In this section, you will explore the following questions:
By using mathematics, ecologists can study how interactions among living organisms and with their environment affect the distribution, abundance, density, and life strategies of species. A population consists of individuals of the same species living within a specific area. Populations fluctuate based on both biotic and abiotic factors. When studying a population, characteristics of that population are quantified and their change is monitored. This statistical study of these changes, demography, investigates how populations respond to these fluctuations. A shift in populations can affect community structure and the ecosystem as a whole.
Information presented and the examples highlighted in the section support concepts outlined in Big Idea 2 and Big Idea 4 of the AP® Biology Curriculum Framework. The AP® Learning Objectives listed in the Curriculum Framework provide a transparent foundation for the AP® Biology course, an inquiry-based laboratory experience, instructional activities, and AP® exam questions. A learning objective merges required content with one or more of the seven science practices.
| Big Idea 2 | Biological systems utilize free energy and molecular building blocks to grow, to reproduce, and to maintain dynamic homeostasis. |
| Enduring Understanding 2.D | Growth and dynamic homeostasis of a biological system are influenced by changes in the system’s environment. |
| Essential Knowledge | 2.D.1 All biological systems from cells and organisms to populations, communities and ecosystems are affected by complex biotic and abiotic interactions involving exchange of matter and free energy. |
| Science Practice | 6.4 The student can make claims and predictions about natural phenomena based on scientific theories and models. |
| Learning Objective | 2.3 The student is able to predict how changes in free energy availability affect organisms, populations, and ecosystems. |
| Essential Knowledge | 2.D.1 All biological systems from cells and organisms to populations, communities and ecosystems are affected by complex biotic and abiotic interactions involving exchange of matter and free energy. |
| Science Practice | 1.3 The student can refine representations and models of natural or man-made phenomena and systems in the domain. |
| Science Practice | 3.2 The student can refine scientific questions. |
| Learning Objective | 2.22 The student is able to refine scientific models and questions about the effect of complex biotic and abiotic interactions on all biological systems, from cells and organisms to populations, communities and ecosystems. |
| Essential Knowledge | 2.D.1 All biological systems from cells and organisms to populations, communities and ecosystems are affected by complex biotic and abiotic interactions involving exchange of matter and free energy. |
| Science Practice | 4.2 The student can design a plan for collecting data to answer a particular scientific question. |
| Science Practice | 7.2 The student can connect concepts in and across domain(s) to generalize or extrapolate in and/or across enduring understandings and/or big ideas. |
| Learning Objective | 2.23 The student is able to design a plan for collecting data to show that all biological systems, including populations, are affected by complex biotic and abiotic interactions. |
| Essential Knowledge | 2.D.1 All biological systems from cells and organisms to populations, communities and ecosystems are affected by complex biotic and abiotic interactions involving exchange of matter and free energy. |
| Science Practice | 5.1 The student can analyze data to identify patterns or relationships. |
| Learning Objective | 2.24 The student is able to analyze data to identify possible patterns and relationships between a biotic or abiotic factors and a biological system, including populations. |
| Big Idea 4 | Biological systems interact, and these systems and their interactions possess complex properties. |
| Enduring Understanding 4.A | Interactions within biological systems lead to complex properties. |
| Essential Knowledge | 4.A.5 Communities are composed of populations of organisms that interact in complex ways. |
| Science Practice | 1.4 The student can use representations and models to analyze situations or solve problems qualitatively and quantitatively. |
| Science Practice | 4.2 The student can design a plan for collecting data to answer a particular scientific question. |
| Learning Objective | 4.11 The student is able to justify the selection of the kind of data needed to answer scientific questions about the interaction of populations within communities. |
| Essential Knowledge | 4.A.5 Communities are composed of populations of organisms that interact in complex ways. |
| Science Practice | 2.2 The student can apply mathematical routines to quantities that describe natural phenomena. |
| Learning Objective | 4.12 The student is able to apply mathematical routines to quantities that describe communities composed of populations of organisms that interact in complex ways. |
| Essential Knowledge | 4.A.5 Communities are composed of populations of organisms that interact in complex ways. |
| Science Practice | 6.4 The student can make claims and predictions about natural phenomena based on scientific theories and models. |
| Learning Objective | 4.13 The student is able to predict the effects of a change in the community’s populations on the community. |
In addition, content from this chapter is addressed in the AP Biology Laboratory Manual in the following lab(s):
Populations are dynamic entities. Populations consist all of the species living within a specific area, and populations fluctuate based on a number of factors: seasonal and yearly changes in the environment, natural disasters such as forest fires and volcanic eruptions, and competition for resources between and within species. The statistical study of population dynamics, demography, uses a series of mathematical tools to investigate how populations respond to changes in their biotic and abiotic environments. Many of these tools were originally designed to study human populations. For example, life tables, which detail the life expectancy of individuals within a population, were initially developed by life insurance companies to set insurance rates. In fact, while the term demographics is commonly used when discussing humans, all living populations can be studied using this approach.
The study of any population usually begins by determining how many individuals of a particular species exist, and how closely associated they are with each other. Within a particular habitat, a population can be characterized by its population size (N), the total number of individuals, and its population density, the number of individuals within a specific area or volume. Population size and density are the two main characteristics used to describe and understand populations. For example, populations with more individuals may be more stable than smaller populations based on their genetic variability, and thus their potential to adapt to the environment. Alternatively, a member of a population with low population density (more spread out in the habitat), might have more difficulty finding a mate to reproduce compared to a population of higher density. As is shown in Figure 36.2, smaller organisms tend to be more densely distributed than larger organisms.
The most accurate way to determine population size is to simply count all of the individuals within the habitat. However, this method is often not logistically or economically feasible, especially when studying large habitats. Thus, scientists usually study populations by sampling a representative portion of each habitat and using this data to make inferences about the habitat as a whole. A variety of methods can be used to sample populations to determine their size and density. For immobile organisms such as plants, or for very small and slow-moving organisms, a quadrat may be used (Figure 36.3). A quadrat is a way of marking off square areas within a habitat, either by staking out an area with sticks and string, or by the use of a wood, plastic, or metal square placed on the ground. After setting the quadrats, researchers then count the number of individuals that lie within their boundaries. Multiple quadrat samples are performed throughout the habitat at several random locations. All of this data can then be used to estimate the population size and population density within the entire habitat. The number and size of quadrat samples depends on the type of organisms under study and other factors, including the density of the organism. For example, if sampling daffodils, a 1 m2 quadrat might be used whereas with giant redwoods, which are larger and live much further apart from each other, a larger quadrat of 100 m2 might be employed. This ensures that enough individuals of the species are counted to get an accurate sample that correlates with the habitat, including areas not sampled.
For mobile organisms, such as mammals, birds, or fish, a technique called mark and recapture is often used. This method involves marking a sample of captured animals in some way (such as tags, bands, paint, or other body markings), and then releasing them back into the environment to allow them to mix with the rest of the population; later, a new sample is collected, including some individuals that are marked (recaptures) and some individuals that are unmarked (Figure 36.4).
Using the ratio of marked and unmarked individuals, scientists determine how many individuals are in the sample. From this, calculations are used to estimate the total population size. This method assumes that the larger the population, the lower the percentage of tagged organisms that will be recaptured since they will have mixed with more untagged individuals. For example, if 80 deer are captured, tagged, and released into the forest, and later 100 deer are captured and 20 of them are already marked, we can determine the population size (N) using the following equation:
Using our example, the population size would be estimated at 400.
Therefore, there are an estimated 400 total individuals in the original population.
There are some limitations to the mark and recapture method. Some animals from the first catch may learn to avoid capture in the second round, thus inflating population estimates. Alternatively, animals may preferentially be retrapped (especially if a food reward is offered), resulting in an underestimate of population size. Also, some species may be harmed by the marking technique, reducing their survival. A variety of other techniques have been developed, including the electronic tracking of animals tagged with radio transmitters and the use of data from commercial fishing and trapping operations to estimate the size and health of populations and communities.
In addition to measuring simple density, further information about a population can be obtained by looking at the distribution of the individuals. Species dispersion patterns (or distribution patterns) show the spatial relationship between members of a population within a habitat at a particular point in time. In other words, they show whether members of the species live close together or far apart, and what patterns are evident when they are spaced apart.
Individuals in a population can be more or less equally spaced apart, dispersed randomly with no predictable pattern, or clustered in groups. These are known as uniform, random, and clumped dispersion patterns, respectively (Figure 36.5). Uniform dispersion is observed in plants that secrete substances inhibiting the growth of nearby individuals (such as the release of toxic chemicals by the sage plant Salvia leucophylla, a phenomenon called allelopathy) and in animals like the penguin that maintain a defined territory. An example of random dispersion occurs with dandelion and other plants that have wind-dispersed seeds that germinate wherever they happen to fall in a favorable environment. A clumped dispersion may be seen in plants that drop their seeds straight to the ground, such as oak trees, or animals that live in groups (schools of fish or herds of elephants). Clumped dispersions may also be a function of habitat heterogeneity. Thus, the dispersion of the individuals within a population provides more information about how they interact with each other than does a simple density measurement. Just as lower density species might have more difficulty finding a mate, solitary species with a random distribution might have a similar difficulty when compared to social species clumped together in groups.
While population size and density describe a population at one particular point in time, scientists must use demography to study the dynamics of a population. Demography is the statistical study of population changes over time; that is, birth rates, death rates, and life expectancies. Each of these measures, especially birth rates, may be affected by the population characteristics described above. For example, a large population size results in a higher birth rate because more potentially reproductive individuals are present. In contrast, a large population size can also result in a higher death rate because of competition, disease, and the accumulation of waste. Similarly, a higher population density or a clumped dispersion pattern results in more potential reproductive encounters between individuals, which can increase birth rate. Lastly, a female-biased sex ratio (the ratio of males to females) or age structure (the proportion of population members at specific age ranges) composed of many individuals of reproductive age can increase birth rates.
In addition, the demographic characteristics of a population can influence how the population grows or declines over time. If birth and death rates are equal, the population remains stable. However, the population size will increase if birth rates exceed death rates; the population will decrease if birth rates are less than death rates. Life expectancy is another important factor; the length of time individuals remain in the population impacts local resources, reproduction, and the overall health of the population. These demographic characteristics are often displayed in the form of a life table.
Life tables provide important information about the life history of an organism. Life tables divide the population into age groups and often sexes, and show how long a member of that group is likely to live. They are modeled after actuarial tables used by the insurance industry for estimating human life expectancy. Life tables may include the probability of individuals dying before their next birthday (i.e., their mortality rate), the percentage of surviving individuals dying at a particular age interval, and their life expectancy at each interval. An example of a life table is shown in Table 36.1 from a study of Dall mountain sheep, a species native to northwestern North America. Notice that the population is divided into age intervals (column A). The mortality rate (per 1000), shown in column D, is based on the number of individuals dying during the age interval (column B) divided by the number of individuals surviving at the beginning of the interval (Column C), multiplied by 1000.
For example, between ages three and four, 12 individuals die out of the 776 that were remaining from the original 1000 sheep. This number is then multiplied by 1000 to get the mortality rate per thousand.
As can be seen from the mortality rate data (column D), a high death rate occurred when the sheep were between 6 and 12 months old, and then increased even more from 8 to 12 years old, after which there were few survivors. The data indicate that if a sheep in this population were to survive to age one, it could be expected to live another 7.7 years on average, as shown by the life expectancy numbers in column E.
| Life Table of Dall Mountain Sheep | ||||
|---|---|---|---|---|
| Age interval (years) | Number dying in age interval out of 1000 born | Number surviving at beginning of age interval out of 1000 born | Mortality rate per 1000 alive at beginning of age interval | Life expectancy or mean lifetime remaining to those attaining age interval |
| 0-0.5 | 54 | 1000 | 54.0 | 7.06 |
| 0.5-1 | 145 | 946 | 153.3 | -- |
| 1-2 | 12 | 801 | 15.0 | 7.7 |
| 2-3 | 13 | 789 | 16.5 | 6.8 |
| 3-4 | 12 | 776 | 15.5 | 5.9 |
| 4-5 | 30 | 764 | 39.3 | 5.0 |
| 5-6 | 46 | 734 | 62.7 | 4.2 |
| 6-7 | 48 | 688 | 69.8 | 3.4 |
| 7-8 | 69 | 640 | 107.8 | 2.6 |
| 8-9 | 132 | 571 | 231.2 | 1.9 |
| 9-10 | 187 | 439 | 426.0 | 1.3 |
| 10-11 | 156 | 252 | 619.0 | 0.9 |
| 11-12 | 90 | 96 | 937.5 | 0.6 |
| 12-13 | 3 | 6 | 500.0 | 1.2 |
| 13-14 | 3 | 3 | 1000 | 0.7 |
Another tool used by population ecologists is a survivorship curve, which is a graph of the number of individuals surviving at each age interval plotted versus time (usually with data compiled from a life table). These curves allow us to compare the life histories of different populations (Figure 36.6). Humans and most primates exhibit a Type I survivorship curve because a high percentage of offspring survive their early and middle years—death occurs predominantly in older individuals. These types of species usually have small numbers of offspring at one time, and they give a high amount of parental care to them to ensure their survival. Birds are an example of an intermediate or Type II survivorship curve because birds die more or less equally at each age interval. These organisms also may have relatively few offspring and provide significant parental care. Trees, marine invertebrates, and most fishes exhibit a Type III survivorship curve because very few of these organisms survive their younger years; however, those that make it to an old age are more likely to survive for a relatively long period of time. Organisms in this category usually have a very large number of offspring, but once they are born, little parental care is provided. Thus these offspring are on their own and vulnerable to predation, but their sheer numbers assure the survival of enough individuals to perpetuate the species.
Design an investigation that a researcher might use to determine the size of a penguin population in the Antarctic using the mark and release method. What biotic or abiotic factors might influence the size of the penguin population in a given area?
Data adapted from Deevey Jr., E. S. (1947, Dec.). Life tables for natural populations of animals. The Quarterly Review of Biology, 22(4), 283314.