Environmental factors influencing immigration behaviour of the invasive earthworm <i>Lumbricus</i> <i>terrestris</i>
Bibliographic record
Abstract
Despite the ecological threats posed to northeastern North American forests by the invasive earthworm Lumbricus terrestris L., 1758 (Oligochaeta: Lumbricidae), the dispersal behaviour of this organism is poorly understood. This study investigated how environmental conditions influence the immigration behaviour of L. terrestris. Experimental mesocosms were used to test for differences in burrow establishment depending on leaf-litter type (sugar maple (Acer saccharum Marsh.) or white pine (Pinus strobus L.)) or the background population density of conspecifics (0, 25, or 100 m−2). Choice chambers were used to test for selection between habitat conditions. Video recording was used to measure the latency between introduction and establishment. A significantly greater proportion of individuals established burrows in the presence of maple over pine litter, although this preference did not result in a significant difference in latency. For higher population density treatments, the time since establishment of the background population of conspecifics had a significant effect on earthworm habitat selection, with an increasing preference for the high-density habitat over time. Population density had a significant effect on latency, with greater latency under low-density conditions. These results suggest that L. terrestris detects differences in litter type and conspecific population density and modifies its immigration behaviour accordingly. Findings may be useful in predicting and responding to future dispersal patterns of this invader.
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 | 0.000 | 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".