Predicting the habitat suitability of the invasive white mango scale, <i>Aulacaspis tubercularis</i>; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The white mango scale, Aulacaspis tubercularis (Hemiptera: Diaspididae), is an invasive pest that threatens the production of several crops of commercial value including mango. Though it is an important pest, little is known about its biology and ecology. Specifically, information on habitat suitability of A. tubercularis occurrence and potential distribution under climate change is largely unknown. In this study, we used four ecological niche models, namely maximum entropy, random forest, generalized additive models, and classification and regression trees to predict the habitat suitability of A. tubercularis under current and future [representative concentration pathways (RCPs): RCP4.5 and RCP8.5 of the year 2070] climatic scenarios, using bioclimatic variables. Models' performance was evaluated using the true skill statistic (TSS), the area under the curve (AUC), correlation (COR), and the deviance. RESULTS: All models sufficiently predicted the occurrence of A. tubercularis with high accuracy (AUC ≥ 0.93, TSS ≥ 0.81 and COR ≥ 0.77). The random forest algorithm had the highest accuracy among the four models (AUC = 0.99, TSS = 0.93, COR = 0.90, deviance = 0.26). Temperature seasonality (Bio4), mean temperature of the driest quarter (Bio9), and precipitation seasonality (Bio15) were the most important variables influencing A. tubercularis occurrence. Models' predictions showed that countries in east, south, and west Africa are highly suitable for A. tubercularis establishment under current conditions. Similarly, Mexico, Brazil, India, Myanmar, Bangladesh, Thailand, Laos, Vietnam, and Cambodia are also highly suitable for the pest to thrive. Under future conditions, the suitable areas might slightly decrease in many countries of sub-Saharan Africa under both RCPs. However, the range of expansion of A. tubercularis is projected to be higher in Australia, Brazil, Spain, Italy, and Portugal under the future climatic scenarios. CONCLUSION: The results reported here will be useful for guiding decision-making, developing an effective management strategy, and serving as an early warning tool to prevent further spread toward new areas. © 2022 Society of Chemical Industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it