Of all shapes and sizes: a theoretical framework for animal‐mediated terrestrial heterogeneity across scales
Bibliographic record
Abstract
Animals redistribute elements throughout their lives by depositing wastes and carcasses. Growing evidence shows that these zoogeochemical processes enhance landscape diversity and heterogeneity worldwide. We provide a descriptive framework for understanding how direct animal depositions (i.e. fecal matter, urine, carcasses, and other body materials) contribute to element heterogeneity across scales, with particular focus on how a species' contributions differ relative to one another. In this framework, we identify mean body mass and population density as the main predictors of element heterogeneity. Secondary predictors, including population strategy, overabundance, habitat preference, elemental composition and predation, are nested within and influenced by body size and density. We then demonstrate how animals can play unique roles within communities, leading to multiscale patterns of elemental heterogeneity within an ecosystem. In doing so, we highlight the importance of studying zoogeochemistry through both an ecosystem ecology and community ecology lens. We illustrate our framework using three spatial scales of animal communities (100 cm2, 100 m2, 100 km2) within an eastern temperate forest, considering both individual species traits and their community interactions at each scale. The community heterogeneity framework provides a theoretical understanding of how individual species and animal communities collectively drive element heterogeneity, allowing a predictive mechanism for the ecosystem contributions of animals across systems and scales.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".