Remote Sensing Vegetation Indices to study migratory insect seasonal movements and population outbreaks.
Bibliographic record
Abstract
Dispersal and migratory movements of phytophagous insects are strongly tight to vegetation phenology. The succeeding broods of seasonally migrating species connect distant regions that differ in the timing of plant growth. Also, the overall plant production and the extent of the growing season determine the breeding capacity of the insects, and thus influence their demographic trends. The use of photosynthetic activity proxies based on remote sensing observations, such as NDVI or EVI, opens new avenues to study migratory patterns of insects, a largely understudied field. Here, we present two applications of NDVI to study the migration of the Painted Lady butterfly, Vanessa cardui, an obligate migratory species with a large migratory range encompassing the Palaearctic and the Afrotropics.We develop a spatio-temporal Species Distribution Model (SDM) to determine monthly suitable areas for reproduction across its migratory range. We use a comprehensive dataset of V. cardui breeding occurrences and time-series of climatic and vegetation growth variables, including EVI and monthly difference (EVI change). Vegetation indexes proved to be relevant variables explaining V. cardui breeding suitability, having higher importance in the Afrotropical region. Moderate EVI values showed best conditions for breeding. EVI change had a better fit with slight increases of vegetation growth, discarding sharp changes in greening. These patterns agree with the phenology of V. cardui herbaceous hostplants in the growing season, distributed in open-areas such as meadows, weeds and bushland. In the temperate zone, vegetation growth was not a limiting factor and suitability was mostly explained by variables related to temperature.At a temporal scale, we assess the role that anomalies in photosynthetic activity might have in modulating demographic trends of V. cardui. We performed a pixel-based time-series analysis of monthly NDVI values from 2000 to 2022. We observe that four demographic outbreaks of the butterfly observed in Europe are immediately preceded by anomalous vegetation growth events in suitable breeding regions in Africa and/or the Middle East, suggesting a strong association between both events. We investigate in higher detail the largest of the outbreak episodes in 2019. The resulting maps of anomalies showed high signal in regions of the Middle East from December 2018 to May 2019. The highest anomalies were detected in rocky deserts and arid and semi-arid shrublands, while sand deserts were not affected. The large extent found with exceptional greening could have functioned as massive breeding grounds for V. cardui. This hypothesis strongly matches massive citizen science data of V. cardui observations that were first observed in the Arabian Peninsula in March, and that further spread all over Europe in unprecedented numbers.Taken together, we highlight the potential of remote-sensing vegetation indices to study seasonal migratory movements of phytophagous insects. We show how NDVI can inform models about the potential shifting distributions of migratory species, and how NDVI anomalies can be used to predict potential population outbreaks. In a world where insects represent the majority of terrestrial diversity, the use of vegetation indices may become standard in the fields of insect movement ecology and population dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".