Special Feature: Intraspecific variation in ecology & evolution
Bibliographic record
Abstract
Species across the tree of life differ in many aspects. Comparative analyses and meta-analyses in both animal and plant kingdoms have brought significant insights on interspecific variation (e.g. Raffard et al., 2019; Siefert et al., 2015). As ecologists, we often work on the assumption that interspecific variation is greater than intraspecific variation, despite the central role intraspecific variation plays in evolutionary theory (Darwin, 1859). Yet, intraspecific variation may improve species' ability to respond and adapt to new environmental conditions, which is pivotal in the current context of global changes where both the mean and the variance of environmental conditions are changing. Therefore, it is surprising that the use of single average values per species has persisted across ecological research, from using mean values of ecological traits in community ecology (e.g. McGill et al., 2006), to mean values of vital rates in population projection models (e.g. Caswell, 2006; Vindenes et al., 2021). Doing so ignores the genetic and phenotypic variation present within species, from individuals through to populations, often over-simplifying the complexity that exists in nature. In this special feature, Rosa et al. (2025) nicely exemplifies how ignoring intraspecific variation in vital rates can bias predictions of population growth rates from demographic models. The authors show that mixing vital rates (e.g. survival, recruitment) from different yellow-bellied toad (Bombina variegata) populations, inhabiting contrasting ecological contexts, in hybrid ‘Frankenstein’ matrices, would rely on the assumption that all populations have similar vital rates, independently on their habitats or encountered weather conditions. This strong and somewhat unrealistic biological assumption can lead hybrid matrices to produce biased predictions of population growth rates with potentially deleterious implications for conservation or management.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.471 | 0.258 |
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".