A multispecies incidence function model for understanding metacommunity dynamics in fragmented landscapes.
Bibliographic record
Abstract
Metapopulation theory is essential for understanding species persistence and distribution in fragmented landscapes. Hanski’s Incidence Function Model (IFM) is a key tool to apply metapopulation theory, but its single-species focus limits its relevance for community-level conservation. We propose a hierarchical multispecies IFM (MIFM), capitalizing on all species information to assess the effects of local and landscape characteristics on species and communities. Applied to bird communities in Mexican cocoa agroforests, the MIFM demonstrates that it outperforms the IFM in generalization, accuracy, and uncertainty representation, while capturing specific environmental influences. Our case study reveals that cocoa plantation size and landscape connectivity were two major factors influencing bird communities. The MIFM’s hierarchical structure leverages additional data to assess environmental effects on communities and individual species, especially the rarest ones. Its versatility, characterizing its monospecific counterpart, makes the MIFM a powerful tool for conservation studies, potentially enhancing the use of existing, under-exploited databases.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".