The nature of science: The fundamental role of natural history in ecology, evolution, conservation, and education
Bibliographic record
Abstract
There is a contemporary trend in many major research institutions to de-emphasize the importance of natural history education in favor of theoretical, laboratory, or simulation-based research programs. This may take the form of removing biodiversity and field courses from the curriculum and the sometimes subtle maligning of natural history research as a "lesser" branch of science. Additional threats include massive funding cuts to natural history museums and the maintenance of their collections, the extirpation of taxonomists across disciplines, and a critical under-appreciation of the role that natural history data (and other forms of observational data, including Indigenous knowledge) play in the scientific process. In this paper, we demonstrate that natural history knowledge is integral to any competitive science program through a comprehensive review of the ways in which they continue to shape modern theory and the public perception of science. We do so by reviewing how natural history research has guided the disciplines of ecology, evolution, and conservation and how natural history data are crucial for effective education programs and public policy. We underscore these insights with contemporary case studies, including: how understanding the dynamics of evolutionary radiation relies on natural history data; methods for extracting novel data from museum specimens; insights provided by multi-decade natural history programs; and how natural history is the most logical venue for creating an informed and scientifically literate society. We conclude with recommendations aimed at students, university faculty, and administrators for integrating and supporting natural history in their mandates. Fundamentally, we are all interested in understanding the natural world, but we can often fall into the habit of abstracting our research away from its natural contexts and complexities. Doing so risks losing sight of entire vistas of new questions and insights in favor of an over-emphasis on simulated or overly controlled studies.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".