 Dzień dobry. Nazywam się Rzeczwińska i po południu mojego kolega Janusz Piątek z Farmów Antropologii Adamickiej Uniwersyjnej w Poznań, będziemy zauważyć się na wszystkie, to znaczy klasyczne, paleodemograficzne metody i nowe metodologii, w research on state of the dynamics of prehistoric populations in this case on Wicina population. And what the idea, the problem is that there is a groups of critics of paleodemographic research because of inaccuracy of sex determination or age of elimination, there is also lack of data according to age. The material size we focus on is usually lower, so no adequate representation of material because of what, lack of child, skeletal remains. So the real population size was probably higher, greater. And that's why researchers have omitted the classical methodology and searched for new methodological approach for new solutions. For example, they reject classical life table, building of life tables. So this is the aim of the presentation to show all that methodological approach, new methods and I show you that the results produced using both are the same. Nowe, we focus on where human remains excavated in 1950s and 60s at Wicina Cemetery. We use over 400 individuals with age and sex determined. So that's the urn field from Wicina. More information will be provided by Eustena during our common presentation at 12. So slightly over 500 graves and 438 individual information on people died. And methodology, classical methods were used. So we built life tables, classical life tables, classical halles, life table for stationary population model without and with the correction of the number of children. As we know, stationary population model with the assumption that fertility and mortality balance each other is a kind of oversimplification. That's why we introduce population growth. It's quite difficult to calculate the value of population growth for prehistoric population. So we adopt the values from model tables produced by wise. And also I calculated them from regression line, regression function of bocat apple. On the basis of life table parameters we estimated fertility parameters, fertility figures and we assess the selection pressure. And new methods, new methodology, the Kaplan-Meier survival function which leases to omid the under registration of individuals of children age 014 and cox proportional hazard models which will be shown during the noon presentation how to capture for example the impact of some cultural variables on age of death. And computation, statistical views then how to calculate information, how to calculate fertility on the basis of life table parameters and population growth were calculated on the basis of methodology provided by Kenneth Weiss first and then it was developed by Heneberg and Stein published in Human Biology bocat apple function from carnatropology and then classical cross index Heneberg and Piontex papers on how to assess measures of the opportunity for natural selection. Having age estimated we grouped people in age categories, children and adult age categories as we know adult would start at the age of 15 and what the pros and cons of the use of life tables which are sometimes criticized. The problem concerns the methodology. There are two methods first on the basis of age of population which is used by for example contemporary demographers but also with regard to 19th century population when we have distribution of people by age I built such life tables for 19th century population from Poland but when we focus on skeletary minds, prehistoric minds there is no data on age on population by age that's why the only information on population are distribution of disease by age. According to many anthropologists the distribution of those who died by age do not represent the real age structure. That's why this method is criticized, this life tables some paleo demographist, anthropologists gauge, love and joy they propose to for example use hazard methods which from my point of view as I am an anthropologist biologist is quite complicated to interpret the results produced with the use of such methods. I belong to a group who still use life tables we for example calculate life table parameters using both methods on the basis of distribution of those who died and on the basis of population by age and differences in life table parameters for the same population, the same period were statistically insignificant so no differences in my opinion the most informative parameter is life expectancy at age zero. This slide presents life expectancy for two model situations as you can see the results after the age of 15 are grouped into groups with first mark with blue line when the number of children was underrepresented and second with red lines with correction of number of children differences concerns what the fact that the number of children was underrepresented so that's why the only fact influencing the results so in the first situation life expectancy at the age of zero was close to 12 while in the second one close to 30 when you introduce the value of population growth the values change a little because of the fact that population growth was very small it was 1.7 or 2.2 according to methods of population growth calculation per 1000 people so MT means model table wise 17.565 means life expectancy probability of surviving at the age of 15 fraction of dying also two groups of lines according to the fact if children were correct or not but what is characteristic the fact that the higher fraction of dying was at the age of 1 and then between the age of 30 and 40 irrespective of the fact if children were correct or not and fraction of surviving which declining with age and also two groups of lines fertility reconstruction wise was the first who implemented these methods or tried to estimated 30 figures on the basis of life table parameters and population growth I did the same for example for 19th century population from greater Poland because of having lack of information fragmentary information on children born to women that way it was impossible to reconstruct the reproductive history of women and in such situations this method is very useful so three population models who have zero population growth and non-zero population growth first adopted from model tables of wise and then calculated on the massive bucket apple regression as you can see the 30 figures are almost the same so the same value of average age specific fertility rate mean birth interval, mean family size but I would like to focus on total fertility rate which inform us about how many children were delivered by women during their whole productive period which is between 15 and age of 49 so the values are close to 6 and slightly over 6 and the question is high or not the number of children is 5 is the value high or not it depends because for example the same values were obtained for 19th century population from Poland but the majority of children born died before the reaching of the onset of reproduction which is confirmed by the opportunity for natural selection the measures informing us about the selection pressure here we have they are also calculated on the basis of life-tail parameters that's why in my opinion life-tales are extremely valuable because they lead to recalculate or calculate complete different parameters first growth index extremely high in the situation when the number of children was corrected 1.5 which means that the mortality of children infants, young children was very high so quite high selection pressure against children then potential reproductive growth which inform us about the selection pressure according adults so in this case 51% of the adult population had a chance to produce children because the majority of them did not survive the onset of reproduction and biological state index which inform us about the participation in producing children in the whole population the population is a whole and here between 20 and 35% of the population has a chance to participate in producing children so high selection pressure against infants and children translated into low chance to participate in reproduction because the majority of children did not survive the onset of reproduction and few people survived to the end of reproduction for comparison Poland in the 1960s and the potential reproductive growth was equal 0.99 the biological state index was equal 0.99 which means that in Poland in the 1960s when the excavations at the China Cemetery was conducted 99% of the population had a chance to participate in reproduction so that's the difference and finally survival probability Kaplan-Meier function I focus more I tell more about this method in the next presentation it's quite useful because when our material touch right now here we have a group of people with lower age lower limits of the age at that so it's very useful when you want to omit the underregistration of deaths and what kind of information is provided with survival probability for example the onset of maturity the age of 15 conventionally 50% of individuals survived up to the age of 15 and for example to the age of 4 about 25% of people and the results are covered with the survival line or survival probability which is produced with the use of classical life table so the results are almost the same so what's the conclusion the classical democratic research which was developed in 1978 is still a very important part of study so there are still very important tools in research on skeletal populations on prehistoric populations of course it's worth looking for searching for new methodology under the condition that the results produced or the use of them are easy to interpret from the point of view of biology since we are talking about the state and dynamics of the population so thank you very much for your attention