How individual links affect network stability in a large‐scale, heterogeneous metacommunity
Bibliographic record
Abstract
Elucidating how dispersal and landscape connectivity influence metacommunity stability will shed light on natural processes structuring ecosystems and help prioritize conservation actions in an increasingly fragmented world. Much of the theoretical and mathematical development of the metacommunity concept has been based on simplified experimental systems or simulated data. We still have limited understanding of how variation in the habitat matrix and species-specific differences in dispersal ability contribute to metacommunity dynamics in heterogeneous landscapes. We model a metacommunity of rainforest mammals in Borneo, a tropical biodiversity hotspot, where protected areas are increasingly isolated by ongoing habitat disturbance and loss. We employ a combination of hierarchical models of local abundance, circuit-theory-based dispersal analysis, and metapopulation models. Our goal was to understand which landscape links were the most important to metapopulation persistence and metacommunity stability. Links were particularly important if they were short and connected two large patches. This was partly because only the very shortest links could be traversed by poorly dispersing species, including small herbivores such as chevrotains (Tragulus spp.) and porcupines. Links that join large patches into a "super-patch" may also promote island-mainland rather than Levins-type metapopulation dynamics for good dispersers, particularly large carnivores such as clouded leopards (Neofelis diardi) and sun bears (Helarctos malayanus), reducing metapopulation extinction risk and thereby enhancing metacommunity stability. Link importance to metacommunity stability was highly correlated between heterogeneous and homogeneous landscapes. But link importance to metapopulation capacity varied strongly across species, and the correlation between heterogeneous and homogeneous landscape matrix scenarios was low for poorly dispersing taxa. This suggests that the environmental conditions in the area between habitat patches, the landscape matrix, is important for assessing certain individual species but less so for understanding the stability of the entire metacommunity.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".