Predator density outweighs experimental warming effects on short‐term carbon and nitrogen loss from arctic shrub litter
Bibliographic record
Abstract
Rapid climate change in the Arctic is altering biological communities and their subsequent effects on ecosystem functioning. For example, warming‐induced shrub expansion accelerates biogeochemical cycles in part by increasing high‐quality litter inputs. Likewise, warming may enable higher densities of wolf spiders, which are dominant invertebrate predators whose activities indirectly alter plant litter decomposition rates. Although shrubs and wolf spiders are responding to climate change simultaneously, it is unclear how more shrub litter and more spiders together will influence elemental cycling in Arctic ecosystems. To test how warming could influence these processes, we used a fully factorial mesocosm experiment to quantify effects of wolf spiders on litter decomposition of an expanding species of dwarf deciduous shrub Betula nana under ambient and warmed conditions. We found higher densities of wolf spiders were consistently associated with more litter mass loss, and more C and N release regardless of warming treatment, indicating biotic interactions may be a stronger driver of short‐term B. nana litter decomposition than warming when wolf spiders are present. Our findings suggest the combined effects of warming‐induced shifts in plant and arthropod communities may further accelerate C and N cycling, which could cause positive feedbacks on Arctic shrub expansion.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".