Aquatic insect subsidies influence microbial composition and processing of detritus in near‐shore subarctic heathland
Bibliographic record
Abstract
Insects are major conduits of resources moving from aquatic to terrestrial systems. While the ecological impacts of insect subsidies are well documented, the underlying mechanisms by which these resources change recipient ecosystems remain poorly understood. Most subsidy inputs enter terrestrial systems as detritus; thus, soil microbes will likely influence the processing of insect subsidies, with implications for plant community composition and net primary productivity (NPP). In a subarctic ecosystem near Lake Mývatn, Iceland where midge (Diptera: Chironomidae) deposition to land is high, we investigated how insect subsidies affected litter processing and microbial communities. We also evaluated how those belowground effects related to changes in inorganic nitrogen, plant composition and NPP. We simulated subsidies by adding midge carcasses to 1‐m 2 heathland plots, where we measured effects on decomposition rates and the plant community. We then studied how fertilization treatments (control, KNO 3 and midge‐carcass addition) affected graminoid biomass and inorganic nitrogen in greenhouse experiments. Lastly, we conducted a soil‐incubation study with a phospholipid fatty acid analysis (PLFA) to examine how midge addition to heathland soils affected microbial respiration, biomass and composition. We found that midge addition to heathland soils increased litter decomposition and graminoid plant cover by 2.6× and 2×, respectively. Greenhouse experiments revealed similar patterns, with midge carcasses increasing graminoid biomass by at least 2× and NH 4 + concentrations by 7×. Our soil‐incubation study found that midge carcasses elevated microbial respiration by 64%, microbial biomass by 43% and shifted microbial functional composition. Our findings indicate that insect subsidies can stimulate soil microbial communities and litter decomposition in subarctic heathlands, leading to increased NPP and changes in plant community composition.
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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".