Determinants of geographic range size in plants
Bibliographic record
Abstract
Geographic range size has long fascinated ecologists and evolutionary biologists, yet our understanding of the factors that cause variation in range size among species and across space remains limited. Not only does geographic range size inform decisions about the conservation and management of rare and nonindigenous species due to its relationship with extinction risk, rarity, and invasiveness, but it also provides insights into fundamental processes such as dispersal and adaptation. There are several features unique to plants (e.g. polyploidy, mating system, sessile habit) that may lead to distinct mechanisms explaining variation in range size. Here, we highlight key studies testing intrinsic and extrinsic hypotheses about geographic range size under contrasting scenarios where species' ranges are static or change over time. We then present results from a meta-analysis of the relative importance of commonly hypothesized determinants of range size in plants. We show that our ability to infer the relative importance of these determinants is limited, particularly for dispersal ability, mating system, ploidy, and environmental heterogeneity. We highlight avenues for future research that merge approaches from macroecology and evolutionary ecology to better understand how adaptation and dispersal interact to facilitate niche evolution and range expansion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".