An important challenge in ecology is to understand variation in species’ maximum intrinsic rate of population increase, ?_???, not least because ?_??? 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 ?_???. In sharks and rays, specifically, ?_??? 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 ?_???. We show that both temperature and depth have separate effects on shark and ray ?_??? estimates, such that species living in deeper waters have lower ?_???. Furthermore, temperature also correlates with changes in the mass scaling coefficient, suggesting that as body size increases, decreases in ?_??? 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 ?_??? 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.

Analyses are reproducible using version 3.3.2 or above (R Core Team 2016).

Files needed for reproducing the analyses are:

chond-data.csv: Data frame with 63 rows (species) and 11 variables. Some of these variables are based on the same life history trait but are transformed for ease of interpretation and analysis.

stein-et-al-single.tree: Phylogenetic tree with scaled branch lengths from Stein et al. (2018) used in analyses. These are freely downloadable from <http://vertlife.org/sharktree/>.

rmax-scaling-analysis.R: R code with minimum working example of how to load data files, fit models phylogenetic linear models using the `pgls` function in the `caper` package, run information-theoretic comparisons, and check diagnostics.