Forest landscape structure influences the cyclic‐eruptive spatial dynamics of forest tent caterpillar outbreaks
Bibliographic record
Abstract
Abstract A fundamental question in forest insect ecology is the role of forest landscape structure, particularly the amount and spatial configuration of host tree species, in shaping the dynamics of recurring forest insect outbreaks. For forest tent caterpillar (FTC), independent studies do not converge on a singular conclusion, although all indicate that forest structure influences outbreak dynamics. These studies also vary in how they treat climate as a covariate. We evaluated the relative importance of host forest landscape composition and configuration, as well as climate, for their influence on FTC outbreak cycling in the twentieth century. We predicted that FTC outbreaks would exhibit greater synchrony and intensity within areas associated with higher abundance of host trees. We reconstructed FTC outbreaks from 1928 to 2006 using tree‐ring analysis within a well‐structured experimental landscape located in northwestern Ontario and northern Minnesota. Time‐series clustering and spatial nonparametric covariance were used to determine whether similarities in time series and patterns in spatial synchrony corresponded with land management history. Using constrained ordination, we compared statistical properties of outbreak time series to landscape variables representing host abundance, forest configuration, and climate. We found no evidence of climatic effects at the scale of this study, but a significant albeit small influence of landscape structure on outbreak dynamics. Outbreaks were more synchronous and more cyclic within managed zones containing a greater relative abundance of aspen and other hardwood host tree species, compared with the more conifer‐dominated Wilderness area. Yet, we also observed asynchronous outbreak dynamics across the study area, such that correlations with slower‐changing forest landscape variables varied starkly among outbreak pulses. Consequently, the strength of relationship between landscape variables and FTC outbreak patterns varied strongly through time—a result that may explain why short‐term studies yield conclusions that are at odds with one another. Our results speak to the importance of long time series, contrasting landscape structure, use of multivariate methods, and controlling for climatic variation when investigating the effects of forest landscape structure on the cyclic‐eruptive spatial dynamics for forest defoliators.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".