Unit 3- Populations 3.1-Testing for Plasticity and Adaptation
Unit 3- Populations 3.1-Testing for Plasticity and Adaptation Chapter 4 pages 90–93, 98–100 Key Terms Phenotype: is a product of the interaction between an organism’s genes; organism's observable characteristics or traits: such as its morphology or development Genotype: genetic makeup of a cell, an organism, or an individual common garden study: representatives from different populations that had distinctive growth forms, which he called ecotypes, were grown under the same environmental conditions in a common location. If plasticity was the only cause of the variation in morphology in a species, then you would expect that plants grown in a common garden would look the same ecotypes: genetically distinct geographic variety, population or race within species (or among closely related), which is adapted to specific environmental conditions. genetic drift: change in gene frequencies in a population due to chance or random events local adaptation: a population survives best in its home environment. If the genetic differences are the result of adaptation, then the growth of each plant in its home environment should be better than in the other environments. reciprocal transplant experiment: used to determine if genetically differentiated populations are adapted to their home environments. Heritability: the proportion of total phenotypic variation in a trait attributable to genetic variation; determines the potential for evolutionary change in a trait homozygous: having identical alleles at a given locus heterozygotes: having different alleles at a given locus Phenotypic Variation in a Desert Lizard -
Ted Case (1976) explored variation in body size among Sauromalus (lizard known at Chuckwalla which lives in hot, dry places) populations at twelve sites distributed across its geographic range, and found that average summer temperatures at his desert study sites ranged from 23.8 C to 35 C, while average annual rainfall varied from approx. 35 to 194 mm. Because the environments in which Sauromalus lives vary greatly across its range, we might expect that selection have favored different characteristic in different parts of the species’ range. Prefers to eat herbaceous plants and the amount of winter rainfall largely determines the amount of plant growth in these desert environments therefore higher rainfall means more available food.
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Variation in rainfall translates into variations in food availability. Lizards at lower elevations on average have access to less food and the amount available on any
given year is unpredictable. -
This case found that lizards from the food-rich higher elevations are approximately 25% longer than those from lower elevations therefore different body mass. Therefore the best predictor of body length with these lizards is average winter rainfall.
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Christopher Tracy (1999) collected 12 to 15 juvenile Chuckwallas from 6 populations in Arizona, California, and Nevada, living at elevations ranging from 200 to 890m. He then raised these lizards under identical environmental conditions in a laboratory to determine the contributions of environmental versus genetic factors to size differences among this population. He concluded that lizards from higher elevations grew to a larger size, approximating in a lab common garden for lizards the pattern of variation in body size found in the field.
Genetic Variation and Heritability -
in an equation variability can be defined as: h2=VG/VP which is genetic variance and phenotypic variance sub dividing VP gives an equation of: h2=VG/(VG+VE) Peter Boag and Peter Grant (1978) the latter formerly a professor at Mcgill, estimated bill width in the Galapagos finch (geospiza fortis) to have a heritability of 0.95. By comparison they estimated that bill length in the species has a heritability of 0.62.
Calculating Gene Frequencies -
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Chia-Chen Tan (1946), Chia-Chen Tan and Ju-Chi Li (1934), and Theodosius Dobzhansky (1937) determined that the variation in colour patterns shown by Harmonia (Asian lady beetles) is due to the effects of more than a dozen alternative alleles for colour pattern Hardy-Weinberg Principle states that in a population mating at random in the absence of evolutionary forces, allele frequencies will remain constant. To maintain constant allele frequencies in a population: • Random mating • No mutations • Large population size • No immigration • All genotypes have equal fitness
Disruptive Selection -
A form of natural selection that favours twos or more extreme phenotypes over the average phenotype in a population.
Acclimation/plasticity or adaptation/evolution? •
Organisms can optimize their performance three key ways: (1) acclimation or developmental plasticity, (2) dispersal or range shifts, and (3) evolution (adaptation) or extinction.
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The first and third ways involve expressing a new phenotype to suit a new environment.
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Two things to keep in mind are that acclimation and plasticity represent the capacity of an individual to adjust to environmental change within their own lifetime, and it occurs very quickly, whereas adaptation is an evolutionary response of a population that occurs from generation to generation and therefore takes more time.
Testing for developmental plasticity 1) Phenotype = Genotype × Environment
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One genotype (or clone) can be grown in different environments. If phenotypic differences are observed between environments then plasticity can be implicated as the cause of that phenotypic variation.
2) P = G × E
•
↑
Held constant
French botanist, Gaston Bonnier, observed in his early studies on variability in plant populations in Europe (Bonnier 1890). On his travels in various mountain ranges he observed that many species of plants grew over a wide range of elevations, and differed dramatically in their form in different locations. These observations prompted him to conduct transplant experiments on the effects of climate on plant morphology.
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Bonnier was able to produce genetically identical clones of a single plant, and transplant one into a lowland site and the other into an alpine site. He then made measurements and drawings comparing each pair of plants. These results show that the phenotypic differences between these plants were caused by developmental plasticity; a single genotype was able to produce morphologically different phenotypes in response to the environment.
Testing for genetic variability among populations •
Gote Turesson (1925) extended this type of research farther and looked for evidence for genetic variability among populations. He used a common garden study in which representatives from different populations that had distinctive growth forms, which he called ecotypes, were grown under the same environmental conditions in a common location. If the morphological differences persisted when grown in the same environment, this would be evidence for genetic differences among populations.
1) P = G × E
•
↑
Held constant
It is important to realize that change in gene frequencies in a population can be due to chance or random events (i.e., genetic drift) and mutation, or it can be the product of natural selection and therefore adaptive.
Testing for adaptation •
To demonstrate adaptation, you would have to show that: (1) phenotypic differences among populations are based on genetic differences, using a common garden study. (2) each population survives best in its home environment (local adaptation) when compared to others derived from other environments. A reciprocal transplant experiment can be used to determine if genetically differentiated populations are adapted to their home environments.
(1) Common garden comparison: If plasticity was the only cause of the variation in morphology in a species, then you would expect that plants grown in a common garden would look the same
(2) Local adaptation (reciprocal transplant) comparison: If the genetic differences are the result of adaptation, then the growth of each plant in its home environment should be better than in the other environments. “Unintentional” experiments to test for adaptation •
An unintentional experiment occurred when three new species of plants in the Sapindaceae family were brought to the United States in the 1950s and were planted farther north than the existing species’ range. Soapberry bugs (Jadera haemotaloma) feed exclusively on plants in this family. They have piercing mouthparts (beaks) that they insert into the fruits to feed, and the length of the beak must be a certain length to reach the seeds within the fruit.
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Scott Carroll and Christin Boyd (1992) seized the opportunity to ask whether the soapberry bugs were able to adapt to these new food sources and expand their range. They first collected eggs from each population of soapberry bug and grew them in a common garden. Differences in beak length among populations were maintained in the common garden, suggesting that beak length is genetically determined
•
The researchers also found a positive correlation between beak length and the radius of fruits each population used, suggesting that each soapberry bug population had adapted to its plant host. But this was only observational evidence.
•
Scott Carroll, Stephen Klassen, and Hugh Dingle (1998) continued the study. They asked whether soapberry bug populations were ecotypes, and answered this question by doing a reciprocal transplant study and measuring juvenile survivorship of each population on different plant hosts.
3.2- Speciation Chapter 4 pages 103–107 Key Terms Inbreeding: mating between close relatives; more likely in small populations microsatellite DNA: short repeating units of DNA that can be used identity relatedness among individuals. Biological species concept: a group of actually or potentially interbreeding populations, which are reproductively isolated from other such groups Isolating Mechanisms: some process that prevents the production of a viable offspring between two individuals. Isolating mechanisms are critical to the species integrity Allpatric Speciation: speciation that occurs when isolating mechanisms evolve among geographically separated populations Parapatric speciation: speciation that occurs when a population expands into a new
habitat-type within the pre-existing range of the parent species Sympatric speciation: speciation that occurs when isolation mechanisms evolve among populations with overlapping geographic ranges Assorative mating: mating among phenotypically similar (positive assortative mating), or dissimilar (negative assortative mating), individuals Parallel Evolution: the independent evolution of similar traits in geographically separated species Genetic Diversity and Butterfly Extinctions -
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Richard Frankham and Katherine Ralls (1998) point out that one of the contributors to higher extinction rats in small populations may be inbreeding. Combining already low genetic variations in small populations with a high rate of inbreeding has several negative impacts on populations, including reduced fecundity, lower juvenile survival, shortened life span and it further accelerates the loss of genetic diversity especially in Plantago lanceolata and Veronica spicata that act as hosts for the Glanville fritillary butterfly, Malitaea cinxia. Ilik Saccheri and colleagues (1998) reported one of the first studies giving direct evidence that inbreeding contributes to extinctions in wild populations. Studied dry meadows. Documented an average of 200 extinctions and 114 colonization’s. Conducted genetic studies on populations of Melitaea in 42 meadows estimating heterozygosity and indicator of genetic variability with respect to seven enzyme systems and one locus of nuclear microsatellite DNA. Results of the study indicated that influence of inbreeding on the probability of extinction was very significant. High inbreeding=probability of extinction.
Speciation: what is a species? -Ernst Mayr (1942) defined species as “groups of actually or potentially interbreeding populations, which are reproductively isolated from other such groups.” Based upon a real ecological concept: reproductive isolation Speciation: what is reproductive isolation? -
isolating mechanisms can be categorized into two groups, pre and postzygotic. Prezygotic isolating mechanisms are processes which prevent two individuals from forming a zygote. Postzygotic isolating mechanisms are equally efficient at maintaining species integrity, but they occur after a zygote has been formed.
Speciation: what causes speciation? -
in both allopatric and parapatric speciation, the evolution of reproductive isolation could occur through drift or natural selection. Since genetic drift is random, fluctuations in allele freguencies in subpopulations will be independent, and thus loss of genetic diversity through drift could lead to reproductive isolation
Reproductive Isolation and Ecological Divergence -
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Dan Funk and colleagues (2006) decided to conduct a broad test of this prediction that increased ecological divergence will be associated with increased reproductive isolation. Using both parametric and nonparametric tests for relations they found that the more different the habitats were the more reproductively isolated the pairs of related species were. Mckinnon and colleagues (2004) wanted to know to what extent ecological difergence plays in the early stages of speciation. What they did was collected fish from marine and stream populations and placed them in an experiemental aquarium to see which individuals would and would not mate. Concluded that reproductive isolation occurred based upon ecological differentiation and not primarily geographic isolation.
3.3 Distribution and Abundance of Populations and Species Chapter 10 pages 255–256, 259–267, 269–274 Distribution Limits -
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G. Caughley and his colleagues (1987) found a close relationship between climate and the distribution of the three largest kangaroos in Australia. One in the eastern third of the continent of Australia that includes several biomes (temperate forest grows in the southeast and tropical forests in the north, and mountains with their varied climates. Another in the southern and western regions of Australia (temperate woodland and shrubland biome). Third in the savanna and desert. These limited distributions may not be determined by climate directly but suggest that climate often influences species distributions through factors such as food production, water supply and habitat. Also studied was the tiger beetle (Cicindela longilabris) that lives at higher latitudes and high elevations that just any other species of tiger beetle in north America. Thomas Schultz, Michael Quinlan, and Neil Hadley (1992) set out to study the environmental physiology of widely separated populations of the species. Found that metabolic rates are higher and its preferred temperatures lower than those of most other tiger beetle species that have been studied. None of
their measurements differed significantly among populations. Distribution Patterns -There are three distributions of patterns: 1. Random Distribution: an individual has an equal probability of occurring anywhere in an area. Neutral interactions between individuals and local environment 2. Regular Distribution: individuals are uniformly spaced through the environment. Antagonistic interactions between individuals or local depletion of resources 3. Clumped Distribution: individuals live in areas of high local abundance which are separated by areas of low abundance Organism Size and Population Density -
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John Damuth (1981) produced one of the first clear demonstrations of the relationship between body size and population density. Focused on herbivourus mammals ranging from small rodents to rhinoceros. Meanwhile average population density of 307 species of herbivorous mammals decreases with increased body size. Building on his analysis Robert Peters and Karen Wassenberg (1983) explored the relationship between body size and average population density for a wider variety of animals. Found that population density decreased with increased body size.
Commonness and Rarity -Deborah Rabinowitz (1981) devised a classification of commonness and rarity based on combinations of three factors: 1. Geographic range of a species 2. Habitat tolerance 3. Local population size - Any one of these factors, if small for a species, is associated with an increased probability of rarity, and a species with all three small is the most vulnerable to extinction.
3.4 Population Dynamics Chapter 11 pages 283–298, Chapter 12 pages 309–312 Key Terms Cohort: a group of individuals of the same age Cohort Life Table: a life table based on individuals born (or beginning life in some other way) at the same time Static Life Table: a life table constructed by recording the age at death of a large number
of individuals; the table is called static because the method Age Distribution: the distribution of individuals among age groups in a population; also called age structure Survivorship Curve: a graphical summary of patterns of survival in a population -
Adolph Murie (1944) had been fired by the U.S. National Park Service to study the interactions between wolves and Dall sheep in Mount Danali National Park, Alaska. The main purpose of his study was to determine whether wolves kill enough sheep to justify the call for reducing the wolf population. Sheep found ways of avoiding the wolves.
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In contrast Ahmad Hegazy (1990) used similar care to study an endangered plant species. Prompted by concern that harvesting was leading to the extinction of this valuable plant (Cleome droserifolia). Through careful analysis of the Cleome population provided a means for managing the species that promotes its survival and allowed its use in traditional medicine
Patterns of Survival: Estimating Patterns of Survival -
three main ways of estimating patters of survival within a population: 1. identify a large number of individuals that are born at about the same time and keep records on them from birth to death (cohort and cohort life table). 2. Go into a field for a narrow window of time and record the age at death of a large number of individuals. Different because individuals are born at different times (static life table). 3. Determines patterns of survival from age distribution that consists of the proportion of individuals of different ages within a population.
Patterns of Survival: High Survival Among the Young -
plotting number of survivors per 1000 births against age produces the survivorship curve. Murie used this when plotting the patterns of life and death within the sheep population.
Patterns of Survival: Three Types of Survivorship Curves -
Type I survivorship curve: a pattern of survivorship in which there are high rates of survival among young and middle-aged individuals followed by high rates of mortality among the aged
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Type II survivorship curve: a pattern of survivorship characterized by constant
rates of survival throughout life -
Type III survivorship curve: a pattern of survivorship in which a period of extremely high rates of mortality among the young is followed by a relatively high rate of survival
Age Distribution -
indicates that reproduction is sufficient to replace the oldest individuals in the population as they die.
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Rosemary and Peter Grant (1989) spent decades studying Galapagos finch populations. The age distributions of the large cactus finch during 1983 and 1987 show that population can be very dynamic. The age distribution tells population ecologists a great deal about the dynamics of population including whether a population is growing, declining, or approximately stable.
3.5 Life Histories and Trade-Offs Chapter 9 pages 226–230 Life history trade-offs -
A species’ life history describes how, over a lifetime, an organism’s energies are allocated to functions such as growth, number of offspring produced, parental investment in offspring, size at reproductive maturity, and dispersal.
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energy is limited and can’t be allocated in such a way as to maximize all of these functions, so there need to be trade-offs between functions. For example, there must be a trade-off between growth and reproduction because if more energy is allocated to growth, then less will be available for reproduction. Because life history trade-offs affect reproduction, they influence population dynamics.
Seed size and recruitment -
Anna Jakobsson and Ove Eriksson (2000) investigated how seed size, which is determined by energy allocation to reproduction by the maternal plant, influences seedling size and seedling survival to reproduction (also known as recruitment). Their results, show that larger seeds produced larger seedlings and that seedlings from larger seeds had a higher recruitment rate. So there is clearly an advantage to those species that have large seeds.
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The seed size and seed number trade-off was shown by Stevens (1932) for plants
in the Asteraceae (daisy), Poaceae (grasses), Brassicaceae (mustards), and Fabaceae (bean); in fact, the offspring size and number trade-off is one of the most common in nature. Trade-offs and survivorship curves -
One implication of producing many offspring is that parents do not have the energy to provide care to all those offspring. Thus, there is often a trade-off between number of offspring and the amount of parental care. Humans, for example, have few offspring but high parental care resulting in a high rate of survivorship and a Type I survivorship curve. Contrast this to the plants had many offspring, low parental care, and low rates of survival.
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Trade-offs exist among many life history functions including (1) growth and reproduction, (2) growth and defense, (3) number and size of offspring, (4) number of offspring and parental care, and (5) number of offspring and parent lifespan.
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Dispersal is another life history function that involves trade-offs.
Dispersal -
Dispersal can be defined as the permanent movement of individuals from their site of origin to a previously unoccupied location. They may disperse to avoid intraspecific competition (competition between individuals of the same species) or interspecific competition (competition between individuals of different species), avoid inbreeding, or to locate suitable habitat for an expanding population.
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Strayer and colleagues (1996) on the zebra mussel (Dreissena polymorpha) in the Hudson River, New York. The species was first detected in 1990, it expanded its range in 1991, and by 1992 it was found everywhere they sampled in the freshwater part of the estuary. By 1992 there were no more places to disperse to.
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Small (young) individuals were prevalent in 1992 (they made up the bulk of the population); however, their numbers were reduced in 1993 (most individuals were in the 10 to 20 mm size classes), and by 1994, their numbers were very low. There was no new recruitment of individuals, because of a lack of dispersal.
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To minimize energy expense, organisms can produce lots of small offspring, or use other organisms or media (e.g., water) to disperse them. To minimize the risk of predation, they could disperse when they are mature and able to defend themselves, or disperse when they are in a protected stage, such as a seed. To minimize the risk of finding a poorer habitat they could produce lots of offspring with the hopes that at least some of them will find suitable habitat. Another strategy would be to produce fewer offspring, but provide them with enough
energy reserves to get established. -
If offspring require protection from parents to avoid being preyed upon, then the organism can’t produce lots of offspring. Producing lots of small offspring to minimize the cost of dispersal means that parental care is minimized. Dispersing when they are old enough to defend themselves means they need more parental care, hence few offspring. Minimizing the risk of getting a poor site in a variable environment by having lots of offspring means that the offspring will be small. Relate these trade-offs back to the three types of survivorship curves; any strategy that results in lots of small offspring with little parental care will likely result in a type III survivorship curve. Strategies that involve only a few offspring and more parental care will likely result in a type I curve.
3.6 Population Growth • Chapter 12 pages 305–309, 313–325, Chapter 9 pages 237–240 Fecundity schedules -
the number of births in each age class. This is referred to as a fecundity schedule.
R0 = Σ lx mx = 2.418 -
This means that every individual is producing 2.418 offspring in its lifetime.
Geometric population growth -
The population size of Phlox next year (Nt+1) can be predicted because we know population size in the current year (Nt) and we know that each individual produced an average of 2.418 seeds. Thus, population size at Nt+1 of Phlox drummondii can be calculated using the following equation:
Nt+1 = Nt × R0 -
Constant R0 produces geometric (exponential) growth. It is called geometric growth because it occurs by the same proportion (i.e., 2.418) in each generation. Note that for annuals such as Phlox that have pulsed reproduction, the textbook uses the following equation where λ is equal to R0: Nt+1 = Nt × λ
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Based on the predicted population size for Phlox based on R0 = 2.418, we can
make general inferences about what net reproductive rate (R0) tells us about population growth: If R0 = 1 then the population is constant, i.e., each individual replaces itself If R0 > 1 then the population increases, as with PhloxIf R0 < 1 then the population decreases, because each individual does not replace itself. -
There are two assumptions that we have made in order to use this as a predictive tool: (1) that there are no limits to growth, and (2) that R0 stays constant over time. As you read the next sections, think about whether these assumptions are valid for natural populations.
Exponential population growth -
To examine population growth in species with overlapping generations, we need to calculate the per capita rate of increase (r), which is the per capita birth rate (b) minus the per capita death rate (d), in the absence of limiting resources: r = b – d. It is a per capita model because growth rate depends on the number of existing breeding individuals in a population. To simplify our consideration of population growth with overlapping generations, we will consider only births and deaths as sources of change in the model, though the effects of immigration and emigration could also be added.
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Like R0, r can be calculated from a life table by combining information on survivorship and age-specific births. It can then be used to predict population size in future years, in the same way that we used R0. Thus, to predict population size next year (Nt+1), we need to know how many individuals there are in the current year (Nt) and r. Because there are overlapping generations, the equation is modified so that the number of new individuals produced (r × Nt) is added to the number of existing individuals Nt: Nt+1 = r × Nt + Nt
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As with our description of population growth in Phlox, when growth rate is a function of population size, populations grow exponentially. A constant, positive r always produces exponential growth. This property can be observed by calculating the growth rate, or change in the number of individuals per change in time (dN/dt) for a population experiencing exponential growth. When a population is in exponential growth, the growth rate (dN/dt) always increases with population size (N).
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But does exponential growth go on forever in natural populations? NO! Resources become limited at some population level, and population growth stops, resulting in a sigmoidal or S-shaped population growth curve.
Logistic population growth -
Sigmoidal population growth, also called logistic population growth, has been
shown in a variety of populations including yeast (Saccharomyces cerevisiae), paramecium (Paramecium caudatum), barnacles (Balanus balanoides), and even African buffalo (Syncerus caffer) on the Serengeti Plain in Africa. -
Initially, populations grow very quickly as in exponential growth; however, at some point the rate of growth slows due to limiting resources and stops when the carrying capacity of the habitat is reached. The carrying capacity is known as K; it is the theoretical maximum population size or number of individuals that can be supported in a given environment.
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realized per capita rate of increase: dN/dt= rmaxN. The reason why r is now called the “realized” per capita rate of increase is that in the logistic growth model, it is defined relative to rmax, or the maximum per capita intrinsic rate of increase, which occurs when N=1.
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Early on in population growth, r is at its maximum, but Nt is low, so the numbers of new individuals being added at each time point are small. The maximum growth rate occurs when r and Nt are at their intermediate points, and this is at half the carrying capacity (K/2). The growth rate approaches 0 when the carrying capacity is reached because r = 0.
Limits to population growth -
In theory, then, logistic growth is density-dependent because population growth rate slows as population size reaches K. To produce this pattern, realized per capita rate of increase, or r, should decrease with population size. The maximum rate of increase, rmax occurs at a very low population size, and when N = K, r = 0 and population growth stops.
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Support for the theoretical prediction for density-dependent population regulation has been found in an experimental manipulation of Daphnia populations (Frank et al. 1957).
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Competition for resources may be one cause of density-dependence; with a higher population there is higher competition, and greater mortality.
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Two other possible mechanisms are attraction of predators by high prey density, and increased infection by disease with higher density.
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What mechanisms are responsible for density-independent population regulation, i.e., if per capita birth and death rates are unrelated to population density? These are generally abiotic factors, and they are often rare, extreme events such as an early hard freeze, or a tornado, flood, landslide, wind storm, etc. Density-independent and density-dependent population controls can interact.
Population growth and life histories: r-selection and K-selection
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How does population growth relate to life histories? One classification system proposed by MacArthur and Wilson (1967) and Eric Pianka (1970) is the concept of r–selection and K–selection.
Ted Case (1976) explored variation in body size among Sauromalus (lizard known at Chuckwalla which lives in hot, dry places) populations at twelve sites distributed across its geographic range, and found that average summer temperatures at his desert study sites ranged from 23.8 C to 35 C, while average annual rainfall varied from approx. 35 to 194 mm. Because the environments in which Sauromalus lives vary greatly across its range, we might expect that selection have favored different characteristic in different parts of the species’ range. Prefers to eat herbaceous plants and the amount of winter rainfall largely determines the amount of plant growth in these desert environments therefore higher rainfall means more available food.
-
Variation in rainfall translates into variations in food availability. Lizards at lower elevations on average have access to less food and the amount available on any
given year is unpredictable. -
This case found that lizards from the food-rich higher elevations are approximately 25% longer than those from lower elevations therefore different body mass. Therefore the best predictor of body length with these lizards is average winter rainfall.
-
Christopher Tracy (1999) collected 12 to 15 juvenile Chuckwallas from 6 populations in Arizona, California, and Nevada, living at elevations ranging from 200 to 890m. He then raised these lizards under identical environmental conditions in a laboratory to determine the contributions of environmental versus genetic factors to size differences among this population. He concluded that lizards from higher elevations grew to a larger size, approximating in a lab common garden for lizards the pattern of variation in body size found in the field.
Genetic Variation and Heritability -
in an equation variability can be defined as: h2=VG/VP which is genetic variance and phenotypic variance sub dividing VP gives an equation of: h2=VG/(VG+VE) Peter Boag and Peter Grant (1978) the latter formerly a professor at Mcgill, estimated bill width in the Galapagos finch (geospiza fortis) to have a heritability of 0.95. By comparison they estimated that bill length in the species has a heritability of 0.62.
Calculating Gene Frequencies -
-
Chia-Chen Tan (1946), Chia-Chen Tan and Ju-Chi Li (1934), and Theodosius Dobzhansky (1937) determined that the variation in colour patterns shown by Harmonia (Asian lady beetles) is due to the effects of more than a dozen alternative alleles for colour pattern Hardy-Weinberg Principle states that in a population mating at random in the absence of evolutionary forces, allele frequencies will remain constant. To maintain constant allele frequencies in a population: • Random mating • No mutations • Large population size • No immigration • All genotypes have equal fitness
Disruptive Selection -
A form of natural selection that favours twos or more extreme phenotypes over the average phenotype in a population.
Acclimation/plasticity or adaptation/evolution? •
Organisms can optimize their performance three key ways: (1) acclimation or developmental plasticity, (2) dispersal or range shifts, and (3) evolution (adaptation) or extinction.
•
The first and third ways involve expressing a new phenotype to suit a new environment.
•
Two things to keep in mind are that acclimation and plasticity represent the capacity of an individual to adjust to environmental change within their own lifetime, and it occurs very quickly, whereas adaptation is an evolutionary response of a population that occurs from generation to generation and therefore takes more time.
Testing for developmental plasticity 1) Phenotype = Genotype × Environment
•
One genotype (or clone) can be grown in different environments. If phenotypic differences are observed between environments then plasticity can be implicated as the cause of that phenotypic variation.
2) P = G × E
•
↑
Held constant
French botanist, Gaston Bonnier, observed in his early studies on variability in plant populations in Europe (Bonnier 1890). On his travels in various mountain ranges he observed that many species of plants grew over a wide range of elevations, and differed dramatically in their form in different locations. These observations prompted him to conduct transplant experiments on the effects of climate on plant morphology.
•
Bonnier was able to produce genetically identical clones of a single plant, and transplant one into a lowland site and the other into an alpine site. He then made measurements and drawings comparing each pair of plants. These results show that the phenotypic differences between these plants were caused by developmental plasticity; a single genotype was able to produce morphologically different phenotypes in response to the environment.
Testing for genetic variability among populations •
Gote Turesson (1925) extended this type of research farther and looked for evidence for genetic variability among populations. He used a common garden study in which representatives from different populations that had distinctive growth forms, which he called ecotypes, were grown under the same environmental conditions in a common location. If the morphological differences persisted when grown in the same environment, this would be evidence for genetic differences among populations.
1) P = G × E
•
↑
Held constant
It is important to realize that change in gene frequencies in a population can be due to chance or random events (i.e., genetic drift) and mutation, or it can be the product of natural selection and therefore adaptive.
Testing for adaptation •
To demonstrate adaptation, you would have to show that: (1) phenotypic differences among populations are based on genetic differences, using a common garden study. (2) each population survives best in its home environment (local adaptation) when compared to others derived from other environments. A reciprocal transplant experiment can be used to determine if genetically differentiated populations are adapted to their home environments.
(1) Common garden comparison: If plasticity was the only cause of the variation in morphology in a species, then you would expect that plants grown in a common garden would look the same
(2) Local adaptation (reciprocal transplant) comparison: If the genetic differences are the result of adaptation, then the growth of each plant in its home environment should be better than in the other environments. “Unintentional” experiments to test for adaptation •
An unintentional experiment occurred when three new species of plants in the Sapindaceae family were brought to the United States in the 1950s and were planted farther north than the existing species’ range. Soapberry bugs (Jadera haemotaloma) feed exclusively on plants in this family. They have piercing mouthparts (beaks) that they insert into the fruits to feed, and the length of the beak must be a certain length to reach the seeds within the fruit.
•
Scott Carroll and Christin Boyd (1992) seized the opportunity to ask whether the soapberry bugs were able to adapt to these new food sources and expand their range. They first collected eggs from each population of soapberry bug and grew them in a common garden. Differences in beak length among populations were maintained in the common garden, suggesting that beak length is genetically determined
•
The researchers also found a positive correlation between beak length and the radius of fruits each population used, suggesting that each soapberry bug population had adapted to its plant host. But this was only observational evidence.
•
Scott Carroll, Stephen Klassen, and Hugh Dingle (1998) continued the study. They asked whether soapberry bug populations were ecotypes, and answered this question by doing a reciprocal transplant study and measuring juvenile survivorship of each population on different plant hosts.
3.2- Speciation Chapter 4 pages 103–107 Key Terms Inbreeding: mating between close relatives; more likely in small populations microsatellite DNA: short repeating units of DNA that can be used identity relatedness among individuals. Biological species concept: a group of actually or potentially interbreeding populations, which are reproductively isolated from other such groups Isolating Mechanisms: some process that prevents the production of a viable offspring between two individuals. Isolating mechanisms are critical to the species integrity Allpatric Speciation: speciation that occurs when isolating mechanisms evolve among geographically separated populations Parapatric speciation: speciation that occurs when a population expands into a new
habitat-type within the pre-existing range of the parent species Sympatric speciation: speciation that occurs when isolation mechanisms evolve among populations with overlapping geographic ranges Assorative mating: mating among phenotypically similar (positive assortative mating), or dissimilar (negative assortative mating), individuals Parallel Evolution: the independent evolution of similar traits in geographically separated species Genetic Diversity and Butterfly Extinctions -
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Richard Frankham and Katherine Ralls (1998) point out that one of the contributors to higher extinction rats in small populations may be inbreeding. Combining already low genetic variations in small populations with a high rate of inbreeding has several negative impacts on populations, including reduced fecundity, lower juvenile survival, shortened life span and it further accelerates the loss of genetic diversity especially in Plantago lanceolata and Veronica spicata that act as hosts for the Glanville fritillary butterfly, Malitaea cinxia. Ilik Saccheri and colleagues (1998) reported one of the first studies giving direct evidence that inbreeding contributes to extinctions in wild populations. Studied dry meadows. Documented an average of 200 extinctions and 114 colonization’s. Conducted genetic studies on populations of Melitaea in 42 meadows estimating heterozygosity and indicator of genetic variability with respect to seven enzyme systems and one locus of nuclear microsatellite DNA. Results of the study indicated that influence of inbreeding on the probability of extinction was very significant. High inbreeding=probability of extinction.
Speciation: what is a species? -Ernst Mayr (1942) defined species as “groups of actually or potentially interbreeding populations, which are reproductively isolated from other such groups.” Based upon a real ecological concept: reproductive isolation Speciation: what is reproductive isolation? -
isolating mechanisms can be categorized into two groups, pre and postzygotic. Prezygotic isolating mechanisms are processes which prevent two individuals from forming a zygote. Postzygotic isolating mechanisms are equally efficient at maintaining species integrity, but they occur after a zygote has been formed.
Speciation: what causes speciation? -
in both allopatric and parapatric speciation, the evolution of reproductive isolation could occur through drift or natural selection. Since genetic drift is random, fluctuations in allele freguencies in subpopulations will be independent, and thus loss of genetic diversity through drift could lead to reproductive isolation
Reproductive Isolation and Ecological Divergence -
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Dan Funk and colleagues (2006) decided to conduct a broad test of this prediction that increased ecological divergence will be associated with increased reproductive isolation. Using both parametric and nonparametric tests for relations they found that the more different the habitats were the more reproductively isolated the pairs of related species were. Mckinnon and colleagues (2004) wanted to know to what extent ecological difergence plays in the early stages of speciation. What they did was collected fish from marine and stream populations and placed them in an experiemental aquarium to see which individuals would and would not mate. Concluded that reproductive isolation occurred based upon ecological differentiation and not primarily geographic isolation.
3.3 Distribution and Abundance of Populations and Species Chapter 10 pages 255–256, 259–267, 269–274 Distribution Limits -
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G. Caughley and his colleagues (1987) found a close relationship between climate and the distribution of the three largest kangaroos in Australia. One in the eastern third of the continent of Australia that includes several biomes (temperate forest grows in the southeast and tropical forests in the north, and mountains with their varied climates. Another in the southern and western regions of Australia (temperate woodland and shrubland biome). Third in the savanna and desert. These limited distributions may not be determined by climate directly but suggest that climate often influences species distributions through factors such as food production, water supply and habitat. Also studied was the tiger beetle (Cicindela longilabris) that lives at higher latitudes and high elevations that just any other species of tiger beetle in north America. Thomas Schultz, Michael Quinlan, and Neil Hadley (1992) set out to study the environmental physiology of widely separated populations of the species. Found that metabolic rates are higher and its preferred temperatures lower than those of most other tiger beetle species that have been studied. None of
their measurements differed significantly among populations. Distribution Patterns -There are three distributions of patterns: 1. Random Distribution: an individual has an equal probability of occurring anywhere in an area. Neutral interactions between individuals and local environment 2. Regular Distribution: individuals are uniformly spaced through the environment. Antagonistic interactions between individuals or local depletion of resources 3. Clumped Distribution: individuals live in areas of high local abundance which are separated by areas of low abundance Organism Size and Population Density -
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John Damuth (1981) produced one of the first clear demonstrations of the relationship between body size and population density. Focused on herbivourus mammals ranging from small rodents to rhinoceros. Meanwhile average population density of 307 species of herbivorous mammals decreases with increased body size. Building on his analysis Robert Peters and Karen Wassenberg (1983) explored the relationship between body size and average population density for a wider variety of animals. Found that population density decreased with increased body size.
Commonness and Rarity -Deborah Rabinowitz (1981) devised a classification of commonness and rarity based on combinations of three factors: 1. Geographic range of a species 2. Habitat tolerance 3. Local population size - Any one of these factors, if small for a species, is associated with an increased probability of rarity, and a species with all three small is the most vulnerable to extinction.
3.4 Population Dynamics Chapter 11 pages 283–298, Chapter 12 pages 309–312 Key Terms Cohort: a group of individuals of the same age Cohort Life Table: a life table based on individuals born (or beginning life in some other way) at the same time Static Life Table: a life table constructed by recording the age at death of a large number
of individuals; the table is called static because the method Age Distribution: the distribution of individuals among age groups in a population; also called age structure Survivorship Curve: a graphical summary of patterns of survival in a population -
Adolph Murie (1944) had been fired by the U.S. National Park Service to study the interactions between wolves and Dall sheep in Mount Danali National Park, Alaska. The main purpose of his study was to determine whether wolves kill enough sheep to justify the call for reducing the wolf population. Sheep found ways of avoiding the wolves.
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In contrast Ahmad Hegazy (1990) used similar care to study an endangered plant species. Prompted by concern that harvesting was leading to the extinction of this valuable plant (Cleome droserifolia). Through careful analysis of the Cleome population provided a means for managing the species that promotes its survival and allowed its use in traditional medicine
Patterns of Survival: Estimating Patterns of Survival -
three main ways of estimating patters of survival within a population: 1. identify a large number of individuals that are born at about the same time and keep records on them from birth to death (cohort and cohort life table). 2. Go into a field for a narrow window of time and record the age at death of a large number of individuals. Different because individuals are born at different times (static life table). 3. Determines patterns of survival from age distribution that consists of the proportion of individuals of different ages within a population.
Patterns of Survival: High Survival Among the Young -
plotting number of survivors per 1000 births against age produces the survivorship curve. Murie used this when plotting the patterns of life and death within the sheep population.
Patterns of Survival: Three Types of Survivorship Curves -
Type I survivorship curve: a pattern of survivorship in which there are high rates of survival among young and middle-aged individuals followed by high rates of mortality among the aged
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Type II survivorship curve: a pattern of survivorship characterized by constant
rates of survival throughout life -
Type III survivorship curve: a pattern of survivorship in which a period of extremely high rates of mortality among the young is followed by a relatively high rate of survival
Age Distribution -
indicates that reproduction is sufficient to replace the oldest individuals in the population as they die.
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Rosemary and Peter Grant (1989) spent decades studying Galapagos finch populations. The age distributions of the large cactus finch during 1983 and 1987 show that population can be very dynamic. The age distribution tells population ecologists a great deal about the dynamics of population including whether a population is growing, declining, or approximately stable.
3.5 Life Histories and Trade-Offs Chapter 9 pages 226–230 Life history trade-offs -
A species’ life history describes how, over a lifetime, an organism’s energies are allocated to functions such as growth, number of offspring produced, parental investment in offspring, size at reproductive maturity, and dispersal.
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energy is limited and can’t be allocated in such a way as to maximize all of these functions, so there need to be trade-offs between functions. For example, there must be a trade-off between growth and reproduction because if more energy is allocated to growth, then less will be available for reproduction. Because life history trade-offs affect reproduction, they influence population dynamics.
Seed size and recruitment -
Anna Jakobsson and Ove Eriksson (2000) investigated how seed size, which is determined by energy allocation to reproduction by the maternal plant, influences seedling size and seedling survival to reproduction (also known as recruitment). Their results, show that larger seeds produced larger seedlings and that seedlings from larger seeds had a higher recruitment rate. So there is clearly an advantage to those species that have large seeds.
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The seed size and seed number trade-off was shown by Stevens (1932) for plants
in the Asteraceae (daisy), Poaceae (grasses), Brassicaceae (mustards), and Fabaceae (bean); in fact, the offspring size and number trade-off is one of the most common in nature. Trade-offs and survivorship curves -
One implication of producing many offspring is that parents do not have the energy to provide care to all those offspring. Thus, there is often a trade-off between number of offspring and the amount of parental care. Humans, for example, have few offspring but high parental care resulting in a high rate of survivorship and a Type I survivorship curve. Contrast this to the plants had many offspring, low parental care, and low rates of survival.
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Trade-offs exist among many life history functions including (1) growth and reproduction, (2) growth and defense, (3) number and size of offspring, (4) number of offspring and parental care, and (5) number of offspring and parent lifespan.
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Dispersal is another life history function that involves trade-offs.
Dispersal -
Dispersal can be defined as the permanent movement of individuals from their site of origin to a previously unoccupied location. They may disperse to avoid intraspecific competition (competition between individuals of the same species) or interspecific competition (competition between individuals of different species), avoid inbreeding, or to locate suitable habitat for an expanding population.
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Strayer and colleagues (1996) on the zebra mussel (Dreissena polymorpha) in the Hudson River, New York. The species was first detected in 1990, it expanded its range in 1991, and by 1992 it was found everywhere they sampled in the freshwater part of the estuary. By 1992 there were no more places to disperse to.
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Small (young) individuals were prevalent in 1992 (they made up the bulk of the population); however, their numbers were reduced in 1993 (most individuals were in the 10 to 20 mm size classes), and by 1994, their numbers were very low. There was no new recruitment of individuals, because of a lack of dispersal.
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To minimize energy expense, organisms can produce lots of small offspring, or use other organisms or media (e.g., water) to disperse them. To minimize the risk of predation, they could disperse when they are mature and able to defend themselves, or disperse when they are in a protected stage, such as a seed. To minimize the risk of finding a poorer habitat they could produce lots of offspring with the hopes that at least some of them will find suitable habitat. Another strategy would be to produce fewer offspring, but provide them with enough
energy reserves to get established. -
If offspring require protection from parents to avoid being preyed upon, then the organism can’t produce lots of offspring. Producing lots of small offspring to minimize the cost of dispersal means that parental care is minimized. Dispersing when they are old enough to defend themselves means they need more parental care, hence few offspring. Minimizing the risk of getting a poor site in a variable environment by having lots of offspring means that the offspring will be small. Relate these trade-offs back to the three types of survivorship curves; any strategy that results in lots of small offspring with little parental care will likely result in a type III survivorship curve. Strategies that involve only a few offspring and more parental care will likely result in a type I curve.
3.6 Population Growth • Chapter 12 pages 305–309, 313–325, Chapter 9 pages 237–240 Fecundity schedules -
the number of births in each age class. This is referred to as a fecundity schedule.
R0 = Σ lx mx = 2.418 -
This means that every individual is producing 2.418 offspring in its lifetime.
Geometric population growth -
The population size of Phlox next year (Nt+1) can be predicted because we know population size in the current year (Nt) and we know that each individual produced an average of 2.418 seeds. Thus, population size at Nt+1 of Phlox drummondii can be calculated using the following equation:
Nt+1 = Nt × R0 -
Constant R0 produces geometric (exponential) growth. It is called geometric growth because it occurs by the same proportion (i.e., 2.418) in each generation. Note that for annuals such as Phlox that have pulsed reproduction, the textbook uses the following equation where λ is equal to R0: Nt+1 = Nt × λ
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Based on the predicted population size for Phlox based on R0 = 2.418, we can
make general inferences about what net reproductive rate (R0) tells us about population growth: If R0 = 1 then the population is constant, i.e., each individual replaces itself If R0 > 1 then the population increases, as with PhloxIf R0 < 1 then the population decreases, because each individual does not replace itself. -
There are two assumptions that we have made in order to use this as a predictive tool: (1) that there are no limits to growth, and (2) that R0 stays constant over time. As you read the next sections, think about whether these assumptions are valid for natural populations.
Exponential population growth -
To examine population growth in species with overlapping generations, we need to calculate the per capita rate of increase (r), which is the per capita birth rate (b) minus the per capita death rate (d), in the absence of limiting resources: r = b – d. It is a per capita model because growth rate depends on the number of existing breeding individuals in a population. To simplify our consideration of population growth with overlapping generations, we will consider only births and deaths as sources of change in the model, though the effects of immigration and emigration could also be added.
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Like R0, r can be calculated from a life table by combining information on survivorship and age-specific births. It can then be used to predict population size in future years, in the same way that we used R0. Thus, to predict population size next year (Nt+1), we need to know how many individuals there are in the current year (Nt) and r. Because there are overlapping generations, the equation is modified so that the number of new individuals produced (r × Nt) is added to the number of existing individuals Nt: Nt+1 = r × Nt + Nt
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As with our description of population growth in Phlox, when growth rate is a function of population size, populations grow exponentially. A constant, positive r always produces exponential growth. This property can be observed by calculating the growth rate, or change in the number of individuals per change in time (dN/dt) for a population experiencing exponential growth. When a population is in exponential growth, the growth rate (dN/dt) always increases with population size (N).
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But does exponential growth go on forever in natural populations? NO! Resources become limited at some population level, and population growth stops, resulting in a sigmoidal or S-shaped population growth curve.
Logistic population growth -
Sigmoidal population growth, also called logistic population growth, has been
shown in a variety of populations including yeast (Saccharomyces cerevisiae), paramecium (Paramecium caudatum), barnacles (Balanus balanoides), and even African buffalo (Syncerus caffer) on the Serengeti Plain in Africa. -
Initially, populations grow very quickly as in exponential growth; however, at some point the rate of growth slows due to limiting resources and stops when the carrying capacity of the habitat is reached. The carrying capacity is known as K; it is the theoretical maximum population size or number of individuals that can be supported in a given environment.
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realized per capita rate of increase: dN/dt= rmaxN. The reason why r is now called the “realized” per capita rate of increase is that in the logistic growth model, it is defined relative to rmax, or the maximum per capita intrinsic rate of increase, which occurs when N=1.
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Early on in population growth, r is at its maximum, but Nt is low, so the numbers of new individuals being added at each time point are small. The maximum growth rate occurs when r and Nt are at their intermediate points, and this is at half the carrying capacity (K/2). The growth rate approaches 0 when the carrying capacity is reached because r = 0.
Limits to population growth -
In theory, then, logistic growth is density-dependent because population growth rate slows as population size reaches K. To produce this pattern, realized per capita rate of increase, or r, should decrease with population size. The maximum rate of increase, rmax occurs at a very low population size, and when N = K, r = 0 and population growth stops.
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Support for the theoretical prediction for density-dependent population regulation has been found in an experimental manipulation of Daphnia populations (Frank et al. 1957).
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Competition for resources may be one cause of density-dependence; with a higher population there is higher competition, and greater mortality.
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Two other possible mechanisms are attraction of predators by high prey density, and increased infection by disease with higher density.
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What mechanisms are responsible for density-independent population regulation, i.e., if per capita birth and death rates are unrelated to population density? These are generally abiotic factors, and they are often rare, extreme events such as an early hard freeze, or a tornado, flood, landslide, wind storm, etc. Density-independent and density-dependent population controls can interact.
Population growth and life histories: r-selection and K-selection
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How does population growth relate to life histories? One classification system proposed by MacArthur and Wilson (1967) and Eric Pianka (1970) is the concept of r–selection and K–selection.
