Diversity, Stability, and the Forecast Challenge in Forest Lepidopteran Predictive Ecology: Are Multi-Scale Plant–Insect Interactions the Key to Increased Forecast Precision?
Bibliographic record
Abstract
I report on long-term patterns of outbreak cycling in four study systems across Canada and illustrate how forecasting in these systems is highly imprecise because of complexity in the cycling and a lack of spatial synchrony amongst sample locations. I describe how a range of bottom-up effects could be generating complexity in these otherwise periodic systems. (1) The spruce budworm in Québec exhibits aperiodic and asynchronous behavior at fast time-scales, and a slow modulation of cycle peak intensity that varies regionally. (2) The forest tent caterpillar across Canada exhibits eruptive spiking behavior that is aperiodic locally, and asynchronous amongst regions, yet aggregates to produce a pattern of periodic outbreaks. In Québec, forest tent caterpillar cycles differ in the aspen-dominated northwest versus the maple-dominated southeast, with opposing patterns of cycle intensity between the two regions. (3) In Alberta, forest tent caterpillar outbreak cycles resist synchronization across a forest landscape gradient, even at very fine spatial scales, resulting in a complex pattern of cycling that defies simple forecasting techniques. (4) In the Border Lakes region of Ontario and Minnesota, where the two insect species coexist in a mixedwood landscape of hardwood and conifers, outbreak cycle intensity in each species varies spatially and temporally in response to host forest landscape structure. Much attention has been given to the effect of top-down agents in driving synchronizable population cycles. However, foliage loss, tree death, and forest succession at stem, stand, and landscape scales affect larval and adult dispersal success, and may serve to override regulatory processes that cause otherwise top-down-driven periodic, synchronized, and predictable population oscillations to become aperiodic, asynchronous, and unpredictable. Incorporating bottom-up effects at multiple spatial and temporal scales may be the key to making significant improvements in forest insect outbreak forecasting.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".