The importance of inter‐individual variation in predicting species' responses to global change drivers
Bibliographic record
Abstract
Abstract Inter‐individual variation in phenotypic traits has long been considered as “noise” rather than meaningful phenotypic variation, with biological studies almost exclusively generating and reporting average responses for populations and species’ average responses. Here, we compare the use of an individual approach in the investigation of extracellular acid–base regulation by the purple sea urchin Paracentrotus lividus challenged with elevated p CO 2 and temperature conditions, with a more traditional approach which generates and formally compares mean values. We detected a high level of inter‐individual variation in acid–base regulation parameters both within and between treatments. Comparing individual and mean values for the first (apparent) dissociation constant of the coelomic fluid for individual sea urchins resulted in substantially different (calculated) acid–base parameters, and models with stronger statistical support. While the approach using means showed that coelomic p CO 2 was influenced by seawater p CO 2 and temperature combined, the individual approach indicated that it was in fact seawater temperature in isolation that had a significant effect on coelomic p CO 2 . On the other hand, coelomic [HCO 3 − ] appeared to be primarily affected by seawater p CO 2 , and less by seawater temperature, irrespective of the approach adopted. As a consequence, we suggest that individual variation in physiological traits needs to be considered, and where appropriate taken into account, in global change biology studies. It could be argued that an approach reliant on mean values is a “procedural error.” It produces an artefact, that is, a population's mean phenotype. While this may allow us to conduct relatively simple statistical analyses, it will not in all cases reflect, or take into account, the degree of (physiological) diversity present in natural populations.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".