time of the year, and so on.
Creating a graph for this particular experiment was challenging, as I was not able to collect as much data as I had thought. The average surface temperature of both Lake Nipissing and Trout Lake were compared with each other, as well as demonstrating the potential impact of the temperature on the number of crayfish caught. I had expected to catch at least 4-5 crayfish in total, however the results collected were much lower as I only caught 1 crayfish in the entire experiment. Although the organization of the graph was difficult to create, it is useful to see the effect of surface temperature at each trap location on the amount of crayfish and species caught. If I were to further explore this idea, I would use a more strategic sampling strategy, where all 5 traps are in prime locations to catch crayfish in both Trout Lake and Lake Nipissing.
To collect my field data I returned the following day, on August 6, 2018 to collect the number of species and number of crayfish I found in each trap. I was discouraged again to find that I had caught no crayfish, although, there were some other species in the trap that I recorded for accuracy as well. The only pattern that seems to be evident is that the temperature of the water, somewhat warm, seems to mean as of right now that crayfish do not enjoy this environment, however, it is very possible that they just didn’t come out that night. In order to eliminate the unknown, it was important to sample this location on 2 different occasions. Unfortunately, the replicates resulted in relatively the same data.
Lake Nipissing
To collect my field data I returned the following day, August 7, 2018 to collect the number of species and number of crayfish I found in each trap. In traps 1-4, there were no other species in the trap, nor were there any crayfish. However, in Trap 5, one that I placed a little closer to the rocks and vegetation, I caught my first crayfish! I was very excited as it looked like the species I was aiming to catch, Orconectes propinquus, as it is supposed to be very common in Northern Ontario freshwater lakes. I measured this crayfish to compare its length to the crayfish found in Trout Lake,. In regards to replicates, I also sampled this location on two different occasions. Unfortunately, no crayfish or any other species were caught. This information made me reflect on my hypothesis, as I was so sure I would catch at least 3 or 4 crayfish in Lake Nipissing as it is known for its shallow, warmer waters where crayfish thrive. Perhaps it was the way I sampled and the locations that I chose that determined the results? Although these thoughts were going through my mind, fisherman Joe confirmed he has used those traps before in this specific area and have caught many crayfish over the past few months, therefore it really could have been the temperature of the water, time of day, temperature of the air, or any other environmental factor that influenced the results.
The collection of the initial data was difficult to say the least. In the beginning, I had created my own traps after watching a few YouTube tutorials. To implement this sampling technique, I needed 5 minnow traps, each costing around $20-$25 each, which was not in my budget so I made them myself. After putting the traps in their locations, I returned the following day and found them sitting in the bush outside of the water. Trying to find a location where pedestrians would not have easy access to was probably the most difficult part of setting up the experiment, as it took 3 days and 2 tries of putting my homemade traps out to realize that they weren’t the best way to catch crayfish. After having them out the second time and returning to only 3 traps, I decided I had to modify my approach by speaking to a real fisherman and buying the minnow traps. Thankfully, Joe the fisherman was kind enough to lend me 5 traps and give me some pointers on where to put them. This time, instead of bread, I used dead minnows as bait for the crayfish and kept the traps in areas of the lake that were rich in vegetation, rocks, and mud. I strongly believed this would have a significant impact on my research.
To identify the specific locations of interest it was important for me to research to understand where it is likely for me to catch Orconectes propinquus in North Bay, Ontario. After trying to research online for answers, I thought it would be more useful to speak to someone who catches crayfish and other species on a daily basis. Joe is a fisherman who owns a bait shop in North Bay, just along Lake Nipissing. At this point, I had already created my own crayfish traps using plastic bottles, fishing line, rope, and a piece of bread. The previous day, I had put these traps out in 5 different locations along the shoreline of Trout Lake, recorded the temperature of the water, air, time of day, etc., and returned to check out what I had caught. Unfortunately, my homemade traps were taken out of the water by pedestrians and left in the bush. I emptied those traps, put a new piece of bread in, and tossed them back into the water for another night while I tried to figure out how I was going to catch the crayfish without spending too much money on minnow traps.
While those traps stayed in the water over night, Joe the fisherman was kind enough to lend me 5 minnow traps, some rope, and dead minnows for the crayfish to eat. He informed me that he usually catches them closer to the dock, where there is more vegetation and the crayfish can hide. Around 11:15am, 21 degrees Celcius outside, and an average surface temperature of water of 18.12 degrees Celcius, I put out the traps in the 5 random locations along the gradient (some vegetation, some rocks, sand/mud, etc.).
Below is a photo of all 5 traps in 5 randomly selected locations:
Trout Lake Trap 1
Trout Lake Trap 2
Trout Lake Trap 3
Trout Lake Traps 4&5
DAY 2: August 6, 2018
Today I went to collect my traps to count the number of crayfish, if any, and number of other species, if any, in the trap. If there were crayfish, I planned on measuring the length of the crayfish to examine if there was a difference in size between Trout Lake and Lake Nipissing, and use that as evidence to the type of feeding of crayfish in that particular environment.
**Note: 3 of 5 homemade traps floated away in the water, 2 of them collected no species. This information was disregarded in the analysis of my study.
Lake Nipissing
After collecting the data for Trout Lake, it was time to move the traps to the other lake in North Bay, Lake Nipissing. In efforts to keep the traps safe, I chose a location where a family member of mine could look after the traps. It was also important that I kept the traps in a relatively similar environment, for example, an area of vegetation, rocks, and mud/sand, as crayfish are more likely to be caught in those areas.
Below are photos of all 5 traps in 5 randomly selected locations:
Lake Nipissing Trap 1
Lake Nipissing Trap 2
Lake Nipissing Trap 3
Lake Nipissing Trap 4
Lake Nipissing Trap 5
Hypothesis:
I hypothesize that there will be a greater abundance of Orconectes propinquus crayfish in Lake Nipissing as opposed to Trout Lake. I believe that the surface temperature of the water has a direct affect in the abundance of crayfish in both lakes.
Response variable: number of Orconectes propinquus crayfish present
Explanatory variable: average surface area temperature of Lake Nipissing and Trout Lake
These variables would be considered continuous variables as they can take on any value between its minimum and maximum value
Sampling technique: Simple Random
Constructed imaginary baselines on the two maps of Lake Nipissing and Trout Lake
Blindly pointed at 10 locations, and then blindly chose 5 of those 10 spots to determine 5 sample locations in each lake
Some underlying processes that may cause the patterns I have observed may be that the average temperature of the spots in Lake Nipissing are generally warmer than the surface area temperature of Trout Lake. This may have an effect on the number of species, especially if the particular species prefers warmer temperatures.
My study design focuses on measuring leaf length of fallen leaves. While this is admittedly easier than climbing trees to collect leaves, fallen leaves bear little loyalty to their parent tree and could blow wherever the wind takes them. Additionally, there is a risk of sampling bias, as more dried leaves take on a dull red colour compared to some of the freshly fallen leaves that have a more vibrant colour. These leaves tend to stand out, which is great but there is some variation in leaf colour, even among freshly fallen leaves. This means those of a duller colour are more likely to be missed. In response to this, I think I will need to use a small quadrat to focus my leaf collection to be exhaustive within a fixed area. That way, all leaves are evaluated to determine if they are freshly fallen enough to be sampled. (Dried leaves are not used because they cannot sustain the manipulation required for measuring and their reduced weight makes them more likely to be dispersed nearby trees by wind from.)
It was a challenge to organize and summarize my data, but once I had figured out what the most important parts of my research were, it was easier for me to visualize them. My prediction was that there would be a greater abundance of large woody vegetation (e.g. trees) on the eastern, westerly facing side of Jack Creek.
Hypothesis: If the landscape has a higher elevation, and is westerly facing, then a greater abundance of large woody vegetation will be present. Prediction: A greater amount of large woody vegetation will be present in western facing slopes with a higher degree of aspect and elevation. Response Variable: Large woody vegetation abundance Explanatory (predictor) Variable: Elevation and aspect
During my field data collection, I took counts of both trees and shrubs to create comparison. I took an average of the vegetation count to the number of data points to create an accurate representation of the data. Below are two graphs representing the average number of species in response to elevation and aspect.
Figure. 1Average number of species is explained in response to changes in elevation. Species are divided into two groups 1. Trees (large woody vegetation) and, 2. Shrubs (small vegetation). Total is included to show linear response over the environmental gradient. In general, total number of species decreased with increases in elevation. The null hypothesis is rejected as there is a greater number of large woody vegetation with increased elevation, however, the greatest abundance of large woody vegetation occurs at an elevation of 540 MASL.
Figure 2.Average number of species is examined in response to varying degrees of aspect (North, North East, East, South East, South, South West, West, North West, and Flat). Species are divided into two groups 1. Trees (large woody vegetation) and, 2. Shrubs (small vegetation). Total is included to show linear response over the environmental gradient. A clear correlation exists between abundance of trees in westerly, south-westerly, and north-easterly facing slopes. This accurately reflects the natural landscape of Jack Creek, which flows north to south within a gully. The null hypothesis is rejected as there is a greater abundance of large woody vegetation on the westerly facing, high elevation slopes.
The outcome of my field studies was slightly different than I expected, however, it reveals that further exploration is necessary as to why there was a greater amount of vegetation in the flat meadows 15 metres on the western side of the creek. Other factors might influence the data, such as disturbance, opens fields, and amount of sunlight.
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.
