Integrating Network and Meta‐Ecosystem Models for Developing a Zoogeochemical Theory
Bibliographic record
Abstract
Human activities have caused significant changes in animal abundance, interactions, movement and diversity at multiple scales. Growing empirical evidence reveals the myriad ways that these changes can alter the control that animals exert over biogeochemical cycling. Yet a theoretical framework to coherently integrate animal abundance, interactions, movement and diversity to predict when and how animal controls over biogeochemical cycling (i.e., zoogeochemistry) change is currently lacking. We present such a general framework that provides guidance on linking mathematical models of species interaction and diversity (network theory) and movement of organisms and non-living materials (meta-ecosystem theory) to account for biotic and abiotic feedback by which animals control biogeochemical cycling. We illustrate how to apply the framework to develop predictive models for specific ecosystem contexts using a case study of a primary producer-herbivore bipartite trait network in a boreal forest ecosystem. We further discuss key priorities for enhancing model development, data-model integration and application. The framework offers an important step to enhance empirical research that can better inform and justify broader conservation efforts aimed at conserving and restoring animal populations, their movement and critical functional roles in support of ecosystem services and nature-based climate solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".