Review: After reading this week’s assigned materials, view the following videos to learn about some methods used to estimate population size. As you watch both videos, consider which methods are best suited to sensitive populations, large populations, migrating populations, and populations that are difficult to measure. Reflect on what each method has to offer in order to best determine when and how a population sampling method should be used.
Population Sampling
WEEK 3 RESOURCES Required Resources Text Smith, T. M., & Smith, R. L. (2015). Elements of Ecology (9th ed.). Boston, MA: Pearson. Chapter 8: Properties of Populations. pp 152-167 Chapter 9: Population Growth. pp 172-186 Chapter 10: Life History. pp 192-215 Multimedia Wellcome Trust. (2014 June 16). Surveying populations (Links to an external site.)Links to an external site. [Video File]. Retrieved from The animation demonstrates several ways in which populations can be sampled. Random and systematic sampling in a woodland of different plant and animal species is reviewed.  The video can aid you in completing the week three discussion.Accessibility Statement (Links to an external site.)Links to an external site.Privacy Policy (Links to an external site.)Links to an external site. Wellcome Trust. (2014, June 16). What’s up, buttercup? Population sampling techniques (Links to an external site.)Links to an external site.[Video File]. Retrieved from Student scientists demonstrate population sampling techniques and use it to answer questions about factors that influence population size. The video is used in the week three discussion.Accessibility Statement (Links to an external site.)Links to an external site.Privacy Policy (Links to an external site.)Links to an external site. KYAfield. (2010, August 24). Mist netting Kentucky’s songbirds (Links to an external site.)Links to an external site. [Video file]. Retrieved from Avian biologist, Kate Heyden from the U.S. Fish and Wildlife demonstrates how the state’s population and diversity of songbirds are sampled. The methodology is explained in terms of following ethical and objective research guidelines. The video is used in the week three discussion.Accessibility Statement (Links to an external site.)Links to an external site.Privacy Policy (Links to an external site.)Links to an external site. Recommended Resources Multimedia University of New Hampshire. (2014, July 8). UNH Bee Research (Links to an external site.)Links to an external site. [Video File]. Retrieved from The University of New Hampshire’s researchers demonstrate conservation methods for supporting native pollinator populations. The video is used in the week three discussion. To learn more about the research: (Links to an external site.)Links to an external site.Accessibility Statement (Links to an external site.)Links to an external site.Privacy Policy (Links to an external site.)Links to an external site.
Population Sampling
CHAPTER 8 Smith, T. M., & Smith, R. L. (2015). Elements of Ecology (9th ed.). Boston, MA: Pearson. 8.1 Organisms May Be Unitary or Modular A population is considered to be a group of individuals, but what constitutes an individual? For most of us, defining an individual would seem to be no problem. We are individuals, and so are dogs, cats, spiders, insects, fish, and so on throughout much of the animal kingdom. What defines us as individuals is our unitary nature. Form, development, growth, and longevity of unitary organisms are predictable and determinate from conception on. The zygote, formed through sexual reproduction, grows into a genetically unique organism (see Chapter  5). There is no question about recognizing an individual. This simplistic view of an individual breaks down, however, when the organism is modular rather than unitary. In modular organisms, the zygote (the genetic individual) develops into a unit of construction, a module, which then produces further, similar modules. Most plants are modular in that they develop by branching, repeated units of structure. The fundamental unit of aboveground construction is the leaf with its axillary bud and associated internode of the stem. As the bud develops and grows, it produces further leaves, each bearing buds in their axils. The plant grows by accumulating these modules (Figure 8.1). The growth of the root system is also modular, growing through the process of branching and providing a continuous connection between above- and belowground modules. There are, however, a variety of growth forms produced by modular growth in plants; some plants spread their modules laterally as well as vertically. For example, some species produce specialized stems that either grow above the surface of the substrate, referred to as stolons (Figure 8.2a), or below the surface of the substrate, referred to as rhizomes (Figure 8.2b). These plants can produce new vertical stems and associated root systems from these laterally growing stolons or rhizomes. Similarly, some plants sprout new stems from the surface roots, which are called suckers (Figure 8.2c). In these laterally growing forms, the new modules may cover a considerable area and appear to be individuals. The plant produced by sexual reproduction, thus arising from a zygote, is a genetic individual, or genet. Modules produced asexually by the genet are ramets. These ramets are clones—genetically identical modules—and are collectively referred to as a clonal colony. The ramets may remain physically linked or they may separate. The connections between the modules may die or rot away, so that the products of the original zygote become physiologically independent units. These ramets can produce seeds (through sexual reproduction) and their own lateral extensions or ramets (asexual reproduction). Whether living independently or physically linked to the original individual, all ramets are part of the same genetic individual. Thus, by producing ramets, the genet can cover a relatively large area and considerably extend its life. Some modules die, others live, and new ones appear. Plants are the most obvious group of modular organisms; however, modular organisms include animals, such as corals, sponges, and bryozoans (Figure 8.3), as well as many protists and fungi. Technically, to study populations of modular organisms, we must recognize two levels of population structure: the module (ramet) and the individual (genet). As such, characterizing the population structure of a modular species presents special problems. For practical purposes, ramets are often counted as—and function as—individual members of the population. Modern genetic techniques, however, have allowed ecologists to determine the structure of these populations in terms of genets and ramets, quantifying the patterns of genetic diversity (see this chapter, Field Studies: Filipe Alberto). Field Studies Filipe Alberto Department of Biological Sciences, University of Wisconsin–Milwaukee Numerous plant species reproduce both sexually and asexually. For example, many grass species form dense mats of ramets through the growth of rhizomes or stolons, yet also produce new offspring by flowering and through seed production (new genets). For plant ecologists, this dual strategy of reproduction presents a critical problem in understanding the structure of plant populations. In theory, a field of grass occupied by a species exhibiting both reproductive strategies (sexual and asexual) could consist of only a single genetic individual (genet) and all of the apparent “individual” grass plants could be genetically identical ramets produced by a single parent plant. In contrast, the field could consist of a diversity of genets, each with an associated clonal colony of ramets that intermingle with ramets from adjacent clones. Although impossible only a few decades ago, the development of new genetic technologies now enables ecologists to analyze the genetic structure of a population, and one of the “new generation” of ecologists that is engaged in this new field of molecular ecology is Filipe Alberto of the University of Wisconsin. The focus of Alberto’s research is to understand the population structure of marine plants and algae that inhabit the shallow waters of coastal environments. One of the species that has been the focus of Alberto’s work is the seagrass Cymodocea nodosa. C. nodosa is common throughout the Mediterranean and Atlantic coast of Africa, where it forms meadows in the shallow nearshore waters (see Figure  18.8). It colonizes disturbed areas where it plays an important role in the stabilization of sediments. The species is dioecious (having separate male and female individuals) reproducing both sexually as well as vegetatively (asexually) through the extension of rhizomes. It exhibits fast clonal growth, with extension rates up to 2 m per year. In one study, Alberto and colleagues examined the genetic structure of a population of C. nodosa inhabiting Cádiz Bay along the southeastern coast of Spain. Within the bay, the researchers established a grid of 20 × 38 m with a grid spacing of 2 m, yielding a total of 220 sampling units (see Figure  1). For each sampling unit the researchers collected three to five shoots belonging to the same rhizome (genet) for genetic analysis. This sampling scheme allowed them to analyze the spatial pattern of relatedness (genetic similarity) among shoots at any point within the grid. Relatedness (similarity in genotypes) of sampled shoots was determined through the use of microsatellites, tandem repeats of one to six nucleotides found along a strand of DNA (see Section 5.2). Tandem repeats occur in DNA when a pattern of two or more nucleotides is repeated and the repetitions are directly adjacent to each other (tandem); for example: ACACACACAC. The repeated sequence is often simple, consisting of two (as with the preceding example of AC), three, or four nucleotides (di-, tri-, and tetranucleotide repeats, respectively). Microsatellites are ideal for population studies because, first, they are typically abundant in all species, and secondly, they are highly polymorphic—that is, for a given microsatellite locus on the DNA, there are typically many different forms of the microsatellite—different alleles. Each sequence with a specific number of repeated nucleotides is designated as an allele. So, a locus with six repeats is one allele (ACACACACACAC or AC6), whereas the same locus in another individual that contains nine repeats is another (different) allele. For the analysis of relatedness in the C. nodosa population, Alberto used nine different microsatellites that he had identified in a previous genetic study of the species. The researchers identified 41 different alleles (across the nine microsatellites) and a total of 83 different genotypes in the sample grid. The 83 different genotypes represent 83 genetically unique individuals that were produced through sexual reproduction. The number of different genotypes also corresponds to the number of clones (where clone is defined as a colony of ramets originating from the same genet) in the population. A map of the genotypes on the sampling grid is presented in Figure 1. The result shows an extremely skewed distribution of clone sizes (Figure 2), with a median clone dimension of 3.6 m. Despite the genetic richness of the meadow (83 different genotypes) and the resulting presence of many unique smaller clones, the meadow (sample grid) is spatially dominated by a few large clones. To determine how the two strategies of reproduction (sexual and asexual) influence the genetic structure of the population across the landscape, Alberto undertook a spatial analysis of relatedness using the data presented in Figure 1. With a plant species that can reproduce asexually through the lateral extension of rhizomes, one would assume that there is a high probability that adjacent shoots are ramets from the same genet (they belong to the same clone). But how would the probability of two shoots belonging to the same clone change for greater and greater distance between them? To answer this question, Alberto calculated the probability of clonal identity for the different distance classes (recall that the sample points are on a grid of 2 m). The probability of clonal identity F r is defined as the probability that a randomly chosen pair of shoots separated by a distance r belong to the same clone (genetically identical). The calculated values of F r for each distance class (0–2 m; 2–4; 4–6; 6–8; … ) is presented in Figure 3. The probability of clonal identity (F r) declines with increasing distance, from approximately 25 percent in the first distance class (0–2 m) to reach zero (meaning no pairs shared the same genotype) at a distance of 25–30 m. This distance (25–30 m) corresponds to the dimensions of the largest clones found in the populations (see Figure  3). The results indicate that in this population of seagrass, sampling shoots at an interval of 30 m or more will assure that the samples represent unique genotypes—genetically unique individuals—rather than ramets. 8.2 The Distribution of a Population Defines Its Spatial Location The distribution of a population describes its spatial location, the area over which it occurs. Distribution is based on the presence and absence of individuals. If we assume that each red dot in Figure 8.4 represents an individual’s position within a population on the landscape, we can draw a line (shown in blue) defining the population distribution—a spatial boundary within which all individuals in the population reside. When the defined area encompasses all the individuals of a species, the distribution describes the population’s geographic range. Population distribution is influenced by a number of factors. We introduced the concept of habitat—the place or environment where an organism lives—in Chapter 7 (Section 7.14). Each species has a range of abiotic environmental and resource conditions under which it can survive, grow, and reproduce. The primary factor influencing the distribution of a population is the occurrence of suitable environmental and resource conditions—habitat suitability. Red maple (Acer rubrum), for example, is the most widespread of all deciduous trees of eastern North America (Figure 8.5). The northern limit of its geographic range coincides with the area in southeastern Canada where minimum winter temperatures drop to −40°C. Its southern limit is the Gulf Coast and southern Florida. Dry conditions halt its westward range. Within this geographic range, the tree grows under a wide variety of soil types, soil moisture, acidity, and elevations—from wooded swamps to dry ridges. Thus, the red maple exhibits high tolerance to temperature and other environmental conditions. In turn, this high tolerance allows a widespread geographic range. A species with a geographically widespread distribution, such as red maple, is referred to as ubiquitous. In contrast, a species with a distribution that is restricted to a particular locality or localized habitat is referred to as endemic. Many endemic species have specialized habitat requirements. For example, the shale-barren evening primrose (Oenothera argillicola; Figure  8.6) is a member of the evening primrose family (Onagraceae). This species is adapted to hot, shale-barren environments that form when certain types of shale form outcrops on south- to southwest-facing slopes of the Allegheny Mountains. Most members of this group of plants are listed as endangered or threatened because they are found in these specific habitats only from southern Pennsylvania through West Virginia to southern Virginia, where shale barrens are formed. The geographic distribution of red maple in Figure 8.5 illustrates another important factor limiting the distribution of a population: geographic barriers. Although this tree species occupies several islands south of mainland Florida, the southern and eastern limits of its geographic range correspond to the Gulf of Mexico and Atlantic coastline. Although environmental conditions may be suitable for establishment and growth in other geographic regions of the world (such as Europe and Asia), the red maple is restricted in its ability to colonize those areas. Other barriers to dispersal (movement of individuals), such as mountain ranges or extensive areas of unsuitable habitat, may likewise restrict the spread and therefore the geographic range of a species (see Chapter 5, Section 5.8 and Figure 5.19 for an example of Plethodon salamanders). Later, we will explore another factor that can restrict the distribution of a population: interactions, such as competition and predation, with other species (Part Four). Within the geographic range of a population, individuals are not distributed equally. Individuals occupy only those areas that can meet their requirements (suitable habitat). Because organisms respond to a variety of environmental factors, they can inhabit only those locations where all factors fall within their range of tolerance (see Chapter 7, Section 7.14). As a result, we can describe the distribution of a population at various spatial scales. For example, in Figure 8.7, the distribution of the moss Tetraphis pellucida is described at several different spatial scales, ranging from its geographic distribution at a global scale to the location of individuals within a single clump occupying the stump of a dead conifer tree. This species of moss can grow only in areas in which the temperature, humidity, and pH are suitable; different factors may be limiting at different spatial scales. At the continental scale, the suitability of climate (temperature and humidity) is the dominant factor. Within a particular area, distribution of the moss is limited to microclimates along stream banks, where conifer trees are abundant. Within a particular locality, it occupies the stumps of conifer trees where the pH is sufficiently acidic. As a result of environmental heterogeneity, most populations are divided into subpopulations, each occupying suitable habitat patches of various shapes and sizes within the larger landscape of unsuitable habitat. In the example of Tetraphis presented in Figure 8.7, the distribution of individuals within a region is limited to stream banks, where temperature and humidity are within its range of tolerance, and stands of conifers are present to provide a substrate for growth. As a result, the population is divided into a group of spatially discrete local subpopulations, (Figure 8.8). Ecologists refer to the collective of local subpopulations as a metapopulation, a term coined in 1970 by the population ecologist Richard Levins of Harvard University. Although spatially separated, these local populations are connected through the movement of individuals among them (Section 8.7). A more detailed discussion of metapopulations is presented in Chapter 19 (Landscape Dynamics). Ecologists typically study these local, or subpopulations, rather than the entire population of a species over its geographic range. For this reason, it is important when referring to a population to define explicitly its boundaries (spatial extent). For example, an ecological study might refer to the population of red maple trees in the Three Ridges Wilderness Area of the George Washington–Jefferson National Forest in Virginia or to the population of Tetraphis pellucida along the Oswagatchie River in in the Adirondack Mountains of New York. 8.3 Abundance Reflects Population Density and Distribution Whereas distribution defines the spatial extent of a population, abundance defines its size—the number of individuals in the population. In Figure 8.4, the population abundance is the total number of red dots (individuals) within the blue line that defines the population distribution. Abundance is a function of two factors: (1) the population density and (2) the area over which the population is distributed. Population density is the number of individuals per unit area (per square kilometer [km], hectare [ha], or square meter [m]), or per unit volume (per liter or m3). By placing a grid over the population distribution shown in Figure 8.4, as is done in Figure  8.9, we can calculate the density for any given grid cell by counting the number of red dots that fall within its boundary. Density measured simply as the number of individuals per unit area is referred to as crude density. The trouble with this measure is that individuals are typically not equally numerous over the geographic range of the population (see Section 8.2). Individuals do not occupy all the available space within the population’s distribution because not all areas are suitable. As a result, density can vary widely from location to location (as in Figure 8.9). How individuals are distributed within a population—in other words, their spatial position relative to each other—has an important bearing on density. Individuals of a population may be distributed randomly, uniformly, or in clumps (aggregated; Figure 8.10). Individuals may be distributed randomly if each individual’s position is independent of those of the others. In contrast, individuals distributed uniformly are more or less evenly spaced. A uniform distribution usually results from some form of negative interaction among individuals, such as competition, which functions to maintain some minimum distance among members of the population (see Chapter  11). Uniform distributions are common in animal populations where individuals defend an area for their own exclusive use (territoriality) or in plant populations where severe competition exists for belowground resources such as water or nutrients (Figure 8.11; see also Figures 11.17 and 11.19). The most common spatial distribution is clumped, in which individuals occur in groups. Clumping results from a variety of factors. For example, suitable habitat or other resources may be distributed as patches on the larger landscape. Some species form social groups, such as fish that move in schools or birds in flocks (see Figure 8.10). Plants that reproduce asexually form clumps, as ramets extend outward from the parent plant (see Figure 8.1). The distribution of humans is clumped because of social behavior, economics, and geography, reinforced by the growing development of urban areas during the past century. In the example presented in Figure 8.9, the individuals within the population are clumped; as a result, the density varies widely between grid cells. As with geographic distribution (see Section 8.2), the spatial distribution of individuals within the population can also be described at multiple spatial scales. For example, the distribution of the shrub Euclea divinorum, found in the savanna ecosystems of Southern Africa, is clumped (Figure  8.11a). The clumps of Euclea are associated with the canopy cover of another plant that occupies the savanna: trees of the genus Acacia (Figure  8.11b). The clumps, however, are uniformly spaced, reflecting the uniform distribution of Acacia trees on the landscape. The regular distribution of Acacia trees is a function of competition among neighboring individuals for water (see Section 11.10). In the example of Tetraphis presented in Figure  8.7, the spatial distribution of individuals is clumped at two different spatial scales. Populations are concentrated in long bands or strips along the stream banks, leaving the rest of the area unoccupied. Within these patches, individuals are further clumped in patches corresponding to the distribution of conifer stumps. To account for patchiness, ecologists often refer to ecological density, which is the number of individuals per unit of available living space. For example, in a study of bobwhite quail (Colinus virginianus) in Wisconsin, biologists expressed density as the number of birds per mile of hedgerow (the birds’ preferred habitat), rather than as birds per hectare (Figure  8.12). Ecological densities are rarely estimated because determining what portion of a habitat represents living space is typically a difficult undertaking. 8.4 Determining Density Requires Sampling Population size (abundance) is a function of population density and the area that is occupied (geographic distribution). In other words, population size = density × area. But how is density determined? When both the distribution (spatial extent) and abundance are small—as in the case of many rare or endangered species—a complete count may be possible. Likewise, in some habitats that are unusually open, such as antelope living on an open plain or waterfowl concentrated in a marsh, density may be determined by a direct count of all individuals. In most cases, however, density must be estimated by sampling the population. A method of sampling used widely in the study of populations of plants and sessile (attached) animals involves quadrats, or sampling units (Figure 8.13). Researchers divide the area of study into subunits, in which they count animals or plants of concern in a prescribed manner, usually counting individuals in only a subset or sample of the subunits (as in Figure 8.9). From these data, they determine the mean density of the units sampled. Multiplying the mean value by the total area provides an estimate of population size (abundance). The accuracy of estimates of density derived from population sampling can be influenced by the manner in which individuals are spatially distributed within the population (Section 8.3). The estimate of density can also be influenced by the choice of boundaries or sample units. If a population is clumped—concentrated into small areas—and the population density is described in terms of individuals per square kilometer, the average number of individuals per unit area alone does not adequately represent the spatial variation in density that occurs within the population (Figure  8.14). In this case, it is important to report an estimate of variation or provide a confidence interval for the estimate of density. In cases where clumping is a result of habitat heterogeneity (habitat is clumped), ecologists may choose to use the index of ecological density for the specific areas (habitats) in which the species is found (for example, stream banks in Figure 8.7). For mobile populations, animal ecologists must use other sampling methods. Capturing, marking, and recapturing individuals within a population—known generally as mark-recapture—is the most widely used technique to estimate animal populations (Figure 8.15). There are many variations of this technique, and entire books are devoted to various methods of application and statistical analysis. Nevertheless, the basic concept is simple. Capture-recapture or mark-recapture methods are based on trapping, marking, and releasing a known number of marked animals (M) into the population (N). After giving the marked individuals an appropriate period of time to once again mix with the rest of the population, some individuals are again captured from the population (n). Some of the individuals caught in this second period will be carrying marks (recaptured, R), and others will not. If we assume that the ratio of marked to sampled individuals in the second sample (n/R) represents the ratio for the entire population (N/M), we can compute an estimate of the population using the following relationship: NM=nrNM=nr The only variable that we do not know in this relationship is N. We can solve for N by rearranging the equation as follows: N=nMRN=nMR For example, suppose that in sampling a population of rabbits, a biologist captures and tags 39 rabbits from the population. After their release, the ratio of the number of rabbits in the entire population (N) to the number of tagged or marked rabbits (M) is N/M. During the second sample period, the biologist captures 15 tagged rabbits (R) and 19 unmarked ones—a total of 34 (n). The estimate of population size, N, is calculated as N=nMR=(34×39)15=88N=nMR=(34×39)15=88 This simplest method, the single mark–single recapture, is known as the Lincoln–Petersen index of relative population size. As with any method of population estimation, the accuracy of the Lincoln–Peterson index depends on a number of assumptions. First, the method assumes that the sampling is random, that is, each individual in the population has an equal probability of being captured. Secondly, the marked individuals must distribute themselves randomly throughout the population so that the second sample will accurately represent the population. Last, the ratio of marked and unmarked individuals must not change between the sampling periods. This is especially important if the method of marking individuals influences their survival, as in the case of highly visible marks or tags that increase their visibility to predators. For work with most animals, ecologists find that a measure of relative density or abundance is sufficient. Methods involve observations relating to the presence of organisms rather than to direct counts of individuals. Techniques include counts of vocalizations, such as recording the number of drumming ruffed grouse heard along a trail, counts of animal scat seen along a length of road traveled, or counts of animal tracks, such as may be left by a number of opossums crossing a certain dusty road. If these observations have some relatively constant relationship to total population size, the data can be converted to the number of individuals seen per kilometer or heard per hour. Such counts, called indices of abundance, cannot function alone as estimates of actual density. However, a series of such index figures collected from the same area over a period of years depicts trends in abundance. Counts obtained from different areas during the same year provide a comparison of abundance between different habitats. Most population data on birds and mammals are based on indices of relative abundance rather than on direct counts. 8.5 Measures of Population Structure Include Age, Developmental Stage, and Size Abundance describes the number of individuals in the population but provides no information on their characteristics—that is, how individuals within the population may differ from one another. Unless each generation reproduces and dies in a single season, not overlapping the next generation (such as annual plants and many insects), the population will have an age structure: the number or proportion of individuals in different age classes. Because reproduction is restricted to certain age classes and mortality is most prominent in others, the relative proportions of each age group bear on how quickly or slowly populations grow (see Chapter 9). Populations can be divided into three ecologically important age classes or stages: prereproductive, reproductive, and postreproductive. We might divide humans into young people, working adults, and senior citizens. How long individuals remain in each stage depends largely on the organism’s life history (see Chapter 10). Among annual species, the length of the prereproductive stage has little influence on the rate of population growth (see Chapter 9). In organisms with variable generation times, the length of the prereproductive period has a pronounced effect on the population’s rate of growth. The populations of short-lived organisms often increase rapidly, with a short span between generations. Populations of long-lived organisms, such as elephants and whales, increase slowly and have a long span between generations. Determining a population’s age structure requires some means of obtaining the ages of its members. For humans, this task is not a problem, but it is for wild populations. Age data for wild animals can be obtained in several ways, and the method varies with the species (Figure 8.16). The most accurate, but most difficult, method is to mark young individuals in a population and follow their survival through time (see discussion of life table, Chapter 9). This method requires a large number of marked individuals and a lot of time. For this reason, biologists may use other, less-accurate methods. These methods include examining a representative sample of individual carcasses to determine their ages at death. A biologist might look for the wear and replacement of teeth in deer and other ungulates, growth rings in the cementum of the teeth of carnivores and ungulates, or annual growth rings in the horns of mountain sheep. Among birds, observations of plumage changes and wear in both living and dead individuals can separate juveniles from subadults (in some species) and adults. Aging of fish is most commonly accomplished by counting rings deposited annually (annuli) on hard parts including scales, otoliths (ear bones), and spines. Studying the age structure of plant populations can prove even more difficult. The major difficulty lies in determining the age of plants and whether the plants are genetic individuals (genets) or ramets (Section 8.1). The approximate ages of trees in which growth is seasonal can be determined by counting annual growth rings (Figure  8.17), a procedure called dendrochronology. But given the time and expense necessary to collect and analyze samples of tree rings, forest ecologists have tried to employ size (diameter of the trunk at breast height, or dbh) as an indicator of age on the assumption that diameter increases with age—the greater the diameter, the older the tree. Such assumptions, it was discovered, were valid for dominant canopy trees; but with their growth suppressed by lack of light, moisture, or nutrients, smaller understory trees, seedlings, and saplings add little to their diameters. Although their diameters suggest youth, small trees are often the same age as large individuals in the canopy. Attempts to age nonwoody plants have met with less success. The most accurate method of determining the age structure of short-lived herbaceous plants is to mark individual seedlings and follow them through their lifetimes. The results of recent studies, however, suggest that annual growth rings form in the root tissues (secondary root xylem) of many perennial herbaceous plants and can be used successfully in the analysis of age structure in this group of plants. However, the use of size or developmental stage classes is often more appropriate than age in describing the structure of plant (and some animal) populations. As we shall see in Chapter 9, size or developmental stage often provides a better indicator of patterns of mortality and reproduction necessary for predicting patterns of population dynamics. Once the age structure of a population has been determined, it can be represented graphically in the form of an age pyramid. Age pyramids (Figure 8.18) are snapshots of the age structure of a population at some period in time, providing a picture of the relative sizes of different age groups in the population. As we shall see, the age structure of a population is a product of the age-specific patterns of mortality and reproduction (Chapter 9). In many plant populations, the distribution of age classes is often highly skewed (Figure 8.19). In forests, for example, dominant overstory trees can inhibit the establishment of seedlings and growth and survival of juvenile trees. One or two age classes dominate the site until they die or are removed, allowing trees in young age classes access to resources such as light, water, and nutrients so they can grow and develop. 8.6 Sex Ratios in Populations May Shift with Age Populations of sexually reproducing organisms in theory tend toward a 1:1 sex ratio (the proportion of males to females). The primary sex ratio (the ratio at conception) also tends to be 1:1. This statement may not be universally true, and it is, of course, difficult to confirm. In most mammalian populations, including humans, the secondary sex ratio (the ratio at birth) is often weighted toward males, but the population shifts toward females in the older age groups. Generally, males have a shorter life span than females do. The shorter life expectancy of males can be a result of both physiological and behavioral factors. For example, in many animal species, rivalries among males occur for dominant positions in social hierarchies or for the acquisition of mates (see Section 10.11). Among birds, males tend to outnumber females because of increased mortality of nesting females, which are more susceptible to predation and attack (Figure 8.21). 8.7 Individuals Move within the Population At some stage in their lives, most organisms are mobile to some degree. The movement of individuals directly influences their local density. The movement of individuals in space is referred to as dispersal, although the term dispersal most often refers to the more specific movement of individuals away from one another. When individuals move out of a subpopulation, it is referred to as emigration. When an individual moves from another location into a subpopulation, it is called immigration. The movement of individuals among subpopulations within the larger geographic distribution is a key process in the dynamics of metapopulations and in maintaining the flow of genes between these subpopulations (see Chapters 5 and 19). Many organisms, especially plants, depend on passive means of dispersal involving gravity, wind, water, and animals. The distance these organisms travel depends on the agents of dispersal. Seeds of most plants fall near the parent, and their density falls off quickly with distance (Figure 8.22). Heavier seeds, such as the acorns of oaks (Quercus spp.), have a much shorter dispersal range than do the lighter wind-carried seeds of maples (Acer spp.), birch (Betula spp.), milkweed (Asclepiadaceae), and dandelions (Taxaxacum officinale). Some plants, such as cherries and viburnums (Viburnum spp.), depend on active carriers such as particular birds and mammals to disperse their seeds by eating the fruits and carrying the seeds to some distant point. These seeds pass through the animals’ digestive tract and are deposited in their feces. Other plants possess seeds armed with spines and hooks that catch on the fur of mammals, the feathers of birds, and the clothing of humans. In the example of the clumped distribution of E. divinorum shrubs (see Figure 8.11), birds disperse seeds of this species. The birds feed on the fruits and deposit the seeds in their feces as they perch atop the Acacia trees. In this way, the seeds are dispersed across the landscape, and the clumped distribution of the E. divinorum is associated with the use of Acacia trees as bird perches. For mobile animals, dispersal is active, but many others depend on a passive means of transport, such as wind and moving water. Wind carries the young of some species of spiders, larval gypsy moths, and cysts of brine shrimp (Artemia salina). In streams, the larval forms of some invertebrates disperse downstream in the current to suitable habitats. In the oceans, the dispersal of many organisms is tied to the movement of currents and tides. Dispersal among mobile animals may involve young and adults, males and females; there is no hard-and-fast rule about who disperses. The major dispersers among birds are usually the young. Among rodents, such as deer mice (Peromyscus maniculatus) and meadow voles (Microtus pennsylvanicus), subadult males and females make up most of the dispersing individuals. Crowding, temperature change, quality and abundance of food, and photoperiod all have been implicated in stimulating dispersal in various animal species (Chapter 11, Section 11.8). Often, the dispersing individuals are seeking vacant habitat to occupy. As a result, the distance they travel depends partly on the density of surrounding subpopulations and the availability of suitable unoccupied areas. The dispersal of individuals is a key feature in the dynamics of metapopulations (see Figure  8.8), where colonization involves the movement of individuals from occupied habitat patches (existing local populations) to unoccupied habitat patches to form new local populations. The role of dispersal and colonization in metapopulation dynamics is discussed further in Chapter 19 (Section 19.7). Unlike the one-way movement of animals in the processes of emigration and immigration, migration is a round trip. The repeated return trips may be daily or seasonal. Zooplankton in the oceans, for example, move down to lower depths by day and move up to the surface by night. Their movement appears to be related to a number of factors including predator avoidance. Bats leave their daytime roosting places in caves and trees, travel to their feeding grounds, and return by daybreak. Other migrations are seasonal, either short range or long range. Earthworms annually make a vertical migration deeper into the soil to spend the winter below the freezing depths and move back to the upper soil when it warms in spring. Elk (Cervus canadensis) move down from their high mountain summer ranges to lowland winter ranges. On a larger scale, caribou (Rangifer tarandus) move from the summer calving range in the arctic tundra to the boreal forests for the winter, where lichens are their major food source. Gray whales (Eschrichtius robustus) move down from the food-rich arctic waters in summer to their warm wintering waters of the California coast, where they give birth to young (Figure  8.23). Similarly, humpback whales (Megaptera novaeangliae) migrate from northern oceans to the central Pacific off the Hawaiian Islands. Perhaps the most familiar of all are long-range and short-range migrations of waterfowl, shorebirds, and neotropical migrants in spring to their nesting grounds and in fall to their wintering grounds (see Chapter 11, Field Studies: T. Scott Sillett). Another type of migration involves only one return trip. Such migrations occur among Pacific salmon (Oncorhynchus spp.) that spawn in freshwater streams. The young hatch and grow in the headwaters of freshwater coastal streams and rivers and travel downstream and out to sea, where they reach sexual maturity. At this stage, they return to the home stream to spawn (reproduce) and then die. 8.8 Population Distribution and Density Change in Both Time and Space Dispersal has the effect of shifting the spatial distribution of individuals and consequently the localized patterns of population density. Emigration may cause density in some areas to decline, whereas immigration into other areas increases the density of subpopulations or even establishes new subpopulations in habitats that were previously unoccupied. In some instances, dispersal can result in the shift or expansion of a species’ geographic range. The role of dispersal in range expansion is particularly evident in populations that have been introduced to a region where they did not previously exist. A wide variety of species have been introduced, either intentionally or unintentionally, into regions outside their geographic distribution. As the initial population becomes established, individuals disperse into areas of suitable habitat, expanding their geographic distribution as the population grows. A map showing the spread of the gypsy moth (Lymantria dispar) in the eastern United States after its introduction in 1869 is shown in Figure 8.24. The story of the introduction of this species is presented in the following section (see this chapter, Ecological Issues & Applications). In other cases, the range expansion of a population has been associated with temporal changes in environmental conditions, shifting the spatial distribution of suitable habitats. Such is the case of the shift in the distribution of tree populations in eastern North America as climate has changed during the past 20,000 years (see Section 18.9, Figure 18.25). Examples of predicted changes in the distribution of plant and animal populations resulting from future human-induced changes in Earth’s climate are discussed later in Chapter 27 . Although the movement of individuals within the population results in a changing pattern of distribution and density through time, the primary factors driving the dynamics of population abundance are the demographic processes of birth and death. The processes of birth and death, and the resulting changes in population structure, are the focus of our attention in the following chapter. Ecological Issues & Applications Humans Aid in the Dispersal of Many Species, Expanding Their Geographic Range Dispersal is a key feature of the life histories of all species, and a diversity of mechanisms have evolved to allow plant and animal species to move across the landscape and seascape. In plants, seeds and spores can be dispersed by wind, water, or through active dispersal by animals (see Section 15.14). In animals, the dispersal of fertilized eggs, particularly in aquatic environments, can result in the dispersal of offspring across significant distances. But dispersal typically involves the movement—either active or passive—of individuals, both juvenile and adult. In recent centuries, however, a new source of long-distance dispersal has led to the redistribution of species at a global scale: dispersal by humans. Humans are increasingly moving about the world. As they do so, they may either accidentally or intentionally introduce plants and animals to places where they have never occurred (outside their geographic range). Although many species fail to survive in their new environments, others flourish. Freed from the constraints of their native competitors, predators and parasites, they successfully establish themselves and spread. These nonnative (nonindigenous) plants and animals are referred to as invasive species . Sometimes these introductions are harmless, but often the introduced organisms negatively affect native species and ecosystems. In the past few centuries, many plants and animals, especially insects, have been introduced accidentally by accompanying imported agricultural and forest products. The seeds of weed species are unintentionally included in shipments of imported crop seeds or on the bodies of domestic animals. Or seed-carrying soil from other countries is often loaded onto ships as ballast and then dumped in another country in exchange for cargo. Major forest insect pests such as the Asian longhorned beetle (Anoplophors galbripennis) are hitchhikers on wooden shipping containers and pallets. Humans have also introduced nonnative plants intentionally for ornamental and agricultural purposes. Most of these introduced plants do not become established and reproduce, but some do. On the North American continent, the ornamental perennial herb purple loosestrife (Lythrum salicaria; Figure  8.25a), originally introduced from Europe in the mid-1800s, has eliminated native wetland plants to the detriment of wetland wildlife. The Australian paperbark tree (Melaleuca quinquenervia; Figure  8.25b), introduced as an ornamental plant in Florida, is displacing cypress, sawgrass, and other native species in the Florida Everglades, drawing down water and fostering more frequent or intense fires. The most notorious plant invader in the United States is cheatgrass (Bromus tectorum; Figure  8.25c), a winter annual accidentally introduced from Europe into Colorado in the 1800s. It arrived in the form of packing material and possibly crop seeds. It spread explosively across overgrazed rangeland and winter wheat fields in the Pacific Northwest and the Intermountain Region. By 1930 it became the dominant grass, replacing native vegetation. Cheatgrass is highly flammable, and densely growing populations provide ample fuel that increases fire intensity and often decreases the time intervals between fires (fire frequency). One of the most widely spread invasive plants in North America is kudzu (Pueraria montana), a species of vine native to Asia. This plant was originally introduced to the United States as an ornamental vine at the Philadelphia Centennial Exposition of 1876. By the early part of the 20th century, kudzu was being enthusiastically promoted as a fodder crop, and rooted cuttings were sold to farmers through the mail. In the 1930s and 1940s, kudzu was propagated and promoted by the Soil Conservation Service as a means of holding soil on the swiftly eroding gullies of the deforested southern landscape, especially in the Piedmont regions of Alabama, Georgia, and Mississippi. By the 1950s, however, kudzu was recognized as a pest and removed from the list of species acceptable for use under the Agricultural Conservation Program, and in 1998 it was listed by the United States Congress as a Federal Noxious Weed. Although it spreads slowly, kudzu completely covers all other vegetation, blanketing trees with a dense canopy through which little light can penetrate (Figure  8.26). Estimates of kudzu infestation in the southeastern United States vary greatly, from as low as 2 million to as high as 7 million acres. The most damaging introduced insect pest of the eastern United States hardwood forests is the gypsy moth (Lymantria dispar). The gypsy moth is found mainly in the temperate regions of the world, including central and southern Europe, northern Africa, central and southern Asia, and Japan. Leopold Trouvelot, a French astronomer with an interest in insects, originally introduced the species into Medford, Massachusetts in 1869. As part of an effort to begin a commercial silk industry, Trouvelot wanted to develop a strain of silk moth that was resistant to disease. However, several gypsy moth caterpillars escaped from Trouvelot’s home and established themselves in the surrounding areas. Some 20 years later, the first outbreak of gypsy moths occurred, and despite all control efforts since that time, the gypsy moth has persisted and extended its range (see Figure 8.24). In the United States, the gypsy moth has rapidly moved north to Canada, west to Wisconsin, and south to North Carolina. Gypsy moth caterpillars defoliate millions of acres of trees annually in the United States (Figure 8.27). In the forests of eastern North America, annual losses to European gypsy moths are estimated at $868 million, and the Asian strain that has invaded the Pacific Northwest has already necessitated a $20 million eradication campaign. The problem that invasive species present is not restricted to terrestrial environments. More than 139 nonindigenous aquatic species that affect native plant and animal species have invaded the Great Lakes by way of global shipping. Most notorious is the zebra mussel (Dreissena polymorpha; Figure  8.28) native to the lakes of southern Russia. The species was introduced from the ballast of ships traversing the St. Lawrence Seaway. Since it first appeared in 1988, the zebra mussel has spread to most eastern river systems (Figure  8.29). In addition to their impact on wildlife, zebra mussels colonize water intake pipes, severely restricting the water flow to power plants or other municipal or private facilities. The San Francisco Bay Area is occupied by 96 nonnative invertebrates, from sponges to crustaceans. Exotic fish, introduced purposefully or accidentally, have been responsible for 68 percent of fish extinctions in North America during the past 100 years and for the population decline of 70 percent of the fish species listed as endangered. Summary Unitary and Modular Organisms 8.1 A population is a group of individuals of the same species living in a defined area. Populations are characterized by distribution, abundance, density, and age structure. Most animal populations are made up of unitary individuals with a definitive growth form and longevity. In most plant populations, however, organisms are modular. These plant populations may consist of sexually produced parent plants and asexually produced stems arising from roots. A similar population structure occurs in animal species that exhibit modular growth. Distribution 8.2 The distribution of a population describes its spatial location, or the area over which it occurs. The distribution of a population is influenced by the occurrence of suitable environmental conditions. Within the geographic range of a population, individuals are not distributed equally throughout the area. Therefore, the distribution of individuals within the population can be described as a range of different spatial scales. Individuals within a population are distributed in space. If the spacing of each individual is independent of the others, then the individuals are distributed randomly; if they are evenly distributed, with a similar distance among individuals, it is a uniform distribution. In most cases, individuals are grouped together in a clumped or aggregated distribution. Abundance 8.3 Abundance is defined as the number of individuals in a population. Abundance is a function of two factors: (1) the population density and (2) the area over which the population is distributed. Population density is the number of individuals per unit area or volume. Because landscapes are not homogeneous, not all of the area is suitable habitat. The number of organisms in available living space is the true or ecological density. Sampling Populations 8.4 Determination of density and dispersion requires careful sampling and appropriate statistical analysis of the data. For sessile organisms, researchers often use sample plots. For mobile organisms, researchers use capture-recapture techniques or determine relative abundance using indicators of animal presence, such as tracks or feces. Age, Stage, and Size Structure 8.5 The number or proportion of individuals within each age class defines the age structure of a population. Individuals making up the population are often divided into three ecological periods: prereproductive, reproductive, and postreproductive. Populations can also be characterized by the number of individuals in defined classes of size or stage of development. Sex Ratios 8.6 Sexually reproducing populations have a sex ratio that tends to be 1:1 at conception and birth but often shifts as a function of sex-related differences in mortality. Dispersal 8.7 At some stage of their life cycles, most individuals are mobile. For some organisms, such as plants, dispersal is passive and dependent on various dispersal mechanisms. For mobile organisms, dispersal can occur for a variety of reasons, including the search for mates and unoccupied habitat. For some species, dispersal is a systematic process of movement between areas in a process called migration. Population Dynamics 8.8 Dispersal has the effect of shifting the spatial distribution of individuals and as a result the localized patterns of population density. Although the movement of individuals within the population results in a changing pattern of distribution and density through time, the primary factors driving the dynamics of population abundance are the demographic processes of birth and death. Invasive Species Ecological Issues & Applications Humans have either accidentally or intentionally introduced plant and animal species to places outside their geographic range. Sometimes these introductions are harmless, but often the introduced organisms negatively affect the populations of native species and ecosystems.
Population Sampling
CHAPTER 9 Smith, T. M., & Smith, R. L. (2015). Elements of Ecology (9th ed.). Boston, MA: Pearson. 9.1 Population Growth Reflects the Difference between Rates of Birth and Death Suppose we were to undertake an experiment in which we monitor a population of an organism that has a very simple life cycle, such as a population of freshwater hydra (Figure 9.2) growing in an aquarium in the laboratory. Most reproduction is asexual, involving a process termed budding, in which a new hydra develops as a bud from the parent (see Figure 9.2). If we assume only asexual reproduction, then all individuals are capable of reproduction and produce a single offspring at a time. We define the population size at a given time (t) as N(t), where N represents the number of individuals. Let us assume that the initial population is small, N(0) = 100 (where 0 refers to time zero at the start of the experiment), so that the food supply within the aquarium is much more than is needed to support the current population. How will the population change over time? Because no emigration or immigration is allowed by the lab setting, the population is closed. The number of hydra will increase as a result of new “births.” Additionally, the population will decrease as a result of some hydra dying. Because the processes of birth and death in this population are continuous—no defined period of synchronized birth or death—we can observe the number of births that occur over some appropriate time interval. Given the rates of budding (reproduction) for freshwater hydra, an appropriate time unit (t) would be one day. We can define the number of new hydra produced by budding over the period of one day as B and the number of hydra dying over the same day as D. In our hypothetical experiment, let us assume that the initial population produced 40 new individuals (births) over the first day (B = 40) and that 10 of the original 100 hydra died (D = 10). The population size at the end of day 1, N(1), can then be calculated from the initial population size, N(0), and the observed numbers of births (B) and deaths (D): N(0) + B − D = N(1) or 100 + 40 − 10 = 130N(0) + B − D = N(1)  or  100 + 40 − 10 = 130 But what if we now want to predict what the population will be the following day, N(2)? How could we use the measures of B and D to determine the number of births and deaths that will occur in our population that is now composed of 130 hydra? Although B and D represent the measure of birth and death in the population, the actual values are dependent on the initial population size, N(0) = 100. For example, if the initial population size was 200 rather than 100, we could assume that the values of B and D would be twice as large. If we wish to calculate an estimate of birthrate that is independent of the initial population size we need to divide the number of hydra born during the day by the initial population size: B/N(0) or 40/100. We can now define the resulting value 0.4 as b, which is the per capita birthrate (per capita meaning per individual). The per capita birthrate is the average number of births per individual during the time period t (one day). Likewise, we can calculate the per capita death rate as D/N(0) of 10/100 = 0.1. The advantage of expressing the observed values of birth (B) and death (D) for the population as per capita rates (b and d) is that if we assume they are constant (do not change over time), we can use b and d to predict the growth of the population over time regardless of the population size N(t). If we start with N(t) hydra at time t, then to calculate the total number of hydra reproducing over the following day (t + 1), we must simply multiply the per capita birthrate (b) by the total number of hydra at time t [N(t)], which is bN(t). The number of hydra dying over the time interval is calculated in a similar manner: dN(t). The population size at the next time period (t + 1) would then be N(t + 1) = N(t) + bN(t) − dN(t)N(t + 1) = N(t) + bN(t) − dN(t) Applied to the hydra population: N(0) = 100N(1) = 100 + 0.4(100) − 0.1(100) = 130N(2) = 130 + 0.4(130) − 0.1(130) = 169N(0) = 100N(1) = 100 + 0.4(100) − 0.1(100) = 130N(2) = 130 + 0.4(130) − 0.1(130) = 169 The resulting pattern of population size as a function of time is shown in Figure 9.3 and is referred to as geometric population growth. We can calculate the rate of change in the population (the population growth rate) by subtracting N(t) from both sides of the preceding equation: N(t + 1) − N(t) = bN(t) − dN(t)N(t + 1) − N(t) = bN(t) − dN(t) or N(t + 1) − N(t) = (b − d)N(t)N(t + 1) − N(t) = (b − d)N(t) Applied to the hydra population over the first two days: N(1) − N(0) = 0.4(100) − 0.1(100) = 30N(2) − N(1) = 0.4(130) − 0.1(130) = 39N(1) − N(0) = 0.4(100) − 0.1(100) = 30N(2) − N(1) = 0.4(130) − 0.1(130) = 39 The term on the left side of the equation [N(t + 1) − N(t)] is the change in the population size N over the time interval [(t + 1) − t]. If we represent the change in population size as ΔN and the change in time (the time interval) as Δt—the mathematical symbol Δ refers to a “change” in the associated variable—we can rewrite the equation for the rate of population change in a simplified form: ΔNΔt= (b − d)N(t)ΔNΔt= (b − d)N(t) Because per capita birthrates and death rates, b and d, are constants (fixed values), we can simplify the equation even further by defining a new parameter r = (b – d). The value r is the per capita growth rate. ΔNΔt= rN(t)ΔNΔt= rN(t) Thus, the population growth rate (ΔN/Δt) defines the unit change in population size per unit change in time, or the slope of the relationship between N(t) and t (the “rise” over the “run”) presented in Figure 9.3. Note that because the pattern of population growth is an upward sloping curve, the rate of population change depends on the time interval being viewed (see Figure 9.3 and preceding calculations). With a population that is growing geometrically, the rate of population growth is continuously increasing as the population size increases. It is important to remember that the model of geometric growth that we have developed for the hydra population can only predict changes in population size on discrete time intervals of one day. This is because the estimates of b and d (and therefore, r) were estimated using observations of birth and death over a one-day period. For populations, such as the hydra, where birth and death are occurring continuously (not daily intervals), population ecologists often represent the processes of birth and death as instantaneous rates and population growth as a continuous process rather than on defined time steps (such as one day). The model is then presented as a differential equation: dNdt= rNdNdt= rN The term ΔN/Δt is replaced by dN/dt to express that Δt (the time interval) approaches a value of zero, and the rate of change becomes instantaneous. The value r is now the instantaneous per capita rate of growth (sometimes called the intrinsic rate of population growth), and the resulting equation is referred to as the model of exponential population growth (in contrast to geometric population growth based on discrete time steps). The model of exponential growth (dN/dt = rN) predicts the rate of population change over time. If we wish to define the equation to predict population size, N(t), under conditions of exponential growth [N(t) at any given value of t], it is necessary to integrate the differential equation presented previously. The result is: N(t) = N(0)ertN(t) = N(0)ert where N(0) is the initial population size at t = 0, and e is the base of the natural logarithms; its value is approximately 2.72. Examples of exponential growth for differing values of r are shown in Figure 9.4. Note that when r = 0 (when b = d), there is no change in population size. For values of r > 0 (when b > d) the population increases exponentially, whereas values of r < 0 (when b < d) result in an exponential decline in the population. As with the pattern of geometric population growth, exponential growth results in a continuously accelerating (or decelerating) rate of population growth as a function of population size. Exponential (or geometric) growth is characteristic of populations inhabiting favorable environments at low population densities, such as during the process of colonization and establishment in new environments. An example of a population undergoing exponential growth is the rise of the reindeer herd introduced on St. Paul, one of the Pribilof Islands, Alaska (Figure 9.5). In the fall of 1911, the United States government introduced 25 reindeer on the island of St. Paul to provide the native residents with a sustained source of fresh meat. Over the next 30 years, the original herd of 4 males and 21 females grew to a herd of more than 2000 individuals. The whooping crane (Grus americana) provides another example of a population exhibiting exponential growth (Figure  9.6). At the time of European settlement of North America, the population of the whooping crane was estimated at more than 10,000. That number dropped to between 1300 and 1400 individuals by 1870, and by 1938, only 15 birds existed. The species was declared endangered in 1967, and thanks to conservation efforts, the 2011 population was estimated at more than 300 individuals. The whooping crane breeds in the Northwestern Territories of Canada and migrates to overwinter on the Texas coast at the Aransas National Wildlife Refuge. Counts of the entire population from the period of 1938 to 2013 have provided the data presented in Figure 9.6. (For an example of exponential population growth for humans, see Chapter 10, Ecological Issues & Applications). 9.2 Life Tables Provide a Schedule of Age-Specific Mortality and Survival As we established in the previous section, change in population abundance over time is a function of the rates of birth and death, as represented by the per capita growth rate r. But how do ecologists estimate the per capita growth rate of a population? For the hydra population, where all individuals can be treated as identical, the rates of birth and death for the population were estimated by counting the number of individuals in the population either giving birth or dying per unit of time. This simple approach was possible because each individual within the population could be treated as identical with respect to its probability of giving birth or dying. But when birthrates and death rates vary with age, a different approach must be used. To obtain a clear and systematic picture of mortality and survival within a population, ecologists use an approach involving the construction of life tables. The life table is simply an age-specific account of mortality. Life insurance companies use this technique, first developed by students of human populations, as the basis for evaluating age-specific mortality rates. Now, however, population ecologists are using life tables to examine systematic patterns of mortality and survivorship within animal and plant populations. The construction of a life table begins with a cohort, which is a group of individuals born in the same period of time. For example, data presented in the following table represent a cohort of 530 gray squirrels (Sciurus carolinensis) from a population in northern West Virginia that was the focus of a decade-long study. The fate of these 530 individuals was tracked until all had died some six years later. The first column of numbers, labeled x, represents the age classes; in this example, the age classes are in units of years. The second column, nx, represents the number of individuals from the original cohorts that are alive at the specified age (x). Of the original 530 individuals born (age 0), only 159 survived to an age of 1 year, whereas of those 159 individuals, only 80 survived to age 2. Only 5 individuals survived to age 5, and none of those individuals survived to age 6 (that is why there is no age class 6). When constructing life tables, it is common practice to express the number of individuals surviving to any given age as a proportion of the original cohort size (nx/n0). This value, lx, referred to as survivorship, represents the probability at birth of surviving to any given age (x). The difference between the number of individuals alive for any age class (nx) and the next older age class (nx+1) is the number of individuals that have died during that time interval. We define this value as dx, which gives us a measure of age-specific mortality. The number of individuals that died during any given time interval (dx) divided by the number alive at the beginning of that interval (nx) provides an age-specific mortality rate, qx. A complete life table for the cohort of gray squirrels, including all of the preceding calculations, is presented in Table  9.1. In addition, the calculation of age-specific life expectancy, ex, which is the average number of years into the future that an individual of a given age is expected to live, is presented in Quantifying Ecology 9.1. Quantifying Ecology 9.1 Life Expectancy Most of us are not familiar with the concept of life tables; however, almost everyone has heard or read statements like, “The average life expectancy for a male in the United States is 72 years.” What does this mean? What is life expectancy? Life expectancy (e) typically refers to the average number of years an individual is expected to live from the time of its birth. Life tables, however, are used to calculate age-specific life expectancies (ex), or the average number of years that an individual of a given age is expected to live into the future. We can use the life table for the cohort of female gray squirrels presented in Table 9.1 to examine the process of calculating age-specific life expectancies for a population. The first step in estimating ex is to calculate Lx using the nx column of the life table. Lx is the average number of individuals alive during the age interval x to x + 1. It is calculated as the average of nx and nx + 1. This estimate assumes that mortality within any age class is distributed evenly over the year. In the example of the gray squirrel, the value of T0 is 578. This means that the 530 individuals in the cohort lived a total of 578 years (some only 1 year, whereas others lived to age 5). The life expectancy for each age class (ex) is then calculated by dividing the value of Tx by the corresponding value of nx. In other words, it is calculated by dividing the total number of years lived into the future by individuals of age x by the total number of individuals in that age group. Note that life expectancy changes with age. On average, at birth gray squirrel individuals can expect to live for only 1.09 years. However, for those individuals that survive past their first birthday, life expectancy increases to 1.47. Life expectancy remains high for age class 2 and then declines for the remainder of the age classes. Why does life expectancy increase for those individuals that survive to age 1 (1.47 as compared to 1.09 for newborn individuals)? Which would have a greater influence on the life expectancy of a newborn (age 0): a 20 percent decrease in mortality rate for individuals of age class 0 (x = 0) or a 20 percent decrease in the mortality rate of age class 4 (x = 4) individuals? Why? 9.3 Different Types of Life Tables Reflect Different Approaches to Defining Cohorts and Age Structure There are two basic kinds of life tables. The first type is the cohort or dynamic life table. This is the approach used in constructing the gray squirrel life table presented in Table 9.1. The fate of a group of individuals, born at a given time, is followed from birth to death—for example, a group of individuals born in the year 1955. A modification of the dynamic life table is the dynamic composite life table. This approach constructs a cohort from individuals born over several time periods instead of just one. For example, you might follow the fate of individuals born in 1955, 1956, and 1957. The second type of life table is the time-specific life table. This approach does not involve following a single or group of cohorts, but rather it is constructed by sampling the population in some manner to obtain a distribution of age classes during a single time period. Although it is much easier to construct, this type of life table requires some crucial assumptions. First, it must be assumed that each age class was sampled in proportion to its numbers in the population. Second, it must be assumed that the age-specific mortality rates (and birthrates) have remained constant over time. Most life tables have been constructed for long-lived vertebrate species having overlapping generations (such as humans). Many animal species, especially insects, live through only one breeding season. Because their generations do not overlap, all individuals belong to the same age class. For these species, we obtain the values of nx by observing a natural population several times over its annual season, estimating the size of the population at each time. For many insects, the nx values can be obtained by estimating the number surviving from egg to adult. If records are kept of weather, abundance of predators and parasites, and the occurrence of disease, death from various causes can also be estimated. Table 9.2 represents the fate of a cohort from a single gypsy moth egg mass. The age interval, or x column, indicates the different life stages, which are of unequal duration. The nx column indicates the number of survivors at each stage. The dx column gives an accounting of deaths in each stage. In plant demography, the life table is most useful in studying three areas: (1) seedling mortality and survival, (2) population dynamics of perennial plants marked as seedlings, and (3) life cycles of annual plants. An example of the third type is Table 9.3, showing a life table for the annual elf orpine (Sedum smallii). The time of seed formation is the initial point in the life cycle. The lx column indicates the proportion of plants alive at the beginning of each stage; the dx column indicates the proportion dying, rather than the actual number of individuals (as in the other examples). 9.4 Life Tables Provide Data for Mortality and Survivorship Curves Although we can graphically display data from any of the columns in a life table, the two most common approaches are the construction of (1) a mortality curve based on the qx column and (2) a survivorship curve based on the lx column. A mortality curve plots mortality rates in terms of qx as a function of age. Mortality curves for the life tables presented in Table 9.1 (gray squirrel) and Table 9.3 (S. smallii) are shown in Figure 9.7. For the gray squirrel cohort (Figure  9.7a), the curve consists of two parts: a juvenile phase, in which the rate of mortality is high, and a post-juvenile phase, in which the rate decreases with age until mortality reaches some low point, after which it increases again. For plants, the mortality curve may assume various patterns, depending on whether the plant is annual or perennial and how we express the age structure. Mortality rates for the Sedum population (Figure 9.7b) are initially high, declining once seedlings are established. Survivorship curves plot the lx from the life table against time or age class (x). The time interval is on the horizontal axis, and survivorship is on the vertical axis. Survivorship (lx) is typically plotted on a log10 scale. Survivorship curves for the life tables presented in Table 9.1 (gray squirrel) and Table 9.3 (S. smallii) are shown in Figure 9.8. Life tables and survivorship curves are based on data obtained from one population of the species at a particular time and under certain environmental conditions. They are like snapshots. For this reason, survivorship curves are useful for comparing one time, area, or sex with another (Figure 9.9). Survivorship curves fall into three general idealized types ( Figure 9.10). When individuals tend to live out their physiological life span, survival rate is high throughout the life span, followed by heavy mortality at the end. With this type of survivorship pattern, the curve is strongly convex, or Type I. Such a curve is typical of humans and other mammals and has also been observed for some plant species. If survival rates do not vary with age, the survivorship curve will be straight, or Type II. Such a curve is characteristic of adult birds, rodents, and reptiles, as well as many perennial plants. If mortality rates are extremely high in early life—as in oysters, fish, many invertebrates, and many plant species, including most trees—the curve is concave, or Type III. These generalized survivorship curves are idealized models to which survivorship of a species can be compared. Many survivorship curves show components of these three generalized types at different times in the life cycle of a species ( Figure 9.11). 9.5 Birthrate Is Age-Specific A standard convention in demography (the study of populations) is to express birthrates as births per 1000 individuals of a population per unit of time. This figure is obtained by dividing the number of births that occurred during some period of time (typically a year) by the estimated population size at the beginning of the time period and multiplying the resulting number by 1000. This figure is the crude birthrate. This estimate of birthrate can be improved by taking two important factors into account. First, in a sexually dimorphic population (separate male and female individuals), only females within the population give birth. Second, the birthrate of females generally varies with age. Therefore, a better way of expressing birthrate is the number of births per female of age x. Because in sexually dimorphic species population increase is a function of the number of females in the population, the age-specific birthrate can be further modified by determining only the mean number of females born to a female in each age group, bx. Following is the table of age-specific birthrates for the gray squirrel population used to construct the life table (Table 9.1): At age 0, females produce no young; thus, the value of bx is 0. The average number of female offspring produced by a female of age 1 is 2. For females of ages 2 and 3, the bx value increases to 3 and then declines to 2 at age 4. By age 5 the females no longer reproduce; thus, the value of bx is 0. The sum—represented by the Greek letter sigma, Σ—of the bx values across all age classes provides an estimate of the average number of female offspring born to a female over her lifetime; this is the gross reproductive rate. In the example of the squirrel population presented previously, the gross reproductive rate is 10. However, this value assumes that a female survives to the maximum age of 5 years. What we really need is a measure of net reproductive rate that incorporates the age-specific birthrate as well as the probability of a female’s surviving to any specific age. 9.6 Birthrate and Survivorship Determine Net Reproductive Rate We can use the gray squirrel population as the basis for constructing a fecundity, or fertility, table (Table 9.4). The fecundity table uses the survivorship column, lx, from the life table together with the age-specific birthrates (bx) described previously. Although bx may initially increase with age, survivorship (lx) in each age class declines. To adjust for mortality, we multiply the bx values by the corresponding lx (the survivorship values). The resulting value, lxbx, is the mean number of females born in each age group, adjusted for survivorship. Thus, for 1-year-old females, the bx value is 2; but when adjusted for survival (lx), the value drops to 0.6. For age 2, the bx is 3; but lxbx drops to 0.45, reflecting poor survival of adult females. The values of lxbx are summed over all ages at which reproduction occurs. The result represents the net reproductive rate, R0, defined as the average number of females that are produced during a lifetime by a newborn female. If the R0 value is 1, on average females will replace themselves in the population (produce one daughter). If the R0 value is less than 1, the females do not replace themselves. If the value is greater than 1, females are more than replacing themselves. For the gray squirrel, an R0 value of 1.4 suggests a growing population of females. Note the significant difference between the gross and net reproductive rates (10 and 1.4, respectively). The difference reflects the fact that only a small proportion of the females born will survive to the maximum age and produce 10 female offspring. Because the value of R0 is a function of the age-specific patterns of birth and survivorship, it is a product of the life history characteristics: the allocation of resources to reproduction, the timing of reproduction, the trade-off between the size and number of offspring produced, and the degree of parental care. The net reproductive rate (R0), therefore, provides a means of evaluating both the individual (fitness) and the population consequences of specific life history characteristics. We will discuss this topic in detail in the following chapter (Chapter 10). 9.7 Age-Specific Mortality and Birthrates Can Be Used to Project Population Growth Age-specific mortality rates (qx) from the life table together with the age-specific birthrates (bx) from the fecundity table can be combined to project changes in the population into the future. To simplify the process, the values for age-specific mortality are converted to age-specific survival. If qx is the proportion of individuals alive at the beginning of an age class that die before reaching the next age class, then 1 – qx is the proportion that survive to the next age class ( Table 9.5) and is designated as sx. With age-specific values of sx and bx, we can project the growth of a population by constructing a population projection table. In year 1, we now have six 1-year-olds and five 2-year-olds, and we are now ready to calculate reproduction (recruitment into age class 0). The bx value of the six 1-year-olds is 2, so they produce 12 offspring. The five 2-year-olds have a bx value of 3, so they produce 15 offspring. Together, the two age classes produce 27 young for year 1, and they now make up age class 0. The total population for year 1 [N(1)] is 38. Survivorship and fecundity are determined in a similar manner for each succeeding year (Table 9.6). Survival is tabulated year by year diagonally down the table to the right through the years, while new individuals are being added each year to age class 0. As we can see, the process of calculating a population projection table from a life table in which life stages are defined by age (or age classes) is relatively straightforward. For many populations, such as perennial plants or fish, however, survival and birth are better described in terms of size rather than age, and rates are expressed as the probability of survival or number of offspring produced per individual of a given size. For these species, the process of developing a population projection table is similar to that presented previously if the size of an individual increases continuously through time. If, however, the size of an individual can either increase or decrease from one time to the next, as is the case with most perennial herbaceous plants, a more complex approach must be taken (see Quantifying Ecology 9.2). Given the population projection table presented in Table 9.6, we can calculate the age distribution for each successive year—the proportion of individuals in the various age classes for any one year—by dividing the number in each age class (x) by the total population size for that year [N(t)] (see Section 8.5). In comparing the age distribution of the squirrel population over time (Table 9.7), we observe that the population attains an unchanging or stable age distribution by year 7. From that year on, the proportions of each age group in the population remain the same year after year, even though the population [N(t)] is increasing. Another piece of information that can be derived from the population projection shown in Table 9.6 is an estimate of population growth. By dividing the total number of individuals in year t + 1, N(t + 1), by the total number of individuals in the previous year, N(t), we can arrive at the finite multiplication rate—? (Greek letter lambda)—for each time period. N(t + 1)/N(t) = λN(t + 1)/N(t) = λ The rate λ has been calculated for each time interval and is shown at the bottom of each column (year) in Table  9.6. Note that initially λ varies between years, but once the population has achieved a stable age distribution, the value of λ remains constant. Values of λ greater than 1.0 indicate a population that is growing, whereas values less than 1.0 indicate a population in decline. A value of λ = 1.0 indicates a stable population size—neither increasing nor decreasing through time. Quantifying Ecology 9.2 Life History Diagrams and Population Projection Matrices The construction of life tables and their use in the development of population projection tables are important approaches in studying the dynamics of age-structured populations. We can represent the steps involved in the construction of the population projection table presented in Table 9.6 graphically using a life history diagram. Each of the circles represents an age class. The red arrows pointing from each age class to the next represent the values of sx, the age-specific values of survival in Table 9.5. The blue arrows from age classes 1 to 4 that point to age class 0 are the age specific birth rates, bx. Although the life history diagram provides a convenient way of graphically representing the age-specific rates of survival and birth from a life table, the real value of this approach is that it summarizes the changes that occur in populations (species) that exhibit a more complex pattern of transition between different developmental stages or size classes. For example, in perennial herbaceous plant species, birthrates (seed or seedling production) are a function of size rather than age. In addition, the die-back and regrowth each year of aboveground tissues (stem, leaves, and flowers) can result in an individual remaining in the same size class (or developmental stage) for multiple years or even reverting to a smaller size class. The population structure of the perennial herb Prinmila vulgaris presented in Figure 8.20 provides an example. In their analysis of the population growth of the forest herb Prinmila vulgaris, Teresa Valverde and Jonathan Silverston represent the population structure using five developmental stage classes based on reproductive stage and size (see legend to figure). The resulting life history diagram is presented here. In the preceding diagram, the arrows labeled with the letter F represent fecundity or birthrates (the number of seedlings produced by the various adult stage classes: Adults 1, 2, and 3). These values are equivalent to the bx values used in the analysis of the grey squirrel population (see Table 9.5). The arrows labeled with the letter G represent the probability of an individual in that stage class growing into the next larger stage class the following year. These values are equivalent to the sx values in Table 9.5. The arrows labeled S represent the probability that an individual of a given stage class will either stay in the same age class or revert into a previous stage class the following year (smaller in size than the previous year). These values do not exist for a population that is described in terms of age classes because an individual cannot revert to a previous age class. Because of the greater complexity of possible transitions, a system of subscripts is required to identify the transitions. Each transition has a two-number subscript, i and j, that represents the probability that an individual of stage class j at year t will move into stage class i the following year (calculated as t + 1). For example, there are three possible transitions for individuals currently in the Stage Class 3. The transition G43 is the probability that an individual in Stage Class 3 will move into Stage Class 4 the following year, whereas the transition S23 is the probability that the individual will revert to Stage Class 2 the following year. The transition labeled S33 is the probability that the individual will remain in Stage Class 3 the following year. The values of fecundity (F) and the transition probabilities (G and S) are organized in the form of a population projection matrix. The top row of the matrix contains the values of fecundity for each of the stage classes (Fij ). The other elements of the matrix contain the transition probabilities between stage classes (Gij and Sij ). The elements of the matrix that have a value of zero represent rates or transitions that are either not possible or do not exist for the population under study. Combined with estimates of the current population structure (number of individuals in each of the stage classes), the population projection matrix can be used to project patterns of population growth and structure in the future, just as was done in Table  9.6 for the grey squirrel population. The procedure involves matrix multiplication—multiplying the preceding matrix by a vector representing the current population structure. To further investigate this technique and apply it to predict patterns of population growth for the population of P. vulgaris studied by Teresa Valverde and Jonathan Silverston and discussed previously, go to Analyzing Ecological Data at The population projection table demonstrates two important concepts of population growth: (1) the rate of population growth, as estimated by λ, is a function of the age-specific rates of survival (sx) and birth (bx), and (2) the constant rate of increase of the population from year to year and the stable age distribution are results of survival and birthrates for each age class that are constant through time. Given a stable age distribution in which λ does not vary, λ can be used as a multiplier to project population size into the future (t + 1). This can be shown simply by multiplying both sides of the equation for λ shown previously by the current population size, N(t), giving: N(t + 1) = N(t)λN(t + 1) = N(t)λ We can predict the population size at year 1 by multiplying the initial population size N(0) by λ, and for year 2 by multiplying N(1) by λ: N(1) = N(0)λN(2) = N(1)λN(1) = N(0)λN(2) = N(1)λ Note that by substituting N(0)λ for N(1), we can rewrite the equation predicting N(2) as: N(2) = [N(0)λ]λ = N(0)λ2N(2) = [N(0)λ]λ = N(0)λ2 In fact, we can use λ to project the population at any year into the future using the following general form of the relationship developed previously: N(t) = N(0)λtN(t) = N(0)λt For our squirrel population, we can multiply the population size at year 0 — N(0) = 30 — by λ = 1.2, which is the value derived from the population projection table, to obtain a population size of 36 for year 1. If we again multiply 36 by 1.20, or the initial population size 30 by λ 2 (1.202), we get a population size of 43 for year 2; and if we multiply the initial population size of 30 by λ10, we arrive at a projected population size of 186 for year 10 (Figure 9.12). These population sizes do not correspond exactly to the population sizes calculated in the population projection table because λ fluctuates above and below the eventual value attained at stable age distribution. Only after the population achieves a stable age distribution does the λ value of 1.20 project future population size. The equation N(t) = N(0)λt describes a pattern of population growth (see Figure 9.12) similar to that presented for the exponential growth model developed in Section 9.1. Recall that when described over discrete time intervals, however, the pattern of growth is termed geometric population growth (see discussion in Section 9.1). In this example, the time interval (Δt) is 1 year, the interval (x) used in constructing the life and fecundity tables from which λ is derived. Note that the equation predicting population size through time using the finite growth multiplier λ is similar to the corresponding equation describing conditions of exponential growth developed in Section 9.1: N(t) = N(0)λt (geometric population growth)N(t) = N(0)ert (exponential population growth)N(t) = N(0)λt (geometric population growth)N(t) = N(0)ert (exponential population growth) In fact, the two equations (finite and continuous) illustrate the relationship between λ and r: λ = er or r = ln λλ = er or r = ln λ For the gray squirrel population, we can calculate the value of r = ln(1.20), or 0.18. Unlike the original calculation of r for the hydra population in Section 9.1, this estimate of the per capita population growth rate does not assume that all individuals in the population are identical. It is derived from λ, which as we have seen is an estimate of population growth based on the age-specific patterns of birth and death for the population. This estimate does, however, assume that the age-specific rates of birth and death for the population are constant; that is, they do not change through time. It is this assumption that results in the population converging on a stable age distribution and constant value of λ. The geometric and exponential models developed thus far provide an important theoretical framework for understanding the demographic processes governing the dynamics of populations. But nature is not constant; systematic and stochastic (random) processes, both internal (demographic) and external (environmental), can influence population dynamics. 9.8 Stochastic Processes Can Influence Population Dynamics Thus far we have considered population growth as a deterministic process. Because the rates of birth and death are assumed to be constant for a given set of initial conditions — values of r or λ and N(0) — both the exponential and geometric models of population growth will predict only one exact outcome. Recall, however, that the age-specific values of survival and birth in the life and fecundity tables (Tables 9.1 and 9.4) represent probabilities and averages derived from the cohort or population under study. For example, the values of bx are the average number of females produced by a female of that age group. For the 1-year-old females, the average value is 2.0; however, some female squirrels in this age class may have given birth to four female offspring, whereas others may not have given birth at all. The same holds true for the age-specific survival rates (sx), which represent the probability of a female of that age surviving to the next age class. For example, in Table 9.5 the probability of survival for a 1-year-old female gray squirrel is 0.5—the same probability of getting a heads or tails in a coin toss. Although survival (and mortality) is expressed as a probability, it is a discrete event for any individual—it either survives to the next year or not, just as the outcome of a single coin toss will be either heads or tails. If we toss a coin 10 times, however, we expect to get on average an outcome of 5 heads and 5 tails. This is in fact what we assume when we multiply the probability of survival (0.5) by the number of females in an age class (10) to project the number surviving to the next year (5) in Table 9.6. But each individual outcome in the 10 coin tosses is independent, and there is a possibility of getting 4 heads and 6 tails (probability p = 0.2051), or even 0 heads and 10 tails (p = 9.765 × 10–4). The same is true for the probability of survival when applied to individuals in a specific age class. The realization that population dynamics represent the combined outcome of many individual probabilities has led to the development of probabilistic, or stochastic, models of population growth. These models allow the rates of birth and death to vary about the mean estimate represented by the values of bx and sx. The stochastic (or random) variations in birthrates and death rates occurring in populations from year to year are called demographic stochasticity, and they cause populations to deviate from the predictions of population growth based on the deterministic models discussed in this chapter. Besides demographic stochasticity, random variations in the environment, such as annual variations in climate (temperature and precipitation) or the occurrence of natural disasters, such as fire, flood, and drought, can directly influence birthrates and death rates within the population. Such variation is referred to as environmental stochasticity. We will discuss the role of environmental stochasticity in controlling the growth of populations later in Chapter 11 (Section 11.13). 9.9 A Variety of Factors Can Lead to Population Extinction When deaths exceed births, populations decline. R0 becomes less than 1.0, and r becomes negative. Unless the population reverses the trend, it may become so low that it declines toward extinction (see Figure 9.4). Small populations—because of their greater vulnerability to demographic and environmental stochasticity (see Section  9.8) and loss of genetic variability (see Section 5.7)—are more susceptible to extinction than larger populations (we shall explore this issue at greater length in Chapter 11). However, a wide variety of factors can lead to population extinction regardless of population size. Extreme environmental events, such as droughts, floods, or extreme temperatures (heat waves or frosts), can increase mortality rates and reduce population size. Should the environmental conditions exceed the bounds of tolerance for the species, the event could well lead to extinction (for examples of environmental tolerances see Figures 5.7, 6.6, and 7.14a). Changes in regional and global climate over the past century (see Chapter 2, Ecological Issues & Applications) have led to a decline in many plant and animal species, and projected future climate change could result in the extinction of many species (see Chapter 27). A severe shortage of resources, caused by either environmental extremes (as discussed previously) or overexploitation, could result in a sharp population decline and possible extinction should the resource base not recover in time to allow for adequate reproduction by survivors. In the example of exponential growth in the population of reindeer introduced on St. Paul in 1911 (see Figure 9.5), the reindeer overgrazed their range so severely that the herd plummeted from its high of more than 2000 in 1938 to 8 animals in 1950 (Figure  9.13). The decline produced a curve typical of a population that exceeds the resources of its environment. Growth stops abruptly and declines sharply in the face of environmental deterioration. From a low point, the population may recover to undergo another phase of exponential growth or it may decline to extinction. (Adapted from Scheffer 1951.) As we discussed in Chapter 8 (Ecological Issues & Applications), when a nonnative species (invasive species) is introduced to an ecosystem through human activity, the resulting interactions with species in the community can often be detrimental. The introduction of a novel predator, competitor, or parasite (disease) can increase mortality rates, having a devastating effect on the target population and causing population decline or even extinction. Ecological Issues & Application The Leading Cause of Current Population Declines and Extinctions Is Habitat Loss A multitude of ecological studies over the past several decades have documented a pattern of population decline and extinction for an ever-growing number of plant and animal species across the planet ( Figure 9.14 ). The primary cause of current population extinctions is the loss of habitat as a result of human activities. The cutting of forests, draining of wetlands, clearing of lands for agriculture, and damming of rivers have resulted in a significant decline in the available habitat for many species and are currently the leading causes of species extinctions on a global scale. Freshwater ecosystems have been particularly vulnerable to habitat destruction over the past century. In the United States alone, there are approximately 75,000 dams impounding some 970,000 km of river, or about 17 percent of the rivers in the nation ( Figure 9.15 ). Dams remove sections of turbulent river and create standing bodies of water (lakes and reservoirs), affecting flow rates, temperature and oxygen levels, and sediment transport. Dams have a particularly negative impact on migratory species, restricting movement upriver to breeding areas. As a result of the degradation of freshwater habitats, between the years 1900 and 2010, 57 species and subspecies of North American freshwater fish have become extinct ( Figure  9.16 ). Birds are perhaps the most extensively monitored group of terrestrial species in North America over the past 50 years and therefore provide some of the best examples of population declines resulting from human activity and land-use change. The North American Breeding Bird Survey (a joint effort between the United States Geological Service and the Canadian Wildlife Service) has conducted annual surveys in the United States and Canada since its initial launch in 1966. These data provide a basis for evaluating population trends that can be related to changes in land use and habitat decline over the same period. Data from the Breeding Bird Survey show that one of the most negatively impacted groups of birds over the past 50 years has been species that inhabit the grassland habitats of the Great Plains of central North America. Beginning in the latter half of the 19th century, the expansion of agriculture west of the Mississippi River has led to the decline of native grasslands (prairies) as land has been converted to cropland ( Figure 9.17 ). More than 80 percent of North American grasslands have been converted to agriculture or other land uses. The loss of habitat has led to a steady decline in grassland bird species in both the United States and Canada ( Figure 9.18 ). The observed decline in populations is not only a result of a reduction in the area of native grasslands to support local populations but also because of declines in local population growth rates as a result of habitat degradation on remaining grassland areas (quality of habitat). Kimberly With of the University of Kansas and colleagues examined the growth rates of the three dominant grassland bird species in the Flint Hills region of the central Great Plains: Dickcissel (Spiza Americana), Grasshopper Sparrow (Ammodramus savannarum), and Eastern Meadowlark (Sturnella magna). Using estimates of annual population growth rate (λ; see Section 9.7) for numerous local populations across the region, the researchers determined that the mean annual growth rates for all three species on remaining habitats are negative (λ < 1). These results indicate that the observed decline in regional populations of these three species is a result of both decreasing area of habitat and negative growth rates for populations on the remaining areas of grassland. Not all species are equally susceptible to extinction from habitat decline. One group of species that are particularly vulnerable is migratory species. Species that migrate seasonally depend on two or more distinct habitat types in different geographic regions (see Section 8.7). If either of these habitats is altered or destroyed, the species will not persist. The more than 120 species of neotropical birds that migrate each year between the temperate zone of eastern North America and the tropics of Central and South America (and the islands of the Caribbean) depend on suitable habitat in both locations ( Figure 9.19 ) as well as stopover habitat in between. An analysis of the North American Breeding Bird Survey data for Canada during the period of 1970 to 2010 shows a decline of more than 50 percent in the populations of bird species that breed in Canada (spring and summer months) and migrate to South America for the winter. The primary reason for this decline in migratory bird populations is the destruction of rain forest habitats in South America. The species most vulnerable to extinction are endemics, which are species found only in a particular locality or localized habitat. Endemic species are particularly susceptible to extinction because of their limited geographic distribution (see Chapter 8, Section 8.2 and Figure 8.6). Environmental changes, disturbances, or human activities within their limited range could result in a complete loss of habitat for the species. For example, the island of Madagascar off the east coast of Africa is home to a diverse flora and fauna, of which approximately 90 percent are endemics and found only on Madagascar. The majority of these species inhabit the island’s tropical rain forest habitats, which have declined in extent steadily over the past 50 years ( Figure 9.20 ). More than 90 percent of the original rain forest has been cleared, and as a result, Madagascar has the largest percentage of plant and animal species listed as threatened or endangered compared to any other geographic region in the world. For example, lemurs are a group of primates endemic to the island of Madagascar that depend on the rain forest habitat. As a result of forest clearing and habitat loss, 91 percent of the known lemur species are threatened. Twenty-three of the species are now considered “critically endangered,” 52 are “endangered,” and 19 are listed as “vulnerable” on the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species. At least 17 species and 8 genera are believed to have become extinct in the 2000 years since humans first arrived in Madagascar. Summary Population Growth 9.1 In a population with no immigration or emigration, the rate of change in population size over time over a defined time interval is a function of the difference between the rates of birth and death. When the birthrate exceeds the death rate, the rate of population change increases with population size. As the time interval over which population change is evaluated decreases, approaching zero, the change in population size is expressed as a continuous function, and the resulting pattern is termed exponential population growth. The difference in the instantaneous per capita rates of birth and death is defined as r, which is the instantaneous per capita growth rate. Life Table 9.2 Mortality and its complement, survivorship, are best analyzed by means of a life table—an age-specific summary of mortality. By following the fate of a cohort of individuals until all have died, we can calculate age-specific estimates of mortality and survival. Types of Life Tables 9.3 We can construct a cohort or dynamic life table by following one or more cohorts of individuals over time. A time-specific life table is constructed by sampling the population in some manner to obtain a distribution of age classes during a single time period. Mortality and Survivorship Curves 9.4 From the life table, we derive both mortality curves and survivorship curves. They are useful for comparing demographic trends within a population and among populations under different environmental conditions and for comparing survivorship among various species. Survivorship curves fall into three major types: Type I, in which individuals tend to live out their physiological life span; Type II, in which mortality and thus survivorship are constant through all ages; and Type III, in which the survival rate of the young is low. Birthrate 9.5 Birth has the greatest influence on population increase. Like mortality rate, birthrate is age-specific. Certain age classes contribute more to the population than others do. Net Reproductive Rate 9.6 The fecundity table provides data on the gross reproduction, bx, and survivorship, lx, of each age class. The sum of these products gives the net reproductive rate, R 0, which is defined as the average number of females that will be produced during a lifetime by a newborn female. Population Projection Table 9.7 We can use age-specific estimates of survival and birthrates from the fecundity table to project changes in population density. The procedure involves using the age-specific survival rates to move individuals into the next age class and age-specific birthrates to project recruitment into the population. The resulting population projection table provides future estimates of both population density and age structure. Estimates of changes in population density can be used to calculate λ (lambda), which is a discrete estimate of population growth rate. This estimate can be used to predict changes in population size through time (geometric growth model). In addition, λ can be used to estimate r, which is the instantaneous per capita growth rate. The estimate of r, based on λ, accounts for differences in the age-specific rates of birth and death. Stochastic Processes 9.8 Because the age-specific values of survival and birth derived from the life and fecundity tables represent average values (probabilities), actual values for individuals within the population can vary. The random variations in birthrates and death rates that occur in populations from year to year are called demographic stochasticity. Random variations in the environment that directly influence rates of birth and death are termed environmental stochasticity. Extinction 9.9 A variety of factors can result in a population declining to extinction, including environmental stochasticity, the introduction of new species, and habitat destruction. Habitat Loss and Extinction Ecological Issues & Applications The primary cause of species extinctions is habitat destruction resulting from the expansion of human populations and activities. Declining populations are a result of both a reduction in the area of habitat available to support populations and a decline in the growth rate of populations that inhabit remaining areas of habitat. The latter is the result of the degradation of remaining habitats because of human activities. Population Sampling CHAPTER 10 Smith, T. M., & Smith, R. L. (2015). Elements of Ecology (9th ed.). Boston, MA: Pearson. 10.1 The Evolution of Life Histories Involves Trade-offs If reproductive success (the number of offspring that survive to reproduce) is the measure of fitness, imagine designing an organism with the objective of maximizing its fitness. It would reproduce as soon as possible after birth, and it would reproduce continuously, producing large numbers of large offspring that it would nurture and protect. Yet such an organism is not possible. Each individual has a limited amount of resources that it can allocate to specific tasks. Its allocation to one task reduces its resources available for other tasks. Thus, allocation to reproduction reduces the amount of resources available for growth. Should an individual reproduce early in life or delay reproduction? For a given allocation of resources to reproduction, should an individual produce many small offspring or fewer and larger offspring? Each possible action has both benefits and costs. Thus, organisms face trade-offs in life history characteristics related to reproduction, just as they do in the adaptations related to carbon, water, and energy balance (discussed in Chapters 6 and 7). These trade-offs involve modes of reproduction; age at reproduction; allocation to reproduction; number and size of eggs, young, or seeds produced; and timing of reproduction. These trade-offs are imposed by constraints of physiology, energetics, and the prevailing physical and biotic environment—the organism’s habitat. As such, the evolution of an organism’s life history reflects the interaction between intrinsic and extrinsic factors. Extrinsic ecological factors such as the physical environment and the presence of predators or competitors directly influence age-specific rates of mortality and survivorship. Intrinsic factors relating to phylogeny (the evolutionary history of the species), patterns of development, genetics, and physiology impose constraints resulting in trade-offs among traits. In our discussion, we explore these trade-offs and the diversity of solutions that have evolved to assure success at the one essential task for continuation of life on our planet, reproduction. 10.2 Reproduction May Be Sexual or Asexual In Chapter 5, we explored how genetic variation among individuals within a population arises from the shuffling of genes and chromosomes in sexual reproduction. In sexual reproduction between two diploid individuals, the individuals produce haploid (one-half the normal number of chromosomes) gametes—egg and sperm—that combine to form a diploid cell, or zygote, that has a full complement of chromosomes. Because the possible number of gene recombinations is enormous, recombination is an immediate and major source of genetic variability among offspring. However, not all reproduction is sexual. Many organisms reproduce asexually (see Section 8.1). Asexual reproduction produces offspring without the involvement of egg and sperm. It takes many forms, but in all cases, the new individuals are genetically the same as the parent. Strawberry plants spread by stolons, modified lateral stems from which new roots and vertical stems sprout (see Figure  8.2). The one-celled paramecium reproduces by dividing in two. Hydras, coelenterates that live in freshwater (see Figure 9.2), reproduce by budding—a process by which a bud pinches off as a new individual. In spring, wingless female aphids emerge from eggs that have survived the winter and give birth to wingless females without fertilization, a process called Parthenogenesis (Greek Parthenos, “virgin”; Latin genesis, “to be born”). Organisms that rely heavily on asexual reproduction revert occasionally to sexual reproduction. Many of these reversions to sexual reproduction are induced by an environmental change at some time in their life cycle. During warmer parts of the year, hydras turn to sexual reproduction to produce eggs that lay dormant over the winter and from which young hydras emerge in the spring to mature and reproduce asexually. After giving birth to several generations of wingless females, aphids produce a generation of winged females that migrate to different food plants, become established, and reproduce parthenogenetically. Later in the summer, these same females move back to the original food plants and give birth to true sexual forms—winged males and females that lay eggs rather than give birth to young. Each form of reproduction, asexual and sexual, has its trade-offs. The ability to survive, grow, and reproduce indicates that an organism is adapted to the prevailing environmental conditions. Asexual reproduction produces offspring that are genetically identical to the parent and are, therefore, adapted to the local environment. Because all individuals are capable of reproducing, asexual reproduction results in a potential for high population growth. However, the cost of asexual reproduction is the loss of genetic recombination that increases variation among offspring. Low genetic variability among individuals in the population means that the population responds more uniformly to a change in environmental conditions than does a sexually reproducing population. If a change in environmental conditions is detrimental, the effect on the population can be catastrophic. In contrast, the mixing of genes and chromosomes that occurs in sexual reproduction produces genetic variability to the degree that each individual in the population is genetically unique. This genetic variability produces a broader range of potential responses to the environment, increasing the probability that some individuals will survive environmental changes. But this variability comes at a cost. Each individual can contribute only one-half of its genes to the next generation. It requires specialized reproductive organs that, aside from reproduction, have no direct relationship to an individual’s survival. Production of gametes (egg and sperm), courtship activities, and mating are energetically expensive. The expense of reproduction is not shared equally by both sexes. The eggs (ovum) produced by females are much larger and energetically much more expensive than sperm produced by males. As we shall examine in the following sections, this difference in energy investment in reproduction between males and females has important implications in the evolution of life history characteristics. 10.3 Sexual Reproduction Takes a Variety of Forms Sexual reproduction takes a variety of forms. The most familiar involves separate male and female individuals. It is common to most animals. Plants with that characteristic are called dioecious (Greek di, “two,” and oikos, “home”; Figure 10.1a). In some species, individual organisms possess both male and female organs. They are hermaphrodites (Greek hermaphroditos). In plants, individuals can be hermaphroditic by possessing bisexual flowers with both male organs, stamens, and female organs, ovaries (Figure 10.1b). Such flowers are termed perfect. Asynchronous timing of the maturation of pollen and ovules reduces the chances of self-fertilization. Other plants are monoecious (Greek mono, “one,” and oikos, “home”). They possess separate male and female flowers on the same plant (Figure  10.1c). Such flowers are called imperfect. This strategy of sexual reproduction can be an advantage in the process of colonization. A single self-fertilized hermaphroditic plant can colonize a new habitat and reproduce, establishing a new population; this is what self-fertilizing annual weeds do that colonize disturbed sites. Among animals, hermaphroditic individuals possess the sexual organs of both males and females (both testes and ovaries), a condition common in invertebrates such as earthworms (Figure 10.2). In these species, referred to as simultaneous hermaphrodites, the male organ of one individual is mated with the female organ of the other and vice versa. The result is that a population of hermaphroditic individuals is in theory able to produce twice as many offspring as a population of unisexual individuals. Other species are sequential hermaphrodites. Animals—such as some mollusks and echinoderms—and some plants may be males during one part of their life cycle and females in another part. Some fish may be females first, then males. Sex change usually takes place as individuals mature or grow larger. A change in the sex ratio of the population stimulates sex change among some animals. Removing individuals of the other sex initiates sex reversal among some species of marine fish (Figure 10.3). Removal of females from a social group among some coral reef fish stimulates males to change sex and become females. In other species, removal of males stimulates a one-to-one replacement of males by sex-reversing females. Among the mollusks, the Gastropoda (snails and slugs) and Bivalvia (clams and mussels) have sex-changing species. Almost all of these species change from male to female. Plants also can undergo sex change. One such plant is jack-in-the-pulpit (Arisaema triphyllum), a clonal herbaceous plant found in the woodlands of eastern North America (Figure  10.4). Jack-in-the-pulpit may produce male flowers one year, an asexual vegetative shoot the next, and female flowers the next. Over its life span, a jack-in-the-pulpit may produce both sexes as well as an asexual vegetative shoot but in no particular sequence. Usually an asexual stage follows a sex change. Sex change in jack-in-the-pulpit appears to be triggered by the large energy cost of producing female flowers. Jack-in-the-pulpit plants generally lack sufficient resources to produce female flowers in successive years; male flowers and pollen are much cheaper to produce than female flowers and subsequent fruits. 10.4 Reproduction Involves Both Benefits and Costs to Individual Fitness To understand how trade-offs function to influence natural selection requires an understanding of the balance between benefits and costs associated with a phenotypic trait. If the objective of reproduction is to maximize the relative fitness of the individual, then the benefit of increasing the number of offspring produced would seem obvious. Yet a central tenet of life history theory is that the behavioral, physiological, and energetic activities involved in reproduction extract some sort of cost to future reproductive success in the form of reduced survival, fecundity, or growth. There are many examples of various activities involved in reproduction that increase an individual’s probability of mortality in addition to the direct physiological costs of reproduction. Activities associated with the acquisition of a mate (see Sections 10.11 and 10.12), defense of a breeding territory (see Chapter 11, Section 11.10), and the feeding and protection of young can reduce the probability of future survival. The work of Tim Cutton-Block of Cambridge University provides an example of the costs of reproduction in terms of increased probability of future survival. In the development of life tables for a population of red deer in central Scotland, he examined differences in the age-specific patterns of mortality for females—referred to as milk hinds—who have reared a calf to weaning age and those who have not—referred to as yeld hinds (Figure 10.5). The higher reproductive costs to milk hinds associated with the care and feeding (lactation) of calves result in higher mortality rates than those observed for yeld hinds (Figure 10.5a). Reproduction can also directly reduce an individual’s ability to produce future offspring. The current reproductive expenditure might leave the individual with insufficient energy resources to produce the same number of offspring during future periods of reproduction (Figure 10.5b). For example, studies by Sveinn Hanssen of the University of Tromso in Norway have shown that current reproduction results in reduced future fecundity in eider ducks. The common eider (Somateria mollissima) is a long-lived sea duck whose females do not eat during the incubation period. As a result, the reproductive effort of the female results in an increased loss of body mass and reduced immune function. In a four-year study, Richard Primack and Pamela Hall of Boston University examined the costs of reproduction in the pink lady’s slipper orchid (Cypripedium acaule). In two eastern Massachusetts populations, the researchers randomly assigned plants to be hand pollinated (increased reproduction) or left as controls, and the treatments were repeated in four successive years. By the third and fourth years of the study, the high cost of reproduction resulted in a lower growth and flowering rate of hand-pollinated plants in comparison with the control plants. For an average-sized plant, the production of fruit in the current year results in an estimated 10–13 percent decrease in leaf area and a 5–16 percent decrease in the probability of flowering in the following year. Increased allocation of resources to reproduction relative to growth diminished future fecundity (Figure 10.6). Interpreting Ecological Data Q1. What is the approximate difference in the probability of flowering in 1987 for individuals with a leaf area of 200 cm2 that produced zero fruits and three fruits during the period from 1984 to 1986? What does this tell you about the impact of the costs of past reproduction on future prospects of reproduction? Q2. According to the preceding figure, the probability of an individual with leaf area of 100 cm2 that produced zero fruits over the past three years (1984–1986) flowering in the following year (1987) is approximately 0.5 (or 50 percent). How large would an individual that bore three fruits over the past three years have to be to have the same probability of flowering? Allocation to reproduction has been shown to reduce allocation to growth in a wide variety of plant and animal species (Figure 10.7). In many species, there is a direct relationship between body size and fecundity (Figure 10.8). As a result, an individual reproducing earlier in age will produce fewer offspring per reproductive period than an individual that postpones reproduction in favor of additional growth. The act of reproduction at a given age, therefore, has potential implications to both age-specific patterns of mortality (survivorship) and fecundity (birthrate) moving forward. For this reason, the age at which reproduction begins—the age at maturity—is a key aspect of the organism’s life history. 10.5 Age at Maturity Is Influenced by Patterns of Age-Specific Mortality When should an organism begin the process of reproduction? Some species begin reproduction early in their life cycle, whereas others have a period of growth and development before the onset of reproduction. If natural selection functions to maximize the relative fitness of the individual, then the age and size at maturity are optimized when the difference between the costs and benefits of maturation at different ages and sizes is maximized. That is when the “payoff” in the fitness of the individual is greatest. An important component of this evolution is the age-specific pattern of mortality because it both shapes and is shaped by the age-specific expenditures of reproductive effort. To explore how natural selection can function to influence the age at maturity, let’s return to think about the age-specific patterns of survival and fecundity that we developed in the previous chapter, that is, the patterns of survival and fecundity that determine the trajectory of population growth (Table   10.1 ; see also Section 9.6 ). Recall that the first column labeled x shows the age or age class of individuals in the population. The column labeled sx shows the age-specific survival rates (the probability of an individual of age x surviving to age x + 1), and column bx represents the average number of female offspring produced by an individual female of age x. The three columns have been divided into three distinct age categories relating to reproduction: prereproductive, reproductive, and postreproductive. Prereproductive age categories represent juveniles, whereas the reproductive and postreproductive categories are referred to as adults. The age of maturity then represents the transition from juvenile to adult, or the age at which first reproduction occurs. In our example, we assume that the organism reproduces repeatedly following the onset of maturity until postreproductive age is achieved; however, this is not always the case, as we will discuss later. Our objective is to understand that both extrinsic and intrinsic factors influence the evolution of age at maturity. Natural selection will favor those individuals whose age at maturity results in the greatest number of offspring produced over the lifetime of an individual. Consider a simple hypothetical example of a species that continues to grow with age only until it reaches sexual maturity and then begins to reproduce. As with the examples presented in Figure 10.8, assume that fecundity increases with body weight—the larger the individual female, the greater the number of offspring produced per time period (reproductive event). Now assume that individuals within the population vary in the age at which they achieve maturity. As a result of differences in body weight, a female that begins to reproduce at age 3 will produce 10 offspring per year over the duration of her lifetime, whereas a female that delays reproduction until age 5 will have a 50 percent greater fecundity, or 15 offspring per year (Figure  10.9). Therefore, we can calculate the cumulative number of offspring produced at any point in each female’s life by summing the number of offspring from the onset of maturity to that age (see Figure  10.9). Note in Figure 10.9 that the female that delayed maturity until year 5 has produced a greater number of offspring over her lifetime. Thus, natural selection should favor delayed maturity. However, this conclusion assumes that the females live to their maximum age (12 years). In fact, before age eight the female that matured early has a greater cumulative number of offspring, and it is only if females survive past year eight that the strategy of delayed maturity increases fitness. Recall the difference between gross and net reproductive rate presented previously (Section 9.6). The value obtained by summing the values in the bx column as was done in this example is a measure of gross reproductive rate, and the strategy of delayed maturity is clearly the winner in terms of fitness. However, the true measure of reproductive rate is net reproductive rate (R 0), as it considers both the age-specific values of fecundity (bx ) and the age-specific values of survivorship (lx ). If survival beyond age eight is an improbable event for this species, then the strategy of early maturity results in the greater fitness. As the preceding hypothetical example demonstrates, the primary fitness advantage of delaying maturity is the larger initial body size obtained by individuals when they first reproduce. The primary cost of delaying reproduction (late maturity) is the increased risk of death before reproduction, or death before the advantage of increased fecundity as a result of delayed maturity are fully realized—in this example, death before age eight. If one assumes that natural selection acts on age-specific potential of producing future offspring, then age at maturity can be predicted from the mean juvenile and adult survival rates (sx column in Table 10.1). Decreases in the ratio of adult-to-juvenile survival (low survival for adults relative to juveniles) appear to favor reductions in age at maturity. As external factors (those unassociated with reproduction) increase adult mortality, selection would be expected to favor genotypes that mature earlier (before those ages), thus increasing their probability of contributing genes to future generations. number of long-term experiments in which patterns of mortality have been manipulated, support the prediction that natural selection favors earlier maturation when adult survival is reduced, and conversely, that it favors delayed maturation when, relative to adult survival, juvenile survival is reduced. David Reznick and colleagues at the University of California–Riverside conducted a long-term experiment on guppies in Trinidad in which the predictions relating to age-specific patterns of mortality and age at maturity are supported. Local populations of guppies on the island differ in their life history characteristics, and differences among populations are closely associated with the identity of the predator species living in their habitat. Predator species alter age-specific survival because they are size specific in their choice of prey. Crenicichla alta (a cichlid), the main predator at one set of the localities, preys predominantly on larger guppies from sexually mature size classes. At other localities, Rivulus hartii (a killifish) is the main predator. Rivulus feeds primarily on small guppies from immature size classes. Guppies from localities with Crenicichla mature at an earlier age than do guppies from localities with Rivulus. To prove that differences in age-specific patterns of survival result from different patterns of predation causing differences in age at maturity, Reznick and colleagues transplanted guppies from a site with Crenicichla to a site that contained Rivulus, but no guppies. This manipulation released the guppies from selective predation on adults and exposed them to selective predation on juveniles. Eleven years (30–60 generations) after the shift in predation-induced mortality from adults to juveniles, guppies responded to the increase in the ratio of adult-to-juvenile survival with significantly increased age at maturity (from 48 to 58 days). The increased age at maturity was accompanied by a larger average size at age of maturity for females and the production of fewer, but larger offspring (Figure 10.10). 10.6 Reproductive Effort Is Governed by Trade-offs between Fecundity and Survival Fecundity is the number of offspring produced per unit of time (bx ), but the energetic costs of reproduction include a wide variety of physiological and behavioral activities in addition to the energy and nutrient demands of the reproductive event, including gonad development, movement to spawning area, competition for mates, nesting, and parental care. Together, the total energetic costs of reproduction per unit time are referred to as an individual’s reproductive effort. The amount of energy organisms invest in reproduction varies. Herbaceous perennials invest between 15 and 20 percent of annual net production in reproduction, including vegetative propagation. Wild annuals that reproduce only once expend 15 to 30 percent, most grain crops—25 to 30 percent, and corn and barley—35 to 40 percent. The common lizard (Lacerta vivipara) invests 7 to 9 percent of its annual energy assimilation in reproduction. The female Allegheny Mountain salamander (Desmognathus ochrophaeus) expends 48 percent of its annual energy budget on reproduction. Reproductive effort is thought to be associated with the adaptive responses to age at maturity discussed previously (Section 10.5). For example, a decline in adult (reproductive) survival rate relative to that of juveniles (prereproductive) is predicted to favor genotypes that mature earlier in life and increase reproductive effort. The probability of future survival (and therefore future reproduction) is low, so early maturity and high reproductive effort will maximize individual fitness. Conversely, an increased juvenile mortality results in delayed maturity and reduced reproductive effort. This is the pattern that was observed by Reznick and colleagues in their experiments with predation-induced mortality in populations of guppies on Trinidad (see Figure 10.10a). Michael Fox and Allen Keast of Trent University in Canada found that pumpkinseed fish (Lepomis gibbosus) inhabiting two shallow ponds that experienced major winterkills (weather-related mortality events during winter months) matured one to two years earlier and at a smaller size (a difference >20 millimeters [m] in length) than individuals of the same species living in an adjacent lake in which winterkill did not occur. In addition, females inhabiting the high-mortality environment had a significantly higher energy allocation to reproduction than those inhabiting low-mortality environments. In both of the aforementioned studies, the researchers found that variations in allocation to reproduction were related to patterns of mortality caused by extrinsic factors (predation or extreme temperatures). Patterns of mortality, however, are also influenced directly by reproductive effort. For example, in the study of red deer presented in Figure 10.5, allocation to reproduction resulted in an increased female mortality rate. Therefore, allocation to reproduction at any time during the life of an individual involves trade-offs between current benefits from the production of offspring and costs in terms of potential reduction in future reproduction. Natural selection functions to optimize the trade-offs between present and future reproduction. An optimized life history is one wherein conflicts between the competing demands for survival and reproduction are resolved to the advantage of the individual in terms of fitness. To explore this relationship, we can examine how fecundity and survival vary as a function of allocation to reproduction at any given time period in an individual’s life. Cor Dijkstra and colleagues at the University of Groningen in the Netherlands examined the trade-off between fecundity and survival in the European kestrel (Falco tinnunculus), which is a predatory bird that feeds on small mammals. Both parents provide food for the brood (offspring); therefore, reproductive allocation associated with the feeding of young can be approximated by the time (hours/day) spent in flight (hunting activity). The brood size for nesting pairs of kestrels in the study area averaged five chicks. The researchers divided the nesting population in the study area into two groups: a nonmanipulated control group and a group in which brood size in the nests was manipulated. Starting when the nestlings were 5 to 10 days old, the researchers removed two nestlings from selected nests, thus reducing brood size by two. These chicks were then transferred to other nests, increasing the size of the brood by two. Brood enlargement forced an increase in daily hunting activities of both parents (Figure 10.11a). Despite the increase, food intake per chick declined with increased brood size. This decline resulted in reduced growth rate of the nestlings and increased nestling mortality (Figure 10.12). In addition, brood enhancement resulted in enhanced weight loss in the female parent, and survival of the parents was negatively correlated with the experimental change in brood size (Figure 10.11b). Increased allocation to reproduction (energy expenditure to the feeding and caring of offspring) resulted in a reduction in the probability of future survival of parents and, therefore, future reproduction. Interpreting Ecological Data Q1. How would a decrease in the probability of offspring survival in Figure 10.13a change the optimal value for current reproductive allocation? Q2. How would a decrease in adult survival in Figure 10.11b change the optimal value for current reproductive allocation? The responses of offspring and parental survival to brood enhancement in the study by Dijkstra reveal two patterns that are essential to understanding how natural selection functions to optimize reproductive effort. First, as reproductive effort increased, the number of offspring increased, but the probability of offspring survival decreased. Therefore, for any given value of reproductive effort, current reproductive success is the product of the two: number of offspring produced multiplied by the probability of their survival. As a result of the inverse relationship between the number of offspring and their probability of survival (Figure 10.13), the resulting pattern of current reproductive success is one of diminishing returns, with each additional unit of reproductive allocation returning a decreasing benefit in terms of current fecundity (Figure 10.14). Second, as reproductive effort increased, parental survival decreased, once again with each additional unit of reproductive allocation representing an increased cost in terms of future fecundity.Figure 10.14 illustrates these patterns in that each additional unit of reproductive allocation returns a decreasing benefit (current reproduction) and increasing cost (reduced future reproduction). The dashed line represents the sum of the values for current and future reproduction for any given allocation to reproduction (value along x-axis). The dashed line reaches its maximum value at intermediate values of reproductive allocation. Fitness of the parent is often maximized at intermediate reproductive effort (investment), particularly for organisms that reproduce repeatedly. In this analysis, optimal allocation to reproduction is not the maximum possible number of offspring that can be produced for a given reproductive event, and it is not the allocation that maximizes the benefit in terms of current fecundity (maximum offspring survival; see Figure 10.14). If natural selection functions to maximize the relative fitness of the parent, the allocation to reproduction represents a trade-off with parental, not offspring, survival. Natural selection functions to maximize fitness over the lifetime of the parent. 10.7 There Is a Trade-off between the Number and Size of Offspring In theory, a given allocation to reproduction can potentially produce many small offspring or fewer large ones (Figure  10.15). The number of offspring affects the parental investment each receives. If the parent produces a large number of young, it can afford only minimal investment in each one. In such cases, animals provide no parental care, and plants store little food energy in seeds. Such organisms usually inhabit disturbed sites, unpredictable environments, or places such as the open ocean where opportunities for parental care are difficult at best. By dividing energy for reproduction among as many young as possible, these parents increase the chances that some young will successfully settle and reproduce in the future. Parents that produce few young are able to expend more energy on each. The amount of energy varies with the number, size, and maturity of individuals at birth. Some organisms expend less energy during incubation. The young are born or hatched in a helpless condition and require considerable parental care. These animals, such as young mice or nestling American robins (Turdus migratorius), are altricial. Other animals have longer incubation or gestation, so the young are born in an advanced stage of development. They are able to move about and forage for themselves shortly after birth. Such young are called precocial. Examples are gallinaceous birds, such as chickens and turkeys, and ungulate mammals, such as cows and deer. The degree of parental care varies widely. Some species of fish, such as cod (Gadus morhua), lay millions of floating eggs that drift freely in the ocean with no parental care. Other species, such as bass, lay eggs in the hundreds and provide some degree of parental care. Among amphibians, parental care is most prevalent among tropical toads and frogs and some species of salamanders. The spotted (Ambystoma maculatum) and redback (Plethodon cinereus) salamanders found in eastern North America provide such an example of contrasting life history strategies relating to the number of young produced and parental care (Figure 10.16). The spotted salamander is found under logs and piles of damp leaves in deciduous forest habitats. During the month of February, individuals migrate to ponds and other small bodies of water to reproduce. After mating, females lay up to 250 eggs in large, compact, gelatinous masses that are attached to twigs just below the water surface. After mating, adults leave the water and provide no parental care of eggs or young. In contrast, the redback salamander occupies similar habitats in mixed coniferous–deciduous forests. After mating, females lay 4 to 10 eggs, which are deposited in a cluster within the crevice of a rotting log or stump. The female then curls about the egg cluster, guarding it until the larvae hatch. Among reptiles, which rarely exhibit parental care, crocodiles are an exception. They actively defend the nest and young for a considerable time. Invertebrates exhibit parental care to varying degrees. Octopi, crustaceans (such as lobsters, crayfish, and shrimp), and certain amphipods brood and defend eggs. Parental care is best developed among the social insects: bees, wasps, ants, and termites. Social insects perform all functions of parental care, including feeding, defending, heating and cooling, and sanitizing. How a given investment in reproduction is allocated, the number and size of offspring produced, and the care and defense provided all interact in the context of the environment to determine the return to the individual in terms of increased fitness (see Quantifying Ecology 10.1). 10.8 Species Differ in the Timing of Reproduction How should an organism invest its allocation to reproduction through time? Thus far we have focused on age-structured populations in which reproduction begins with the onset of maturity and continues over some period of time until either reproduction ceases (postreproductive period) or senescence occurs. Organisms that produce offspring more than once over their lifetime are called iteroparous. Iteroparous organisms include most vertebrates, perennial herbaceous plants, shrubs, and trees. As we have explored, the timing of reproduction for iteroparous species involves trade-offs. Early reproduction means earlier maturity, less growth, reduced fecundity per reproductive period, reduced survivorship, and reduced potential for future reproduction. Later reproduction means increased growth, later maturity, and increased survivorship but less time for reproduction. In effect, to maximize contributions to future generations, an organism balances the benefits of immediate reproduction and future reproductive prospects, including the cost to fecundity (total offspring produced) and its own survival (Section 10.6). Another approach to reproduction is to initially invest all energy in growth, development, and energy storage, followed by one massive reproductive effort, and then death. In this strategy, an organism sacrifices future prospects by expending all its energy in one suicidal act of reproduction. Organisms exhibiting this mode of reproduction are called semelparous. Semelparity is employed by most insects and other invertebrates, by some species of fish (notably, salmon), and by many plants. It is common among annuals, biennials, and some species of bamboos. Many semelparous plants, such as ragweed (Ambrosia spp.), are small, short-lived, and found in ephemeral or disturbed habitats. For them, it would not be efficient, fitness-wise, to hold out for future reproduction because their chances of success are slim. They gain maximum fitness by expending all their energy in one bout of reproduction. Other semelparous organisms, however, are long-lived and delay reproduction. Mayflies (Ephemeroptera) may spend several years as larvae before emerging from the surface of the water for an adult life of several days devoted to reproduction. Periodical cicadas spend 13 to 17 years belowground before they emerge as adults to stage a single outstanding exhibition of reproduction. Some species of bamboo delay flowering for 100 to 120 years, produce one massive crop of seeds, and die. Hawaiian silverswords (Argyroxiphium spp.) live 7 to 30 years before flowering and dying. In general, the fitness of species that evolved semelparity must increase enough to compensate for the loss of repeated reproduction. As we have seen, optimal reproductive effort per unit time (per reproductive event) is the balance between current and future reproduction that functions to maximize the individual’s (parental) fitness. Within this framework, semelparity implies that one single maximum reproductive effort followed by death represents the optimal strategy for the individual in the context of its environment (external constraints). It follows that iteroparity evolved through a change in conditions such that less than the maximum possible reproductive effort is optimal on the first reproductive attempt and the organism survives to reproduce during future time intervals. What type of change in conditions might bring about the shift from semelparity to iteroparity? If the external environment imposes a high adult mortality relative to juvenile mortality, and if individuals reach maturity, chances are that they will not survive much longer; therefore, future reproductive expectations are bleak. Under these conditions, semelparity would be favored. If the opposite holds true and juvenile mortality is high compared to adult mortality, an individual has a good chance of surviving into the future once it survives to maturity; hence, prospects of future reproduction are good. Under these conditions iteroparity is favored. Quantifying Ecology 10.1 Interpreting Trade-offs Many of the life history characteristics discussed in this chapter involve trade-offs, and understanding the nature of trade-offs involves the analysis of costs and benefits for a particular trait. One trade-off in reproductive effort discussed in Section  10.7 involves the number and size of offspring produced. The graph in Figure 1 is similar to the one presented in Figure 10.15, showing the trade-off relationship between seed size and the number of seeds produced per plant. The example assumes a fixed allocation (100 units); therefore, the number of seeds produced per plant declines with increasing seed size. Based on this information alone, it would appear that the best strategy for maximizing reproductive success would be to produce small seeds, thereby increasing the number of offspring produced. However, we must also consider any benefits to reproductive success that might vary as a function of seed size. The reserves of energy and nutrients associated with large seed size have been shown to increase the probability of successful establishment, particularly for seedlings in low-resource environments. For example, J. A. Ramírez-Valiente of the Center for Forestry Research (CIFOR: Madrid, Spain) found that average seed size in local populations of cork oak (Quercus suber) in Spain increases with decreasing precipitation ( Figure  2), and that increased seed size was positively related to seedling survival under dry conditions (decreasing precipitation) (also see an example of the relationship between seed size and seedling survival in shade in Chapter  6, Field Studies: Kaoru Kitajima). A generalized relationship between seed size and seedling survival for two different environments (wet and dry) is plotted in Figure 3. In both environments, survival increases with seed size; however, in dry environments, the probability of survival declines dramatically with decreasing seed size. By multiplying the number of seeds produced by the probability of survival, we can now calculate the expected reproductive success (the number of surviving offspring produced per plant) for plants producing seeds of a given size in both the wet and dry environments (Figure 4). In wet environments, where all seed sizes have comparable probabilities of survival, the strategy of producing many small seeds results in the highest reproductive success and fitness. In contrast, the greater probability of survival makes the strategy of producing large seeds the most successful in dry environments, even though far fewer seeds are produced. Interpreting the trade-offs observed in life history characteristics, such as the one illustrated between seed size and the number of seeds produced, requires understanding how those trade-offs function in the context of the environment (both biotic and abiotic) in which the species lives. Costs and benefits of a trait can change as the environmental conditions change. The diversity of life history traits exhibited by species is testimony that there is no single “best” solution for all environmental conditions. In the example just presented, natural selection should favor plants producing small seeds in wet environments and plants that produce larger seeds in dry environments, resulting in a difference in average seed size in these two environments. What might you expect in an environment where annual rainfall is relatively high during most years (wet) but in which periods of drought (dry) commonly persist for several years? The seeds of shade-tolerant plant species are typically larger than those of shade-intolerant species. How might this difference reflect a trade-off in life history characteristics relating to successful reproduction in sun and shade environments? See the discussion of shade tolerance in Chapter 6 and the Field Studies feature in that chapter. 10.9 An Individual’s Life History Represents the Interaction between Genotype and the Environment Natural selection acts on phenotypic variation among individuals within the population and variation in life history characteristics, such as age at maturity, allocation to reproduction, and the average number and size of offspring produced, is common among individuals within a population (see Chapter  5). The observed phenotypic variation within populations can arise from two sources: genotypic variation among individuals and interactions between the genotype and environment. Recall that most phenotypic traits are influenced by the environment; that is to say, the phenotypic expression of the genotype is influenced by the environment (see Chapter 5, Section 5.4). The ability of a genotype to give rise to different phenotypic expressions under different environmental conditions is termed phenotypic plasticity, and the set of phenotypes expressed by a single genotype across a range of environmental conditions is referred to as the norm of reaction (see Figure 5.4). Just as with the examples of phenotypic plasticity related to physiological, morphological, and behavioral characteristics involved in the thermal, energy, and water balance of plants and animals, the characteristics related to life history also exhibit reaction norms as a result of interactions between genes and environment (see Chapters 6 and 7). One life history trait that has received a great deal of focus regarding response to environmental variation is the relationship between age and size at maturity. Let’s begin by examining the expected patterns of size and age at maturity for a given genotype. Figure 10.17 shows the graph of a growth curve for a hypothetical fish species that under the best of conditions can mature at two years of age and at a weight of 4 kilograms (kg). Now let us change the environmental conditions by reducing the availability of food. The result is a slower rate of growth (Figure  10.17). The question now becomes when would the initiation of reproduction (onset of maturity) maximize fitness for the slow-growing fish? There are three possible ways to go. First, individuals could always mature at the same size (blue line in Figure  10.17). The problem with this approach is that it now requires an additional four years to reach maturity, increasing the probability of mortality before the individual has the opportunity to breed (blue dashed line). The second approach is to always mature at the same age (green line in Figure 10.17). This approach also presents a downside; now the individual would weigh only 1.75 kg and smaller individuals produce fewer offspring (green dashed line). Somewhere between these two approaches is a compromise between the increased costs represented by the increased risk of mortality and that of reduced fecundity. The species could evolve to possess a norm of reaction for age and size at maturity (red line in Figure  10.17). The optimal solution for any growth rate would depend on the relationship between size and fecundity and the age-specific patterns of juvenile mortality. Nicolas Tamburi and Pablo Martin of the Universidad Nacional del Sur in Argentina examined patterns of phenotypic plasticity in the age and size at maturity in the freshwater applesnail (Pomacea canaliculata) native to South America. It has a broad geographic range, and its local populations exhibit variation in life history traits. In their experiments, the researchers reared full sibling snails in isolation under a gradient of seven different levels of food availability determined by size-specific ingestion rates. The reaction norms for age and size at maturity for both male and female snails are presented in Figure  10.18. They show a marked difference between males and females. Males showed less variation in age at maturity but a wide variation in shell size. Size is largely irrelevant in gaining access to females, and male fitness can be maximized through fast maturation at the expense of size at maturity. In contrast, a minimum size is required for females to reach maturity, so there is a much greater variation in age at maturity rather than size. In both cases, the reaction norms reflect a trade-off between age and size at maturity. The differences between the reaction norms of males and females reflect basic differences in the trade-offs between the sexes as they relate to fitness. 10.10 Mating Systems Describe the Pairing of Males and Females In all sexually reproducing species there is a social framework involving the selection of mates. The pattern of mating between males and females in a population is called the mating system (also see Chapter 5). The structure of mating systems in animal species ranges from monogamy, which involves the formation of a lasting pair bond between one male and one female, to promiscuity, in which males and females mate with one or many of the opposite sex and form no pair bond. The primary mating systems in plants are outcrossing (cross-fertilization in which pollen from one individual fertilizes the ovum of another) and autogamy (self-fertilization). However, a mixed mating system, in which plants use both outcrossing and autogamy, is common. The mating system of a species has direct relevance to its life history because it influences allocation to reproduction, particularly in males. Competition among males for mates, courtship behavior, territorial defense, and parental care (feeding and protection of offspring) can represent a significant component of reproductive allocation. In addition, we shall see that the degree of parental care differs among mating systems and parental care has a direct effect on offspring survival. As such, a mating system is both influenced by and influences age-specific patterns of fecundity and mortality. Monogamy is most prevalent among birds and rare among mammals, except for several carnivores, such as foxes (Vulpes spp.) and weasels (Mustela spp.), and a few herbivores, such as the beaver (Castor spp.), muskrat (Ondatra zibethica), and prairie vole (Microtus ochrogaster). Monogamy exists mostly among species in which cooperation by both parents is needed to raise the young successfully. Most species of birds are seasonally monogamous (during the breeding season) because most young birds are helpless at hatching and need food, warmth, and protection. The avian mother is no better suited than the father to provide these needs. Instead of seeking other mates, the male can increase his fitness by continuing his investment in the young. Without him, the young carrying his genes may not survive. Among mammals, the situation is different. The females lactate (produce milk), which provides food for the young. Males often contribute little or nothing to the survival of the young, so it is to their advantage in terms of fitness to mate with as many females as possible. Among the mammalian exceptions, the male provides for the female and young and defends the territory (area defended for exclusive use and access to resources; see Section 11.10 for discussion). Both males and females regurgitate food for the weaning young. Monogamy, however, has another side. Among many species of monogamous birds, such as bluebirds (Sialia sialis), the female or male may “cheat” by engaging in extra-pair copulations while maintaining the reproductive relationship with the primary mate and caring for the young. By engaging in extra-pair relationships, the female may increase her fitness by rearing young sired by two or more males. The male increases his fitness by producing offspring with several females. Polygamy is the acquisition of two or more mates by one individual. It can involve one male and several females or one female and several males. A pair bond exists between the individual and each mate. The individual having multiple mates—male or female—is generally not involved in caring for the young. Freed from parental duty, the individual can devote more time and energy to competition for more mates and resources. The more unevenly such crucial resources as food or quality habitat are distributed, the greater the opportunity for a successful individual to control the resource and several mates. The number of individuals of the opposite sex an individual can monopolize depends on the degree of synchrony in sexual receptivity. For example, if females in the population are sexually active for only a brief period, as with the white-tailed deer, the number a male can monopolize is limited. However, if females are receptive over a long period of time, as with elk (Cervus elaphus), the size of a harem a male can control depends on the availability of females and the number of mates the male has the ability to defend. Environmental and behavioral conditions result in various types of polygamy. In polygyny, an individual male pairs with two or more females. In polyandry, an individual female pairs with two or more males. Polyandry is interesting because it is the exception rather than the rule. This system is best developed in three groups of birds: the jacanas (Jacanidae; Figure  10.19), phalaropes (Phalaropus spp.), and some sandpipers (Scolopacidae). The female competes for and defends resources essential for the male and the males themselves. As in polygyny, this mating system depends on the distribution and defensibility of resources, especially quality habitat. The female produces multiple clutches of eggs, each with a different male. After the female lays a clutch, the male begins incubation and becomes sexually inactive. The nature and evolution of male–female relationships are influenced by environmental conditions, especially the availability and distribution of resources and the ability of individuals to control access to resources. If the male has no role in feeding and protecting the young and defends no resources available to them, the female gains no advantage by remaining with him. Likewise, the male gains no increase in fitness by remaining with the female. If the habitat were sufficiently uniform, so that little difference existed in the quality of territories held by individuals, selection would favor monogamy because female fitness in all habitats would be nearly the same. However, if the habitat is diverse, with some parts more productive than others, competition may be intense, and some males will settle on poorer territories. Under such conditions, a female may find it more advantageous to join another female in the territory of the male defending a rich resource than to settle alone with a male in a poorer territory. Selection under those conditions will favor a polygamous relationship, even though the male may provide little aid in feeding the young. 10.11 Acquisition of a Mate Involves Sexual Selection For females, the production and care of offspring represents the largest component of reproduction expenditure. For males, however, the acquisition of a mate is often the major energetic expenditure that influences fitness. The flamboyant plumage of the peacock (Figure 10.20) presented a troubling problem for Charles Darwin. Its tail feathers are big and clumsy and require a considerable allocation of energy to grow. They are also conspicuous and present a hindrance when a peacock is trying to escape predators. In the theory of natural selection, what could account for the peacock’s tail? Of what possible benefit could it be (see Chapter  5)? In his book The Descent of Man and Selection in Relation to Sex, published in 1871, Darwin observed that the elaborate and often outlandish plumage of birds and the horns, antlers, and large size of polygamous males seemed incompatible with natural selection. To explain why males and females of the same species often differ greatly in body size, ornamentation, and color (referred to as sexual dimorphism), Darwin developed a theory of sexual selection. He proposed two processes to account for these differences between the sexes: intrasexual selection and intersexual selection. Intrasexual selection involves male-to-male (or in some cases, female-to-female) competition for the opportunity to mate. It leads to exaggerated secondary sexual characteristics such as large size, aggressiveness, and organs of threat, such as antlers and horns (Figure 10.21), that aid in competition for access to mates. Intersexual selection involves the differential attractiveness of individuals of one sex to another (see this chapter, Field Studies: Alexandra L. Basolo). In the process of intersexual selection, the targets of selection are characteristics in males such as bright or elaborate plumage, vocalizations used in sexual displays, and the elaboration of some of the same characteristics related to intrasexual selection (such as horns and antlers). It is a form of assortative mating in which the female selects a mate based on specific phenotypic characteristics (see Section  5.7). There is intense rivalry among males for female attention. In the end, the female determines the winner, selecting an individual as a mate. The result is an increase in relative fitness for those males that are chosen, shifting the distribution of male phenotypes in favor of the characteristics on which female choice is based (see Chapter  5). But do characteristics such as bright coloration, elaborate plumage, vocalizations, or size really influence the selection of males by females of the species? Marion Petrie of the University of Newcastle, England, conducted some experiments to examine intersexual selection in peacocks (Pavo cristatus). She measured characteristics of the tail feathers (referred to as the train) of male peacocks chosen by females as mates over the breeding season. Her results show that females selected males with more elaborate trains. In particular, she found a positive correlation between the number of eyespots a male had on his train (see Figure 10.20c) and the number of females he mated with. She then altered the tail feathers from a group of males with elaborate trains and found that reduction in the number of eyespots led to a reduction in mating success. However, the train itself is not what is important; it is what the elaborate tail feathers imply about the individual. The large, colorful, and conspicuous tail makes the male more vulnerable to predation, or in many other ways, reduces the male’s probability of survival. A male that can carry these handicaps and survive shows his health, strength, and genetic superiority. Females showing preference for males with elaborately colored tail feathers produce offspring that carry genes for high viability. Thus, the selective force behind the evolution of exaggerated secondary sexual characteristics in males is preferred by females. In fact, later experiments by Petrie found that the offspring of female peacocks that mated with males having elaborate tail feathers had higher rates of survival and growth than did the offspring of those paired with males having less elaborate trains (Figures 10.20a and 10.20b). A similar mechanism may be at work in the selection of male birds with bright plumage. One hypothesis proposes that only healthy males can develop bright plumage. There is evidence from some species that males with low parasitic infection have the brightest plumage. Females selecting males based on differences in the brightness of plumage are in fact selecting males that are the most disease resistant (for example, see Section 15.7). In some animal species, male vocalizations play an important role in courtship behavior, and numerous studies have found evidence of female mate choice based on the complexity of a male’s song. In aviary studies, Myron Baker and colleagues at the University of Trondheim (Norway) found that female great tits (Parus major) were more receptive of males with more varied or elaborate songs (Figure 10.22). In a 20-year study of song sparrows (Melospiza melodia) inhabiting Mandarte Island, British Columbia (Canada), Jane M. Reid of Cambridge University (England) and colleagues found that males with larger song repertoires were more likely to mate and that repertoire size predicted overall measures of male and offspring fitness. Males with larger song repertoires contributed more independent offspring—those hatching and reaching independence from parental care—and recruited offspring into the breeding population on the island; furthermore, those males also contributed more independent and recruited grand-offspring to the island population (Figure 10.23). This was because these males lived longer and reared a greater proportion of hatched chicks to independence from parental care, not because females mated to males with larger repertoires laid or hatched more eggs. Furthermore, independent offspring of males with larger repertoires were more likely to recruit and then to leave more grand-offspring than were offspring of males with small repertoires. Field Studies Alexandra L. Basolo School of Biological Sciences, University of Nebraska–Lincoln The elaborate and often flamboyant physical traits exhibited by males of many animal species—bright coloration, exceedingly long feathers or fins—have always presented a dilemma to the traditional theory of natural selection. Because females in the process of mate selection often favor these male traits, sexual selection (see Section 10.11) will reinforce these characteristics. However, male investment in these traits may also reduce the amount of energy available for other activities that are directly related to individual fitness, such as reproduction, foraging, defense, predator avoidance, and growth. The effect of such trade-offs in energy allocation on the evolution of animal traits is the central focus of ecologist Alexandra L. Basolo’s research, which is changing how behavioral ecologists think about the evolution of mate selection. Basolo’s research focuses on the small freshwater fishes of the genus Xiphophorus that inhabit Central America. One group of species within this genus, the swordtail fish, exhibits a striking sexual dimorphism in the structure of the caudal fin. Males have a colorful, elongated caudal appendage, which is termed the sword ( Figure 1 ), which is absent in females. This appendage appears to play no role other than as a visual signal to females in the process of mate selection. To test the hypothesis that this trait results partly from female choice (intersexual selection), Basolo undertook a series of laboratory experiments to determine if females exhibited a preference for male sword length. Her test subject was the green swordtail, Xiphophorus helleri, shown in Figure 1. These experiments allowed females to choose between a pair of males differing in sword length. Five tests with different pairs of males were conducted in which the sword differences between paired males varied. Female preference was measured by scoring the amount of time a female spent in association with each male. Results of the experiments revealed that females preferred males with longer swords. The greater the difference in sword length between two males, the greater was the difference in time that the female spent with them (Figure 2). The results suggest that sexual selection through female choice will influence the relative fitness of males. The benefit of having a long sword is the increased probability of mating. But what is the cost? Locomotion accounts for a large part of the energy budget of fish, and the elongated caudal fin (sword) of the male swordtails may well influence the energetic cost of swimming. The presence of the sword increases mating success (via female choice) but may well negatively affect swimming activities. To evaluate the costs associated with sword length, Basolo undertook a series of experiments using another species of swordtail, the Montezuma swordtail (Xiphophorus montezumae) found in Mexico. Like the green swordtail, the Montezuma swordtail males have an asymmetric caudal fin as a result of an extended sword, and the presence of this sword increases mating success. The experiments were designed to quantify the metabolic costs of the sword fins during two types of swimming—routine and courtship—for males with and without sword fins. Males having average-length sword fins were chosen from the population. For some of these males, the sword was surgically removed (excised). Comparisons were then made between males with and without swords for both routine (no female present) and courtship (female present) swimming. Male courtship behavior involves a number of active maneuvers. Routine swimming by males occurs in the absence of females, whereas the presence of females elicits courtship-swimming behavior. Basolo placed test males into a respirometric chamber—a glass chamber instrumented to measure the oxygen content of the water continuously. For a trial where a female was present, the female was suspended in the chamber in a cylindrical glass tube having a separate water system. During each trial, water was sampled from the chamber for oxygen content to determine the rate of respiration. Higher oxygen consumption indicates a higher metabolic cost (respiration rate). Results of the experiments show a significant energy cost associated with courtship behavior (Figure 3). A 30 percent increase in net cost was observed when females were present for both groups (males with and without swords) as a result of increased courtship behavior. However, the energy cost for males with swords was significantly higher than that for males without swords for both routine and courtship swimming behavior (Figure 3). Thus, although sexual selection via female choice favors long swords, males with longer swords experience higher metabolic costs during swimming, suggesting that sexual and natural selection have opposing effects on sword evolution. The cost of a long sword to male swordtails extends beyond the energetics of swimming. Other studies have shown that more conspicuous males are more likely to be attacked by predators than are less conspicuous individuals. In fact, Basolo and colleague William Wagner have found that in green swordtail populations that occur sympatrically (together) with predatory fish, the average sword length of males in the population is significantly shorter than in populations where predators are not present. These results suggest that although sexual selection favors longer swords, natural selection may have an opposing effect on sword length in populations with predators. Despite the cost, both in energy and probability of survival, the sword fin of the male swordtails confers an advantage in the acquisition of mates that must offset the energy and survival costs in terms of natural selection. 10.12 Females May Choose Mates Based on Resources A female exhibits two major approaches in choosing a mate. In the sexual selection discussed previously, the female selects a mate for characteristics such as exaggerated plumage or displays that are indirectly related to the health and quality of the male as a mate. The second approach is that the female bases her choice on the availability of resources such as habitat or food that improve fitness. Numerous studies have shown that in some species, mate choice by females appears to be associated with the acquisition of resources, usually a defended high quality habitat or territory (see Section 11.10). Ethan Temeles of Amherst College (Amherst, Massachusetts) and J. John Kress of the National Museum of Natural History (Smithsonian Institution, Washington, D.C.) found that female purple-throated carib hummingbirds (Eulampis jugularis) on the island of Dominica in the West Indies preferred to mate with males that had high standing crops of nectar on their flower territories (Figure  10.24). A male’s ability to maintain high nectar standing crops on his territory not only depended on the number of flowers in his territory but also on his ability to enhance his territory through the prevention of nectar losses to intruders. Andrew Balmford and colleagues at Cambridge University (England) examined the distribution of females across male territories to assess mate choice in puku (Kobus vardoni) and topi (Damaliscus lunatus), which are two species of grazing antelope in southern Africa. In both species, males defend areas (territories) in which they have exclusive use of resources. Both species are polygamous, and visitation to territories by females was found to be a good predictor of where females tended to mate. In both species, female choice (visitation rate) was correlated with the quality of forage in different territories, indicating that female choice was influenced by the quality of defended resources. 10.13 Patterns of Life History Characteristics Reflect External Selective Forces Nature presents us with a richness of form and function in the diversity of life that inhabits our planet. Some species are large, and others are small. Some mature early, and others mature later in their lives. Some organisms produce only a few offspring over their lifetime, whereas other species produce thousands in a single reproductive event. Some organisms fit an entire lifetime into a single season, and others live for centuries. Are the characteristics exhibited by any given species a random assemblage of these traits, or is there a discernable pattern? What we have seen so far is that these characteristics, which define the life history of an individual, are not independent of one another. These characteristics are products of evolution by natural selection, the possible outcomes molded by the external environment, and constrained by trade-offs relating to fundamental physiological and developmental processes. Ecologists have long recognized that the set of characteristics that define a species’ life history covaries, forming what appear to be distinctive “suites” of characteristics that seem to be a product of broad categories of selective forces. A number of empirical models have developed to account for the observed covariation among life history traits. One such model is the fast–slow continuum hypothesis. The fast–slow continuum hypothesis emphasizes the selective forces imposed by mortality at different stages of the life cycle. Under this scheme, species can be arranged along a continuum from those experiencing high adult mortality levels to those experiencing low adult mortality. This differential mortality is responsible for the evolution of contrasting life histories on either end of the continuum. Species undergoing high adult mortality are expected to have a shorter life cycle (longevity) with faster development rates, early maturity, and higher fecundity than those experiencing low adult mortality. This approach has proven accurate in predicting patterns of life history characteristics in many groups of species and is generally consistent with the patterns presented in the preceding sections. Other approaches to understanding the observed correlations among life history traits have focused on constraints imposed by the abiotic environment. If the life history characteristics and mating system exhibited by a species are the products of evolution, would they not reflect adaptations to the prevailing environmental conditions under which natural selection occurred? If this is the case, do species inhabiting similar environments exhibit similar patterns of life history characteristics? Do life history characteristics exhibit patterns related to the habitats that species occupy? One way of classifying environments (or species habitats) relates to their variability in time. We can envision two contrasting types of habitats: (1) those that are variable in time or short-lived and (2) those that are relatively stable (long-lived and constant) with few random environmental fluctuations. The ecologists Robert MacArthur of Princeton University, E. O. Wilson of Harvard University, and later E. Pianka of the University of Texas used this dichotomy to develop the concept of r- and K-selection. The theory of r- and K-selection predicts that species adapted to these two different environments will differ in life history traits such as size, fecundity, age at first reproduction, number of reproductive events during a lifetime, and total life span. Species popularly known as r-strategists are typically short-lived. They have high reproductive rates at low population densities, rapid development, small body size, large number of offspring (with low survival), and minimal parental care. They make use of temporary habitats. Many inhabit unstable or unpredictable environments that can cause catastrophic mortality independent of population density. Environmental resources are rarely limiting. They exploit noncompetitive situations. Some r-strategists, such as weedy species, have means of wide dispersal, are good colonizers, and respond rapidly to disturbance. K-strategists are competitive species with stable populations of long-lived individuals. They have a slower growth rate at low populations, but they maintain that growth rate at high densities. K-strategists can cope with physical and biotic pressures. They possess both delayed and repeated reproduction and have a larger body size and slower development. They produce few seeds, eggs, or young. Among animals, parents care for the young; among plants, seeds possess stored food that gives the seedlings a strong start. Mortality relates more to density than to unpredictable environmental conditions. They are specialists—efficient users of a particular environment—but their populations are at or near carrying capacity (maximum sustainable population size) and are resource-limited. These qualities, combined with their lack of means for wide dispersal, make K-strategists poor colonizers of new and empty habitats. The terms r and K used to characterize these two contrasting strategies related to the parameters of the logistic model of population growth (presented in Chapter 11); r is the per capita rate of growth, and K is the carrying capacity (maximum sustainable population size). Using the classification of r and K, strategies for comparing species across a wide range of sizes is of limited value. For example, the correlation among body size, metabolic rate, and longevity in warm-blooded organisms results in species with small body size generally being classified as r species and those with large body size as K species (see Chapter 7). The concept of r species and K species is most useful in comparing organisms that are either taxonomic or functionally similar. The plant ecologist J. Phillip Grime of the University of Sheffield, England, used a framework similar to that used by MacArthur and Wilson to develop a life history classification for plants. Grime’s life history classification of plants is based on the assumption that any habitat can be classified into one of two categories: stress and disturbance. Stress is defined as conditions that restrict plant growth and productivity, such as shortages of light, water, mineral nutrients, or suboptimal temperatures (see Chapter 6). Disturbance is associated with the partial or total destruction of plant biomass that arises from the activity of herbivores, pathogens, or natural disasters such as wind, fire, or flooding. When the four permutations of high and low stress couple with high and low disturbance, it is apparent that only three are suitable as habitat for plants, because in highly disturbed environments, stress does not allow for the reestablishment of plant populations. Grime suggests that the remaining three categories of habitat are associated with the evolution of distinct types of plant life history strategies—R, C, and S (Figure 10.25). Species exhibiting the R, or ruderal, strategy rapidly colonize disturbed sites. These species are typically small in stature and short-lived. Allocation of resources is primarily to reproduction, with characteristics allowing for a wide dispersal of seeds to newly disturbed sites. Predictable habitats with abundant resources favor species that allocate resources to growth, favoring resource acquisition and competitive ability (C species). Habitats in which resources are limited favor stress-tolerant species (S species) that allocate resources to maintenance. These three strategies form the end points of a triangular classification system that allows for intermediate strategies, depending on resource availability (levels of stress) and frequency of disturbance (Figure 10.26). Ecological Issues & Applications The Life History of the Human Population Reflects Technological and Cultural Changes The history of the human population presents what appears to be a classic example of exponential population growth, yet on closer inspection, what emerges is a story of a species that has redefined its life history through a series of technological, cultural, and economic changes over the past two centuries. With the end of the last glacial period (~18,000 bp) and the development of agriculture some 10,000 years ago, human demographers estimate that the human population was approaching 5 million. By 1 ad the population had risen to approximately 250 million, and it would take until the beginning of the 19th century before that number would reach a billion. By the 19th century, the human population entered a period of expansive growth, rising to 2 billion by 1930. Adding the next billion would take only 30 years (1960), and on October 31, 2011, the United Nations officially declared that the human population had reached 7 billion. So what are the prospects for the future? The United Nations’ projection of future population growth shows the global population continuing to expand over the next several decades before peaking near 10 billion later in the 21st century. Although this may appear as an astonishingly large number, it represents a significant decline in population growth rates moving forward and the possibility of population numbers stabilizing in the foreseeable future. When combined with projections of future growth over the next century, it becomes apparent the human population is not following a continuous pattern of exponential growth. Rather, the graph of the human population presented in Figure  10.27 suggests three distinct periods, or phases, of population growth in modern time (19th century forward). In phase 1, the period before the early 20th century, population growth is slow and steady. By the early 20th century, however, Phase 2 began, which was a period of dramatic exponential growth. This period of growth continued until the latter part of the 20th century when the population growth rate began to slow. We have now entered Phase 3 as the population growth rate declines and the population potentially peaks at 10 billion. What has caused these three phases? Why did the population growth rate explode in the early 20th century, and what caused it to decline as the 20th century came to a close? These three phases of population growth are the central components of what human demographers—ecologists who study the human population—refer to as the demographic transition. The demographic transition describes the transition from high birthrates and death rates to low birthrates and death rates as countries move from a preindustrial to an industrialized social and economic system (Figure 10.28). Phase 1 is associated with premodern times and is characterized by a balance between high rates of birth and death. This was the situation of the human population before the late 18th century. This balance between birthrate and death rate resulted in a slow growth rate (<0.05 percent). Death rates were high because of the lack of sanitation, knowledge of disease prevention and cures, and occasional food shortages (usually climate related). The infant mortality rate in the United Kingdom and the United States during the 18th century was as high as 500 per 1000, or one in every two infants born. With high child mortality rates, there was little incentive in rural societies to control fertility. By the early 19th century death rates began to decline, first in Europe and then in other countries, over the next 100 years. The decline in death rates would lead to Phase 2 of the transition characterized by exponential growth as the population growth rate rose (difference between birthrate and death rate increased; see Figure 10.28). The decline was a result of improved food supply and sanitation (particularly water supplies). This decline gained momentum in the early 20th century with significant improvements in public health. Improved sanitation and the identification of causes of and cures for infectious diseases led to a dramatic decline in death rates as the 20th century progressed. The greatest reduction in death rates was realized by children; infant mortality rates declined steadily in the 20th century ( Figure  10.29). The reduced infant mortality rates had a swift and dramatic effect on population growth rates. Increases in life expectancy for older individuals (post-reproductive) has little effect on population growth rates; in contrast, increased survival rate of infants results in those individuals entering the reproductive ages and adding to overall population growth (see Section 9.7). Phase 3 of the transition moved the population toward stability through a decline in birthrates. This phase began as early as the end of the 19th century in northern Europe and then spread to other places over the next several decades ( Figure  10.30). In the second half of the 20th century, birthrates declined, and by the early 1960s, the world population growth rate peaked at more than 2 percent and has been declining ever since. The number of new individuals added to the global population each year peaked in the 1990s. There are a number of factors that contributed to this decline, although many of them remain speculative and are the focus of continued research by social scientists. In rural areas where children played an important role in farm life, the continued decline in infant mortality meant that at some point parents realized the need to control family size. Likewise, increased urbanization changed the traditional value of large family size that was essential to farm life. The increased role of women in the workforce and the improvements to contraception in the second half of the 20th century led to even further declines in birthrates, and by the early 1960s the world population growth rate had peaked at slightly more than 2 percent. So begins Phase 3 of the transition. The global population growth rate has been declining since its peak in the 1960s and the number of new individuals added to the global population each year peaked in the 1990s. Demographic transition describes the patterns of human population growth for all regions of the planet, but the timing of the transition has differed for different regions. The more industrialized economies began the transition earlier, with many countries in Western Europe and Asia, such as Poland, Germany, and Japan, now exhibiting a negative growth rate. In contrast, many of the developing countries of the world are still in the mid to latter phases of Phase 2, exhibiting growth rates that still exceed 2 percent. From an ecological perspective, the amazing point of the demographic transition is that it represents a modification of the life history of our species, not as a result of natural selection but by means of “social evolution.” Humans have dramatically altered age-specific patterns of birth and death through changes in technology and cultural changes that have occurred as the social structure has changed from rural agrarian to industrial urbanized society. Summary Trade-offs 10.1 Organisms face trade-offs in life history characteristics related to reproduction. Trade-offs are necessitated by the constraints of physiology, energetics, and the prevailing physical and biotic environment. Trade-offs involve conflicting demands on resources or negative correlation among traits. Asexual and Sexual Reproduction 10.2 Fitness is an organism’s ability to leave behind reproducing offspring. Organisms that contribute the most offspring to the next generation are the fittest. Reproduction can be asexual or sexual. Asexual reproduction, or cloning, results in new individuals that are genetically the same as the parent. Sexual reproduction combines egg and sperm in a diploid cell, or zygote. Sexual reproduction produces genetic variability among offspring. Forms of Sexual Reproduction 10.3 Sexual reproduction takes a variety of forms. Plants with separate males and females are called dioecious. An organism with both male and female sex organs is hermaphroditic. Plant hermaphrodites have bisexual flowers, or if they are monoecious, separate male and female flowers on the same individual. Some plants and animals change sex. Benefits and Costs 10.4 The behavioral, physiological, and energetic activities involved in reproduction represent a cost to future reproductive success of the parent in the form of reduced survival, fecundity, or growth. Age at Maturity 10.5 Natural selection favors the age at maturity that results in the greatest number of offspring produced over the lifetime of an individual. Environmental factors that result in reduced adult survival select for earlier maturation, and conversely, environmental factors that result in reduced juvenile survival relative to that of adults select for delayed maturation. Reproductive Effort 10.6 Optimal reproductive effort represents a trade-off between current and future reproduction. Allocation to current reproduction functions to increase current fecundity but reduces parental survival, resulting in decreased future reproduction. Fitness is often maximized by an intermediate reproductive effort, particularly for organisms that reproduce repeatedly over their life spans. Number and Size of Offspring 10.7 Organisms that produce many offspring have a minimal investment in each offspring. They can afford to send a large number into the world with a chance that a few will survive. By so doing, they increase parental fitness but decrease the fitness of the young. Organisms that produce few young invest considerably more in each one. The fitness of the young of such organisms is increased at the expense of the fitness of the parents. Timing of Reproduction 10.8 To maximize fitness, an organism balances immediate reproductive efforts against future prospects. One alternative, semelparity, is the investment of maximum energy in a single reproductive effort. The other alternative, iteroparity, is the allocation of less energy to repeated reproductive efforts. Reaction Norms 10.9 The characteristics related to life history, such as age at maturity, exhibit reaction norms (phenotypic plasticity) as a result of the interaction between genes and the environment. Mating Systems 10.10 The pattern of mating between males and females in a population is the mating system. In animal species, mating systems range from monogamy to promiscuity. Sexual Selection 10.11 In general, males compete with males for the opportunity to mate with females, but females finally choose mates. Sexual selection favors traits that enhance mating success, even if it handicaps the male by making him more vulnerable to predation. Male competition represents intrasexual selection, whereas intersexual selection involves the differential attractiveness of individuals of one sex to the other. By choosing the best males, females ensure their own fitness. Resources and Mate Selection 10.12 Females may also choose mates based on the acquisition of resources, usually a defended territory or habitat. By choosing a male with a high-quality territory, the female may increase her fitness. Life History Strategies 10.13 The set of characteristics that define a species’ life history covary, forming what appear to be distinctive suites of characteristics that seem to be a product of broad categories of selective forces. A number of hypotheses have been developed to explain these patterns. The fast–slow continuum hypothesis says that species can be arranged along a continuum from high to low adult mortality. High adult mortality results in selection for a shorter life cycle, faster development rates, and higher fecundity than populations experiencing low adult mortality. Another hypothesis is based on two contrasting types of habitats: those that are variable in time or short-lived and those that are relatively stable. The former habitat type creates selection pressure for short life cycle, fast development, and high reproductive rates, and high fecundity, and the latter for longevity, delayed maturity, and lower reproductive effort distributed over a longer period of time. Human Life History Ecological Issues & Applications The history of the human population is described by the transition from high birthrates and death rates to low birthrates and death rates as countries move from a preindustrial to an industrialized social and economic system This dynamic is known as the demographic transition and represents a modification of the life history of our species, not as a result of natural selection, but by means of “social evolution.”

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