From genes to populations: how fisheries‐induced evolution alters stock productivity

Abstract

By removing individuals with certain heritable characteristics such as large body size, harvesting may induce rapid evolutionary change in fish life history. There is controversy, however, as to the prevalence of fisheries-induced evolution (FIE) and to what extent it should be considered as part of sustainable resource management. Recent research has shown that FIE can be difficult to detect and its economic effects might not always be significant. Here, we show how population growth rate (r), a critical factor affecting sustainability and recovery, is affected by FIE through the analysis of a simulation model that demonstrates the link between individual-level genetic processes and stock dynamics. We examine how different levels of evolvability, fishing intensity, and density-dependence interact to influence r in three commercially harvested species: Atlantic cod (Gadus morhua), lake whitefish (Coregonus clupeaformis), and yellow perch (Perca flavescens). We demonstrate that at low harvest levels, evolution has minimal effect on r for all three species. However, at the harvest rates experienced by many fish stocks, evolution increases r and reduces the risk of collapse for cod and whitefish. During the initial stages of a harvest moratorium, a switch occurs, and r becomes reduced as a consequence of evolution. These results explain how evolution increases stock resilience, but also impedes recovery after periods of intense harvesting.

Size‐selective fishing and the potential for fisheries‐induced evolution in lake whitefish

“…For example, fisheries‐induced evolution can be buffered by negative density dependence in somatic growth rate that is the outcome of intraspecific competition for limiting resources (Dunlop et al., 2015; Gobin et al., 2016). On the one hand, Healey (1980) provides experimental evidence of fisheries‐induced, negative density‐dependent growth at the whole lake level in small lakes, and Lorenzen and Enberg (2002) discuss more generally the importance of considering negative density‐dependent growth as a population regulation mechanism in fish populations.…”

“…Concern about fisheries‐induced evolution has pervaded discourse on fisheries management and poses important questions about whether (and how) fisheries‐induced evolution should be controlled or reversed (e.g., Dunlop et al., 2015; Heino et al., 2013). A frequent recommendation is to protect large fecund fish, which could be done, for example, by fishing using a gear type with bell‐shaped size selectivity (Hixon et al., 2014; Law, 2007).…”

“…In eco‐genetic models of fisheries‐induced evolution of maturation traits in lake whitefish, somatic growth rate is assumed to be negatively density‐dependent according to a specified functional form. This allows for feedbacks between ecological and evolutionary processes in response to alternative forms and intensities of size‐selective fishing (Dunlop, Eikeset, & Stenseth, 2015; Dunlop, Heino et al., 2009; Wang & Höök, 2009). Eco‐genetic modeling also has been used to explore how different intensities of density‐dependent growth affect population dynamics and stock productivity (Gobin, Lester, Fox, & Dunlop, 2016).…”

Global connectivity and cross-scale interactions create uncertainty for Blue Growth of Arctic fisheries

“…For example, high harvesting pressure affects population dynamics through size-specific survival and can cause evolution of life-history traits with impact on the population growth rate and recovery potential [70,71]. The evolutionary effects of fishing, particularly on age and size at maturation, have been studied in the northern cod stocks of the Baltic Sea, Norwegian Sea and Icelandic shelf [72][73][74], and also in the Norwegian Spring-spawning herring stocks [75].…”

“…Increasing the understanding on individual level processes and behavior, and how they link to population and community dynamics [70,131,132] may, however, enable a new generation of modeling tools to study the emergence of cross-scale dynamics. Such models may take into account that underlying mechanisms may occur at different scales than observed patterns, such as regime shifts and collapses [133].…”