II LABORATORY WORK

Variation and variability

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!

Morphological characteristics

• Quantitative

– Metric

– Meristic

• Qualitative

• The characteristics can be determined by detailed observation of the chosen species

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

• 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

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

QUALITATIVE TRAITS

QUANTITATIVE TRAITS

HABITAT DATA

Qualitative traits

• Can be only described

• Subjective

• ...Quality, How is it something?

• Variation in coloration

• Variation

• 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!

Color of flowers

Numerical category Possibility (variation)

1 Yellow

2 Light yellow

3 White

4 Purple

Color of leaf

Numerical

category

Possibility

(variation)

1 Dark green

2 Light green

Hairs on the stam

Numerical category Possibility (variation)

1 Very hairy

2 Slightly hairy

Heterostilia – mechanism to avoid self fertilization

Numerical category Possibility (variation)

1 Antheras are longer then stigma

2 Antheras are shorter than stigma

Picture the habitat of the chosen species

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

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

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 methods1. 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

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

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)

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

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)

• 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

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 AgricultureAgricultural 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

• 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

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

• 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

• 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!

Color of flowers

Numerical category Possibility (variation)

1 Yellow

2 Light yellow

3 White

4 Purple

Color of leaf

Numerical

category

Possibility

(variation)

1 Dark green

2 Light green

Hairs on the stam

Numerical category Possibility (variation)

1 Very hairy

2 Slightly hairy

Heterostilia – mechanism to avoid self fertilization

Numerical category Possibility (variation)

1 Antheras are longer then stigma

2 Antheras are shorter than stigma

III. Results and discussion

• Quantitative traits– Univariant statistics

– T test

• Qualitative traits– Chi square analyses

• Download PAST software!

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

• Mean = Aritmetička sredina

– Sum of all results divided with total number of results

• MedianVidiii

• 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

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

• 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

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

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

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

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

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!

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

Komparativni prikaz distance na osnovu svih kvantitativnih osobina

KLASTER ANALIZA (Eulidean distance)

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

Igman v.s Jahorina za boju cvjetovaDeg. 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.

Procjena frekvencija za kvalitativne osobine

Bjelašnica

Žuta

Svjetložuta

Bijela

Ljubičasta

Žuta – 48,56% (17 jedinki)Svijetložuta – (45,71% (16 jedinki)Bijela – nema ni jedna jedinkaLjubičasta – 5,71% (2 jedinke)

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

Jahorina

Žuta

Svjetložuta

Bijela

Ljubičasta

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

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.

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

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?

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