Deteriorating microbiomes in agriculture - the unintended effects of pesticides on microbial life
Bibliographic record
Abstract
There is emerging concern regarding the unintentional and often unrecognized antimicrobial properties of “non-antimicrobial” pesticides. This includes insecticides, herbicides, and fungicides commonly used in agriculture that are known to produce broad ranging, off-target effects on beneficial wildlife, even at seemingly non-toxic low dose exposures. Notably, these obscure adverse interactions may be related to host-associated microbiome damage occurring from antimicrobial effects, rather than the presumed toxic effects of pesticides on host tissue. Here, we critically review the literature on this topic as it pertains to the rhizosphere microbiome of crop plants and gut microbiome of pollinator insects (namely managed populations of the western honey bee, Apis mellifera), since both are frequent recipients of chronic pesticide exposure. Clear linkages between pesticide mode of action and host-specific microbiome functionalities are identified in relation to potential antimicrobial risks. For example, inherent differences in nitrogen metabolism of plant- and insect-associated microbiomes may dictate whether neonicotinoid-based insecticides ultimately exert antimicrobial activities or not. Several other context-dependent scenarios are discussed. In addition to direct effects (e.g., microbicidal action of the parent compound or breakdown metabolites), pesticides may indirectly alter the trajectory of host-microbiome coevolution in honey bees via modulation of social behaviours and the insect gut-brain axis - conceivably with consequences on plant-pollinator symbiosis as well. In summary, current evidence suggests: (1) immediate action is needed by regulatory authorities in amending safety assessments for “non-antimicrobial” pesticides; and (2) that the development of host-free microbiome model systems could be useful for rapidly screening pesticides against functionally distinct microbial catalogues of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".