The primary diet data set (compared later with other studies) was measured in Bream collected during a large survey in Botany Bay and Georges River system, New South Wales, Australia (Pease et al., 1981). Fish (n = 1,130, 1.1–40.5 cm total length) were sampled using a variety of commercial fishing methods [gill net (various sizes), beach seine, and beam trawl] across the entire estuary between February 1978 and October 1979. Biometric information [total length (mm), weight (g)] was collected prior to fish dissection, and then fish stomachs were removed and prey contents sorted. Prey species were identified to the highest taxonomic level possible, were blotted and weighed, and expressed as the proportion-by-biomass of the total stomach contents for each individual, which was then averaged across all non-empty stomachs. Energy density per wet mass (kJ g^–1) of prey types were taken from published sources (Supplementary Table 1).

The routine metabolic rate (RMR; Chabot et al., 2016) of Bream was estimated using respirometry experiments. Mass-specific RMR to be used in a bioenergetic model was determined as:

Where R_T is RMR (g O₂ g^–1 h^–1) at temperature T (°C), W is body weight (g), and R_a and R_b are constants (Table 1). R_a (gO₂ g^–1 day^–1) was derived in respirometry experiments (0.0163) and R_b was taken from the literature as a mass-specific universal allometric scaling exponent (−0.25; Brown et al., 2004) because there was limited variation in Bream size in the respirometry experiments, and available evidence suggests -0.25 is approximately a median exponent value for fish (Clarke and Johnston, 1999; Payne et al., 2015).

Respirometry trials of smaller Bream (n = 12, 0.015–0.08 kg) were performed at a different time and location to large Bream (n = 5, 0.411–0.738 kg). Smaller fish were caught by hook and line in Sydney Harbour, Australia, and transported to the Sydney Institute of Marine Science (33° 50′ 31.6″ S; 151° 14′ 51.18″ E) where they were kept in captivity throughout the respirometry trials (during Autumn 2016). Bream were held in a 1000-L seawater flow-through holding tank that was maintained at 24°C (approximately ambient water temperature at time of capture) using aquarium bar heaters and provided natural light. Fish were fed daily a diet of frozen fish and invertebrates, and held for one week before respirometry trials began. The RMR of fish was measured individually at 24°C in an insulated and darkened 8-L rectangular custom-built respirometry chamber. Individuals were fasted for 24 h prior to the respirometry to prevent any increased oxygen consumption associated with digestion, based on the gastric evacuation rates of other species and temperature used for the trials here (Plaut, 2000; Chang et al., 2005; Das et al., 2018). After a 3-ho acclimation period inside the chamber, the chamber was isolated from atmospheric air, and oxygen content of the water measured (Hach HQ40d, Loveland, CO, United States). Each respirometry trial ran until dissolved oxygen in the chamber reached 80%. All respirometry trials were performed in the morning to minimize diurnal changes in metabolic rate or activity (Clark et al., 2013). A background respirometry trial was performed without any fish, to account for any microbial respiration, which was subtracted from the oxygen consumption measured for each individual fish. Respirometry trials of larger Bream used the same protocol and equipment as the smaller bream, but were undertaken in August 2012 at the Cronulla Fisheries Research Centre (34° 04′ 21″ S, 151° 08′ 56″ E), and on animals that had been captured in the nearby Port Hacking estuary and subsequently held in large (10 m × 5 m × 2 m) flow-through mesocosms for several years prior to experimentation. These Bream were captured by netting and transferred to 1,000-L flow-through tanks one week prior to respirometry trials, in which they were held under the same conditions as the small Bream. They were then fasted for 24 h and individual Bream transferred to a darkened 91-L respirometer. The respirometer was continuously flushed with fresh seawater for the 3-h acclimation period, then sealed while RMR was measured at 24°C for 20–30 min per fish (sufficient time to see large, linear declines in dissolved oxygen concentration). Background respiration trials were run daily as per small Bream experiments.

For all fish, the mass-dependent intercept of RMR (R_a) was estimated using linear regression of log-transformed RMR values with mass-specific slope (R_b) forced at −0.25 (Brown et al., 2004). All statistical models were done using R statistical computing (v3.3.1; R Core Development Team, 2016). Experiments were conducted under approval of the University of New South Wales Animal Care and Ethics Committee (No. 15/152B), and all experiments were performed in accordance with relevant guidelines and regulations of this ethics approval as well as the Guide for the Care and Use of Laboratory Animals (8th edition, National Academies Press).

Consumption rate estimates for Bream were estimated using a bioenergetic model, based on the format of Kitchell et al. (1977) and modified as in Lawson et al. (2018):

Where C_J is consumption rate (J day^–1), R_J is metabolic rate (J day^–1), ACT is a multiplier of metabolic rate to account for costs of activity, G is energy required for growth (J day^–1), and A is the proportion of ingested energy lost to assimilation costs (Table 1). Assimilation was assumed constant and taken from the literature (Table 1; Hartman and Brandt, 1995). A generalized metabolic rate R_J (J day^–1) was determined, using R_T estimated in the respirometry experiments, by:

Where R_q is the slope of the temperature-dependent function of RMR (Clarke and Johnston, 1999), T is ambient water temperature (°C; variable, see below), T_r (°C) is the temperature at which respirometry trials were performed to derive R_T, and oxy is the caloric-coefficient of oxygen used to convert metabolic rate from oxygen consumption (g O₂) to energy requirement (J) (Table 1). R_T was multiplied by 24 to determine daily metabolic rate. Energy cost of growth G (J day^–1), at body weight W, was determined by:

Where ΔW is the growth rate (g day^–1) at body weight W (g), and F is the energy density of Bream tissue (J g^–1; Table 1). Growth rate was taken as the slope at W of a published von Bertalanffy growth function for Bream (Pollock, 1982; Table 1) and converted from fork length (FL, cm) as in Steffe et al. (1996):

To convert an energy requirement (in joules) accurately to a food requirement (in grams), information on the ED of prey consumed is required. Based on the diet reported here and associated prey ED values found in the literature (Supplementary Table 1), a linear LOESS regression function (smoothing parameter = 0.75; Cleveland, 1979) was fitted to determine the relationship between prey energy density and Bream length (Lawson et al., 2018). This indicates the mean prey energy density (ED, J g^–1) throughout ontogeny of Bream. Prey energy density was used to convert energy consumed (C_J; J day^–1) to consumption rate of prey C (g day^–1):

Consumption: biomass ratio (Q:B) is a metric often used to compare annual consumption rates of populations (Polovina, 1984). Q:B (g g^–1 year^–1), at body weight W, was calculated:

Where 365 is a multiplier to calculate annual Q:B. Bream lifespan (7 years), growth rate and maximum size parameters for the bioenergetic model were taken from Pollock (1982). Q:B was calculated across the life-span of a fish by calculating weight-dependent growth, consumption, and size-dependent prey energy density at each age based on the above relationships. Temperature was also varied according to a seasonal cycle, assuming a birth date of 1 September (start of Spring; Pollock, 1982). Daily temperature was measured at the inlet to the aquarium at the Sydney Institute of Marine Science in Sydney Harbour (33° 50′ 31.6″ S; 151° 14′ 51.18″ E) over 3 years (2013–2016) to generate a sine function (range 15.6–22.1°C) that provided seasonal variation in T. The bioenergetic model using the diet data found in the current study (n = 1,130) will hereafter be referred to as the “primary model scenario.”

To compare consumption and growth rates using different sources of diet data, we produced nine comparative model scenarios based on four published studies on Bream diet (Bell et al., 1984; Morton et al., 1987; Hadwen et al., 2007; Sheaves et al., 2014). Each study provided diet as percent composition of major prey types (Supplementary Table 2), but variability among methods meant we were able to build in multiple comparative model scenarios from two studies. Two scenarios were created using diet data from Hadwen et al. (2007); the first using GCA and the second using stable isotope analysis (SIA). Five scenarios were created from Morton et al. (1987) GCA; Autumn (day and night combined), Spring (day and night combined), day (Spring, Summer, Autumn combined), night (Autumn, Winter, Spring combined), and mean (using all data). One scenario was created from Sheaves et al. (2014) using gut contents analysis, and one scenario was created from Bell et al. (1984) using gut contents analysis. Each comparative model scenario was compared against the primary scenario described above, to test the effect of using different sources of diet data on consumption and growth rate estimates.

A weighted mean ED of prey was calculated for each comparative scenario based on the proportion (by volume) of each prey item and its ED (Supplementary Tables 1, 2). This weighted mean ED was used as a constant prey ED for each comparative scenario to produce estimates of consumption rate. Unlike the primary model dataset, there was insufficient data to produce ontogeny-specific prey ED values in comparative scenarios. The lifetime and daily consumption rate produced by each comparative scenario were then compared with the primary scenario presented here. All parameters except ED were identical in all scenarios. Temperature of Sydney Harbour was used in all scenarios, despite some scenarios using dietary data from different regions, to isolate the effect of dietary composition. ED values for identical prey groups used the same values in the comparative scenarios as the primary scenario, with ED values for new prey items taken from the literature (Supplementary Table 1).

Differences in diet may not always reflect proportional change in consumption, and energy budgets can be dynamic, such that an increase in energy content of the diet may not represent a decrease in consumption rate—instead, consumption may remain the same, but the consumer is able to invest more energy into growth (Kitchell et al., 1977). We explored this in additional scenarios; we examined the effect of diet composition on potential growth rate if consumption rate (in mass) was held constant. Differences in growth rate based on the diet quality of the comparative scenarios were estimated when Bream were 100 g (∼1.4 year), 250 g (∼2.7 year), and 400 g (∼4.5 year; representing fast, moderate, and slow instantaneous growth rates, respectively). Growth was estimated by keeping consumption rate constant, based on the mean percentage of body mass consumed daily of the primary and all comparative scenarios (described above) at each of the three chosen masses. The total ingested energy, and therefore energy allocated for growth, differed between the scenarios depending on the prey composition. Energy allocated for growth (J day^–1) was calculated as:

Where C_B is percent body mass consumed daily (%) estimated as the mean consumption rate of all scenarios above at each weight (100, 250, and 400 g) and ED_mis the weighted mean prey energy density of each respective scenario (J g^–1). Growth rate (ΔW, g day^–1) was calculated as:

Gut content analysis of Bream using the primary diet data (n = 1,130; Pease et al., 1981) showed a diverse diet with ontogeny, with prey items from at least 34 orders and 13 phyla (Table 2). Actual prey diversity was likely higher, as some prey items were only identified to phylum or class (e.g., many polychaetes). The most common prey groups were crustaceans (found in 70.1% of guts), polychaetes (50.3%), and bivalves (26.4%; Table 2). Of the crustaceans, amphipods (31.6%) and decapods (18.9%) were the most prevalent (Table 2). Invertebrates made up at least 80% of the diet by volume at all ages (Figure 1).

Some ontogenetic diet changes were observed, namely crustaceans (primarily amphipods and copepods) constituting up to 70% of diet by volume for Bream under 5 cm, but then the contribution of crustaceans generally decreased with size, and bivalves increased for Bream >10 cm. However, ontogenetic diet change had only a small influence on mean prey ED (Figure 2).

The bioenergetic model showed mass-specific consumption rate was higher in younger individuals. A one-year old individual (14.5 cm FL) consumed 3.90% of its body weight daily, with a Q:B of 14.25, whereas a seven-year old adult (28.8 cm FL) consumed 1.42–2.12% (mean 1.79%) of its body weight daily, with a Q:B of 5.21–7.75 (mean 6.52; Figure 3). Oscillations in predicted consumption rate estimates were caused by seasonal fluctuations in water temperature and its subsequent impact on RMR (Figure 3).

The comparative scenarios that used prey compositions derived from the literature estimated lifetime consumption (g) of Bream between 24.9% lower (Morton—Autumn) and 14.7% higher (Hadwen GCA) than the primary model presented here (maximum difference 52.7%, range 11.7–17.8 kg; Table 3). The daily consumption rate (g day^–1) for the scenario with the highest consumption rate (Hadwen GCA) was up to 53% higher than the scenario with the lowest consumption rate (Morton Autumn; 7.7–11.8 g day^–1; Figure 4).

Keeping consumption rate (in mass) constant resulted in highly variable growth rates among the model scenarios. There were consistently greater than five-fold ranges in daily growth rates among scenarios; the fastest (Moreton Autumn) and slowest growing scenario (Hadwen GCA) in the 250 g size class had a daily growth range of 0.04–1.07 g day^–1 (Table 4). The slowest growing model scenarios in the 400 g size class showed negative growth rates (Table 4), i.e., energy consumption was inadequate to meet the demands of assimilation, RMR and activity, so these individuals must either divert energy away from these processes, increase their consumption rate, or lose weight.

We show that there are substantial differences in consumption or growth rate estimates despite using seemingly detailed diet data (i.e., more detailed than simply choosing the one or two most common prey items to represent the entire diet composition; Lawson et al., 2018). The diet data described here includes ontogenetic changes in prey composition and therefore ED, but ontogenetic diet changes were not included in the comparative model scenarios because the previously published diet studies had a limited sample size or had missing/limited data for specific fish sizes. This poor ontogenetic diet resolution accounts for some of the observed differences in consumption and growth rates among model scenarios.

When consumption (in mass) was kept constant, negative growth rates were observed in two comparative scenarios, i.e., the energy consumed was less than that required for RMR, assimilation and activity costs, and so energy was taken from bodily stores. Consistent use of energy stores leads to weight loss and, if representative of actual diet composition, implies three options for individuals. First, this poor-quality diet composition is temporary and represents only a brief period when individuals rely on stored energy. Second, the individuals move to other areas or increase foraging time if prey quality declines. Third, changes in metabolic efficiency reduce the cost of standard metabolic rate to avoid weight loss (Halsey, 2018; Redman et al., 2018). Similarly, a decline in activity may also reduce total energy expenditure when experiencing a poor-quality diet, and thus prevent weight loss, but could reduce foraging intensity. So while loss of body mass is possible under certain conditions, in reality these negative growth scenarios indicate conditions under which individuals would need to either increase consumption or divert less energy to foraging or reproductive activity. This highlights the importance of accurate dietary information when using consumption and growth rate estimates to inform ecosystem models.

When diet information is lacking for a certain region, it can be common practice to instead use dietary information from a different region. Regional dietary information may be absent in terms of both prey composition and nutritional values such as ED. Here, each comparative scenario used different prey compositions that drove the discrepancies in consumption and growth estimates, likely because the original studies that informed the scenarios were conducted in multiple locations and habitats along eastern Australia. Similarly, the lower consumption rates estimated by the comparative Morton scenarios (up to 24.9% over 7 years; Table 3) arise largely from a high ED of terrestrial invertebrate prey (Supplementary Table 1). However, the ED data for terrestrial prey items were collected from different regions from the diet information of Bream, as no local data were available. The resultant high ED values of terrestrial prey used in the models may not necessarily produce erroneous consumption estimates, as terrestrial invertebrates appear to be more energy-dense in general than aquatic invertebrates. Cummins (1967) summarized ED values for 78 invertebrate species and found the mean ED of aquatic invertebrates sampled was 12.27 kJ g^–1 dry weight, compared to 22.61 kJ g^–1 for terrestrial invertebrates. Nevertheless, the actual ED values of terrestrial prey of Bream in eastern Australia, and hence associated error in consumption estimates, remain unknown. Therefore, local diet information is important for estimating consumption rates.

The method used to assess diet may also have implications for the overall diet composition and prey ED. SIA was more effective at estimating typical prey than GCA for small sample sizes, and similar trends are observed with other species (Hadwen scenarios; Table 2; Parkyn et al., 2001; Davis et al., 2012). Unlike gut contents, SIA can provide dietary information from previous weeks or months (Hyslop, 1980; Davis et al., 2012). This also accounts for individuals sampled that have empty stomachs, which can be a large portion of the total sample if species are irregular binge feeders or eject their stomach contents on capture (Hughes et al., 2014; Schilling et al., 2017). Stable isotopes may also be useful for examining diel and seasonal variation in diet that are not easily measured by GCA. For example, large differences were seen in prey composition, and hence ED, between Bream in the same location depending on season and between day and night (Table 2; Morton et al., 1987), and across these timeframes the use of stable isotopes may enable a more realistic representation of the standard diet. Hence it may be important to consider the effect that sampling method has on the resulting diet composition when estimating consumption rates. Like other methods of diet sampling, SIA requires calibration to determine the ED of prey items.

Most studies report ED as energy per dry mass, but estimating consumption requires the ED of prey as wet mass (i.e., in the form that the predator consumes the food). It may be easy to quantify how much dry prey material a consumer requires to meet a certain energy intake, but percent water content is needed to determine the amount of prey that consumer must actually forage. The difference between ED per dry mass and ED per wet mass—determined by percent water—is therefore crucial for use in bioenergetic models, but can vary substantially between prey items. For example, percent water in invertebrates ranges from 41 to 93% which, when converting from ED per dry mass to ED per wet mass, creates an eight-fold range in possible values (James et al., 2012). Most studies do not report the water content of their study species, and so ED per dry mass must be converted to ED per wet mass based on a handful of publications that state percent water content, as was performed here (Supplementary Table 3). For example, in an attempt to form a regression of prey ED vs dry-to-wet mass ratio to eliminate the need for bomb calorimetry, James et al. (2012) found just eight published studies that provided ED and percent water of any invertebrate (regardless of habitat, location, or season); just one study was found from Australia, and provides data only for coleopterans (beetles). It is therefore possible that unknown water content values contribute substantially to errors in the mean ED of prey, and hence consumption and growth rates.

There are sources of error in bioenergetic models other than diet quality/source that arise from uncertainty of the input parameters. This is especially true for species that lack basic bioenergetic data such as the scaling of metabolic rate with temperature or body size, which can cause several-fold ranges of energy requirement estimates (Lawson et al., 2019). Similarly to prey energy density, uncertainty in assimilation efficiency can lead to linear deviations in consumption estimates, i.e., a 10% change in assimilation efficiency can cause a 10% change in consumption estimate (Essington, 2003). However, while assimilation efficiency typically ranges from 65 to 95% (Peck et al., 2003; Fu et al., 2005; Fitzgibbon et al., 2007), prey ED may vary >10-fold (Supplementary Table 1; McCluskey et al., 2016).

While the present study focused on prey ED, it should be noted that predator ED also varies. An individual’s ED—and hence their cost of growth—can vary with ontogeny or season (Pothoven et al., 2012; Canale and Breck, 2013). For a given amount of energy allocated to growth, the growth rate (in mass) of an individual is inversely proportional to the individual’s ED (see methods). Here, a 60% linear increase in bream ED (4,000–6,400 kJ) with ontogeny results in a 1.1% reduction in lifetime consumption compared to using the mean bream ED value (Supplementary Material). This difference is likely greater at particular life stages or seasons; a variable bream ED causes up to a 30% increase in growth rate for 100-g individuals compared to a constant bream ED (Supplementary Material). However, at the same age, differences in prey ED among our model scenarios caused a 521% difference in growth rate estimates. Therefore, the effect of variable predator ED appears to be substantially smaller than the effect of variable prey ED.

Similarly, the individuals involved in the respirometry of the present study may have had altered metabolic rates or activity costs that aren’t representative of wild animals. Such differences may be associated with capture stress or a diet requiring minimal search and foraging costs. However, the objective of this study was to isolate and quantify the effect of uncertainty in one parameter—the prey source–while keeping other parameters constant. Importantly, any sources of error in such parameters will affect the early stage of the bioenergetic model, i.e., when its output remains in energy (kJ) and not mass of prey (g). As a result, the errors will be applied to all of the comparative model scenarios here equivalently, and it is unlikely that the results of the study experience substantial change. For example, if RMR of individuals was 10% lower than estimated here, the absolute amount of prey required to meet energetic demands would be altered but the relative differences among the model scenarios would be identical to those presented here.