Local‐regional relationships and the geographical distribution of species
Bibliographic record
Abstract
ABSTRACT Aim Local‐regional (LR) species diversity plots were conceived to assess the contribution of regional and local processes in shaping the patterns of biological diversity, but have been used also to explore the scaling of diversity in terms of its alpha, beta, and gamma components. Here we explore the idea that patterns in the geographical ranges of species over a continent can determine the shape of small region to large region (SRLR) plots, which are equivalent to LR plots when comparing the diversity of sites at two regional scales. Location To test that idea, we analysed the diversity patterns at two regional scales for the mammals of North America, defined as the mainland from Alaska and Canada to Panama. Method We developed a theoretical model relating average range size of species over a large‐scale region with its average regional point species diversity (RPD). Then, we generated a null model of expected SRLR plots based on theoretical predictions. Species diversities at two scales were modelled using linear and saturation functions for Type I and Type II SRLR relationships, respectively. We applied the models to the case of North American mammals by examining the regional diversity and the RPD for 21 large‐scale quadrats (with area equal to 160,000 km2), arranged along a latitudinal gradient. Results Our model showed that continental and large‐scale regional patterns of distribution of species can generate both types of SRLR relationship, and that these patterns can be reflected in LR plots without invoking any kind of local processes. We found that North American nonvolant mammals follow a Type I SRLR relationship, whereas bats follow a Type II pattern. This difference was linked to patterns in which species of the two mammalian groups distribute in geographical space. Conclusion Traditional LR plots and the new SRLR plots are useful tools in exploring the scaling of species diversity and in showing the relationship between distribution and diversity. Their usefulness in comparing the relative role of local and regional processes is, however, very limited.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".