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Let's say I want to evolve a bacterium that is resistant to an antibiotic. I want to do this by growing initially clonal populations of bacteria in presence of this antibiotic for a long time.
I have two options:
- Make one 50 ml flask culture
- Make ten 5 ml tube cultures
What are the comparative advantages and disadvantages of these two? Clearly, the ten culture version is better at probing the space of all possible resistance mechanisms. However, what about the rate of evolution? If I just want to get a resistant strain as fast as possible, which one should I use?
Note that "antibiotic" is just a proxy for any selective pressure, and "bacteria" is proxy for any organism. If possible, I think it will be interesting to address the effects of sexual vs. asexual reproduction in the organism that is being evolved.
There is already a question, How does rate of evolution/innovation scale with population size? that is very similar. I think mine is different, because it is not one small population vs. one big population, but one big population vs. many small populations. In other words, I am attempting to control for number of selection/mutation events, and focus only on the stratification of the total population.
Note: This is not an area where I know the litterature well
Where are many counteracting processes to consider for this question. For instance, the rate of evolution will be affected by the rate of mutation, the distribution of positive and deleterious mutations, strength of selection, whether the fitness effects are small or large, if fitness effects depend on interactions between multiple mutations or single mutations with large effect, asexual vs. sexual populations, and processes such as genetic drift.
A recent review that is useful for your question is Lanfear et al (2014). They focus on how substitution rate is affected by effective population size, and go through both theoretical and empirical evidence. They conclude that in most cases, most mutations are deleterious, and that drift and selection are the most important effects in determining rates of evolution. They also state the effect of effective population size (Ne) is likely to depend if mutations are dominated by deleterious or advantagious mutations, and they expect the rate of evolution to be negatively correlated with Ne when deletious mutations dominate evolution and a positive correlation to Ne when advantagious mutations dominate.
A paper by Rozen et al (2008) indicates that while large populations are more effective in fixing mutations with large positive effects, small populations can be more effective in complex "rugged" adaptive landscapes.
While such determinism speeds adaptation on the smooth adaptive landscape represented by the simple environment, it can limit the ability of large populations from effectively exploring the underlying topography of rugged adaptive landscapes characterized by complex environments.
However, this does not deal directly with the rate of evolution, but mostly about attaining the highest possible fitness in a particular fitness landscape. Another interesting paper is Desai et al. (2007), but I haven't looked through this yet. However, they find (for asexual bacteria) that the speed of adaptation scales less than linearly with population size, and similar results have also been found in studies from Lenski's group. Their interpretation is that evolution is driven by multiple concurrent mutations of small effect and not one-by-one fixation of single mutations (which would favour large populations more).
For your case of evolution of resistance, I think one crucial thing is if you expect resistance to be driven by a few single mutations or combinations of many mutations that interact (related to whether resistance is "easy" or "hard" to evolve). From what I understand, the first case should favour a single large population (since they will fix mutations for resistance more quickly), while the second case might favour many smaller populations (since you might get more effective exploration of combinations of mutations)
These are just a few starting points in a very large field, and I hope that you find them useful.
With very large population sizes like this the effect of genetic drift goes to zero, which is greatly simplifying. I'm also assuming selection is very strong, so that fixation times are small, and that mutation rates and population size more or less cancels out, so there are a finite number of mutations and the selection space is relatively unexplored. If selection is weak everything takes longer, but I'm pretty sure the results are the same. There's greater exploration of alternatives, but that doesn't affect our thought experiment comparing population size.
In asexual populations: 10 cultures will give you ten answers to your selection criteria, one of which will be 'best'. One culture will give you the best solution a little bit later (larger populations take longer to reach fixation).
In sexual populations: One culture is better. The space of alleles is larger, and positive or conditionally positive mutations will be tested against more possible synergistic partners.
Consider that there are ten positive mutations split across your population. A sexual population in ten sub-populations has a tiny chance of getting all ten mutations into the same gene pool, but all ten sub-populations combined make it near-certainty that all ten mutations will make it into the same organism.
There's a limit to sexual population size above which evolution doesn't accelerate with additional individuals, because any additional mutations they bring to the table are eventually guaranteed to already exist in the gene pool. That limit is related to the genome size and mutation rate and probably other things, but for biological species it is enormous.
Both have its benefits and drawbacks.
The single big culture would have a bigger risk of contamination, but a bigger chance that the resistant mutated individuals are present, so in this case you would have a lower risk of complete extermination of your culture.
The smaller several cultures on the other hand will let you try several dosages of the antibiotic without fearing exterminating your culture. On the other hand each individual culture probably will have lower diversification, so lowers the chance of new strains.
Much of the literature on social evolution is pervaded by the old debate about the relative merits of kin and group selection. In this debate, the biological interpretation of processes occurring in real populations is often conflated with the mathematical methodology used to describe these processes. Here, we highlight the distinction between the two by placing this discussion within the broader context of evolution in structured populations. In this review we show that the current debate overlooks important aspects of the interplay between genetic and demographic structuring, and argue that a continued focus on the relative merits of kin versus group selection distracts attention from moving the field forward.
Present address: Centre d’Écologie Fonctionnelle et Évolutive, UMR 5175, CNRS 1919 route de Mende, 34293 Montpellier Cedex 5, France
An “Appreciable Fraction” of Variation
Darwin’s core insight was that organisms with disadvantageous traits would slowly be weeded out through negative (or purifying) selection, while those with advantageous features would reproduce more often and pass those features on to the next generation (positive selection). Selection would help to spread and refine those valuable traits. For most of the first half of the 20th century, population geneticists largely attributed genetic differences between populations and species to adaptation through positive selection.
But in 1968, the famed population geneticist Motoo Kimura resisted the adaptationist perspective with his neutral theory of molecular evolution. In a nutshell, he argued that an “appreciable fraction” of the genetic variation within and between species is the result of genetic drift — that is, the effects of randomness in a finite population — rather than natural selection, and that most of these differences have no functional consequences for survival and reproduction.
The following year, the biologists Jack Lester King and Thomas Jukes published “Non-Darwinian Evolution,” an article that likewise emphasized the importance of random genetic changes in the course of evolution. A polarized debate subsequently emerged between the new neutralists and the more traditional adaptationists. Although everyone agreed that purifying selection would weed out deleterious mutations, the neutralists were convinced that genetic drift accounts for most differences between populations or species, whereas the adaptationists credited them to positive selection for adaptive traits.
Much of the debate has hinged on exactly what Kimura meant by “appreciable fraction” of genetic variation, according to Jeffrey Townsend, a biostatistician and professor of evolutionary biology at the Yale School of Public Health. “Is that 50 percent? Is it 5 percent, 0.5 percent? I don’t know,” he said. Because Kimura’s original statement of the theory was qualitative rather than quantitative, “his theory could not be invalidated by later data.”
Nevertheless, neutral theory was rapidly adopted by many biologists. This was partly a result of Kimura’s reputation as one of the most prominent theoretical population geneticists of the time, but it also helped that the mathematics of the theory was relatively simple and intuitive. “One of the reasons for the popularity of the neutral theory was that it made things a lot easier,” said Andrew Kern, a population geneticist now at the University of Oregon, who contributed an article with Matthew Hahn, a population geneticist at Indiana University, to a special issue of Molecular Biology and Evolution celebrating the 50th anniversary of neutral theory.
To apply a neutral model of evolution to a population, Hahn explained, you don’t have to know how strong selection is, how large the population is, whether mutations are dominant or recessive, or whether mutations interact with other mutations. In neutral theory, “all of those very hard parameters to estimate go away.”
The only key input required by the neutral model is the product of the population size and the mutation rate per generation. From this information, the neutral model can predict how the frequency of mutations in the population will change over time. Because of its simplicity, many researchers adopted the neutral model as a convenient “null model,” or default explanation for the patterns of genetic variation they observed.
Some population geneticists were not convinced by Kimura’s argument, however. For instance, John Gillespie, a theoretical population geneticist at the University of California, Davis (and Kern’s doctoral adviser), showed in the early 1970s that some natural selection-based models could explain patterns observed in nature as well as neutral models, if not better.
More fundamentally, even when there aren’t enough data to disprove a neutral-theory null model, it doesn’t mean that natural selection isn’t happening, said Rebekah Rogers, an evolutionary geneticist at the University of North Carolina, Charlotte. “Any time you have limited data, the arguments get really fierce,” she said.
For decades, that was the crux of the problem: Kimura had proposed neutral theory at a time before inexpensive sequencing technology and the polymerase chain reaction became available, when gene sequence data were sparse. There was no simple way to broadly prove or disprove its tenets except on theoretical grounds because we didn’t know enough about genomic variation to resolve the dispute.
To demonstrate the Hardy-Weinberg equilibrium, assume G and g are the dominant and recessive alleles for a trait where GG = green, gg = yellow, and Gg = orange. In our imaginary population of 1,000 individuals, assume that 600 have the GG genotype, 300 are Gg, and 100 are gg. The allele and genotype frequency for each allele is calculated by dividing the total population into the number for each genotype:
After the allele frequency has been determined, we can predict the frequency of the allele in the first generation of offspring.
First, determine the total number of alleles possible in the first generation. In this imaginary case, because each organism has 2 alleles and there are 1,000 organisms, the number of possible alleles in the first generation of offspring is:
Next, examine the possibility of each allele. For the G allele, both GG and Gg individuals must be considered. Taken separately,
The letter p is used to identify the allele frequency for the dominant allele (.75) and q for the recessive allele (.25). Note that p + q = 1.
The frequency for the G allele is therefore:
For the g allele, the calculation is similar:
The frequency for the g allele is therefore:
Hardy-Weinberg can also predict second-generation genotype frequencies. From the previous example, the allele frequencies for the only possible alleles are p = .75(G) and q = .25(g) after meiosis. Therefore, the probability of a GG offspring is p p = p 2 or (.75) (.75) = 55 percent. For the gg possibility, the allele frequencies are q q or (.25) (.25) = 6 percent. For the heterozygous genotype, the dominant allele can come from either parent, so there are two possibilities: Gg = 2pq = 2(.75)(.25) = 39 percent.
Note that the percentages equal 100, and the allele frequencies (p and q) are identical to the genotype frequency in the first generation! Because there is no variation in this hypothetical situation, it is in Hardy-Weinberg equilibrium, and both the gene and allele frequencies will remain unchanged until acted upon by an outside force(s). Therefore, the population is in a stable equilibrium with no innate change in phenotypic characteristics. As mentioned in Historical Development and Mechanisms of Evolution and Natural Selection, populations tend not to stay in Hardy-Weinberg equilibrium for very long because of environmental pressures.
The Hardy-Weinberg equation highlights the fact that sexual reproduction does not alter the allele frequencies in a gene pool. It also helps identify a genetic equilibrium in a population that seldom exists in a natural setting because five factors impact the Hardy-Weinberg equilibrium and create their own method for microevolution. Note that the first four do not involve natural selection:
- Gene migration
- Genetic drift
- Nonrandom mating
- Natural selection
Refer to Historical Development and Mechanisms of Evolution and Natural Selection for a more in-depth coverage of the natural selection model.
A mutation is an inheritable change of a gene by one of several different mechanisms that alter the DNA sequencing of an existing allele to create a new allele for that gene.
A primary mechanism for microevolution is the formation of new alleles by mutation. Spontaneous errors in the replication of DNA create new alleles instantly while physical and chemical mutagens, such as ultraviolet light, create mutations constantly at a lower rate. Mutations affect the genetic equilibrium by altering the DNA, thus creating new alleles that may then become part of the reproductive gene pool for a population. When a new allele creates an advantage for the offspring, the number of individuals with the new allele may increase dramatically through successive generations. This phenomenon is not caused by the mutation somehow overmanufacturing the allele, but by the successful reproduction of individuals who possess the new allele. Because mutations are the only process that creates new alleles, it is the only mechanism that ultimately increases genetic variation.
Gene migration is the movement of alleles into or out of a population either by the immigration or emigration by individuals or groups. When genes flow from one population to another, that flow may increase the genetic variation for the individual populations, but it decreases the genetic variability between the populations, making them more homogeneous. Gene migration is the opposite effect of reproductive isolation, which tends to be genetically near the Hardy-Weinberg equilibrium.
Genetic drift is the phenomenon whereby chance or random events change the allele frequencies in a population. Genetic drift has a tremendous effect on small populations where the gene pool is so small that minor chance events greatly influence the Hardy-Weinberg arithmetic. The failure of a single organism or small groups of organisms to reproduce creates a large genetic drift in a small population because of the loss of genes that were not conveyed to the next generation. Conversely, large populations, statistically defined as greater than 100 reproducing individuals, are proportionally less affected by isolated random events and retain more stable allele frequency with low genetic drift.
Two types of genetic drift act when a large population is modified to be considered statistically as a small population: fragmentation effect and pioneer effect.
The fragmentation effect is a type of genetic drift that occurs when a natural occurrence, such as a fire or hurricane, or man-made event, such as habitat destruction or overhunting, unselectively divides or reduces a population so it contains less genetic variability than the once-large population. A large population may become fragmented when a man-made dam creates a large lake where once an easily forded river provided no obstruction of movement. Likewise, a new highway can isolate species on either side. The net result is a small fragment that becomes reproductively separated from the main group. The fragmented group did not become isolated because of natural selection, so it may contain a fragment or all of the genetic variation of the larger population.
Even when small populations recover, their genetic variability is still so low that they remain in danger of extinction from a single catastrophic event.
Zoos spend a great deal of time, money, and energy in an effort to increase their genetic diversity by locating new breeding organisms for mating, usually from other zoos.
The pioneer effect occurs whenever a small group breaks away from the larger population to colonize a new territory. Like the fragmentation effect, the pioneers, which may consist of only a single seed or mating pair, remain an extinction threat because they do not have the genetic diversity of the main body and therefore are less likely to produce offspring capable of surviving changes in the environment. Even though a pioneer population reproduces successfully, the gene variation has not increased. So the danger involved with survival in a changing environment still exists!
The Hardy-Weinberg equation assumes that all males have an equal chance to fertilize all females. However, in nature, this seldom is true because of a number of factors such as geographical proximity, as is the case in rooted plants. In fact, the ultimate nonrandom mating is the act of self-fertilization that is common in some plants. In other cases, as the reproductive season approaches, the number of desirable mates is limited by their presence (or absence) as well as by their competitive premating rituals. Finally, botanists and zoologists practice nonrandom mating as they attempt to breed more and better organisms for economic benefit.
How does evolution work?
Evolution is simply the change in the gene pool of a population over time. Individual organisms don’t evolve once you have your genes, they can’t really be changed except for a very few rare circumstances. But there are five different ways that genes can change a population over time:
Mutation is very important in evolution because it’s the only way that completely new genes ever happen. In fact, every single gene in the world started as a mutation! The other four mechanisms are just different ways that genes can be reshuffled, but with mutation, it’s something new that’s never been seen before.
Most mutations don’t have any effect on the organism, or they may even have a negative effect. But, every once in a while, a mutation happens that actually improves the organism in some way. Maybe it’s just a bit faster, has sharper teeth, or a better brain. When this happens, the organism is more likely to survive, reproduce, and pass on the new gene to its offspring. When those genes spread in the population, it’s said to have evolved.
When organisms move in and out of an area, they also take their genes with them!
In conservation, one of the main concerns is how the wild landscape is becoming increasingly fragmented. More roads, farms, and shopping malls are built, and shy animals don’t move around like they did historically.
Now, a very real possibility is that some populations will become too isolated and may eventually evolve to become inbred. If this happens, they’re more likely to succumb to disease or be unable to adapt to a changing environment because they won’t have access to new genes that may help them survive better.
It might seem like “natural selection” is a difficult-to-understand concept dating back to Darwin and his Galapagos finches, but it’s actually pretty simple.
Organisms are exposed to different conditions that affect how likely they are to survive and have babies. That’s it! These different conditions, called selective pressures, can be external (in the environment) or internal (within their own bodies).
Examples of selective pressures might be the pH of ocean water (crab shells will dissolve when it’s too acidic), a new disease (Tasmanian devils are evolving to become more resistant to an infectious facial tumor), or how attractive an organism is to others (“beautiful” or “handsome” animals are more likely to find mates and reproduce than their “ugly” counterparts).
Artificial selection is similar to natural selection, except the limitation to which organisms are allowed to reproduce is decided by humans. We do this because we want to develop a certain trait in an organism, like high-productivity wheat or friendlier kitties.
Check out this cool example of artificial selection in some things you may eat all the time:
Dogs are a great example of artificial selection. There are 340 different breeds of dog in the world, all created by people for a certain purpose. In some cases, such as English Bulldogs or Chihuahuas, these dogs’ genes are manipulated to make them physically attractive but can actually cause unhealthy side effects. It’s not very likely that these dogs would have evolved as such in the wild.
Most genes don’t have selective pressures on them forcing them to evolve one way or another. They just casually float along in a population, and each time a new organism is born, its genes from its parents get reshuffled randomly.
This has some interesting effects. If a population is very small, it’s more likely to show a phenomenon called genetic drift—random changes in the gene pool. Larger populations serve as big reservoirs of rare genes so it’s hard to lose them completely. On the flip side, it’s easier for rare genes to be lost from small populations because there aren’t very many to begin with.
For example, let’s think of a small population of just 20 birds. If only one of these birds has a rare gene and that bird dies, that gene is lost from the population. But, if there are 10,000 birds and 50 of those birds have the rare gene, that gene is much more likely to stay in the population by passing it on to offspring. It’s unlikely that all 50 of the special birds would be struck by lightning at the same time!
Advances in medical science
Human medicine didn&rsquot see many big advancements or improvements until around 1800s, when the world&rsquos population rose to 1 billion for the first time, and we have not looked back since.
As advances happened in science and medicine and agricultural practices revolutionized the way we feed the world, people began to live longer, more and more children survived into childbearing age and had families of their own. The population grew &ndash all over the world.
The rise of industry and large-scale agriculture meant that families could be much larger than in the past. The social impact of the urban-rural divide led to more complex societies, cities, and more people.
To this day, the advantage that Asia has enjoyed over the last 10,000 years has not disappeared.
If a third of the world&rsquos population 1,000 years ago was centered in Asia, namely in China and India, it makes sense that about a third of the world&rsquos population is still there today! Population growth dynamics and factors that allowed for larger families and more abundant food supplies are still present in these two populous nations.
Obviously, other social, cultural, religious and political factors play into this ultimately one-sided population, but the fact is that populations increase exponentially. Essentially, with a higher base level to begin within China and India, the population explosion of the past 200 years is more prominently seen there. As a prime example of our increasingly imbalanced planet, more than 51.5% of the world&rsquos population exists in an Asian bubble that contains only 19 countries.
Evolution: Genetic Drift, Gene Flow, Mutations, Random Change
- Key factors that can cause evolution:
- – Small populations are more variable to changes in allele frequencies
- -non-random mating opportunities result in only those “preferred” traits being passed onto future populations
- – new alleles may be created when mutations occur (changes the frequencies of new and original alleles)
- -migration causes changes in the relative abundance of alleles
- -natural selection takes place when individuals with certain alleles have greater reproductive success than others
- Ex: in population of 100 endangered frogs,1 frog that contained a particular allele and half of population got wiped out due to severe droughts
- 2 results possible for the frequency of this allele:
- This frog might be one of 50 that died (rare allele eliminated from gene pool)
- By some chance this frog survived and the allele frequency doubled
Global population growth
Two centuries of rapid global population growth will come to an end
One of the big lessons from the demographic history of countries is that population explosions are temporary. For many countries the demographic transition has already ended, and as the global fertility rate has now halved we know that the world as a whole is approaching the end of rapid population growth.
This visualization presents this big overview of the global demographic transition – with the very latest data from the UN Population Division.
As we explore at the beginning of the entry on population growth, the global population grew only very slowly up to 1700 – only 0.04% per year. In the many millennia up to that point in history very high mortality of children counteracted high fertility. The world was in the first stage of the demographic transition.
Once health improved and mortality declined things changed quickly. Particularly over the course of the 20th century: Over the last 100 years global population more than quadrupled.ਊs we see in the chart, the rise of the global population got steeper and steeper and you have just lived through the steepest increase of that curve. This also means that your existence is a tiny part of the reason why that curve is so steep.
The 7-fold increase of the world population over the course of two centuries amplified humanity’s impact on the natural environment. To provide space, food, and resources for a large world population in a way that is sustainable into the distant future is without question one of the large, serious challenges for our generation. We should not make the mistake of underestimating the task ahead of us. Yes, I expect new generations to contribute, but for now it is upon us to provide for them. Population growth is still fast: Every year 140 million are born and 58 million die – the difference is the number of people that we add to the world population in a year: 82 million.
In red you see the annual population growth rate (that is, the percentage change in population per year) of the global population. It peaked around half a century ago. Peak population growth was reached in 1968 with an annual growth of 2.1%. Since then the increase of the world population has slowed and today grows by just over 1% per year. This slowdown of population growth was not only predictable, but predicted. Just as expected by demographers (here), the world as a whole is experiencing the closing of a massive demographic transition.
This chart also shows how the United Nations envision the slow ending of the global demographic transition. As population growth continues to decline, the curve representing the world population is getting less and less steep. By the end of the century – when global population growth will have fallen to 0.1% according to the UN’s projection – the world will be very close to the end of the demographic transition. It is hard to know the population dynamics beyond 2100 it will depend upon the fertility rate and as we discuss in our entry on fertility rates hereꃾrtility is first falling with development – and then rising with development. The question will be whether it will rise above an average 2 children per woman.
The world enters the last phase of the demographic transition and this means we will not repeat the past. The global population has quadrupled over the course of the 20th century, but it will not double anymore over the course of this century.
The world population will reach a size, which compared to humanity’s history, will be extraordinary if the UN projections are accurate (they have a good track record), the world population will have increased more than 10-fold over the span of 250 years.
We are on the way to a new balance. The big global demographic transition that the world entered more than two centuries ago is then coming to an end: This new equilibrium is different from the one in the past when it was the very high mortality that kept population growth in check. In the new balance it will be low fertility keeps population changes small.
The UN population projection by country and world region until 2100
The chart shows the change of the total population since 1950 and from 2015 it shows the UN population projection until the end of the century.
This interactive visualization you can change to any other country or world region.
By switching to the map view you can explore the projection of the distribution of the global population.
As we see here, there is a significant fall in the population growth rate, particularly in the second half of the 21st century. Although the world population is still rising at the end of the century, it’s doing so very slowly. We would therefore expect growth to come to an end very soon after 2100.
In this projection the world population will be around 10.88 billion in 2100 and we would therefore expect ‘peak population’ to occur early in the 22nd century, at not much more than 10.88 billion.
Appendix C. Stochastic Modeling
The model presented above is deterministic. We introduce stochasticity by updating species counts N1 and N2 using distribution functions based on equations (A1).
At each time step, the counts of species 1 and 2 are updated to account for logistic growth:
Exponential Growth Plus Death
To introduce a net death rate, we develop the expression of the deterministic logistic growth into a (positive) exponential growth term and a (negative) density-dependent death term.
At each time step, a random fraction of individuals of species 1 in deme x are randomly moved into demes x + dx and x − dx . All demes are processed sequentially in a random order with the following scheme:
• draw B = Binomial ( N 1 , p = D 1 d t / dx 2 )
• draw B left = Binomial ( B , p = 0.5 )
• update N 1 ( x , t + dt ) → N 1 ( x , t ) − B
• update N 1 ( x − dx , t + dt ) → N 1 ( x − dx , t ) + B left
• update N 1 ( x + dx , t + dt ) → N 1 ( x + dx , t ) + ( B − B left )
In practice, we set dt = 1 and dx = 1 without loss of generality. We also keep r1, r2, D1, and D2 small enough to ensure that the probabilities in binomial distributions remain smaller than 1.
Evolutionary Simulations without Trade-Off
At each time point, each division gives rise to a mutation, with a probability of 5%. Each new phenotype is randomly drawn from a normal distribution centered on the ancestor phenotype, with a standard deviation of 0.1 times the phenotype in each direction. To speed up the simulations, subpopulations that did not reach a certain size after a certain time since they appeared are cleared up, and their counts are randomly distributed over the remaining populations.
Evolutionary Simulations with Trade-Off
At each time point, each division gives rise to a mutation, with a probability of 1%. Each new phenotype randomly falls on the trade-off line (uniform distribution), which is split into 500 bins within the first quadrant of the space ( r , D ) .
Organisms, Populations and Communities: Mark And Recapture Practical
This was originally written in Star Office and then copy and pasted office. The numbers seen must be put into a table to make sense. Other than that it is a good example for practical lab reports.
Organisms, Populations and Communities
The Mark and Recapture method of estimating population size is used in the study of animal populations where individuals are highly mobile. It is of no value where animals do not move or move very little. The number of animals caught in each sample must be large enough to be valid. There is a simple formula used to find the approximate number of organisms in the area. This formula is:
No. of animals in 1st sample * No. of animals in 2nd sample
Total population = Number of marked animals recaptured
Using this simple technique you are able to gather an approximate number of organisms in the area.
To use matches to simulate the mark and recapture method. We will use the mark and recapture method to estimate the counting of an organism population.
I feel that this practical will not accurately portray the mark and recapture method of counting population. I do not think that the sample we are to use is big enough for an accurate result. We are only using a box of matches which at most will contain 60 matches.
Marked matches pulled from sample of 20
Estimated population of matches:33+33+33+50+40
Estimated population of matches: 38
Actual number of matches: 36
The results from the practical were relatively accurate. The estimated number of matches differed only by 2 in comparison with the actual number of matches. By using 5 samples inaccuracies were evened out to make sure a relatively accurate number was given at the end. If only one or two samples were taken then you could gather a figure unusually high or unusually low.
To make sure that the samples were accurate four steps were taken. We marked the matches with highlighter, so that when selecting our matches we were not bias as we could not know which ones were marked, unlike if we marked them with tape or a tag. We mixed the marked matches back with the rest of the unmarked matches in a jar and shook the jar to make sure there was full integration. Then someone would pull matches from the jar while not looking in order not to make a bias selection. Finally five different samples were taken in order to gather a variety of answers.
There are some problems with the mark and recapture method of counting populations. If consequent sampling of the organisms is taken too soon after marking or re-counting then chances are the organisms have not had time to mix back into their habitat which means when they are recaptured you are given the indication that there are a lower number of organisms than what there really are. If subsequent samples are taken too long after marking or re-counting than there is a very high possibility that the organisms are dead or have moved to a different habitat. This gives the indication that there are more animals in the population that what there really are, as the calculation will be wrong.
There are assumptions made in this method of sampling that, if were not true, would cause the method to be inaccurate and fail. The method assumes that it is being used for a large population, not a small isolated population. If the population sampled was small and isolated than subsequent sampling would prove inaccurate. It also assumes that the animals move. If animals are sampled that do not move than you will get inaccurate results as you will be resampling the exact same animals in the same area. It also assumes that the animals stay in the one area all the time. If the animals migrate at all then later samples will prove inefficient as there will be none or few animals to recapture.
There are many animals that this method would be unsuitable for. They include migrating animals, stationary animals, territorial animals and humans. The method would not work with these kinds of organisms as they go against the assumptions that the method uses. The method assumes that the sampled animal is one that moves, interacts with others of its kind and stay in the one area all the time. The technique also assumes random mixing after tagging. If this does not happen then the results obtained will be inaccurate.
There are three main ways of marking animals for this method. Marking the animal with paint or some other marking device (pen, ink etc.) is the simplest method for marking organisms. However, it has many drawbacks. The mark may come off due to rain, water, or the organism shedding skin. Also, the marks may be confused with other natural marks on the animal, such as dirt and grime. Also the paint or ink may poison the animal causing death (an example of this is canetoads and paint).
Another method of marking organisms can be tagging them. This involves a tag with information being placed onto the animal at the time of capture. This is a good way of marking as more information can be put onto the animal than marking with ink or paint such as sample number and date collected. However, the tags can only carry limited information and may come off the animal from scratching or natural wear. This means that an animal may not be counted as being previously captured as it has no tag.
The third and most effective way of marking animals is with an electronic microchip. This is where a small microchip is placed under the animals skin and when scanned delivers information put on the chip. This method allows large amounts of data to be put onto the chip unlike normal tags. It is also harder for the tags to dislodge or fall off as they are secured under the animals skin, not connected to it like normal tags. However, this method would be ineffective for small animals such as small crabs and mollusks as the tags would be too big. It is also a very expensive way to mark animals and would be more suited for larger fish, mammals, reptiles and other large organisms.
We used matches to simulate the mark and recapture method counting populations. The practical worked accurately which refuted our hypothesis. The sample number of matches was large enough to use as an example. We also overcame the problem of the marked matches integrating with the unmarked matches by mixing them together in a glass jar. The practical was an accurate example of the mark and recapture method of counting population.