This document provides instructions for a laboratory work on variation and variability in plant species. It outlines choosing two populations of a species from different environments to observe and compare quantitative and qualitative morphological traits. Students are instructed to perform basic statistical analyses to test for significant differences between traits in populations and relate any differences to environmental adaptation. The document describes the structure of the laboratory work report and provides examples of quantitative and qualitative traits that could be observed and measured in the plant species Primula vulgaris.

1. II LABORATORY WORK
Variation and variability

2. Tasks – big picture; step by step
• Choose at least two populations of one type of species that you are going
to observe
– The populations must be located in different environments and potentially
isolated enough
– The number of individuas per observed population/loaclity must be minimum
• Chose the morphological characteristics you are going to observe
– Observe the variation of quantitative traits of the chosen species (at least
identify 3 quantitative traits)
– Observe the variation of qualitative traits of the chosen species (at least
identify two qualitative traits)
• Perform basic statistic analyses to see if there is any statistically significant
differences between the chosen traits in the two populations
– If there is, this tells us that that trait is different because of adaptation and
evoultion
• Discuss how and why could this be!

3. Morphological characteristics
• Quantitative
– Metric
– Meristic
• Qualitative
• The characteristics can be determined by
detailed observation of the chosen species

4. Quantitative traits
• Traits that show a certain amount of variation
and are registered in two ways:
• Metric
– You register them by counting
• Meristic
– You register them by measuring

5. • Quantitative (meristic) traits example:
• Number of flowers in the roseta–in one individuals
• Number of leaves in the roseta– indicates the number of leaves in the
roseta
• Number of sepale latice) – indicates the number of sepales of one flower
belonging to one individual
• Quantitative (metric) traits example:
• Lenght of the longest stem in the roseta
• Lenght of the longest leaf in the roseta
• Width of the largest leaf in the roseta
• Width of the largest flower in the roseta
Primula vulgaris

6. Salamandra atra
• Quantitative (meristic) traits example:
• Number of dots on the whole body in one individuals
• Number of dots on the parotid gland– blue circles on the right
picture
• Number of body ridges – only on the body not the tail
• Quantitative (metric) traits example:
• Lenght of the head (from nose till gular ridge)
• Lenght of the body (from nose tip till cloaca)
• Width of the head (between two mouth angles
• Width of the cloaca

8. QUALITATIVE TRAITS
QUANTITATIVE TRAITS
HABITAT DATA

9. Qualitative traits
• Can be only described
• Subjective
• ...Quality, How is it something?
• Variation in coloration
• Variation

10. • Qualitative traits example:
• Color of flowers
• Possibilities: Dark Yellow,Light yellow, Purple
• Color of leafes
• Possibilities: Dark green, Light green
• Heterostilia
• Possibilities: Yes/ No
• Hairs on the stem
• Possibilities: a lot/ small amount
Primula vulgaris
Assign numerical chategories
for each possibility of every
chosen qualitative trait!

11. Color of
flowers
Numerical category Possibility (variation)
1 Yellow
2 Light yellow
3 White
4 Purple

12. Color of leaf
Numerical
category
Possibility
(variation)
1 Dark green
2 Light green

13. Hairs on the stam
Numerical category Possibility (variation)
1 Very hairy
2 Slightly hairy

14. Heterostilia – mechanism to avoid self fertilization
Numerical category Possibility (variation)
1 Antheras are longer then stigma
2 Antheras are shorter than stigma

15. Picture the habitat of the chosen species

16. AIM of the research
• Comparative analyses of chosen morphological traits in order to see if
there is any statisticlly significance between each of them
– If there is, that can be a sgin of adaptation! Describe and discuss what kind of
adaptation!
– Find the link between the trait and adaptation
• Stems are more hairy where is colder
– It is colder on higher altitudes
• Determine the variation (variation of phenotype) of the species Primula
vulgaris according to the analysed quantitative and qualitative traits

17. Dependent/Indipendent variable
• Indipendent variable
– Type of habitat - Its characteristics and
environmental factors
• Sunlight, humidity, temperature, type of soil, exposition
• Dependent variable
– Chosen morphological traits that are observed

18. Structure of laboratory work
• parts:
1. Introduction
1. Definition of variation, variability, adaptation and evolution
2. General data regarding the chosen specie and its biology
2. Material and methods
1. Highlight the differences in habitats between the chosen
population you are observing
2. Present the types of chosen traits and their numerical
assignment (for qualitative data)
3. Pictures of measurments and habitat
4. List the needed material you used during field work
5. List statistical analysis you will perform in order to prove the
hypothesis

19. Structure of laboratory work
• parts:
– . Results
• .1 Present the statistical analyses in form of tables
with titles
– . Discussion
• .1 Discuss what could be the link between the
specifis trait that showed statistically significant
difference and its environment
• .2 Discuss why other triats did’t show statistically
significant variation

20. Structure of laboratory work
• parts:
– . Conclusion
• .1 Link all the traits that show statisticlly significanse
difference, with your conclusion why there is the
difference? Which environmental factor couses
difference and why in that particular trait?
– . Literature
• List all used resources: web-sites, books (writer, title,
year of publishing and editor)

21. Structure of laboratory work
• Maximum 18 pages with literature and
pictures
– Who has more, he will get minus points
– Pictures can be given in an “appendix”
• Aim: Short, clear, try to identify which data
are mandatory and which are not important
to mention!!!!!!
• Each table and every picture is numerated
and has a title

22. Comparative analyses of chosen morphological characteristics between
three populations of Primula vulgaris from the locality: mt IGMAN, mt
BJELAŠNICA and mt JAHORINA
Exampl e of f orm of l aborat ory work
Claim/Hypothesis: The variation in chosen
characteristics of three populations, is strictly
corelated with different strategy of adaptation
to the observed environments (3 mountains)

23. • Varijabilnost i varijacija (lat. varius –
različit) su termini koji se u našem
jeziku obično poistovjeduju i
međusobno i sa pojmom
promjenljivosti uopde.
• Varijabilnost (engl. variability) opisuje
sposobnost i pojavu vremenskog
(alohroničnog) mijenjanja istih živih
sistema, dok se varijacija (engl.
variation) primarno odnosi na
prostornu ili sinhroničnu nejednakost
različitih bioloških sistema
(Hadžiselimovid, 2005).
I NTRODUCTI ON

24. General characteristics of Primula
vulgaris
Imperium: Eukaryota Whittaker &
Margulis, 1978
Regnum: Plantae Haeckel, 1866
Subregnum: Tracheobionta
Phylum: Spermatophyta
(=Anthophyta)
Subphylum: Magnoliophytina
(=Angispermae)
Classis: Magnoliatae (=Dicotyledonae)
Subclassis: Dilleniidae
Nadred: Ericanae
Ordo: Primulales
Familia: Primulaceae Vent.
Genus: Primula L.
Species: Primula vulgaris Huds
Pri mul aceae Vent .
Familija obuhvata 18 rodova sa 255 registrovanih vrsta u
Germplasm Resources Information Network (GRIN) (United
States Department of Agriculture
Agricultural Research Service, Beltsville Area)
Pri mul a vul gari s Huds.
Kozmopolitska je biljka, rasprostranjena na
području zapadne i južne Evrope, sjeverne
Afrike i južne Azije. Spada među najranije
proljetnice o čemu govori i njeno ime - lat.
primus znači prvi, dok lat. vulgaris znači
običan, svakodnevan.
Biological
Classification
table

25. • Proljetni jaglac na dugačkoj čvrstoj
stabljici nosi mnogo sitnih žutih
cvjetova, sa 5 tamnih pjega u ždrijelu
vjenčida (cvijet pojedinačan, sraslih
latica i lapova).
• Ocvijede je dvostruko građeno od 5
latica i 5 lapova. Vršci latica su
rascijepani na 2 dijela. Cijev vjenčida je
produžena i valjkasta. Boja cvjeta varira,
može biti žuta, ljubičasta i bijela. Jaglac
naraste do 15 cm.
• Listovi su prizemni, cjeloviti, dužine
5 -10 cm sa kratkom lisnom drškom i
oblikuju rozetu, odozdo su gusto
dlakavi. Mladi listovi su natrag svinuti i
mrežasto naborani.
• Postoje brojni hortikulturni oblici koji se
upotrebljavaju u dekorativne svrhe.
• Jedna od karakteristika ove vrste je i
pojava heterostilije

26. MATERIAL AND METHODS
• Chosen localities: Bjelašnica,
Igman, Jahorina
• Number of individuals:
individua (po 35 iz svake populacije)
• Date of field work(s): .4. i 6.4.2009.
• Tools needed for field work: rooler,
scissors...par gumenih rukavica, lopata,
pinceta, linijar, papiridi za obilježavanje
individua, fotoaparat, sveska A4 formata
• Protocol for measurment and pictures:
se vrši dok su individue u svježem stanju,
tj. na licu mjesta uzorkovanja.
• Used softwares for statistical
analyses: PAST i Microsoft Office
Excel 2007

27. • Quantitative (meristic) traits example:
• Number of flowers in the roseta–in one individuals
• Number of leaves in the roseta– indicates the number of leaves in the
roseta
• Number of sepale latice) – indicates the number of sepales of one flower
belonging to one individual
• Quantitative (metric) traits example:
• Lenght of the longest stem in the roseta
• Lenght of the longest leaf in the roseta
• Width of the largest leaf in the roseta
• Width of the largest flower in the roseta
Primula vulgaris

28. • Qualitative traits example:
• Color of flowers
• Possibilities: Dark Yellow,Light yellow, Purple
• Color of leafes
• Possibilities: Dark green, Light green
• Heterostilia
• Possibilities: Yes/ No
• Hairs on the stem
• Possibilities: a lot/ small amount
Primula vulgaris
Assign numerical chategories
for each possibility of every
chosen qualitative trait!

29. Color of
flowers
Numerical category Possibility (variation)
1 Yellow
2 Light yellow
3 White
4 Purple

30. Color of leaf
Numerical
category
Possibility
(variation)
1 Dark green
2 Light green

31. Hairs on the stam
Numerical category Possibility (variation)
1 Very hairy
2 Slightly hairy

32. Heterostilia – mechanism to avoid self fertilization
Numerical category Possibility (variation)
1 Antheras are longer then stigma
2 Antheras are shorter than stigma

33. III. Results and discussion
• Quantitative traits
– Univariant statistics
– T test
• Qualitative traits
– Chi square analyses
• Download PAST software!

34. UNIVARIANTN STATISTIC ANALISES for
each quntitative trait separately of each
population
• Number of samples
• Max
• Min
• Median
• Mean
• Standard devation
– Variance
• Standard error of mean

35. • Mean = Aritmetička sredina
– Sum of all results divided with total number of
results
• Median
Vidiii
• Varianca (measure of variability)
– Odstupanja od aritmetičke sredine pojedinacnih
rezultata
• Primjer s prosjekom; stranica 62
– Square of variance = Standard deviation
• Standard for measuring variability
• Important for T test

36. Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj cvjetova
za populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj listova za
populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj latica
za populaciju Bjelašnice
% of samples results are
in the range between – to +
result (3.49 – 17.25 = big
range of variation!!! )
Add to mean
and
substract to
mean

37. • Standard error
– If a trait varies a lot within a population, the less samples you measure, there is higher
probability your final mean is not the actual one (as if you had measured for example 2 times
more samples
– The error is less if we increase the number of samples, but it doesn’t lower in a propotional
trend
• It lowers proportionally if we square the total number of measurments
• STANDARD ERROR
• It is higher if the standard deviation is higher
• It tells you how much is truly possible that the calculated mean is truly the actual
one
• It showes us the odstupanja of calculated aritmetic means (of each trait) from the
real, true aritmetic mean of that trait for that population
• You need it for calculating T test

38. Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj cvjetova
za populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj listova za
populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj latica
za populaciju Bjelašnice
% of possibility that our
calculated mean for that
trait doesn’t diverge from
the true, actual means for
that population samples
from 9.21 - 11,53
Add to mean
and
substract to
mean

39. Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj cvjetova
za populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj listova za
populaciju Bjelašnice
Prikaz osnovnih statističkih podataka
za posmatranu osobinu : broj latica
za populaciju Bjelašnice

40. Prikaz osnovnih statističkih podataka za
posmatranu osobinu : broj listova za
populaciju Igmana
Prikaz osnovnih statističkih podataka za
posmatranu osobinu : broj lapova za
populaciju Igmana
Prikaz osnovnih statističkih
podataka za posmatranu
osobinu : broj cvjetova za
populaciju Igmana

41. Prikaz osnovnih statističkih podataka za
posmatranu osobinu : broj cvjetova za
populaciju Jahorina
Prikaz osnovnih statističkih podataka za
posmatranu osobinu : broj listova za
populaciju Jahorina
Prikaz osnovnih statističkih
podat aka za posmat r anu osobi nu
: br oj l at i ca za popul aci j u
Jahor i na

42. T- TEST: TEST SLIČNOSTI I DISTANCE
(Euclidean model procjene distance i sličnosti
Matrix distance)
• The test is performed by using mean values (of all quantitative traits respectively: number of sepals,
number of leaves, number of flowers). This test is performed to see the distance between
populations (in terms of evolution). The conclusion is therefore, made by comparing means of each
trait per popualtion.
• You prove your null hypothesis in this way
– There are no statistifically significant differences between each population for the observed trait
– if p is smaller then 0.05 (meaning that the result you got could only just by chance happen in 5 cases out of 100) we
reject the null hypothesis concluding that the observed difference is statistically significant
• Degree of feedom is a korigiran number of results (confirmed) so you avoid an untrue result (N-1):
(N1-1)+(N2-2); N1 npr, number of samples in bjelasnica; N2 number of samples inIgman
• T tables: dobijeno t mora biti vece ili jednako od t vrijednosti ocitane sa tablice za vrijednost
p=0.05, da bi se razlika izmedju populacija smatrala statisticki znacajnom (showes in red using
statistical programs)
• % isto sto i 0,05 nivo statisticke znacajnosti : znaci ako postoji razlika izmedju populacija za
odredjenu kvantitativnu osobinu, t je vece ili jednako datoj vrijednosti sa tablice (pa je p manji od
.05); da ne postoji razlika, t bi bio manji od vrijednosti date u tablici (a p veci od 0,05 u tom
slucaju)...
• ako je nasa t vrijednost veca ili jednaka vrijednosti sa tablice za razinu znacajnosti 5%, onda znaci da
rezultate koje smo dobili, nismo slucajno dobili, odnosno postoji samo 5% slucajnosti da bi dobiveni
rezultati bili tek tako slucajno tu, e pa onda ne moze nikako biti da je slucajno nego fakat postoji
razlika!

43. Mat r i ks di st ance za posmat r anu osobi nu: br oj
l i st ova
Mat r i ks di st ance za posmat r anu osobi nu: br oj l at i ca

44. Komparativni prikaz distance na osnovu svih kvantitativnih osobina

45. KLASTER ANALIZA (Eulidean distance)

46. KVALITATIVNA ANALIZA – hi kvadrat test
Bjelašnica vs. Igman za boju cvjetova
Deg. Freedom: 34
• Chi^2: 18,583
• p(same): 0,98533
Bjelašnica v.s Jahorina za boju cvjetova
Deg. freedom: 33
• Chi^2: 18,083
• p(same): 0,98363

47. Igman v.s Jahorina za boju cvjetova
Deg. freedom: 34
• Chi^2: 18,833
• p(same): 0,98355
• p > 0,05 Odbacujemo nul hipotezu i zaključujemo da postoji razlika između
uočene i očekivane frekvencije za boju cvjetova kod Bjelašnice i Igmana,
Bjelašnice i Jahorine, Igmana i Jahorine.

48. Procjena frekvencija za kvalitativne osobine
Bjelašnica
Žuta
Svjetložuta
Bijela
Ljubičasta
Žuta – 48,56% (17 jedinki)
Svijetložuta – (45,71% (16 jedinki)
Bijela – nema ni jedna jedinka
Ljubičasta – 5,71% (2 jedinke)

49. Bjelašnica
Žuta
Svjetložuta
Bijela
Ljubičasta
Žuta – 11,43% (4 jedinke)
Svijetložuta – 80% (28 jedinki)
Bijela – 5,71% (2 jenike)
Ljubičasta – 2,86% (1 jedinka)
Igman

50. Jahorina
Žuta
Svjetložuta
Bijela
Ljubičasta
Žuta – 17,14% (6 jedinki)
Svijetložuta – 40% (14 jedinki)
Bijela – 34,3% (13 jedinki)
Ljubičasta – 5,71% (2 jedinke)

51. ANALIZA DIVERZITETA
• Indeksi raznovrsnosti (heterogenosti) objedinjuju indekse zastupljenosti
i indekse ravnomjernosti u jednu brojčanu vrijednost.
• Razvijen je veliki broj indeksa raznovrsnosti, a u radu su korišteni sljededi
indeksi:
 Simpsonov indeks raznovrsnosti
(varira između 0 i 1)
 Shannonov indeks raznovrsnosti:
zasnovan je na informacionoj teoriji i predstavlja mjeru srednjeg stepena
nesigurnosti u predviđanju kojoj vrsti slučajno odabrane individue iz
populacije pripadaju.

52. A – Bjelašnica, B - Igman, C - Jahorina
A B C
Taxa_S 1 1 1
Individuals 353535
Dominance_D 1 1 1
Shannon_H0 0 0
Simpson_1-D 0 0 0
Analiza Shannonov-og indeksa i Simpsonov-og indeksa

53. Conclusion
• Conclude accoridng to each measure and statistics the
results and link it with environment characteristics!
• Each statistic analyses has to be discussed and
interpretated!
• Which trait show the more variation?
– Why? Could it be an adaptation if you assume that that
particular population lives in a slightley different environment?
• Is it statistically significant and what does that mean?

54. Literatura
• Petz, B. 1997: «Osnovne statističke metode za nematematičare», naklada
IV izdanje
• Past software manual
• Izvod iz magistarskog rada: „KOMPLEKSNA ANALIZA RAZLIČITIH MODELA
• PROUČAVANJA GENETIČKE DISTANCE I NJENIH MOGUDIH FAKTORA U
STANOVNIŠTVU BIH” (Naris Pojskid, 2003)
• Izvod iz doktorske disertacije: “POLIMORFIZAM MIKROSATELITNIH
MARKERA NUKLEARNOG GENOMA U BH. POPULACIJAMA
SALMONIDA“, (Pojskid Naris, 2005)
• Izvod iz magistarskog rada: “DISTRIBUCIJA HAPLOTIPOVA
MITOHONDRIJALNE DNK I GENETIČKE OSOBENOSTI LJUDKIH POPULACIJA
U BOSNI I HERCEGOVINI” (Lejla Kapur, 2004)
• Hadžiselimovid Rifat, 2005: “Bioantropologija, diverzitet recentnog
čovjeka”. Institut za genetičko inženjerstvo i biotehnologiju, Sarajevo