Farmed crickets (Orthoptera: Gryllidae) raised with dermestids (Coleoptera: Dermestidae) suffer from reduced and delayed growth, but not enough to explain reports of dramatic yield loss
Bibliographic record
Abstract
The mass production of insects for food and feed is an expanding North American industry. Facilities that mass rear insects are at risk of pest infestations because the optimal environmental conditions for rearing beneficial species may also support the development of pest species. Here, we present the first recorded results detailing the interactions between dermestids and farmed crickets. Cricket farms have reported extremely low harvest yield during heavy dermestid infestations, but the exact reasons for this low yield are unknown. Many dermestid larvae are covered in dense, detachable, barbed setae called hastisetae, which are used by the larvae as an active trapping system against arthropod predators. We designed a series of experiments to test the hypotheses that a dermestid pest of cricket farms, black larder beetle (Dermestes ater DeGeer (Coleoptera: Dermestidae)), may be directly impacting Gryllodes sigillatus Walker (Orthoptera: Gryllidae) yield through the physical effects of hastisetae ingestion and/or indirectly impacting cricket yield through competition for fishmeal, a primary source of protein in conventional cricket feed. Our predictions that G. sigillatus life history and survival would be negatively affected by dermestids were largely refuted. Females fed infested diets grew less mass, but not smaller body size, compared to females fed uninfested diets. We also found that while G. sigillatus experienced delayed growth early in life after living with dermestids, they were able to tolerate living with, and consuming, dermestid larvae. We discuss how these findings have led to new hypotheses concerning how dermestid infestations drive reductions in cricket farm yield.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".