Soil food web nitrogen cycling in eastern temperate forests: a tracer experiment
Bibliographic record
Abstract
Nitrogen cycling in temperate forests is a critical ecological process, predominantly driven by the decomposition of plant-derived organic matter by microbial activity. However, the role of soil macro-arthropods, particularly ground beetles, in mediating these processes has remained largely underexplored despite their potential to influence nutrient cycling and ecosystem dynamics. This study investigated how macro-arthropods, with a focus on ground beetles, contributed to nitrogen cycling in eastern temperate forests. Using 15N-labeled black birch leaf litter, we traced nitrogen movement through soil food webs in young and old forest stands at the Yale Myers Forest in Connecticut. Litter bags, with and without access for large invertebrates, were deployed to evaluate the contributions of macro-arthropods to litter decomposition and nitrogen cycling. We hypothesized that forest age influenced ground beetle community composition, with younger forests harboring more detritivorous beetles and older forests supporting predominantly carnivorous species with elevated 15N levels. Litter decomposition rates, soil nitrogen pools, and isotopic signatures of 15N in soil invertebrates and ground beetles were measured across three growing seasons (2022–2024). Results: We collected a total of 30,309 with the dominant groups including ground beetles, spiders, and millipedes, and detected a weak pulse of 15N in their biomass. This study provides insights into the trophic interactions and functional roles of soil macro-arthropods in forest nutrient dynamics, with implications for understanding forest ecosystem resilience and management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".