Loss of ash trees in riparian forests from emerald ash borer infestations has implications for aquatic invertebrate leaf-litter consumers
Bibliographic record
Abstract
Rapid loss of ash (Fraxinus spp.) trees in riparian forests from an invasive insect, the emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888), could pose risk of altering organic matter inputs to water bodies that underpin many aquatic ecosystem processes. We measured the composition of riparian forests and their leaf-litter contributions to headwater streams and determined the relative palatability of ash leaves and leaves of three other common riparian trees to aquatic invertebrate leaf-litter consumers (the stonefly (Pteronarcys sp.) and the cranefly (Tipula sp.)) in laboratory microcosms and whole invertebrate communities in forest streams. Ash trees contributed, on average, 24% to riparian tree density and 20% to total litterfall. Among the four common streamside trees accounting for 65% of total litterfall, ash was the first or second most preferred food source for consumers. Leaf packs without ash decomposed at slower rates than packs containing 25%–100% ash leaves. Preferential feeding on ash leaves infers a high-quality food source selected by consumers, and this concurred with comparatively high N content and low C–N ratio of ash leaves. Aquatic invertebrate communities on leaf packs in streams differed among leaf mixtures with or without ash, although community dissimilarity was low. The loss of ash in riparian forests represents an EAB-induced reduction in a high-quality resource subsidy to organic matter consumers in streams. We discuss how this has implications for risk predictions and management response strategies.
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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".