Bibliographic record
Abstract
We present an approach for the analysis of spatiotemporal patterns and scale-dependence in the dynamics of plant communities that combines well-known methods to reduce complexity arising from the high-dimensionality of ecological data. Our approach takes into account both correlations and autocorrelations in the structure and dynamics of the community. In application to a sand dune annual plant community, we find answers to questions regarding the nature of community response in space and time, and identify significant spatiotemporal interactions and spatiotemporal scales. Community-level spatial correlograms reveal a fixed pattern in time that is not apparent from species-level dynamics. In this sense, the community is shown to be more stable than the sum of its parts. This form of stability is not a simple artifact of averaging species-specific responses, and points to some consistency in species interactions. Temporal correlograms are highly spatially-specific. In this sense, the inclusion of the spatial information is shown to change our view of temporal dynamics. The relative importance of biological processes versus an environmental gradient is examined in the temporal community response. We find that community response to rainfall is also spatially-specific and that the temporal autocorrelation dynamics shows similar patterns even when the effect of rainfall is removed. Our findings are relevant to both empirical and theoretical work aimed at understanding the interaction between space and time in ecological 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".