The preferences of <i>Lumbricus terrestris</i> earthworms for weed seeds typical of the Northern Great Plains agroecosystems
Bibliographic record
Abstract
Abstract Earthworms are postdispersal seed predators that can influence weed communities in temperate agroecosystems. Recent studies have found that seed feeding by earthworms tends to be driven by the active selection of certain seed species rather than random encounter. Numerous seed traits are expected to affect seed selection by earthworms, including seed size, shape, coat hardness, and nutritional content. The impact of these traits on seed selection by earthworms tends to vary depending on seed species identity and earthworm species identity, rendering the outcome of earthworm–seed interactions hard to predict. We carried out laboratory experiments to investigate the impact of seed physical and chemical traits on seed choice by the common earthworms ( Lumbricus terrestris ). Seeds of six weed species typical of the Northern Great Plains agroecosystem, wild mustard ( Sinapis arvensis L.), field pennycress ( Thlaspi arvense L.), shepherd’s purse [ Capsella bursa-pastoris (L.) Medik.], catchweed bedstraw ( Galium aparine L.), green foxtail [ Setaria viridis (L.) P. Beauv.], and redroot pigweed ( Amaranthus retroflexus L.), were offered to L . terrestris in multiple-choice feeding arenas. The results showed that seeds of S. arvensis and C. bursa-pastoris , both of which have high lipid content, were the most consumed. Seed ingestion was negatively influenced by irregular seed shapes and long seed length, but these physical traits did not override the strong preference for lipid-rich seeds. These findings suggest that seed selection by L . terrestris earthworms was strongly influenced by the lipid content of the seed when seed morphology (i.e., size and shape) varied within certain limits. Therefore, seed nutrients are likely to play an important role in weed seed choice by L . terrestris earthworms when seed physical traits do not impose major constraints on ingestion.
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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".