Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Temporal Uncoupling between Energy Acquisition and Allocation to Reproduction in a Herbivorous-Detritivorous Fish

  • Francisco Villamarín ,

    Affiliations Australian Rivers Institute - ARI, Griffith University, Brisbane, Australia, Coordenação de Pesquisas em Biodiversidade, Instituto Nacional de Pesquisas da Amazônia - INPA, Manaus, Brazil, Programa Ciência Sem Fronteiras, Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, Brasilia, Brazil

  • William E. Magnusson,

    Affiliation Coordenação de Pesquisas em Biodiversidade, Instituto Nacional de Pesquisas da Amazônia - INPA, Manaus, Brazil

  • Timothy D. Jardine,

    Affiliation School of Environment and Sustainability, University of Saskatchewan, Saskatoon, Canada

  • Dominic Valdez,

    Affiliation Australian Rivers Institute - ARI, Griffith University, Brisbane, Australia

  • Ryan Woods,

    Affiliation Australian Rivers Institute - ARI, Griffith University, Brisbane, Australia

  • Stuart E. Bunn

    Affiliation Australian Rivers Institute - ARI, Griffith University, Brisbane, Australia

Temporal Uncoupling between Energy Acquisition and Allocation to Reproduction in a Herbivorous-Detritivorous Fish

  • Francisco Villamarín, 
  • William E. Magnusson, 
  • Timothy D. Jardine, 
  • Dominic Valdez, 
  • Ryan Woods, 
  • Stuart E. Bunn


Although considerable knowledge has been gathered regarding the role of fish in cycling and translocation of nutrients across ecosystem boundaries, little information is available on how the energy obtained from different ecosystems is temporally allocated in fish bodies. Although in theory, limitations on energy budgets promote the existence of a trade-off between energy allocated to reproduction and somatic growth, this trade-off has rarely been found under natural conditions. Combining information on RNA:DNA ratios and carbon and nitrogen stable-isotope analyses we were able to achieve novel insights into the reproductive allocation of diamond mullet (Liza alata), a catadromous, widely distributed herbivorous-detritivorous fish. Although diamond mullet were in better condition during the wet season, most reproductive allocation occurred during the dry season when resources are limited and fish have poorer body condition. We found a strong trade-off between reproductive and somatic investment. Values of δ13C from reproductive and somatic tissues were correlated, probably because δ13C in food resources between dry and wet seasons do not differ markedly. On the other hand, data for δ15N showed that gonads are more correlated to muscle, a slow turnover tissue, suggesting long term synthesis of reproductive tissues. In combination, these lines of evidence suggest that L. alata is a capital breeder which shows temporal uncoupling of resource ingestion, energy storage and later allocation to reproduction.


Linkages among habitats and the flux of matter across ecosystem boundaries have important implications for biomass production of animals and plants [14]. As part of spawning movements and migrations, many species of fish cross ecosystem boundaries. For example, in the wet-dry tropics, fish that typically live in river channels, waterholes and estuaries during the dry season move onto floodplains during the wet season, where they take advantage of abundant resources. Subsequently, as flood waters recede, many fish disperse back to waterholes, river channels and estuaries. It has been well documented that fish movements may transfer nutrients and aquatic production [3,5], but information on how this production contributes to individual energy allocation and reproductive investment is scarce (but see [4]). Much of this energy might be obtained from floodplain productivity.

Floodplains provide large amounts of high quality resources to consumers [4,6], with higher production of macrophytes, phytoplankton and attached algae during periods of inundation [7]. In general, abundant food resources support earlier maturation and higher fecundity in fish (see [8]). However, it is not well understood how the temporally abundant subsidies from floodplains are allocated to consumer tissues. According to life-history theory, allocation to reproduction results in a trade-off with somatic growth or survival because of limitations of the resource budget [914]. However, trade-offs between somatic growth and reproduction have rarely been observed under natural conditions [14,15].

Organisms differ in their allocation of resources to reproduction. Diadromous species engage in energetically expensive migrations for spawning and use surplus energy stored from previous periods to fuel reproductive output [8,1618], a strategy known as ‘capital breeding’ [1921]. Conversely, the coupling of the reproductive cycle with temporally abundant resources during the breeding period is characterized as ´income breeding´. On floodplains with short inundation periods (~2 months) in the Australian wet-dry tropics, the development of reproductive tissues was fueled by resources available at the time of spawning in the herbivorous fish, Nematalosa come [4]. Therefore, the reproductive cycle of this ‘income-breeding’ fish, not known to undertake upstream spawning migrations [22], is coupled with the temporally abundant resources from floodplains, despite the short inundation duration.

Another common species in the Australian wet-dry tropics, the diamond mullet (Liza alata), is a catadromous fish broadly distributed in the Indo-west-central Pacific. This herbivorous-detritivorous fish breeds in estuaries and spawns large numbers of pelagic non-adhesive eggs [23], as do most of the other members of the Mugilidae [24]. Juveniles recruit in estuarine areas from where they move up river. Although information on reproduction of this species is scarce, Bishop [23] found higher values of gonadosomatic index (GSI) during the early-wet season in the Alligator Rivers region and suggested that spawning migrations must occur during the wet season, which is the only time when seasonally isolated water bodies are connected to the sea for more than 4 months. It is unknown whether this species is a capital or an income breeder. If resources from floodplains are instantly allocated to reproduction, then greater growth of reproductive tissues is expected during the wet season. On the other hand, if mullet carry out spawning migrations to the sea during the wet season, they must have enough stored resources to synthesize gonads before flooding begins and we would expect higher reproductive investment during the dry season. In this case, reproductive investment would be temporally uncoupled from resource availability from floodplains.

Most studies under natural conditions have relied on morphological estimates of reproductive investment, such as total clutch mass, the number of young in a clutch and frequency of clutches (see [14]). However, besides the limitations on quantifying fecundity or reproductive effort based on counting fish eggs [8], those estimates do not express instantaneous growth of reproductive or somatic tissues. An alternative method, RNA:DNA ratio of a cell, is a biochemical indicator of recent growth in aquatic organisms [2528]. This is because the amount of DNA present in a cell remains relatively constant, whereas RNA concentrations vary in proportion to protein synthesis [2931]. Thus, by quantifying the ratio of these nucleic acids in the cells of fish it is possible to estimate the instantaneous investment in growth of different tissues. This can allow inferences as to whether the fish was investing in somatic or reproductive growth at the time of capture. Another method, stable-isotope analysis (SIA) has become a common tool for investigating trophic interactions and energy pathways in food webs [3235] and can be used to determine the origin of resources in reproductive tissues [4]. Isotope values of vertebrate tissues depend in part on the isotopic turnover rates, with liver having a shorter half-life than muscle [3639]. Half-lives of carbon and nitrogen in metabolically active fish tissues, such as liver, range in the order of days to weeks, while structural tissues, such as muscle, show half-lives in the range of weeks to months [3941]. Therefore, theoretically, liver has the potential to provide more recent dietary information. Comparing isotope ratios of these fast and slow turnover tissues with those of gonads may be useful to indicate whether gonads were formed using resources available at the time of capture or a few months in advance [4].

Here we combine information on RNA:DNA ratios and stable-isotope analyses to achieve novel insights into the reproductive allocation strategies of diamond mullet. We analysed RNA:DNA and ratios of stable isotopes of carbon (δ13C) and nitrogen (δ15N) in multiple tissues of this fish to estimate the temporal importance of different aquatic habitats as a subsidy for the synthesis of somatic and reproductive tissues. Specifically, we asked the following questions: 1) When do mullet allocate energy resources for reproductive and somatic growth? 2) Is there a trade-off between reproductive and somatic investment? 3) How different are short- and long-term turnover tissues in terms of carbon and nitrogen stable isotopes?

Materials and Methods

Study area

This study was undertaken within the limits of Kakadu National Park, a protected area located in the Alligator Rivers region in the Northern Territory, Australia (Fig 1). This region is situated about 150 km east of the city of Darwin and is part of a bio-geographical region known as the Australian wet-dry tropics. The most conspicuous climatic characteristic of this region is the presence of a warm dry season and a warm-humid wet season [42,43]. The dynamic hydrology drives most ecosystem processes and structure, including primary productivity and subsidies to food webs through fish movements [4,44].

Fig 1. Study area.

Location of mullet sampling sites during dry and wet seasons (black and white dots, respectively) in the Alligator Rivers region—Northern Territory, Australia. Yellow triangles represent locations where primary sources were collected.

One fourth of the area of the Australian wet-dry tropics is comprised of floodplain wetlands [44]. These ecosystems experience extensive seasonal inundations and high river flows during the wet season between November and April. As rainfall decreases during the dry season, flows are reduced to zero in most rivers and waterbodies are contracted and isolated [45,46]. During this period, creeks and floodplain areas dry out except for a few permanent swamps and lagoons, known locally as billabongs or waterholes [47]. The Alligator Rivers region area is covered by water during March and April, which recedes to approximately 25%–30% of its maximum extent by August and September. It takes about 5 months to reduce to 50% of the maximum recorded flooded area in a given wet season [48]. Tidal influence extends for 70–90 km along the major rivers [47].

Permission to access biological resources in a commonwealth area for non-commercial purposes was provided by the Australian Government. Permit number: AU-COM2012-171.

Mean monthly water-level data at the South Alligator River Data Warehouse (12°39´42´´S, 132°30´26´´E) from 1979 to 2011 was obtained from the Department of Land Resource Management Water Data Portal <>. These data were used to broadly characterize water-level variation at the study area.

Fish and primary resources sampling

We sampled 13 water bodies, including waterholes and floodplains, but none of those waterbodies were visited during both dry and wet seasons because of lack of water or access. Nevertheless, most waterbodies sampled during the dry season were represented by their surrounding floodplains during the wet season (Fig 1). We concentrated sampling during the late-dry (October-November, 2013) and late-wet seasons (April-May, 2014).

Mullet were caught mainly using gill nets. Electrofishing was used in a few waterholes during dry-season sampling. Fish were measured (Standard length, ±1 mm), weighed (±1 g) and dissected to collect samples of muscle, liver, gonads and eggs, when present. We placed all tissue samples in labeled cryogenic vials and stored them immediately on ice for SIA and in liquid nitrogen for RNA:DNA analyses. All animals were euthanized using clove oil, and all efforts were made to minimize suffering. Only 42 of the 56 individuals collected were of adult size (standard length > 27 cm), and juveniles were not used in analyses.

The use of animals in this study was approved by Griffith University’s Animal Ethics Committee in accordance with the Australian Government’s code for the care and use of animals for scientific purposes. Permit Number: ENV/08/11/AEC "NABH-Northern Australia Biodiversity Hub".

We included data of other components of aquatic foodwebs, including biofilm, detritus and filamentous algae as possible primary resources for fish (S2 Table). These data were collected as part of a broader foodweb project between 2012 and 2014. The samples of these primary sources were collected in ten of the same sites where mullet were captured, and samples from 11 additional sites were also included in analyses (Fig 1). Specific methods used to collect end-member organisms are summarized in [49].

RNA:DNA laboratory processing

On arrival at the laboratory, samples were removed from liquid nitrogen and kept frozen at -80°C for a maximum of 15 days. We randomly took sub-samples weighing between 0.001 and 0.3g for analyses. We added 200μl of 0.5% Sarcosil-TE buffer (0.5% sarcosyl; 10mM Tris-HCl, pH 7.5; 1mMEDTA) and two sterile beads (3mm) to the samples in order to induce cell lysis by high-frequency oscillation. RNA and DNA content were quantified using a QubitTM flourometer and fluorescent dyes and standards from Qubit® dsRNA—DNA BR Assay Kits. RNA and DNA content were expressed as μg/mL. RNA:DNA represents the ratio of these concentrations.

SIA Laboratory processing

In the laboratory, all samples were kept frozen at -20°C for 2–4 weeks. We then dried the samples in an oven at 60°C for at least 24 h before grinding and homogenizing them with a mortar and pestle. Samples of 0.6–1.0 mg were used in the analyses.

Samples were combusted in a EuroEA 3000 (EuroVector, Italy) or Europa GSL (Sercon Ltd, Crewe, UK) elemental analyzer and the resulting N2 and CO2 gas were chromatographically separated and fed into an IsoPrime (Micromass,UK) or Hydra 20–22 (Sercon Ltd, Crewe UK) isotope-ratio mass spectrometer. This measures the ratio of heavy and light isotopes in a sample and compares them to a standard. Elemental ratios (C/N) are expressed in %C and %N by mass and isotope ratios (δ) as parts per mil (‰), defined as δ(‰) = (Rsample/Rstandard—1)*1000, where Rsample and Rstandard are the isotope ratios of the sample and standard, respectively. Isotopic standards used were referenced to PeeDee Belemnite (PDB) for carbon, and atmospheric air for nitrogen [32]. Secondary standards of Ammonium Sulfate and Sucrose were used in each run. Acetanilide was used to cross reference elemental compositions of secondary standards.

Data analysis

We used exploratory bi-plots and regressions to examine relationships between length and body mass, and used regression residuals as an index of body condition. Because high C/N ratios in animal tissues are indicative of high lipid content [5052], we used C/N as a secondary indicator of condition. Also, because high lipid levels can cause isotopic fractionation when C/N is higher than 4.0 [50], we performed chemical lipid extractions on a subset of 29 samples (10 muscle, 9 gonads and 10 liver) using a chloroform:methanol solution following the protocol from Bligh and Dyer [53]. After re-analyzing these samples for stable isotopes, we compared the resulting δ13C values with those mathematically lipid corrected using C/N and the most common equations in the literature [51, 52, 5457]. We used the slopes and fit (r2) of the relationships to choose the most appropriate equation to correct the remainder of the samples. The equation from [51] yielded results that best matched values from our extracted samples, so we mathematically corrected samples with C/N ratios ≥4.0 using that equation (S1 Fig).

We explored the relationships between reproductive and somatic tissues in terms of RNA:DNA ratios and carbon and nitrogen stable isotopes using simple and multiple linear regressions. Specifically, we tested whether δ13C and δ15N of reproductive tissues (gonads) were predicted by somatic tissues (muscle and liver) and also whether long-term (muscle) is predicted by short-term turnover tissue (liver).

To test for differences between dry and wet seasons on carbon and nitrogen SI of mullet tissues (muscle, liver and gonads) and primary resources (detritus, filamentous algae and biofilm), we used one-way Analyses of Variance (ANOVA).

We used a two-way ANOVA to compare RNA:DNA means using season and tissue type as factors. We used Analyses of Covariance (ANCOVA) to test if gonadal RNA:DNA ratios are related to gonadal δ15N and season.

We also included the site of capture nested within seasons as factor to control for confounding effects of different locations being sampled. R software [58] was used for all statistical analyses and graphics.


We found no evidence suggesting that the site of capture influenced any of the patterns explained regarding body condition, RNA:DNA ratios and δ15N of different tissues (F<2.4; p>0.067 in all cases). However, we found a significant influence of the site where mullet were caught on δ13C of sampled tissues (F = 2.8; p = 0.022), suggesting site-specific δ13C signatures being incorporated on mullet tissues. During the wet season, most individuals had immature or early developing gonads. During the dry season, most had early developing gonads, but four had ripe eggs. There was a significant positive correlation between standard length and body mass (log(y) = -5.13 + 3.18*log(x); r2 = 0.69; p<0.001). During the wet season, most individuals were heavier for a given length, and conversely during the dry season they were lighter than expected for their length (S2 Fig). We used the residuals from the length—body mass regression as an index of condition.

Significant differences in body condition were found between seasons (t-test; t = -8.91, df = 33, p< 0.001), and all individuals had better body condition during the wet season. Although not quantified, we observed the presence of large mesenteric fat bodies comprising around one third of body volume in individuals during the wet season (Fig 2).

Fig 2. Adult individual of Liza alata.

During the wet season, individuals of this species possess large mesenteric fat bodies (blue arrow) representing up to one third of the body volume.

There was a strong positive, though non-linear, correlation between C/N ratios of muscle tissue and the regression residuals, suggesting that the two indicators were in good agreement, and C/N ratios were also higher during the wet season (t = -2.86, df = 16.1, p = 0.011) (Fig 3).

Fig 3. Relationships between body condition, reproductive investment and lipid content in somatic tissues.

Regression residuals of standard length and body mass were used as surrogates of body condition (X axis). The Y axis represents RNA:DNA ratios in gonads, a proxy of reproductive investment (black circles). The Z axis represents C/N ratios, a proxy of lipid content in muscle tissue (blue squares). Individuals from dry and wet seasons are represented by solid and open symbols, respectively.

When data from wet and dry seasons were combined, body condition (BC) predicted RNA:DNA (R/D) in gonads (R/D = 21.02–63.5 BC, F = 15.8, r2 = 0.36, p<0.001). R/D was negatively related to BC, suggesting a trade-off between reproduction and somatic investment throughout the year. However, this relationship was not evident when analyzing each season separately (Dry season: r2 = 0.11, p = 0.159; Wet season: r2 = 0.04, p = 0.222) (Fig 3).

A two-way ANOVA on RNA:DNA values showed a significant interaction between season and tissue type (F = 18.93, p<0.001), suggesting that despite the lower body condition and lipid levels, most growth occurred in gonads during the dry season, when water level was at its lowest (Fig 4 and Table 1).

Fig 4. Somatic and reproductive growth.

Growth of tissues is indicated by RNA:DNA ratios in relation to the flooding cycle (mean monthly water level data at the South Alligator River Data Warehouse, black line). Developed eggs, gonads and muscle tissues are represented by red, blue and green dots, respectively.

No significant differences in δ13C and δ15N of mullet tissues and primary resources (filamentous algae and biofilm) were found between dry and wet seasons. However, detritus δ15N was 2.3‰ more enriched during the wet season (Table 2).

Table 2. Stable isotopes of C and N of L. alata and its primary sources.

Carbon stable-isotope ratios (δ13C) of somatic and reproductive tissues were highly correlated. Liver δ13C significantly predicted both gonad (δ13CG = -8.55 + 0.69δ13CL, F = 62.9, r2 = 0.65, p<0.001) and muscle (δ13CM = -12.08 + 0.57δ13CL, F = 30.1, r2 = 0.46, p<0.001) (S3A and S3B Fig, respectively). Gonad δ13C was also predicted by muscle δ13C (δ13CG = -5.91 + 0.79δ13CM, F = 49.1, r2 = 0.59, p<0.001) (S3C Fig). Standardized regression coefficients from a multiple linear regression model (R2 = 0.72, p<0.001) indicated that the variation in δ13C from gonads was equally predicted by the variation in liver (bstandardized = 0.43, p<0.001) and that in muscle (bstandardized = 0.43, p = 0.003), but season had no significant effect (bstandardized = -0.19, p = 0.64).

Nitrogen stable isotopes from liver (δ15NL) had a relatively weak relationship with gonads (δ15NG = 0.78 + 0.76 δ15NL, F = 21.01, r2 = 0.43, p<0.001) and muscle (δ15NM = 3.4 + 0.55 δ15NL, F = 9.08, r2 = 0.22, p = 0.005) (Fig 5A and 5B, respectively), but the relationship between muscle and gonad was stronger (δ15NG = -0.55 + 0.89 δ15NM, F = 80.43, r2 = 0.74, p<0.001). Gonads were consistently lower in δ15N values relative to muscle, which caused the relationship to fall below the 1:1 line (Fig 5C). Standardized regression coefficients from a multiple regression model (δ15NG = -3.16 + 8.24δ15NM+ 3.62δ15NL + 3.08Season, R2 = 0.85, p<0.001) indicated that variation in δ15N in gonads was better predicted by δ15N in muscle (bstandardized = 0.73, p<0.001) than that in liver (bstandardized = 0.32, p = 0.001) or by season (bstandardized = 0.4, p = 0.004).

Fig 5. Relationships between δ15N values in somatic and reproductive tissues of Liza alata.

The dashed line represents the 1:1 expected relationship. The solid line represents the least-squares linear regressions of: A) liver vs. gonads, B) liver vs. muscle and, C) muscle vs. gonads. Individuals from dry and wet seasons are represented by solid and open symbols, respectively.

Analysis of covariance indicated that RNA:DNA ratios were significantly related to δ15N in gonads (p = 0.04), and season (p = 0.02), but there was no interaction between season and δ15N (p = 0.09).


Using a novel combination of chemical indicators, we found evidence of temporal uncoupling between resource availability and allocation of energy to reproduction by Liza alata in the Alligator Rivers region. Fish were in better condition in the wet season when food availability peaks, yet were actively synthesizing gonads in the dry season. A trade-off between reproductive and somatic investment was evident and carbon and nitrogen stable isotopes suggested that the long-term diet was mainly contributing to reproductive growth.

We used the residuals from the length-mass relationship of mullet as an index of body condition. A strong correlation between a second indicator of condition, C/N ratios of muscle tissue, and the regression residuals of length and body mass suggests that residuals can appropriately be used as an index of body condition. The relationship between C/N and the regression residuals is likely nonlinear because, in addition to storing fat in muscle, this species also stores fat in specialized mesenteric fat bodies. Therefore, although they may be confounded by reproductive tissue or stomach fullness, indices based on length-weight relationships appear more sensitive than muscle C/N ratios for assessing fish condition.

Although mullet in this study showed evidence of gonadal growth during both dry and wet seasons, RNA:DNA ratios imply that higher growth occurred during the dry season. This was unexpected because the dry season is the period of the year when water level is at its lowest, as is primary productivity and food availability in the Alligator Rivers region [7]. Given the high prevalence of mesenteric fat bodies and the higher lipid content of muscle (higher C/N) in fish captured during the wet season, this suggests that mullet store most of their energy for reproductive and somatic growth as fat when resources are more abundant and they can attain a favorable body condition. The fat is then re-mobilized to the gonads during the dry season, reducing condition but increasing gonad mass in preparation for spawning in the early-wet season.

Although RNA:DNA values of muscle tissue were slightly higher during the dry season, this tissue grew significantly less relative to gonads during both seasons. Slow growth rates, a common characteristic of related species within the Mugilidae [59], might be causing this pattern. Some Mugilids attain about 75% of their maximum size in their first 3–4 years of life, with greatest mean annual growth increments during the first year and decreasing markedly after age 3–5 [60]. All mature mullet analyzed in this study had standard lengths between 27 and 42 cm. It is plausible that these individuals were growing slowly and with limited food consumption rates outside of the wet season.

Furthermore, low values of RNA:DNA found in developed eggs might be the result of increasing volumes of lipid granules during pre-ovulatory stages, typical of other Mugilidae species [61]. Throughout oogenesis, early oocytes are rich in protein and RNA within the yolk nucleus. Subsequently, with the approach of the breeding season, during the vitellogenic period, lipid droplets accumulate in the cytoplasm [61]; this might explain such low values of RNA:DNA in developed eggs relative to gonads.

Life-history theory states that organisms have a limited resource budget and thus, allocation to reproduction arises as a trade-off against somatic growth or survival. The "Principle of Allocation" [62] predicts negative correlations between reproduction and somatic growth [914]. Somatic investment is usually used as a surrogate of body condition (e.g. fat content or body mass per length) [14]. The strong negative correlation between body condition and RNA:DNA in mullet gonads in this study provides evidence of a trade-off between reproduction and somatic investment. Although expected, this trade-off has rarely been measured in natural conditions, mainly because individual variation in resource acquisition exceeds that of resource allocation [15]. Estimates of reproductive investment in most studies under natural conditions rely on values such as total clutch mass, the number of young in a clutch and frequency of clutches (see [14]). However, there are difficulties associated with quantifying fecundity or reproductive effort of fish based on counting eggs [8] because there could be confounding effects, such as downregulation of secondary oocytes by atresia which may reduce the final number of eggs ovulated [6366]. Since RNA:DNA ratios are molecular measures of instantaneous growth of specific tissues, such as gonads, they are likely to be useful estimates of reproductive investment. This can overcome the difficulties of estimating reproductive effort, especially in capital breeding fishes.

Because capital breeders [6769] use stocks of energy in their body to sustain reproduction, a positive correlation between reproductive investment and pre-breeding body stores is expected [14]. This is true in the case of mullet in this study. Low values of gonadal RNA:DNA during the period when body condition is at its highest suggest that the wet season is the time of the year when mullet store most energy. On the other hand, elevated values of gonadal RNA:DNA ratios during the dry season suggest that these energy stocks are allocated to reproduction months after they were acquired. In combination, these lines of evidence suggest that L. alata is a capital breeding fish which shows a temporal uncoupling of resource ingestion, energy storage and allocation to reproduction.

Further evidence for capital breeding is provided through carbon and nitrogen stable isotope analyses. Isotopic equilibrium depends on turnover rates of tissues, and recent dietary sources are more rapidly reflected in fast-turnover tissues such as liver [3740]. Diamond mullet in this study showed strong correlations of δ13C values in reproductive (gonads) and somatic tissues (liver and muscle), suggesting that both short- and long-term diet could be contributing to gonadal growth. If gonads were being synthesized using only energy stored months before when mullet had access to resources from floodplains, then we would expect δ13C values of gonads to be poorly correlated with those of liver and more correlated with tissues showing slower turnover rates, such as muscle. However, we also found a significant positive correlation between δ13C values for liver and muscle tissues. This strong correlation may be due to overall isotopic similarity between wet and dry season habitat resources. Although filamentous algae showed a slight increase in δ13C values which might explain the slight increase in liver δ13C values during the wet season, we found no significant differences between dry and wet season δ13C values of other potential mullet resources, such as detritus and biofilm.

Results of δ15N analyses were more consistent with expectations for a capital breeder. Although gonad δ15N values showed significant correlations with liver values, a stronger correlation was found with muscle tissue, as revealed by a significantly greater slope and r2. Higher δ15N in muscle tissue that turns over more slowly than in fast-turnover liver tissue might be a reflection of the significantly higher δ15N found in detritus in the wet season. Detritus is an important resource for this species and during the wet season, mullet forage in more productive floodplains than during the dry season. It has been observed that high denitrification processes in wetland areas cause primary consumers to have higher δ15N isotopic values than in areas with less wetland coverage [70].

The low correlations between δ15N of muscle and gonads (likely produced during wet season) with liver (produced in less productive areas during the dry season) suggests that N-bearing proteins in gonads may be more readily mobilized from protein obtained during the wet season than sourced from a maintenance diet during gonadal formation in the dry season. Although there was a strong correlation between gonads and muscle tissue, gonads showed consistent depletions in 15N relative to muscle. Furthermore, we found that instantaneous reproductive growth (gonadal RNA:DNA) was negatively correlated with δ15N values during the dry season. Both of these patterns are consistent with results obtained by [71] for humans, who demonstrated that δ15N values of hair can become low due to anabolic processes, such as those occurring during gestation. The authors hypothesized that higher retention of urea might be helping to re-incorporate N to the metabolic pool for protein synthesis. However, fish excrete N in the form of ammonia which is highly toxic, which makes it unlikely that mullet would be retaining and recycling excretory N. In general, N cycling is complex and can lead to enrichment or depletion depending on the tissue and physiological state of the organism (see review by [72]), and the amino acid profile of different tissues can also affect δ15N because essential amino acids such as phenylalanine exhibit no fractionation relative to the diet whereas others such as glutamic acid exhibit strong fractionation [73]. Detailed physiological explanation for 15N depletion in mullet gonads is beyond the scope of this study, but a subject worthy of future investigation. To our knowledge, this is the first record of 15N depletion in fish tissues related to energy allocation to reproduction.

Although the sites where mullet were captured during the wet season were not exactly the same as those during the dry season because of lack of access or water availability, respectively, it is important to stress that the patterns found relating somatic and reproductive growth and δ15N of mullet tissues were not affected by these differences. On the other hand, δ13C values of mullet tissues were significantly influenced by the site of capture, this suggests that site-specific δ13C signatures are being incorporated on mullet tissues. This is an expected result given that δ13C values of primary aquatic producers depend mainly on CO2 difusion rates and local isotopic composition of the dissolved inorganic carbon pool (DIC) [74].

The temporal and spatial uncoupling between energy acquisition and allocation to reproduction of this common fish has important implications for the preservation of the natural hydrological regimes of floodplain areas. Diamond mullet typically inhabit waterholes during the dry season and move into floodplains during the wet season where they may obtain most of their energy. Subsequently, as flood waters recede, some individuals migrate back to waterholes and others eventually migrate to saltwater to spawn [23]. Despite the low primary productivity found in remnant waterholes during the dry season [7], findings from this study emphasize the importance of these habitats as zones where reproductive allocation takes place. More importantly, wet season habitats such as floodplains are critical in providing most energy for growth and reproduction. Therefore, the maintenance of natural hydrological regimes would enhance the capacity for fishes of this region to maintain viable populations.

Supporting Information

S1 Fig. Relationships between lipid-extracted and mathematically-corrected δ13C data using published lipid-correction equations.

The X axis represents mathematically-corrected (δ13C´) minus uncorrected (δ13C) values. The Y axis represents chemically-extracted (δ13Cext) minus uncorrected (δ13C) values. Gonad, liver and muscle tissues are represented by black dots, triangles and squares, respectively. The dashed line represents the expected 1:1 relationship. The solid line represents a least squares regression of δ13Cextδ13C on δ13C´- δ13C. The correction equations used were as follows: (A) [47]: δ13C´ = δ13C - 2.98*log(C/N) + 3.09; (B) [50]: δ13C´ = δ13C +(6-(22.2/C/N)); (C) [51]: δ13C´ = (δ13C*C/N + 7.08*(C/N-3.7)) / C/N; (D) [52]: δ13C´ = δ13C + (0.322* C/N) - 1.175; (E) [48]: δ13C´ = δ13C - 3.32 + (0.99* C/N); (F) [53]: δ13C´ = δ13C + (6.3* ((C/N—4.2) / C/N)).


S2 Fig. Linear regression between log standard length and log body mass of Liza alata.

Individuals captured during dry and wet seasons are represented by solid and open symbols, respectively.


S3 Fig. Relationships between δ13C values in somatic and reproductive tissues of Liza alata.

The dashed line represents the 1:1 expected relationship. The solid line represents the least-squares linear regressions of: A) liver vs. gonads, B) liver vs. muscle and, C) muscle vs gonads. Individuals from dry and wet seasons are represented by solid and open symbols, respectively.


S1 Table. Data of underlying findings.

Raw data of the individuals of L. alata from which we draw the conclusions presented in the manuscript. Abreviations of attributes are as follows: sl_mm = standard length in milimeters, mass_g = mass in grams, %c = percentage of carbon, %n = percentage of nitrogen, le = lipid-extracted samples, d13c = δ13C, d15n = δ15N, r_d = RNA:DNA ratio, M = muscle, L = liver, G = gonad, EGG = eggs. Sex: F = female, M = male, U = unknown.


S2 Table. Primary sources data of underlying findings.

Raw data of the primary sources (biofilm, detritus and filamentous algae) from which we draw the conclusions presented in the manuscript. Abreviations of attributes are as follows: d13c = δ13C, d15n = δ15N, %c = percentage of carbon, %n = percentage of nitrogen.



We are thankful to Gundjeihmi Aboriginal Corporation and the support from Ross Nobobbob and William Alderson. All Kakadu National Park Rangers and Staff including Steve Winderlich, Anne O'dea, Garry Lindner, Jonathon Nadji, Sean Nadji, Fred Hunter and Calvin Murakami were very helpful providing support in the field. Housing/lodging was provided by ERISS (Environmental Research Institute of Supervising Scientists). Michael Douglas, Samantha Setterfield, Jaana Deilenberg, Peter Kyne, Dave Crook, Duncan Buckle, Damian McMaster from Charles Darwin University; Doug Ward and Mark Kennard from Griffith University, Neil Pettit from Western Australia University and Tom Rayner offered valuable help. Two anonymous reviewers gave suggestions that greatly improved the manuscript. This research was conducted with the support of funding to SB from the Australian Government’s National Environmental Research Program and from Land and Water Australia. FV received support from the Brazilian Government’s Science Without Borders Program.

Author Contributions

Conceived and designed the experiments: FV SEB DV TDJ. Performed the experiments: FV DV RW. Analyzed the data: FV WEM. Contributed reagents/materials/analysis tools: FV DV RW SEB. Wrote the paper: FV TDJ WEM.


  1. 1. Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. The River Continuum Concept. Can J Fish Aquat Sci [Internet]. NRC Research Press; 1980 Jan 1;37(1):130–7. Available from:
  2. 2. Polis GA, Anderson WB, Holt RD. Toward an Integration of Landscape and Food Web Ecology: The Dynamics of Spatially Subsidized Food Webs. Annu Rev Ecol Syst [Internet]. Annual Reviews; 1997;28:289–316. Available from:
  3. 3. Vanni MJ. Nutrient Cycling by Animals in Freshwater Ecosystems. Annu Rev Ecol Syst [Internet]. Annual Reviews; 2002;33:341–70. Available from:
  4. 4. Jardine T, Pusey B, Hamilton S, Pettit N, Davies P, Douglas M, et al. Fish mediate high food web connectivity in the lower reaches of a tropical floodplain river. Oecologia [Internet]. Springer-Verlag; 2012;168(3):829–38. Available from:
  5. 5. Moore JW, Schindler DE, Carter JL, Fox J, Griffiths J, Holtgrieve GW. Biotic control of stream fluxes: Spawning salmon drive nutrient and matter export. Ecology [Internet]. Ecological Society of America; 2007 May 1;88(5):1278–91.
  6. 6. Junk WJ, Bayley PB, Sparks RE. The flood pulse concept in river-floodplain systems. Can Spec Publ Fish Aquat Sci. 1989;106(1):110–27.
  7. 7. Pettit NE, Bayliss P, Davies PM, Hamilton SK, Warfe DM, Bunn SE, et al. Seasonal contrasts in carbon resources and ecological processes on a tropical floodplain. Freshw Biol [Internet]. Blackwell Publishing Ltd; 2011 Jun 1;56(6):1047–64. Available from:
  8. 8. McBride RS, Somarakis S, Fitzhugh GR, Albert A, Yaragina NA, Wuenschel MJ, et al. Energy acquisition and allocation to egg production in relation to fish reproductive strategies. Fish Fish [Internet]. 2015 Mar 1;16(1):23–57.
  9. 9. Williams GC. Natural Selection, the Costs of Reproduction, and a Refinement of Lack’s Principle. Am Nat [Internet]. [University of Chicago Press, American Society of Naturalists]; 1966;100(916):687–90. Available from:
  10. 10. Gadgil M, Bossert WH. Life Historical Consequences of Natural Selection. Am Nat [Internet]. The University of Chicago Press; 1970;104(935):1–24. Available from:
  11. 11. Calow P. The cost of reproduction—A physiological approach. Biol Rev [Internet]. Blackwell Publishing Ltd; 1979 Feb 1;54(1):23–40.
  12. 12. Partridge L, Sibly R, Beverton RJH, Hill WG. Constraints in the Evolution of Life Histories [and Discussion]. Philos Trans R Soc London B Biol Sci [Internet]. 1991 Apr 29;332(1262):3–13. Available from:
  13. 13. Stearns SC. The evolution of life histories. Oxford University Press Oxford; 1992.
  14. 14. Glazier DS. Trade-offs between reproductive and somatic (storage) investments in animals: a comparative test of the Van Noordwijk and De Jong model. Evol Ecol. Springer; 1999;13(6):539–55.
  15. 15. Van Noordwijk AJ, de Jong G. Acquisition and allocation of resources: their influence on variation in life history tactics. Am Nat. JSTOR; 1986;137–42.
  16. 16. Boulcott P, Wright PJ. Critical timing for reproductive allocation in a capital breeder: evidence from sandeels. Aquat Biol. 2008;3(1):31–40.
  17. 17. Moles MD, Johnston TA, Robinson BW, Leggett WC, Casselman JM. Is gonadal investment in walleye (Sander vitreus) dependent on body lipid reserves? A multipopulation comparative analysis. Can J Fish Aquat Sci. NRC Research Press; 2008;65(4):600–14.
  18. 18. Palstra AP, van den Thillart GE. Swimming physiology of European silver eels (Anguilla anguilla L.): energetic costs and effects on sexual maturation and reproduction. Fish Physiol Biochem. Springer; 2010;36(3):297–322.
  19. 19. Henderson BA, Wong JL, Nepszy SJ. Reproduction of walleye in Lake Erie: allocation of energy. Can J Fish Aquat Sci. NRC Research Press; 1996;53(1):127–33.
  20. 20. Rijnsdorp AD, Witthames PR. Ecology of reproduction. Flatfishes Biol Exploit. Wiley Online Library; 2005;68–93.
  21. 21. Marshall CT, Kennedy J, Skjæraasen JE, Nash RDM, Thorsen A, Slotte A, et al. Do capital breeders like Atlantic herring (Clupea harengus) exhibit sensitive periods of nutritional control on ovary development and fecundity regulation? Can J Fish Aquat Sci. NRC Research Press; 2009;67(1):16–27.
  22. 22. Uehara M, Tachihara K. Reproductive traits of the western Pacific gizzard shad Nematalosa come: implications for conservation and management in a population at its distributional margin. Environ Biol Fishes. Springer; 2015;98(1):1–10.
  23. 23. Bishop K, Allen S, Pollard D, Cook M. Ecological studies on the fishes of the Alligator Rivers Region, Northern Territory (Final report in 3 parts). Superv Sci Alligator Rivers Reg Jabiru. 1980.
  24. 24. Lake JS. Freshwater fishes and rivers of Australia. Nelson Melbourne; 1971.
  25. 25. Holm-Hansen O, Sutcliffe WH Jr, Sharp J. Measurement of deoxyribonucleic acid in the ocean and its ecological significance. Limnol Ocean. 1968;13(3):507–14.
  26. 26. Bulow FJ. RNA-DNA ratios as indicators of recent growth rates of a fish. J Fish Board Canada. NRC Research Press; 1970;27(12):2343–9.
  27. 27. Clemmesen C. The effect of food availability, age or size on the RNA:DNA ratio of individually measured herring larvae: laboratory calibration. Mar Biol. Springer; 1994;118(3):377–82.
  28. 28. Berdalet E, Roldán C, Olivar MP, Lysnes K. Quantifying RNA and DNA in planktonic organisms with SYBR Green II and nucleases. Part A. Optimisation of the assay. Sci Mar. 2005;69(1):1–16.
  29. 29. Bergeron J. Nucleic acids in ichthyoplankton ecology: a review, with emphasis on recent advances for new perspectives. J Fish Biol. Wiley Online Library; 1997;51(sA):284–302.
  30. 30. Bulow FJ. RNA-DNA ratios as indicators of growth in fish: a review. age growth fish. The Iowa State University Press Ames, IA; 1987;45–64.
  31. 31. Buckley L, Caldarone E, Ong T-L. RNA—DNA ratio and other nucleic acid-based indicators for growth and condition of marine fishes. In: Molecular Ecology of Aquatic Communities. Springer; 1999. p. 265–77.
  32. 32. DeNiro MJ, Epstein S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta. Elsevier; 1981;45(3):341–51.
  33. 33. Peterson BJ, Fry B. Stable isotopes in ecosystem studies. Annu Rev Ecol Syst. JSTOR; 1987;293–320.
  34. 34. Fry B. Stable isotope ecology, 308 pp. Springer, New York.[Links]; 2006.
  35. 35. Layman CA, Araujo MS, Boucek R, Hammerschlag-Peyer CM, Harrison E, Jud ZR, et al. Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol Rev. Wiley Online Library; 2012;87(3):545–62.
  36. 36. Tieszen LL, Boutton TW, Tesdahl KG, Slade NA. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ13C analysis of diet. Oecologia. Springer; 1983;57(1–2):32–7.
  37. 37. Hobson KA, Clark RG. Assessing Avian Diets Using Stable Isotopes I: Turnover of 13C in Tissues. Condor [Internet]. Cooper Ornithological Society; 1992;94(1):181–8. Available from:
  38. 38. Suzuki KW, Kasai A, Nakayama K, Tanaka M. Differential isotopic enrichment and half-life among tissues in Japanese temperate bass (Lateolabrax japonicus) juveniles: implications for analyzing migration. Can J Fish Aquat Sci [Internet]. NRC Research Press; 2005 Mar 1;62(3):671–8. Available from:
  39. 39. Buchheister A, Latour RJ. Turnover and fractionation of carbon and nitrogen stable isotopes in tissues of a migratory coastal predator, summer flounder (Paralichthys dentatus). Can J Fish Aquat Sci [Internet]. NRC Research Press; 2010 Feb 11;67(3):445–61. Available from:
  40. 40. Heady WN, Moore JW. Tissue turnover and stable isotope clocks to quantify resource shifts in anadromous rainbow trout. Oecologia. Springer; 2013;172(1):21–34.
  41. 41. Ankjærø T, Christensen JT, Grønkjær P. Tissue-specific turnover rates and trophic enrichment of stable N and C isotopes in juvenile Atlantic cod Gadus morhua fed three different diets. Mar Ecol Ser. 2012
  42. 42. Finlayson CM, Woodroffe CD. Wetland vegetation. In: Landscape and vegetation ecology of the Kakadu region, northern Australia. Springer; 1996. p. 81–112.
  43. 43. Finlayson CM. Plant ecology of Australia’s tropical floodplain wetlands: a review. Ann Bot. Annals Botany Co; 2005;96(4):541–55.
  44. 44. Warfe DM, Pettit NE, Davies PM, Pusey BJ, Hamilton SK, Kennard MJ, et al. The “wet–dry”in the wet–dry tropics drives river ecosystem structure and processes in northern Australia. Freshw Biol. Wiley Online Library; 2011;56(11):2169–95.
  45. 45. McDonald NS, McAlpine J. Floods and droughts: the northern climate. Monsoonal Aust Landscape, Ecol Man North Lowl AA Balkema, Rotterdam, Netherlands. 1991;41–62.
  46. 46. Kennard MJ, Pusey BJ, Olden JD, Mackay SJ, Stein JL, Marsh N. Classification of natural flow regimes in Australia to support environmental flow management. Freshw Biol [Internet]. Blackwell Publishing Ltd; 2010 Jan 1;55(1):171–93. Available from:
  47. 47. Finlayson CM, Cowie ID, Bailey BJ. Characteristics of a seasonally flooded freshwater system in monsoonal Australia. In: Wetland ecology and management: Case studies. Springer; 1990. p. 141–62.
  48. 48. Ward DP, Petty A, Setterfield SA, Douglas MM, Ferdinands K, Hamilton SK, et al. Floodplain inundation and vegetation dynamics in the Alligator Rivers region (Kakadu) of northern Australia assessed using optical and radar remote sensing. Remote Sens Environ. Elsevier; 2014;147:43–55.
  49. 49. Jardine TD, Hunt RJ, Faggotter SJ, Valdez D, Burford MA, Bunn SE. Carbon from periphyton supports fish biomass in waterholes of a wet–dry tropical river. River Res Appl. Wiley Online Library; 2013;29(5):560–73.
  50. 50. McConnaughey T, McRoy CP. Food-Web structure and the fractionation of Carbon isotopes in the bering sea. Mar Biol [Internet]. Springer-Verlag; 1979;53(3):257–62. Available from:
  51. 51. Logan JM, Jardine TD, Miller TJ, Bunn SE, Cunjak RA, Lutcavage ME. Lipid corrections in carbon and nitrogen stable isotope analyses: comparison of chemical extraction and modelling methods. J Anim Ecol. Wiley Online Library; 2008;77(4):838–46.
  52. 52. Post DM, Layman CA, Arrington DA, Takimoto G, Quattrochi J, Montana CG. Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia. Springer; 2007;152(1):179–89.
  53. 53. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. NRC Research Press; 1959;37(8):911–7.
  54. 54. Fry B. Stable isotopic indicators of habitat use by Mississippi River fish. J North Am Benthol Soc [Internet]. The University of Chicago Press; 2002;21(4):676–85. Available from:
  55. 55. Sweeting CJ, Polunin NVC, Jennings S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun Mass Spectrom [Internet]. John Wiley & Sons, Ltd.; 2006 Feb 28;20(4):595–601. Available from: 10.1002/rcm.2347
  56. 56. Bodin N, Le Loc’h F, Hily C. Effect of lipid removal on carbon and nitrogen stable isotope ratios in crustacean tissues. J Exp Mar Bio Ecol [Internet]. 2007 Mar 5;341(2):168–75. Available from:
  57. 57. Smyntek PM, Teece MA, Schulz KL, Thackeray SJ. A standard protocol for stable isotope analysis of zooplankton in aquatic food web research using mass balance correction models. Limnol Oceanogr. Wiley Online Library; 2007;52(5):2135–46.
  58. 58. Team RC. R core team (2014). R A Lang Environ Stat Comput R Found Stat Comput Vienna, Austria URL 2014.
  59. 59. Hickling CF. A Contribution to the Natural History of the English Grey Mullets [Pisces, Mugilidae]. J Mar Biol Assoc United Kingdom. Cambridge Univ Press; 1970;50(03):609–33.
  60. 60. Koutrakis ET, Sinis AI. Growth analysis of grey mullets (Pisces, Mugilidae) as related to age and site. Isr J Zool. Taylor & Francis; 1994;40(1):37–53.
  61. 61. Abraham M, Blanc N, Yashouv A. Oogenesis in five species of grey mullets (Teleostei, Mugilidae) from natural and landlocked habitats. Isr J Zool. Taylor & Francis; 1966;15(3–4):155–72.
  62. 62. Cody ML. A General Theory of Clutch Size. Evolution (N Y) [Internet]. Society for the Study of Evolution; 1966;20(2):174–84. Available from:
  63. 63. Murua H, Kraus G, Saborido-Rey F, Witthames PR, Thorsen A, Junquera S. Procedures to estimate fecundity of marine fish species in relation to their reproductive strategy. J Northwest Atl Fish Sci. 2003;33:33–54.
  64. 64. Kjesbu OS, Jakobsen T, Fogarty MJ, Megrey BA, Moksness E. Applied fish reproductive biology: contribution of individual reproductive potential to recruitment and fisheries management. Fish Reprod Biol Implic Assess Manag. Wiley-Blackwell United Kingdom; 2009;293–332.
  65. 65. McElroy WD, Wuenschel MJ, Press YK, Towle EK, McBride RS. Differences in female individual reproductive potential among three stocks of winter flounder, Pseudopleuronectes americanus. J Sea Res. Elsevier; 2013;75:52–61.
  66. 66. Witthames PR, Armstrong M, Thorsen A, Solemdal P, Kjesbu OS. Contrasting development and delivery of realised fecundity in Atlantic cod (Gadus morhua) stocks from cold and warm waters. Fish Res. Elsevier; 2013;138:128–38.
  67. 67. Drent RH, Daan S. The prudent parent: energetic adjustments in avian breeding. Ardea. 1980;68(1–4):225–52.
  68. 68. Jönsson KI. Capital and income breeding as alternative tactics of resource use in reproduction. Oikos. JSTOR; 1997;57–66.
  69. 69. Bonnet X, Bradshaw D, Shine R. Capital versus income breeding: an ectothermic perspective. Oikos. JSTOR; 1998;333–42.
  70. 70. Diebel MW, Vander Zanden MJ. Nitrogen stable isotopes in streams: effects of agricultural sources and transformations. Ecol Appl. Eco Soc America; 2009;19(5):1127–34.
  71. 71. Fuller BT, Fuller JL, Sage NE, Harris DA, O’Connell TC, Hedges REM. Nitrogen balance and δ15N: why you’re not what you eat during nutritional stress. Rapid Commun Mass Spectrom. Wiley Online Library; 2005;19(18):2497–506.
  72. 72. McCue MD, Pollock ED. Stable isotopes may provide evidence for starvation in reptiles. Rapid Commun Mass Spectrom. Wiley Online Library; 2008;22(15):2307–14.
  73. 73. Chikaraishi Y, Steffan SA, Ogawa NO, Ishikawa NF, Sasaki Y, Tsuchiya M, et al. High-resolution food webs based on nitrogen isotopic composition of amino acids. Ecol Evol. Wiley Online Library; 2014;4(12):2423–49.
  74. 74. Keeley JE, Sandquist DR. Carbon: freshwater plants. Plant Cell Environ. Wiley Online Library; 1992;15(9):1021–35.