Stoichiometric and structural uncertainty in soil food web models
Bibliographic record
Abstract
Abstract Soil food web models are a powerful tool for calculating the carbon and nitrogen mineralized by different soil organisms. Two limitations of the current models are that they use (a) fixed parameters for determining the relative efficiency of carbon and nitrogen conversion and (b) a generic set of trophic species (i.e. nodes). We propose a new method for analysing soil food webs that varies production efficiency and diet mechanistically with resource stoichiometry. Then, we calculated how the lumping or splitting of trophic species affects carbon and nitrogen mineralization for both net mineralization rates as well as the distribution of mineralization (i.e. sources of mineralization). Our models with additional stoichiometric details better represent organisms that consume basal resources with high C:N ratios. This is important because we show that lumping together trophic species at low trophic levels, which often differ in C:N ratios (i.e. microbial taxa), causes the largest deviations in estimates of carbon and nitrogen mineralization. One reason for the large effect of C:N ratio is that it impacts diet and production efficiency in our stoichiometric model. Conversely, lumping together species at higher trophic levels causes the largest errors when those species have different production and assimilation efficiencies. Finally, we demonstrate that differences in death rate and biomass are mostly important when lumping species that have different diets. We suggest that C:N ratios are especially important when grouping microbial taxa because microbial C:N ratios vary widely and microbes feed at low trophic levels where differences in C:N ratio mattered the most. Conversely lumping higher consumers will be most problematic when they have differences in the conversion efficiencies. We provide an approach to calculate structural error and report it with parameter uncertainty. Then, we demonstrate, using oribatid mites as an example, that considering the life history of different taxa can help us group organisms more efficiently into trophic species. In doing so, we provide a way to reduce and report the uncertainty in soil food web models and to stimulate future empirical work measuring key life history parameters. A free Plain language summary can be found within the Supporting Information of this article.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".