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Few studies have examined multiple life‐history traits across a latitudinal gradient to test whether variation in growth rate and mortality schedules induces trends predicted by life‐history theory. We collected data for the following life‐history traits for 75 Eurasian perch (Perca fluviatilis) populations: growth coefficient (K) and asymptotic body length (L∞) from the von Bertalanffy growth model, size at ages one and two years, specific juvenile growth rate, instantaneous adult and juvenile mortality rates, life span, age and length at maturity, and reproductive life span and investment. All life‐history traits except L∞ were significantly correlated with latitude. In general, growth rates, mortality rates, and reproductive investment decreased with latitude, whereas age at maturity, size at maturity, and life span increased with latitude. Populations could be grouped into two categories based on variation in L∞: stunted (small sized) vs. piscivorous (large sized). Four trait–latitude relationships differed between these two types: the growth coefficient (K) and the juvenile growth rate were larger, and age and length at maturity were lower in the stunted populations compared with piscivorous populations. Perch from southern populations tend to grow fast and experience high juvenile and adult mortality rates. As predicted from life‐history theory, this selects for an early age and small size at maturity and relatively large investment in reproduction. The opposite pattern was found for northern populations.

Behavioural syndromes, defined as correlated behaviours in different contexts, have been studied across species and taxa including humans as part of a personality concept. While most studies have focused on solitary individuals, less is known on how shoaling fish compromise between own personality and group behaviour. Risk-taking behaviour in 1-year-old perch (Perca fluviatilis) was observed to compare individual behaviour when in a group and when alone. An experimental design gave the fish the choice between foraging in an open area in the presence of a piscivore and hiding in the vegetation. We quantified the variation accountable by the effect of individuals being in a group, individuals alone and repeated measurements, using hierarchical mixed effects models. Within-group variances were low, but when individuals were later tested alone, individual differences explained most of the variation. Still, the individual best linear unbiased predictors (BLUPs) of time spent in the open area, extracted from the random effects of the mixed effects model, were positively correlated with the corresponding BLUPs when alone. The results indicate that individual behavioural traits are to some degree expressed also within groups. Most fish showed a shyer behaviour when alone, but bolder individuals changed less between treatments than did shyer ones, suggesting a more influential role of bold fish in the group.

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Summary 1.The gape size of the predator and the body depth of its prey are the main factors deciding whether a gape-limited piscivore can ingest a potential prey fish. The connection between morphological variation in pike and its prey was studied in five lakes with different proportions of pike, perch and roach. 2. At low prey abundance the prey fish are larger than in lakes with a higher prey density. In contrast, the pike are smaller but have relatively high gapes in the lake with the lowest prey availability, and larger but with smaller gapes at the highest prey density. There is a positive correlation with relative gape size of pike and relative body depth of prey among the five lakes. 3. Using the body depth in perch and roach and the gape size in pike, the proportion of the prey populations that could be eaten by the pike was calculated. In the low prey density lakes the pike could ingest a smaller proportion of the prey than in the high prey density lakes where most pike can eat all available prey sizes. 4. Competition for food in a situation where prey are big may cause a selection for a higher gape size. Our results indicate that this mechanism could be the reason that pike from different lakes vary in relative gape size.