Long-term impacts of invasive insects and pathogens on composition, biomass, and diversity of forests in Virginia's Blue Ridge Mountains
Bibliographic record
Abstract
Exotic forest insects and pathogens (EFIP) have become regular features of temperate forest ecosystems, yet we lack a long-term perspective on their net impacts on tree mortality, carbon sequestration, and tree-species diversity. Here, we analyze three decades (1987-2019) of forest monitoring data from the Blue Ridge Mountains ecoregion in eastern North America, including 67 plots totaling 29.4 ha, along with a historical survey from 1939. Over the past century, EFIP substantially affected at least eight tree genera. Tree host taxa had anomalously high mortality rates (≥6% yr-1 from 2008-2019, versus 1.4% yr-1 for less-impacted taxa). Following the arrival of EFIP, affected taxa declined in abundance (-25 to -100%) and live aboveground biomass (AGB; -13 to -100%) within our monitoring plots. We estimate that EFIP were responsible for 21-29% of ecosystem AGB loss through mortality (-87 g m-2 yr-1) from 1991-2013 across 66 sites. Over a century, net AGB loss among affected species totaled roughly 6.6-10 kg m-2. The affected host taxa accounted for 23-29% of genera losses at the plot-scale, with mixed net effects on α-diversity. Several taxa were lost from our monitoring plots but not completely extirpated from the region. Despite these losses, both total AGB and α-diversity were largely recovered through increases of sympatric genera. These results indicate that EFIP have been an important force shaping forest composition, carbon cycling, and diversity. At the same time, less-affected taxa in these relatively diverse temperate forests have conferred substantial resilience with regards to biomass and α-diversity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".