Linking ecological processes with spatial and non‐spatial patterns in plant communities
Bibliographic record
Abstract
Summary 1. Attempts to infer underlying ecological process from observed patterns in ecology have been widespread, but have generally relied on first‐order (non‐spatial) community characteristics such as the species abundance distribution (SAD). This measure has become an important test of several theories of species coexistence, but has proved unsuccessful in distinguishing between them. 2. Spatially explicit data are increasingly available for a range of ecological communities, and analysis methods for these data are well developed. However, relatively little work has investigated the potential of these data for similar inference about mechanisms of coexistence. 3. In this study, we systematically investigate the spatial and non‐spatial signals of simulated ecological processes. We include neutral, niche, lottery, Janzen–Connell and heteromyopia models, deriving and comparing first‐ and second‐order measures for the patterns they generate. 4. We find that the SAD is unable to distinguish reliably between underlying models, with random variation in its shape concealing any systematic differences. 5. A new second‐order summary measure of spatial structure derived in this paper, in contrast, proves highly sensitive to the type of ecological interaction being modelled, and is robust to random variation. 6. Synthesis. A simple summary measure of the spatial structure of plant communities is presented and found to be a more powerful indicator of underlying process in simulated data than a widely used first‐order measure, the SAD. The potential for answering important ecological questions using spatial statistics, particularly concerning mechanisms of coexistence in diverse communities, appears to be great.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".