LOCAL AND REGIONAL ZOOPLANKTON SPECIES RICHNESS: A SCALE-INDEPENDENT TEST FOR SATURATION
Bibliographic record
Abstract
Assemblages of coexisting species are formed by immigration from a regional pool of colonists and local interactions among species and with the physical environment. Theory suggests that the shape of the relationship between regional and local species richness may indicate the relative roles of dispersal and local interactions in limiting local diversity. Here we examine patterns of regional and local species richness in freshwater crustacean zooplankton to test whether linear (suggesting dispersal limitation) or curvilinear (suggesting saturation, via strong local control) functions best fit the data. Local richness appeared saturated when regions of different spatial extents were included on the same graph. However, this pattern was influenced by differences in scale among surveys. We corrected for the effects of regional scale by plotting mean local richness against the residuals of the species–landscape area relations. Controlling for the extent of the regional scale produced much more linear patterns, suggesting strong dispersal limitation. We present a simple graphical model to explain how variation among surveys in the geographic size of regions can produce apparent saturation of local diversity even if the underlying pattern of local and regional richness is linear. We also compare the predictive power of residual regional richness on local richness with that of several local features in a multiple regression model. Local richness exhibits strong relationships with both residual regional richness and pH. We argue that the relative strengths of local and regional processes depend on the definition of the regional scale. A variety of evidence suggests that local processes play a major role in generating differences in zooplankton diversity among lakes within a biogeographic region. Evidence for the importance of dispersal limitation comes largely from comparisons of lakes across very large scales. Our analysis suggests that linear patterns of local and regional diversity are not incompatible with strong local interactions.
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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".