Ants in Agriculture: Their Diversity, Ecological Role, and Utility as a Model Soil Organism for Ecotoxicological Studies
Bibliographic record
Abstract
There is substantial evidence that non-target insects are at risk from exposure to many commonly used insecticides. Most recent agricultural research has been dedicated to the impacts of insecticides on bees. However, this research has primarily focused on above-ground, oral exposure to insecticides. There is a knowledge gap for non-target soil insects that can be used for laboratory ecotoxicology research which have easily measurable sublethal endpoints for contact exposure. Ants (Hymenoptera; Formicidae) are a diverse, abundant, widespread, soil-nesting group of insects with positive and negative impacts on agroecosystems. However, there have been few studies of ant richness, abundance, distribution, and ecology in agroecosystems in Canada. This thesis includes a survey of the diversity of ant species and their ecology on farms in southern Ontario, Canada which is the first of its kind for this region. Based on this survey, I selected L. neoniger as a representative species of soil-nesting ant to use for ecotoxicological assays of agricultural pesticides. I have developed a simple, replicable, inexpensive and standardized method for raising this species in the lab with low control mortality and which maintains nesting and grooming behaviours similar to those observed in the field. This rearing method also facilitated the application of insecticides through a field-realistic soil drench. The soil drench protocol was then tested using a common agricultural insecticide, imidacloprid, and I observed quantifiable detrimental effects at relevant field application rates. The rearing and testing method for Lasius neoniger described in this thesis could be used as a backbone for the broader evaluation of non-target impacts of soil-applied pesticides in North America.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".