Data and analysis from: Body mass, temperature, and depth shape the maximum intrinsic rate of population increase in sharks and rays
Bibliographic record
Abstract
An important challenge in ecology is to understand variation in species' maximum intrinsic rate of population increase, 𝑟𝑚𝑎𝑥, not least because 𝑟𝑚𝑎𝑥 underpins our understanding of the limits of fishing, recovery potential, and ultimately extinction risk. Across many vertebrate species, terrestrial and aquatic, body mass and environmental temperature are important correlates of 𝑟𝑚𝑎𝑥. In sharks and rays, specifically, 𝑟𝑚𝑎𝑥 is known be lower in larger species, but also in deep-sea ones. We use an information-theoretic approach that accounts for phylogenetic relatedness to evaluate the relative importance of body mass, temperature and depth on 𝑟𝑚𝑎𝑥. We show that both temperature and depth have separate effects on shark and ray 𝑟𝑚𝑎𝑥 estimates, such that species living in deeper waters have lower 𝑟𝑚𝑎𝑥. Furthermore, temperature also correlates with changes in the mass scaling coefficient, suggesting that as body size increases, decreases in 𝑟𝑚𝑎𝑥 are much steeper for species in warmer waters. These findings suggest that there are (as-yet understood) depth-related processes that limit the maximum rate at which populations can grow in deep sea sharks and rays. While the deep ocean is associated with colder temperatures, other factors that are independent of temperature, such as food availability and physiological constraints, may influence the low 𝑟𝑚𝑎𝑥 observed in deep sea sharks and rays. Our study lays the foundation for predicting the intrinsic limit of fishing, recovery potential, and extinction risk species based on easily accessible environmental information such as temperature and depth, particularly for data-poor species. This repository contains the data and a minimum working example of the model-fitting process used for the article "Body mass, temperature, and depth shape productivity in sharks and rays", which is currently in press at Ecology and Evolution.
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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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.021 |
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".