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.
Category: Uncategorized
Blog Post 6: Data Collection (Percy) (New)
Trout Lake
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.
Blog Post 5: Design Reflections (Percy) (New)
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.
Blog Post 3: Ongoing Field Observations (Percy) (New Project)
DAY 1: August 5, 2018
Trout Lake
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:
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:
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.
Blog Post 8 for Katarina Duke
Compiling the table was relatively easy; however, establishing a ranking system for algal growth significance required some thought. In the end, I chose to use a ranking from 0 to 5 with 5 indicating “very significant quantity of algal growth.” Summarizing the data within the table was easy as the SonoTek Flow Tracker 2 used takes the 20-25 discharge and depth measurements taken within the creek and provides an average depth and total discharge.
Deciding on a graph format to use was completed with trial and error. Initially I had completed a graph plotting both discharge and depth against algal growth significance. Instead, I chose to create two separate graphs (i.e. discharge vs. algal growth significance and depth vs. algal growth significance) to convey the data as algal growth significance showed a correlation with discharge but no relationship could be inferred between depth and algal significance. This surprised me as water temperature increases with shallower water and temperature increases encourage algal growth, thus making me presume that a relationship between algal growth and water depth could be established.
Many abiotic factors influence the growth of algal. It would be interesting to determine at what temperature does the influence of temperature on algal growth surpass the influence of discharge on algal growth (or vice versa). I would also be curious in exploring further if there is in fact no relationship between water depth and algal growth by conducting a controlled experiment where few abiotic factors are changing such as observing algal growth in a stagnant tank with only the water level changing.
The influence of discharge on algal growth was as expected; increase in discharge inhibits algal growth.
Not depicted within the graphs but displayed in the table is the relationship between turbulent or laminar flow and algal growth. As expected algal growth was observed in areas of laminar flow.
Blog Post 3: Ongoing Field Study – Heather Lean
For my field study, I have decided to focus on a group of Goldfinger Potentilla Shrubs that are located in a circle in the park behind my house. They are separated into three groups.
Group A is located 301º NW in the circle and has minimal sun exposure due to larger trees blocking the sun. It is noted that the shrubs have less dense foliage and the number of flowers is less compared to the other plants.
Group B is located 68º E in the circle and has full sun exposure from sunrise to afternoon. These plants have dense foliage and a greater number of flowers throughout.
Group C is located 190º S. They have full sun exposure throughout part of the afternoon and evening. The plants are noted to have a similar appearance to Group A. They have a decreased number of flowers and foliage.
Due to the positioning of groups A and C, it would appear they have greater competition for sunlight then group B due to larger trees blocking out the sun. The decrease of direct sunlight may be a consideration as to the variations between the groups.
My hypothesis is as follows:
Are Goldfinger Potentilla shrubs are more likely to produce more flowers when in direct sunlight and does the competition of other plant species around affect the number of flowers on the shrubs? I do think that the amount of sun plays a part in the number of flowers the shrubs produce. I also think that they are directly affected by the competition of the surrounding trees.
Blog Post 2: Sources of Scientific Information. By Heather Lean
The article I found was an online article about Fluctuating resources in plant communities.
My reasons for this being that the authors who wrote it are experts working in the field from credible research institutes. Even though the article is more about the theory they have developed, they still include their methods and results. They have also acknowledged the article was referee by several people making this an academic peer-reviewed article.
Davis, M. A., Grime, P., & Thompson, K. (n.d.). Fluctuating resources in plant communities: A general theory of invasibility. Journal of Ecology. Retrieved August 2, 2018, from https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1046/j.1365-2745.2000.00473.x.
Blog Post 1: Observations (NEW PROJECT)
The areas I have selected to observe are aquatic bodies of water in North Bay, Ontario. There are two large lakes in North Bay that are home to a variety of species, as well are known for different fish such as Bass, Muskee, crayfish, etc.
Site A: Lake Nipissing
- Surface area of 873.3 km2, 196m above sea level, average depth of 4.5 m (shallow), max depth 64m & max length 65 km
- 3rd largest lake in Ontario
- Located between Ottawa River and Georgian Bay
- Topography: flat land
- Vegetation: shallow water lake
- August 1, 2018, 2:00pm, 22 degrees Celcius, scattered showers (light rain)
There are many species that are abundant in Lake Nipissing, such as Bass and Muskee. What interests me is the abundance of a particular species in Lake Nipissing as opposed to Trout Lake (Site B). Lake Nipissing is near a sewage treatment plant; therefore the sewage is most likely dumped into this lake causing the temperature to be warmer. Lake Nipissing is also known to be very shallow, perhaps contributing to the temperature of the water as well.
Site B: Trou
t Lake
- Surface area of 348.1 km2, 11.27 km long, 202 m above sea level, 4km wide
- 6km east of Lake Nipissing
- Exists eastward into the Matter River, flowing via the Ottawa River to the St. Lawrence River
- Source of the Mattawa River
- Located on a well-known historic North American fur-trading route
- North Bay drinking water obtained from this lake
Trout Lake is known for its cooler, deeper waters. The species of fish are endless, as they haven’t found the bottom of this lake yet, which spikes interest to me on the differences between abundance of the same species within the two different lakes. The lake is very deep, perhaps contributing to the temperature of the water, as well as the types of species that live in the shallower areas closer to the shoreline.
I am interested in examining a species that may be important to the life of bigger fish within the two lakes, and am intrigued by the idea of the water temperature having an affect on the abundance of this species. Perhaps this is something I will examine for my field study.
- Does water temperature have an affect on the abundance of crayfish in either lake? What are the repercussions of this?
- Does water depth have an affect on the temperature of the water? If so, are crayfish more abundant in shallow or deep waters?
- Does water temperature have an affect on the species of crayfish present? If so, does this affect other species?
Blog Post 7 for Katarina Duke
Theoretical basis of my research project is to demonstrate how algae growth varies depending on the flow regime of freshwater creeks. The growth of algae can act as an indicator for water quality (i.e. water pollution) and as a predictor for the maintenance of water supply systems (i.e. intake pipes and filter lines). Algae can deplete the oxygen in water, release toxins, and lead to taste and odour issues. More turbulence leads to more oxygen absorbed by water, thus counteracting the oxygen depleted by algae. The creeks sampled in my research project have proven to be fish bearing through previously conducted fish presence studies and observation. Establishing the connection between algae growth and flow regimes within freshwater creeks will aid in maintaining a healthy ecosystem for fish and predict creeks potentially at risk for loss of fish.
Ecological processes that my hypothesis will touch on are the hydrologic cycle and nutrient cycling.
It is also important to acknowledge that temperature, seasonality, weather, and unknown anthropogenic activities can affect the growth rate of algae. Other studies have been completed focusing on the relationship between nutrient levels and algae growth as well as temperature and algae growth.
Keywords: Algae growth, flow regime, water depth, turbulent
Blog Post 4 for Katarina Duke
Three sampling methods were used in gathering data from the Mohn Mill community using the virtual forest tutorial: haphazard, random, and systematic.
An equal number of quadrats were sampled (i.e. 30 each) with the systematic sampling technique having the fastest sampling time but, the sampling time for all three methods remained within the range of 15 to 16 hours. The haphazard method had a sampling time of 15 hours and 57 minutes, and the random sampling method has a sample time of 15 hours and 49 minutes.
In all three sampling strategies, Red Maple and White Oak were determined to be the two most common species; However, the results for the two rarest species differed for each method (i.e. American Basswood, Sweet Birch, White Ash, and Hawthorn).
- Haphazard or convenience sampling
Using the area, haphazard sampling technique for the Mohn Mill community, American basswood and Hawthorn were the two rarest species as indicated by the actual importance value.
| · Hawthorn
Actual importance value: 0.6 Calculated importance value: 0.4
|
· Sweet birch
Actual importance value: 0.2 Calculated importance value: 0.7
|
- Random sampling
Using the area, random sampling technique for the Mohn Mill community, White ash and Hawthorn were the two rarest species as indicated by the actual importance value.
| · White Ash
Actual importance value: 0.2 Calculated importance value: 0.6
|
· Hawthorn
Actual importance value: 0.6 Calculated importance value: 0.6
|
- Systematic sampling
Using the area, systematic sampling technique for the Mohn Mill community, American basswood and Sweet birch were the two rarest species as indicated by the actual importance value.
| · American basswood:
Actual importance value: 0.2 Calculated importance value: 1.5
|
· Sweet birch:
Actual importance value: 0.2 Calculated importance value: 0.7
|
For all three sampling methods—haphazard, random, and systematic sampling—the accuracy improved with abundance.
Of the three methods, the random sampling method had the highest accuracy.
I found it interesting that the systematic method of sampling had skewed the density of the rare species to such a substantial extent, making a haphazard sampling approach appear to be a more desirable sampling method. I was also surprised to see haphazard having the degree of accuracy it did.
A reason for the lack of accuracy using the systematic sampling method could potentially be using a transect sampling method in conjunction with the systematic method. I selected the samples at regular distances along the transect, with the initial point randomly chosen. As stated in “Tutorial: Sampling techniques,” systematic sampling can produce problems if the points correspond to an underlying environmental pattern, which perhaps is the case for Mohn Mill community.
I am curious about the results stratified sampling and transects would obtain. For stratified sampling, the tree population would be split into somewhat homogenous groups (same species). I predict the accuracy for stratified sampling would be equivalent to, if not better than, the accuracy of random sampling and that the common species determined would match. I think stratified sampling would determine the rarest species to be Hawthorn and Sweet birch due to their occurrence in two of sampling methods used.
A method I am aware of that is commonly used in the forestry industry is the point-centered quarter method, where a point in the center of the forest is identified and then the area surrounding it is separated into four quarters. I am surprised this method was not within the tutorial given its common use in relation to trees. I’d be interested in seeing how the method compares to those used within the tutorial in terms of the rare species determined and accuracy.
