Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model

Abstract

Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life‐history parameters (mortality, maturity, growth) and stock‐recruit parameters (juvenile production). However, there has been little research to simultaneously predict life‐history and stock‐recruit parameters. I develop the first data‐integrated life‐history model, which extends a simple model of evolutionary dynamics to field measurements of life‐history parameters as well as historical records of spawning output and subsequent recruitment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortality, maturity, growth, and size, and uses these to predict intrinsic growth rate (r) for all described fishes. The model confirms previous analysis showing little correlation between steepness and either natural mortality or asymptotic maximum size (W∞). However, it does reveal taxonomic patterns, where family Sebastidae has lower steepness (mean=0.72) and Salmonidae has elevated steepness (mean=0.79) relative to the prediction for bony fishes (class Actinopterygii, mean=0.74). Similarly, genus Sebastes has growth rate r (0.09) approaching that of several shark families (Lamniformes: 0.02; Carcharhiniformes: 0.02). A cross‐validation experiment confirms that the model is accurate, explains a substantial portion of variance (32%–67%), but generates standard errors that are somewhat too small. Predictive intervals are tighter for species than for higher‐level organizations (e.g. families), and predictions (including intervals) are available for all fishes worldwide in R package FishLife. I conclude by outlining how multivariate predictions of life‐history and stock‐recruit parameters could be useful for stock assessment, decision theory, ensemble modelling and strategic management.

Life histories determine divergent population trends for fishes under climate warming

“…Even though actual recruitment is not possible to predict because of the general lack of data on early-life histories, we can predict the differential effects of temperature change on potential population growth based on the basic reproductive number, R 0 , which measures the mean per capita offspring production. Consequently, estimating the differential temperature effects on population growth does not require recruitment estimates (but see a recent study providing an assessment of life histories, recruitment, and population growth rate r for about 150 populations 22 ). Thus, we parameterized a life-table model with the length-based growth, maturation, and survivorship functions based on available population-specific life-history data.…”

“…Consequently, life-history traits inform the reference points for fisheries species and likelihood of recovery for the overfished populations 20 , 21 . Temperature effects on life-history changes and population resistance, however, have mainly been evaluated for single traits (but see 22 ). For example, the warming-induced body size decreases have been argued to have dramatic reproductive consequences for marine fishes, given the positively allometric relationship between female body mass and reproductive output 23 .…”

Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

“…Developing steepness priors based on life‐history models (e.g. Thorson, 2020) is a promising and active area of stock assessment research, but further work is needed to understand the impact of misdiagnosing the spatial distribution of productivity.…”

An updated life‐history scheme for marine fishes predicts recruitment variability and sensitivity to exploitation

“…Additionally, these analyses use mean life‐history traits for each stock and are based on a moderately small sample of stocks and species, especially with respect to patterns of variability in bet‐hedgers and the salmonic strategy. Databases such as FishLife set the path by providing robust estimates of fish traits and might support more extensive analysis of fish life history at the species level and across supraspecific taxonomic levels (e.g., Thorson, 2020; Thorson et al, 2017). However, life‐history traits are plastic, and a range of phenotypes might be present within each fish stock.…”