Which tends to increase the size of a population

The Net Reproductive Rate The net reproductive rate r is the percentage growth after accounting for births and deaths. In the example above, the population reproductive rate is 0. Suppose we came back many years later, the net reproductive rate was still the same, but now the population had grown to 1,, How many new individuals would be added each year now?

Simply multiply the population by the reproductive rate: 1,, x 0. The net reproductive rate is the same as before, but because the population is so much bigger, many more individuals are added. Exponential Growth If a population grows by a constant percentage per year, this eventually adds up to what we call exponential growth. In other words, the larger the population grows, the faster it grows!!

A curve of exponential growth is an upward sweeping growth curve. In this case, if nothing else is done, the population size approaches infinity. But the earth's resources are limited, and such a curve is a physical impossibility. Instead, things become limiting: food, habitats and shelter, disease, etc. In that case population tends to reach an upper limit, known as the carrying capacity k for that environment.

Then, you get the yellow curve in the figure above, known as the logistic model. Here, as the population approaches a theoretical upper limit, the net reproductive rate decreases. In exponential growth, it stays constant. The logistic curve is the more realistic, even though it is still an abstraction most populations don't behave so nicely in the real environment - they tend to bounce around, and r tends to change through time in ways that are unpredictable, due to stochastic unpredictable changes.

Some Population Statistics for Humans At the end of the s, Robert Malthus, a priest, wrote one of the most influential essays in the world - He was pondering why there was so much suffering among humans, and came to the conclusion that human population growth tended to always outstrip food supply.

The Core Principles of Malthus are: 1. Food is necessary for human existence. Human population tends to grow faster than the power in the earth to produce subsistence, and that 3. The effects of these two unequal powers must be kept equal. Woolfenden and Fitzpatrick used this approach to estimate N e for Florida Scrub-Jays -- their estimate of N e was A major problem, though, is how to account simultaneously for all these various effects -- the jury is definitely still out on that one.

Technical discussions of N e include important papers by Caballero , Crow and Denniston , Harris and Allendorf , various papers by Nunney, Vucetich et al. Waples showed how to estimate N e by using temporal fluctuations in allele frequencies. Wang and Whitlock incorporated variation in both time and space as bases for estimating N e. In the literature, you may see "variance effective size" and "inbreeding effective size".

The former focuses on changes in genetic variance, on consequences for the offspring generation and hence naturally leads to consideration of interpopulation divergence.

The latter focuses on changes in heterozygosity, on consequences for the parental generation, and hence naturally leads to consideration of the level of inbreeding within populations. See Crow and Kimura's text pp. References: Caballero, A. Review article: Developments in the prediction of effective population size. Heredity Chesser, R. Effective sizes and dynamics of uniparentally and diparentally inherited genes. Genetics Crow, J. Inbreeding and variance effective population effective numbers.

An Introduction to Population Genetics Theory. Burgess Publishing, Minneapolis, MN. Felsenstein, J. Inbreeding and variance effective numbers in populations with overlapping generations. Harris, R. Genetically effective population size of large mammals: an assessment of estimators. Only one race was found in , corresponding to a race found in Europe, suggesting that Europe was the source of the introduction.

Since the original introduction, mutations have created new pathotypes in the single introduced genetic background Steele et al. Chestnut blight Cryphonectria parasitica in North America also shows some characteristics of a founder population as it has much less genetic diversity than populations in Asia. It appears that the center of diversity and possible center of origin is in Japan Milgroom et al. Go to References. Next Section. Log In Bookstore Join Renew.

It looks like your browser does not have JavaScript enabled. Please turn on JavaScript and try again. Page Content. We will consider these in the context of pathogen populations in plant pathosystems: Small recurring population size occurs when there are not many host plants in the area to infect, or when the environment is not optimal for infection.

A genetic bottleneck, or severe reduction in population size, occurs when the plant population is removed e. A founder effect occurs when a small number of individuals, representing only a small fraction of the total genetic variation in a species, starts a new population. A founder event occurs when one or two infected plants slip through a quarantine and introduce a disease into an area where the disease did not previously exist.

Measuring Genetic Drift The magnitude of genetic drift depends on N e , the effective population size, for the population. The degree of change increases as the population size decreases. Genetic Drift Decreases Gene Diversity and Leads to Population Subdivision The chance of fixing an allele due to genetic drift depends on the effective population size as well as the frequency distribution of alleles at a locus.

Genetic Drift in Pathogen Populations In agroecosystems, pathogen populations usually become very large as a result of the genetic uniformity of the host plant, so genetic drift may not play a large role in the evolutionary process within a farmer's field in the real world. Examples of Genetic Drift Mycosphaerella graminicola causes Septoria tritici leaf blotch on wheat. All rights reserved. The herds started small, but with plentiful resources and few predators, they grew quickly.

Figure 1: The American bison population in northern Yellowstone National Park grew exponentially between and After being driven nearly to extinction in the s, the population began growing again due to conservation efforts implemented by governments and private landowners in the early s. All rights reserved. The yearly increase in the northern YNP bison population between and can be described as exponential growth. A population that grows exponentially adds increasingly more individuals as the population size increases.

The original adult bison mate and have calves, those calves grow into adults who have calves, and so on. This generates much faster growth than, say, adding a constant number of individuals to the population each year. Exponential growth works by leveraging increases in population size, and does not require increases in population growth rates. This meant that the herd only added between 4 and 9 individuals in the first couple of years, but added closer to 50 individuals by when the population was larger and more individuals were reproducing.

Speaking of reproduction, how often a species reproduces can affect how scientists describe population growth see Figure 2 to learn more. Figure 2: Bison young are born once a year — how does periodic reproduction affect how we describe population growth? The female bison in the YNP herd all have calves around the same time each year — in spring from April through the beginning of June Jones et al. This type of periodic reproduction is common in nature, and very different from animals like humans, who have babies throughout the year.

When scientists want to describe the growth of populations that reproduce periodically, they use geometric growth. Geometric growth is similar to exponential growth because increases in the size of the population depend on the population size more individuals having more offspring means faster growth! Exponential growth and geometric growth are similar enough that over longer periods of time, exponential growth can accurately describe changes in populations that reproduce periodically like bison as well as those that reproduce more constantly like humans.

Photo courtesy of Guimir via Wikimedia Commons. The power of exponential growth is worth a closer look. If you started with a single bacterium that could double every hour, exponential growth would give you ,,,, bacteria in just 48 hours!

The YNP bison population reached a maximum of animals in Plumb et al. That's more than thirteen times larger than the largest population ever thought to have roamed the entire plains region! The potential results may seem fantastic, but exponential growth appears regularly in nature. When organisms enter novel habitats and have abundant resources, as is the case for invading agricultural pests, introduced species , or during carefully managed recoveries like the American bison, their populations often experience periods of exponential growth.

In the case of introduced specie s or agricultural pests, exponential population growth can lead to dramatic environmental degradation and significant expenditures to control pest species Figure 3. Figure 3: If this much money is being spent on something, it must be important!

Understanding population growth is important for predicting, managing, monitoring, and eradicating pest and disease outbreaks. Many introduced species, including agricultural pests and infectious diseases, grow exponentially as they invade new areas, and billions of dollars are spent predicting and managing the population growth and dispersal of species that have the potential to destroy crops, harm the health of humans, wildlife, and livestock, and affect native species and natural ecosystem functioning.

Let's think about the conditions that allowed the bison population to grow between and The total number of bison in the YNP herd could have changed because of births, deaths, immigration and emigration immigration is individuals coming in from outside the population, emigration is individuals leaving to go elsewhere. The population was isolated, so no immigration or emigration occurred, meaning only births and deaths changed the size of the population.

Because the population grew, there must have been more births than deaths, right? Right, but that is a simple way of telling a more complicated story. Births exceeded deaths in the northern YNP bison herd between and , allowing the population to grow, but other factors such as the age structure of the population, characteristics of the species such as lifespan and fecundity , and favorable environmental conditions, determined how much and how fast. Changes in the factors that once allowed a population to grow can explain why growth slows or even stops.

Figure 4 shows periods of growth, as well as periods of decline, in the number of YNP bison between and Growth of the northern YNP bison herd has been limited by disease and predation, habitat loss and fragmentation, human intervention, and harsh winters Gates et al. Figure 4: The YNP bison population has increased and decreased in size over the past century in response to factors such as disease, predation, habitat loss, human intervention, and environmental conditions.

Scientists with the National Park Service and Colorado State University recently published these data showing both the number of bison counted in YNP on an annual basis blue dots and the number of bison removed from the population grey columns for the purposes of herd management.

Management of the bison population in YNP has been fairly controversial — to learn more about this controversy check out Plumb et al. Factors that enhance or limit population growth can be divided into two categories based on how each factor is affected by the number of individuals occupying a given area — or the population's density.

As population size approaches the carrying capacity of the environment, the intensity of density-dependent factors increases. For example, competition for resources, predation, and rates of infection increase with population density and can eventually limit population size. Other factors, like pollution, seasonal weather extremes, and natural disasters — hurricanes, fires, droughts, floods, and volcanic eruptions — affect populations irrespective of their density, and can limit population growth simply by severely reducing the number of individuals in the population.

The idea that uninhibited exponential growth would eventually be limited was formalized in by mathematician Pierre-Francois Verhulst.