Jessica Gurevitch, Samuel M. Scheiner, and Gordon A. Fox - The Ecology of Plants - 2002 / The Ecology of Plants Jessica Gurevitch, Samuel M. Scheiner, and Gordon A. Fox; 2002 / Ch 09

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Figure 9.1

Digitalis purpurea (foxglove, Schrophulariaceae) is a “bienni- al”—a short-lived monocarpic perennial. These plants take two or more years to reach maturity, then reproduce in a single bout in which an enormous fraction of their available resources is devoted to reproduction. Relatively few plant species are strict biennials; in most so-called biennials, some plants reproduce in two years, and some in more. (Photograph courtesy of M. Rees.)

ble. Even if we substituted definite numbers for words such as “unlimited” and “indefinitely,” it would not be possible for a plant to maximize all of these components of fitness simultaneously; trade-offs would be necessary. A good way to see this is to consider the size and number of seeds.

The number of seeds per reproductive bout can vary tremendously. There are some plants that mature only a few dozen or so seeds per flowering episode, such as Cocos nucifera (coconut palms, Arecaceae). A mature cottonwood tree (Populus fremontii, Salicaceae), on the other hand, may produce tens of thousands of seeds per episode; some orchids may produce hundreds of thousands to millions of seeds. If natural selection favors those genotypes that leave behind the most representa-

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tives over time, why don’t all plants produce huge numbers of seeds?

Most plant ecologists studying this question have focused on the idea of a trade-off between the number of seeds a plant can make and the sizes of those seeds. Larger seeds generally contain more endosperm (or other nutritional stores), and thus generally have a greater chance of establishment success, than seeds provided with fewer resources. Large amounts of endosperm in the seeds of some arid-zone perennials, for example, allow them to sink deep roots quickly, increasing their chances of survival.

Thus, in fitness gained through maturing seeds (often called a plant’s female fitness), there is a tradeoff in the use of limited resources. Producing more (but smaller) seeds probably reduces the chances of success for each seed because smaller seeds typically grow more slowly. On the other hand, there are likely to be diminishing returns in provisioning only one seed. The joint chance of success of, say, two medium-sized seeds can be greater than the chance of success of a single large seed. Thus, natural selection on maternal plants favors those that can divide resources among seeds so as to optimize the number of descendants left behind. Is there a single best seed size, or is it best to produce seeds in a range of sizes? What is the best size or range of sizes?

John Harper (1977) suggested that variation in seed size is quite limited in most plant species, as was suggested by the data available at the time. However, studies in the 1980s and 1990s revealed much variation in seed size within populations, even when natural selection was acting to constrain seed size because a particular size was optimal for a species (Mazer and Wolfe 1992; Platenkamp and Shaw 1993; Mojonnier 1998). In fact, individual plants often produce seeds of quite varied size (Venable 1985; Winn 1991; see Figure 5.1)

Using a graphical model, Christopher Smith and Stephen Fretwell (1974) showed that there is a single optimal size for seeds (Figure 9.2) if environmental conditions are predictable. McGinley et al. (1987) showed that Smith and Fretwell’s conclusion holds under fairly broad circumstances. However, their analysis found that variable seed size is sometimes favored when the environment varies over time and the geometric mean of fitness (see Chapter 7) is used as the criterion for fitness. The geometric mean gives the average rate of population growth over a series of variable years, and is therefore the appropriate measure of fitness.

Individual plants actually produce seeds of variable size, as noted above, so this theoretical result seems to differ from what we actually see in nature. McGinley et al. (1987) suggested that this difference might be a consequence of evolutionary constraints that limit the abilities of plants to reduce variation in seed size. For example, many species in the sunflower family (Asteraceae)

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1

Figure 9.2

Model for finding the seed site that maximizes fitness of the parental plant. The curve gives the fitness of seeds of a particular size under particular environmental conditions.

Increasing seed size increases fitness, but above a certain size, there are diminishing returns in producing large seeds. Below a minimum size, the seeds have no fitness. The optimal seed size is the value of the curve tangent to the 45degree line. (After Smith and Fretwell 1974.)

have flowering heads with two kinds of flowers, disk florets and ray florets, which can be constrained to produce different-sized seeds by their physical shape (Figure 9.3; Venable 1985). Seeds within a legume pod can differ in size because those more proximal (closer) to the maternal plant are able to garner more resources than more distal (farther) seeds (Silvertown 1987). It is important to realize that other factors besides natural selection determine the outcome of evolution (see Chapter 5). Even when selection favors a single seed size and when natu-

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ral selection is the dominant evolutionary force, it may not be possible to eliminate variation in seed size within or between plants, because this variation is also determined by environmental and developmental factors.

In determining seed size, there is an inherent conflict between the fitness of the maternal sporophyte and the fitness of the individual seeds. Natural selection on maternal plants favors those that leave the largest number of descendants, but it acts differently on seeds: the favored seeds are those that have the best individual size—often, the largest. An analogy may be helpful: if a pair of parents have a large sum of money to leave their children, they may best assure the success of the largest number of descendents by dividing the money evenly among the children—but an individual child may best assure his or her success by getting as large an individual inheritance as possible. This sort of competition among offspring—and conflict between parents and off- spring—also occurs in plants.

Individual seeds can vary in their efficiency at sequestering a share of the maternal sporophyte’s resources. Genetic differences, positional effects, differences in the timing of fertilization, or environmental variation can lead to differences in seed size. Selection on seeds favors increased ability to garner maternal resources whenever the fitness gained by doing so exceeds the inclusive fitness lost by depriving siblings of those resources. An individual’s inclusive fitness is a measure of how well that individual passes on its genes as well as how well its relatives pass on theirs, weighted by how closely they are related. For example, if every seed in a fruit has the same pollen parent, then, selection favors an individual’s increasing its seed size if doing so increases its fitness by more than the fitness lost by two siblings, because, on average, the seeds share half their genes with one another.

Disk florets

Ray florets

Figure 9.3

Helianthus annuus (sunflower, Asteraceae) is a widespread North American annual plant that is widely cultivated for its seeds, but also has many wild populations. Sunflower inflorescences (also called heads) are composed of two types of flowers (florets), each of which can produce a single seed. Disk florets compose the center of the head, while ray florets—which have a single very large petal and four very small petals—occur around the outside of the head. In some species in this family, seeds produced by different kinds of florets can differ in size, shape, and dispersal and dormancy properties. (Photograph by S. Scheiner.)

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Classic studies of seed size were conducted by Edward Salisbury in England (1942) and Herbert Baker in California (1972). Both of them focused on the ecological correlates of seed size. Baker, for example, concluded that plants with larger seeds were characteristic of more open habitats, such as deserts and coastal dunes. These classic studies have stimulated much research, partly because this kind of study makes it difficult to distinguish the effects of selection from the effects of phylogeny. For example, seed size may have different ecological consequences in different environments, as Baker concluded. But it may also be true that open habitats have many large-seeded plants simply because the plants that colonized those habitats are from taxa with large seeds. In an extensive study of the flora of the Indiana Dunes at the southern end of Lake Michigan (where Cowles conducted his pioneering studies of succession; see Chapter 13), Susan Mazer (1989) was able to control for these different effects statistically. She found that habitat accounted for only 4% of the variance in seed size by itself. In the Indiana Dunes, the relationship between seed size and habitat is thus largely caused by the history of the taxa that occur in each habitat.

Another factor that can affect selection on seed size and number is granivory, or seed predation. If a substantial fraction of seeds are eaten by granivores, there can be selective advantages to reducing seed size and increasing seed number. While tiny seeds may have reduced chances of establishment, they may also be too small to be worth the attention of granivores. There is a large body of theory concerning animal behavior, called “optimal foraging theory,” concerned with how animals should be expected to search for, select, and handle food items. This theory predicts that granivores should not pay attention to seeds that are too small, and this prediction is supported by much empirical evidence. In addition, small seeds, scattered widely, are far more difficult for vertebrate seed predators such as rodents or jays to find than are a few large seeds (this is not true for fungal seed pathogens, however).

Seed size may also be dictated by dispersal strategy. If seeds are dispersed by wind (see Figure 8.14A), then there may be an optimal size to maximize the distance traveled. It is not just the resources invested in the seed that matter, but also the resources invested in the dispersal structure (see Figure 8.15). Variation in seed size may ensure that seeds are dispersed different distances away from the parent plant, resulting in less competition among siblings.

In this discussion of the ecology and evolution of seed size and number, we have assumed that there is a fixed total amount of resources allocated to reproduction at any time. When this is true, the problem reduces to the trade-off between number and size of seeds. This assumption is useful in gaining an initial insight. But it

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is clear that both the timing of reproduction and the amount of resources allocated to reproduction are often genetically variable and have evolved over time. This realization leads to the more general problem of the evolution of life histories.

Life History Strategies

We now consider the more general problem: how does selection act on the schedule of reproduction and survival throughout a plant’s life? Martin Cody (1966) suggested that throughout their lives, organisms must apportion resources among competing demographic functions—survival, growth, and reproduction. How an organism does this determines its fitness. Many studies have been conducted using the assumption that these three demographic functions really are competing. It has become clear, however, that this is not always the case, because the functions of plant body parts do not correspond in any simple way to the three demographic categories (see “Reproductive allocation” below). However, there is no doubt that life history traits are subject to natural selection.

Arabidopsis thaliana (Brassicaceae; Figure 9.4) is a weedy European plant that is now widely naturalized throughout the temperate world. It is perhaps best known as the “fruit fly” of plant molecular biology—

Figure 9.4

Arabidopsis thaliana (mouse-ear cress, Brassicaceae) is a small annual mustard. Native to central Europe, it has become naturalized in much of the temperate world. A. thaliana has one of the smallest genome known in any angiosperm, and therefore is widely used in genetic studies. Because laboratory strains have been selected for extremely short life cycles— as little as a few weeks—the species is widely used in a number of other studies, including a growing number of ecological studies. (Photograph courtesy of K. Scheiner.)

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that is, as an important model organism—because it has a small genome and reproduces quickly. Under some circumstances, some Arabidopsis genotypes can begin flowering three weeks after germinating. There is also remarkable variation in Arabidopsis life spans. Individuals from some wild European populations live as long as a year. Arabidopsis from other populations can have two generations within a year—one of the few plants that do so. In some places with mild winters, such as the mid-latitude U.S. states of Kentucky and North Carolina, this plant is a winter annual—germinating in the fall, growing slowly through the winter, and flowering in the spring. Where the winters are colder, as in the northeastern United States, it is a spring annual—germinat- ing in the early spring, growing quickly, and flowering by late spring or early summer. There is a genetic basis for much of this variation within Arabidopsis (Kuittinen et al. 1997). Those genotypes with the shortest life histories have been the focus of most research because of their convenience, but evolutionary ecologists are now exploring the molecular basis of life history variation in this species.

Seed Germination

A number of different environmental factors help to trigger germination. In many plants, temperature plays a key role in regulating germination. For example, winter annuals in the Sonoran Desert do not germinate during the summer rainy season, and the reverse is true of summer annuals. At least in some plants, some cell membranes in seeds undergo temperature-dependant phase transitions that render cells either more or less permeable to water, thus allowing (or preventing) imbibition (uptake of water by seeds) depending on the season (Bewley and Black 1985). Light also plays a key role in many species. For example, many weedy species require light to germinate; this is why many species germinate following soil disturbance. There are also species such as Eschscholzia californica (California poppy, Papaveraceae) in which germination is inhibited by light.

When should a seed germinate? This might seem to be obvious: it should germinate when conditions are favorable. But can plants use environmental conditions at germination time to predict subsequent conditions— that is, can plants evolve predictive germination? The answer depends critically on the predictability of the environment. In a maritime climate (see Figure 18.15C), for example, summers are often warm and wet. Germination in late spring usually provides a fairly good chance of surviving to maturity. In contrast, rainfall in desert environments can be highly unpredictable (see Figure 18.20), and many plants that germinate do not survive to maturity (Fox 1989; Venable and Pake 1999).

In a study of the annual Plantago insularis (Plantaginaceae) growing in the Sonoran Desert, Maria Clauss

and Lawrence Venable (2000) found only a slight correlation between rain at germination time and subsequent rain during the growing season. This correlation was greatest at mesic sites (that is, favorable microhabitats). However, populations at xeric sites (unfavorable microhabitats) would be expected to experience the strongest selection for being able to predict when adequate rainfall would be available. Clauss and Venable pointed out that predictive germination might still be occurring in these plants, but if so, it is a complicated phenomenon. For example, heavy rain at germination time does predict subsequent rain in xeric (but not mesic) sites in El Niño years (see Chapter 18).

Predictive germination may involve factors other than climate. Annuals in fire-prone shrublands in California are stimulated to germinate by compounds in smoke (Keeley and Fotheringham 1997). Fire reduces aboveground biomass and the density of competitors, and minerals in the ash add nutrients to the soil; therefore, conditions following fire are more favorable for growth of annuals. Many species classified as weedy (ruderal) use cues of recent disturbance as signals for germination.

Life Span

Annuals, as we saw above, are plants in which the vegetative life cycle is completed in less than a year. Individual plants are “born” as seeds; since most annual plants have seed banks, this means that most annuals that you see growing are actually more than a year old. In temperate-zone environments, many annuals can be categorized as either winter annuals (like Arabidopsis in the southern United States) or summer annuals. These terms can be confusing. Winter annuals germinate in the autumn, overwinter as vegetative plants, and flower and die in the spring. Summer annuals complete their aboveground life cycle in the warm months. Research with annuals in the Sonoran Desert (Mulroy and Rundel 1977) has showed that winter and summer annuals differ considerably in their developmental and physiological traits, and this is probably true of winter and summer annuals in other environments as well.

Annuals are especially common in certain environments, including deserts, many dunes, and recently disturbed sites. Most common herbaceous weeds are annuals. Annuals in warm deserts have been the subject of much ecological investigation. In years favorable to plant growth, these plants produce many seeds. Subsequent years may well have poor rainfall, leading to poor germination, survival, and growth for these plants. It is only in “good” years that population growth may actually be positive (see Figure 7.13).

Semelparity includes plants with a range of life spans. Biennials (Figure 9.5; see also Figure 9.1) are semelparous plants that flower after two or more years.

Figure 9.5

Cirsium canescens (Platte thistle, Asteraceae) is a “biennial.” (Photograph courtesy of S. Louda.)

The term itself is something of a misnomer, as it takes most biennial species longer than two years to reach flowering size, and individuals in most biennial populations vary in this respect. Semelparous perennials are plants that live for a number of years before flowering. There is no firm boundary between biennials and semelparous perennials (Young and Augspurger 1991), although generally biennials die back to ground level in winter (or the unfavorable season), persisting only underground, while semelparous perennials have substantial aboveground structures year-round.

Both theoretical and empirical studies point to two factors that might select for semelparous life histories: a critical size for reproduction and diminishing returns from retaining resources for subsequent bouts of reproduction. Biennials and short-lived semelparous plants often occur in early successional habitats, where canopy closure after several years causes a decrease in the quality of the environment. Long-lived semelparous plants are characteristic of relatively unproductive but persistent habitats, such as deserts, many grasslands, and alpine regions. Factors such as competition for pollinators may be more important in selecting for semelparity in these settings.

Flowering stalk

Nonflowering ramet

Figure 9.6

Agaves (century plant, Agavaceae) have semelparous rosettes that flower only once, mobilizing all the energy stored during a lifespan of a few decades to produce a large inflorescence with hundreds of flowers. The genet itself is iteroparous, because vegetative reproduction precedes flowering. (Photograph © Frans Lanting/Photo Researchers, Inc.)

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or even longer. While most woody plants live for at least a few years, the reverse is not necessarily true. Some herbaceous plants, such as many tundra plants, live for centuries. Among forest trees there are species that live for decades and others that live for centuries, even in the same type of habitat. In pine flatwoods in the southeastern United States, for example, Pinus palustris (longleaf pine, Pinaceae) can live for 300 or more years, while Cersis canadensis (eastern redbud, Fabaceae) lives for only a few decades. Typically, long-lived perennials have shortlived seeds and a small or nonexistent seed bank. Most perennials, however, have the ability to become dormant as vegetative plants during winter or the dry season. Perennials have more ability to withstand variable and harsh environmental conditions in their aboveground phase than do annuals; this ability increases with the longevity of the species (long-lived temperate trees, for example, are more likely to withstand a hard freeze or a drought than a short-lived herbaceous perennial would be).

How do we explain all of these different life histories, as well as the variation within each? What are their consequences for population growth? Early efforts at such explanations focused on the theory of r- and K- selection, while recent work has centered on demographic approaches.

r- and K-selection

Robert MacArthur and his students and colleagues developed their ideas about life history evolution, focused on the concept of r- and K-selection, during the 1960s. This work stimulated much of the early research on the evolution of life histories. The concept of r- and K-selection plays little role in current research on life histories. Nevertheless, it continues to be influential in popular writing on ecology, in textbooks, and in some aspects of community ecology, so we explain it briefly here.

Using the logistic model of population growth,

where r is the intrinsic rate of population growth, K is the carrying capacity, and N is the population size, MacArthur and Wilson (1967; MacArthur 1972) suggested that at low population densities, selection will be strongest on traits that increase the intrinsic rate of population growth, r. At high densities, they suggested, selection will be strongest on traits that increase population size, or the carrying capacity, K. Loosely, they argued that there is stronger selection for productivity than for efficiency at low densities, and vice versa at high densities. Selection for increasing r and K was widely interpreted as counterposed. There is, however, no reason why there must be a trade-off between r and K. It is

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mathematically straightforward to show that selection under logistic growth always favors increases in both parameters (Boyce 1984; Emlen 1984).

Using the assumption that high population densities lead to reduced survival of juveniles, Eric Pianka (1970) developed detailed predictions for animal populations, which were later extended to plants: K-selection should favor delayed reproduction and a reduced number of offspring, while r-selection should favor early reproduction and an increased number of offspring. This prediction is what most people mean when they refer to r- and K-selection: weedy plants, for example, are taken to be r-selected, while forest trees are taken to be K- selected. As Boyce (1984) put it, references to “r strategists” generally mean small organisms that reproduce quickly and are poor competitors, and thus should be favored in disturbed habitats, while references to “K strategists” generally imply that organisms have the opposite characteristics, although these characterizations were not derived from the original model.

Perhaps the greatest problem with r- and K-selec- tion theory is that its predictions are qualitative and relative, rather than quantitative, and so are difficult to test. Plants that are r-selected, for example, are predicted to evolve an earlier age at first reproduction and greater seed numbers than those that are K-selected. But what can one measure to test this? Plants within one community might be classified as r-strategists or K-strate- gists based on which reproduced at an earlier age, but this classification might be different in another community, or in a comparison of one of these species with another species. What is more, such a classification says nothing about whether the differences among the species are actually a result of selection on age of first reproduction, and certainly not whether the differences are part of an entire life history strategy. Finally, the emphasis on the distinction between plants that live mainly in disturbed habitats (r-strategists) and those that live in stable habitats (K-strategists) may have been misplaced. Many “stable” habitats, such as mature forests, have been shown to depend on disturbance for their maintenance (see Chapter 13). Previous notions of climax communities and stable equilibria have changed dramatically in recent years, reducing the utility of this equilibrium-based theory.

Grime’s Triangular Model

Responding to some of the limitations of r- and K-selec- tion theory, plant ecologist Philip Grime (1977, 1979) proposed an extension of that theory, classifying life histories as being influenced primarily by selection for one of three traits: colonizing ability (which he called R selection, for ruderal plants), competitive ability (C selection), or stress tolerance (S selection). Grime’s theory was not derived from any particular model of population growth

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or evolution. He assumed that there are necessary tradeoffs between these syndromes. In fact, Grime (1979) pointed out that plants could have any combination of traits associated with R, S, and C selection. Like r- and K- selection theory, then, Grime’s theory made predictions that do not lend themselves to critical tests. While C-S- R theory made assumptions and predictions about life histories, it had a particular emphasis on the relative importance of competition in different habitats (see Chapter 10). Consequently, this theory has received a fair amount of attention and generated considerable controversy among plant community ecologists, but has not had much influence in the study of life history evolution.

Demographic Life History Theory

The demographic approach to life history theory centers on studying how variation in survival and fecundity as individuals vary in size, age, or other life history stages affects population growth. Its utility can be seen in its solution of “Cole’s paradox.” In studying the consequences of schedules of reproduction, Lamont Cole (1954) developed a model that compared the fitnesses of a perennial phenotype with an annual phenotype in the same species. If a fraction p of the plants survive to reproduce each year, and produce F seedlings if they do reproduce, then the perennial’s fitness is λ P = p(F + 1), while the annual’s fitness is λ A = pF. Cole concluded that annuals need only increase their reproductive output by one new seedling per year to do as well as perennials. He pointed out that this finding was paradoxical because most organisms are perennial, not annual, suggesting that there must be other advantages to being perennial.

Using a demographic approach, Eric Charnov and William Schaffer (1973) pointed out a flaw in Cole’s reasoning: his result depends on the assumption that rates of survival and fecundity do not depend on age. If his model is modified slightly to give first-year plants a different chance of surviving than older perennials, the paradox disappears. Charnov and Schaffer demonstrated this as follows: If the fraction of first-year plants surviving is c, and the surviving annuals produce FA seeds while the surviving perennials produce FP seeds, then λ P = cFP + p and λ A = cFA. Thus the population growth rates of both phenotypes are given by the chance of individuals surviving to reproduce times their fecundity if they do survive; for the perennials, this rate is increased by the chance of surviving from year to year. Therefore, the annuals have greater fitness only when λ P < λ A, that is, when FA > FP + p/c, or when their fecundity exceeds the perennials’ fecundity plus the survival chances of adults relative to those of juveniles. This means that where survival of adults is much greater than survival of juveniles, the annuals’ reproductive output would need to exceed that of the perennials by a considerable

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amount for the annuals to have a greater lifetime fit- ness—thus resolving the paradox.

Reproductive Allocation

If it matters when a plant reproduces, and how much it reproduces at each life stage or age, the obvious question becomes, how does selection act on the allocation of resources at each stage? This question, especially with the added assumption of a trade-off between reproduction and survival, has provided the focus for much research in life history evolution.

Key to addressing this problem is the concept of reproductive value (v; see Chapter 7). William Schaffer and Michael Rosenzweig (1977) showed that fitness is maximized at every age if the sum of present reproduction plus future reproduction weighted by relative reproductive value,

F+ pi vi + 1

ivo

is maximized at every age. The fraction of resources allocated to reproduction at age or stage i is the reproductive effort at that age or stage. Schaffer and Rosenzweig analyzed selection on life histories graphically by plotting current and future reproduction against reproductive effort.

Figure 9.7 shows two simple alternatives. In Figure 9.7A, the curves for both present reproduction (Fi) and future reproduction [(pivi+1)/v0] are convex, so their sum is maximized when reproductive effort is either 0 or 1 at any given age or stage. These curves can differ for different ages or stages. Since a reproductive effort of 1 means putting all resources into reproduction and none into survival, this graph describes selection for a semelparous life history. In Figure 9.7B, both curves are concave, so selection favors intermediate reproductive effort at every age after the onset of reproduction—an iteroparous life history. Combinations of curves of different shapes can thus describe a wide variety of life histories.

Testing this kind of theory has proved difficult. One problem, pointed out by Schaffer and Rosenzweig (1977), is that in many cases more than one life history is optimal. But there is a deeper problem in actually measuring reproductive effort: How does one assign the fraction of resources used for reproduction? Little is known about the underlying developmental and biochemical processes of reproduction. An early study (Hickman and Pitelka 1975) found that the dry weight of plant parts, except for some unusual tissues, is highly correlated with their energy content. This result led to hundreds of studies comparing the dry weights of plant parts under varied circumstances.

But how does one assign a plant part to “current” versus “future” reproduction? For flowers and fruits, the choice is clear, but this is not so for organs such as roots.

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Figure 9.7

Selection for semelparity versus iteroparity. Fitness is greatest when the sum of present reproduction plus future reproduction (green curves) is maximized at every age. (A) Semelparity is favored when the curves for both present and future reproduction are convex—fitness is greatest when reproductive effort is always either 0 or 1. (B) Iteroparity is favored when both curves are concave—fitness is greatest at intermediate reproductive effort once maturity is reached. (After Schaffer and Rosenzweig 1977.)

One attempt at handling this problem was a study by Edward Reekie and Fakhri Bazzaz (1987). These investigators measured the amount of root mass present during flowering of a grass and the amount present at other times, and found no difference. They concluded that none of the root mass counted toward reproduction.

The problem with testing theory of this kind is that the parts of a plant do not relate in any simple way to their demographic consequences. Moreover, reproductive effort is a ratio. As such, it does not uniquely describe the life history (Schaffer 1983) because there are many different life histories that could yield a measurement of, say, 75% allocation to reproduction at a particular time. This observation led to models using more

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sophisticated mathematics, such as optimal control theory, which can handle the technical problem of optimizing reproductive allocation throughout the life span. Additional work along these lines has been slow because meaningful predictions require that the models be much more biologically realistic. Further work in developmental biology and physiology is thus needed to make much more progress in this approach to life history theory.

Matrix models (see Chapter 7) play an important role in both theoretical and empirical studies of life histories. In the simple case of the Charnov-Schaffer model of perennial life histories, the original model stated that the numbers of juveniles and adults (J and A, respec-

tively) next year will be Jt+1 = cFPJt + pFPA and At+1 = cJt + pAt. Rewritten in matrix form, this is

Analysis of this model tells us that the population will eventually grow at the rate λ P = cFP + p, the dominant eigenvalue of the matrix. At that time, the population will be composed of FP times as many juveniles as adults. In this simple case, it is easy to see the consequences of varying the survival and fecundity rates. For example, increasing juvenile survival c increases λ P at the rate FP. With more realistic models, it is not so easy to do this sort of thing visually. However, the methods described in Chapter 7 make it possible to completely analyze the effects of varying any survival or fecundity rate on population growth or composition.

Bet Hedging in a Variable Environment

Year-to-year variation in survival and fecundity also affects fitness. How can fitness variation among years be reduced when some of that variation is inherently unpredictable? Many plants appear to achieve this by “hedging their bets” (spreading the risk) among years. To see why variation among years changes fitness, compare a hypothetical annual species that always produces an average of 1.5 seeds every year (that is, half of the plants in the population produce 1 seed, and half produce 2) with another annual that produces 0.5, 1, and 3 seeds in dry, average, and wet years. If these types of years occur with equal frequency (1/3), the average number of descendants of the variable species will be 0.51/3 11/3 31/3 ≈ 1.14, or 0.36 fewer than the number produced by the invariant species. In general, increasing the variance in fitness among years decreases its long-term geometric mean. This implies that there is a trade-off between mean and variance in fitness: gaining added fitness in “good” years can come at the expense of reducing long-term average fitness. For matrix models, the problem is somewhat more complex, because the

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order of multiplication matters. Analyses of both structured and unstructured population models suggest that selection often acts to increase fitness by reducing among-year variation in fitness.

Spreading reproduction among more years or more evenly among years, increasing the area over which seeds are dispersed, and increasing dispersal through time by means of seed banks are all examples of bet hedging. Seed banks are perhaps the best studied of these mechanisms. Most annual plants have seed banks. Without environmental variation, among-year seed dormancy is opposed by selection to germinate immediately, since seeds that remain dormant in the soil cannot reproduce, but those that germinate can reproduce. A number of studies (e.g., Brown and Venable 1991; Kalisz and McPeek 1993) suggest that seed banks play an important role in buffering populations from environmental variation.

Difficulties in Measuring Trade-Offs

There are an enormous number of studies comparing species or higher taxa that show a negative correlation between traits such as longevity and fecundity. Nevertheless, a substantial number of studies at the population level—where selection must be acting on these trade-offs—not only fail to show a negative correlation, but often show the opposite. This kind of result led a few scientists in the 1980s to argue that there are really no trade-offs. The problem was clarified when Arie van Noordwijk and Gerdien de Jong (1986) pointed out that a positive correlation between, say, reproductive allocation and survival-related traits could actually be expected if the total amount of resources available varied among individuals. They suggested a useful analogy: for people operating under a fixed income, the amount of money available to spend on housing is negatively correlated with the amount available to spend on cars. Nevertheless, if you looked at the data, you would find that people who spend more on housing also spend more on cars—because they have greater total wealth. The trade-offs are real, but they operate in the context of variable access to resources.

A critical distinction must be made between those traits that must be traded off because the physical rules of the universe demand it, and traits that are traded off because selection has molded species that way. An example of the first category is resource allocation to leaves versus flowers: limiting resources (carbohydrates, nitrogen, or water) used to produce and maintain leaves cannot also be used to produce flowers. A possible example of the second category is an apparent trade-off between speed of wood growth and wood toughness. Fast-grow- ing trees such as quaking aspens (Populus tremuloides, Salicaceae) have weak wood, and individual trees generally live only 50 or 60 years. Consequently, the quak-

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ing aspen tends to be an early successional species (see Chapter 13). We cannot tell whether the growth rate versus wood density trade-off is inherent in the biochemistry of wood, or if the right mutation has simply not come along, or if selection always favors just these two combinations of traits.

Thus, we need to know more about variation among individuals in resource availability, as well as about the mechanisms determining resource allocation to different functions. Using a genetic model, David Houle (1991) showed that even when trade-offs do exist, genetic correlations between life history traits could evolve to be either negative or positive, depending on the genetic details. This finding points to the importance of increasing our understanding of the biological basis of life history traits.

Phenology: Within-Year Schedules

of Growth and Reproduction

Phenology—the timing of growth and reproductive activity within a year—can vary greatly among species, populations, and even individuals. In temperate climates, there are usually some plants flowering at all times between the first and last frost. Some woody species are evergreen, while others are deciduous. Even within groups that we usually think of as all of one type—such as conifers, which we think of as evergreen, there may be exceptions—such as the deciduous conifer species Larix laricina (the larch or tamarack, Pinaceae; Figure 9.8). In general, plant phenologies are constrained by seasonality, mainly by temperature or moisture availability. Phenologies have a more complex relationship with animal pollinators, seed dispersers, and herbivores because the plants and animals can be agents of selection on each other.

Vegetative Phenology

In temperate deciduous forests, many herbaceous plants on the forest floor expand their leaves and flower before the canopy trees begin leaf expansion. The consequence is that these plants do most of their growing and reproducing in temperatures that are far colder than those experienced by the canopy trees during the same life stages. Many studies have shown that this timing is actually advantageous to the forest-floor herbs. Once the canopy closes, very little sunlight—sometimes as little as 1%—gets through to the forest floor. Thus, the for- est-floor herbs’ growth and reproduction would be even more limited by light availability than they are by cool temperatures.

In most temperate species, it appears that temperature and photoperiod (day length) are the main factors determining vegetative phenologies. It is important to realize that temperature plays this role in a particular