Using the Species–Area Relationship to Set Baseline Targets for Conservation
Bibliographic record
Abstract
This paper demonstrates how the power form of the Species-Area Relationship (SAR) can be used to set conservation targets for land classes using biodiversity survey data. The log-transformation of the power model is a straight line; therefore, if one knows the average number of species recorded per survey site and can estimate the true species number present in the land class, using EstimateS software, it is possible to calculate the slope of the curve, or z-value. The z-value is the exponent in the power model and it can then be used to estimate the proportion of area required to represent a given proportion of species present in any land class. This application of the SAR is explored using phytosociological relevé data from South Africa's Succulent Karoo biome. We also provide suggestions for extrapolating the estimated z-values to other land classes within a bioregion that lack sufficient survey data, using the relationship between z-values and remotely determined landscape variables such as habitat diversity (topographic diversity) and geographic location (latitude and longitude). The SAR predicts that for most Succulent Karoo vegetation types a conservation target of 10% of the land area would not be sufficient to conserve the majority of species. We also demonstrate that not all land classes are equal from a plant biodiversity perspective, so applying one target to all land classes in a region will lead to significant gaps and inefficiencies in any reserve network based on this universal target.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".