Temperature dependence of the reproduction niche and its relevance for plant species distributions
Bibliographic record
Abstract
Abstract The distribution and abundance of plant species are intimately related to their reproductive success, which in turn is affected by a large number of environmental variables. Yet, reproductive success is rarely taken into account in species distribution models ( SDM s). In this paper we examine the extent to which consideration of the reproduction niche and its relationship with temperature could improve SDM s. We review the literature on plant reproductive responses to temperature and the influence of these relationships on species range delimitation. We define the reproduction niche and discuss how temperature influences several stages of the reproductive process. Furthermore, we review examples that illustrate how the reproduction niche influences species distributions and discuss how aspects of the reproduction niche could be considered in SDM s. We show that the reproduction niche fundamentally influences species distributions and that in principle it is easy to include aspects of the reproduction niche in SDM s, although sufficient data are only available for a restricted number of species. Bayesian methods and inverse parameterization may be the most efficient way to use existing data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".