Estimating extinction from species–area relationships: why the numbers do not add up
Bibliographic record
Abstract
Researchers commonly use species-area relationships (SAR) to estimate extinction rates caused by habitat loss by reversing the SAR, extrapolating backward from area to calculate expected species loss. We have previously shown that the backward SAR method considerably overestimates extinction rates due to a previously unrecognized sampling artifact. Jacob Bock Axelsen, Uri Roll, Lewi Stone, and Andrew Solow recently argued that the backward SAR method is correct and the method does not overestimate extinction rates. In this paper, we further elaborate and clarify our previous results. We show that the backward SAR method gives the correct extinction rate only under a strict complementary-area sampling design, which is not used in practice because it requires knowing which species are endemic to the area of destroyed habitat, or the number of species in the complementary area. Because of this problem, researchers substitute a power-law model for the SAR in the backward SAR equation. However, this substitution violates the backward SAR method's requirement for complementary sampling. With this model substitution, the backward SAR equation is no longer correct, except in the special case of randomly distributed species. For the complementary sampling or random distribution of species, the first individual of a species to be encountered and the last individual to be encountered to lose the species are exchangeable (or the same individual). But this is not the case for other sampling designs or if species are not randomly distributed and explains why the backward SAR method fails to correctly estimate extinction rates. Our proofs and results are general and explain the widely recognized overestimation of extinction by the backward SAR method. We suggest future directions for developing general theory for estimating species extinction from species-area relationships. Until then, however, the backward SAR method should not be used to estimate species extinction in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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; both teacher heads agree on what is shown here.
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".