Metabolic traits in brown trout (Salmo trutta) vary in response to food restriction and intrinsic factors

Abstract

Metabolic rates vary hugely within and between populations, yet we know relatively little about factors causing intraspecific variation. Since metabolic rate determines the energetic cost of life, uncovering these sources of variation is important to understand and forecast responses to environmental change. Moreover, few studies have examined factors causing intraspecific variation in metabolic flexibility. We explore how extrinsic environmental conditions and intrinsic factors contribute to variation in metabolic traits in brown trout, an iconic and polymorphic species that is threatened across much of its native range. We measured metabolic traits in offspring from two wild populations that naturally show life-history variation in migratory tactics (one anadromous, i.e. sea-migratory, one non-anadromous) that we reared under either optimal food or experimental conditions of long-term food restriction (lasting between 7 and 17 months). Both populations showed decreased standard metabolic rates (SMR-baseline energy requirements) under low food conditions. The anadromous population had higher maximum metabolic rate (MMR) than the non-anadromous population, and marginally higher SMR. The MMR difference was greater than SMR and consequently aerobic scope (AS) was higher in the anadromous population. MMR and AS were both higher in males than females. The anadromous population also had higher AS under low food compared to optimal food conditions, consistent with population-specific effects of food restriction on AS. Our results suggest different components of metabolic rate can vary in their response to environmental conditions, and according to intrinsic (population-background/sex) effects. Populations might further differ in their flexibility of metabolic traits, potentially due to intrinsic factors related to life history (e.g. migratory tactics). More comparisons of populations/individuals with divergent life histories will help to reveal this. Overall, our study suggests that incorporating an understanding of metabolic trait variation and flexibility and linking this to life history and demography will improve our ability to conserve populations experiencing global change.

Keywords: anadromy; climate change; metabolism; partial migration; phenotypic flexibility; plasticity.

Figure 1

(A) Residual standard metabolic rate (rSMR) values (body mass corrected) for brown trout offspring derived from an anadromous background population (AB) and a non-anadromous background population (non-AB) that were reared under two experimental food treatments: optimal food rations (High) and 25% of optimal daily rations (Low) (black vertical bars represent the SD around the mean (shown as a gap in the bars), and sample size is shown as ‘N =’). (B) Cumming estimation plots for each population background and food treatment combination with effect sizes shown as black dots (i.e. the mean differences in rSMR among the groups), the distributions (shaded curves) and 95% CIs (back bars) of the effect sizes obtained from non-parametric bootstrap resampling (5000 resamples).

Figure 2

(A) rMMR values (body mass corrected) for brown trout offspring derived from an anadromous background population (AB) and a non-anadromous background population (non-AB) that were reared under two experimental food treatments: optimal food rations (High) and 25% of optimal daily rations (Low) (black vertical bars represent the SD around the mean (shown as a gap in the bars), and sample size is shown as ‘N =’). (B) Cumming estimation plots for each population background and food treatment combination with effect sizes shown as black dots (i.e. the mean differences in rMMR among the groups), the distributions (shaded curves) and 95% CIs (back bars) of the effect sizes obtained from non-parametric bootstrap resampling (5000 resamples).

Figure 3

(A) rAS values (body mass corrected) for brown trout offspring derived from an anadromous background population (AB) and a non-anadromous background population (non-AB) that were reared under two experimental food treatments: optimal food rations (High) and 25% of optimal daily rations (Low) (black vertical bars represent the SD around the mean (shown as a gap in the bars), and sample size is shown as ‘N =’). (B) Cumming estimation plots for each population background and food treatment combination with effect sizes shown as black dots (i.e. the mean differences in rAS among the groups), the distributions (shaded curves) and 95% CIs (back bars) of the effect sizes obtained from non-parametric bootstrap resampling (5000 resamples).

Figure 4

Size-independent relationships between: (A) residual standard metabolic rate (rSMR) and residual maximum metabolic rate (rMMR); (B) rSMR and rAS; and (C) rMMR and rAS for brown trout offspring derived from an anadromous background population (AB) and a non-anadromous background population (non-AB) that experienced two food reduction treatments: optimal food rations (High) and 25% of optimal rations (Low).

Figure 5

Gardner–Altman estimation plots for: (A) standard metabolic rate (SMR), (B) maximum metabolic rate (MMR) and (C) aerobic scope (AS) of male and female brown trout, which show the residual (body mass corrected) SMR/MMR/AS on the left axes and the effect size (mean difference between females and males) is represented by the black dot on the right axes, along with the distribution (shaded curve) and 95% CI (black bars) of the effect size, obtained via non-parametric bootstrap resampling (5000 resamples).