A null model for quantifying the geometric effect of habitat subdivision on species diversity
Bibliographic record
Abstract
Abstract Aim To derive null models for the expected number of species shared among multiple samples or habitat patches, allowing exploration of the geometric effects of subdivision on species diversity. Location Global. Major taxa studied Predominantly sessile organisms. Methods The occurrence probability of a species in a subdivided area depends on its abundance and spatial pattern over a known habitat extent. The joint probability that two subareas share a species is the product of the probability of species occurrence in each subarea provided that the latter probability is independent. The sum of this probability over all species is the number of species the two subareas share, or zeta diversity of order 2. Generalizing from 2 tomsubareas yields a null model for zeta diversity of orderm. From zeta diversity, many metrics (e.g., beta and gamma diversity) for themhabitat patches can be calculated, revealing the effects of increasing habitat fragmentation. Results The null models show the geometric effects of subdivision depend on patterns of spatial distribution of species within a landscape and evenness of species abundance distribution. For aggregated assemblages, increasing subdivision decreases shared species, increases beta diversity and results in higher total species richness in subdivided habitat than an equal contiguous area. Main conclusions To correctly interpret diversity patterns in fragmented habitat the geometric effects of subdivision must be considered. Our models explain why fragmented habitat could have higher diversity than continuous habitat of equal area but predict a threshold patch‐size above which this will not occur (herec. 100 ha). Apparently positive diversity effects of subdivision, including more species in groups of small patches, are probable outcomes of spatial aggregation of assemblages. The shared species null models offer an analytical tool for exploring the geometric effects of subdivision on diversity while controlling for total habitat area.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".