Patch size distribution and network connectivity affect species invasion dynamics in dendritic networks
Bibliographic record
Abstract
Abstract Biological invasions are globally affecting ecosystems, causing local species loss and altering ecosystem functioning. Understanding the success and unfolding of such biological invasions is thus of high priority. Both local properties and the spatial network structure have been shown to be determinants of invasion success, and the identification of spatial invasion hubs directly promoting invasion dynamics is gaining attention. Spatial dynamics, however, could also indirectly alter invasion success by shaping local community structure: in many ecosystems, such as riverine networks, regional properties such as patch size distribution are known drivers of local community structures, which themselves may affect the establishment success of invading species. Using microcosm experiments in dendritic networks, we disentangled how patch size distribution and dispersal along specific network topologies shaped local communities, and, subsequently, affected the establishment success of invading species. We find that inherent patch size distributions shaped composition and diversity of local communities, and, subsequently, modulated invasion success. Specifically, the relationship between local diversity and invasion success changed across an increasing patch size gradient from a negative to a positive correlation, while overall increasing patch size reduced invasion success. Connectivity did not have a direct effect on invasion success but indirectly affected invasions by shaping diversity patterns in the whole network. Our results emphasize the relevance of indirect, landscape-level effects on species invasions, which need to be considered in the management of spatial habitat networks.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".