lab ecology, final

7/29/2019 Lab Ecology, Final

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Agata Kozowska, IB WORLD SCHOOL NO. 001309, BIOLOGY HL

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IB WORDL SCHOOL NO. 001309

LAB REPORT

Biology Higher LevelSimulation of capture - mark - release - recapture

method of estimating the population size

Agata Kozowska, Biology HL

SUPERVISOR: Katarzyna Radzikowska

DATE: 15.05.2012

DURATION: 1.5 hour

Lab partner: Monika Maciejewska


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INTRODUCTION

Capture-mark-release-recapture is a common method used in ecology for estimation of the

size of population. The method includes capturing a group of individuals and marking them.

After that this group is released back to their natural environment. After some time another

random group is captured and the marked individuals are counted. This procedure is repeated

and then the number of individuals in the population is estimated by the use of a special

formula. This method is really useful, due to the fact that counting all the individuals in a

population is quite impossible. In this investigation we are going to estimate the number of

individuals in a population with this method by doing a simulation on pea seeds.

PROCEDURE

Students divided themselves into six pairs or groups of three. Each group took similar amount

of pea seeds (circa 250ml) and put them into a plastic bag. It was the equivalent of the whole

population. Each group counted all the seeds and wrote down the number which represented

the number of individuals in the population. After that each group had to mark a particular

number of seeds using a pen. I was working in a pair with my friends Monika, and we were

the group A which means we marked 60 seeds. The numbers of marked pea seeds are

shown in the table below:

Table 1. Numbers of pea seeds marked in different groups

Group Number of pea seeds (item)[1]

A 60

B 90

C 120

D 150

E 180

F 210

After that the marked seeds were placed back in the whole population and mixed with not

marked seeds. Secondly, a random sample was taken out the whole population by the use of a

cup (all groups used a cup of the same size and shape). Than the seeds from the cup were


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counted. The whole number and the number of marked seeds was written down. This

procedure was repeated 5 times in every group.

RAW DATATable 2. Overall numbers of pea seeds and numbers of marked pea seeds taken out from the population

GROUP

Number of marked seeds from

the random sample taken from

the whole population(item)[1]

Number of all seeds from the

random sample taken from the

whole population(item)[1]

A

Total population size:

1192(item)[1]

26 544

33 529

30 536

28 571

24 532

BTotal population size:

1141(item)[1]

55 49249 599

44 571

40 554

46 582

C


982(item)[1]

55 558

49 585

57 619

67 574

72 627

D


1138(item)[1]

80 611

71 59380 545

88 666

75 628

E


1240(item)[1]

65 454

68 474

76 489

68 470

64 479

F


1110(item)[1]

123 602

124 605

132 608

129 616

136 607

Consider that each groups repeated the procedure five times.


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DATA PROCESSING

The first thing that has to be done during the data processing is estimating the population size

by the use of a proper formula:

N

n

n

n1

2

3

Where:

n1number of marked individuals in the whole population

n2total number of individuals in the random sample

n3number of marked individuals in the random sample

Nestimated number of individuals in the total population

The value we are looking for is described as N. In order to calculate the value we have to

transform the formula. The final formula is shown below:

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n

nnN

Although the total number of the individuals in the population is known, we let it to be

unknown. We want to check to what extent the estimated value will be similar to the real

value (we counted the seeds in the population as you saw in the tables on previous pages). In

this step we make an assumption that we do not know the number of individuals in the wholepopulation and we try to estimate it.

It has also to be stated that each group have done their own calculations and only the final

values were given to all the students. Calculations done by my group are shown below.

125538.125526

54460

N


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Table 3. Estimated numbers of individuals in the populationsGROUP Number of individuals in

the estimated population

A

1255

962

1072

1224

1330

B

805

1100

1168

1247

1139

C

1217

1433

1303

1028

1045

D

1146

1253

1022

1135

1256

E

1257

1255

1158

1244

1347

F

10281025

967

1003

937

As you should mention, counting the random sample was repeated 5 times in every group. As

it is visible in the table above, the estimated sizes of populations are not the same in each

probe. Five repetitions of the procedure were done to make the final result more accurate. The

mean value of the estimated size of the population should be calculated.

In order to calculate the mean value the formula taken from MinkaPeters Weem Biology forthe IB Diploma will be used:


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Table 4. Means of estimated values of population sizes

GROUP Mean estimated size of

population(item)[1]

A 1169

B 1092

C 1205

D 1162

E 1252

F 992

An important point of the investigation is to calculate the standard deviation for the values of

estimated sizes of populations. The values of standard deviation will be later used for creating

the error bars on the graph, which show us the variation of the data.The following formula

1

was used:

Where:

x represents each single value of population size

is represents the mean value

N represents the number of trials

GROUP A:

1 http://www.sohcahtoa.org.uk (22.05.2012)


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Table 5. Values of standard deviation of the data for each group

GROUP Value of standard deviation [3 s.f.]

A 133

B 151

C 154

D 86.8

E 59.9

F 35.1

Consider that the greater was the initial value of marked seeds the smaller is the value of

standard deviation. It means that the results should become more accurate with the increase of

marked individuals in a certain population. Data from Table4. is presented on the graph

below.

Graph1. Means of estimated number of individuals in a population.


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In this step of the report I will calculate the differences between estimated and actual values

of number of individuals in the whole populations which is called the error of estimation.

Error of estimation = | |

Example: difference between estimated and actual number of individuals in the total

population in group A (60 pea seeds marked in the primary sample)

Error of estimation = | | = 24Table 5. Values of the errors of estimation in each group

GROUP Value of the error of estimation

A 23

B 49

C 223

D 24

E 12

F 118

The next step will be the calculation of the percentage error of estimated number of

individuals in the whole populations.

Percentage deviation =| | 100%

Percentage deviation =|| 100% = 1. 92953=1.93 [%] (3 s.f.)


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Table 6. Values of the percentage deviation of estimated number of individuals in the whole population

Number marked

individuals from the

primary sample

Estimated number of

individuals in the whole

population

Actual number of

individuals in the total

population

Percentage error of

estimated number of

individuals in the whole

population [%] (3 s.f.)

60 1169 1192 1.93

90 1091 1141 4.38

120 1205 982 22.7

150 1162 1138 2.11

180 1252 1240 0.968

210 992 1110 10.6


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CONCLUSION & EVALUATION

The experiment which was carried out was a simulation of a real method that ecologists use to

estimate the number of individuals in a population. Notice that each pea seed was an

equivalent of an living individual. If it was not a simulation the individuals would be for

example squirrels (Sciurus vulgaris) or the common polypody (Polypodium vulgare) in a

forest. The Capture-mark-release-recapture method is commonly used in ecology. It

includes capturing a group of individuals from a certain area, marking them and releasing

them back to their natural environment. After a period of time, another portion of individuals

is captured, and the marked individuals are counted. As the number of marked individuals in

the captured group should be proportional to the number of marked individuals in the whole

population, the number of organisms in the whole populations can be estimated. It is assumed,

that the probability of being captured in the second sample is the same, no matter if the

individual was captured in the first one or not. The figures below show animals marked by

the ecologists.

Figure1. Two marked individuals: on the left - rock hyrax (Procavia capensis), on the right - Chittenango ovate

amber snail (Novisuccinea chittenangoensis).

Another filed, to which this method is often applied is epidemiology. Typical use includes

estimation of the number of people who are in need for particular services and help like

children with disabilities or from the areas of extreme poverty, medical help for elderly

people. This method is also useful in estimation of the number of individuals suffering from a

particular disease, i.e. people infected with the HIV virus. Knowledge about the scale of a

problem for exampledrug addiction or crime is crucial for fighting against it, therefore the

called in another words multiple systems estimation is a method of great importance, not

only in application to ecology or zoology but also social sciences.


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Nevertheless, this method has also its drawbacks. It is only an estimation of the real

population size and it is hard to check the reliability of the results obtained. In order to

evaluate the uncertainty of the capture-recapture method, we conducted an experiment, which

was a reconstruction of the real method in conditions, were calculating the real number of

individuals in a population was possible. When ecologists need to know the number of

individuals in a particular population it is impossible for them just to count the organisms so

they have to estimate the number using this method. There are several reasons why the

population could not be counted directly:

Living organisms (animals) could migrate The number of individuals can change any time because some of the organisms may

die and there could be also new individuals born

Individuals are in a constant state of movement

The aim of our experiment was to examine the reliability of this method. We asked ourselves

to what extent is this method trustworthy and accurate? Our hypothesis included two

assumptions:

The method is useful in estimating the population size, although there is a relativelysignificant boundary of error as it is only an estimation

The greater the number of marked individuals, the more accurate the result

Because our population was technically not able to move, reproduce or die during the

experiment and was easy to count we had the chance to compare the real values of the number

of individuals in the population with those that were estimated. The differences between the

real values and the values estimated through the investigation and the percentage error of

estimation are shown in the table Table7


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Table 7. Comparison of the estimated and actual numbers of individuals in each group.

GROUP Mean estimated size of

population(number of

individuals, item)[1]

Real size of the

population(number of

individuals, item)[1]

Percentage error of

estimated number of

individuals in the

whole population[%]

(3 s.f.)

A 1169 1192 1.93

B 1092 1141 4.38

C 1205 982 22.7

D 1162 1138 2.11

E 1252 1240 0.968

F 992 1110 10.6

As you can see from the table above, the estimated numbers of individuals in a population

except groups C and F, where the percentage errors equal consecutively to 22.7% and

10.6% our first assumption turned out to be true the results obtained are close to the

actual numbers. The results are additionally presented on the graph below to make them

more clear to analyze.

Graph 2. Real and estimated values of the numbers of individuals in the populations


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Although the method generally worked, our next assumption that the greater the marked

sample the more accurate the results appeared to be not true after conducting the

investigation. The group that was thought to be the most accurate turned to be the less

accurate in comparison to other groups. Nevertheless, there are several reason that might

possibly result in such outcome:

Errors in manual and mathematical calculations. Each group used different pea seeds, and although it was exactly the same species the

individuals might slightly differ.

Some of the pea seeds could possibly fall down from the table and change thecalculations.

We were not able to check how precise were the manual and mathematicalcalculations of other groups (it seems to be possible that some of the groups were not

precise enough).

The pea seeds were marked by the use of a pen when placed back in the population,because we had not enough time they were not dry enough and they might

accidentally mark other random individuals which might have an impact on the

results.

It turned to be hard to count hundreds of pea seeds during a lesson. There was a lot ofnoise and we had to hurry all the time, so some students might feel disrupted and make

some errors in manual counting and later mathematical calculations.

Because we were not counting all the populations by our own (we were divided intogroups) we had to take the results from other groups after the experiment. It seems to

be possible that mistakes occurred while writing down the results from the whiteboard.

The results might be affected by the human error, we might possibly subconsciouslychoose some individuals instead of acting totally random

As we can see, several factors might possibly affect the experiment causing errors.

Nevertheless, we learn from our mistakes so now I am willing to present some ways of how

the investigation could be improved. The first thing is time. We had only 1.5 hour for

counting all the seeds and it turned out to be not enough. Such exercises require precision, so

hurrying up all the time influences the experiment in a negative way. If we were to do the

experiment once again we should definitely plan more time. Another aspect that could beimproved is using a closed box instead of the table, so the pea seeds will not fall down. It


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would be also better to mark the individuals long before the exercise in order to let them dry.

Another useful idea might be using small plastic balls or some other objects of exactly the

same shape and structure. It appears that mixing the individuals by the use of a closed box

will be more random and therefore accurate, than using a plastic bag. A greater number of

trials would also increase the accuracy of our study. The last but not least thing is to put the

results directly into the computer, not on the whiteboard to avoid some further errors.

To sum up, the capture-mark-release-recapture method is only a statistical method of

estimating the size of population based on random samples which might but are not always

representative. Notice that I use the word estimate through the whole lab report. And there is

a significant difference between estimation and calculation. The estimated number is never

exact. Despite of all the drawbacks of this method it is still really useful as long as ecologists

do not have the opportunity to count the individuals living in the natural environment.


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References

Peters Weem M.,Biology for the IB Diploma, IBID Press

Krebs, Charles J. (2009).Ecology (6th ed.). p. 119

http://stattrek.com/statistics/formulas.aspx 23.05.2012 18:07

http://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Peters

en_method22.05.2012 13:40
http://stattrek.com/statistics/formulas.aspxhttp://stattrek.com/statistics/formulas.aspxhttp://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Petersen_methodhttp://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Petersen_methodhttp://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Petersen_methodhttp://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Petersen_methodhttp://en.wikipedia.org/wiki/Mark_and_recapture#Adaptation_of_the_Lincoln.E2.80.93Petersen_methodhttp://stattrek.com/statistics/formulas.aspx

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