Potential impact of climate change on Nearctic leafhopper distribution and richness in North America
Bibliographic record
Abstract
Abstract Climate change significantly contributes to shifts in the geographical range of pests and diseases. Leafhoppers (Hemiptera: Cicadellidae), known vectors of phytoplasmas pathogens, are linked to the transmission of more than 600 diseases affecting a thousand plant species worldwide. Despite this, the potential effects of climate change on leafhopper vectors of phytoplasmas remain a critical knowledge gap. To address this gap, our study investigated the potential impact of climate change on 14 species of Nearctic leafhoppers previously associated with phytoplasma-related diseases. Using the MaxEnt species distribution algorithm and other ecological niche modeling techniques, we assessed (i) the expected species richness under current climate conditions and four future scenarios and (ii) the environmental niche similarity among these species across these scenarios. Our projections suggest that the eastern region of North America holds the potential for the highest species richness, a trend expected to persist across all future scenarios, gradually expanding eastward. Notably, our findings indicate the increasing suitability of northern Canada for more species. Network analysis further revealed a remarkable similarity in environmental niches among most leafhopper species. Moreover, across the four future scenarios, there is a tendency for an increase in this similarity. Altogether, our study underscores the potential persistent presence of Nearctic leafhoppers in their current habitats while pointing to a shift toward northern North America in future scenarios. These findings have significant implications for sustainable pest management practices, prompting a necessary discussion on strategies to mitigate climate change and pest migration’s impact on agricultural systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".