Information

19.1: Population Demographics and Dynamics - Biology

19.1: Population Demographics and Dynamics - Biology


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Populations are dynamic entities. In fact, while the term “demographics” is sometimes assumed to mean a study of human populations, all living populations can be studied using this approach.

Population Size and Density

Populations are characterized by their population size (total number of individuals) and their population density (number of individuals per unit area). A population may have a large number of individuals that are distributed densely, or sparsely. There are also populations with small numbers of individuals that may be dense or very sparsely distributed in a local area. Population size can affect potential for adaptation because it affects the amount of genetic variation present in the population. Density can have effects on interactions within a population such as competition for food and the ability of individuals to find a mate. Smaller organisms tend to be more densely distributed than larger organisms (Figure 19.1.1).

ART CONNECTION

As this graph shows, population density typically decreases with increasing body size. Why do you think this is the case?

Estimating Population Size

The most accurate way to determine population size is to count all of the individuals within the area. However, this method is usually not logistically or economically feasible, especially when studying large areas. Thus, scientists usually study populations by sampling a representative portion of each habitat and use this sample to make inferences about the population as a whole. The methods used to sample populations to determine their size and density are typically tailored to the characteristics of the organism being studied. For immobile organisms such as plants, or for very small and slow-moving organisms, a quadrat may be used. A quadrat is a wood, plastic, or metal square that is randomly located on the ground and used to count the number of individuals that lie within its boundaries. To obtain an accurate count using this method, the square must be placed at random locations within the habitat enough times to produce an accurate estimate. This counting method will provide an estimate of both population size and density. The number and size of quadrat samples depends on the type of organisms and the nature of their distribution.

For smaller mobile organisms, such as mammals, a technique called mark and recapture is often used. This method involves marking a sample of captured animals in some way and releasing them back into the environment to mix with the rest of the population; then, a new sample is captured and scientists determine how many of the marked animals are in the new sample. This method assumes that the larger the population, the lower the percentage of marked organisms that will be recaptured since they will have mixed with more unmarked individuals. For example, if 80 field mice are captured, marked, and released into the forest, then a second trapping 100 field mice are captured and 20 of them are marked, the population size (N) can be determined using the following equation:

number marked first catch × total number second catchnumber marked second catch = Nnumber marked first catch × total number second catchnumber marked second catch = N

Using our example, the population size would be 400.

80 × 10020 = 40080 × 10020 = 400

These results give us an estimate of 400 total individuals in the original population. The true number usually will be a bit different from this because of chance errors and possible bias caused by the sampling methods.

Species Distribution

In addition to measuring density, further information about a population can be obtained by looking at the distribution of the individuals throughout their range. A species distribution pattern is the distribution of individuals within a habitat at a particular point in time—broad categories of patterns are used to describe them.

Individuals within a population can be distributed at random, in groups, or equally spaced apart (more or less). These are known as random, clumped, and uniform distribution patterns, respectively (Figure 19.1.2). Different distributions reflect important aspects of the biology of the species; they also affect the mathematical methods required to estimate population sizes. An example of random distribution occurs with dandelion and other plants that have wind-dispersed seeds that germinate wherever they happen to fall in favorable environments. A clumped distribution, may be seen in plants that drop their seeds straight to the ground, such as oak trees; it can also be seen in animals that live in social groups (schools of fish or herds of elephants). Uniform distribution is observed in plants that secrete substances inhibiting the growth of nearby individuals (such as the release of toxic chemicals by sage plants). It is also seen in territorial animal species, such as penguins that maintain a defined territory for nesting. The territorial defensive behaviors of each individual create a regular pattern of distribution of similar-sized territories and individuals within those territories. Thus, the distribution 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.

Demography

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: birth rates, death rates, and life expectancies. These population characteristics are often displayed in a life table.

Life Tables

Life tables provide important information about the life history of an organism and the life expectancy of individuals at each age. They are modeled after actuarial tables used by the insurance industry for estimating human life expectancy. Life tables may include the probability of each age group dying before their next birthday, the percentage of surviving individuals dying at a particular age interval (their mortality rate, and their life expectancy at each interval. An example of a life table is shown in Table 19.1.1 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.

mortality rate = number of individuals dyingnumber of individuals surviving × 1000mortality rate = number of individuals dyingnumber of individuals surviving × 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 give the mortality rate per thousand.

mortality rate = 12776 × 1000 ≈ 15.5mortality rate = 12776 × 1000 ≈ 15.5

As can be seen from the mortality rate data (column D), a high death rate occurred when the sheep were between six months and a year 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.

This life table of Ovis dalli shows the number of deaths, number of survivors, mortality rate, and life expectancy at each age interval for Dall mountain sheep.

Table 19.1.1: Life Table of Dall Mountain Sheep1
ABCDE
Age interval (years)Number dying in age interval out of 1000 bornNumber surviving at beginning of age interval out of 1000 bornMortality rate per 1000 alive at beginning of age intervalLife expectancy or mean lifetime remaining to those attaining age interval
0–0.554100054.07.06
0.5–1145946153.3
1–21280115.07.7
2–31378916.56.8
3–41277615.55.9
4–53076439.35.0
5–64673462.74.2
6–74868869.83.4
7–869640107.82.6
8–9132571231.21.9
9–10187439426.01.3
10–11156252619.00.9
11–129096937.50.6
12–1336500.01.2
13–143310000.7

Survivorship Curves

Another tool used by population ecologists is a survivorship curve, which is a graph of the number of individuals surviving at each age interval versus time. These curves allow us to compare the life histories of different populations (Figure 19.1.3). There are three types of survivorship curves. In a type I curve, mortality is low in the early and middle years and occurs mostly in older individuals. Organisms exhibiting a type I survivorship typically produce few offspring and provide good care to the offspring increasing the likelihood of their survival. Humans and most mammals exhibit a type I survivorship curve. In type II curves, mortality is relatively constant throughout the entire life span, and mortality is equally likely to occur at any point in the life span. Many bird populations provide examples of an intermediate or type II survivorship curve. In type III survivorship curves, early ages experience the highest mortality with much lower mortality rates for organisms that make it to advanced years. Type III organisms typically produce large numbers of offspring, but provide very little or no care for them. Trees and marine invertebrates exhibit a type III survivorship curve because very few of these organisms survive their younger years, but those that do make it to an old age are more likely to survive for a relatively long period of time.

Section Summary

Populations are individuals of a species that live in a particular habitat. Ecologists measure characteristics of populations: size, density, and distribution pattern. Life tables are useful to calculate life expectancies of individual population members. Survivorship curves show the number of individuals surviving at each age interval plotted versus time.

Figure 19.1.1 As this graph shows, population density typically decreases with increasing body size. Why do you think this is the case?

Smaller animals require less food and others resources, so the environment can support more of them per unit area.

Multiple Choice

Which of the following methods will provide information to an ecologist about both the size and density of a population?

A. mark and recapture
B. mark and release
C. quadrat
D. life table

C

Which of the following is best at showing the life expectancy of an individual within a population?

A. quadrat
B. mark and recapture
C. survivorship curve
D. life table

D

Human populations have which type of survivorship curve?

A. Type I
B. Type II
C. Type III
D. Type IV

A

Free Response

Describe how a researcher would determine the size of a penguin population in Antarctica using the mark and release method.

The researcher would mark a certain number of penguins with a tag, release them back into the population, and, at a later time, recapture penguins to see what percentage was tagged. This percentage would allow an estimation of the size of the penguin population.

Footnotes

  1. 1 Data Adapted from Edward S. Deevey, Jr., “Life Tables for Natural Populations of Animals,” The Quarterly Review of Biology 22, no. 4 (December 1947): 283-314.

Glossary

demography
the statistical study of changes in populations over time
life table
a table showing the life expectancy of a population member based on its age
mark and recapture
a method used to determine population size in mobile organisms
mortality rate
the proportion of population surviving to the beginning of an age interval that dies during that age interval
population density
the number of population members divided by the area being measured
population size
the number of individuals in a population
quadrat
a square within which a count of individuals is made that is combined with other such counts to determine population size and density in slow moving or stationary organisms
species distribution pattern
the distribution of individuals within a habitat at a given point in time

Population Demographics and Dynamics

Populations are dynamic entities. Their size and composition fluctuate in response to numerous factors, including 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 populations is called demography: a set of mathematical tools designed to describe populations and investigate how they change. Many of these tools were actually 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 sometimes assumed to mean a study of human populations, all living populations can be studied using this approach.

Population Size and Density

Populations are characterized by their population size (total number of individuals) and their population density (number of individuals per unit area). A population may have a large number of individuals that are distributed densely, or sparsely. There are also populations with small numbers of individuals that may be dense or very sparsely distributed in a local area. Population size can affect potential for adaptation because it affects the amount of genetic variation present in the population. Density can have effects on interactions within a population such as competition for food and the ability of individuals to find a mate. Smaller organisms tend to be more densely distributed than larger organisms ([link]).

As this graph shows, population density typically decreases with increasing body size. Why do you think this is the case?

Estimating Population Size

The most accurate way to determine population size is to count all of the individuals within the area. However, this method is usually not logistically or economically feasible, especially when studying large areas. Thus, scientists usually study populations by sampling a representative portion of each habitat and use this sample to make inferences about the population as a whole. The methods used to sample populations to determine their size and density are typically tailored to the characteristics of the organism being studied. For immobile organisms such as plants, or for very small and slow-moving organisms, a quadrat may be used. A quadrat is a wood, plastic, or metal square that is randomly located on the ground and used to count the number of individuals that lie within its boundaries. To obtain an accurate count using this method, the square must be placed at random locations within the habitat enough times to produce an accurate estimate. This counting method will provide an estimate of both population size and density. The number and size of quadrat samples depends on the type of organisms and the nature of their distribution.

For smaller mobile organisms, such as mammals, a technique called mark and recapture is often used. This method involves marking a sample of captured animals in some way and releasing them back into the environment to mix with the rest of the population then, a new sample is captured and scientists determine how many of the marked animals are in the new sample. This method assumes that the larger the population, the lower the percentage of marked organisms that will be recaptured since they will have mixed with more unmarked individuals. For example, if 80 field mice are captured, marked, and released into the forest, then a second trapping 100 field mice are captured and 20 of them are marked, the population size (N) can be determined using the following equation:

Using our example, the population size would be 400.

These results give us an estimate of 400 total individuals in the original population. The true number usually will be a bit different from this because of chance errors and possible bias caused by the sampling methods.

Species Distribution

In addition to measuring density, further information about a population can be obtained by looking at the distribution of the individuals throughout their range. A species distribution pattern is the distribution of individuals within a habitat at a particular point in time—broad categories of patterns are used to describe them.

Individuals within a population can be distributed at random, in groups, or equally spaced apart (more or less). These are known as random, clumped, and uniform distribution patterns, respectively ([link]). Different distributions reflect important aspects of the biology of the species they also affect the mathematical methods required to estimate population sizes. An example of random distribution occurs with dandelion and other plants that have wind-dispersed seeds that germinate wherever they happen to fall in favorable environments. A clumped distribution, may be seen in plants that drop their seeds straight to the ground, such as oak trees it can also be seen in animals that live in social groups (schools of fish or herds of elephants). Uniform distribution is observed in plants that secrete substances inhibiting the growth of nearby individuals (such as the release of toxic chemicals by sage plants). It is also seen in territorial animal species, such as penguins that maintain a defined territory for nesting. The territorial defensive behaviors of each individual create a regular pattern of distribution of similar-sized territories and individuals within those territories. Thus, the distribution 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.

Demography

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: birth rates, death rates, and life expectancies. These population characteristics are often displayed in a life table.

Life Tables

Life tables provide important information about the life history of an organism and the life expectancy of individuals at each age. They are modeled after actuarial tables used by the insurance industry for estimating human life expectancy. Life tables may include the probability of each age group dying before their next birthday, the percentage of surviving individuals dying at a particular age interval (their mortality rate, and their life expectancy at each interval. An example of a life table is shown in [link] 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 give 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 six months and a year 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.

This life table of Ovis dalli shows the number of deaths, number of survivors, mortality rate, and life expectancy at each age interval for Dall mountain sheep.
Life Table of Dall Mountain Sheep 1
A B C D E
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

Survivorship Curves

Another tool used by population ecologists is a survivorship curve, which is a graph of the number of individuals surviving at each age interval versus time. These curves allow us to compare the life histories of different populations ([link]). There are three types of survivorship curves. In a type I curve, mortality is low in the early and middle years and occurs mostly in older individuals. Organisms exhibiting a type I survivorship typically produce few offspring and provide good care to the offspring increasing the likelihood of their survival. Humans and most mammals exhibit a type I survivorship curve. In type II curves, mortality is relatively constant throughout the entire life span, and mortality is equally likely to occur at any point in the life span. Many bird populations provide examples of an intermediate or type II survivorship curve. In type III survivorship curves, early ages experience the highest mortality with much lower mortality rates for organisms that make it to advanced years. Type III organisms typically produce large numbers of offspring, but provide very little or no care for them. Trees and marine invertebrates exhibit a type III survivorship curve because very few of these organisms survive their younger years, but those that do make it to an old age are more likely to survive for a relatively long period of time.

Section Summary

Populations are individuals of a species that live in a particular habitat. Ecologists measure characteristics of populations: size, density, and distribution pattern. Life tables are useful to calculate life expectancies of individual population members. Survivorship curves show the number of individuals surviving at each age interval plotted versus time.

[link] As this graph shows, population density typically decreases with increasing body size. Why do you think this is the case?

[link] Smaller animals require less food and others resources, so the environment can support more of them per unit area.

Multiple Choice

Which of the following methods will provide information to an ecologist about both the size and density of a population?

Which of the following is best at showing the life expectancy of an individual within a population?

Human populations have which type of survivorship curve?

Free Response

Describe how a researcher would determine the size of a penguin population in Antarctica using the mark and release method.

The researcher would mark a certain number of penguins with a tag, release them back into the population, and, at a later time, recapture penguins to see what percentage was tagged. This percentage would allow an estimation of the size of the penguin population.

Footnotes

    Data Adapted from Edward S. Deevey, Jr., “Life Tables for Natural Populations of Animals,” The Quarterly Review of Biology 22, no. 4 (December 1947): 283-314.

Glossary


Estimating Population Size

The most accurate way to determine population size is to count all of the individuals within the area. However, this method is usually not logistically or economically feasible, especially when studying large areas. Thus, scientists usually study populations by sampling a representative portion of each habitat and use this sample to make inferences about the population as a whole. The methods used to sample populations to determine their size and density are typically tailored to the characteristics of the organism being studied. For immobile organisms such as plants, or for very small and slow-moving organisms, a quadrat may be used. A quadrat is a wood, plastic, or metal square that is randomly located on the ground and used to count the number of individuals that lie within its boundaries. To obtain an accurate count using this method, the square must be placed at random locations within the habitat enough times to produce an accurate estimate. This counting method will provide an estimate of both population size and density. The number and size of quadrat samples depends on the type of organisms and the nature of their distribution.

For smaller mobile organisms, such as mammals, a technique called mark and recapture is often used. This method involves marking a sample of captured animals in some way and releasing them back into the environment to mix with the rest of the population then, a new sample is captured and scientists determine how many of the marked animals are in the new sample. This method assumes that the larger the population, the lower the percentage of marked organisms that will be recaptured since they will have mixed with more unmarked individuals. For example, if 80 field mice are captured, marked, and released into the forest, then a second trapping 100 field mice are captured and 20 of them are marked, the population size (N) can be determined using the following equation:

Using our example, the population size would be 400.

These results give us an estimate of 400 total individuals in the original population. The true number usually will be a bit different from this because of chance errors and possible bias caused by the sampling methods.


Dynamics and Correlation Among Viral Positivity, Seroconversion, and Disease Severity in COVID-19 : A Retrospective Study

Background: The understanding of viral positivity and seroconversion during the course of coronavirus disease 2019 (COVID-19) is limited.

Objective: To describe patterns of viral polymerase chain reaction (PCR) positivity and evaluate their correlations with seroconversion and disease severity.

Design: Retrospective cohort study.

Setting: 3 designated specialty care centers for COVID-19 in Wuhan, China.

Participants: 3192 adult patients with COVID-19.

Measurements: Demographic, clinical, and laboratory data.

Results: Among 12 780 reverse transcriptase PCR tests for severe acute respiratory syndrome coronavirus 2 that were done, 24.0% had positive results. In 2142 patients with laboratory-confirmed COVID-19, the viral positivity rate peaked within the first 3 days. The median duration of viral positivity was 24.0 days (95% CI, 18.9 to 29.1 days) in critically ill patients and 18.0 days (CI, 16.8 to 19.1 days) in noncritically ill patients. Being critically ill was an independent risk factor for longer viral positivity (hazard ratio, 0.700 [CI, 0.595 to 0.824] P < 0.001). In patients with laboratory-confirmed COVID-19, the IgM-positive rate was 19.3% in the first week, peaked in the fifth week (81.5%), and then decreased steadily to around 55% within 9 to 10 weeks. The IgG-positive rate was 44.6% in the first week, reached 93.3% in the fourth week, and then remained high. Similar antibody responses were seen in clinically diagnosed cases. Serum inflammatory markers remained higher in critically ill patients. Among noncritically ill patients, a higher proportion of those with persistent viral positivity had low IgM titers (<100 AU/mL) during the entire course compared with those with short viral positivity.

Limitation: Retrospective study and irregular viral and serology testing.

Conclusion: The rate of viral PCR positivity peaked within the initial few days. Seroconversion rates peaked within 4 to 5 weeks. Dynamic laboratory index changes corresponded well to clinical signs, the recovery process, and disease severity. Low IgM titers (<100 AU/mL) are an independent risk factor for persistent viral positivity.


19.1: Population Demographics and Dynamics - Biology

KEYWORDS: human-wildlife conflict, human disturbance, MaxEnt, off-trail recreation, tourism, visitor steering, wildlife management

Outdoor recreation, particularly in winter, causes pressure on wildlife. While many species seem to adjust well to predictable on-trail recreation activities, unpredictable off-trail activities are considered harmful. Measures to minimise human disturbance require the identification of ‘conflict-sites’ where human activities are likely to interfere with the requirements of wildlife. We used winter recreation data combined with spatial modelling to predict where recreationists move from marked trails into wildlife habitats in winter and to determine the environmental factors that trigger this off-trail behaviour. We surveyed marked winter trails in the southern Black Forest, Germany, by foot or ski for tracks of people leaving the trail, with three types of recreationists distinguished: hikers, snowshoe users and cross-country skiers. Using a maximum entropy approach, the probability of leaving the trail was modelled as a function of topographic, forest structure and tourism infrastructure variables. By combining the results with previously mapped habitat information of two disturbance sensitive species, the capercaillie Tetrao urogallus and the red deer Cervus elaphus , we identified conflict sites where mitigation measures would be most effective. All models were effective in predicting the locations where people left the trails and the three types of recreationists showed a similar pattern: the presence of closed summer trails and signposts along these trails proved to be the factors most strongly affecting the probability of leaving marked trails, followed by slope, which was negatively correlated with the probability of going off-trail. People leaving directly into the forest, not using a summer trail, were most positively influenced by the successional stages ‘regeneration’ and ‘old forest’, whereas increasing canopy cover decreased the probability of leaving the trail. The models were extrapolated to all marked trails in the study area. Locations with a high probability of people leaving the trails were identified and intersected with the previously mapped key habitats of the two wildlife species, thereby showing the locations where leaving the trail would be linked with a high potential of human-wildlife conflict. By indicating what triggers people to leave the trails, and identifying the critical locations, our results contribute to the determination of adequate management measures.


Contents

Although the seeds of a source-sink model had been planted earlier, [1] Pulliam [2] is often recognized as the first to present a fully developed source-sink model. He defined source and sink patches in terms of their demographic parameters, or BIDE rates (birth, immigration, death, and emigration rates). In the source patch, birth rates were greater than death rates, causing the population to grow. The excess individuals were expected to leave the patch, so that emigration rates were greater than immigration rates. In other words, sources were a net exporter of individuals. In contrast, in a sink patch, death rates were greater than birth rates, resulting in a population decline toward extinction unless enough individuals emigrated from the source patch. Immigration rates were expected to be greater than emigration rates, so that sinks were a net importer of individuals. As a result, there would be a net flow of individuals from the source to the sink (see Table 1).

Pulliam's work was followed by many others who developed and tested the source-sink model. Watkinson and Sutherland [3] presented a phenomenon in which high immigration rates could cause a patch to appear to be a sink by raising the patch's population above its carrying capacity (the number of individuals it can support). However, in the absence of immigration, the patches are able to support a smaller population. Since true sinks cannot support any population, the authors called these patches "pseudo-sinks". Definitively distinguishing between true sinks and pseudo-sinks requires cutting off immigration to the patch in question and determining whether the patch is still able to maintain a population. Thomas et al. [4] were able to do just that, taking advantage of an unseasonable frost that killed off the host plants for a source population of Edith's checkerspot butterfly (Euphydryas editha). Without the host plants, the supply of immigrants to other nearby patches was cut off. Although these patches had appeared to be sinks, they did not become extinct without the constant supply of immigrants. They were capable of sustaining a smaller population, suggesting that they were in fact pseudo-sinks.

Watkinson and Sutherland's [3] caution about identifying pseudo-sinks was followed by Dias, [5] who argued that differentiating between sources and sinks themselves may be difficult. She asserted that a long-term study of the demographic parameters of the populations in each patch is necessary. Otherwise, temporary variations in those parameters, perhaps due to climate fluctuations or natural disasters, may result in a misclassification of the patches. For example, Johnson [6] described periodic flooding of a river in Costa Rica which completely inundated patches of the host plant for a rolled-leaf beetle (Cephaloleia fenestrata). During the floods, these patches became sinks, but at other times they were no different from other patches. If researchers had not considered what happened during the floods, they would not have understood the full complexity of the system.

Dias [5] also argued that an inversion between source and sink habitat is possible so that the sinks may actually become the sources. Because reproduction in source patches is much higher than in sink patches, natural selection is generally expected to favor adaptations to the source habitat. However, if the proportion of source to sink habitat changes so that sink habitat becomes much more available, organisms may begin to adapt to it instead. Once adapted, the sink may become a source habitat. This is believed to have occurred for the blue tit (Parus caeruleus) 7500 years ago as forest composition on Corsica changed, but few modern examples are known. Boughton [7] described a source—pseudo-sink inversion in butterfly populations of E. editha. [4] Following the frost, the butterflies had difficulty recolonizing the former source patches. Boughton found that the host plants in the former sources senesced much earlier than in the former pseudo-sink patches. As a result, immigrants regularly arrived too late to successfully reproduce. He found that the former pseudo-sinks had become sources, and the former sources had become true sinks.

One of the most recent additions to the source-sink literature is by Tittler et al., [8] who examined wood thrush (Hylocichla mustelina) survey data for evidence of source and sink populations on a large scale. The authors reasoned that emigrants from sources would likely be the juveniles produced in one year dispersing to reproduce in sinks in the next year, producing a one-year time lag between population changes in the source and in the sink. Using data from the Breeding Bird Survey, an annual survey of North American birds, they looked for relationships between survey sites showing such a one-year time lag. They found several pairs of sites showing significant relationships 60–80 km apart. Several appeared to be sources to more than one sink, and several sinks appeared to receive individuals from more than one source. In addition, some sites appeared to be a sink to one site and a source to another (see Figure 1). The authors concluded that source-sink dynamics may occur on continental scales.

One of the more confusing issues involves identifying sources and sinks in the field. [9] Runge et al. [9] point out that in general researchers need to estimate per capita reproduction, probability of survival, and probability of emigration to differentiate source and sink habitats. If emigration is ignored, then individuals that emigrate may be treated as mortalities, thus causing sources to be classified as sinks. This issue is important if the source-sink concept is viewed in terms of habitat quality (as it is in Table 1) because classifying high-quality habitat as low-quality may lead to mistakes in ecological management. Runge et al. [9] showed how to integrate the theory of source-sink dynamics with population projection matrices [10] and ecological statistics [11] in order to differentiate sources and sinks.

Why would individuals ever leave high quality source habitat for a low quality sink habitat? This question is central to source-sink theory. Ultimately, it depends on the organisms and the way they move and distribute themselves between habitat patches. For example, plants disperse passively, relying on other agents such as wind or water currents to move seeds to another patch. Passive dispersal can result in source-sink dynamics whenever the seeds land in a patch that cannot support the plant's growth or reproduction. Winds may continually deposit seeds there, maintaining a population even though the plants themselves do not successfully reproduce. [12] Another good example for this case are soil protists. Soil protists also disperse passively, relying mainly on wind to colonize other sites. [13] As a result, source-sink dynamics can arise simply because external agents dispersed protist propagules (e.g., cysts, spores), forcing individuals to grow in a poor habitat. [14]

In contrast, many organisms that disperse actively should have no reason to remain in a sink patch, [15] provided the organisms are able to recognize it as a poor quality patch (see discussion of ecological traps). The reasoning behind this argument is that organisms are often expected to behave according to the "ideal free distribution", which describes a population in which individuals distribute themselves evenly among habitat patches according to how many individuals the patch can support. [16] When there are patches of varying quality available, the ideal free distribution predicts a pattern of "balanced dispersal". [15] In this model, when the preferred habitat patch becomes crowded enough that the average fitness (survival rate or reproductive success) of the individuals in the patch drops below the average fitness in a second, lower quality patch, individuals are expected to move to the second patch. However, as soon as the second patch becomes sufficiently crowded, individuals are expected to move back to the first patch. Eventually, the patches should become balanced so that the average fitness of the individuals in each patch and the rates of dispersal between the two patches are even. In this balanced dispersal model, the probability of leaving a patch is inversely proportional to the carrying capacity of the patch. [15] In this case, individuals should not remain in sink habitat for very long, where the carrying capacity is zero and the probability of leaving is therefore very high.

An alternative to the ideal free distribution and balanced dispersal models is when fitness can vary among potential breeding sites within habitat patches and individuals must select the best available site. This alternative has been called the "ideal preemptive distribution", because a breeding site can be preempted if it has already been occupied. [17] For example, the dominant, older individuals in a population may occupy all of the best territories in the source so that the next best territory available may be in the sink. As the subordinate, younger individuals age, they may be able to take over territories in the source, but new subordinate juveniles from the source will have to move to the sink. Pulliam [2] argued that such a pattern of dispersal can maintain a large sink population indefinitely. Furthermore, if good breeding sites in the source are rare and poor breeding sites in the sink are common, it is even possible that the majority of the population resides in the sink.

The source-sink model of population dynamics has made contributions to many areas in ecology. For example, a species' niche was originally described as the environmental factors required by a species to carry out its life history, and a species was expected to be found only in areas that met these niche requirements. [18] This concept of a niche was later termed the "fundamental niche", and described as all of the places a species could successfully occupy. In contrast, the "realized niche", was described as all of the places a species actually did occupy, and was expected to be less than the extent of the fundamental niche as a result of competition with other species. [19] However, the source-sink model demonstrated that the majority of a population could occupy a sink which, by definition, did not meet the niche requirements of the species, [2] and was therefore outside the fundamental niche (see Figure 2). In this case, the realized niche was actually larger than the fundamental niche, and ideas about how to define a species' niche had to change.

Source–sink dynamics has also been incorporated into studies of metapopulations, a group of populations residing in patches of habitat. [20] Though some patches may go extinct, the regional persistence of the metapopulation depends on the ability of patches to be re-colonized. As long as there are source patches present for successful reproduction, sink patches may allow the total number of individuals in the metapopulation to grow beyond what the source could support, providing a reserve of individuals available for re-colonization. [21] Source–sink dynamics also has implications for studies of the coexistence of species within habitat patches. Because a patch that is a source for one species may be a sink for another, coexistence may actually depend on immigration from a second patch rather than the interactions between the two species. [2] Similarly, source-sink dynamics may influence the regional coexistence and demographics of species within a metacommunity, a group of communities connected by the dispersal of potentially interacting species. [22] Finally, the source-sink model has greatly influenced ecological trap theory, a model in which organisms prefer sink habitat over source habitat. [23]

Land managers and conservationists have become increasingly interested in preserving and restoring high quality habitat, particularly where rare, threatened, or endangered species are concerned. As a result, it is important to understand how to identify or create high quality habitat, and how populations respond to habitat loss or change. Because a large proportion of a species' population could exist in sink habitat, [24] conservation efforts may misinterpret the species' habitat requirements. Similarly, without considering the presence of a trap, conservationists might mistakenly preserve trap habitat under the assumption that an organism's preferred habitat was also good quality habitat. Simultaneously, source habitat may be ignored or even destroyed if only a small proportion of the population resides there. Degradation or destruction of the source habitat will, in turn, impact the sink or trap populations, potentially over large distances. [8] Finally, efforts to restore degraded habitat may unintentionally create an ecological trap by giving a site the appearance of quality habitat, but which has not yet developed all of the functional elements necessary for an organism's survival and reproduction. For an already threatened species, such mistakes might result in a rapid population decline toward extinction.


Biology and population dynamics of cowcod (Sebastes levis) in the southern California Bight

Cowcod (Sebastes levis) is a large (100-cm-FL), long-lived (maximum observed age 55 yr) demersal rockfish taken in multispecies commercial and recreational fisheries off southern and central California. It lives at 20–500 m depth: adults (>44 cm TL) inhabit rocky areas at 90–300 m and juveniles inhabit fine sand and clay at 40–100 m. Both sexes have similar growth and maturity. Both sexes recruit to the fishery before reaching full maturity. Based on age and growth data, the natural mortality rate is about M =0.055/yr, but the estimate is uncertain. Biomass, recruitment, and mortality during 1951–98 were estimated in a delay-difference model with catch data and abundance indices. The same model gave less precise estimates for 1916–50 based on catch data and assumptions about virgin biomass and recruitment such as used in stock reduction analysis. Abundance indices, based on rare event data, included a habitat-area–weighted index of recreational catch per unit of fishing effort (CPUE index values were 0.003–0.07 fish per angler hour), a standardized index of proportion of positive tows in CalCOFI ichthyoplankton survey data (binomial errors, 0–13% positive tows/yr), and proportion of positive tows for juveniles in bottom trawl surveys (binomial errors, 0–30% positive tows/yr). Cowcod are overfished in the southern California Bight biomass during the 1998 season was about 7% of the virgin level and recent catches have been near 20 metric tons (t)/yr. Projections based on recent recruitment levels indicate that biomass will decline at catch levels > 5 t/yr. Trend data indicate that recruitment will be poor in the near future. Recreational fishing effort in deep water has increased and has become more effective for catching cowcod. Areas with relatively high catch rates for cowcod are fewer and are farther offshore. Cowcod die after capture and cannot be released alive. Two areas recently closed to bottom fishing will help rebuild the cowcod stock.


Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study

Background COVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, climate, surveillance, and policy responses. The Philippines is a LMIC that has had a relatively severe COVID-19 outbreak but has recently curtailed transmission while gradually easing interventions.

Methods We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a “Minimum Health Standards” policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and projected the impacts of easing interventions.

Results Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-32%. The December 2020 case detection rate was estimated at ∼14%, population recovered at ∼12%, and scenario projections indicated high sensitivity to MHS adherence.

Conclusions COVID-19 dynamics in the Philippines are driven by age, contact structure, and mobility, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS measures should allow the Philippines to maintain epidemic control, but disease resurgence remains a threat due to low population immunity and detection rates.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by the World Health Organization Regional Office for the Western Pacific to provide modelling advice to Member States. JMS is supported by a NASA Ecological Forecasting grant (NNX17AI21G). EDLT, TRT, MRJEE, and RFRS are supported by a project grant from the Philippine Council for Health Research and Development, Department of Science and Technology, Philippines. JMT is supported by an Early Career Fellowship from the National Health and Medical Research Council (APP1142638). The funding bodies had no role in the study design, analysis, interpretation of data, and in writing the manuscript.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

No IRB oversight was needed for this study.

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.


Dynamical Systems in Population Biology

The conjoining of nonlinear dynamics and biology has brought about significant advances in both areas, with nonlinear dynamics providing a tool for understanding biological phenomena and biology stimulating developments in the theory of dynamical systems. This research monograph provides an introduction to the theory of nonautonomous semiflows with applications to population dynamics. It develops dynamical system approaches to various evolutionary equations such as difference, ordinary, functional, and partial differential equations, and pays more attention to periodic and almost periodic phenomena. The presentation includes persistence theory, monotone dynamics, periodic and almost periodic semiflows, traveling waves, and global analysis of typical models in population biology. Research mathematicians working with nonlinear dynamics, particularly those interested in applications to biology, will find this book useful. It may also be used as a textbook or as supplementary reading for a graduate special topics course on the theory and applications of dynamical systems.

Dr. Xiao-Qiang Zhao is a professor in applied mathematics at Memorial University of Newfoundland, Canada. His main research interests involve applied dynamical systems, nonlinear differential equations, and mathematical biology. He is the author of more than 40 papers and his research has played an important role in the development of the theory of periodic and almost periodic semiflows and their applications.

Dr. Xiao-Qiang Zhao is a professor in applied mathematics at Memorial University of Newfoundland, Canada. His main research interests involve applied dynamical systems, nonlinear differential equations, and mathematical biology. He is the author of more than 40 papers and his research has played an important role in the development of the theory of periodic and almost periodic semiflows and their applications.

"This is a highly technical research monograph which will be mainly of interest to those working in the field of mathematical population dynamics. … The book provides a comprehensive coverage of the latest theoretical developments, particularly in the purely mathematical sophistications of the field … ." (Tony Crilly, The Mathematical Gazette, March, 2005)

"This book provides an introduction to the theory of periodic semiflows on metric spaces and their applications to population dynamics. … This book will be most useful to mathematicians working on nonlinear dynamical models and their applications to biology." (R.Bürger, Monatshefte für Mathematik, Vol. 143 (4), 2004)

"The main purpose of the book, in the author’s words, ‘is to provide an introduction to the theory of periodic semiflows on metric spaces’ and to apply this theory to a collection of mathematical equations from population dynamics. … The book presents its mathematical theory in a coherent and readable fashion. It should prove to be a valuable resource for mathematicians who are interested in non-autonomous dynamical systems and in their applications to biologically inspired models." (J. M. Cushing, Mathematical Reviews, 2004 f)


Conclusions

The observed results reveal relationships between female sex, age, symptom duration, and disease severity and the persistence and loss of serum IgG levels in individuals who have recovered from COVID-19 for the first time in the Brazilian Amazon region. The percentage of patients exhibiting antibody loss was high in the present study, which may have implications for seroepidemiological investigations [36], especially those conducted recently, possibly leading to underestimated calculations of the prevalence of infection or even the susceptibility of the population to possible reinfection and thus compromising the success of current vaccination campaigns. Therefore, the use of tests with high sensitivity and specificity as chemiluminescent microparticle immunoassay, can enhance the accuracy and capacity of diagnosis of anti-SARS-CoV-2 antibodies [37].


Watch the video: OpenStax Concepts of Biology Population Demographics and Dynamics (December 2022).