Towards a mechanistic understanding of animal–ecosystem interactions
Bibliographic record
Abstract
Research Highlight: Ferraro, K. M., Welker, L., Ward, E. B., Schmitz, O. J., & Bradford, M. A. (2023). Plant mycorrhizal associations mediate the zoogeochemical effects of calving subsidies by a forest ungulate. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.14002. Animals play large roles in ecosystem elemental cycling but predicting effects in diverse contexts remains a substantial challenge. Fundamental to progress is (1) identifying mechanisms by which animals impact nutrient distribution and cycling, and (2) disentangling how environmental context mediates the operation of alternative mechanisms. In an elegant field experiment, Ferraro et al. (2023) provide the first detailed exploration of the impact of nutrient inputs from mammalian parturition on soil functioning and the stoichiometry of plant tissues. The authors find that nitrogen from experimental additions of ungulate parturition material (natal fluids) is rapidly incorporated into microsite soil organic pools and plant tissues. They also find that soil processes (soil microbial biomass, rates of carbon mineralization, nitrogen mineralization and nitrification) and the nitrogen content of plant tissues above- and belowground are increased by addition of parturition material. Notably, the authors identify that increases in some soil processes and plant tissue nitrogen are weaker in microsites dominated by ericoid mycorrhizal plants than those dominated by ectomycorrhizal plants. These findings demonstrate that parturition depositions, a ubiquitous but overlooked mechanism of mammalian impacts on ecosystems, impact ecosystem processes and plant tissue stoichiometry. Furthermore, plant-fungal associations are a predictive axis of context dependency mediating zoogeochemical effects at fine scales. Ferraro et al.'s (2023) novel approach simultaneously advances mechanistic understanding of animal-ecosystem interactions at fine scales and facilitates prediction of ungulate effects on nutrient availability at landscape extents.
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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 | 0.004 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".