Using energetic budgets to assess the effects of environmental stress on corals: are we measuring the right things?
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- Lesser, M.P. Coral Reefs (2013) 32: 25. doi:10.1007/s00338-012-0993-x
Historically, the response of marine invertebrates to their environment, and environmentally induced stress, has included some measurement of their physiology or metabolism. Eventually, this approach developed into comparative energetics and the construction of energetic budgets. More recently, coral reefs, and scleractinian corals in particular, have suffered significant declines due to climate change-related environmental stress. In addition to a number of physiological, biophysical and molecular measurements to assess “coral health,” there has been increased use of energetic approaches that have included the measurement of specific biochemical constituents (i.e., lipid concentrations) as a proxy for energy available to assess the potential outcomes of environmental stress on corals. In reading these studies, there appears to be some confusion between energy budgets and carbon budgets. Additionally, many assumptions regarding proximate biochemical composition, metabolic fuel preferences and metabolic quotients have been made, all of which are essential to construct accurate energy budgets and to convert elemental composition (i.e., carbon) to energy equivalents. Additionally, models of energetics such as the metabolic theory of ecology or dynamic energy budgets are being applied to coral physiology and include several assumptions that are not appropriate for scleractinian corals. As we assess the independent and interactive effects of multiple stressors on corals, efforts to construct quantitative energetic budgets should be a priority component of realistic multifactor experiments that would then improve the use of models as predictors of outcomes related to the effects of environmental change on corals.
KeywordsEnergy budgetsMetabolismCarbon budgetsClimate changeCorals
A short history of physiological ecology and energetic budgets
Organismal performance under both “normal” and “stressful” conditions is fundamentally determined by the energetic status of the individual that can ultimately affect fitness (i.e., reproductive output). Energy is the currency of ecology, and its acquisition and use have been of seminal interest to physiologists and ecologists for years. Energetics in an ecological context, by definition, is the flow of energy through ecological systems. Energy is stored in the chemical bonds of the basic biochemical constituents of the cell: proteins, lipids and carbohydrates. The International System of Units’ designation to describe energy is the joule (J). Energy is released under a series of well-controlled oxidation–reduction reactions and captured in the form of ATP and/or reducing power (i.e., NADPH) that then facilitates the work of essential reactions in metabolic pathways and physiological processes. Physiological ecology, principally a bottom-up approach to understand the interaction of an organism with its environment, as a field of study came out of the works of Brody (1945), Crisp (1971) and Kleiber (1975), who developed energetic approaches grounded in thermodynamic principles and laws of metabolic scaling in an ecological context. In a marine context, this approach developed through the 1950s and 1960s on studies of bivalves and crustaceans and led to breakthrough studies on marine mussels by Widdows and Bayne (1971), Thompson and Bayne (1974) and Bayne and Newell (1983), leading to the development of energetic concepts such as “scope for growth” (Widdows and Johnson 1988) and the energetic efficiency of protein turnover (Hawkins 1985).
Energetic approaches, along with assessments of growth efficiencies, metabolic fuel preferences and proximate biochemical composition, have been used very successfully to assess the effects of pollution, water flow and food availability, developmental energetics, and the energetics of larval swimming on a variety of marine organisms (Sprung 1984; Zamer 1986; Zamer and Shick 1987, 1989; Sebens 1987; Shilling and Manahan 1990; Lesser et al. 1992; Coma et al. 1998; Marsh et al. 1999; Wendt 2000; Halldórsson et al. 2005). Both physiological and biochemical approaches to understand the ecology of organisms can be best appreciated in the comprehensive reviews by Crisp (1984), Prosser (1991) and Hochachka and Somero (2002).
Environmental stress and scleractinian corals
The physiology of scleractinian corals has always been of fundamental interest (e.g., Gladfelter 1985) but none more so than during recent decades where the decline of this sentinel functional group on coral reefs is attributed to a number of anthropogenically induced environmental changes such as increased seawater temperatures and ocean acidification (Hoegh-Guldberg et al. 2007). Since the 1980s, coral reefs have undergone unprecedented changes, and one of the primary reasons has been the mortality of scleractinian corals from the primary and secondary effects of environmental stress most commonly manifested in the stress response known as coral bleaching (Lesser 2004). One of the unique features of coral physiology is that corals, unlike many marine invertebrates, are living very close to their upper thermal limits such that small upward deviations from normal temperatures can result in coral bleaching which is exacerbated, if not directly caused, by simultaneous exposure to high solar irradiances (Lesser 2006, 2011). This generalized stress response occurs as a series of cellular events beginning with the production and accumulation of reactive oxygen and nitrogen species, and subsequently oxidative stress as antioxidant defenses are overwhelmed (Lesser 2006, 2011). Subsequently, this can lead to apoptosis, autophagy or necrosis, and the expulsion of the photosynthetic Symbiodinium symbionts from the host (Lesser 2006, 2011). The loss of this source of energy in the form of translocated photosynthate is believed to be one of several causes of coral mortality.
In assessing how corals respond to environmental stress, it has been suggested that in addition to traditional approaches such as measuring photosynthesis we incorporate new concepts such as the measurement of protein turnover with its important energetic underpinnings (Gates and Edmunds 1999). What actually has occurred is that the use of indirect measures has become common. In particular, the use of less intrusive approaches such as active chlorophyll fluorescence, which measures the quantum yield of fluorescence for photosystem II (PSII) and assesses the proportion of functional PSII units (Warner et al. 2010), has been broadly used as a tool to monitor “coral health”. This approach is easy to use and has the advantage of collecting data quickly on a large number of corals, and while many early studies were plagued by misinterpretations of the fluorescent data, this approach has provided essential, but incomplete, information on coral performance under different types of environmental stress (Warner et al. 2010). Additionally, we live in the all “omics” all the time era with the implicit assumption that if we understand what is occurring at the level of the metagenome or transcriptome, we will have a quantitative understanding of the stress response of corals. What we have gained are very important insights into mechanisms (Hofmann and Todgham 2010), and the limitations of those insights (Feder and Walser 2005), but not the effects on the differential performance of individuals, populations and communities. Others have argued effectively that the emphasis on the stress biology of scleractinian corals and their relatives has actually limited our understanding of coral biology generally and with it our ability to understand and fully assess the stress response of corals (Edmunds and Gates 2003). While symbiotic associations, as multicompartmental systems, are certainly more complicated to tease apart whatever the question may be (e.g., Zamer and Shick 1987; Mayfield et al. 2009), it should not deter from the construction of complete energetic budgets that are ecologically informative. Recently, Stumpp et al. (2011) constructed energy budgets for sea urchin larvae under different pH conditions to assess the effects of ocean acidification. Their results show an increase in energetic costs associated with exposure to lower pH seawater that led to significantly less energy available for growth and development and decreased survivorship. These data show that energetic shortfalls occurred during the feeding stages of development and were associated with increased costs during environmental stress. Sea urchin larvae represent a straightforward, one-compartment system to quantify energetic budgets, growth and survivorship, but multicompartment systems like symbiotic sea anemones and corals are much more complicated. Recently, there has been an increase in the use of energetic approaches to assess the status of corals under environmental stress. But are we measuring the right things?
Carbon budgets, energy budgets and metabolic quotients
Energy available to the cell is not contained in the fundamental elements (e.g., carbon) but in the major macromolecules (i.e., proteins, lipids and carbohydrates) of every cell. Carbon does not equal energy per se, but it can be converted to energetic equivalents using carefully considered conversion factors (Gnaiger and Bitterlich 1984). Because of the emphasis on productivity studies and associated measurements of photosynthesis and carbon fluxes, the question of, and interest in, the energetics of corals has varied over time. Recently, several studies on corals appear to have confounded carbon budgets, optical models of photosynthesis in corals, the effects of flow on photosynthesis in corals or costs associated with stress based solely on lipid content with changes in energetic costs or energy budgets (e.g., Sebens et al. 2003; Hoogenboom et al. 2006, 2008; Jones and Berkelmans 2011; Leuzinger et al. 2003) without actually having determined whether there are really changes in an energetic budget by assessing all inputs and outputs in energetic terms and closing the budget on individual colonies.
Falkowski et al. (1984) reported on their extensive work on Stylophora pistillata from the Red Sea and developed a detailed carbon budget for this coral under high and low irradiances, assuming certain metabolic quotients [i.e., respiratory (RQ; the molar ratio of CO2 produced versus O2 consumed) and photosynthetic (PQ; the molar ratio of O2 produced versus CO2 fixed)]. The authors then did some basic energetic conversions of the carbon fixed and translocated to the host and showed that only corals in the high irradiance environment could support net positive growth. Both the metabolic quotients and energetic conversions used were reasonable at the time of these studies (see below for discussion). Then, and now, carbon budgets are fraught with difficulty, as demonstrated by Muscatine et al. (1981) in their seminal paper on the description of the contribution of carbon from zooxanthellae to animal respiration (CZAR) calculation. The problems identified include using short-term measurements of photosynthesis and respiration extrapolated to 24 h to calculate photosynthesis-to-respiration (P/R) ratios, quantifying the percent of carbon translocated, separating animal respiration from coral respiration and using PQ and RQ quotients based on broad assumptions, or taken from other systems, to convert oxygen fluxes to carbon units. Another important component of both carbon and energy budgets is the need for studies on the uptake of dissolved free amino acids (DFAA) which has been demonstrated (Ferrier 1991; Grover et al. 2008) but not incorporated into carbon or energetic budgets for corals.
Subsequently, a series of more detailed energetic budgets, using a scope-for-growth approach, of the holobiont, were constructed for the Caribbean coral Porites porites by Edmunds and colleagues (Edmunds and Davies 1986, 1989), and several species of coral from Hawaii (Davies 1991) that examined the effects of irradiance and stress (i.e., sedimentation) on the energetic budgets of corals. The approach was more rigorous than previous efforts to construct energy budgets for corals and had as a common thread the use of oxy-Joule conversions for the net oxygen fluxes associated with photosynthesis and respiration but still assumed that lipid was the primary energy source for respiration. These budgets also identified significant energetic losses in the form of mucous or dissolved organic carbon that had not previously been quantified and supported a preeminent role for photoautotrophy in the energy budgets of corals. From an energetic budget perspective, it is important to recognize that both Porter et al. (1984) and Edmunds and Davies (1988) demonstrated that respiration rates of corals are greater in the light then in the dark and likely represent the costs of digestion and biosynthesis of macromolecules from respiring translocated photosynthate (i.e., specific dynamic action). This means that assessing standard metabolic costs is difficult unless the differences between daytime and nighttime respiration rates in corals are assessed and incorporated into energy budgets. As a result, many published energy costs extrapolated over a 24-h period using only nighttime respiration measurements are largely underestimated as recognized by Edmunds and Davies (1988). The most detailed carbon and energetic budgets constructed for symbiotic associations are for the sea anemone Anthopleura elegantissima (Zamer 1986; Zamer and Shick 1987, 1989) and the corals Goniastrea retiformis and Porites cylindrica (Anthony and Fabricius 2000) and are essential reading for anyone considering an energetic approach to assess the effects of environmental stress. These examples of energetic budgets, and carbon and nitrogen budgets as well, for multicompartmental symbiotic associations are the most comprehensive examples that include all of the components required to simultaneously construct accurate carbon, nitrogen and energy budgets under a variety of environmental conditions. Specifically, they include quantitative assessments of both photosynthetic and heterotrophic inputs into the energy budget, a comprehensive assessment of proximate biochemical composition and energy stores available, measurements of metabolic fuel preferences, and energetic costs associated with excretory losses such as mucous and ammonia. These measurements were used to construct not only energetic budgets but also CZAR and the extremely informative bioenergetics metric scope for growth (SFG) which is the energy available for growth and reproduction after maintenance costs have been accounted for.
The use of metabolic quotients (PQ and RQ) from other systems for the calculation of CZAR and in energetic budgets for corals needs to be reconsidered given the availability of PQ and RQ values for corals (Gattuso and Jaubert 1988, 1990). Metabolic quotients for corals from different depths show that the RQ of corals ranges from 0.65 to 0.99; shallow corals exhibit an RQ = 0.99, which suggests that a mixed source of biochemical constituents (primarily glycerol but some lipid as well) is being used to provide energy for standard metabolic needs (Gattuso and Jaubert 1990) and not solely lipids as is often assumed. Alternatively, as carbohydrate and lipid pools are drawn down, protein could be used as the primary respiratory fuel. Since the excretory product of protein oxidation in corals is ammonia, the RQ should range from 0.94 to 0.99 (Gnaiger 1983; Shick 1991). Some of this variability in RQ will also be affected by the degree of heterotrophy (i.e., carnivory) for each species of coral since increased heterotrophy supplies more protein that can be used as a respiratory substrate. Additionally, PQs range from 1.14 to 1.57 in corals and reveal a shift from the production of glycerol to lipids as irradiance decreases with both RQs and PQs, exhibiting a significant dependence on irradiance (Gattuso and Jaubert 1990). We need more direct measurements of metabolic quotients for scleractinian corals for multiple species under a variety of conditions in order to accurately assess both the carbon budgets and the energetics of corals.
The assumption of recently published studies that corals rely on lipid as the most important source of energy reserves and that the RQ (0.72) for lipid oxidation should be used for modeling or conversion to energetic units is flawed. As a source of energy, lipid content in corals (~10–40 % of dry weight) is highly variable both within and between species (Stimson 1987; Harland et al. 1993). As a result, assuming lipids are the preferred metabolic fuel, at least under non-stressful conditions and in shallow waters, cannot be broadly applied and is very likely specific to species, site and environmental conditions. Additionally, it is far more likely that during stress, but especially during prolonged periods of stress where corals are in negative energy balance, corals would be drawing on all biochemical pools, both their storage and structural components for energy once food acquisition by photoautotrophy has been compromised. This is a common response observed during starvation in animals (McCue 2010), and during stress, corals exhibit a similar response (Porter et al. 1989; Rodrigues and Grottoli 2007; Rodrigues et al. 2008). In many organisms, starvation and the co-occurring negative energy balance lead to a point where other cellular processes are initiated such as autophagy leading to cell death (Yin et al. 2009), and there is some evidence that this also occurs in symbiotic sea anemones and corals as part of the bleaching response to environmental stress (Weis 2008).
The importance of proximate biochemical composition
A critically important component of any energetic approach is quantifying the proximate biochemical composition of all constituents (i.e., proteins, lipids and carbohydrates) and converting individual masses into their respective energetic equivalents based on specific enthalpies of combustion (Gnaiger and Bitterlich 1984). Studies that use the total mass of proteins, lipids and carbohydrates as a measure of energy reserves (e.g., Grottoli et al. 2006) are potentially confounded because any particular total mass of these constituents is equivalent to a range of different energetic contents because of the different energetic conversions. Without considering the differences in energetic content and measuring the changes in each component of the proximate biochemical composition, we will not be able to quantify the energetic reserves available to corals normally, or the changes during recovery from environmental stress (see discussion of Rodrigues and Grottoli 2007 below).
Early work assessing the proximate biochemical composition of the symbiotic dinoflagellates (Symbiodinium sp.) and host tissues of Pocillopora damicornis showed that host tissues exposed to oligotrophic conditions are composed of approximately 80 % protein, 12 % carbohydrates and 8 % lipids, whereas the Symbiodinium sp. from these corals is composed of approximately 29 % protein, 64 % carbohydrate and 7 % lipid (Achituv et al. 1994). Using the enthalpies of combustion for protein (−23.9 J mg−1), carbohydrate (−17.5 J mg−1) and lipid (−39.5 J mg−1) from Gnaiger and Bitterlich (1984), one can readily see that for P. damicornis host tissues, proteins and carbohydrates are far more abundant energy reserves than lipid, and it would be potentially misleading to use mass-specific lipid content of the host as a marker for energy reserves. A recent study showed that the tissues (host and symbiont) of Porites compressa and Montipora capitata from Hawaii are composed of ~48 % protein, ~17 % carbohydrate and ~35 % lipid, and ~38 % protein, ~14 % carbohydrate and ~49 % lipid, respectively, at normal temperatures (Rodrigues and Grottoli 2007). If the energy content of these constituents using the mass data (g gdw−1) and the enthalpies of combustion described above is calculated, it can be found that P. compressa actually had 27 % more total energy reserves than M. capitata prior to being exposed to temperature stress despite the fact that M. capitata had higher energy reserves as lipids (~24 %). As these experiments were conducted similar to Grottoli et al. (2006), it is important to note that these calculations do not deter from the main findings of that study but that the underlying reason for increased heterotrophy in M. capitata compared with P. compressa may actually be the lower amount of energy reserves present in M. capitata to begin with. Lastly, Lesser et al. (1994) showed that for P. damicornis, photosynthetically fixed carbon was consistently found in the greatest amounts in the protein fraction relative to lipid and carbohydrate in both the host and symbiotic compartments. Another study of proximate biochemical composition on seven species of coral from the Great Barrier Reef showed that there was significant interspecific variation in the relative amounts of each biochemical constituent, but it was consistently observed that carbohydrate was found in the lowest concentrations in these corals generally followed by protein and then lipids (Leuzinger et al. 2003). Collectively, the data show that assuming lipid is the most important source of energy reserves is not universal among corals of different species, in different habitats, in different seasons and under different environmental conditions.
In regard to lipids, most studies do not distinguish the difference between storage (i.e., triglycerides and wax esters) versus structural lipids (i.e., polar lipids and sterols). It is often assumed that structural lipids cannot be used as energetic equivalents to storage lipids, but in fact all forms of structural lipids have fatty acid moieties that could be oxidized, and there is ample evidence that corals mobilize structural lipids under times of stress (Harland et al. 1992; Rodrigues et al. 2008; Imbs and Yakovleva 2012). Measuring the relative proportion of storage versus structural lipids and assessing the energetic content of the respective lipid pools would be a fruitful area of coral biochemistry and energetic research in the future.
One analytical issue not widely considered is the contribution of glycoproteins (e.g., collagen in mesoglea) in the tissues of cnidarians that can confound standard protein measurements such as the Bradford or Lowry procedures (Zamer et al. 1989). The common wet biochemistry techniques utilized apparently cleave the protein and carbohydrate moieties with differential efficiency and therefore can significantly affect protein measurements. As a result, it has been argued that using a stoichiometric approach with elemental analysis (Gnaiger and Bitterlich 1984) is a more accurate way of assessing the proximate biochemical composition of cnidarian tissues (Zamer et al. 1989). Alternatively, one could isolate coral protein to use as standards in spectrophotometrically based protein measurements to improve the accuracy of assessing this pool (Zamer et al. 1989).
Another important consideration that applies to both carbon and energy budgets is that corals are hypoxic or anoxic at night (Dykens and Shick 1982; Shashar et al. 1993; Kühl et al. 1995). This leads to the induction of anaerobic pathways of energy metabolism using either protein or carbohydrate substrates for which many invertebrates, including cnidarians, are very capable of, but the consequence is lower energy production in the form of ATP compared with the oxidation of lipid (Sebens 1987; Shick et al. 1988). Even more important, and related to this discussion on corals, is that during anoxia, lipids cannot be used as a metabolic fuel (Shick et al. 1988; Shick 1991). Assumptions made for previous studies of coral energy budgets, where it is almost always assumed that corals maintain full aerobic scope over a 24-h period of time and use lipids as a principle source of energy, should be interpreted with increased scrutiny. One solution would be specific studies on corals using continuous measurements of tissue oxygen concentrations (e.g., microelectrodes) in the dark along with enzyme measurements and biochemical measurements of the metabolic pathways and products of anaerobic metabolism. This would allow an assessment of what pathways are being used and an estimation of the molar fluxes of those pathways. These fluxes can then be converted to energetic equivalents and used to more accurately quantify the energetic costs during hypoxia or anoxia in corals and therefore better calculations of the ratio of photosynthesis to respiration (P/R).
Modeling of energetic budgets
The energetic budgets, and the embedded measurements, discussed above (e.g., SFG) fall under the category of net production models. Where appropriate, this approach can also accommodate allometric scaling of physiological processes, so the energy budget of any size animal can be estimated or the results of assessing the allometric scaling can be used to standardize the results to a common size animal. When all components are measured directly, they provide robust empirical data but are largely applicable only for that point in time, set of conditions and life history stage of the organism being evaluated. Recently, two models of energetics have emerged in ecology, which has resulted in vigorous, and sometimes contentious, discussion. One is the metabolic theory of ecology (MTE) developed and promoted by West et al. (1997) and the other is the dynamic energy budget (DEB) theory originally described by Kooijman (1986). At their core, both theories claim to provide a comprehensive description of the organismal performance of all life history stages of an organism, based principally on the scaling of metabolic rate, under varying conditions (van der Meer 2006). One of the principle controversies has been which scaling exponent to use, M3/4 or M2/3, respectively, where M equals mass and the fractional exponent describes the proportional increase in a rate function with increases in mass. In addition, the MTE is premised on the idea that animals maintain a specific fractal network to service organs, tissues and cells as they increase in size in order to satisfy the demand for increasing amounts of resources (i.e., symmorphosis, Weibel et al. 1991), and there is little need, or apparent desire, to understand the mechanistic basis for the models’ predictive capabilities (Brown et al. 2004). In addition to concerns that only three parameters may not be enough to describe all of the features of metabolism (van der Meer 2006), the application of MTE to corals is also problematic because of the dogmatic insistence on the use of the M3/4 power law, which is inconsistent with what we know about scaling exponents for corals and other cnidarians with tissue grade construction and diffusion-dependent processes that dominate their metabolic performance (Shick 1991; Patterson 1992; Zamer and Van Dorp 1994). In comparison, DEB models describe the rates at which organisms assimilate and utilize energy for maintenance, growth and reproduction. The DEB theory is based on physical and chemical assumptions for the energetics of individuals and is not dependent on any specific fractal geometry. As a result, the model can accommodate a range of scaling exponents from M2/3 to M1 (van der Meer 2006) consistent with coral geometry and known scaling exponents for metabolism in cnidarians. A recent application of DEB modeling for the bioenergetics and growth of the oyster, Crassostrea gigas (Alunno-Bruscia et al. 2011), showed that it was able to simulate the growth dynamics of both spat and adult stages of C. gigas accurately over time and at different culture sites. The model fully captured the growth of oysters in the spring, the timing and amplitude of spawning events, and the loss of somatic mass in autumn and winter when food was limiting.
DEB theory has also been applied recently to scleractinian corals. In one example, Eynaud et al. (2011) used published models describing syntrophic symbioses (Muller et al. 2009) and the photoinhibition of photosynthesis (Muller 2011) to assess the effects of ultraviolet radiation (UVR) on photoinhibition in scleractinian corals. In this application of DEB models on scleractinian corals, the Muller (2011) paper used biological weighting functions for the effects of UVR on Symbiodinium (clade B1) from Lesser (1996) which were constructed from experiments on cultures that are physiologically more like phytoplankton than Symbiodinium sp. in hospite. There are several, including depth-dependent, biological weighting functions available for corals (Lewis and Lesser 1996; Lesser 2000) that, when compared with cultured Symbiodinium weighting functions, exhibit significantly decreased effects of UVR. As a result, in order for the DEB modeling to be effective in predicting the photoinhibition of photosynthesis by UVR on corals, these environmentally more realistic weighting functions should be incorporated into the model. Additionally, the model is limited by any inclusion for the change in solar radiation, both its visible and UVR components, with depth such that assuming at 10 m the underwater light field is composed of 1 % ultraviolet-B and 10 % ultraviolet-A radiation is unrealistic given the available data and the potential for variability in the optical properties of tropical waters (Banaszak and Lesser 2009). The empirical data for the effects of ultraviolet radiation on corals are available as are many descriptions of underwater irradiances, and like any model, the inputs are as important as the outputs.
Another recent application of DEB modeling on corals by Edmunds et al. (2011) shows that DEB could be a feasible approach to establishing baseline metrics of physiological processes for scleractinian corals using data from widely differing analytical techniques and species. These “benchmarks” for a fully acclimatized, non-stressed, theoretical coral could then be used to forecast the effects of environmental stress or ask interesting questions about the full range of phenotypic capabilities of a coral, especially those characteristics determined by different types of Symbiodinium. In the study by Edmunds et al. (2011), for some processes (e.g., calcification), a modeling approach could not adequately synthesize and compare studies that used different experimental and analytical approaches. While these authors could not envision a scenario where additional empirical data would resolve the underlying relationships and interactions of coral calcification and various environmental variables, new theories of how photosynthesis and calcification work in scleractinian corals (Jokiel 2011) have not been fully tested and might provide unique empirical data to obtain unique new insights using DEB modeling.
Finally, another recent paper on corals using a modeling approach, including a DEB component, to predict mortality from bleaching in the face of environmental stress (Anthony et al. 2009) also incorporates an empirical energetic approach. This approach provides an important example of how the integration of empirical energy budgets and modeling could be accomplished, but many of the same issues described above for the energetic budget component described in this study limit its utility. Again, the lack of any data on components of the proximate biochemical composition other than lipids, use of metabolic quotients not applicable to corals and the use of carbon versus energetic units could all be corrected and the modeling outcomes significantly improved.
Energetic approaches can be a powerful technique to assess the effects of environmental stress on corals. Surprisingly, there are few comprehensive energetic budgets for scleractinian corals under either “normal” or stressed conditions, and this is a looming issue for forecasting the future of corals especially under the combined threats of rising seawater temperatures and ocean acidification. Currently, the coral reef science community has many of the tools available to produce robust energetic budgets but have not been using them (e.g., ability to quantify heterotrophic inputs, appropriate RQ and PQs, stoichiometric approach to measure proximate biochemical composition and appropriate conversions to energetic values). If an energetics approach is employed, we should be collecting quantitative data on all components of an energetic budget in order to understand both carbon flux and the energetic balance under stress conditions that will impact fitness. Ultimately, additional studies will be needed to improve some of the assumptions in these energetic budgets (e.g., metabolic quotients) or the development of novel approaches to assess metabolic quotients contemporaneously with other measurements (i.e., photosynthesis and respiration) on individual corals under the conditions being assessed. Modeling outputs using the DEB approach can then be significantly improved with a wide range of different types of empirical data (i.e., energy budgets, calcification, carbon budgets, photosynthesis and respiration) relevant to the time scale and the effects being assessed. Recently, this approach has been advocated to predict effects on a broad suite of life history phases and on multiple time scales (e.g., Nisbet et al. 2012), and initial efforts on corals (Anthony et al. 2009) are promising. Because physiology, energetics and biochemistry studies have decreased in recent times compared with tremendous increase in “omics”-based studies, this effort will require a redirection of effort, resources and training along with collaborative interdisciplinary work that includes modelers to help guide what empirical data are required and for physiologists to provide those data sets.
The author would like to thank NSF, ONR and NOAA for supporting his work on coral biology, biochemistry and physiology. The manuscript was significantly improved in critical areas by comments from Bill Zamer. A special thank you to Lisa Rodrigues and Andrea Grottoli for access to original data.