© 2002 by The Society for Integrative and Comparative Biology
| ||||||||||||||||||||||||||||||||||||||||||||||||||||
Energetic Constraints, Size Gradients, and Size Limits in Benthic Marine Invertebrates1
1 Biology Department, University of Maryland, College Park, College Park, Maryland 20742
 |
SYNOPSIS |
|---|
Populations of marine benthic organisms occupy habitats with a range of physical and biological characteristics. In the intertidal zone, energetic costs increase with temperature and aerial exposure, and prey intake increases with immersion time, generating size gradients with small individuals often found at upper limits of distribution. Wave action can have similar effects, limiting feeding time or success, although certain species benefit from wave dislodgment of their prey; this also results in gradients of size and morphology. The difference between energy intake and metabolic (and/or behavioral) costs can be used to determine an energetic optimal size for individuals in such populations. Comparisons of the energetic optimal size to the maximum predicted size based on mechanical constraints, and the ensuing mortality schedule, provides a mechanism to study and explain organism size gradients in intertidal and subtidal habitats. For species where the energetic optimal size is well below the maximum size that could persist under a certain set of wave/flow conditions, it is probable that energetic constraints dominate. When the opposite is true, populations of small individuals can dominate habitats with strong dislodgment or damage probability. When the maximum size of individuals is far below either energetic optima or mechanical limits, other sources of mortality (e.g., predation) may favor energy allocation to early reproduction rather than to continued growth. Predictions based on optimal size models have been tested for a variety of intertidal and subtidal invertebrates including sea anemones, corals, and octocorals. This paper provides a review of the optimal size concept, and employs a combination of the optimal energetic size model and life history modeling approach to explore energy allocation to growth or reproduction as the optimal size is approached.
|
INTRODUCTION |
|---|
Marine invertebrate populations generally span habitats with a range of physical and biological characteristics. In populations of intertidal invertebrates, for example, energetic costs increase with temperature and aerial exposure (stress), and prey intake increases with immersion time. The result is a size gradient with small individuals dominating the upper distributional limits of intertidal populations (e.g., sea anemones, Sebens, 1980
, 1981, 1982b, 1983). Wave action also affects energy intake, limiting feeding time or success, although certain species benefit from wave dislodgment of their prey. Gradients of size and morphology are thus observed from wave exposed to sheltered shores in intertidal and subtidal populations. Water flow brings particles to passive suspension feeders, and thus habitats with more flow can support larger and faster growing individuals or colonies. For a population of shallow subtidal octocorals (Sebens, 1984), mean and maximum colony sizes were larger in habitats with more flow, and also along the top edges of subtidal rock walls, which receive more flow than do lower regions.
An energetic approach has been developed to examine size gradients and limits to the size of individuals in populations spread across variable habitats (Sebens, 1977, 1979, 1982a). The magnitude of the difference between energy intake and metabolic (and/or behavioral) costs can be used to determine an energetic optimal size (EOS) for individuals in such populations. For modular organisms, the size of individual units (e.g., polyps, zooids) affects the energetics of an entire colony or clone and there can be energetic optima for both units and whole colonies (Sebens, 1979; Kim and Lasker, 1998).
The comparison of an optimal individual size, based on energetic considerations (EOS), to the maximum predicted size based on mechanical constraints, and the ensuing mortality schedule, provides a mechanism to study and explain organism size gradients in intertidal and subtidal habitats (Sebens, 1982a; Denny, 1999). For species whose energetic optimal size is well below the maximum size that could persist under a certain set of wave/flow conditions, it is probable that energetic constraints dominate. When the opposite is true, populations of small individuals may dominate habitats with strong dislodgment or damage probabilities, or where feeding structures are deformed by flow reducing energy intake (Denny et al., 1985; Denny, 1999). Alternatively, when the maximum size of individuals is far below either energetic optima or mechanical limits, other sources of mortality (e.g., predation) may favor energy allocation to early reproduction rather than to continued growth. In general, larger individual or colony size results in greater reproductive output or capacity, however, the fitness disadvantages of small size can be outweighed by earlier reproduction (Stearns, 1992). Predictions based on optimal size models have now been tested for a few species of intertidal and subtidal invertebrates including sea anemones, corals, and octocorals. In this paper, the general approach of optimal size modeling is discussed and these findings related to intertidal and subtidal organisms. Even when an energetic optimal size exists, mortality sources and schedules may affect the allocation of resources during growth as the EOS is approached. The allocation of energy to growth and reproduction is also examined in this study, using a combination of the EOS model and a life history modeling approach.
|
THE OPTIMAL SIZE APPROACH |
|---|
A number of authors have attempted to define an optimal size for a variety of animals (Belovsky, 1978
; Sebens, 1977, 1979, 1982a; Case, 1979), plants (e.g., leaves, Givnish and Vermeij, 1976) and colonial organisms (Sebens, 1979; Kim and Lasker, 1998). The general approach has been to determine the body size where organism fitness or some component of fitness is at a maximum, and to consider this the optimal size (OS). In energetic terms, optimal size models are based on the fact that physiological processes scale differentially with size (Schmidt-Nielsen, 1984; Peters, 1983; Calder, 1984; Reiss, 1989; West et al., 1997). An organism can be either too small to make the best use of resources or so large that it is expending too much energy on maintenance, energy that could instead have been used for reproduction. One hindrance to determining an energetic optimal size has been the emphasis, in physiological studies, on efficiency of energy use. Efficiency is not necessary to maximize reproductive output, or fitness, in a given time period. A less efficient creature able to garner more resources can easily do better than a more efficient one with limited intake. Therefore, it is clearly the absolute difference between energy intake and energetic cost that determines the ability of an organism to produce offspring. Below the EOS, smaller individuals are limited by lower intake rates and higher metabolic costs per unit biomass, and above the EOS, larger individuals are limited by less efficient energy intake even though metabolic cost per unit biomass is lower (i.e., more efficient).
The energetic optimum considers only one component of fitness, however. Sebens (1982a, 1987) noted that high mortality rates could select for early reproduction and growth cessation at a size (OS) below the energetic optimum, and that a size escape from predation or a competitive advantage could select for growth to a size (OS) larger than the energetic optimum. More recently, the entire optimality approach based on energetics has been criticized (Kozlowski, 1996) because some authors have equated energetic optima with maximum fitness, ignoring the contribution of size- or age-dependent mortality. In energetic models of optimal size, the difference between energy intake and energetic cost is maximized (Sebens, 1979, 1982a). This difference has been termed "scope for growth" (Warren and Davis, 1967) and "energy surplus" (Sebens, 1979) depending on which energetic costs are included. For example, if the costs of building gonad are included then this difference is truly "scope for growth." If those costs are omitted, and only metabolic maintenance costs are used in the calculation, then this difference is an "energy surplus" that can be used for either growth or reproduction. Most studies do not make this distinction. If oxygen consumption is used to determine metabolic cost, such a measurement includes anabolic energy utilization for tissue or gonad construction, but it would not include energy content of the compounds sequestered in gonad or new tissue. For an optimal energetic size determination, energetic costs should be defined as all costs not involved with tissue or gonad construction, but should include costs of activity, digestion, and repair. Energy surplus, available for either growth or reproduction (Es), is defined as:
|
|
where M is body mass, k1 and c1 are empirically fit constants for energy intake and k2 and c2 are fit constants for metabolic cost. As long as the exponent for energy intake (c1) is less than that for energetic cost (c2), and the constant k1 is sufficiently greater than k2, there will be a size optimum (Mopt) where Es is maximized (Fig. 1). The derivative of energy surplus as a function of mass is determined, and set to zero to find the maximum (Sebens, 1979).
|
|
This mass (Mopt) represents the EOS (Fig. 1) in that it provides the greatest amount of energy for reproduction (e.g., annual gonad production). However, other energetic constraints will modify this optimum. One possible limiting factor is the rate at which energy surplus can be converted to offspring, which may depend on the body cavity volume in which gonads or brooded larvae are housed (Sebens, 1982a). Such morphological constraints will set the true optimum size (OS) higher than that derived in eq. 3. Although this method defines an EOS, other factors may select for growth above this optimum (e.g., competitive advantage, escape from predation) or cessation of growth well below the EOS (e.g., size-specific mortality, mechanical dislodgment). Rex et al. (1999) for example, argued that the trend toward larger individuals within taxa, from continental shelf to abyssal depths, runs counter to the predictions of an EOS model. They propose that the ability to take large rare prey when encountered, and the metabolic efficiency afforded larger individuals, can be strong selective forces for large size in deep sea habitats.
|
|
ALTERNATIVE DEFINITIONS OF OPTIMAL SIZE |
|---|
The difference between intake and cost will not allow an optimal size derivation when both energy intake and metabolism scale similarly with body mass, as occurs in some vertebrates (Brown et al., 1993
). If this is the case, then the rate at which energy can be converted to offspring becomes especially important in determining an optimal size. Brown et al. (1993) provided a model based on rate of net resource acquisition (K0, intake minus cost) and conversion rate of energy to reproduction (K1), that scale as simple power functions of mass with exponents b0, b1 and empirically fit constants C0 and C1.
|
|
They define reproductive power as:
 |
which is maximized at M*, the optimal body mass:
|
|
This can be considered an EOS because this size provides the maximum energy that can actually be allocated to reproduction given the constraints of K1. In this model, the optimal size exists because the organism is able to acquire more resources as it grows, although the rate of conversion of resources into "reproductive work" declines with size. Although the concept of a constraint on conversion rate is valid and important, the specific mathematical formulation of this model and the choice of exponents used has been criticized (Kozlowski, 1996; Perrin, 1998; Bokma, 2001). A number of authors have attempted to explain the evolution of body size and observed body size distributions within specific higher taxa (e.g., birds) based on this model (Brown et al., 1993; Jones and Purvis, 1997; Chown and Gaston, 1997). This use of the model has also come under substantial criticism because of the number of alternative factors influencing fitness (Bokma, 2001) and because the scaling parameters are likely to be species specific, not constant within a higher taxon (Blackburn and Gaston, 1996; Kozlowski and Weiner, 1997).
|
HABITAT QUALITY, SIZE GRADIENTS, AND INDETERMINATE GROWTH |
|---|
TOP
SYNOPSISINTRODUCTIONTHE OPTIMAL SIZE APPROACHALTERNATIVE DEFINITIONS OF... HABITAT QUALITY, SIZE GRADIENTS,... MODULAR ORGANISMS: OPTIMAL UNIT...A LIFE HISTORY APPROACH...MECHANICAL LIMITS TO SIZEReferences |
|---|
Temperature is one of the primary factors affecting metabolic rate of ectotherms; it also varies significantly over time and across microhabitats (Helmuth, 2002
). High temperatures result in elevated metabolism, and at the extremes, may also require replacement of damaged proteins (Hofmann and Somero, 1995, 1996) and production of heat shock proteins (Helmuth and Hofmann, 2001), both of which represent additional costs in terms of synthesis and materials. At low temperatures, respiration is greatly reduced. This can be beneficial to an organism as long as it can carry out all necessary functions, including digestion, movement, pumping and all anabolic pathways. However, low temperatures are likely to be detrimental to a greater or lesser extent depending on the organism in question. For a sedentary passive suspension feeder capturing particles suspended in water, or capturing prey that swim toward it, there may exist a broad range of temperatures below the stress threshold but above the range where ciliary action and digestion would be compromised. In contrast, active suspension feeders such as bivalve mollusks must pump water across their gills, at rates that are temperature-dependent. For all organisms, digestive and metabolic enzymes have distinct temperature optima. Such enzymes will function below their maximum rates above and below those temperature optima.
Size optima (EOS) thus change with habitat temperature as well as with food abundance and quality. Recent studies indicate that the seastar Pisaster ochraceus, for example, decreases its crawling speed, and does not forage well, in the mid intertidal zone during periods of colder water temperature (Sanford, 1999, 2002). In experiments where mussel prey were provided ad libidum, however, lower intake rates were balanced by lower energetic costs, and growth rates were similar over a range of temperatures. With limited prey, the net result is that periods of lower temperature are energetically costly. As in this example, for invertebrates that rely on motility or pump water to acquire prey, there will be a narrower range of temperatures that allow growth to occur. Another advantage of decreased metabolic rate is that low temperatures may allow organisms to survive periods of low food availability, as occurs for suspension feeders during the winter on temperate zone shores.
Besides temperature, intertidal habitats differ in duration of exposure and immersion, flow forces encountered, low or high salinity stress, and in many biological parameters as well (e.g., competition, predation, distance to neighbors). Each of these factors can affect cost and/or intake as a function of size. Feeding time, for example, is affected by immersion time, predator avoidance, and possibly by interspecific or intraspecific competitors interfering with feeding structures or foraging activity. Similarly, wave action influences foraging ability and length of foraging bouts in some mobile species. Interaction among both physical and biological determinants of the energy regime may produce complicated size patterns across habitats. Determination of an EOS, and experimental investigation of factors causing size to deviate from that EOS can help explain observed size gradients in such environments.
Many intertidal and subtidal invertebrates have some form of indeterminate growth, where the maximum size achieved is habitat dependent. In the most extreme cases (e.g., seastars, sea anemones, sea urchins), not only are adult sizes highly variable, but individuals can readjust their body mass larger or smaller if conditions change (Paine, 1976; Sebens, 1982a, 1983) via growth and degrowth. This ability allows individuals to shrink down to the EOS when conditions deteriorate, and thus potentially achieve the maximum reproductive output for a given time period even if they started out much too large. Without this ability, such individuals would find themselves expending all or most of their available energy on metabolic costs. This type of indeterminate growth has an asymptotic size for a given set of environmental conditions. By some definitions, indeterminate growth means that an asymptotic size is never reached (Sebens, 1987). Mat-forming colonial or clonal invertebrates fit this definition, and can occupy large habitat areas when a particularly fit genotype finds itself in an appropriate environment with little competition (Sebens, 1982b, 1987). The combination of phenotype and environment can also affect the outcome of interspecific competition, which can be strongly size dependent (Sebens, 1982c). Some degree of indeterminate growth, combined with habitat heterogeneity, can also lead to increasing size variability in a cohort over time, a phenomenon termed growth depensation (Pfister and Stevens, 2002).
|
MODULAR ORGANISMS: OPTIMAL UNIT SIZE |
|---|
Metabolic rate increases with organism size, usually at a power of mass less than 1.0. However, colonial organisms and those that grow as thin sheets can have metabolic rates that scale linearly with mass (Sebens, 1987
). For a given mass, many small units are metabolically costly compared to one large one, but the advantage of a large surface area for intake much more than outweighs the increased cost per unit mass of small units. The advantage of specialized, even non-feeding, units in colonies may also outweigh the added energetic costs. When the optimal size model is applied to a colony of constant mass, divided into units of variable size, the model predicts that the units should be as small as possible while still able to capture the available prey, particles, nutrients or photons (Sebens, 1979). Such units must also be able to produce gonads and carry out other basic functions and thus there is a limit to how small they can be.
Given a distribution of prey size, with total energy available from prey (considering handling time) normally distributed across sizes (a simplification for the model), a trade-off between being able to capture larger prey and having larger feeding surface area sets the optimal size of units within a colony (Sebens, 1979). Above that optimum, each unit is able to capture larger prey and thus use more of the available prey energy distribution. Below that size, less of the distribution is available but the total capture surface area is larger. The optimum unit size is that which is able to capture prey just above the mean prey size in the distribution. When prey are large and infrequently encountered, a large solitary individual is predicted by this model. These predictions conform to observed patterns, where colonial organisms feeding on very small prey (or photons) have small and constrained unit size, those feeding on larger prey (e.g., zooplankton) have variable but relatively small unit size, and those specializing on dislodged benthic invertebrates (e.g., sea anemones capturing mussels or sea urchins) are large solitary polyps (Sebens, 1979, 1987, 1996).
Among scleractinian corals, most species have polyps a few millimeters in diameter, spread over a high surface to volume skeleton (e.g., branches, plates) (Porter, 1976; Sebens, 1996). Fewer species have large polyps, one to several centimeters diameter, generally with low surface to volume colony morphologies (e.g., mounds, small clusters, solitary polyps). Sebens (1996) showed that the common small polyp morphology results in a greatly elevated feeding surface to colony biomass ratio and thus a great advantage for particle capture, nutrient uptake and light interception for photosynthesis by symbiotic algae. Corals with larger polyps can capture larger prey items, although comparisons of prey capture by a broad range of polyp sizes showed very similar prey size distributions (Sebens et al., 1996, 1998). Within the anemone genus Anthopleura, there is a clear pattern of large solitary individuals feeding on large rare prey (e.g., mussels), and clonal forms with small unit size feeding on small benthic prey and zooplankton (Sebens, 1981).
Aside from the prey size advantage, there may be other strong selective pressures for corals to have large polyps. One such advantage is their position in the competitive hierarchy. Large polyps have large masses of mesenteric filaments and can develop large sweeper tentacles, both of which are used in interspecific competition, and thus large polyp size correlates with a species' position in the competitive hierarchy. Large solitary polyps, for example, are often the best competitors by this mechanism (Lang and Chornesky, 1988), although fast growing corals with small polyps and high colony surface to volume ratios may win by overtopping, shading and smothering corals that are better competitors using extracoelenteric digestion or sweeper tentacles. Sebens (1996) proposed that a stable coexistence occurs on reefs among corals of different polyp sizes because small polyp corals can grow rapidly (energy maximizers) and take space from other corals, but large polyp corals can prevent overgrowth or even outcompete small polyp species in habitats with less light, or when large prey are available. In addition, some corals with high surface to volume morphologies (branches, plates) are more susceptible to storm damage, and thus are removed regularly, leaving behind the mounding forms. Some mounding corals also have small polyps, and may represent an intermediate strategy, one that survives storms well and still has a relatively high intake surface to biomass relationship.
Across higher taxa, modular organisms have unit sizes that reflect their primary prey types. Cnidarian polyps which capture zooplankton, are relatively large—on the order of millimeters to centimeters diameter. The few cnidaria known to feed on phytoplankton (Fabricius et al., 1995) have small polyps and their tentacles have small side branches, pinnae, that appear to be adaptations allowing interception of small particles. Other phyla which specialize on phytoplankton have smaller unit sizes within colonies, a millimeter or much less in diameter (e.g., ascidians, bryozoans). Finally, bacteriovores have unit sizes smaller yet. The feeding units of a sponge, for example, are the choanocytes, which are only tens of microns in diameter. As predicted by the optimal unit size model (Sebens, 1979), unit size in such modular organisms is small, but of a size that allows a particular prey group to be captured. The great variability in cnidarian polyp size reflects both the large range of zooplankton and epibenthic invertebrate prey sizes, and their particular methods of intraspecific competition.
|
A LIFE HISTORY APPROACH TO OPTIMAL SIZE MODELS |
|---|
One valid criticism of optimal size models is that the energetic definition of optimality does not equate with fitness. This fact was discussed extensively by Sebens (1982a
, 1987) who suggested using life history models that incorporate mortality to compare optimal size defined by energetic constraints (EOS) to that predicted by overall fitness (OS). The life history approach was also suggested by Perrin et al. (1987), and Zera and Harshman (2001), because it can deal with the trade-off between life history characteristics and physiological scaling. This approach was employed by Perrin and Sibly (1993), who used lifetime energy allocation to reproduction as a fitness measure, and determined how production rate (i.e., energy surplus) and mortality schedule both affected this fitness measure. The energetic optimum, where production rate was at a maximum, was defined as in eq. 2 (Sebens, 1979) with the exponent for metabolic cost higher than that for energy intake. A particularly useful comparison was incorporated into their model; the derivative of lifetime production rate versus size measures the expected increase in lifetime reproductive output (in energy units) when a unit of energy is allocated to growth instead of to immediate reproduction. The optimal size was defined as that point where this derivative drops to a value of one. Optimal size, OS, coincided with the EOS only under specific conditions of mortality. Optimal size was found to be either above or below the production rate maximum for a variety of mortality rates as a function of size. However, the definition of fitness used in this model is also incomplete. Lifetime reproductive output, in energy or number of offspring, is still only a component of fitness. It is easy to imagine two life history strategies that have equal lifetime reproductive output, but where one strategy produces offspring earlier. This strategy will have the higher rate of offspring production over several generations, and the higher rate of allele transmission within a population. Many life history models incorporate this concept by examining the effect of changes in life history on "R," the rate of increase of a population composed of individuals displaying a particular life history trait compared to a population of individuals with some alternate trait (reviewed in Stearns, 1992).
This life history modeling approach can be used to examine the effect of specific mortality factors on optimal size for a given species. It can also be used to determine how energy is allocated to growth and reproduction as the EOS is approached (see also Kozlowski, 1992; Charnov et al., 2001). Is it better to allocate most energy to early reproduction, resulting in a slow rate of growth but still stopping growth at the energetic optimum? Or, is it better to put all energy into growth so that the energetic optimum is reached rapidly and the organism thus achieves maximum reproductive output for a greater fraction of its lifetime?
In this study, I use the basic model (Sebens, 1979, 1982a) set out in eqs. 1–4 to set the EOS and to determine the energy available for either growth (Eg) or reproduction in a given time period. The third term in eq. 7 represents energy allocation to reproduction and k3 can be chosen so that Eg = 0 at Mopt.
|
|
Population growth is modeled using a standard Leslie matrix population growth model without density dependence (Caswell, 2001) with 20 age classes, run for up to 50 time periods (e.g., years). The rate of population growth after a stable age distribution is achieved is used as an estimate of "R," the rate of increase for a population under a defined growth and mortality schedule (equal to the dominant eigenvalue of the Leslie matrix). In tests of this model, a stable age distribution was generally reached in 10 time steps. In each time step (e.g., year), growth of an individual was set by the fraction of available energy surplus allocated to growth multiplied by a conversion factor for energy to body mass. For this model, c1 = 0.7, c2 = 1.0 and c3 =1.0. Reproduction was set by the fraction of available energy surplus not allocated to growth, multiplied by a factor to convert energy to offspring number.
This model was run with a number of different survivorship schedules, superimposed on the same set of environmental conditions and thus with the same predicted EOS. The age at first reproduction was varied and some of the available energy could then be allocated either toward reproduction in a given year, or all energy could be allocated to growth. Once the EOS was achieved, all energy was allocated to reproduction (Fig. 2). For simplicity, a zero allocation of energy to reproduction was chosen at mass zero, with a linear increase in allocation to 100 percent once the EOS was achieved. First, the model incorporated three conditions where mortality was highest in large individuals, below and at the EOS (Fig. 3). In one case (LMORT1), mortality was zero below 50 grams mass, then went from 0 to 100% from 50 to 200 g. In a second case (LMORT2), mortality was zero below 100 g then went from 0 to 100% between 100 and 200 g. In a third case, mortality was zero below 50 g and went from 0 to 100% between 50 and 150 g (LMORT3).
|
|
The R value for each life history was compared to that with no mortality at any size (maximum R). For the three mortality schedules, the maximum value of R was always achieved if reproduction commenced in the first year, even though that resulted in a slower growth rate and a slower approach to the EOS. Thus, a greater probability of mortality when large did not select for delayed early reproduction. High mortality when large selected for termination of growth (OS) below the energetic optimum. Obviously, if the probability of mortality is 100% at and above a size below that of the EOS, cessation of growth below the EOS and allocation of all energy surplus to reproduction will produce the highest fitness (R). This model was used to explore the effect of allocating all energy to reproduction at various sizes below the EOS; an example is provided using the mortality schedule LMORT2 (Fig. 4). Here, the maximum R value coincided with a mass of 33 g, less than a fifth the mass at the EOS.
|
Second, three schedules of high mortality at a small size were chosen and the R value was compared to that without mortality (Fig. 3). Again, age at first reproduction was varied from 1 to 5 yr. In the first case (SMORT1), mortality was zero above 50 g and went from 50% (at mass = zero) to 0% at 50 g. In the second case (SMORT2), mortality was zero above 150 g and went from 50% (at mass = zero) to 0% at 150 g. In the third case (SMORT3), morality was zero above 7 g and 90% below. In all cases with mortality, the maximum R value was achieved when reproduction was delayed one year. For two of the scenarios, the difference between no delay, one year delay and two years delay was minimal but longer delays resulted in much lower R values. For the third and most extreme case of mortality at a small size, there was a large difference between delaying reproduction one year and no delay, because this delay allowed individuals to spend less time in the smallest size categories which had high mortality rates. This is a common situation for intertidal and subtidal invertebrates, which often have very high mortality during the post-settlement juvenile period. For this set of conditions, the EOS was achieved eventually. However, it is also enlightening to examine the stable size and age distributions of each model population. In several of the scenarios used above, the stable size distribution contained very few individuals at and near the EOS because mortality affected most individuals before they achieved that size. This illustrates that, in actual populations, it is useful to compare maximum sizes in populations as well as mean sizes and entire size-frequency distributions (e.g., Sebens, 1984
); only the largest individuals may approximate the EOS. |
MECHANICAL LIMITS TO SIZE |
|---|
Several investigators have proposed that mechanical forces during extreme wave action can set limits to the size of animals and plants in the intertidal zone (e.g., Denny et al., 1985
, 1998; Carrington, 1990, 2002; Denny, 1995, 1999; Gaylord, 1999, 2000). Measurements of forces during breaking waves and long-term wave height distributions, combined with models of detachment probability, predict that some species can have population size structures set by wave-induced mortality. For other species, such mechanical limits are far above the mean or maximum sizes observed in populations. Considering algae and colonial animals, storm damage may set a maximum size by causing partial mortality, for example the loss of branches. Birkeland (1974) examined Caribbean sea fans (Gorgonia ventalina) dislodged during storms, and proposed that drag-induced mortality could have a strong effect on population size structure. Intertidal mussel beds, and the sizes of mussels within them, are clearly affected by severe wave action and the resulting mussel dislodgment (Paine and Levin, 1981; Whitman, 1987; Carrington, 2002) and presumed mortality.
A comparison of mechanical limits to size, versus energetic limits, can provide important information needed to understand size gradients and maximum sizes in intertidal and subtidal populations. However, there is no reason to expect that such limits will always be far apart. For passive suspension feeders such as octocorals, size and growth rate are positively affected by water flow (Sebens, 1984). Habitats with greater wave-induced mean flow speeds, however, are also likely to have greater extremes of flow during storms. In a population of octocorals (Alcyonium siderium) in Massachusetts followed for seven years, mortality was higher at sites with greater wave action, as were growth rates and both mean and maximum sizes (Sebens, 1984 and unpublished data). Octocoral colonies definitely reached size asymptotes in certain habitats and during certain time periods (years), but the most exposed site had growth trajectories that were not approaching any size limit during years when mortality removed large colonies. Across a gradient of habitats increasing in flow speed, it appears that populations of this species switched from energetic limitation to mechanical limitation of maximum size. It is also likely that at any one site, colonies approached an energetic limit during some years, and not during others.
These examples illustrate how mechanical and biological factors influence size-dependent mortality, and modify the optimal size of individuals within a species. In some cases, there may be a wide separation between the size predicted for dislodgment and the optimal size based on energetics. In such cases, it may be possible to determine which factor sets the upper limit. In other cases, as in the octocoral example above, the two limits may be similar and only studies across habitats and longer time periods will allow this separation. Nonetheless, the EOS concept provides a testable hypothesis. Are individuals in a particular population reaching a size asymptote near this predicted size, or are they either suffering mortality or stopping growth at some other size? If there is a large disparity between the predicted EOS and the maximum sizes within a population, this result can direct studies toward physical and biological factors other than those impacting size-dependent energetics. Understanding size limits and size gradients across habitats ideally involves a multidisciplinary approach, applying physiological, mechanical and ecological methodologies.
|
ACKNOWLEDGMENTS |
|---|
I thank Bob Paine and Gordon Orians for encouraging me to think about size gradients, Vince Gallucci for teaching me some useful math, Brian Helmuth for instigating this study, and Emily Carrington and Brian Helmuth for improving the manuscript. Portions of this study were funded by the National Science Foundation, Biological Oceanography Program (#OCE9811577, 9811576).
|
FOOTNOTES |
|---|
1 From the Symposium Physiological Ecology of Rocky Intertidal Organisms: From Molecules to Ecosystems presented at the Annual Meeting of the Society for Comparative and Integrative Biology, 2–7 January 2002, at Anaheim, California.
2 E-mail: k595{at}umail.umd.edu
|
References |
|---|
Belovsky, G. E. 1978. Diet optimization in a generalist herbivore, the moose. Theor. Pop. Biol, 14:105-134.[CrossRef][ISI][Medline]
Birkeland, C. 1974. The effect of wave action on the population dynamics of Gorgonia ventalina Linnaeus. In F. M. Bayer and A. J. Weinheimer (eds.), Prostaglandins from Plexaura homomalla, Studies in tropical oceanography, No. 12, pp. Rosenstiel School of Marine and Atmospheric Sciences, University of Miami Press, Coral Gables, Florida.
Blackburn, T. M., and K. J. Gaston. 1996. On being the right size: Different definitions of ‘right’. Oikos, 75:551-557.
Bokma, F. 2001. Evolution of body size: Limitations of an energetic definition of fitness. Funct. Ecol, 15:696-699.[CrossRef]
Brown, J. H., P. A. Marquet, and M. L. Taper. 1993. Evolution of body size: Consequences of an energetic definition of fitness. Amer. Nat, 142:573-584.[CrossRef][ISI]
Carrington, E. 1990. Drag and dislodgment of an intertidal macroalga: Consequences of morphological variation in Mastocarpus papillatus Kützing. J. Exp. Mar. Biol. Ecol, 139:185-200.[CrossRef]
Carrington, E. 2002. Seasonal variation in the attachment strength of blue mussels: Causes and consequences. Limnol. Oceanogr, 47:1723-1733.
Calder, W. A. 1984. Size, function and life history. Harvard University Press, Cambridge, Massachusetts.
Case, T. J. 1979. Optimal body size and an animal's diet. Acta Biotheoretica, 28:54-69.[Medline]
Caswell, H. 2001. Matrix population models. Construction, analysis and interpretation. Sinauer Press, New York.
Charnov, E. L., T. F. Turner, and K. O. Winemiller. 2001. Reproductive constraints and the evolution of life histories with indeterminate growth. Proc. Natl. Acad. Sci. U.S.A, 98:9460-9464.
Chown, S. L., and K. J. Gaston. 1997. The species-body size distribution: Energy, fitness and optimality. Funct. Ecol, 11:365-375.
Denny, M. W. 1995. Predicting physical disturbance: Mechanistic approaches to the study of survivorship on wave-swept shores. Ecol. Monogr, 65:371-418.
Denny, M. W. 1999. Are there mechanical limits to size in wave-swept organisms? J. Exp. Biol, 202:3463-3467.[Abstract]
Denny, M. W., T. L. Daniel, and M. A. R. Koehl. 1985. Mechanical limits to size in wave-swept organisms. Ecol. Monogr, 51:69-102.
Denny, M. W., B. Gaylord, B. Helmuth, and T. Daniel. 1998. The menace of momentum: Dynamic forces on flexible organisms. Limnol. Oceanogr, 43:955-968.
Fabricius, K. E., Y. Benayahu, and A. Genin. 1995. Flow dependent herbivory and growth in zooxanthellae-free soft corals. Limnol. Oceanogr, 40:1290-1301.
Gaylord, B. 1999. Detailing agents of physical disturbance: Wave-induced velocities and accelerations on a rocky shore. J. Exp. Mar. Biol. Ecol, 239:85-124.[CrossRef]
Gaylord, B. 2000. Biological implications of surf-zone complexity. Limnol. Oceanogr, 45:174-188.
Givnish, T. J., and G. J. Vermeij. 1976. Size and shapes of liana leaves. Am. Natur, 110:743-778.[CrossRef][ISI]
Helmuth, B. S. T. 2002. How do we measure the environment? Linking intertidal thermal physiology and ecology through biophysics. Integr. Comp. Biol. (In press).
Helmuth, B. S. T., and G. E. Hofmann. 2001. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull, 201:374-384.
Hofmann, G. E., and G. N. Somero. 1995. Evidence for protein damage at environmental temperatures: Seasonal changes in levels of ubiquitin conjugates and hsp70 in the intertidal mussel, Mytilus trossulus. J. Exp. Biol, 198:1509-1518.
Hofmann, G. E., and G. N. Somero. 1996. Protein ubiquitination and stress protein synthesis in Mytilus trossulus occurs during recovery from tidal emersion. Molec. Marine Biol. Biotechnol, 5:175-184.
Jones, K. E., and A. Purvis. 1997. An optimal body size for mammals? Comparative evidence from bats. Funct. Ecol, 11:751-756.[CrossRef]
Kim, K., and H. R. Lasker. 1998. Allometry of resource capture in colonial cnidarians and constraints on modular growth. Funct. Ecol, 12:646-654.[CrossRef]
Kozlowski, J. 1992. Optimal allocation of resources to growth and reproduction: Implications for age and size at maturity. Trends Ecol. Evol, 7:15-19.[CrossRef]
Kozlowski, J. 1996. Energetic definition of fitness? Yes, but not that one. Am. Natur, 147:1087-1091.[CrossRef]
Kozlowski, J., and J. Weiner. 1997. Interspecific allometries are by-products of body size optimization. Am. Natur, 149:352-380.[CrossRef][ISI]
Lang, J. C., and E. A. Chornesky. 1988. Competition between scleractinian reef corals: A review of mechanisms and effects. In Z. Dubinsky (ed.), Ecosystems of the world: Coral reefs, pp. 209–252. Elsevier, Amsterdam.
Paine, R. T. 1976. Size-limited predation: An observational and experimental approach with the Mytilus-Pisaster interaction. Ecology, 57:858-873.[CrossRef]
Paine, R. T., and S. A. Levin. 1981. Intertidal landscapes: Disturbance and the dynamics of pattern. Ecol. Monogr, 51:145-178.[CrossRef]
Perrin, N. 1998. On body size, energy and fitness. Funct. Ecol, 12:500-502.[CrossRef]
Perrin, N., M. Ruedi, and H. Saiah. 1987. Why is the cladoceran Simocephalus retulus not a "big bang" strategist? A critique of the optimal-body-size model. Funct. Ecol, 1:223-228.
Perrin, N., and R. Sibly. 1993. Dynamic models of energy allocation and investment. Ann. Rev. Ecol. Syst, 24:379-410.[CrossRef][ISI]
Peters, R. H. 1983. The ecological implications of body size. Cambridge University Press, Cambridge.
Pfister, C. A., and F. R. Stevens. 2002. The genesis of size variability in plants and animals. Ecology, 83:59-72.[ISI]
Porter, J. W. 1976. Autotrophy, heterotrophy and resource partitioning in Carribbean reef building corals. Am. Nat, 110:731-742.[CrossRef][ISI]
Reiss, M. J. 1989. The allometry of growth and reproduction. Cambridge University Press, Cambridge.
Rex, M. A., R. J. Etter, A. J. Clain, and M. S. Hill. 1999. Bathymetric patterns of body size in deep-sea gastropods. Evolution, 53:1298-1301.
Sanford, E. 1999. Regulation of keystone predation by small changes in ocean temperature. Science, 283:2095-2097.
Sanford, E. 2002. Water temperature, predation, and the neglected role of physiological rate effects in rocky intertidal communities. Integr. Comp. Biol. (In press).
Sebens, K. P. 1977. Habitat suitability, reproductive ecology, and the plasticity of body size in two sea anemone populations (Anthopleura elegantissima and A. xanthogrammica). Ph.D. Diss., University of Washington, Seattle.
Sebens, K. P. 1979. The energetics of asexual reproduction and colony formation in benthic marine invertebrates. Am. Zool, 19:683-697.
Sebens, K. P. 1980. The regulation of asexual reproduction and body size in the sea anemone Anthopleura elegantissima (Brandt). Biol. Bull, 158:370-382.
Sebens, K. P. 1981. The allometry of feeding, energetics, and body size in three sea anemone species. Biol. Bull, 161:152-171.
Sebens, K. P. 1982a. The limits to indeterminate growth: An optimal size model applied to passive suspension feeders. Ecology, 82:209-222.
Sebens, K. P. 1982b. Asexual reproduction in Anthopleura elegantissima (Brandt) (Anthozoa:Actiniaria): Seasonality and spatial extent of clones. Ecology, 63:434-444.[CrossRef][ISI]
Sebens, K. P. 1982c. Competition for space: Growth rate, reproductive output, and escape in size. Am. Natur, 120:189-197.[CrossRef][ISI]
Sebens, K. P. 1983. Population dynamics and habitat suitability of the intertidal sea anemones Anthopleura elegantissima and A. xanthogrammica. Ecol. Monogr, 53:405-433.
Sebens, K. P. 1984. Water flow and coral colony size: Interhabitat comparisons of the octocoral Alcyonium siderium. Proc. Natl. Acad. Sci. U.S.A, 81:5473-5477.
Sebens, K. P. 1987. The ecology of indeterminate growth in animals. Ann. Rev. Ecol. Syst, 18:371-407.[CrossRef][ISI]
Sebens, K. P. 1996. Adaptive responses to water flow: Morphology, energetics, and distribution of reef corals. Proc. 8th Int. Coral Reef Symposium, 2:1053-1058.
Sebens, K. P., K. Vandersall, L. Savina, and K. Graham. 1996. Zooplankton capture by two scleractinian corals, Madracis mirabilis and Montastrea cavernosa in a field enclosure. Mar. Biol, 127:303-318.[CrossRef]
Sebens, K. P., S. Grace, B. Helmuth, E. Maney, and J. Miles. 1998. Water flow and prey capture by three scleractinian corals, Madracis mirabilis, Montastrea cavernosa and Porites porites in a field enclosure. Mar. Biol, 131:347-360.[CrossRef]
Schmidt-Nielsen, K. 1984. Scaling: Why animal size is so important. Cambridge University Press, Cambridge.
Stearns, S. C. 1992. The evolution of life histories. Oxford University Press, Oxford.
Warren, C. E., and G. E. Davis. 1967. Laboratory studies on the feeding, bioenergetics and growth of fish. In S. D. Gerking (ed.), The biological basis of fish production, pp. 175–214. Blackwell, Oxford.
West, B., J. H. Brown, and B. J. Enquist. 1997. A general model for the origin of allometric scaling laws in biology. Science, 276:122-126.
Witman, J. D. 1987. Subtidal coexistence: Storms, grazing, mutualism and the zonation of kelps and mussels. Ecol. Monogr, 57:167-187.
Zera, A. J., and L. G. Harshman. 2001. The physiology of life history trade-offs in animals. Ann. Rev. Ecol. Syst, 32:95-126.[CrossRef][ISI]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. P. O'Connor, A. E. Sieg, and A. E. Dunham Linking physiological effects on activity and resource use to population level phenomena Integr. Comp. Biol., December 1, 2006; 46(6): 1093 - 1109. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. J. Angilletta Jr., T. D. Steury, and M. W. Sears Temperature, Growth Rate, and Body Size in Ectotherms: Fitting Pieces of a Life-History Puzzle Integr. Comp. Biol., December 1, 2004; 44(6): 498 - 509. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||