Research Regional Diversity of Insects in North America
Bibliographic record
Abstract
ABSTRACT Data on the regional distribution in America north of Mexico of>3,550 insect species belonging to a representative sample of taxonomically relatively well-known groups, chiefly families, shows that more species occur in southern regions (71 % of all species occur south of a line along state boundaries from the Arizona-California to Georgia-South Carolina borders) than in northern regions (50 % of all species occur north of the Oregon-California to New England-New York borders). More species occur in western regions (73 % only west of Manitoba, Canada, to Texas) than in eastern regions (56%). Texas-Oklahoma and Arizona-New Mexico contain the most species, followed by California, Georgia-Florida, and the mid-Atlantic region (New York and other states). The per-centages of species recorded in different areas depend on the state of knowledge as well as on the geographical restriction of the groups. Knowledge of most groups of North American insects is inadequate for any meaningful distributional analysis of the sort carried out for these representative samples. Although about one-third of the species analyzed are relatively widespread, the many geographic differ-ences confirm that quantifying biodiversity (especially for purposes of preservation or management) requires local or regional, and not just global, analyses. BIODIVERSITY,MOSTSIMPLYDEFINEDASTHENUMBEROFSPECIESINANarea, has attracted considerable interest recently, much of it focused on large areas, such as the world, the tropics, or North America. Such focus obscures the fact that patterns of regional diversity are especially instructive, and, moreover, provide more realistic estimates of the work required to improve our under-standing of diversity and allow for its preservation and manage-ment. However, from a narrower perspective, most attention has been paid to the distributions of individual species, grouping them into a limited number of continental or more restricted range types
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".