Mutualistic interaction of aphids and ants in pepper, Capsicum annuum and Capsicum frutenscens (Solanaceae)
Bibliographic record
Abstract
Introduction: Adequate biological identification is fundamental for establishing integrated pest management programs and identifying the trophic and mutualist relationships that can affect pest population dynamics. Aphids are the main pest of pepper Capsicum spp. (Solanaceae) crops in Southwestern Colombia, due to their role as vectors of viruses. However, the identification of aphid species is complex, limiting the investigations performed to address their interactions with other organisms. Ants and aphids present a facultative mutualistic relationship, that promotes the growth of hemipteran colonies, for this reason, the study of the ecological mutualistic association between aphids and ants is important. Objective: The main objective was to discriminate the aphid species present in commercial crops of Capsicum spp., and to identify the ant community that attends the aphid colonies and its effects on the size of the aphid colonies. Methods: Aphid species, and their ant mutualist, were collected from Capsicum annuum and Capsicum frutescens, in the Cauca valley, Southwestern Colombia. We use the DNA barcoding approach to identify aphid species, and the ants were identified by morphology‐based taxonomy. To evaluate the effect of ant care on the size and structure of aphid colonies, generalized linear models were calculated using as the response variables the total number of aphids for each colony and the proportion of nymphs. Results: The aphid species that attack pepper crops, are: Aphis gossypii and Myzus persicae (Hemiptera: Aphididae), with A. gossypii being the species that interacts with ants (19 ant species). A. gossypii colonies attended by ants had larger sizes and more nymphs per colony, than those not attended. Conclusions: Although the aphid-ant interaction is not species-specific, it is necessary to consider its role in the propagation of viral diseases in peppers and to determine how this interaction may affect regional biological control strategies.
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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 | 0.000 | 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 | 0.000 |
| 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".