Carrying capacity for species richness as a context for conservation: a case study of North American breeding birds
Bibliographic record
Abstract
Aim To demonstrate that the concept of carrying capacity for species richness (SK) is highly relevant to the conservation of biodiversity, and to estimate the spatial pattern of SK for native landbirds as a basis for conservation planning. Location North America. Methods We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities. We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables. The predictor variables included the latest and probably most accurate satellite and simulation-model derived products. Results The best model of SK included mean annual and inter-annual variation in gross primary productivity and potential evapotranspiration. This model explained 70% of the variation in landbird species richness. Geographically, predicted SK was lowest at higher latitudes and in the arid west, intermediate in the Rocky Mountains and highest in the eastern USA and the Great Lakes region of the USA and Canada. Main conclusions Areas that are high in SK but low in human density are high priorities for protection, and areas high in SK and high in human density are high priorities for restoration. Human density was positively related to SK, indicating that humans select environments similar to those with high bird species richness. Federal lands were disproportionately located in areas of low predicted SK.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".