Realistic simulation of the effects of abundance distribution and spatial heterogeneity on non-parametric estimators of species richness
Bibliographic record
Abstract
: Several non-parametric estimators have been proposed for estimating species richness from a spatial sample. While the label non-parametric may suggest that a method makes few assumptions, these estimators are known to rely on a homogeneous community with certain abundance distributions. In this paper we simulate the effect of different abundance distributions (geometric series, log-normal, and broken-stick models) and of different types of spatial heterogeneity (species-specific aggregation, gradients, and an edge effect) on the performance of four non-parametric estimators of species richness for presence-absence data (Jack1, Jack2, Chao2, and ICE). In order to focus on parameter settings likely to be encountered in real communities, we derived simulation parameters from real data from four agricultural habitat types in central Switzerland. Based on an ANOVA of relative bias, all estimators failed for communities simulated under the geometric model and were considerably affected by a simulated edge effect, but species-specific aggregation, an environmental gradient, and differences between community types had little effect on estimator performance. Species abundance distribution and spatial heterogeneity influence estimator performance by decreasing the proportion of species represented in the sample, which may be counteracted by adapting the sampling design. For reasonably complete samples, Chao2 was the least biased, but suffered from a large variance, as did Jack2. We recommend using the first order jackknife Jack1 or the incidence-based coverage estimator ICE, but only for samples that contain at least 80% of the species.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".