Characterizing landscape configuration effects on eastern spruce budworm infestation dynamics
Bibliographic record
Abstract
Context: , (L.) Mill.) at the stand level. At the landscape level, the ability of SBW to disperse over long distances suggests that the configuration of available resources may also play an important role in affecting infestation success and outcomes. Objectives: We hypothesized that fragmented landscapes characterized by smaller and more dispersed conifer patches intermixed with other land cover types may promote infestations thanks to the ability of SBW to saturate the landscape and avidly consume limited resources. To test our hypothesis, we investigated to what degree landscape configuration plays a significant role in modulating defoliations using spectrally-segmented forest stands. Next, we determined the major drivers of infestation probabilities and how they have evolved as a result of landscape configuration changes. Methods: We capitalized on a combination of Landsat imagery, Forest Resource Inventory (FRI) data, and probability estimates from random forest models to investigate multi-scale effects of landscape configuration on SBW infestations over 13 years in eastern Quebec, Canada, in a spatially-explicit fashion. Based on annual best available pixel composites of surface reflectance derived from Landsat, we superimposed a 400 m tessellation over which we extracted six landscape configuration metrics describing area, aggregation, and shape for infested and non-infested conifer forest patches. Next, probability estimates from two sets of random forest models were extracted from the configuration metrics at annual time steps and for the entire length of the time series. Results: Landscapes characterized by greater fragmentation of conifer patches had a higher risk of infestation. In such landscapes, greater fragmentation was indicated by smaller and more variable-sized conifer patches, with a mean patch area < 40 ha (CV > 100 ha) and a landscape patch index < 50 %. In addition, such areas had more isolated patches and more complex shapes, as indicated by cohesion < 97 %, landscape shape index > 3, and shape > 1.35. The landscape patch index, quantifying the percentage of landscape covered by the largest coniferous patch, had the greatest influence on SBW infestations. These results confirmed our initial hypothesis that a higher level of fragmentation of conifer patches may favor infestation establishment. Conclusions: We demonstrated the use of freely available Landsat imagery to extract configuration metrics in a spatially-explicit fashion. Further, we highlighted the value of using probability estimates to capture landscape configurations at higher risk of infestations. This knowledge can inform forest management practices, such as where harvesting operations may be carried out on the landscape or where planting may be prioritized to reduce conifer stand fragmentation and infestation risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-025-02203-z.
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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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".