Potential Distribution of Nysius simulans (Hemiptera: Lygaeidae) in Soybean Crops in South America Under Current and Future Climate
Bibliographic record
Abstract
Nysius simulans (Stål) is a suctorial, fluid feeding herbivore that can transmit toxins and spread pathogens via saliva and is an economically important pest for soybean in South America. Currently, N. simulans in soybean is predominantly found in Argentina, but future changes in the distribution from both dispersal and range shifts due to climate change may affect soybean cultivation in southern South America. We developed a species distribution model to examine the distribution range of N. simulans. We compared the potential distribution of N. simulans under current and future projected climatic conditions in order to identify future areas of natural occurrence with ecological niche models using Maxent. Current records of N. simulans show that while the species is present in Argentina, and some areas of Brazil, Paraguay, Peru, and Uruguay, our models suggest that many new suitable areas will be available for N. simulans under climate change including other regions of Argentina, and southern Chile. Our results also predict potential future range shifts and distributions into Bolivia, but not Peru nor Brazil. In our model, seasonal trends in temperature were shown to have the greatest contribution to the potential distribution, whereas isothermality (i.e., temperature variability) was correlated to potential future distribution ranges. We conclude that current populations of N. simulans may be expanding its distribution range by diffusion (i.e., range expansion over generations at the margins of populations), and regions with potential future N. simulans distribution should be closely monitored.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".