Effects of study design and analysis on the spatial community structure detected by multiscale ordination
Bibliographic record
Abstract
Abstract Question: What are the effects of different aspects of data collection and analysis on the spatial partitioning of ordination results by multiscale ordination? Locations: Heterogeneous pasture at Marchairuz in the Jura mountains, Switzerland and Mixed hardwood‐pine forest in Oostings Natural Area, NC, USA. Methods: We evaluated the efficiency of different sampling designs for identifying the spatial structure of plant communities and analysed two datasets with multiscale ordination. We compared the effects of quadrat size, the number of species included in the analysis, data type, detrending and ordination method on the shape and precision of the community variogram summarizing spatial community structure. Results: A three‐block sampling design provided a more even distribution of the number of pairs of observation per distance class than random, transect or grid designs. The precision of variogram estimates depended more strongly on the number of species than on the number of quadrats. In contrast, the choice of data type (abundance transformation) had little influence on the shape of the variogram. Detrending reduced the range of spatial autocorrelation. An increase in quadrat size resulted in a smoother variogram and stronger spatial autocorrelation. Principal components analysis (PCA) and Redundancy analysis (RDA) resulted in a larger range of spatial autocorrelation values than did Correspondence analysis (CA) and Canonical correspondence analysis (CCA), but the shape of the variograms was similar. Conclusion: Random samples as well as transects and regular grids may not be efficient sampling designs for spatial analysis of community structure. While the number of species considered strongly affects the precision of a community variogram, its shape depends on the size of the sampling units. Earlier studies may have overestimated the spatial scale of internal organization in plant communities.
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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 | 0.392 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".