Observation of insect abundance and diversity through pan trap sampling methods in Danby Woodlot and Grassland, York University
Bibliographic record
Abstract
This study was done in collaboration with 4 other team members consisting of Muhammad Akram, Daniel Germani, and Markian Plawiuk, and Nicole Gallagher. The purpose of this study was to collect a dataset of organisms gathered from using pan traps in order to familiarize group members with this method of sampling. Pan traps are particularly applicable towards the estimation of the abundance and diversity of flying insects, and are great at capturing pollinators. The experiment took place at the Danby Woodlot and Danby Grassland at York University Keele campus in Toronto, Ontario, on Tuesday September 29th, 2015. The Danby Grassland consisted of a large abundance of plant species no taller than knee height, with a clumped dispersion pattern, as “patches” of individuals crowding together were common. The Danby Woodlot consisted of an abundance of different species of trees of various heights and canopy coverage. Overall, there were high amounts of canopy coverage within the entire Woodlot, as low amounts of sunlight were able to pass through. This resulted in less precipitation present within the Woodlot compared to the amount of precipitation present in the Grassland. There were minor amounts of debris present within both areas at the time of study. The temperature was approximately 17 degrees Celsius, and it was rainy and gloomy with cloudy conditions, with continuous and constant wind travelling at approximately 23 km/h. The precipitation continued throughout the entire duration of experimentation, which started at approximately 3:19PM. Pan traps were completely set up by 3:37PM, data was collected at 4:20PM, and the experiment concluded by 4:34PM. Within the Grassland, nine pan traps were set up in a linear formation 2 metres apart each (18 metres on length total), alternating in colour: white, blue, yellow. The differences in colour of the traps were to account for the ability to attract different species of pollinators. Soapy water was filled into each trap approximately 1cm deep to account for the precipitation that was occurring to minimize the possibility of overflow. The pan traps were set up at the edge of the Grassland closest to the Woodlot, where there was the least chance of disturbance to the traps by activity ocurring in the Grassland. This setup also allowed maximal sunlight exposure (although it was a rather gloomy day), and the pan traps were ensured to not be placed under heavy vegetative cover. Within the Woodlot, the same procedures of setup were repeated – however, the nine pan traps alternated in colour in a white, yellow, blue arrangement instead. The pan traps were placed close to the centre of the Woodlot in a linear formation. The pan traps were left undisturbed in both areas for about 1 hour. Upon data collection, the contents of the bowls were poured through a sieve for easier examination of the specimen collected, and for counting the frequency of individuals collected in each bowl. Further inspection was conducted by using metal prongs to pick up each organism to take a closer look at the species in order to assign an RTU to it. Overall, 18 pan traps were set up – 9 were located in the Grassland, while the other 9 were located in the Woodlot. Efficiency and collaborative efforts within the group was ensured by having a discussion between all group members in regards to experimental setup and design prior to the actual setup within the areas of study. While Bonnie Duong and Nicole Gallagher were the main coordinators for this particular data set, other group members worked efficiently by helping gathering materials needed, ensuring setup was done correctly and provided input, and aided in cleanup efforts after data collection was completed. Due to the constant precipitation during experimentation, it is likely to have affected the overall data collected. The rain likely played a role in lowering the number of organisms collected, as well as lowering the number of different RTU’s collected since more organisms were likely to be caught in the absence of rainy weather.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".