The bioenergetics of a coastal forage fish: Importance of empirical values for ecosystem models

Deep–Sea Research II xxx (xxxx) xxx

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Deep-Sea Research Part II journal homepage: http://www.elsevier.com/locate/dsr2

The bioenergetics of a coastal forage fish: Importance of empirical values for ecosystem models Georgina Dawson a, b, 1, Iain M. Suthers a, b, Stephanie Brodie a, c, James A. Smith a, c, * a

School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, NSW, 2052, Australia Sydney Institute of Marine Science, Chowder Bay Road, Mosman, NSW, 2088, Australia c Institute of Marine Sciences, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95062, USA b

A R T I C L E I N F O

A B S T R A C T

Keywords: Standard metabolic rate Energy density Energy budget Respiration Small pelagic Bomb calorimetry

Forage fish are a vital trophic group in marine ecosystems and numerical models, linking plankton with higher trophic levels. The bioenergetics of a key forage fish in eastern Australia, yellowtail scad Trachurus novaeze­ landiae, was measured using static respirometry and bomb calorimetry to assess their trophic contribution as both predator and prey. The temperature-dependent standard metabolic rate (SMR) of yellowtail scad was 0.62 mgO2 g 0.79 h 1 and Q10 of 1.98. The SMR was used with dietary information to calculate a minimum annual prey consumption of 17.8 g g 0.79 y 1 (at 20 � C), which is equivalent to an SMR-specific consumption:biomass ratio (Q:BSMR) of 6.77 (100 g adult). This was incorporated into a standard energy balance model to estimate a total Q:B of at least 10.6 at 20 � C (or 7.2 at 14 � C), which is 2 to 3-fold larger than most values used to represent this species in ecosystem models. Implications of underestimating forage fish consumption could include errors in estimated prey biomass, ecotrophic efficiencies, or strength of top-down and bottom-up control. Yellowtail scad were a moderate-high value prey, with a mean energy density of 6 kJ g 1 (�0.97 s.d.). Energy density declined with body size and showed considerable inter-individual and spatial-temporal variation, indicating the potential to influence predator consumption rates at seasonal and fine spatial scales. This research highlights the value of measuring species-specific bioenergetics information for improving our understanding of trophic dy­ namics in marine ecosystems and models.

1. Introduction Ecosystem models are a common tool for understanding marine food webs (Fulton et al., 2011; Hunsicker et al., 2011; Pauly et al., 2000), and these models rely on an accurate representation of an ecosystem’s tro­ phic links. Consumption determines the rate of energy transfer through these trophic links, and both consumption rate and the energy density of a species are key to understanding its contribution to the ecosystem as predator and prey (Surma et al., 2018). Due to large data requirements of ecosystem models, these bioenergetic parameters are often taken from generic relationships (Bulman et al., 2006; Fulton et al., 2018; Froese and Pauly, 2018), inferred from other species (Griffiths et al., 2010), or borrowed from previous ecosystem models (Goldsworthy et al., 2013). More empirical research is needed on the bioenergetics of key trophic groups to help inform ecosystem models (Heymans et al., 2016) as well as the varied applications of generalized bioenergetics models (Chipps

and Wahl, 2008; Deslauriers et al., 2017; Smith et al., 2019). A key group is forage fish, which are often the dominant trophic link in pelagic marine systems between primary producers and higher tro­ phic levels (Cury et al., 2011). Forage fish biomass can be closely linked with the biomass of their piscivorous predators and zooplankton prey (Bulman et al., 2011; Frank et al., 2011; Pikitch et al., 2014), and de­ clines in their biomass have been associated with increases in the biomass of squid and jellyfish (Bulman et al., 2011; Roux et al., 2013). Changes in the energy density or nutritional quality of forage fish tissues can also negatively affect their predators, and it is thought that decreased forage fish quality contributed to a widespread breeding failure of seabirds and population decline of Steller sea lions in Alaska € (Osterblom et al., 2008). Variation in the energy density of forage fish can be seasonal and species-specific, and vary spatially and according to fish body size (Albo-Puigserver et al., 2017; Pedersen and Hislop, 2001; Tirelli et al., 2006). This variation can be large even when species are

* Corresponding author. School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, NSW, 2052, Australia. E-mail address: [email protected] (J.A. Smith). 1 Present address: New South Wales Department of Planning, Industry and Environment, Parramatta, NSW 2150, Australia. https://doi.org/10.1016/j.dsr2.2019.104700 Received 28 March 2019; Received in revised form 22 November 2019; Accepted 23 November 2019 Available online 27 November 2019 0967-0645/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Georgina Dawson, Deep–Sea Research II, https://doi.org/10.1016/j.dsr2.2019.104700

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congeneric (Albo-Puigserver et al., 2017; Anthony et al., 2000; Dubreuil and Petitgas, 2009). Despite this clear link between forage fish and the broader marine ecosystem, as well as the strong species-specific varia­ tion among forage fish, empirical bioenergetics data are missing for numerous forage fish species, including in the Southwest Pacific Ocean. Consumption rate can be estimated by quantifying metabolic rate for an energy budget model that accounts for additional energy re­ quirements (such as growth and activity; Kitchell et al., 1977; Olson and Boggs, 1986). In ecosystem models, the total energy requirements of a species are often reported as a relative consumption rate, i.e. an annual consumption to biomass ratio, Q:B (Christensen and Pauly, 1992). There is extensive literature on the ecosystem importance of forage fish (Cury € et al., 2011; Osterblom et al., 2008; Pikitch et al., 2014), including ecosystem models that include Q:B values for forage fish groups (Forrest, 2008; Watson et al., 2013). Yet the reliability of Q:B values for forage fish is uncertain, given the extent to which they are inferred using a multi-species regression containing few pelagic forage fish (Palomares and Pauly, 1998), or borrowed from other species and ecosystems. There is information on the energy density of forage fish (Albo-Puigserver et al., 2017; Anthony et al., 2000; Gatti et al., 2018), but more research is needed to quantify the spatial and temporal variation of this energy density. There is much to be gained by improving the regional accuracy of this bioenergetics information, given that consumption rate changes can influence estimates of biomass and indices of ecosystem function (Steenbeek et al., 2018), and that energy density is one of the most influential parameters when calculating predator consumption rates (Lawson et al., 2018). Yellowtail scad (Trachurus novaezelandiae) is an abundant zoo­ planktivorous forage fish, which schools in pelagic waters (~18–28 � C) off New Zealand and eastern and southern Australia, from Queensland to Western Australia (20–38� S; Rowling et al., 2010). Yellowtail scad are slow growers, reaching ~30 cm and 15 years of age (Rowling et al., 2010; Stewart and Ferrell, 2001), and are known prey for numerous predatory fish in eastern Australia (Hughes et al., 2014; Schilling et al., 2017). This species is included in numerous Australian ecosystem models (Forrest, 2008; Goldsworthy et al., 2013; Watson et al., 2013), yet there is a lack of empirical data on its bioenergetics. The goal of this study was to determine the trophic contribution of this abundant forage fish in eastern Australia, by providing empirical information on its bioenergetics (metabolic rate, Q:B, and tissue energy density). Our specific aims were to: 1) calculate the temperature-dependent energetic requirements of yellowtail scad, and compare this to the measured or inferred requirements for forage fish in Australia and globally; and 2) measure the energy density of yellowtail scad, and how this varies spatially and temporally, and compare this with other forage fish species to identify their relative value as prey.

2.2. Static respirometry and metabolic rate The SMR (mgO2 g 0.79 h 1) of yellowtail scad was measured at five temperatures, indicative of the warmer northern part of their range (18, 21, 24, 27 or 30 � C), to ensure an accurate measurement of Q10. Yellowtail scad used in experiments (n ¼ 55; 9–25 cm total length; 16–170 g) were fished from within Sydney Harbour in January and February 2017 and transported to aquarium facilities at the Sydney Institute of Marine Science for the respirometry experiment. Fish were allocated to one of five treatment tanks (500 L) and acclimated at local ambient water temperature (24 � C) for at least seven days (Barrionuevo and Fernandes, 1998). Each tank was then warmed or cooled 1 � C per day (Mora and Maya, 2006) until the five temperature treatments were reached. Fish were acclimated for an additional seven days to these temperature treatments prior to starting the respirometry experiment. Individual fish were tested at only one temperature, with 1–4 fish tested per day. The temperature treatments tested each day was not fully randomized, because the total number of fish required could not be caught in a brief period (instead over an extended period, ~1 month). Priority was instead given to filling and acclimating specific temperature treatments in order minimize the range of acclimation times within each treatment. This meant that some temperature treatments were completed before others. We note that the water temperature of the environment during the capture period was mostly constant (�1 � C). The minimum and maximum durations an individual was held in the aquarium before being tested was 15–30 days. Fish were fed daily on aquaculture fish pellets (Marine float range, Ridley Aquafeed, Australia) to satiation. Yellowtail scad were not fed for ~24 h before the respirometry experiment, so that metabolic rates were exclusive of digestion (Jobling, 1981). At the beginning of the static respirometry experiment, an indi­ vidual was placed into one of two respirometry chambers (7 L or 20 L) depending on their body size (Chabot et al., 2016), with fish > 20 cm (fork length) always placed in the larger chamber (the mean density was 5.6 g L 1). Fish were acclimated for 3 h (Brodie et al., 2016) in the chamber before it was sealed, with consistent supply of air and flow-through water sourced from the treatment tank, after which the chamber was made airtight and the flow of water stopped. An oxygen probe (HQ40D, Hach Company, Colorado USA) measured the decrease in oxygen (in 10 s intervals), and the experiment was continued until a decline of 1 mg L 1 dissolved oxygen (O2), or 80% saturation was reached. Fish were then removed from the respirometer and weighed to the nearest gram. Control trials were done at each temperature to measure the background respiration (Clark et al., 2013). This involved running a 20-min trial without a fish and measuring the change in dis­ solved oxygen, which was then subtracted from the change measured during trials with fish (see below). One control was done per tempera­ ture treatment each day. Measured rates of oxygen consumption (mgO2 L 1 h 1) for each individual fish were converted to mass-specific metabolic rate (mgO2 g 0.79 h 1) by multiplying oxygen consumption by the volume of the respirometer (7 or 20 L), subtracting the background respiration (Clarke and Johnston, 1999), and accounting for the assumed scaling of meta­ bolic rate with body mass (defined by a power curve with exponent 0.79; Clarke and Johnston, 1999) by dividing this result by mass0.79 (in grams). It was important to account for this scaling with body mass, so that the effect of temperature on metabolic rate could be measured across a range of fish sizes. The temperature-dependence of metabolic rate was defined (Eqn (1)):

2. Materials and methods 2.1. Energy requirements of yellowtail scad The energetic requirements of yellowtail scad were calculated in two steps. The first step measured the standard metabolic rate (SMR; mgO2 g 0.79 h 1) of yellowtail scad using static respirometry, while the second step used published diet information and measured prey density to convert this metabolic rate to consumption (g g 1 y 1). SMR is defined as the minimal maintenance metabolic rate of a post-absorptive resting ectotherm, below which physiological function is impaired (Norin and Malte, 2011). The effect of body mass and temperature on metabolic rate were accounted for by assuming a metabolic scaling exponent (Clarke and Johnston, 1999) and by measuring SMR at a range of temperatures. The temperature-dependence of SMR was reported using the Q10 metric, which is the factorial increase in metabolic rate with every 10 � C in­ crease in temperature (Clarke and Johnston, 1999).

SMR ¼ aebT

(1)

where SMR is the standard metabolic rate (mgO2 g 0.79 h 1), T is tem­ perature (� C) and a and b are constants. Equation (1) was fitted using linear regression of logged variables in R (v3.4.4; R Core Team, 2018). The temperature-specific metabolic rate metric, Q10, was calculated 2

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by taking the exponential of the constant (b) from Eqn (1), multiplied by 10 (Eqn (2)):

CSMR ¼

(2)

Q10 ¼ e10b

CSMRJ ¼ debT EDz

where d ¼

Metabolic rate was then converted to units of energy (Joules) using an oxy-calorific coefficient Qox ¼ 14.14 J mg 1 O2 (Elliott and Davison, 1975), and multiplied by 24 to estimate daily mass-specific energy re­ quirements (i.e. consumption) of yellowtail scad (Eqn (3)):

(4)

24aQox EDz .

where CSMRJ is the daily mass-specific minimum energy requirements (J g 0.79 d 1). This is termed the ‘minimum’ consumption, as this repre­ sents the amount needed to satisfy SMR only.

A common metric for each functional group in ecosystem models is the consumption to biomass ratio (Q:B) (Christensen and Pauly, 1992; Palomares and Pauly, 1989), which represents the relative consumption rate by a predator. Q:B was used in this study to allow a comparison of yellowtail scad consumption between different sources. It was calcu­ lated by multiplying CSMR by the ratio of the mean mass of an adult yellowtail scad (including the mass-specific exponent) to its mean mass, and multiplying by 365 days in a year to get Q:BSMR in units of g g 1 y 1 (Eqn (5)):

2.3. Estimating consumption

Q : BSMR ¼ 365CSMR

(3)

CSMR J ¼ 24Qox SMR

The second step of estimating the energetic requirements of yellowtail scad was to use diet information to convert their minimum consumption rate from joules (CSMRJ; J g 0.79 d 1) to grams of prey. This requires information on the type of prey yellowtail scad consume, and their respective energy densities. The average energy density of yellowtail scad prey (EDz) was calculated using two separate stomach content studies that identified prey composition in yellowtail scad, one sampling fish from coastal waters of northern NSW (J. Smith, unpub­ lished data, n ¼ 20), and one sampling fish from the waters of Sydney Harbour (H. Schilling, unpublished data, n ¼ 24). Energy density of individual prey items were taken from the literature and weighted using diet composition percentages to calculate a weighted average EDz for each diet study (Table 1), and these were averaged to create the final EDz for yellowtail scad. Prey mass is challenging to ascertain from stomach contents, so diet percentages were measured as counts. The relationship between temperature and mass-specific minimum consumption (CSMR; g g 0.79 d 1) was calculated by dividing CSMRJ by the average energy density of yellowtail scad prey (EDZ) (Eqn (4)):

Diet composition (% by count)

Energy density

Study 1

(J g

Shrimp/prawn 23 Fish remains 7 Chaetognatha 6 Amphipoda 3 Bivalve 2 Cyprid 2 Copepod 41 Nauplii Decapoda Cladocera Other 17 Weighted mean EDz Study 1 Weighted mean EDz Study 2 Mean EDz

Study 2

10 41 25 1 20 3

1

(5)

where m is the mean body mass (g) of an adult yellowtail scad, estimated from the individuals in this study. The value of Q:BSMR is that it is well defined by empirical data, and represents an accurate minimum consumption rate, but SMR is only one part of an organism’s energy budget and the Q:B metric commonly used in bioenergetics and ecosystem models refers to total consumption. Thus, we used a standard energy budget model (Brodie et al., 2016; Olson and Boggs, 1986) to estimate total Q:B. This standard model states that 1) total energy requirements are the sum of the requirements of active metabolic rate and growth, divided by the proportion of consumed energy not lost to assimilation, egestion, and excretion (here, ‘assimilation efficiency’); and 2) that active metabolic rate can be esti­ mated from SMR by using an activity multiplier. Thus, we estimated total Q:B (g g 1 y 1) from Q:BSMR (Eqn (6)): Q : B¼

Q : BSMR Act Gm EDf þ A A EDZ m

(6)

where Act is a routine activity multiplier (Beaudreau and Essington, 2009; Winberg, 1956), A is proportional assimilation efficiency, Gm is growth rate (g d 1 � 365) of yellowtail scad at body mass m, and EDf is the energy density (J g 1) of yellowtail scad. We derived the growth rate (g d 1) for yellowtail scad using a von Bertalanffy growth curve for yellowtail scad (with L∞ ¼ 30.8 cm, K ¼ 0.18, t0 ¼ 4.1; Stewart and Ferrell, 2001), and a length-weight relationship: W ¼ 0.0282L2.77 (Taylor and Willis, 1998). The activity multiplier is often used when there is no information on swimming speeds in the field (Ney, 1990), and can be as high as Act ¼ 2–3 for active predators (Kitchell et al., 1977; Ney, 1990; Winberg, 1956). Given the possibly less energetic schooling behaviour and zooplanktivorous diet of yellowtail scad, we used a multiplier of 1.5 (used for more sedentary teleosts; Beaudreau and Essington, 2009). Assimilation efficiency was taken as a widely used estimate for predatory teleosts, A ¼ 0.681 (Brodie et al., 2016; Rice et al., 1983), as derived in Lawson et al. (2018). A is typically within 0.6–0.8, with lower trophic levels having lower efficiency (Christensen et al., 2005; Welch, 1968). Sensitivity tests for A and EDZ showing their influence on total Q:B are illustrated in Supp. Material Fig. S1. This bioenergetics model does not account for the cost of reproduction, so we assume that these costs can be met through flexibility of the modelled energy budget (e.g. via a trade-off between production of reproductive tissue and somatic tissue). The model also assumes a constant EDz, so the estimated mean total Q:B would underestimate the variation of con­ sumption spatially and temporally. Three sources of total Q:B values were used for comparison of yellowtail scad consumption: ‘measured Q:B’ (this study); ‘estimated Q: B’ (using the commonly used multiple regression model of Palomares and Pauly, 1998); and ‘ecosystem model Q:B’ (taken from multiple ecosystem models of eastern Australia which include Q:B for functional groups with yellowtail scad or similar species of forage fish). ‘Measured’

Table 1 Diet composition of yellowtail scad with energy density of individual prey items, and a weighted mean for the total prey energy density (EDz) used in this study. Two sources of diet information are shown (Study 1, J. Smith unpublished data, Northern NSW; Study 2, H. Schilling unpublished data, Sydney Harbour). Values are means. Prey

m0:79 m

wet mass)

3840a 5188b 1800c 3800c 1602d 2160e 5900c 3270f 4820d 2623g 3499h 4539 4131 4335

a

Lawson et al. (2018). Mean of yellowtail scad (this study) and H. vittatus from Lawson et al. (2018). c Wang and Jeffs (2014). d Thayer et al. (1973). e Foy and Paul (1999). f Mean of Cirripedia nauplius and Decapoda zoea from Foy and Paul (1999). g Karjalainen et al. (1997). h Mean of energy density of all species in this table. b

3

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and ‘estimated’ Q:B values were calculated at two temperatures (14 and 20 � C) to allow better comparison with ecosystem models, which experience a range of mean water temperatures. The ‘estimated’ Q:B was calculated (Eqn (7); Palomares and Pauly, 1998): log10 Q : B ¼ 7:964

0:204 log10 W∞

as prey by predators and how this quality may fluctuate through space and time (Spitz et al., 2010). Quality was determined in this study to allow comparison among other forage fish species. The energy density of yellowtail scad and various other forage fish from the literature were assigned a prey quality as per Spitz et al. (2010), where energy density < 4000 J g 1 ¼ low quality, 4000 to 6000 ¼ moderate quality, and �6000 ¼ high quality.

1:965T ’ þ 0:083AR þ 0:532h

þ 0:398d

(7)

where W∞ is the asymptotic weight of yellowtail scad, T0 is the mean water temperature (1000/(T þ 273.15), where T is temperature in � C), AR is the aspect ratio of their caudal fin, and h and d are related to feeding preference. W∞ was calculated by converting asymptotic length (L∞) to asymptotic weight (W∞, g) using the length-weight relationship described above. Feeding preference variables h (herbivore) and d (de­ tritivore), taking the value 0 or 1, were set to zero as yellowtail scad are primarily zooplanktivorous. Aspect ratio (AR) for their caudal fin was calculated using the height (H, cm) and surface area (s, cm2) (Eqn (8); Palomares and Pauly, 1998): AR ¼

H2 s

3. Results 3.1. Energy requirements The standard metabolic rate (SMR) of yellowtail scad increased with temperature (Fig. 1a), where a ¼ 0.159 (95% CI 0.098–0.257) and b ¼ 0.068 (95% CI 0.048–0.088) (see Eqn (1)). A 1 � C increase in temper­ ature was associated with a 7.1% increase in metabolic rate, approxi­ mately doubling for every 10 � C (Q10 ¼ 1.98). The SMR of yellowtail scad in this study was similar to routine metabolic rate for sardine (Sardinops sagax) measured with respirometry (van der Lingen, 1995), with sardine having a metabolic rate of 0.226 mgO2 g 1 h 1 at 20 � C compared to 0.237 mgO2 g 1 h 1 for a 100 g yellowtail scad at 20 � C. It was also lower than the SMR of sprat (Sprattus sprattus) measured with respirometry, which was 0.340 mgO2 g 1 h 1 at 19–20 � C (Meskendahl et al., 2010). The diet composition and proportion of prey types varied between the two stomach content studies of yellowtail scad (Table 1). However, the main prey item and its proportion were identical in both studies (41% Copepod). The mean energy density for yellowtail scad prey (EDz) was 4335 J g 1. The minimum consumption of yellowtail scad increased with tem­ perature (Fig. 1b), and an average adult yellowtail scad in this study (at 20 � C) required 17.82 g g 0.79 y 1 (CSMR), which is equivalent to 1.85 g d 1 for a 100-g adult (given EDz; Table 1). The corresponding minimum annual consumption to biomass ratio (Q:BSMR) for yellowtail scad was 6.77, with a total Q:B estimated at 15.54, given Act ¼ 1.5, A ¼ 0.681, and Gm ¼ 0.83 g d 1 (Fig. 1c). If we ignore the increased metabolic costs of activity (i.e. Act ¼ 1) then total Q:B ¼ 10.57. This value is similar to the widely-used empirical formula (AR ¼ 3.4, W∞ ¼ 375 g, T’ ¼ 3.41, Q:B ¼ 10.4) and less than one ecosystem model (Q:B ¼ 13.9, Table 2).

(8)

The caudal fins of 20 yellowtail scad (fork length 15–25 cm) were photographed and their dimensions measured using ImageJ software (Rasband, 1997). 2.4. Energy density of yellowtail scad The second aim of this study was to explore the role of yellowtail scad as prey, by measuring the energy density of its tissue (and its temporal and spatial variation), and comparing this with published in­ formation on other forage fish. Energy density was measured using bomb calorimetry. Yellowtail scad were collected once within each season from fishing co-ops across eastern Australia, with scad commercially harvested at 5 locations during 2016 (spring) and 2017 (summer, autumn and winter). Sample sites were Moreton Bay (27� S), Coffs Harbour (30� S), Newcastle (32� S), Wollongong (34� S) and Ulladulla (35� S). Not all sites were sampled across all seasons due to limited availability of yellowtail scad. The fish were caught predomi­ nantly with purse seine, although methods for the capture of these fish can vary between the commercial fisheries operating in each area (Rowling et al., 2010). Fish were refrigerated on capture and frozen at 20 � C until pro­ cessing. Within two months of capture, fish were defrosted, weighed, and measured. Ten replicate fish were processed per location and sea­ son, and ranged from 18-32 cm fork length. Following the method of Glover et al. (2010), whole fish were cut into small pieces, pulverized in a hand-operated meat grinder, then homogenized by hand. A ~40 g subsample was weighed and oven-dried at 70 � C to a constant mass (�0.01 g between days) with the final dry-mass recorded. Dried samples were ground into a powder by a blade coffee grinder and re-dried to a constant mass with at least two 0.1–0.2 g pellets pressed and produced. Pellets were ignited in a bomb calorimeter (model 6400, Parr Instrument Company, Illinois, US) with the energy released from the combustion measured as energy content of the dried fish (J g 1 dry mass). All energy densities are reported per unit wet mass as this indicates the scale on which predation (i.e. energy gain) occurs in the marine environment. Energy density per dry mass was converted to energy density per wet mass (J g 1 wet mass) using the ratio of dry and wet mass for each fish (mean 0.284 � 0.038 s.d.). The difference in energy density among seasons and locations, while accounting for fish body length, was tested with a two-factor ANCOVA using R. We note that the data were non-orthogonal (not all locations were sampled in each season), and to improve this one site (Moreton Bay, only sampled once) was removed from the ANCOVA. Prey ‘quality’ is sometimes used to identify the nutritional value of prey in terms of their energy density, so that inferences can be made about their selection

3.2. Energy density The energy density of individual yellowtail scad varied from 3820 to 8470 J g 1 across the east-coast distribution sampled in this study (Fig. 2), with a mean (�s.d.) energy density of 6048 � 0.967 J g 1 and the individuals spread evenly across the ‘high’ and ‘moderate’ prey quality classifications (Spitz et al., 2010, Fig. 3). There was no clear latitudinal trend in energy density of yellowtail scad, except possibly in spring (Fig. 2). Energy density did differ significantly among locations (Table 3), but this was not consistent among seasons. There was also a significant negative effect of fish body length on energy density (P ¼ 0.01; Table 3), with energy density decreasing by ~160 J g 1 per 1 cm increase in body length (Supp. Material, Fig. S2). It was possible to sex 45 individuals, and there was no effect of sex on energy content (one-­ factor ANOVA, P ¼ 0.65). Spring had the largest mean (�s.d.) energy density (6499 � 1115 J g 1), and winter had the lowest (5601 � 553 J g 1). Data collected from published studies revealed that as a trophic group, forage fish energy densities show considerable intra-specific and inter-specific variation, and generally encompasses two prey quality classifications (Table 4). 4. Discussion This study showed that yellowtail scad have a moderate-high nutri­ tional quality as a prey item, and a higher than expected consumption rate as a lower trophic level consumer in Australian marine ecosystems. 4

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Fig. 1. (a) Relationship between standard metabolic rate (SMR, mgO2 g 0.79 h 1) and temperature (� C) for yellowtail scad. The line is the mean fitted relationship (Eqn (1)), dotted lines are the 95% confidence interval of the mean, and points are measurements from individual yellowtail scad. (b) The calculated mean rela­ tionship between mass-specific minimum consumption (g g 0.79 d 1) of yellowtail scad and temperature (Eqn (4)). (c) The relationship between Q:B and temperature for an adult 100 g yellowtail scad, for total Q:B (Eqn (6)) including activity (Act ¼ 1.5) and excluding activity (Act ¼ 1), and for Q:BSMR (Eqn (5)). Temperaturedependent growth is not incorporated in (c) due to a lack of data, but could increase the temperature-dependence of Q:B.

ecosystem models of eastern Australia. This study measured a ‘mini­ mum’ Q:BSMR for yellowtail scad of 6.8, and estimated total Q:B to be at least 10.6 at 20 � C (or 7.2 at 14 � C), which is 2–3 times larger than the total Q:B used in some ecosystem models (Table 2). However, this consumption rate agreed with the published Q:B regression of Palomares and Pauly (1998). This mismatch between measured and inferred con­ sumption for yellowtail scad highlights the value of conducting bio­ energetics experiments for key species in marine ecosystems. Yellowtail scad was a moderate-high quality (i.e. energy dense) species but the energy density varied temporally and spatially. This variation included significant intra-survey variation, but inter-survey variation may be due to small-scale ecosystem or oceanographic pro­ cesses (such as upwelling and food availability) which will have a large impact on the bioenergetics of their predators. Therefore, highly repli­ cated sampling on a fine scale is necessary to account for the variation in energy density of forage fish.

Table 2 Comparison of total Q:B values for an adult yellowtail scad (or most similar species) that have been measured (this study, for 100 g adult); estimated using the multiple regression of Palomares and Pauly (1998); and values used in four Australian ecosystem models for relevant forage fish groups. Measured and Estimated Q:B values at 20 � C and at 14 � C (also see Fig. 1c). Data type

Q:B

Grouping

Study

Yellowtail scad

This study (20 � C, 14 � C)

Yellowtail scad

This study (20 � C, 14 � C)

Modelc Model

10.6, 7.2 15.5, 10.5 10.4, 7.6 3.3 4.5

Palomares and Pauly (1998) (20 � C, 14 � C) Goldsworthy et al. (2013) Watson et al. (2013)

Model

3.8

Model

13.9

Multiple regression (103 species) ‘Jack mackerel’ ‘Small pelagic carnivorous teleost fished’ ‘Large inshore pelagic omnivorous fish’ ‘Small scombrids and carangids’d

a

Measured

Measuredb Estimated

Forrest (2008) Griffiths et al. (2010)

4.1. Temperature dependence of energetic requirements

a

Incorporates SMR, assimilation efficiency and fish growth, but not activity (Act ¼ 1). b Incorporates SMR, assimilation efficiency, fish growth, and activity (Act ¼ 1.5). c An updated model for the same system uses Q:B ¼ 3.10 for a ‘mackerel’ group which includes yellowtail scad (Fulton et al., 2018). d Yellowtail scad not one of the listed species, but similar forage fish species included.

The metabolic rate of yellowtail scad increased exponentially with temperature, as expected from established metabolic theory (Brown et al., 2004; Clarke and Fraser, 2004). Our derived relationship between metabolic rate and temperature is valuable for informing energetic and consumption requirements in generalized bioenergetics models (Deslauriers et al., 2017), large regional-scale ecosystem models, and for exploring changes in consumption across seasons and in a changing climate (Hobday and Lough, 2011; Last et al., 2011; Megrey et al., 2007). Yellowtail scad had a Q10 of 1.98, which is similar to other forage fish species, such as pilchard Sardinops sagax, whiting Sillaginodes punctatus and sprat Sprattus sprattus, with Q10’s of 1.8 (van der Lingen, 1995), 2.1 (Mazloumi et al., 2017) and 2.2 (Meskendahl et al., 2010) respectively. The temperature dependence of energetic requirements indicates that yellowtail scad energetics would vary considerably across its range, with probably twice the consumption requirements for

The estimates of temperature-dependent standard metabolic rate (SMR) for yellowtail scad are the first for any forage fish species in eastern Australia, and provide a valuable resource for developing our under­ standing of the bioenergetics of forage fish in general. The consumption rate estimate for yellowtail scad indicated that this species can have a relatively large trophic impact which is likely underestimated in most 5

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Fig. 2. Seasonal (a–d) energy density (kJ g 1 of wet mass) of yellowtail scad throughout their east-coast Australia distribution (e–f) at Moreton Bay (MB, 27� S), Coffs Harbour (CH, 30� S), Newcastle (NC, 32� S), Wollongong (WO, 34� S) and Ulladulla (UL, 35� S). Points have been jittered for clarity. Horizontal lines are the prey quality classifications by Spitz et al. (2010), poor < 4, moderate 4–6, high > 6 kJ g 1. Fish were not able to be sampled in all seasons at each location. The þ symbols indicates the mean energy density at each location.

summer in south-eastern Queensland compared to winter in southern New South Wales (i.e. a ~10 � C change).

growth, reproduction, and assimilation. Thus, SMR reflects a minimum energy requirement, but one that can be accurately measured. We found that the consumption required to meet the energetic requirements of SMR in yellowtail scad was Q:BSMR ¼ 6.77 at 20 � C (or 4.49 at 14 � C). Scaling this up to total Q:B using an energy budget model incorporates more uncertainty, especially when accounting for activity using an ac­ tivity multiplier (Act). A multiplier of 1.5 (used for more sedentary

4.2. Comparative consumption to biomass estimates Standard metabolic rate (SMR) is only one component of an organ­ ism’s energy budget, which includes the additional costs of activity, 6

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for the mesopredator tailor (Lawson et al., 2018). For our lower estimate of total Q:B (10.56), SMR made up 63% of the total energy requirements (43% for the upper estimate). This is partly due to lower assumed ac­ tivity costs in yellowtail scad, but further exploration of proportional contribution of energy components across a range of species may reveal whether this is also due to fundamental differences in bioenergetics among trophic groups. The empirical regression model of Palomares and Pauly (1998) estimates a Q:B of 10.4 for yellowtail scad at 20 � C, which supports our lower estimate. This regression result was driven largely by the large caudal fin aspect ratio (AR ¼ 3.4) of yellowtail scad, which correlates strongly with metabolic rate (Killen et al., 2016). A study of active metabolic rate using field data (e.g. Brodie et al., 2016) would help indicate whether their large AR is representative of total energy costs. In contrast to the regression tool (Palomares and Pauly, 1998), ecosystem models specified lower total Q:B values for yellowtail scad, with three of four models applying a Q:B for forage fish between 3.3 and 4.5 (Table 2). Even given the effect of temperature, our results suggest these values are underestimates. One reason for this may be the approach in ecosystem models of combining species into trophic groups. Species are often combined to create simpler models, with decisions for groupings based loosely on similarities in organism size, diet, and mortality (Bulman et al., 2006), and resulting parameter values (e.g. Q: B) are averaged among all species in a group. However, yellowtail scad was consistently grouped with similar species in the ecosystem models

Table 3 Results of the ANCOVA testing the effects of location, season, and fish fork length (cm) on energy density (kJ) of yellowtail scad. Location Season Length Location*Season Residuals

Df

SS

MS

F value

P

3 3 1 8 132

13.31 17.74 3.08 37.02 61.51

4.44 5.91 3.08 4.63 0.47

9.52 12.69 6.61 9.93

<0.001 <0.001 0.011 <0.001

teleosts; Beaudreau and Essington, 2009) gives total Q:B ¼ 15.54. Appropriate use of the activity multiplier relies on having measured true SMR, but for a schooling species like yellowtail scad there may be behavioural stress being alone in a respirometer (Meskendahl et al., 2010). It is also possible that repeated measurements of SMR, compared to our single measurement, could provide a lower estimate of mean SMR (Chabot et al., 2016). Thus, it is possible that our measured SMR is elevated and may indicate routine metabolic rate, which is elevated above basal metabolic requirements. For this reason, we also calculated Q:B without the activity multiplier (Q:B ¼ 10.56) which may be more accurate for adult yellowtail scad under natural conditions. In fact, the range in total Q:B (10.56–15.54) corresponding to this range in Act (1–1.5), could provide a useful measure of uncertainty in mean con­ sumption rate. SMR can be a minor component of a total energy budget, such as 9–22% for yellowfin tuna (Olson and Boggs, 1986), or 25–35%

Table 4 Summarised energy density and prey quality of common forage fish from various locations. Energy density <4000 J g 1 is low quality, 4000 to 6000 is moderate quality, and �6000 is high quality (Spitz et al., 2010). Values are means � standard deviation (*standard error) and aggregate often large seasonal and body size variation. Species

Common name

Energy density (J g

Scomber scombrus Sardina pilchardus Thaleichthys pacificus Sprattus sprattus Sprattus sprattus Scomber scombrus Engraulis encrasicolus Trachurus trachurus Scomber colias Sardinops sagax Sprattus sprattus Clupea harengus Trachurus novaezelandiae Sardina pilchardus Trachurus trachurus Clupea harengus Trachurus mediterraneus Engraulis encrasicolus Trachurus novaezelandiae Ammodytidae Sardinella aurita Scomber australasicus Ammodytes hexapterus Engraulis encrasicolus Micromesistius poutassou Triglops pingeli Clupea harengus pallasi Mallotus villosus Hypomesus pretiosus Merlangius merlangus Hyperlophus vittatus Merlangius merlangus

Atlantic mackerel European pilchard Eulachon European sprat European sprat Atlantic mackerel European anchovy Horse mackerel Atlantic chub mackerel Australian pilchard European sprat Atlantic herring Yellowtail scad European pilchard Horse mackerel Atlantic herring Horse mackerel European anchovy Yellowtail scad Sand lance Round sardinella Blue mackerel Pacific sand lance European anchovy Blue whiting Ribbed sculpin Pacific herring Capelin Surf smelt Whiting Sandy sprat Whiting

7500 � 1500 7500 � 2000 7490 � 190* 7260 � 364* 7200 � 1300 7170 � 1300 7000 � 960 7000 � 1300 6930 � 870 6840 � 330* 6770 � 820* 6705 � 390* 6048 ± 967 6030 � 1170 6030 � 610 6015 � 457* 5820 � 1020 5800 � 900 5780 � 590* 5700 � 600 5640 � 1030 5430 � 340* 5400 � 80* 5350 � 610 4900 � 800 4800 � 430* 4765 � 90* 4605 � 170* 4390 � 350* 4241 � 107* 4240 � 190* 4170 � 124*

a b c d e f g h

Spitz and Jouma’a (2013). Anthony et al. (2000). Hislop et al. (1991). Albo-Puigserver et al. (2017). Dubreuil and Petitgas (2009). Lawson et al. (2018). Pedersen and Hislop (2001). This study. 7

1

wet mass)

Prey quality

Location

High High High High High High High High High High High High High High High High Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate

Bay of Biscaya Bay of Biscaya Northern Gulf of Alaskab Northern Scottish Watersc Bay of Biscaya West Mediterranean Sead Bay of Biscaye Bay of Biscaya West Mediterranean Sead Eastern Australiaf North Seag Northern Scottish Watersc Eastern Australiah West Mediterranean Sead West Mediterranean Sead North Seag West Mediterranean Sead Bay of Biscaya Eastern Australiaf Bay of Biscaya West Mediterranean Sead Eastern Australiaf Northern Gulf of Alaskab West Mediterranean Sead Bay of Biscaya Northern Gulf of Alaskab Northern Gulf of Alaskab Northern Gulf of Alaskab Northern Gulf of Alaskab North Seag Eastern Australiaf Northern Scottish Watersc

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of prey (Ware and Thomson, 2005), or local oceanographic processes such as upwelling (Rey et al., 2015), are known to potentially influence the energy density of fish. It is likely that these processes are driving the differences in energy density in this study, but do not appear to influence energy density along a latitudinal gradient, or predictably with season. Seasonal variation in energy density has been observed for forage fish, possibly due to reproductive onset (Albo-Puigserver et al., 2017; Dubreuil and Petitgas, 2009; Gatti et al., 2018; Tirelli et al., 2006). The increased energy density for yellowtail scad in spring at the lower lati­ tude locations may indicate a spawning signal, although the spawning period may be protracted beyond spring (Horn, 1993; Smith et al., 2018). Increased abundance or prey quality during the maximum and secondary plankton blooms in east coast Australia (Thompson et al., 2009) could also explain the higher energy density of yellowtail scad at some locations during spring and summer (Ware and Thomson, 2005). Oceanographic patterns (e.g. upwelling, eddies) of the East Australia Current (EAC) are different for all the locations sampled in this study (Everett et al., 2014) and could be another cause of the variability of energy density of yellowtail scad. Accounting for this variability would be a key way to derive more accurate estimates of Q:B in predators of yellowtail scad, especially for ecosystem models operating at a sub-annual time step, given that there can be a 1:1 relationship between percent change in energy density and percent change in a predator’s calculated Q:B (Supp. Material Fig. S1; Lawson et al., 2018).

(Forrest, 2008; Goldsworthy et al., 2013; Watson et al., 2013), such as various horse mackerels Trachurus spp. and jack mackerel Trachurus declivis, although occasionally also blue mackerel Scomber australasicus. The ecosystem model by Griffiths et al. (2010), which was the only model with a Q:B value (13.9) close to the estimated value in this study, did not specifically include yellowtail scad in the trophic group, but did include forage fish species such as redbait and blue mackerel. The source of their large Q:B value was a generic value for horse mackerel from another study (Pillar and Barange, 1998), but was derived through a bioenergetics model based on stomach content analysis. Forrest (2008) had a comparable Q:B to the other models (3.8), and this value was approximated using indirect methods rather than empirical information. The remaining models (Goldsworthy et al., 2013; Watson et al., 2013) obtained Q:B values for species or trophic groups from previous ecosystem models (mainly Bulman et al., 2006), and in the case of Goldsworthy et al. (2013) also used derived information from Fishbase (Froese and Pauly, 2018), although we note that Fishbase calculates Q:B values comparable to our own for yellowtail scad given appropriate asymptotic weight and aspect ratio values. There appears to be a history of models borrowing values from other models, and it is unclear which forage fish groups in these models used empirically-derived species-s­ pecific consumption rates. A recommendation is thus to place more emphasis on measuring Q:B for key species using bioenergetics experi­ ments. We also note that ecosystem models from elsewhere can estimate larger Q:B values for similar species, e.g. Q:B ¼ 11.4 for a group con­ taining rough scad Trachurus lathami in the Gulf of Mexico (Geers et al., 2016). There may be numerous implications from underestimating forage fish consumption in an ecosystem model. The strength of top-down control could be underestimated, due to underestimated predation mortality of prey groups (all else being equal). In a mass-balanced framework like Ecopath with Ecosim, increasing Q:B will trade-off against parameters such as prey biomass, prey production, or eco­ trophic efficiency, especially if Q:B consumption is a specified parameter (i.e. not fitted). If ecotrophic efficiency is increased in order to balance an increase in Q:B, then the primary production required to sustain forage fish catches would be underestimated (Pauly and Christensen, 1995). A change in Q:B may also propagate through a modelled ecosystem; for example, perturbation of model input parameters, including consumption, has been shown to influence biomass estimates and indices of ecosystem functioning (Steenbeek et al., 2018). Ulti­ mately, the impact of error in Q:B may be difficult to predict, and depend on a particular foodweb structure, as well as model structure, including compensatory mechanisms, the model-balancing procedure, and which parameters are specified and which fitted (Christensen and Walters, 2004; Essington, 2007).

5. Conclusions The energetic requirements of yellowtail scad may be under­ estimated in some ecosystem models of eastern Australia, which high­ lights the value of further bioenergetics studies of forage fish in this region. Quantifying the energetic traits of the key species is important to reduce ‘species borrowing’ when parameterizing ecosystem models. For the estimation of forage fish energy density, this study indicates that it is necessary to sample forage fish over large spatial and temporal ranges to incorporate natural variation. To determine mechanistic links between fish energy density and the oceanographic environment, future research should focus on sampling forage fish and their prey across fine spatial and temporal gradients. Improved reliance on empirical bioenergetics information, and increased acknowledgement of variation in energy density when estimating consumption rates of predators, will enhance our understanding of the trophic role of forage fish. Author contribution statement JAS, IMS, and SB conceptualised the study, GD collected and pro­ cessed the data, GD and JAS analysed the data, and GD led the writing, with contribution from all authors.

4.3. Energy density

Declaration of competing interest

This study found that yellowtail scad, across their east-coast distri­ bution, are a moderate to high quality prey item, but there was considerable variation between individuals of the same sample, signif­ icant variation across sampling time and location, and a decrease in energy content at the larger fish lengths (Fig. S2). The mean energy density was within the range of other forage fish species, and similar to a previous study of yellowtail scad (Table 4), although the broad range across all species indicates that estimates of predator consumption rates will benefit from regional and species-specific energy density data of their prey. The large temporal and seasonal variation observed for yellowtail scad should be considered when predator consumption rates need to be resolved spatially or seasonally. However, the significant differences in energy density of yellowtail scad among locations were not consistent through time. This means that there are processes occurring that will lead to a general change in mean energy density of yellowtail scad, but that they are not easily explained by season or latitude. The reproductive status (Hislop et al., 1991), nutritional value

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was funded by the Australian Research Council Linkage Project (LP150100923) and a School of Biological, Earth and Environ­ mental Sciences Student Research Grant. We are grateful to Anna Burke and Hayden Schilling for fieldwork assistance, and Sydney Institute of Marine Science staff for assistance with aquarium facilities. All experi­ ments were approved by the UNSW Animal Care and Ethics Committee (Approval number 15/152B).

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Appendix A. Supplementary data

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