Characterizing landscape configuration effects on eastern spruce budworm infestation dynamics
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».