Checkerboard score–area relationships reveal spatial scales of plant community structure
Bibliographic record
Abstract
Identifying the spatial scale at which particular mechanisms influence plant community assembly is crucial to understanding the mechanisms structuring communities. It has long been recognized that many elements of community structure are sensitive to area; however the majority of studies examining patterns of community structure use a single relatively small sampling area. As different assembly mechanisms likely cause patterns at different scales we investigate how plant species co‐occurrence patterns change with sampling unit scale. We use the checkerboard score as an index of species segregation, and examine species C‐score1–sampling area patterns in two ways. First, we show via numerical simulation that the C‐score–area relationship is necessarily hump shaped with respect to sample plot area. Second we examine empirical C‐score–area relationships in arctic tundra, grassland, boreal forest and tropical forest communities. The minimum sampling scale where species co‐occurrence patterns were significantly different from the null model expectation was at 0.1 m 2 in the tundra, 0.2 m 2 in grassland, and 0.2 ha in both the boreal and tropical forests. Species were most segregated in their co‐occurrence (maximum C‐score) at 0.3 m 2 in the tundra (0.54 3 0.54 m quadrats), 1.5 m 2 in the grassland (1.2 3 1.2 m quadrats), 0.26 ha in the tropical forest (71 3 71 m quadrats), and a maximum was not reached at the largest sampling scale of 1.4 ha in the boreal forest. The most important finding is that the dominant scales of community structure in these systems are large relative to plant body size, and hence we infer that the dominant mechanisms structuring these communities must be at similarly large scales. This provides a method for identifying the spatial scales at which communities are maximally structured; ecologists can use this information to develop hypotheses and experiments to test scale‐specific mechanisms that structure communities.
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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 | 0.001 | 0.001 |
| 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".