Effects of Economic Prosperity on Numbers of Threatened Species
Bibliographic record
Abstract
Abstract: We used data from over 100 countries to investigate the link between numbers of threatened species and per‐capita gross national product. We corrected for factors that might otherwise confound such a relationship. Our study was motivated by the continuing debate over the relationship between environmental degradation and per‐capita income. Proponents of the environmental Kuznets‐curve hypothesis argue that although environmental degradation may increase initially, increases in per‐capita income will eventually result in greater environmental quality. Theoretical objections and the lack of widespread empirical evidence recently have thrown doubt on the existence of such a pattern. Treating threat to biodiversity as one potential indicator of environmental degradation, we divided threatened species into seven taxonomic groups ( plants, mammals, birds, amphibians, reptiles, fishes, and invertebrates) and analyzed each group separately. Count‐data regression analysis indicated that the number of threatened species was related to per‐capita gross national product in five of seven taxonomic groups. Birds were the only taxonomic group in which numbers of threatened species decreased throughout the range of developed countries' per‐capita gross national product. Plants, amphibians, reptiles, and invertebrates showed increasing numbers of threatened species throughout this same range. If these relationships hold, increasing numbers of species from several taxonomic groups are likely to be threatened with extinction as countries increase in prosperity. A key challenge is to understand the interactions among consumer preferences, biology, and institutions that lead to the relationship observed for birds and to see whether this knowledge can be applied to conservation of other taxa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".