Animal-vectored nutrient flows across resource gradients influence the nature of local and meta-ecosystem functioning
Bibliographic record
Abstract
Abstract Organisms moving across landscapes connect ecosystems in space and time, mediating nutrient, energy, and biomass exchanges. Meta-ecosystem ecology offers a framework to study how these flows affect ecosystem functions in space and time. However, meta-ecosystem models often represent consumer movement as diffusion along gradients of resources. Crucially, this assumes that consumer movement connects the same trophic compartments among patches of the same ecosystem. Yet, empirical evidence shows that organisms move across different ecosystems and connect diverse trophic compartments in diffusive and non-diffusive ways. Here, we derive a two-patch meta-ecosystem model that accounts for both types of organismal movement, and we investigate their influences on local and meta-ecosystem functions. We integrate two novel approaches in this classic meta-ecosystem model: a dispersers’ pool to capture the fraction of moving organisms and time scales separation to partition local and regional dynamics. We show that non-diffusive consumer movement increases landscape heterogeneity while diffusive consumer movement enhances source-sink dynamics. Local ecosystem differences driven by consumer movement type are less prevalent at meta-ecosystem extents. Thus, movement type is essential for predicting local ecosystem dynamics. Our results support recent calls to explicitly consider the role of consumers in shaping and maintaining ecosystem functions in space and time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".