Limitations Of Optimization In Evolution Research Paper

Academic Writing Service

Sample Limitations Of Optimization In Evolution Research Paper. Browse other  research paper examples and check the list of research paper topics for more inspiration. If you need a religion research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our research paper writing service for professional assistance. We offer high-quality assignments for reasonable rates.

In biological evolution, natural selection is often seen as an optimizing force: an organism’s adaptations are optimal in performing their function because selection has molded them over long periods of time. Individuals with better (i.e., closer to optimal) features have more offspring, which inherit these improvements. More critical scrutiny of adaptations, however, reveals that many are demonstrably suboptimal. In this research paper, the way natural selection works in natural populations is briefly outlined; a discussion of several reasons why the fitness-maximizing action of selection is limited then follows, and the paper concludes with a short review of the consequences for evolutionary biology brought about by limitations of optimization.

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% OFF with 24START discount code


1. Natural Selection

Some organisms, by virtue of the interaction of the environment with aspects of their morphology, behavior, physiology or other phenotypic features, leave more offspring than others with different phenotypes. Hence, on average, these fitter individuals, which are said to have a high fitness, contribute more of their genetic material to the next generation than the less fertile types. If there is some heritable component to these phenotypic differences, then the proportion of offspring exhibiting the fitter phenotype will increase. This process, continuing generation after generation, ensures that the population matches its environment and, eventually, this match can become optimal, in the sense that no better phenotype can occur.

Nevertheless, optimization may not occur, because of biological processes and properties at any of the stages described. In order to understand why, it is necessary to consider just what is being optimized. As described above, natural selection acts at the level of individual organisms. While such organismal-level selection clearly has an effect on the population, it need not maximize any property of that population. In addition, selection can (and does) act at different levels (e.g., genes, chromosomes, cells and populations), in addition to the traditional, Darwinian, individual level.




2. Reasons For Nonoptimality

Natural selection may fail to optimize for several reasons. Some of the causes of nonoptimality do, of course, fall under more than one of the headings.

2.1 Organisms Are Variable

Ironically, the within-population variability, on which natural selection acts to produce a good match of an organism’s phenotype to its environment, may also prevent this fit becoming perfect. Even if selection has optimized a population’s mean phenotype, individuals within that population will exhibit slightly different phenotypes, some of which may be suboptimal. The phenotypic variation in this optimized population may reflect environmental differences, over either time or space, during the various individuals’ development, or genetic differences among individuals, or it may be a manifestation of the interaction of environmental and genetic differences. The effect of genetic differences is known as genetic load, the difference in fitness between the best possible phenotype and the average fitness of the population. The most important form of load, segregational load, is caused by genetic variants that are found at non-negligible frequencies in the population; such variants are said to segregate in the population. The paradigmatic case is that of human sickle-cell anemia in central Africa, where two alleles, HbA and HbS, are found at the β-globin locus involved in the disease. In spite of having mild anemia, heterozygous individuals have the highest fitness because of their enhanced resistance to malaria, and selection acts to maintain both alleles in the population. An inevitable consequence of Mendelian inheritance, however, is that both sorts of homozygotes occur in each generation and these genotypes produce phenotypes with lower fitnesses, HbA HbA individuals being susceptible to malaria and HbS HbS individuals dying early from severe anemia.

A second form of load is mutational load, effected by recurrent deleterious mutations concurrently being eliminated by purifying selection. Because mutation is a rare event, there are usually very few highly deleterious mutants in a population and mutational load is less important as an explanation for a lack of optimality than segregational load.

In the absence of environmental or genetic differences, phenotypic variation can still arise because of random events during the development of an individual (developmental noise) that lead to differences among individual phenotypes. Hence, even though selection may act to maximize the mean fitness of a population, many individuals may be suboptimal. Of course, if the amount of phenotypic variation due to developmental noise is large and a significant number of individuals are far from the optimum, selection can be expected to act to reduce the level of noise.

2.2 Evolution Is Contingent

Natural selection may also fail to optimize because the right sort of phenotypic variation is absent. Evolution is a contingent process; selection can only act on phenotypic variation that is already present in the population. It may be energetically more efficient to have wheels than legs, but if all individuals always have legs, the population will never optimize its energy efficiency. Of course, the phenotypic variation need not initially encompass the optimum. Selection can move a population towards it gradually, via a series of minor improvements, with new phenotypes arising each generation through mutational changes in the gene pool and, especially, genetic recombination. The availability of small phenotypic differences on which selection can act to produce optimality can also be viewed as a developmental constraint (see Sect. 2.9).

A second aspect of contingency is that selection is blind, always acting to move a population towards the nearest local optimum, rather than towards a global optimum. In the ‘legs or wheels’ example above, this blindness would mean that selection would act to improve the efficiency of legs, rather than start afresh with imperfect, less efficient ‘proto-wheels’ and then optimizing.

Different authorities put different emphases on the importance of contingency in evolution. Some parts of evolutionary game theory (see Dugatkin and Reeve 1998, for recent work in this field), for example, stress that over time a vast range of mutations can occur and so the first aspect of contingency is less relevant. Even the second aspect can be avoided if a broad enough spectrum of mutation can occur.

2.3 Variation May Not Be Heritable

If there is no heritable component to the phenotypic variation, natural selection cannot move the population towards the optimum phenotype. Nevertheless, the vast majority of traits examined (e.g., by carrying out selection experiments on populations of a plant or animal species) do show some degree of heritability. Hence, this explanation for nonoptimality is likely to be relatively unimportant. Note, however, that a population subject to segregational load will display a lack of heritability for that trait, even though there is genetic variation underlying the phenotypic variation. Of course, if the selection pressures on that population change, then heritability will cease to be zero and the population will respond to this new selective regime. In the case of sickle-cell anemia, for instance, the absence of malaria in North America has led to a decline in the frequency of the HbS allele in the US Afro-American population.

Non-heritable variation can also aid optimization. Phenotypic plasticity is the ability of a particular genotype to develop differently in different environments. Crucially, the different phenotypes produced are appropriate (maybe even optimal) in the different environments. For instance, when grown in hot, sunny conditions, some plants reduce exposure to the sun, thereby lowering leaf temperature, by orienting their leaves closer to the vertical. Appropriate match to the environment is thereby achieved in a variety of habitats.

2.4 Trade-Offs Among Different Traits

An important reason why aspects of a phenotype are not optimal is a consequence of the logical impossibility of maximizing two or more functions simultaneously. Traits may have contradictory requirements and the evolutionary consequence is a trade-off between these conflicting demands. For example, the greater a bone’s weight, the stronger it is. But heavier bones are more expensive to produce and carry around, so the weight is a balance between the fitness benefits of strength and lightness. Neither the strength nor the weight of the bone is optimal. Nevertheless, this sort of trade-off can still be viewed as resulting in optimality: fitness rather than weight or strength is optimized. This view of optimality, however, reduces the applicability of Darwin’s natural selection to explain why traits are the way they are because many traits must be considered simultaneously.

A second sort of trade-off arises because of the way most, if not all, genes affect several traits in an organism, a property known as pleiotropy. Traits simultaneously affected by variation at one or more loci are said to be ‘genetically correlated.’ If substituting an allele at a pleiotropic locus has opposite consequences for different components of fitness, the result is an evolutionary trade-off. One of the best examples of antagonistic pleiotropy (as this phenomenon is known) is that of early and late fecundity in Drosophila fruit flies. Flies bearing alleles that lead to greater numbers of offspring early in life have fewer offspring as they age and vice versa; no flies have the ideal maximal fecundity at all ages. But, as above, this sort of trade-off still results in fitness (in this case, lifetime fertility) being optimized.

The concept of trade-offs illustrates that it is vital to know just what is being optimized. Selection, at least that acting at the level of the individual, seeks to optimize an organism’s overall fitness, not particular fitness components (e.g., early fecundity). Explanations invoking selection have been criticized for breaking up organisms, which may be treated better as integrated wholes, into separate traits, each of which is considered to be optimal (for example, Gould and Lewontin 1979). Nevertheless, some degree of ‘atomization’ is necessary for scientific enquiry, to avoid a stultifying holism that prevents asking any questions about aspects of an organism. We must not be prevented from studying a bird’s wing simply because it is but part of a bird. Moreover, many traits must be at least ‘quasi-independent’ to give even the semblance of optimality seen in natural populations (Lewontin 1985).

2.5 Selection Is Not Constant

Selection pressures are temporally and spatially variable as a consequence of changes in the environment and the genetic constitution of the population itself. Seasonal variation is an obvious source of environmental change that may result in changes in thfitnesses of different phenotypes. The annual cycling of frequencies of different chromosomal arrangements (called inversions) in some Drosophila populations appears to be an example of seasonal fitness differences. Hence, selection may be continually molding a population, never quite reaching optimality.

Unique events too (such as the arrival of a novel predator or disease) can also change a selective regime, although in the long term selection would be expected to move the population towards optimality. If the environment changes frequently, however, then selection will not have sufficient time to do so. The important issue is just how quickly the environment does change.

Different populations live in different local environments. In the absence of migration between populations, each can become locally adapted. Gene flow, the genetic exchange brought about by migration, inhibits this local process, however, and no population is absolutely optimal. Gene flow is invoked to explain the failure of most species to expand their range continually. Populations at the edge of the distribution cannot adapt to the conditions there (let alone expand to colonize new habitat) and should be demonstrably less than optimal.

Fitnesses may also depend on the frequency of different phenotypes, genotypes or alleles in the population. Such frequency-dependent selection often arises when unusual types have some sort of advantage by virtue of their rarity. In predator–prey situations, for instance, a rare form of the prey may be overlooked because it does not fit the predator’s search image. Similarly, fitnesses may be density-dependent, affected by the population’s size. These sorts of selection can drive continual genetic change in a population, with optimality never being reached. Even when frequency dependent or density-dependent selection moves a population to a genetic equilibrium, this point need not be (and usually is not) an optimum.

2.6 Other Processes Interfere With Selection

Natural selection is not the only process effecting evolutionary change in natural populations. Each generation, the genetic recombination that occurs as part of sexual reproduction breaks up assemblages of alleles, genes and chromosomes that have survived the filtering action of selection and then re-forms less fit types. The sickle-cell anemia example demonstrates this phenomenon: the optimal heterozygosity regenerates suboptimal homozygotes through sexual reproduction every generation. A second example is the reassortment of genes that may be advantageous in certain combinations by chromosomal recombination during meiosis.

At the level of the population, genetic drift, a consequence of the finiteness of populations, randomly alters allele frequencies every generation. The size of these changes is inversely proportional to the size of the population: in large populations, the effect of drift is negligible, whereas, in small populations, it is often the major agent of change. Obviously, such random changes can move a population away from optimality, although just how far away would depend on the strength of selection for the optimum. But genetic drift can also enhance optimization by enabling a population to escape a local optimum so that selection can move it to another, possibly global, optimum.

Migration and mutation can also prevent optimization, as mentioned above. Nevertheless, mutation is also essential to optimization, providing the raw variation on which selection acts to optimize. Migration from other populations, too, can bring in novel variation on which selection can act.

Nonrandom mating, the occurrence of mating pairs at frequencies different from that expected by chance, can also interfere with the action of selection. Indeed, a population’s mean fitness decreases in some simple models of one common form of nonrandom mating, selective mating, in which mates are more (or less) phenotypically or genetically similar for a particular character or gene.

2.7 Selection Need Not Optimize

Perhaps the most counterintuitive reason for optimization being limited is that numerous selective regimes do not maximize fitness. While most verbal descriptions of natural selection (including Darwin’s) emphasize the optimizing aspect of fitness, mathematical models show that the conditions under which fitness reaches a maximum (even a local maximum) are rather restrictive. Ignoring the effects of genetic drift, migration, mutation and nonrandom mating as described above, constant selection, arising from viability differences among different phenotypes reflecting genetic variation at a single locus, will maximize the mean fitness of the population. If selection pressures vary in any way, if selection arises because of fertility or fecundity differences or if more than one locus is involved, the population’s mean fitness need not be maximized. It is even possible to construct simple mathematical models under which mean fitness is minimized. A straightforward verbal scenario (quoted in Gould and Lewontin 1979, p. 592) in which selection acts but does not improve mean fitness concerns an allele for increased fecundity, which selection rapidly fixes in the population. If the population is otherwise unchanged, no more adults are likely to survive than previously, and the size of the population is unchanged (and may even decrease if predators key into the more abundant juveniles). Neither individuals nor the population is optimized.

The non-optimizing property of fertility selection is a reflection of its formal parallels with selective mating: the number of offspring (‘fitness’) is a property of the mated pair, ‘fertility’ for fertility selection and ‘the chance of mating’ for selective mating. Mean fitness can decrease in both sorts of systems. Nevertheless, it can be argued that the sorts of fitnesses that lead to such non-intuitive behavior are not often found in nature. This rarity may arise either because genetic variants bearing such fitnesses arise infrequently (a form of mutational bias) or because they are weeded out by selection in the longer term.

Systems with two or more loci do not optimize mean fitness because of genetic linkage and the nonindependence of the effects of genes at the different loci (a property known as epistasis). The recombinatory effects of sexual reproduction prevent the population’s mean fitness being maximized. But, again, a case can be made that alleles with strong epistatic interactions are unusual and, as a result, genes (as well as traits) are selectively quasi-independent.

2.8 Conflicts Among Different Levels Of Selection

The above account describes selection acting at the level of the individual. But, at least in principle, selection can act at several levels simultaneously and, as a consequence, an optimum is not reached at one or both levels. The so-called t allele system (now known to involve several loci locked in a chromosomal rearrangement or inversion) in mice provides a paradigmatic example. Heterozygous males produce about 95 percent t sperm, rather than the 50 percent expected under standard Mendelian segregation. This difference is an example of selection at the level of the gamete, favoring the t allele: it is as if t-bearing sperm win the race to fertilize an egg more often than these males’ wild-type sperm. But at the level of the organism there is strong selection against the t-allele, since homozygous males are sterile. Finally, there is selection at the population level reducing the frequency of t. Mouse populations are often small and genetic drift, together with the gametic-level selection, can occasionally result in a population having only tt males. Such populations then become extinct because of the sterility of these males.

Conflicts among levels of selection can be viewed in a similar way to conflicts among selection on different traits: selection is still optimizing some overall property of the system, a balance between action at the different levels. It should also be noted that the efficacy of selection at higher levels, say, populations, is less than that at lower levels, say, individuals, because population colonization and extinction rates are lower than individual birth and death rates. Nevertheless, there are clearly limits on the optimizing power of selection at a particular level.

2.9 Developmental Constraints

The way in which the developmental system of an organism is structured and operates can constrain the phenotypic response to optimizing selection. Developmental constraints can be divided into two categories (Maynard Smith et al. 1985), ‘universal’ and ‘local,’ although it is not always easy to distinguish between them. Universal constraints are those applying to all organisms because, for instance, they are a consequence of the laws of physics. The abovementioned trade-off between the weight and strength of a bone is an example of a universal developmental constraint.

Local constraints, by contrast, pertain to a particular group of organisms: can an organism’s developmental system produce a phenotype that selection will use as a step towards optimality? For example, Maynard Smith et al. (1985) argue that the failure to evolve a process of secondary thickening forces palm trees to be unbranched. Such constraints could also be described as phylogenetic or historical and are a manifestation of the contingency of evolution (see Sect. 2.2). Once evolution has started down a particular pathway, certain developments are precluded. At the origin of life, for example, the choice (for whatever reason) of left-handed amino acid isomers has restricted subsequent evolution to this option. Local developmental constraints may be overcome by selection: some species of palm trees do have branches, for instance. The importance of such constraints clearly depends on their strength, which can be difficult to gauge.

3. Consequences For Evolutionary Biology

Deciding whether optimization has been achieved is difficult. At one extreme, suboptimality is acknowledged, but explained away by invoking trade-offs, developmental constraints, or chance. Everything is considered to be as good as possible, given the circumstances, a position Gould and Lewontin (1979) pilloried as the Panglossian Paradigm. Williams (1966) has suggested that natural selection be invoked as an explanation for a trait only as a last resort. Consequently, a trait need not be considered optimal and the initial focus, at least, is on nonselective processes that may have led to its evolution. Mayr (1983), however, argued that there was nothing wrong in searching for a selective explanation for a phenotype, provided we acknowledge that evolution need not always optimize. Indeed this ‘adaptationist program’ has provided biology with its most powerful explanatory methodology: Krebs and Davies (1997), for instance, exemplify the productive use of this approach in the study of behavior. Moreover, Seger and Stubblefield (1996) demonstrate that using an optimization approach to study a biological phenomenon can expose sub-optimality. Nevertheless, for all the above reasons, it should be remembered that the traditional heuristic interpretation of the action of selection can be misleading. Consequently, optimization will often be limited.

Bibliography:

  1. Dugatkin L A, Reeve H K (eds.) 1998 Game Theory and Animal Behavior. Oxford University Press, New York
  2. Gould S J, Lewontin R C 1979 The spandrels of San Marco and the Panglossian paradigm: A critique of the adaptationist programme. Proceedings of the Royal Society of London Series B, Biological Sciences 205: 581–98
  3. Krebs J R, Davies N B (eds.) 1997 Behavioural Ecology: An Evolutionary Approach, 4th edn. Blackwell Science, Oxford
  4. Lewontin R C 1985 Adaptation. In: Levins R, Lewontin R C (eds.) The Dialectical Biologist. Harvard University Press, Cambridge, MA
  5. Maynard Smith J, Burian R, Kauffman S, Alberch P, Campbell J, Goodwin B, Lande R, Raup D, Wolpert L 1985 Developmental constraints and evolution. Quarterly Review of Biology 60: 265–87
  6. Mayr E 1983 How to carry out the adaptationist program? American Naturalist 121: 324–34
  7. Seger J, Stubblefield J W 1996 Optimization and adaptation. In: Rose M R, Lauder G V (eds.) Adaptation. Academic Press, San Diego, CA
  8. Williams G C 1966 Adaptation and Natural Selection. Princeton University Press, Princeton, NJ

 

Evolution Of Primates Research Paper
Natural Selection Research Paper

ORDER HIGH QUALITY CUSTOM PAPER


Always on-time

Plagiarism-Free

100% Confidentiality
Special offer! Get 10% off with the 24START discount code!