Indirect Effects of Emerald Ash Borer-Induced Ash Mortality and Canopy Gap Formation on Epigaeic Beetles
Bibliographic record
Abstract
Exotic herbivorous insects have drastically and irreversibly altered forest structure and composition of North American forests. For example, emerald ash borer (Agrilus planipennis Fairmaire) from Asia has caused wide-scale mortality of ash trees (Fraxinus spp.) in eastern United States and Canada. We studied the effects of forest changes resulting from emerald ash borer invasion on epigaeic or ground beetles (Coleoptera: Carabidae) along a gradient of ash dieback and gap sizes in southeastern Michigan. Ground beetles were sampled in hydric, mesic, and xeric habitats in which black (Fraxinus nigra Marshall), green (Fraxinus pennsylvanica Marshall), and white (Fraxinus americana L.) ash were the most common species, respectively. During 2006-2007, we trapped 2,545 adult ground beetles comprising 52 species. There was a negative correlation between percent ash tree mortality in 2006 and catches of all beetles. Catches of Agonum melanarium Dejean (in 2006) and Pterostichus mutus (Say) (in 2006-2007) were negatively correlated with tree mortality and gap size, respectively. However, catches of Pterostichus corvinus Dejean were positively correlated with gap size in 2006. As ash mortality and average gap size increased from 2006 to 2007, catches of all beetles as well as P. mutus and Pterostichus stygicus (Say) increased (1.3-3.9 times), while species diversity decreased, especially in mesic and xeric stands. Cluster analysis revealed that beetle assemblages in hydric and mesic stand diverged (25 and 40%, respectively) in their composition from 2006 to 2007, and that hydric stands had the most unique beetle assemblages. Overall, epigaeic beetle assemblages were altered in ash stands impacted by emerald ash borer; however, these impacts may dissipate as canopy gaps close.
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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 | 0.000 |
| 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 | 0.000 |
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".