BIODIVERSITY IN A CAMPUS

APPLIED PROBABILITY AND STATISTICS FOR ENGINEERS
1
BIODIVERSITY IN OUR CAMPUS
a project report Submitted by
BEZALIN RAJ.R (UR16CS153)
AKHIL ANTONY (UR16CS154)
JOEL RAJ BATHULA (UR16CS155)
GAUTHAMLAL T.V (UR16CS156)
ANIKETH SAHE (UR16CS157)
C.ALVINO ROCK (UR16CS158)
LIVIN P.KURIAKOSE (UR16CS159)
in partial fulfillment for the award of the degree
of
BACHELOR OF ENGINEERING
in
COMPUTER SCIENCE AND ENGINEERING
under the supervision of
Ms.J.Grayna
Assistant Professor
Department of Mathematics
DEPARTMENT OF MATHEMATICS
SCHOOL OF SCIENCE AND HUMANITIES
(Karunya Institute of Technology and Sciences)
(Declared as Deemed-to-be-under Sec-3 of the UGC Act, 1956)
Karunya Nagar, Coimbatore - 641 114. INDIA
NOVEMBER 2016

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CERTIFICATE
Certified that the project report“BIODIVERSITYIN OUR CAMPUS”is
a bonafide record submitted to Karunya University,in partial fulfilment of
the requirements for the award of the Degree of Bachelor of Engineering
in Computer Science is a record of orginal research work done by of
“Bezalin Raj.R (UR16CS153),Akhil Antony, (UR16CS154), Joel Raj
Bathula(UR16CS155),Gauthamlal.T.V(UR16CS156), Aniketh Saha
(UR16CS157), Alvino Rock.C (UR16CS158) ,Livin.P.Kuriakose
(UR16CS159), during the period 2016-2017 of their study at Karunya
university under my supervision and guidance.This dissertation has not
formed the basis for the award of Degree / Diploma /Asssociateship
/Fellowship or other similar title to any candidate of any University
This dissertation represents an independent work on the part of the
candidate
SIGNATURE SIGNATURE
HEAD OF THE DEPARTMENT SUPERVISOR

3
DECLARATION
We, Bezalin Raj.R (UR16CS153),Akhil Antony, (UR16CS154), Joel Raj
Bathula(UR16CS155),Gauthamlal.T.V(UR16CS156), Aniketh Saha
(UR16CS157)C. Alvino Rock.C (UR16CS158) ,Livin.P.Kuriakose
(UR16CS159),herebydeclare that the dissertation entitled BIODIVERSITYIN
OUR CAMPUS submitted at Karunya University,in partial fulfillment of the
requirements for the award of the degree of Bachelor of Engineering in
Computer Science is a record oforginal and independent research work doneby
us during the period 2016-2017 under the supervision and guidance of Ms.
Grayna , Assistant Professor , Department of Mathematics, Karunya
University,and it has not formed the basis for the award of Degree / Diploma
/Asssociateship /Fellowship or other similar title to any candidate of any
University.
Signature of the Candidates

4
ACKNOWLEDGEMENTS
“If I make the seven oceanssink,
If I make the trees my pen,
If I make the earth my paper,
Even then the glory of God cannotbe written”
-Kabir
First and foremost,I would like to thank the KING OF THE
UNIVERSE,with a humble heart,for providing me the strength to take up this
research work and complete it with his grace.
We sincerely thank his Excellency Dr.Paul Dhinakaran,Founder and
Chancellor,Karunya University,forproviding spiritualand infrastructural
supportto carry out the research work.
We thank Dr.M.J.Xavier,ViceChancellor,Karunya University,for
providing all the supportand serving as an inspiration to carry out my research
work.Hehas taken major steps to promote the research activities in the
institution.
We thank Dr.C.JosephKennady ,Registrar,Karunya University,for all
the encouragementand motivation to our research .His passion for research
has guided me into moving successfully in our research work.
We whole heartedly thank our supervisor Ms.Grayna,Assistant
Professor ,Departmentof Mathematics,Karunya University ,who heiped me to
sail through rough and unfamiliar territories.Wethank her for guiding me in
the proper way to do the research and providing me strong support.Without
her we would have not achieved our academic goal.
We also thank the Director,Schoolof Computer sciences and
technology,Karunya University,Dr.R.ElijahBlessing for allhis inspiration and
encouragement to climb higher up in our research work
We thank the entire team of Faculty members in the Department
of Computer Science for all their support.

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TABLE OF CONTENTS
CONTENTS Page No.
CERTIFICATE 2
DECLARATION 3
ACKNOWLEDGEMENTS 4
1.POPULATION PARAMETERS VERSUS SAMPLE STATISTICS 7
A.SAMPLESTATISTICS
 SAMPLEMEAN
 SAMPLEVARIANCEAND STANDARD VARIATION
B.POPULATIONPARAMETER 11
 POPULATIONMEAN
2.BIODIVERSITYINDICES 16
A.IMPORTANCEOF SPECIES EVENNESS
B.SIMPSONS DIVERSITYINDEX
C.SHANNONINDEX
 EFFECTS OF SPECIES RICHNESS ONSIMPSON’S DIVERSITY
 TABLE SHOWING SPECIES RICHNESS AND SPECIES EVENNESS
3.TRIAGE 17
A.ISLANDS PROTECT TO CONSERVE1/2 OF THE DIVERSITY
B.DIVERSITYINCOMPOSITIONSIMILARAND DIFFERENT
C.COMPOSITIONSIMILAR COMMUNITIES ORDEMES
4.PHYLOGENICITY&DIFFERENTIATION 18
A.DIFFERFORPHYLOGENETIC ENTROPHY AND DIVERSITY
B.BEHAVIOROF G ST AS DIFFERENTIATIONINCREASES.
5.EVIDENCES &PARTITION
A.EMPIRICAL EVIDENCE
B.PARTITIONDIVERSEINTO WITHIN- AND BETWEEN-GROUP
COMPONENTS
C.PARTITIONING DIVERSITY
6.DIFFERENCIATIONAND LINKING 19
A.COMPOSITION SAME& DIFFERENTIATION

6
B.MEASURE OF DIFFERENTIATIONTO REPLACEG ST ORF ST
C.LINKING DIVERSITYTO ECOLOGICAL AND GENETIC MODELS
7.SPECIES AND POPULATION 20
A.SPECIATION
B.PEOPLEIGNORE PROBLEMS SO LONG
C.POPULATIONSIZE INFLUENCEAMPHIBIANDETECTIONPROBABILITY
8.THINGS AND METHODS 21
A.LEARNING WAYS WITH PLACE
B.DATA
C.DATA ANALYSIS
D.EXPERIMENTS OF CANDIDATE
9.RESULTS OF VARIETIES 23
A.ALYTES OBESTRICANS
B.TRYTURUS CRISTATUS
10.SIZEAND THUMB 25
A.SPATIAL VARIATIONINPOPULATIONSIZEAND SPECIES
CONSERVATION
B.RULE OF THUMB
11.PERSISTENCEAND METHODOLOGIES 26
A.PROBABILITYOF RESISTANCE
B.DATAS AND METHODOLOGIES
BIBLIOGRAPHY 27
APPENDIX 28
CONCLUSION 34

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1.POPULATION PARAMETERS VERSUS SAMPLE STATISTIC
As noted in the Introduction, a fundamental task of biostatistics is to analyze
samples inorder to make inferences about the populationfromwhich the
samples were drawn. To illustrate this, consider the population of
Massachusetts in 2010, which consisted of 6,547,629 persons.One
characteristic (or variable) of potential interest might be the diastolic blood
pressureof the population. There are a number of ways of reporting and
analyzing this, which will be considered in the module on Summarizing Data.
However, for the time being, we will focus on the mean diastolic blood
pressureof all people living in Massachusetts. Itis obviously notfeasibleto
measureand record blood pressures for of all the residents, but one could take
samples of the population in order estimate the population's mean diastolic
blood pressure.
Systolic
Blood
Pressure
Diastolic
Blood
Pressure
Total
Serum
Cholesterol
Weight Height Body
Mass
Index
141 76 199 138 63.00 24.4
119 64 150 183 69.75 26.4
122 62 227 153 65.75 24.9
127 81 227 178 70.00 25.5
125 70 163 161 70.50 22.8
123 72 210 206 70.00 29.6
105 81 205 235 72.00 31.9
113 63 275 151 60.75 28.8
106 67 208 213 69.00 31.5
131 77 159 142 61.00 26.8

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A.SAMPLE STATISTICS
In order to illustrate the computation of sample statistics, we selected a small
subset(n=10) of participants in the Framingham HeartStudy. The data values
for these ten individuals are shown in the table below. The rightmost column
contains the body mass index (BMI) computed using the height and weight
measurements. We will come back to this example in the module on
Summarizing Data, but it provides a usefulillustration of someof the terms
that have been introduced and will also serveto illustrate the computation of
some samplestatistics.
Data Values for a Small Sample
 SAMPLEMEAN
There are severalstatistics that describe the center of the data, but for now we
will focus on the samplemean, which is computed by summing all of the
values for a particular variable in the sample and dividing by the sample
size. For the sample of diastolic blood pressures in the table above, the sample
mean is computed as follows:
 SAMPLE VARIANCE AND STANDARD DEVIATION
If there are no extreme or outlying values of the variable, the mean is the most
appropriatesummary of a typical value, and to summarizevariability in the
data we specifically estimate the variability in the samplearound the sample
mean. If all of the observed values in a sample are close to the sample mean,
the standard deviation will be small (i.e., close to zero), and if the observed
values vary widely around the sample mean, the standard deviation will be
large. If all of the values in the sample are identical, the sample standard
deviation will be zero.
Table - Diastolic Blood Pressures and Deviations from the Sample Mean

9
X=Diastolic Blood Pressure Deviation from the Mean
76 4.7
64 -7.3
62 -9.3
81 9.7
70 -1.3
72 0.7
81 9.7
63 -8.3
67 -4.3
77 5.7
The table below displays each of the observed values, the respective deviations
fromthe sample mean and the squared deviations from the mean.
X=Diastolic
Blood Pressure
Deviation from
the Mean
Squared Deviation from
the Mean
76 4.7 22.09
64 -7.3 53.29
62 -9.3 86.49
81 9.7 94.09
70 -1.3 1.69
72 0.7 0.49
81 9.7 94.09
63 -8.3 68.89

10
67 -4.3 18.49
77 5.7 32.49
The squared deviations are interpreted as follows. The first participant's
squared deviation is 22.09 meaning that his/her diastolic blood pressureis
22.09 units squared fromthe mean diastolic blood pressure, and the second
participant's diastolic blood pressureis 53.29 units squared from the mean
diastolic blood pressure. A quantity that is often used to measurevariability in
a sample is called the sample variance, and it is essentially the mean of the
squared deviations. The sample varianceis denoted s2
and is computed as
follows:
B.PopulationParameter
The previous page outlined the sample statistics for diastolic blood pressure
measurement in our sample. If wehad diastolic blood pressuremeasurements
for all subjects in the population, we could also calculate the population
parameters as follows:
 PopulationMean
Typically, a population mean is designated by the lower case Greek
letter µ (pronounced 'mu'), and the formula is as follows:
where"N" is the
populations size.

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2. BIODIVERSITY INDICES
Itis including species which is rich and even.
Species richness datas ion our campus collected by us
SPECIES RICHNESS
Itincludes unique variety of species
SPECIES EVENNESS
Itincludes same species
A.IMPORTANCEOF SPECIES EVENNESS
Although species richness is starting step to amount diversity of life.but we
astonished how species evenness is becoming best.We took example of
karunya poochithat result lack to other species.1 extra species to the richness
list of insects but decreaseevenness destroy
the
biodiversity.
B.SIMPSONS DIVERSITY INDEX
Itwas found by English statician in 1949.His practicalmethod is used even
today. We selected approximately an individual from a model .We found
probability fromdifferent species..greater probability of different
species,greater the diversity.Itis represented as
50%
40%
10%
"SIMILIARITY ALPHA/GAMMA
GINI SIMPSON INDEX
SHANNON ENTROPHY
SPECIES RICHNESS

12
𝐷 = 1 − {
𝑛1( 𝑛1−1) + 𝑛2( 𝑛2 − 1) + ⋯. . +𝑛 𝑠( 𝑛 𝑠 − 1)
2𝑁( 𝑁 − 1)
}
S=Number of species,n=number of individuals , 1-D=Simpson index,D=gini
simpson index
C.SHANNON INDEX
Itis optional way of simpson’s indexis shannon index.Simpson’s indexcannot
used for tropic areas.Itmakes ecologicalsenseto dream the equal population.
Sizes with the dangers. If 1 predator gets to consume many Species to live.
Listed down,thespecies of parrot in karunya campus.31 species werepresent
within the campus.
SPECIES LOWERDORSEYTEAM PIG PENSTREAM.
Red parrot 7 18
Green parrot 6 3
Blue parrot 5 15
Pink parrot 7 11
White parrot 4 17
Violet parrot 1 31
Whit and violet mixed
parrot
2 1
We gathered datas from2 places.Place1 is karunya forest.Place2 is karunya
university entrance.The foll2owing data table shows this
SPECIES PLOT1:KARUNYA
FOREST
PLOT2:KARUNYA
UNIVERSITYENTRANCE
Karunya poochi 55 39
Monarch butterfly 35 0
Seven-spotlady beetle 36 50
mosquito 11 45

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*We found out whether biodiversity will be more in karunya forestor karunya
university entrance.
Simpson diversity index found by English statician in 1949 .Ithas principleof
probability.Itmakes us understand counting and probability.Ituses probability
for finding high biodiversity region.
We went to frontyard and backyard and added the insects.wefound 2 insect-
honeybee and lady beetle
INSECTS BACK YARD FRONTYARD TOTALS
HONEYBEE 10 35
LADYBEETLE 50 40
TOTAL
In maximum 60 insects in backyard,75 insects in the frontyard.
*We used the back yard data similarly with front yard data..Weput the insects
frombackyard in a bottle.We removed 1 species and then the next without
replacement.We found the probability when both the individuals from
honeybee,lady beetle,different species.
We prepared a big information list having greater than 3 species.In which we
had butterfly,cranefly,honey bee,mosquito,karunya poochi.Totally we found S
species
SPECIES TYPE NUMBER OF SINGULARVARIETY
Species 1 𝑛1
Species 2 𝑛1
Species 3 𝑛1
: 𝑛1
Species S 𝑛1
Total individuals N
Probability of two single single variety from different species

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𝐷 = 1 − {
𝑛1( 𝑛1−1) + 𝑛2( 𝑛2 − 1) + ⋯. . +𝑛 𝑠( 𝑛 𝑠 − 1)
2𝑁( 𝑁 − 1)
}
*We found D close=1.We found the biodiversity of place great or less.
*We calculated D for karunya forestand karunya university entrance.Wefound
out of the two places which place has high D value.
*Fromthis we also found how to countbiodiversity
 EFFECTS OF SPECIES RICHNESS ON SIMPSON’S DIVERSITY
𝐷 = 1 − {
𝑛1( 𝑛1−1) + 𝑛2( 𝑛2 − 1) + ⋯. . +𝑛 𝑠( 𝑛 𝑠 − 1)
2𝑁( 𝑁 − 1)
}
=1 −
∑ 𝑛1(𝑛1−1)𝑆
𝑖=1
𝑁(𝑁−1)
It includes species richness & species evenness.Species richness includes
individual organisms of a place.Species evenness is same varieties of
organism.Wefound out why weare establish D.Wegot high index value having
1 reasons.Wealso found out how the mathematics model project
works.Another research noted was that same species found once cannot be
found again in environment.Weprepared everything mainly on species
evenness.
VARIETYTYPE Number of individuals
Variety 1 50
Variety 2 50
Variety 3 50
Variety 4 50
Total varieties 200
*We found that the place having 4 varieties with 50 unique individuals from a
type and calculated D
*When we had trials of having total varieties as 300,400.I compared these
trials with my previous finding of total variety.As wehad v=4,thevalue of

15
variety and individual increased proportionally as we increased the total
varieties
*We found out even by keeping 4 varieties as it is but individuals as n and
calculated d for the example.We also found n increase,population same,D got
increased.
*We took species,n individuals in each species.Wefound out D is function of s
and n.And also D also has S large,n fixed.We also found s saw ill death in
highing species richness
lim
𝑆→∞
( 𝐷(𝑆, 𝑛))
D got changed as n became big,s is constant.n differed and found the effect of
rising the individuals when population constant.
lim
𝑛→∞
( 𝐷(𝑆, 𝑛))
 Table showing species richness andspecies evenness
PLANTS GARDEN 1 GARDEN 2
Caryota urens L 4 6
Delonix regia 5 6
Ficus sp. 6 6
Musa acuminate colla 4 6
Bombax celba 2 6

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3.TRIAGE
protecting scientists should always adopt protection plans or make judgments
about how many sites to preservein a given region, given limited conservation
resources.
Gene scientists should always takemore subpopulations of an organisms
which are endangered need protection to preserveits genetic diversity.
• 1/20 islands had the similiar number of individuals, the
similiar number of species, and the similiar 2 group of organism
frequency.Assume absenceof shared species between islands; every island
has a full individual 2 group of organism.
 Their diversities are all equal regardless of one’s diverseconcept.
A.ISLANDS PROTECT TO CONSERVE1/2 OF THE DIVERSITY
Every island havefull differing 2 group organisms and each is equally diverse,
we should saveat ½ islands (10 islands) for saving a ½ archipelago.
B.DIVERSITY IN COMPOSITION SIMILAR AND DIFFERENT
• Between-group diversity (beta diversity) ranges from 1 to N. Itcan be
changed into a gap time to give remedy of relation similar or differentiation.
• There are many houses of such measures, each parameterized`by q. We
can make overlap measures, measures of shared diversity, measures of
community turnover, etc.
C.COMPOSITION SIMILAR COMMUNITIES OR DEMES
Species richness (allele number) gives the reasonableanswer, 0.05=1/20, the
small taken number for 20 equally large communities.this shows correctly that
the islands are completely dissimilar.
Shannon entropy gives a similarity ratio is 0.57, wrongly suggesting
considerablesimilarity.

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4.PHYLOGENICITY & DIFFERENTIATION
A.DIFFER FOR PHYLOGENETIC ENTROPHY AND DIVERSITY
Example: Tropical canopy of butterfly and understory communities
B.BEHAVIOR OF 𝑮 𝑺𝑻 AS DIFFERENTIATION INCREASES.
We begin having 2 similiar small populations (4 similiar common alleles,
10000 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑠
𝑎𝑙𝑙𝑒𝑙𝑒
𝑠𝑢𝑏 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
. We then successively add unique alleles to each
subpopulation(
10,000 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑠
𝑎𝑙𝑙𝑒𝑙𝑒
) and graph G and D (the measureof
differentiation defined later). Though real differentiate highs constantly
starting to right, G reaches a maximum (0.0345) and then falls back to zero.
G is accounted fromcorrect people allele frequency, so this is not a sampling
issue.
Measures of differentiation;shouldincrease withincreasing differentiation:
SPECIES 1 SPECIES 2 SPECIES 3
𝑫 𝑺𝑻 0 0.24 0.07
𝑮 𝑺𝑻 0 0.39 0.06
D 0 0.66 1
Measures of similiarity;shouldincrease withdecreasing differentiation:
SPECIES 1 SPECIES 2 SPECIES 3
𝑯 𝑺 𝑯 𝑻 1 0.51 0.94
0
1
2
3
4
5
6
1 2 3 4
NUMBER OF GstAS DIFFERENTIATIONPROCESS
Gst D

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5.EVIDENCES & PARTITION
A.EMPIRICAL EVIDENCE
• Microsatellites in 2 small organisms belonging to fish Tripterygion delaisi
that were distinguishablebehaviourally, geographically, and via mtDNA
• The authors werepuzzled that F was very low between subspecies, even
at loci with no shared alleles between subspecies
B.PARTITION DIVERSE INTO WITHIN- AND BETWEEN-GROUP COMPONENTS
Method commonly used in ecology and universally used in genetics is
“additive partitioning” .
• Total (gamma) diversity = mean within-group diversity (alpha) +between-
group (beta) diversity
• gamma = alpha + beta
understand evolutionary processes
*Additive partitioning of the Gini-Simpson index (heterozygosity) produces
between-a group component that is confounded with the within-group
component. This is fatal.
C.PARTITIONING DIVERSITY
• Partitioning into within- and between-group components should be
complete: the within-group component should contain no information about
the between-group component, and vice versa.
• In particular situations in the real world, of course, within- and between-
group diversity can be correlated or anticorrelated. We wantthese
correlations to reflect only the behavior of the real world.Wedon’twant them
to be artefacts of our mathematics.

19
6.DIFFERENTIATION AND LINKING
A.COMPOSITION SAME & DIFFERENTIATION
• Between-group diversity (beta diversity) ranges from 1 to N. Itcan be
changed into a gap time to gives reading of connective same or differentiation.
*Something special happens when q=2. For this value of q, the measure of
community onjoin with measureof shared diversity arethe same. This is the
Morisita-Horn index.
B.MEASURE OF DIFFERENTIATION TO REPLACE 𝑮 𝑺𝑻 OR 𝑭 𝑺𝑻
[(H – H )/(1 – H )] [n/(n-1)]
• If all n subpopulations consistof k equally common alleles, this measure
result “∞“ of each subpopulation’s contrastgenewhich have individuaity to
that subpopulation.
• Account of pure differentiation, independent of average within-
subpopulation heterozygosity.
• Linear in shared diversity
• Itshould replace G when differentiation is the quantity of interest
C.LINKING DIVERSITY TO ECOLOGICAL AND GENETIC MODELS
• m
m
Hubbell’s neutral model of biodiversity
• Finite island model in population genetics
N N N

20
7.SPECIES AND POPULATION
A.SPECIATION
*generation = little or no differentiation.dry cover to speciation
• This is wrong sinceit only tells us that 𝐺𝑆𝑇 will be low, not that real
differentiation will be low. 𝐺𝑆𝑇 can be low even for completely differentiated
subpopulations, or can be high even when subpopulations show little
differentiation.
B.PEOPLE IGNORE PROBLEMS SO LONG
• Ecologists and geneticists often treat measures as mere tools for the
extraction of p-values
• Statistical significance depends As many we can get on samplesize as on
the magnitude of the effect being measured.
• Ecological problems should usually be cast in terms of estimating a
meaningful parameter, with confidence intervals, rather than testing an
always-falsenullhypothesis (which will surely lose if sample sizeis large
enough).
C.POPULATION SIZE INFLUENCE AMPHIBIAN DETECTION PROBABILITY.
Monitoring is important for species conservation.Monitoring programs must
take irregular detection of species into count frequent.Itsuggests thatfinding
probability might be foundoutby population size.butthis relationship is not
found empirically.Population sizebecause it increases heterogenicity in finding
out probability. And hence it will bias the biodiversity.Wesearch for place
occupancy to reach data fromhelper based amphibian monitoring programto
get how well different variables explain change in finding out probability.A
note to presentweight of people. Contact between population sizeand find
probability was good.Weather variable gives probabilities of detection for
2
6
species.Approximatesite occupy probability connect with population index
was or was not used to frame detection probability.Thecontact between
content and finding have effects to model watch and organism protection

21
8.THINGS AND METHODS
A.LEARNING WAYS WITH PLACE
Volunteer based amphibian monitoring program of vthe Swiss Canton Aargau
has upto 1999.Itdreamto survey status and population status of summer
baby producing endangered amphibian organism . Canton has 10 crore each
one have 30 breeding sitesand show a spatial hotspotof amphibian.Every year
2−3
10
𝑐𝑟𝑜𝑟𝑒 are choosed randomly..Thefirst2 visits done at night using april and
may.third visit in june or july.Asingletrained helper is always inchargefor
5−10 𝑏𝑟𝑒𝑒𝑑𝑖𝑛𝑔 𝑠𝑖𝑡𝑒𝑠
1 𝑦𝑒𝑎𝑟
does
3 𝑣𝑖𝑠𝑖𝑡𝑠
1 𝑠𝑖𝑡𝑒
.Helpers account anurans by walk to water edge
and note visual encounter and call..Newts are powerfully probed with nets in
extra to visualcontactors..Thesurvey is doneaccordingly to perfect
protocol.which says precisetime rules for the meeting of each place
corresponding to the size.Helpers also explained pond show veg site,site
structurefor 3rd
survey.
B.Data
Study based on the data gather the model of monitoring program in years
1999-2006.Outof years wechooseonly the final and secondry final survey
have is general fromyears 2004-2006and 2nd
gather information have on the
equal amphibian baby producing place was on averagedone 2 years
before.For experiment on amphibians and place was complete(i.e,result
answer=n=561)
We selected 6 amphibian organisms made
permission comparebetween organism;high sound calls challenging silent calls
,newts challenging anurans.Thespecies were:Thewife toad Alytes
obestricans,the Alytes obestricians, Bufo calamita,frogs of the water frog
Pelophylax esculentus-compexand the two newt.Every organism was detailed
individually.Although volunteers say accounts for every past stages for all
species,analysed on adults counts only.Adults counts always underestimate
true more .The + contact among truth many and the numbering.Thecounts
may thus help as usefulidea to amphibian population size.

22
C.DATA ANALYSIS
We checked the describing majic different covariates on finding probability by
including covariates to the mark recapture similar place take over models
made by MacKenzieet val.[27] and Tyreetal [11]
The data analysis is processed outwith the statistic
softwarePRESENCE2.0which makes assuming of detection probability.and site
occupying in connection to varying covariates.Weopted little exemplary
alkaike reports criterion.Weconsidered models as well joined by data if Akaike
weight was bigger than 0.05.Samplesizewas thenumber of amphibian
breeding sites included in processing information
D.EXPERIMENTS OF CANDIDATE
For each species we join a little number of candidate models to the
information.Models havecommon joining for all 3 gathering binformation.,1
covariate per experiment for 2 midnight gathering and also a single but varying
constantterm for 3rd
daytime survey.This yield logistic regression models of
the form
pit;l is the detection probability ,covit is an detailed covariateat site i during
surveyt. α, β and γ are the intercept and slopes of the logistic regression. Place
presence, which was not the concentrate of this learning, was experimented as
a same term.
To find which covariates describe detection probability won, we used
covariates that explained the data well in before, same collect information
(e.g.,[14],[16],[18],[25]), butalso somenovel ones. Before used covariateadd
pond having scape, decoration plant cover, swimming sea vegies, sea hidden

23
vegies, pond get), phenology ,weather covariates ( fastblowing of air, wind
temperature, rain).
9.RESULTS OF VARIETIES
Table 1 shows a summary of the model selection results: Only models with an
Akaike weight greater than 0.1 are shown. Table S1 shows the full model
selection results.
A.Alytesobstetricans…
2 experiments adding climate covariates haveAkaike sizebreadthwise ∼0.3 &
2 experiments previous people contents and phenology, , having Akaike size
breadthwise∼0.1 (Table1). With the 2, they calculated atmost 85% of the
Akaike sizebreadthwise. The experiment well done helped by the information
adding the letters explaining land heat. For this experiment, land temperature
have “+” impacts on detection probability (slope estimate ± SE on the logit
scale: β = 1.14±0.47) (Fig. 1). Theapproximate calculating for probability of
detection on the daytime place (3rd site visit) was γ = 0.27±0.07 (estimate± SE

24
on the good measuring scale). The experiment conducted adding only past
population index received weak supportfrom the data. In this Experiment,
previous people contents had a “+” effects on detection probability (slope
account ± SE on the logit measurement scale: β = 0.68±0.50). For thebest
modelling include paster population indexes, the estimating the sites occupant
probability was Ψ = 0.26±0.06 (estimating ± SE). For the best modelling without
paster population indexes, the estimating of the sites occupant probability was
Ψ = 0.22±0.03 (estimate± SE).
B.Trituruscristatus
2 experiments best explaining the datas (Table 1). Both accounting for atmost
90% of the Akaikeweights. Together experiments including paster population
indexes. The modelling of paster population indexes and span finishing
because the final information reportwas by long the high by the datas.
= 0.12±0.04 (estimate± SE).

25
10.SIZE AND THUMB
A..SPATIAL VARIATION IN POPULATION SIZE AND SPECIES CONSERVATION
the implications of the relationship between population study for the
conserving of threatening organisms. Therelate implies that small populations
are likely to be missed during surveys and in monitoring programs. This may
have two consequences. First, if subpopulations arenotdetected, then they
cannot be the point of conservation work and thereforethey may be more
mostly to go reach death (also see[46]). Second, if a population that was
known to occur at a site is no longer detected becausepopulation sizes are
small, then conservation managers may stop species-specific management
actions. As a consequence, the species may go locally extinct.
B.RULE OF THUMB
We counted the number of hibiscus of differentvarieties in our karunya
campus.Then we wegot these list of varieties 4,6,7,3,8,1.I wanted to spotthe
ways that the next hibiscus I select is from a varying type.I can find out only
using rule of thumb.The formula is
n1
𝑁
,N=Total number,n1=total variety
N=4+6+7+3+8+1=29,n1=3,thereforechanceof new variety of hibiscus=
n1
N
=
3
29
=0.1034
Rule of thumbs is false, since 𝐺𝑆𝑇 can be arbitrarily close to zero even for
completely differentiated subpopulations.

26
11.PERSISTENCE AND METHODOLOGIES
A.PROBABILITY OF PERSISTENCE
We tried for spotting important place which show the present distribution.and
took all datas equal. We found shortcutmethod of preserve is to use
persistencein probability.wefound probability gives riseto consistency. Used
for integrating different ways and method that spoil the protection of karunya
campus. To do this method of connection of preserving areas which gave
highest probability of persistencein our karunya campus to us.Especially the
European trees in our karunya campus need less than half as many places as
the earlier procedureto find the species atleast 0.95.aternatively we chose
value 1 ending to 50,procedurehighs the probability more than 10%..This is
very good for least variety species.and will connect the particular place.This
method include amount,social,politicalproblems
B.DATAS AND METHODOLOGIES
Our research initiated with book of organisms. Startfrom the spreading
map.We haveused 54 427 records to identify the presentdistribution of 148
organisms and small organisms of Europetrees have timber organisms.Itis a
small set of AFE information,crossed on the 40×40km cells of UTM. GRID.The
place surrounded gap 2204 grid cells in places like Germany and the
surrounding area,notincludesoviet union.becauseexampling work in this
place is approximately intensive and same.Datas on evil and difficulty is on
process of success ,wefound out

27
BIBLIOGRAPHY
1.Pollock KH, Nichols JD, Simons TR, Farnsworth GL, Bailey LL, Sauer JR
(2002) Largescalewildlife monitoring studies: statistical methods for design
and analysis. Environmentrics 13: 105–119.
2.Weber D, Hintermann U, Zangger A (2004) Scaleand trends in species
richness: considerations for monitoring biological diversity for political
purposes. GlobalEcology and Biogeography 13: 97–104.
3.Nichols JD, Williams BK (2006) Monitoring for conservation. Trends in
Ecology & Evolution 21: 668–673.
4.Lindenmayer DB, Likens GE (2010) Effectiveecological monitoring. CSIRO
Publishing, Collingwood.
5.Anderson DR(2001) Theneed to get the basics right in wildlife field
studies. Wildlife Society Bulletin 29: 1294–1297.
6.Yoccoz NG, Nichols JD, Boulinier T (2001) Monitoring biological diversity in
spaceand time. Trends in Ecology & Evolution 16: 446–453.
7.Preston FW (1979) Theinvisible birds. Ecology 60: 451–454.
8.Kéry M, Schmidt BR (2008) Imperfectdetection and its consequences for
monitoring for conservation. Community Ecology 9: 207–216.

28
9.Link WA, Sauer JR (1998) Estimating population changefrom countdata:
application to the North American Breeding Bird Survey. Ecological
Applications 8: 258–268.
10.Kéry M, Schmid H (2004) Monitoring programs need to take into account
imperfect species detectability. Basic and Applied Ecology 5: 65–73.
11.TyreAJ, Tenhumberg B, Field SA, Niejalke D, Parris K, et al. (2003)
Improving precision and reducing bias in biological surveys: estimating
false-negative error rates. Ecological Applications 13: 1790–1801.
12.Bailey LL, Simons TR, Pollock KH (2004) Estimating site occupancy and
species detection probability parameters for terrestrial salamanders. Ecological
Applications 14: 692–702.
Appendix
QUESTIONAIRE SURVEY
A.QUESTIONAIRE DATAS
1. Own role by selecting from the following list
2. Area of research
3. How would formal management of biodiversity research data be useful to you?
Researcher Role
0
10
20
30
40
50
60
Role
Academic staff Research assistant Pgrad/Pdoc
Visiting researcher Independent researcher DOC manager
0
5
10
15
20
25
30
35
Formal management of biodiversity research data
Of significant advantage to my work Useful but not of major significance
Interesting but not particularly useful Of no interest to me
Not sure what this means

29
4. What kinds of non-digital data do you generate or collect for your research (or have
you generated or collected in the past)?
Non-digital data types collected
5. What kinds of digital data do you generate or collect for your research?
Digital data types collected
6. In what formats are these digital information sources held?
Formats of digital information sources
7. Are the research data you generate sometimes a combination of different data
formats?
Vaule of formal management of biodiversity research data
27%
43%
21%
6% 3%
Of significant advantage
to my work
Useful but not of major
significance
Interesting but not
particularly useful
Of no interest to me
Not sure what this
means
0
10
20
30
40
50
60
Digital data types
audio/video
bibliography
databases
datasets
derived data
digital images
digital objects
drawing plots
geospatial data
plans/maps
raw/experimental data
remote sensing data
statistical data
text-based files
none selected
0
10
20
30
40
50
60
Formats of digital information sources
CAD
Database files
XML
GIS
HTML
Image files
Plain text
PDF
Ref mgmt software
Rich text files
Spreadsheets
Stats software
Word processed files
Unsure
No comment

30
38% of respondents agreed that
the data they generate is ‘Often’ a
combination of different data
formats and 35% agreed that this
is ‘Sometimes’ the case. A further
8% agreed that data is
‘Potentially’ a combination of
different data formats.
8. How large (in total) are your digital research data? Please estimate.
9. How long do you think your research data will have value?
Combination of different data formats
often
sometimes
rarely never
potentially
unsure
no comment
0
5
10
15
20
25
30
Size (in total) of your digital data
<100MB
100MB-1GB
1GB-1TB
>1TB
Don't know
No comment
0
5
10
15
20
25
Data size
Size (in total) of your digital data
82%
3%
15%
<1TB
>1TB
Don't know/no comment
82%
18%
Often/sometimes/potentially
Rarely/never/unsure/no comment

31
10. What data storage and back up system(s) do you currently have in place?
Single v Multiple Storage Sites
11. Who currently manages your data?
How long data will have value
>5 yrs
>10 yrs
10> yrs
don't know
no comment
0
5
10
15
20
25
30
35
40
45
Data life
Data storage and back up system(s)
0
10
20
30
40
50
60
70
Data storage
Own hard drive
External hard drive
CD/DVD
USB
LAN
Offsite storage
Third party
Who currently manages your data?
0
10
20
30
40
50
60
70
80
Who manages your data?
I manage
Project manager
Designated person on project
External project partners
ITS
IT staff in my department
Research assistant
Data storage and back up systems
>1 storage site
87%
single storage site
13%

32
12. Do you currently have a formal ResearchData Management Plan in place and, if
not, please indicate the reasons.
13. repositories – and how often - do you submit your research data
a. repositories do submit our research data
b. submit research data to a repository?
14. Can you please indicate what types of metadata you consider important to assignto
your data.
Who currently manages your data?
64%
3%
32%
1%
Just me
Project manager/IT staff
Me plus others
No comment
Do you currently have a formal Research Data Management
Plan in place?
0
5
10
15
20
25
30
35
Do you have a formal Research Data Management Plan?
Yes
No, not yet considered
No, I don't see the need
No, I don't have time
No, but I would consider this
I don't know enough to respond
No comment
To which repositories do you
submit your data?
0
10
20
30
40
50
To which repositories do you submit your
research data?
Landcare
NIWA
DOC
other NZ
International
None, intend to,
no comment

33
15. At what stage are metadata assignedto your research data?
16. Who assigns metadata to your research data?
0
10
20
30
40
50
60
metadata you consider important to assign to your data
author
date
project date
format
funding source
geospatial
project title
project description
project identifier
subject keywords
specimen taxonomic name
title of data set
none
don't know
no comment
At what stage are metadata assigned?
0
5
10
15
20
25
30
When are metadata assigned?
Prior to data creation
Part of indexing process
During file saving
When submitting to repository
After submitting to repository
No metadata assigned
Don't know
No comment
Who assigns the metadata?
0
10
20
30
40
50
I assign
Research colleagues
Research support staff
Library/Information
services
Repository
administrators
Generated automatically
None are assigned
Are metadata assigned?
56%
44%
Yes
No/don't know/no comment

34
54% of researchers selected
only ‘I decide which terms to
use and I assign them’
although the free text
comments, associated with
this response, contradict this
statement somewhat:
CONCLUSION
The rapid decline in biodiversity now taking place is believed by someto be the
startof one of the greatest mass extinctions in the history of life on earth.
Taking action on this requires not only political will, but also sound methods
for quantifying and reasoning about diversity — because withoutthem, werisk
channelling conservation funds in the wrong directions.
54%
18%
28% Only I assign
I and/or others assign
none assigned/don't
know/no comment

35
 Diversity matters not only for trees, birds, and so on, but also for
invisible life such as the bacteria that live in your gut, and for designing
effective vaccines against different strains of viruses — wherewe
humans tend to think of diversity as bad, not good. There are deep
mathematical problems here, and solving them has involved some
surprising branches of mathematics that are often seen as distant from
any conceivable application. Biodiversity is a concept that has no
general definition. Usually it is used in a context that stresses the need
for attention on our living environmentand the sustainableuseof
natural resources.
 Biodiversity can be divided in different types such as habitat, species
and genetic diversity.
 The integrated approach used in coastal zone management is an
adequate method in dealing with the matter of biodiversity.
 The problems and benefits of biodiversity are many. They focus on the
need for sustainabledevelopment and adequate use of coastal
resources.
 Loss of biodiversity and biodiversity conservation areconcepts that
providethe basis for biodiversity management.
 The management of biodiversity is a complex matter that needs the
involvement of many different partners ranging from governmental
organisations to private companies, NGO's and volunteers. This aside,
national and international commitment, legislation and enforcement
offer an essential framework for promoting and maintaining
biodiversity.

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