Global scientific progress and shortfalls in biological control of the fall armyworm Spodoptera frugiperda
Bibliographic record
Abstract
Since 2016, the fall armyworm (FAW) Spodoptera frugiperda has spread over extensive areas of the tropics and subtropics, imperiling food security, economic progress and the livelihoods of millions of cereal farmers. Although FAW has received long-standing scientific attention in its home range in the Americas, chemical inputs feature prominently in its mitigation and biological control uptake is globally lagging. Here, building upon a quantitative review of the global literature, we methodically dissect FAW biological control science. Of the known entomopathogens (46), parasitoids (310) and predators (215) of FAW, approx. 40% have been subject to laboratory- or field-level scrutiny. Laboratory-level performance has partially been assessed for 14–18% of the above invertebrate taxa. Yet, organismal, geographic, methodological and thematic biases hamper efforts to relate in-field animal biodiversity to biological control services. Often, single-guild ‘snapshot’ surveys are preferred over comprehensive bio-inventories or population dynamics appraisals, trophic interactions remain undocumented, standard pest infestation metrics are lacking and natural enemy censuses are performed arbitrarily. Diurnal biota receive inordinate attention, while egg and pupal predation - the main biotic sources of mortality - are routinely overlooked. Multiple microbial and invertebrate biota are investigated with a view towards mass-rearing and augmentative release. Meanwhile, conservation biological control receives marginal attention and cross-disciplinary engagement with the agroecology domain is lagging. We lay out several steps, including standardized methodologies, smart use of biodemographic toolkits, networked field trials and a fortification of its ecological underpinnings, to sharpen the science of (FAW) biological control and urge further momentum in its global implementation.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".