Paleodemographical analyses of plague outbreaks in before and after the of 1347-48.

Irene Barbiera, Researcher, University of Padova, [email protected], ORCID: orcid.org/0000-0002-3251-5960 ; tel work: 0039 049 827 4113; mobile: 0039 3498109814

Maria Castiglioni, Associate Professor, University of Padova, [email protected], ORCID: orcid.org/0000-0003-3977-8517

Gianpiero Dalla Zuanna, Full Professor, University of Padova, [email protected], ORCID: orcid.org/0000-0001-7011-4028

Keywords: paleodemography, medieval population, Black Death, d index.

Abstract

In this study we show how the paleodemographic analyses of skeletal data – found in Italian cemeteries dated before, concurrently and after the Black Death – can offer useful insights to understand population dynamics of the past, with a special focus on the centuries affected by strong and recurrent epidemic outbreaks. Our research on the one hand contributes to recent debates around the that afflicted dating from the Early and on the other it offers an interdisciplinary contribution to the study of mortality crises, auspicated by the same scholars involved in these debates.

The aim of this research is manifold. First of all we developed a reliable method to trace mortality trends both in periods of normal mortality and in periods affected by mortality crisis. Secondly, applying such method we contribute to the ongoing debates concerning the impact of Y-pestis in Italian medieval population. We show that both the First (Justinian

Plague, 6th-8th centuries) and the Second (Black Death, 14th-15th centuries) Pandemics restrained the growth of the Italian population, with consequent changes in the population pyramid and demographic equilibrium. However the spread and lethality of the Black Death have been more intense than those caused by the Justinian Plague.

1

Before discussing our methods and outcomes, we will consider in some detail the most recent debates concerning the outbreaks which have affected Italy and Europe for three centuries, from 1348.

1. DEBATES AROUND THE BLACK DEATH

«I say, then, that the sum of thirteen hundred and forty-eight years had elapsed since the fruitful Incarnation of the Son of God, when the noble city of Florence, which for its great beauty excels all others in Italy, was visited by the deadly pestilence. Some say that it descended upon the human race through the influence of the heavenly bodies, others that it was a punishment signifying God’s righteous anger at our iniquitous way of life. But whatever its cause, it had originated some years earlier in the East, where it had claimed countless lives before it unhappily spread westwards, growing in strength as it swept relentlessly on from one place to the next (Translated by G.H. McWilliams 1995).

With these words Giovanni Boccaccio witnessed the arrival of the Black Death in Italy in the famous introduction to the Decameron. The contagion was at first brought to

(Sicily) by a Genovese jailer arriving from Crimea in September of 1347. From there it spread throughout Sicily, reaching the port of Marseille in November and from 1348 it spread to Western Europe, reaching Great Britain probably in April of that same year.

This disease of rodents, which particularly affects rats, can be transmitted to humans by a vector: the flea. When a flea sucks blood from an infected rodent, some of the bacteria settle in the flea’s proventricolus where they multiply, preventing the flea from getting proper nourishment. The flea does not get infected, rather it becomes a healthy carrier. When the rat dies, the hungry flea can bite humans, regurgitating the plague- carrying bacteria into their tissue, from where the infection reaches the lymphatic system, producing the buboes in

2 correspondence of lymph glands (Audoin-Rouzeau 2003; Gratz 1999; Tikhomirov 1999; Del

Panta 1980). In concomitance with the appearance of buboes, other symptoms of Yersina pestis infection are high fever, headache, malaise, shaking chills, inflammation of interior organs. For some reasons still unclear, plague can cause a blood infection, generating a septicemia form which is inevitably lethal and kills within 24 hours. In other cases, in the same way unclear, plague can generate an infectious pneumonia, provoking a secondary pneumonic form of plague, which kills within three days, having a lethality tax of 99%. In this case the disease can be transmitted person-to-person, through saliva, coughing and sneezing provoking a primary pneumonic plague which is also transmissible person-to – person (Poland and Dennis 1999; Gani and Leach 2004; Alfani 2010).

If scholars now agree that the plague represented the most devastating epidemic ever to hit Europe in Medieval and Early Modern times, still many doubts remain about the forms it took, the timing of its transmission, the symptoms and the mortality levels it provoked. More recently the topic of plague has once again become of prime interest, becoming the object of a vivid debate between those who began doubting that the Black Death was provoked by the bacillus of Yersina Pestis, identify by Alexander Yersin during the plague which spread in

Hong Kong in 1894,1 and those who still maintain the strong connection between Yersina

Pestis and Black Death.

A fundamental contribution to these debates has recently been offered by genetic analyses aimed at identifying the DNA of Yersina pestis in medieval skeletons and at tracing its evolution through time.

In a first study, carried on in 1998, scholars extracted DNA from the dental pulp of a number of individuals who had died of plague and who were buried in common pits connected to two hospitals in Labesc and Marseille. These were used as places of quarantine

1 It would have been anthrax according to TWIGG (1984), viral hemorrhagic fever similar to Ebola according to DUNCAN, SCOTT (2004). According to a more conciliatory hypothesis advanced by THEILMANN, CATE (2007), Yersina Pestis would have occurred in concomitance with other virulent diseases. 3 for people affected by the Plague in 1590 and in 1772. According to the authors of this study, fragments of genome belonging to Yersina pestis could be identify in the pulp of the investigated teeth (Drancourt et al. 1998). Subsequent analyses, performed on diverse samples from Northern European sites did not succeed in identifying the genome of plague bacillus. Doubts were thus raised about the validity of the first study (Gilbert et al. 2007).

More recently, new sequencing techniques allowed traces of Yersina Pestis DNA to be identified once more in some skeletal samples excavated in different sites in Europe:

Hereford (UK), Bergen op Zoom (The Netherlands), Augsburg (Germany), Parma (Italy),

Saint-Laurent-de la Cabresse () (Haensch et al. 2010) and Smithfield in London- the well known cemetery used during the Black Death epidemic between 1348 and 1349 (Bos et al. 2011). These new analyses highlighted new pathogens related to Yersina Pestis, suggesting that the Ninth-century was derived from the Fourteenth-century plague, which had changed over time (Geddes de Filicaia and Geddes de Filicaia 2015;

Green 2014). Similarly, the Justinianic Plague also turned out to be provoked by a bacillus related to the biovars involved in subsequent pandemics (Wiechmann and Grupe 2005; Cui et al. 2013; Wagner et al. 2014). Given such relations, scholars now agree that the of Justiniaic

Plague (541 B.C.) can be defined as First , The Black Death as Second Pandemic and the 19th century Bubonic Plague, as Third Pandemic (Green, 2014).

Despite the fact that many doubts about the etiology of the plague are now being resolved, there are still several unclear points concerning the lethality and the modes of its transmittance in the Middle Ages.

The mechanism of plague transmission: from rat to flea and from flea to humans, detected for the Third Pandemic does not seem compatible with the speed of transmission of the Second Pandemic. First of all, the flea which transmits the plague bacillus, Xenophilla cheopis, prefers rats to humans, therefore it will most probably attack humans only after the

4 population of rats dies out. Moreover, the transfer from rats to humans requires a quite close cohabitation among the two. Written sources of the medieval period describing the plague do not mention rats nor any blight of rodents. Contrary to these observations some scholars emphasize that the lack of rats from medieval sources might simply be due to the fact that at that time people were unaware of the mechanism of transmission and of role of rats in carrying the plague (Theilmann and Cate 2007). These scholars hypothesize that the disease probably assumed the bubonic and pneumonic forms simultaneously, thus, triggering direct transmission person-to-person (Theilmann and Cate 2007; Del Panta 2007). Also the modalities of micro-diffusion of the Nonantala plague in the epidemic of 1630 seem difficult to interpret without hypothesizing a person-to-person transmission of the disease (Alfani and

Bonetti in press).

On the other hand, pneumonia plague occurs only after the diffusion of the bubonic form and under very particular climatic and environmental conditions; it is not certain that these circumstances occurred in the Middle Ages (Alfani 2015). In addition, bubonic plague, as transmitted by the flea, spreads in hot or mild temperatures, i.e. between summer and early autumn, while pneumonia spreads predominantly in winter. In this regard, an analysis of the occurrences of testamentary acts in the cities of , Orvieto, Siena, Arezzo, Florence and Perugia in 1348, carried out by Cohn, shows that these became particularly numerous between June and September with considerably high peaks in July, indicating a greater incidence of deaths at this time of year (Cohn 2002). Also in Bologna there is a peak of testamentary acts dating from June and July of 1348. It should also be noted, as Shona Kelly

Wray (2009) observes, that during the epidemic notary offices experienced a time of organizational difficulty as well, which, if it did not prevent the drafting of the acts, certainly limited the full efficiency of the office. It therefore cannot be excluded that in these critical months the acts would have been even more numerous in reality. Likewise, the Florentine

5 death records indicate that, in March 1400, the deaths number around 164 compared to an average of 125; in June it rose to 2,697, with a peak in July of 5,005 falling back to 1,947 in

August (Cohn 2002). In the same way the burial records of Santa Maria Novella in Florence also indicate that in plague years mortality was significantly greater in July (Herly and

Klapisch-Zuber 1978). These trends seem to be in line with the seasonal cycles of bubonic plague observed in India and China, linked to the life cycle of the flea. But, as Cohn asks, could the European summer climate of the Medieval Period be compatible with the humid hot weather of the Indian continent, preferred by the Xenophilla Cheopis flea?

Recently, several studies suggest that plague actors were not just rats and Xenophilla

Cheopis, but a larger number of rodents such as marmots, gerbils, guinea pigs, prairie dogs and parasites such as human fleas and lice (Buhnerkempe 2011; Carmichel 2014).

An additional controversial aspect, partly related to the transmission and diffusion of the contagion, concerns the level of mortality triggered by Black Death. Shrewsbury argues, for example, that in its first appearance in the mid-14th century it did not cause more than

20% of the deaths and that in mortality was around 5% (Shrewsbury 2005).

However, it has been shown that in Cambridgeshire mortality reached 75% (Dubois 1988;

Page 1934), while in mountainous and less densely populated Wales mortality reached much lower levels (Rees 1920). In Italy, Dubois suggests a loss of life of about 55% in Albi between 1343 and 1357, while Comba suggests that in that same period losses in the Susa

Valley ranged between 20 and 55% (Comba 1993). In the case of Florence and its countryside, the plague would have reduced the population between 62.9% and 67.5% (Herly and Klapisch-Zuber 1978).

The data, therefore, varies considerably from zone to zone, often for unclear reasons, although a reverse relationship is clearly visible between mortality rates and population density. In addition, estimates are still difficult since the size of local and macro regional

6 populations prior to 1347 are not known. The subsequent cycles of plague, on the other hand, caused – in general terms – less victims and took on different connotations. In 1360 the plague affected mainly children so that it was termed "the plague of children" (Cohn 2010); during the fourteenth century, it assumed more marked social features, affecting the most disadvantaged and the poor (Alfani 2010b).

It is precisely on the magnitude of mortality during the centuries in which the plague remained endemic that the paleodemographic methods we apply to this work can offer an interesting contribution.

2. PALEOGRAPHIC ANALYSES AND THE STUDY OF MORTALITY IN ITALY

The sources of medieval are scarce and problematic. It is only after the Council of Trent (1545-1563) made ecclesiastical records mandatory that baptism, marriage and burial records, together with Parish family books, started being produced with greater regularity, becoming reliable only by mid-17th century, exactly when, the plague left Italy after three centuries. Although many studies have recently focused on the study of the plague, giving new light to the demographic dynamics of the 14th and 15th centuries, sources are rather sporadic for earlier periods for which only occasional censuses, tax declarations or death records are preserved (mainly in cities), allowing for reconstruction of the rates of fertility and mortality only in a few cases (Leverotti 1982; Alfani and Bonetti in press).

Ancient and medieval demography is therefore largely a matter of speculation. Due to the scarcity of written sources, archaeology has filled an important role in the understanding of demographic mechanisms, thanks also to the development of more and more sophisticated methods of excavation, recognition, publication and interpretation. At the same time, methodological advances in bimolecular archaeology (DNA and isotopic analyses) opened up

7 innovative ways of studying ancient human remains, contributing to the understanding of living standards in the past (Barbiera, Castiglioni and Dalla Zuanna 2016a).

In this context, paleodemography, that is the use of skeletons to reconstruct demographic dynamics of the past underwent important developments over the last decade without using the expensive chemical and biological analyses, and reconsidering also information on skeletons collected and published in the past. We, too, have proposed a reconstruction of Imperial Roman and Medieval mortality trends in Italy before the Black

Death by applying a method developed by Bouchet-Appel and Naji (Bouchet-Appel and Naji

2006; Barbiera and Dalla Zuanna 2007; Barbiera and Dalla Zuanna 2009). This method has been developed taking into consideration the two main distortions inherent in funerary data:

(1) the number of children under 5 years of age is generally underrepresented, either because their fragile skeletons do not hold the test of time, or because they were buried in different areas than the adults; (2) age at death can be precisely determined until age 20 according to the growth stages of teeth and the closures of epiphyses, while for the adults the age of death cannot be determined with precision (Hoppa and Vaupel 2002). Keeping such limitations in mind, the method aims at calculating the following ratio for each site considered:

d = D5-19/D5+ the ratio between the number of deaths at ages 5-19, the period during

which age can be precisely estimated, and the number of deaths at age

5+, excluding children of age 0-5, who are underrepresented.

As shown in the following part, in the case of stationary populations the index d corresponds to the probability of dying between the fifth and the twentieth birthday. In stationary population, as d grows, mortality increases since early (children) deaths are more numerous of that of adults and elderly individuals. However, in the real world, the matter can

8 be more complicated. Thus, to be able to employ d in demographic analyses it is necessary to comprehend its relation with the measures of mortality in the various conditions which occur in reality, considering also the demography of populations affected by serious and recurrent epidemic outbreaks

3. THE D INDEX AND THE MORTALITY CRISIS

To understand the meaning of d, let’s consider unrealistic situations first, where the relationship between different measures is easy to master, and then we will approach the “real world”.

The first example concerns stationary populations, that are closed to migrations, with grow rate equal to 0 and with crude death and birth rates equal and stable over time. In such cases, as already mentioned, d acquires a precise demographic meaning. In fact if the population is stationary, effective deaths (D) are equal to those of the life table (d) and consequently D5-19= d5-19 and D5+= l5. Thus d = D5-19/D5+= d5-19/l5= q5-19.

It is possible to use the standard life tables developed by Coale&Demeny (1983) to obtain a series of survival measures starting from different levels of mortality of stationary population.2 Table 1 shows the strict relation that connects d with some parameters of mortality: the relationship is linear or parabolic not only for measures of mortality above 5 years of age but also for mortality before the 5th birthday, which does not contribute to the estimate of d. Thus, as can be inferred from table 1, as d grows the probability of dying (q0, q1-4, q0-4) increases.

Table 1. Relationship between d and some measures of mortality. Stationary population and Coale & Demeny Standard Life Tables, Family West.

2 On the problems of using standard life tables for describing mortality in past periods, see: BARBIERA, DALLA ZUANNA, 2007; SÉGUI, BUCHET, 2013.

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C&D Death 5+ Death 5-19 D n = m Females

Levels (*) % % D5-19/D5+ ‰ q0 ‰ q1-4 ‰ q0-4 ‰ e0 e5 -2 ------0,238 65,8 452,3 358,1 660,9 15,3 32,8 -1 ------0,221 59,0 420,3 333,6 615,8 16,7 34,0 0 ------0,205 53,1 390,2 310,5 573,4 18,4 35,3 1 46,8 8,9 0,191 50,0 365,5 261,8 531,6 20,0 36,6 2 50,8 8,9 0,175 44,4 334,0 237,6 492,2 22,5 38,3 3 54,5 8,7 0,160 40,0 305,6 215,8 455,4 25,0 40,1 4 57,9 8,5 0,147 36,4 279,7 196,0 420,9 27,5 41,7 5 61,1 8,2 0,134 33,3 256,1 177,9 388,5 30,0 43,4 6 64,2 7,9 0,123 30,8 234,4 161,3 357,9 32,5 45,0 7 67,1 7,5 0,112 28,6 214,3 145,9 328,9 35,0 46,6 8 69,8 7,1 0,102 26,7 195,6 131,6 301,5 37,5 48,2 9 72,5 6,7 0,092 25,0 178,2 118,3 275,4 40,0 49,8 10 74,9 6,3 0,084 23,5 161,9 105,8 250,6 42,5 51,4 11 77,3 5,8 0,075 22,2 146,6 94,1 226,9 45,0 52,9 12 79,6 5,3 0,067 21,1 132,2 83,1 204,3 47,5 54,4 Linear R2 with d (levels 1 to12) 0,982 0,999 0,999 0,999 0,988 0,992 Parabolic R2 with d (levels 1 to 12) 0,999 1 1 1 0,999 0,999 (*) The values of the mortality indexed for the levels 0, -1 e -2 have been projected by interpolating and extrapolating the values of d with a straight line (q0, q1-4, q0-4) or a parabola (n=m, e0, e5).

But what happens if we leave the world of the stationary populations? Let’s start by examining the case of stable populations (without migrations, with natural growth rate r different from zero and with crude death and birth rates stable over time). Figure 1 shows the relationship between d and r for three levels of mortality in stable populations analyzed by

Coale & Demeny (West Family). At constant mortality levels, the value of d changes according to the variation of r, in fact if the birth rate is greater (or smaller) than mortality, the proportion of infants increases (or decreases) and consequently , the proportion of deaths of children varies accordingly (Ségui and Buchet 2013). For example, in cases in which mortality is very high (such as level 1 Coale & Demeny, e0,F=20), where the population increases annually by1%, d=0.250;, reversely, if the population decreases by 1% then d=0.139.

These results instill doubts about the possibility of using directly d as an indicator of mortality level. Suppose that we know, for a single cemetery, only the value of d=0.137, without knowing the natural growth rate of the population. This value of d would be

10 compatible with e0,F=20 and r=-11‰, with e0,F=30 and r=0, and also with e0,F=40 and r=+11‰.

However, if the annual population growth rate is low, then the influence of r over d is quite limited: from example, again in the case of high mortality (level 1 Coale & Demeny, e0,F=20), if r varies within the interval ±3‰ then 0,177

Table 1. In addition, figure 1 also shows that the relationship between r and d is sufficiently linear and regular for mortality levels typical of pre-industrial societies (e0<40). Therefore, on the basis of the empirical relationships visible in Figure 1, it is possible to correct d:

d* = d – 0,0045r where r is expressed in ‰ and the angular coefficient has an

intermediate value between those in Figure 1

The index d* can be used to estimate the level of mortality when the rate of natural growth r is known. In this case, it is d* (rather than d) to be connected with the measures of mortality indicated in Table 1. For example, in a population where d=0.250 and r=+10‰, then d*=0.205, which corresponds to e0,F=18.5 (and not e0,F<15, as would result when using d in a direct way).

Figure 1. Relationship between d=(D5-19/D5+) and r, stable populations of Coale & Demeny, West Family

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Otherwise, in periods characterized by recurrent and intense mortality crises – such as in Italy in the three centuries between 1350 and 1650 and, in all likelihood, between the sixth and the seventh centuries – the assumption of a stable population does not hold (Bocquet-

Appel and Bacro 2008; Ségui at al. 2006; Signoli et al. 2002; Castex and Cartron 2007) because during the crisis the number of deaths could increase 5-10 fold compared to normal years, but also because the crisis influenced other aspects of demography (Del Panta 1980).

In particular, during periods of crisis the number of births could significantly decrease and a much larger number of people could migrate to increase their chances of survival. Moreover, following a crisis marriages and births generally increased whereas mortality decreased, especially if the crisis swept away the weakest individuals.

To understand the meaning of d in such circumstances, we performed a simulation.

Starting from the female stationary population connected to mortality A of Coale & Demeny level 1 Family West (e0,F=20.0 and crude birth and mortality rate = 0.05) we hypothesized that – over a century – there is a year of super-mortality B which occurs every 15 years, along

12 the lines of that observed in the parish of St. Botolph in London during the plague of 1604

(Hollingsworth and Hollingsworth 1971; Del Panta 1980). The year of the plague was followed by five years characterized by mortality C, 20% lower than the normal level, and then by nine years of normal mortality A. In each cycle lasting fifteen years, the sequence of mortality levels is thus: AAAABCCCCCAAAAA (Table 2): in the first five-year period mortality is (4A + B)/5; in the second five-year period it is 5C/5 and in the third it is 5A/5.

In addition, in the simulation the birth rate is reduced by 40% in the year following the crisis, increasing in the following four-year period (+60% compared to a normal year), then gradually approaching pre-crisis levels (+20% compared to the levels registered during the final five years). These trends are close to those visible in the parish of Nonantola (MO) during the plague of 1630 (Alfani and Cohn 2007a). Therefore, in the three five-year cycles, the birth rate is respectively 0.05 (to C & D level 1), 0.07 and 0.06.

Under these hypotheses, the population fluctuates around the value observed at the beginning of the period (r=+0.35 ‰ over the century). Also, d oscillates as shown in Figure

2 with an average level of 0.24 (life expectancy at birth slightly above 15 years). So, the distribution of deaths among young adults and older people is close to that observed when the same life expectancy remains constant in time, in a stationary population regime (back to

Table 1).

Table 2 – Probability of dying in three five-year cycles of normality, crisis and recovery

(A) (B) (*) (C) C&D level 1 Crisis years Recovery years 0 4 0,428 0,800 0,342 5 9 0,215 0,539 0,172 10 14 0,061 0,300 0,049 15 19 0,065 0,318 0,052 20 24 0,082 0,339 0,066 25 29 0,097 0,263 0,077 30 34 0,109 0,277 0,087 35 39 0,120 0,324 0,096

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40 44 0,129 0,273 0,103 45 49 0,137 0,292 0,109 50 54 0,158 0,317 0,126 55 59 0,197 0,256 0,158 60 64 0,260 0,341 0,208 65 69 0,346 0,378 0,277 70 74 0,444 0,649 0,355 75 79 0,580 0,790 0,464 80 84 0,731 0,747 0,585 85 + 0,811 0,811 0,649 (*) In the crisis year the additional mortality detected in St. Botolph, during the plague of 1604, was added to the normal one (column A).

Figure 2. Trend of d index in the simulation of mortality and demographic crisis

Therefore, in periods disrupted by the stop and go of major epidemics, the value of d may have been relatively high, corresponding to a kind of average mortality among the years of very frequent epidemic outbreaks and that of years of normal mortality or recovery . If recovery was determined by the increase of birth rate, then d could be augmented by the high number of births as well.

Summarizing the calculations, observations and simulations described above, it can be argued that the indicator d=D5-19/D5+ is useful for estimating mortality when we only have at our disposal data on deaths derived from skeletal samples, notwithstanding the limits of synthesizing an entire mortality regime by one parameter. In the case of stationary

14 populations or populations with a rate of natural growth r between ±3‰, the indicator is well correlated to some basic parameters of mortality: as d grows, mortality increases. If r is larger or smaller, however, it is appropriate to correct the value of d to take into account the variation of population age structure, and thus of deaths, due to the increasing or decreasing birth rate, according to the formula d* = d-0.0045r‰.

In general, since d is influenced by the growth rate, it is not appropriate to use it to estimate mortality for a single cemetery or for a population of unknown trends over time. On the contrary – as in the example shown here – the index is most useful when observed in a large number of cemeteries (or in any case referring to data on deaths related to several populations) so that the impact of anomalies, frequent in past populations, is diluted, and when population trends are known. Less problematic is the use of d to estimate mortality of populations characterized by sudden dips and subsequent recoveries. In fact in these cases d is associated in the long run to measures of mortality in a manner not very different to those detected in stationary or stable populations. However, if d can be a reliable estimator of mortality over the fifth birthday, caution is needed when estimating overall mortality, which in fact could be greatly affected by the relatively abnormal mortality of children. Finally, the index d can also be used to detect different mortality patterns among males and females in past society (see Barbiera, Castiglioni and Dalla Zuanna 2016c).

4. THE TRENDS OF D AND THE ITALIAN POPULATION BEFORE AND AFTER THE BLACK

DEATH

Starting from a previous study on the trends of d and its relationship with the variations in mortality between the Roman time and the end of the twelfth century (Barbiera and Dalla

Zuanna 2007; Barbiera and Dalla Zuanna 2009), we extended the data collection and the estimation of d to a later period, running from the 13th to the 15th century. For this purpose we considered only cemeteries with more than 40 individuals aged 5+, excluding those where

15 more than twenty per cent of adult skeletons were of unknown sex or age. In addition, we included only sites that have a final d value between 0.15 and 0.30. In this way we were able to collect data from 43 sites, including 5,506 individuals over five years of age (Barbiera,

Castiglioni and Dalla Zuanna 2016b). For each macro period we gathered at least 1,200 individuals (figures 3 and 4).

Figure 3. Map of investigated sites, dated between the 1st and the 15th centuries

Figure 4. Analyzed sample size, according the macro periods

1,800

1,600 6-9 century 1-5 century 1-5 century -15 century. -13 century

1,400 14

10

1,200

1,000

800

600

Number of analzyed individuals 400

200

0

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From our sample it emerges that the value of d is relatively low between the 1st and the 4th centuries, it grows in the following period, while between the 9th and the 13th centuries it again reaches a value similiar to that of Imperial Roman times. By contrast, in the centuries marked by plague the index d is much higher (figure 5). Hence we can notice an increase of d in perods caracterized by mortality crisis.

Undoubtably, this data should be taken with caution because of all the problems inherent in the use of the indicator d as the only flag of mortality. For example, there may be conditions with very high infant mortality but low juvenile mortality, and vice versa: in this case, the indicator d might underestimate (or overstate) the intensity of mortality: we recall that in the past the death of children under 5 years of age could even reach 50% of the total. Unfortunately, cemeteries can tell us little about the levels of child mortality

(Barbiera and Dalla Zuanna 2009).

Nonetheless, a positive point is that the trend of d mirrors that of the population

(estimates of Lo Cascio and Malanima 2005): when d grows, the population decreases, and vice versa. In particular, the period after the Black Death is characterized – as previously mentioned – by cemeteries with high values of d that continued in the following centuries

(see Figure 5). In fact, the population took two centuries to recover from the real collapse of 1348-49, returning to the mid-300 levels only in the sixteenth century.

Figure 5. Trends of d and of the Italian population between the Imperial Roman period and the Middle Ages.

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15 0.30

14 0.25 13

12 po 0.20 p

in milionsin 11 d 10 0.15

9 0.10 8

7 0.05 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th

The high d values in this period seem to reflect the typical trends of a lengthy mortality crisis triggered by plague and characterized by alternate stages of high mortality followed by periods of high birth rates.

As the epidemic makes it appearance, the number of deaths increases rapidly to a maximum peak after which the number of deaths begins to fall again. Simultaneously with the mortality crisis, the number of marriages decreases drastically due to grieving, to the disaggregation of kinship and social relations, to the slowdown in economic and trade activities and to emigration towards less affected areas (Del Panta 1980; Alfani and Cohn

2007b). Therefore, a sharp decline follows in conceptions and births, which, together with deaths, will cause the population to be decimated. The population, however, is extremely resilient, following the typical Malthusian pattern. In fact, after a year of plague, there has generally been a strong recovery in weddings and births over the years, due to a decline in the age at marriage, a reduction of those never merried and a shortening of the gap between births (Dalla Zuanna et al. 2012).

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For example, the parish baptism registers of the city of Florence indicate that in the

15th century the number of children baptized decreased by an average of 18% in the years of plague and immediately afterwards. Subsequently when the outbrake ended, the number of baptisms rose to higher values than in those years before the plague. So, for instance, during the outbreak of 1457 the number of children baptized was 1,882; in preceeding years (1453-1456) it was on average 2,113; while in the following years, between 1459 and

1461, an avarege of 2,123 baptisms were recorded (Herly and Klapisch-Zuber 1978). Also the cronists of the period reported a strong increase in marriages and fertility following an outbreak. In these phases the population pyramid broadens, with a larger presence of children. And these new born individuals, once they reach adult age, will contribute even more to the growth of the population, getting married at young ages and giving birth to many children. The population will therefore be young and vulnerable once again to a new epidemics, since those who were immunized will have since died. It seems in fact that the epidemic cycles of the years 1363/64, 1374, 1383 and 1400 affected mainly children who were not immunized by previous outbreaks (Herly and Klapisch-Zuber 1978). The epidemic cycles at the end of the 15th century, which cyclically decimated extraordinary numbers of children and youths every ten years, have considerably modified the age structure of the Italian population and compromised its chance of recovery. These trends of mortality and recovery and of selective mortality by age, seems to be well represented by the high values of d, as found in Italian cemetereis of this period. In fact, because the increase of d reflects the rising number of individuals buried beetween ages 5-19 compared to the total population, it can reflect both an increase in deaths at that age (which corresponds by extension to an increase in overall mortality) and an increase in births (and consequently an increase of the young deaths, subsequently buried in the cemetereis)

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(Bocquet-Appel and Naji 2006; McCaa 2005). Our simulations described above seem to confirm this interpretation.

5. CONCLUSIONS

Using age-related data on death from cemeteries, we were able to gather information about the mortality regime not only of "normal" periods but also of those affected by recurrent major epidemic cycles. In the case of Italy, our data confirms that it was precisely the recurrent outbreaks of plague that restrained the growth of the Italian population during the 6th-8th centuries (following the spread of the Justinian Plague) and during the 14th-15th centuries (after the Black Death). Data on deaths from cemeteries scattered across the regions of the Italian peninsula show, in fact, an increase in mortality exactly when these two epidemic cycles spread. They also confirm that the spread and lethality of the Black Death, with consequent changes in the population pyramid and therefore the demographic equilibrium, have been more intense than those caused by the Justinian Plague.

The method we employed and developed has been proven to be fruitful in assessing ancient demographic trends, as we show in this study. It is therefore auspicated that this path of research will be further developed and deepened to investigate other geographical areas and chronological periods. In our specific case-study in particular archaeological data should be implemented by providing newly excavated and analyzed osteological data particularly for the ; in this way it would be possible to appreciate regional differences and to systematically compare mortality trends obtained from skeletal samples with the data from cadastres, which would allow reconstruction of the age structure of the population

(although sometimes underestimation of children is evident) in different areas and historical moments (Dalla Zuanna et al. 2012). It would therefore be possible to understand more deeply how plague (or plagues) altered family life and population dynamics. Putting together

20 different types of sources – including paleo-demographic ones – may help contribute to understand population dynamic preceding the diffusion of parish registers.

Acknowledgments

The authors wish to profoundly thank Lorenzo Del Panta and Guido Alfani for reading the present text and for their precious and punctual suggestions.

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