After the previous outing I decided to no longer spend any time sampling the back 15 acres of my property. This is because it is a heavily dense forest, therefore the crown cover is much to dense and cannot support the growing of Giant Hogweed as it is shade intolerant. That was the difficulty I found in my previous outings, too much time spent in the higher elevation and dense forest plots that would throw the results off of the findings. It is evident that they grow in disturbed sites and with lots of sun.
In the bottom portion of the property (disturbed site) I continued to use the random sampling method, as well as built a 1 m2 quadrant . Using a larger quadrant is appropriate for this study as the plants are much larger, so having a larger sampling plot will give a better representation of the 15 acre disturbed site. At each site I take 3 sample plots 5m apart in a random direction. This allows for more area being sampled.
This field research project has been very enlightening, both because I conducted a field research project from scratch and learned first hand the results and observations of my study of the effect of ambient temperature on bird activity, but also because it was the first field research project I ever completed and I learned along the way many errors that I had made that could have potentially affected the study. First of all, when I had first made my observations in my garden about bird activity in warm weather I didn’t take into account other variables such as migration timing. I began my observations in April when bird migration was beginning but conducted my field research in May and June when some bird species may have been fully settled. I wish that when I first noticed a potential pattern that I had done more research into what previous studies have shown as this would have helped me to properly form a hypothesis. My initial hypothesis was incorrectly formulated, mostly because I was inexperienced.
Next, I had some trouble during my field research as I had a hard time identifying some of the bird species. Woodpeckers, blue jays and cardinals were easy to identify but I often fumbled with my identification of smaller species such as sparrows, wrens and nuthatches, all of which are common in New Jersey.
Other variables affected my research, predominantly competition for food with other species. Although the bird feeder I used for point count research was squirrel proof, within a few days the squirrels did learn how to use it and it appeared that certain species of birds would not approach the feeder when squirrels were present. However, while this may have affected the number of birds that were counted and thus may affect the results regarding bird abundance and ambient temperature, competition for food sources is a real threat for bird species and needs to also be evaluated.
In hindsight, I feel as though my field research project would have benefited by having conducted thorough research at the beginning which would have aided me in my hypothesis. Secondly, finding an alternative way to identify species would have helped. Perhaps if I had first spent time becoming more accurate in this regard it would have helped. Lastly, if I had been able to better control competition with the squirrels, it would not have had a potential affect on the results.
In my study, I seek to compare the daily behaviours of dabbling ducks in an engineered urban wetland to similar species in natural environments. Urban wetland areas (both natural and constructed) provide important benefits by filtering pollutants and pathogens from wastewater, mitigating flood risk, absorb carbon from the atmosphere and providing aesthetically pleasing recreational areas. While beneficial to human-kind, these areas are also incredibly biodiverse, especially compared to surrounding urbanized areas. Urban wetland areas are often targeted for development in cities looking to squeeze what they can from the available land, putting diverse ecosystems at risk of destruction.
The ecological processes my research focuses on would be behavioural ecology and resource ecology. While the scope of my study obviously isn’t comprehensive enough for a “real” ecological study, I hope that by comparing the behaviours of ducks living in urban wetland environments to those in more natural settings I can provide an argument for the protection of existing wetlands, and perhaps for increased development of new wetland areas in urban settings through my hypothesis that the ducks I study will exhibit similar behaviour patterns to their comparators in natural environments. Significant differences in behaviour between populations may produce additional hypotheses for future investigation into the construction/design techniques employed.
Using the virtual forest, I sampled the Snyder-Middleswarth Natural area first systematically, then randomly, then haphazardly. Each of the different sampling strategies had a sample size of 24. The most efficient strategy was area-based systematic sampling, which was estimated to take 12 hours, 6 minutes to sample. Area-based random and haphazard sampling would take 12 hours, 48 minutes and 13 hours, 9 minutes respectively. Percentage error was calculated using the estimated and true values of species density.
Area-based systematic sampling:
Eastern Hemlock: 21.5%
Sweet Birch: 27.7%
Striped Maple: 100%
White Pine: 100%
Area-based random sampling:
Eastern Hemlock: 39.2%
Sweet Birch: 14.9%
Striped Maple: 100%
White Pine: 142.9%
Area-based haphazard sampling:
Eastern Hemlock: 13.2%
Sweet Birch: 19.2%
Striped Maple: 0.09%
White Pine: 100%
Based on these percentage errors, the most accurate sampling method for the Eastern Hemlock was haphazard sampling with only 13.2% error, and random sampling for Sweet Birch with 14.9% error. These are the two most common species of trees in the Snyder-Middleswarth Natural area. The two most rare species, Striped Maple and White Pine, were most accurately sampled using haphazard and systematic/haphazard sampling respectively. Haphazard sampling of Striped Maple resulted in only a 0.09% error and both systematic and haphazard sampling resulted in 100% error as there were no White Pines recorded. In random sampling, White Pines were over-represented and had an error of 142.9%.
Accuracy increased with abundance of a species, as seen by the significantly lower percentage errors in the more common species vs. the rare species. Although each strategy used 24 samples to gather data, White Pine was undetected in both systematic and haphazard sampling, suggesting that the total number of samples was insufficient to truly capture the number of species in the community and their abundance. Of the three strategies, haphazard sampling seemed to most accurately estimate the abundance of each species in the area, as the percentage errors for the common species were relatively low and Striped Maple (rare species) was present.
So far I’ve collected behavior data on 18 birds, with a goal of 150 (50 in the morning period, 50 midday, and 50 in the evening). I’ve run into a few problems along the way and have made small adjustments to my sampling design as a result.
Initially, my study was going to focus on Mallard ducks only. I’ve noticed however, that at times I cannot find a single mallard on the pond. The Mallard is just one of several species of “dabbling duck” present in the pond so I’ve expanded my data collection to all dabbling duck species. Thus far I’ve found Blue Winged Teal and Gadwall, in addition to Mallards.
It’s June in Alberta – Nothing makes you remember how quickly the weather can change for the worse around here than sitting on the far side of a pond without shelter! I’ve had 2 data collection periods cut short by sudden bad weather so I’ve started taking advantage of “good” collection periods (ie: good weather AND a good number of ducks on the pond) by taking extra readings when the opportunity arises. I still plan to collect the same number of replicates (150), will still keep my time frame to the same 3 hour windows previously stated, and will still strive to collect data on unique individuals at each visit, however I won’t be strictly sticking to the “10 day: 3 collection periods: 5 birds per period” structure I’d previously designed.
I’ve noticed some interesting patterns in my observations.
The ducks I observe are frequently in pairs, usually a male and female, but occasionally 2 females.
In the evening collection period, I’ve been having a hard time finding ANY mallards to observe. I wonder if they live elsewhere and have returned “home” for the night?
The PM observation period is associated with a lower number of all birds, including species that I am not studying.
I don’t have enough data yet to draw any comparisons to my hypothesis yet, but I look forward to seeing if my ducks exhibit similar behavior patterns to ducks in natural wetland habitats.
I used the area random sampling method. I built a 0.25m2 quadrat to determine bud density and measured the width of base branch growth for each replicate to gain an understanding of the effects of crowding on white spruces.The main difficulty that I encountered in my collection of data was in Location 1, the trees are so close together it is hard to walk between them. It was very hard to find my tagged replicates and carry my quadrat, measuring tape and field journal while fighting through the dense branches. Sometimes the outer branches of trees overlapped those beside them, making it harder for me to distinguish which buds belonged to the replicate I was sampling. The data was not surprising, it aligned with my hypothesis, which is that trees that are subject to crowding will be less productive than those that have ample space to themselves. The new growth bud density in Location 1 (most crowded) was on average lower than both that of Location 2 and 3 (least crowded). Despite my difficulties, I think this sampling strategy is the best one for my project. I still have to figure out how I am going to test the soil properties for each location (if I am even able to do so).
Although this project was fairly simple in nature, I have a new appreciation for how complicated research projects in ecology can be. Especially having to write a report afterwards attempting to explain, as clearly as possible, the entire research project.
Initially, I wanted to collect information on the bird species and the number of individuals for each species. I attempted to collect the number of individuals, however, I came to realize that collecting this information would be near impossible. Unless each individual would be tagged it was very difficult to determine whether an individual had been counted already.
The following graph was created based on the data collected for the field research project:
This graph shows the total number of bird species (bar chart) observed with the ambient air temperature overlaid (line chart). As there were only two variables collected for this research project it was fairly easy to layout the data in a graph.
The outcome was not as I was expecting. I was expecting an linear increase in the number of bird species observed as temperatures increased. It would be interesting to see if the same trend exists if more data is collected.
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:
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.
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.
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.
I’m going to select one observation point to work from, in order to prevent bias from wandering around looking for birds.
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.
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
The three sampling strategies used in the virtual forest were haphazard, random, and systematic. Haphazard was the fastest of the three methods (estimated time of 5 hours and 17 minutes), random being moderate in terms of time (estimated 5 hours and 44 minutes), and systematic being the slowest of the methods (estimated 14 hours and 33 minutes). It is safe to assume that haphazard sampling has the fastest sampling time due the limited travel between sample points.
The two most common species in my sampling scenario where the Red Maple and the Chestnut Oak. These two species accuracy (in terms of percent error) were greater than the species that were less commonly sampled.
While doing trying multiple sampling techniques, it was relevant that the use of random sampling posed the least amount of percent error, and this could be do to the fact that there is very little/no overlapping in the sampling plots.
