Distribution of abundance across the range in eastern North American trees
Bibliographic record
Abstract
ABSTRACT Aim We analysed spatial datasets of abundance across the entirety, or near entirety, of the geographical ranges of 134 tree species to test macroecological hypotheses concerning the distribution of abundance across geographical ranges. Location Our abundance estimates came via the USDA Forest Service Forest Inventory and Analysis Eastwide Database, which contains data for 134 eastern North American tree species. Methods We extracted measures of range size and the spatial location of abundance relative to position in the range for each species to test four hypotheses: (a) species occur in low abundance throughout most of their geographical range; (b) there is a positive interspecific relationship between abundance and range size; (c) species are more abundant in the centre of their range; and (d) there is a bimodal distribution of spatial autocorrelation in abundance across a species range. Results Our results demonstrate that (a) most species (85%) are abundant somewhere in their geographical range; (b) species achieving relatively high abundance tend to have larger range sizes; (c) the widely held assumption that species exhibit an ‘abundant‐centre distribution’ is not well supported for the majority of species; we suggest ‘abundant‐core’ as a more suitable term; and (d) there is no evidence of a bimodal distribution of spatial autocorrelation in abundance. Main Conclusions For many tree species, high abundance can be achieved at any position in the range, though suitable sites are found with less frequency towards range edges. Competitive relationships may be involved in the distribution of abundance across tree ranges and species with larger ranges (and possibly broader niches) may be affected more by biotic interactions than smaller ranging 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".