Naturalists, Natural History, and the Nature of Biological Diversity
Bibliographic record
Abstract
Abstract In this essay, I argue that natural history--observing the natural world and deciphering its patterns--is as essential today as it was during Darwin's lifetime to the continuing development of ecology and evolutionary biology. This tradition, which I illustrate through the example of E. O. Wilson's discovery of the taxon cycle 50 years ago, is still very much alive, but there is a growing tendency for observation to serve theory rather provide new insight or to test the predictions of theory. This tendency manifests itself in the failure of ideas about the diversity, distribution, and abundance of species to be informed by patterns in nature that are readily apparent. On the one hand, supporters of neutral theory have sidestepped the unrealistically slow dynamics of random processes in large metacommunities, and they have failed to note global correlations in species numbers and population sizes within taxa. On the other hand, proponents of niche theory have disregarded the implications of variation in distribution and abundance among close relatives, which implies population regulation largely by species-specific agents, such as pathogens. Nor has community niche theory addressed the independence of distribution and abundance with respect to number of close relatives (and presumed competitors). The diversity, abundances, and distributions of species represent the unfolding of many processes over a historically and geographically contingent landscape, for which experimental methods of scientific inquiry are poorly suited. To interpret patterns of diversity, we must continue to depend on inductive reasoning inspired by the data of natural history.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".