An empirical investigation of why species–area relationships overestimate species losses
Bibliographic record
Abstract
It is generally assumed that, when natural habitat is converted to human-dominated land cover, such habitat is lost to its native species. Most literature assumes that species richness should vary as a function of remaining natural area, following the well-known species-area relationship (i.e., classic SAR). However, classic SARs have consistently overestimated species losses resulting from conversion of natural forested land cover to human-dominated landscapes. Moreover, richness is sometimes a peaked function of remaining natural habitat. Recent studies propose modified SAR models based on species' utilization of multiple habitat types, yet none fully explain a peaked species-area relationship. Here, we evaluate the responses of total avian richness, forest bird richness, and open-habitat bird richness to remaining natural land cover within 991 quadrats, each 100 km2, across southern Ontario, Canada. Total bird species richness peaks at roughly 50% natural land cover. Richness of forest birds varies as a classic power function of forested area. In contrast, richness of birds that prefer open habitats does not increase monotonically with either natural- or human-dominated land cover. Richness of open-habitat species can be predicted when we partition human-dominated land cover into an "available human-dominated" component and "lost" habitat. Disiinguishing three land-cover types (natural, available human-dominated, and lost) can thus permit accurate predictions of species richness in landscapes with differing levels of natural habitat conversion.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".