Population declines of tuna and relatives depend on their speed of life

Abstract

Larger-bodied species in a wide range of taxonomic groups including mammals, fishes and birds tend to decline more steeply and are at greater risk of extinction. Yet, the diversity in life histories is governed not only by body size, but also by time-related traits. A key question is whether this size-dependency of vulnerability also holds, not just locally, but globally across a wider range of environments. We test the relative importance of size-and time-related life-history traits and fishing mortality in determining population declines and current exploitation status in tunas and their relatives. We use high-quality datasets of half a century of population trajectories combined with population-level fishing mortalities and life-history traits. Time-related traits (e.g. growth rate), rather than size-related traits (e.g. maximum size), better explain the extent and rate of declines and current exploitation status across tuna assemblages, after controlling for fishing mortality. Consequently, there is strong geographical patterning in population declines, such that populations with slower life histories (found at higher cooler latitudes) have declined most and more steeply and have a higher probability of being overfished than populations with faster life histories (found at tropical latitudes). Hence, the strong, temperature-driven, latitudinal gradients in life-history traits may underlie the global patterning of population declines, fisheries collapses and local extinctions.

Coherent assessments of Europe’s marine fishes show regional divergence and megafauna loss

“…This is due to late age at maturity, low maximum rates of population increase and (often) strong density dependence in recruitment 24 , which gives large fishes reduced resilience to fishing, compared with smaller species. Maximum population growth rate and related 'speed-oflife' traits may be the ultimate correlate of extinction risk, whereas body size is only the proximate, but more easily measured, correlate 25 . Most analyses of life history correlates have been for species within assemblages (limited geographic scale) rather than species across different assemblages.…”

Global reconstruction of life‐history strategies: A case study using tunas

“…(, ), broad similarities in survival, maturation and spawning duration could be drawn based on whether the species predominantly inhabits tropical or temperate waters during its annual cycle. The slower life‐history strategies of temperate species, especially bluefin tunas, is likely to be a contributing factor in their overexploitation (Juan‐Jordá et al., ). Tropical species also returned predominantly larger credible intervals associated with annual fecundity, compared to the temperate species.…”

“…To resolve a full set of fecundity parameters, information on the seventh life‐history trait, annual fecundity per capita ( V , mean number of oocytes produced per individual per year), was also included for the two populations with published estimates (Figure ; Supporting Information Table ). Finally, as broad similarities in life‐history strategy can be drawn between species of tuna that inhabit the same biome (Juan‐Jordá, Mosqueira, Freire, & Dulvy, , ), each population was assigned a binary habitat variable ( H ) delineating tropical or temperate preference (Figure ). To promote convergence across life‐history parameters with different scales (Congdon, ), each trait was standardized to a mean of 0 and a standard deviation of 1.…”

Eco‐evolutionary processes underlying early warning signals of population declines

“…Net reproductive rate also affected the stability of populations and the strength and reliability of abundance‐based EWS of population collapse. Higher net reproductive rates buffered populations from declining immediately after the environment shifted (Juan‐Jordá et al, ). Indeed, for the same rate of change in the external environment, populations with lower reproductive rate recovered more slowly due to a wide basin of attraction compared to populations with higher R 0 (Figure S1).…”