Bibliographic record
Abstract
Ecosystem multifunctionality, the simultaneous production of multiple ecosystem functions, depends on community diversity, composition, productivity, and spatial scale. In metacommunities, each of these community properties is affected by how species disperse between local patches to track environmental change. Here we use a consumer-resource metacommunity model of resource competition to show how dispersal affects the link between diversity, composition, and ecosystem multifunctionality. When species differ in their functional traits and environmental niche, metacommunity multifunctionality becomes highly dependent upon dispersal, which allows community diversity to be maintained when environmental conditions change. Dispersal promotes multifunctionality in two ways: (1) species sorting, whereby species track local environmental changes by shifting in space, thus preserving diversity and ensuring high biomass productivity, and (2) mass effects, whereby source-sink dynamics allow species to persist in suboptimal environments, thus increasing local diversity. Changing the rate at which species disperse affects the strength of these metacommunity processes, and so metacommunity multifunctionality exhibits a unimodal relationship with dispersal, peaking when dispersal is intermediate. Species-sorting dynamics also provide spatial insurance whereby compensatory dynamics stabilize the fluctuations of each function through time at the regional scale. However, this does not extend to the local scale, where species sorting results in high temporal variability for each function, even though the overall rates of multifunctionality are high. Our results suggest that metacommunity processes are important determinants of ecosystem multifunctionality, and thus effective management of multiple ecosystem functions requires consideration of landscape connectivity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".