THE IMPACT OF INSECT DEFOLIATION ON A DECEDIOUS FOREST
Bibliographic record
Abstract
Temperate forests are an important global carbon sink. However, various environmental disturbances can impact carbon sequestration capabilities of these forests. In 2021, a record-breaking defoliation, caused by the spongy moth (Lymantria dispar L., formerly knows as the gypsy moth) occurred in eastern North America. In this study, we assess the impact of this spongy moth defoliation on carbon uptake in a mature oak-dominated temperate forest in the Great Lakes region in Canada, using eddy covariance flux data from 2012 to 2022. The forest is more than 90 years old and known as CA-TPD site in the AmeriFlux and global FLUXNET networks. Study results showed that prior to spongy moth defoliation the forest was a carbon sink with mean annual gross ecosystem productivity (GEP) of 1,367 ± 104, ecosystem respiration (RE) of 1,201 ± 145 and, net ecosystem productivity (NEP) of 197 ± 74 g C m−2 yr−1 over the 2012–2020 period. However, due the defoliation in the early growing season in 2021, GEP declined to 959 g C m-2 yr-1 and RE increased to 1,345 g C m-2 yr-1 causing the forest to became a large source of carbon with annual NEP of -351 g C m-2 yr−1. This large decline in annual NEP was a result of both reduced GEP (30%) and elevated RE (12%). However, in 2022, forest carbon fluxes recovered to pre-infestation levels, with a GEP value of 1,671 g C m-2 yr-1, an RE value of 1,287 g C m-2 yr-1, and an NEP value of 298 g C m-2 yr-1, indicating that the forest was once again a large carbon sink. This research demonstrates that major transient natural disturbances such as the 2021 spongy moth defoliation can have a significant impact on forest carbon dynamics in a future warmer climate. The extent to which North American temperate forests will remain a major carbon sink will depend on the severity and intensity of these disturbance events and rate of recovery of forests following the disturbance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".