THE LOCAL–REGIONAL RELATIONSHIP: IMMIGRATION, EXTINCTION, AND SCALE
Bibliographic record
Abstract
While local processes (e.g., competition, predation, and disturbance) presumably cause species exclusion and thus limit diversity in individual communities, regional processes (e.g., historical events, immigration, and speciation) are assumed to provide a source of species to colonize and thus enrich local communities. Ecologists have attempted to distinguish between these two sets of processes using graphical evidence for local assemblage saturation. However, such efforts have been controversial and are antithetical to the fact that local diversity bears an imprint of both. We examine the local–regional species richness relationship from the perspective of the theory of island biogeography and develop a model that can produce the full range of observed local–regional richness relationships from linear to curvilinear. Importantly, unlike previous models, we do not require species interactions to produce the curvilinear pattern. Curvilinear relationships arise if per-species stochastic extinction rates are substantially higher than colonization rates, while linear relationships result if colonization rates are higher than extinction rates. Because we also show that merely changing the sampling scale can make local–regional relationships appear either saturated or unsaturated, an inference of ecological processes, derived solely from local–regional relationships, is unwarranted.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".