Microbial invasions and inoculants: a call to action
Bibliographic record
Abstract
The use of non-genetically modified microbial inoculants for beneficial purposes in agriculture, bioremediation, medicine, and infrastructure is increasing. The intentional introduction of plants and animals for similar purposes has a long history, but despite successes, has resulted in thousands of plant and animal species becoming invasive, with catastrophic consequences for the environment, public health, and society. Hundreds of microbial invasions are known, and although microbial inoculants can provide benefits, they have similar potential to negatively impact ecosystems. Little action has been taken to guard against the threat of microbial invasions from non-genetically modified microbial inoculants. Now is the time to develop an effective research and management infrastructure for these microbial inoculants to avoid catastrophic outcomes similar to those caused by intentionally-introduced plants and animals. Here, we propose a unified research and management approach to spur action by regulators and practitioners. Three aims need to be addressed: developing (1) a coherent mechanistic understanding of how microbial inoculants effect invasions, (2) predictive models forecasting which microbes pose risks of invasion, and (3) effective management strategies. To guide mechanistic understanding, we develop seventeen key hypotheses. For predictive modeling, quantitative trait analysis and risk maps will be critical. Management strategies will depend on both understanding and predictions, but prevention rather than eradication or control of invasive microbes is likely to be most effective, and a precautionary regulatory approach should immediately be applied to inoculants. Multiple data types will be instrumental for understanding and predicting which microbial inoculants have invasive potential: experimental data from microcosm and mesocosm experiments, and large-scale observational data from microbial surveillance. Both phenomenological and mechanistic modeling approaches will be key in achieving these fundamental and applied research aims. Moreover, each stage of the invasion process --- transport, establishment, spread, and impacts --- often need to be investigated separately. The unified approach developed here provides a roadmap for developing a research and management infrastructure to guard against the threat of microbial invasions from non-genetically modified microbial inoculants.
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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 | 0.043 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".