Differential plasticity of metabolic rate phenotypes in a tropical fish facing environmental change

Abstract

Summary Individual differences in metabolic rate have been linked with variations in behaviour and key life‐history traits and can affect ecological patterns within animal populations. Yet, almost nothing is known of the plasticity of the metabolic response under dynamically changing conditions that are representative of the natural environment. This is surprising since the capacity for animals to cope with rapidly changing environments depends on phenotypic variation and plasticity among members of the population. We measured the standard metabolic rate (SMR), maximum metabolic rate (MMR) and aerobic scope (AS) of 60 juvenile barramundi (Lates calcarifer) under acclimation conditions (35 ppt salinity, 29 °C, normoxia) and when the fish were sequentially faced with low salinity (10 ppt), high temperature (35 °C) and hypoxia (45% air saturation) with each treatment separated by ∼12 days. The overall degree of interindividual variation in body‐mass‐standardised SMR, MMR and AS changed with environmental conditions, and a metabolic coupling was revealed between SMR and MMR when the fish were faced with the environmental changes. The metabolic response to environmental change differed widely and predictably at an individual level. Individuals that had elevated metabolic attributes under acclimation conditions showed little change in SMR, MMR or AS in response to low salinity and high temperature, but MMR and AS were greatly depressed by hypoxia. In contrast, individuals with low‐metabolic attributes under acclimation conditions displayed a substantial increase in SMR, MMR and AS in response to high temperature and (to a lesser extent) low salinity, but hypoxia had very little effect. These findings reveal how phenotypic diversity in key physiological traits can create differential plasticity towards environmental change within a population by showing how individual fish can remain metabolically insensitive to one environmental stressor at the cost of being highly sensitive to another.

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“…We use data from a published study on metabolic traits of fishes (Norin et al, 2016) but the analyses are applicable to any aspect of the phenotype, including behavioural traits. The annotated script is available online (http://dx.doi.org/10.…”

“…For complementary R functions, refer to Nakagawa and Schielzeth (2010) and Wolak et al (2012). Norin et al (2016) took repeated measurements of body mass, standard metabolic rate (SMR) and maximum metabolic rate (MMR) in 60 juvenile barramundi (Lates calcarifer), each sequentially exposed to five different environmental treatments consisting of different temperature, salinity and oxygen levels (900 measurements in total). Their objective was to examine whether individuals exhibit differences in their ability to cope with rapidly changing environmental conditions.…”

“…As a second step, we add time (trials 1-5 specified as a continuous variable) and experimental conditions (three separate factors: temperature, salinity and oxygen) as predictors in the model, with mass as a response variable to control for the variance due to these Plots illustrating inter-and intra-individual variance using data from Norin et al (2016). Body mass (M b ), standard metabolic rate (SMR) and maximum metabolic rate (MMR) were measured five times each in 60 juvenile barramundi fish (Lates calcarifer) under different experimental treatments (trial 1: 35 ppt salinity, 29°C, normoxia; trial 2: 10 ppt salinity; trial 3: 35°C; trial 4: 45% air saturation; trial 5: same as trial 1).…”

“…So far, we have analysed data from Norin et al (2016) using a random-intercept LMM: individual identity was specified as a random factor in the model such that the y-intercept for each individual was allowed to vary relative to the population intercept. However, as we saw, fish grew substantially during the experiment (Fig.…”

Reply to Zhang & Gilbert (2017): Comment on ‘Measurement and relevance of maximum metabolic rate in fishes by Norin & Clark (2016)’

“…and Table ). We iteratively chose to evaluate in 3 min time bins because it provided a suitably high‐resolution assessment of with reliable slopes of oxygen concentration over time (high R 2 ), and it closely matched the duration of chase protocols commonly applied for L. calcarifer and other fish species (Norin & Malte, , ; Clark et al ., ; Roche et al ., ; Norin et al ., , ; Rummer et al ., ). The proposed R 2 of > 0·9 mentioned by Zhang and Gilbert may be a good rule of thumb, although Zhang et al .…”