Predicting future threats to biodiversity from habitat selection by humans
Bibliographic record
Abstract
Global biodiversity is threatened by the human use, alteration and destruction of habitat. The distribution of humans among habitats should, as in other animals, be governed by densitydependent feedback on fitness. It should be possible, therefore, to merge human habitat selection – and, perhaps, other adaptive behaviours – with projections of human population size to forecast future threats to biodiversity. We evaluate this deceptively simple postulate by developing a model of human habitat selection. We test it with World Resources Institute (WRI) data and use the resulting pattern to predict threats to biodiversity. Humans select urban over rural habitats in a way that is consistent with an evolutionarily stable strategy of habitat selection. The choice of habitat is modified by per capita energy use. The pattern of habitat use is associated with increased threats to the biodiversity of mammals, birds and higher plants, but not that of reptiles. We use WRI projections of future human populations to predict the anticipated pattern of human habitat use in 2020. We then use the new distribution to calculate changes in threats to biodiversity and rank nations according to their projected threats. African nations rank consistently higher than nations from any other region. Preventive global conservation might, therefore, be most productively concentrated on helping Africa.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".