The potential impacts of climate factors and malaria on the Middle Palaeolithic population patterns of ancient humans

Quaternary International xxx (xxxx) xxx

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The potential impacts of climate factors and malaria on the Middle Palaeolithic population patterns of ancient humans ´jer a, *, Viktor Sebesty´en a, Endre Domokos a Attila J. Tra a

Sustainability Solutions Research Lab, University of Pannonia, Egyetem Utca 10, Veszpr´em, H-8200, Hungary

A R T I C L E I N F O

A B S T R A C T

Keywords: Aridity index Precipitation Plasmodium Extrinsic development Structural networks

Previous studies that observed the fact that Middle Palaeolithic sites mainly were concentrated in arid and semiarid areas in Africa and Southwest Asia, concluded that climate factors determined the distribution patterns. We argue that biological factors could have been equally important. In present-day sub-Saharan Africa, mosquitoborne diseases and especially falciparum malaria have a serious impact on human populations. This study was aimed to investigate the possible former effect of falciparum malaria on Middle Palaeolithic site distribution patterns and explain why ancient humans avoided the humid areas in the tropical and subtropical regions. It was found that the early human settlements situated in those regions of Africa and Southwest Asia where the po­ tential annual development period of falciparum parasites was short in the mosquitoes, the area was not too humid, and the potential falciparum malaria incidence values were low or moderate. In the Indian Peninsula, precipitation played a less significant role in determining human settlements. The number of the months when the extrinsic development of Plasmodium falciparum parasites was possible showed the strongest structural overlap with the modelled malaria incidences according to the spatial occurrence of the Middle Paleolithic archaeological sites in the case of Africa and in Southwest Asia. In the Indian Peninsula, climatic factors showed the strongest structural overlap with the modelled malaria incidences according to the occurrence patterns of the Middle Palaeolithic archaeological sites.

1. Introduction The Middle Palaeolithic era and as a part of that the last interglacial period (the Eemian) 130–115 ka, was a critical time of human evolution. The earliest split of modern human populations that persist to the pre­ sent time occurred during this period (Macaulay and Richards 2006). That time, anatomically modern humans share the world with other, archaic human species, like Homo sapiens neanderthalensis and Homo sapiens denisova (Lari et al., 2015; Sawyer et al., 2015; Warren 2018). The divergence of the first modern human populations occurred be­ tween 350 and 260 ka according to the molecular evidence (Schlebusch et al., 2017) which result fits well to the 286 kys age of the most ancient anatomically modern human fossils (Jebel Irhoud, Morocco; Richter et al., 2017). Despite the presence of Homo sapiens fossils in areas outside Africa, as the 210 kys old Apidima-1 fossil in Greece (Harvati et al., 2019) or the 185 kys old Misliya-1 fossil in Israel (Hershkovitz et al., 2018), the emergence of the people who became the ancestors of the present human populations should have begun somewhen in the Last Interglacial Period. Kim et al. (2014) also found that Khoisan

hunter-gatherers have been the largest population of modern humans in the Middle Palaeolithic period, and their split from the non-Khoisan populations occurred at 100–150 ka (Kim et al., 2014). The better un­ derstanding of the correlation between the former environmental factors and the occurrence of ancient populations of people would be of great help in understanding how these groups migrated and separated from each other providing the founding populations of modern mankind. It is known that the distribution of the Middle Palaeolithic sites in­ dicates that the early anatomically modern human populations inhabi­ ted rather the former hyperarid and arid (0–200 mm), semi-arid and dry (200–600 mm) climate areas in Northeast Africa, the Arabian Peninsula and the other parts of Southwest Asia than the humid (1000–1600 mm) and hyperhumid (1600 mm<) regions (Groucutt et al., 2015A,B). The middle stone age sites of South Africa were also absent from those areas where the annual precipitation sum was more than 1000 mm (compare the Middle Palaeolithic site map of Wadley 2015 and the climate maps of Groucutt and Blinkhorn 2013). Modern humans might have spread from Africa along the shorelines of Arabia toward southern Asia during or soon after Eemian. In previous works, some authors argued that humans adapted to the arid regions only during the Holocene (Veth et al., 2005;

* Corresponding author. Sustainability Solutions Research Lab, University of Pannonia, Egyetem Utca 10, Veszpr´em, H-8200, Hungary. E-mail address: [email protected] (A.J. Tr´ ajer). https://doi.org/10.1016/j.quaint.2020.10.056 Received 23 October 2019; Received in revised form 13 October 2020; Accepted 22 October 2020 Available online 27 October 2020 1040-6182/© 2020 Elsevier Ltd and INQUA. All rights reserved.

Please cite this article as: Attila J. Trájer, Quaternary International, https://doi.org/10.1016/j.quaint.2020.10.056

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Abbreviations

G(yi) U

T monthly average temperature (◦ C) Tlimit,min lower limitation factor (extremum) of the extrinsic development of P. falciparum parasites (◦ C) P monthly precipitation (mm) Plimit,min precipitation minimum extrema of falciparum malaria occurrence (mm) iMal malaria incidence per 1000 inhabitants iMalmax maximum malaria incidence in Africa relative falciparum malaria incidence (between 0 and 1) iMalrel NoEx the number of months when the development of the parasite is possible in the mosquito vector aTAI the annual Thornthwaite Agrometeorological Index (mm/ ◦ C) aPsum the annual sum of precipitation (mm) the annual mean temperature (◦ C) aTmean a starting-point constant b1-4 unstandardized regression weights xi the sampled values of the modelling variables in the human settlements yi the sampled values of the modelling variables in the blind pattern ̂ F(xi ) the cumulative distribution function of the human

nxi nyi W exi1,2 dx1,2 JSD D xaTmean xNoEx xiMalrel xaTAI xaPsum

settlements the cumulative distribution function of the blind pattern the number of times, where x precedes y in an ordered arrangement of the elements in the two independent samples the number of the samples or Human settlements the number of the samples in the blind pattern is the sum of the ranks in the samples the weight of the edge between the given two nodes the distance between the nodes x1 and x2 Jensen-Shannon Divergence Kullback-Leibler divergence the sampled values of the annual mean temperature in the human settlements (◦ C) the sampled values of number of months when the development of the parasite is possible in the mosquito vector in the human settlements the sampled values of relative falciparum malaria incidence in the human settlements the sampled values of the annual Thornthwaite Agrometeorological Index in the human settlements (mm/ ◦ C) the sampled values of the annual sum of precipitation in the human settlements (mm)

archaeological sites, migration (e.g. Drake et al., 2013) or genetic events and environmental factors. For example, Kim et al. (2014) found that a climatic event which occurred at 80–100 ka, resulting in the desiccation of west-central Africa, potentially could contribute to the severe decline in the ancestors of the current Ntu language-speaker (Bantu) pop­ ulations while the size of populations of the ancestors of the current Khoisan people was affected by a much lesser degree (Kim et al., 2014). Groucutt et al. (2015a) found a coincidence between interior humidity and the distance of the hominin archaeological sites from the coasts of Arabia. Groucutt et al. (2015a) showed that the rapidly fluctuating climate of the Middle Palaeolithic era impacted strongly the opportunity for interregional dispersal. Blome et al. (2012) found little relation be­ tween large scale demographic and climate change in southern Africa in the Last Interglacial Period, but they proved that the hominin archae­ ological sites of the Sahara were positively impacted by the wetter climate of Eemian. Crassard et al. (2013) also found that the human population history was strongly impacted by the changing climatic continental conditions during the Middle Palaeolithic and Neolithic periods in the Arabian Peninsula. The occurrence of early human settlements was rarely explained by non-environmental, non-climatic, but biological factors. The potentially significant impact of different diseases on early human populations was a long time ago suggested but difficult to prove a hypothesis. For example, it was proposed that viral diseases or transmissible spongiform encephalopathies could play a role in the extinction of Neandertal populations (e.g. Underdown 2008; Wolff and Greenwood (2010). Divale as early as 1972 raised that certain diseases such as malaria and dysentery were responsible for large numbers of deaths in the Middle and Upper Palaeolithic eras influencing the population dynamic of the former hunter-gatherer societies (Divale 1972). Wadley (2012) pro­ posed that in the Middle Palaeolithic age, in the site Sibudu, malaria or similar diseases may have discouraged the habitation of humans. Ma­ laria is such a strong evolutionary stressor that caused the long-term accumulation of some defective allele of haemoglobin in the affected populations, mainly in Sub-Saharan Africa. Sickle-cell disease is the most prevalent and the best-known example of this phenomenon (Piel et al., 2010). Due to the abnormality of the oxygen-carrying protein haemoglobin S (HbS protein), Plasmodium parasites can less infect the

Barker and Gilbertson 2000). This idea seems to be unsustainable today (Groucutt and Blinkhorn 2013) because the occupation of open and semi-arid environments, even in the Early Pleistocene, has been demonstrated in such semi-arid and arid areas as Israel and Egypt (Yeshurun et al., 2011; Wendorf et al., 1993). It should be noted that most of the Palaeolithic records of arid areas that are now desert are known from the sediments of springs and palaeolakes due to tapho­ nomical reasons that which does not mean that it is only in such places that ancient human communities occurred in the arid and semi-arid regions (Dennell 2013). For example, Campbell (1992) demonstrated the Middle Palaeolithic adaptation of humans to arid conditions in Egypt. These findings are in accordance with the results of Walter et al. (2009) who confirmed the presence of humans along the Eritrean coast of the Red Sea during the last interglacial, around 125 ka. The coasts of the Red Sea were a dry area in the Middle Palaeolithic era as it is today (Groucutt and Blinkhorn 2013). This is important because human mi­ grations to Eurasia originated in this area. While one of the most important migration waves of modern humans towards the South Asia and Oceania happened about 60–70 ka along the coastal regions of South Asia (Rito et al., 2019), recent evidence suggest that another significant ‘out of Africa’ dispersal events of early humans rather happened about 130–115 ky years ago (Bae et al., 2017; Armitage et al., 2011; Cruciani et al., 2011; Smith et al., 2007). It is striking that, in contrast to East Africa and Southwest Asia, the Middle Palaeolithic sites of the Indian Peninsula equally can be found apparently in the former dry and semi-arid, subhumid humid and hyperhumid areas (Groucutt et al., 2015B). Based on the above-mentioned observations, the wide-scale, climatederived settlement structure of Middle Palaeolithic humans is a widely accepted idea. The investigation of the impact of past climatic changes on former human populations and historical cultures is a hot potato of archaeology (e.g. Buckley et al., 2010; Haug et al., 2003; Weiss et al., 1997). Based on the so-called ‘Water Optimisation Hypothesis’ Homo occupied intermediate positions in the humidity spectrum, inhabiting the semi-arid to sub-humid rainfall regimes on the Earth (Finlayson et al., 2013). This hypothesis may explain the occurrence of early humans e.g. in Africa during the Middle Palaeolithic era. Several studies found correlations between the distribution of the Middle Palaeolithic 2

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abnormal red blood cells which can be a lifesaver when a child gets infected (Hill et al., 1991). About 80% of sickle-cell disease cases are plausibly occurring in Sub-Saharan Africa where heterozygotes have an adaptive advantage against malaria against those who have normal al­ leles. In the present times, the HbS allele frequency is the highest in Central Africa and the West Sub-Saharan region where there is tropical rainforest or tropical monsoon or tropical savanna climate, the malaria suitability is high and there is diverse malaria mosquito fauna in the region (let’s compare the maps of Peel et al., 2007, Grosse at al. 2011, Kibret et al., 2015 and Sinka et al., 2013). It is interesting that long-term agricultural practices could impact the sickle-cell allele frequency. For example, the cut of clearings in the forests can increase the amount of mosquito breeding habitat standing waters (Laland and O’Brien 2010). Although the malaria risk in wet environments could be high, set­ tlements close to waters have had several advantages (food, water source, natural protection, etc.) for human populations in the past. In­ direct evidence suggests that Homo erectus Dubois, 1893 was able to colonize the islands of Southeast Asia passing through wide sea gaps (Smith, 2001). Based on anatomical features, H. erectus and its relatives could possess more aquatic adaptations than extant humans do (Ver­ haegen 2013). Seafaring started between 110 and 35 kys BP in the Aegean Sea by Homo sapiens neanderthalensis King, 1864 (Ferentinos et al., 2012). The late Pleistocene Klasies River Mouth is one of the most famous examples of coastal archaeological sites related to early modern humans in South Africa (Deacon and Wurz, 2005). In general, the coastlines of the seas could play a very important role in human evo­ lution and dispersal (Erlandson, 2017). Furthermore, it is likely that the proximity of aquatic habitats could play a role in the early development of the genus Homo (Hardy, 1960). Anatomical and physiological traits suggest that human ancestors could live in or close to freshwater, brackish water (estuarine or mangrove environment) and coastline habitats raising the possibility that our ancestors could live a semi­ aquatic lifestyle (e.g. Hardy 1960; Morgan 1990; Verhaegen 2013; Rhys-Evans 2020). However, the malaria risk in the above-mentioned environments can be quite different. For example, Anopheles gambiae Giles, 1902 – an important malaria vector – and several other competent malaria-vector mosquito species only prefer freshwater habitats (Gimonneau et al., 2010). Anopheles subpictus Grassi, 1899 can inhabit the brackish waters of the mangrove swamps and estuarine habitats in India (Sahu, 1998) and Anopheles culicifacies Giles, 1901 can also breed in brackish waters in Sri Lanka. In those arid and semi-arid areas, where human settlements were found to be at the former coastlines, the malaria risk could be low due to the lack of freshwater and brackish water breeding habitats for mosquitoes. Pleistocene human societies of the temperate region such as the H. erectus populations of Bilzingsleben, Germany, could inhabit the coasts of lakes and wetlands (Mania and Mania, 2005) without the notable risk of becoming infected with malaria due to the low annual mean temperature conditions. It should also be noted that the ancestral, early Holocene Nilo-Saharan and Chadic populations preferred the coasts of the lakes of North Africa, inhabiting, for example, the Southern Chad Lake Basin (MacEachern, 2012) where falciparum malaria is prevalent in the present era (WHO malaria, 2017). In their case, the motivation is an easily available source of food, the fish could be. Malaria is prevalent in such tropical and subtropical regions as Af­ rica, Southwest Asia and the Indian Peninsula where the high rainfall results in stagnant waters at least in the wet season. The consistently elevated temperature and humidity support the development of anopheline larvae and the high activity and longer survival of the ima­ goes. Although about 30 Plasmodium species — the causative agent of malaria — can naturally infect non-human primates, the malaria para­ sites of humans are strictly adapted to humans and the vector Anopheles mosquitoes. In the historical and prehistorical times, malaria should strongly impact the ancient and modern populations of Homo sapiens (Sabbatani et al., 2010), but the potential role of the influence of the former falciparum malaria with the climatic factors on Middle

Palaeolithic archaeological site patterns previously was not studied. 2. Aims It was aimed to explain why ancient humans preferred rather the arid and semi-arid environments in the Middle Palaeolithic era than the more humid tropical and subtropical regions where theoretically the inter­ annual food availability and drinking water supply could have been more balanced. To answer this question, it was the purpose of the study to analyse whether the climate played the most important role in the distribution of the Middle Palaeolithic archaeological sites or whether mosquito-borne diseases such as falciparum malaria influenced the se­ lection of settlement locations and the possible migration routes in the Middle Palaeolithic era. 3. Materials and methods 3.1. The workflow of the study To map the connections between the falciparum malarial incidence and the human settlement structure, complex modelling of four main parts was performed (see Fig. 1.). The available climate models, the incidence of malaria and the locations of human settlements were pro­ cessed in a GIS model, which was used to approximate the incidence of malaria using a regression model. The effect on the settlement structure was statistically analyzed, and the sensitivity analysis of the modelling variables was elucidated using a multilayer network model. Fig. 1 shows all the variables used in the analysis and the equations in the same order, that the presentation and explanation in the materials and methods section. As shown in the figure, the analysis begins with the imple­ mentation of a GIS model, which is supplemented by a regression model. Then, it is followed by the analysis of the relationships between the results of the GIS model with statistical analysis and network science tools. The new type of correlation exploratory approach shown in Fig. 1 can be applied to the analysis of other problems as well by changing the variables in the blue cluster. 3.2. The used climate models The downscaled reference and past climate data were retrieved from the WorldClim - Global Climate Data database. It is a set of global climate layers (gridded climate data) with a spatial resolution of about 1 km2. The monthly average minimum temperature (◦ C) and the monthly total precipitation sums (mm) were used. The Last Interglacial Period’s climatic model was gained from the published model of Otto-Bliesner et al. (2009). Naturally, during the studied period, the climate of the Earth continuously changed. Although, the climate of the Last Inter­ glacial Period could be very similar to the present conditions according to the climatic reconstruction of Otto-Bliesner et al. (2006). Comparing the Last Interglacial Period model of Otto-Bliesner et al. (2006) or the Meghalayan interglacial model of Fordham et al. (2017) to other, glacial climate models, it can be stated that the glacial climates could be globally drier (in general) than the interglacial climates. This means that the equatorial belt’s climate in the Late Pleistocene era generally could be somewhat drier than in the used climate model of the present study. 3.3. Middle Palaeolithic sites A total of 449 Middle Palaeolithic sites were involved in the study. 94 of them can be found in the Indian Peninsula, the other 355 in Africa, the Arab Peninsula and Southwest Asia The source of the locality of the archaeologic sites was as follows: Wadley (2015), South Africa; Tryon and Faith (2013), East Africa; Hilbert et al. (2016), Northeast Africa and the Arab Peninsula; Shea (2003), Levant; d’Errico et al. (2009), North Africa; Been et al. (2017), Levant; Groucutt et al. (2015a), South and East Africa; Groucutt et al. (2015b), Northeast Africa, Southwest Asia 3

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Fig. 1. The workflow of the study.

and the Indian Peninsula. The overlying archaeologic sites were corre­ sponded to avoid the production of redundancy and the overlapping site localisations were eliminated before the evaluation of the data. The involved archaeological sites can be used with a critical approach. Comparing the demographic patterns of Africa (Nelson, 2004) with the ¨ppen-Geiger climatic zones of the continent (Peel et al., present Ko 2007), the HbS allele frequency according to the modelled map of Grosse at al. (2011), the distribution of the dominant malaria mosquito assemblages on the bases of the model of Sinka et al. (2013) and the malaria stability according to Kibret et al. (2015), strong coincidences among these factors can be recognized. Because the HbS allele frequency is the long-term genetic consequence of malaria, it can be hypothesized that the coincidence between these values belongs to a common, and long-standing aetiologic complex. The base of the causative chain is the climate which determines – among other factors – the biome of an area and, consequently, the distribution of the malaria mosquito assem­ blages, the length of the malaria transmission season, and, finally, the malaria stability. The long-term malaria stability determines the HbS allele frequency and, due to the high children’s mortality, the human population density patterns. Based on these observations, -accepting the fact that the archaeological dataset is incomplete and somewhat distorted-it can be thought to be representative and can be used for modelling purposes investigating large spatial scales.

malaria is characterized by true or false (0; 1) values according to the Boolean algebra. Using climatic factors, deterministic unit step functions can be written the boundary condition in the following form: { 0 if T ≤ Tlimit.min 1(T) = 1 if Tlimit.min < T where T represents the georeferenced climate model data of the monthly average temperature (◦ C), the Tlimit. min is the lower limitation factor (extremum) of the extrinsic development of P. falciparum parasites in ◦ C. The Tlimit. min of the extrinsic (sporogonic) cycle of P. falciparum parasites is 17 ◦ C, according to Patz et al. (2006). The temperature climate model is available in a monthly resolution. The number of months when the development of the parasite is possible in the mosquito vector can be calculated as follows: 12 ∑

Noex =

1(Tmin )

(1)

i=1

3.4.1. Malaria data and the modelling of malaria incidence patterns Various climatic factors were identified which influence the inci­ dence of malaria including the temperature, topography, relative hu­ midity and precipitation (e.g. Weis et al., 2014; Li et al., 2013; Stresman 2010). We selected the following primary and derived environment factors: 1) mean ambient temperature (◦ C), precipitation sum (mm), the number of months when the development of the parasite is possible in the mosquito vector and the Thornthwaite Agrometeorological Index, which is a simple and robust aridity index (Thornthwaite 1948; Kemp 1990). For modelling purposes and for the determination of the corre­ lation between the abiotic and biotic factors and the falciparum malaria incidence values, the climatic values of the period 1970–2000 was applied. The 2000–2017 countrywide malaria incidence (per 1000 in­ habitants) data was gained from the database of the Institute for Health Metrics and Evaluation, University of Washington (IHME 2017). The original malaria incidence data were normalized by converting the

3.4. Model identification The visualization of the malaria prevalence patterns in 2017 in Africa was based on the Data Visualization tool of the Institute for Health Metrics and Evaluation, University of Washington (IHME, 2017a,b). Modelling results were displayed using GIS (Quantum GIS software). The Lambert Azimuthal Equal Area (EPSG:3035) was used as a projec­ tion system. There is a distribution function within the extrema which shows the distribution maximum for the given factor, but in the proposed approach the distribution of the internal interval is neglected, the appearance of 4

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highest incidence value to 1, producing relative malaria incidence values as follows: iMalrel =

iMal iMalmax

Table 1 Correlation matrix of the multiple regression analyses (NoEx: number of months when the development of the parasite is possible in the mosquito vector, aTAI: the annual Thornthwaite Agrometeorological Index, aPSum: the annual sum of precipitation, aTmean: the annual mean temperature, iMalrel: falciparum malaria incidence per 1000 inhabitants).

(2)

where the iMalrel is the relative falciparum malaria incidence (between 0 and 1), the iMal is the malaria incidence per 1000 inhabitants, iMalmax is the maximum malaria incidence in Africa. The steps of modelling were as follows:

NoEx aTAI PSum aTmean iMalrel

1) based on the multiple regression results, the basic correlations be­ tween the number of months when the development of the parasite is possible in the mosquito vector, the Thornthwaite Agro­ meteorological Index, the annual sum of precipitation, the annual mean temperature and the mean malaria incidence in 2000–2017 per 1000 inhabitants and then 2) based on the seasonal and annual climate data of WordClim climate database (WorldClim - Global Climate Data) for 1970–2000 and the Last Interglacial period, the malaria incidence values were calculated according to the reference and the past period.

10

9 aPsum aTmean + 12.2

Psum

aTmean

iMalrel

0.068 1 0.973 − 0.029 0.109

0.29 0.973 1 0.196 0.228

0.95 − 0.029 0.196 1 0.493

0.553 0.109 0.228 0.493 1

Source

SS

Df

MS

F

P

Regression Residual Total

40.0538 84.0836 124.1374

4 84 88

10.0135 1.001

10

<0.0001

lgiMalrel = − 7.7975 + − 4.4555lgNoex + 5.1433lgTAI + − 2.7729lgaPsum

(3)

+ − 2.7729lgaTmean (5)

where the iMalrel is the relative falciparum malaria incidence (between 0 and 1), NoEx is the number of months when the development of the parasite is possible in the mosquito vector, aTAI is the annual Thornthwaite Agrometeorological Index in mm◦ C− 1, aPsum: the annual sum of precipitation, aTmean: the annual mean temperature. A multiple linear regression model was performed to explore the interconnectedness of malaria incidence and bioclimatic factors. The overall significance of the regression models was tested by F value and Prob(F) statistics test. Although the Thornthwaite Agrometeorological Index values are used to classify the aridity level of an area in monthly resolution in most of the cases, its extension to an annual resolution does not constitute any theoretical barrier. The annual mean of the Thornthwaite Agro­ meteorological Index can be calculated according to the following formula: aTAI =

aTAI

1 0.068 0.29 0.95 0.553

Table 2 ANOVA table of the multiple regression analyses.

The used multiple regression equation is the following: iMalrel = a + b1 NoEx + b2 aTAI + b3 aPsum + b4 aTmean

NoEx

where the lgiMalrel is the logarithmic falciparum malaria incidence values per 1000 inhabitants, lgNoEx is the logarithmic number of months when the development of the parasite is possible in the mosquito vector, lgaTAI is the logarithmic annual Thornthwaite Agrometeorological Index in mm◦ C− 1, lgaPsum: the logarithmic annual sum of precipitation, lgaTmean is the logarithmic annual mean temperature. After obtaining the logarithmic values, increasing the values to ten, we get the relative incidence of falciparum malaria. 3.5. The verification of the malaria model The most notable differences in the modelled falciparum malaria prevalence values of the Last Interglacial and the reference period can be seen in around the mountainous areas of the Rift Valley and in South Africa. While the run of the south border of the high incidence falcipa­ rum malaria zone was very similar to the present situation in the Last Interglacial Period in South Africa, the extent areas could be free of malaria. Although the incidence patterns in the Rift Valley area showed a heterogenous picture like today, the extension of the malaria-free and low-malaria incidence areas could be more notable in the Last Inter­ glacial Period compared to the present situation (Fig. 2A–B). Comparing the modelled and observed falciparum malaria patterns, it can be stated that the model gave back in enough accuracy the present spatial malaria prevalence trends in Africa (Fig. 2B–C).

(4)

where the aTAI is the annual Thornthwaite Agrometeorological Index in mm◦ C− 1, aPsum is the annual sum of precipitation in mm and aTmean is the annual mean temperature in ◦ C. 3.4.2. Correlation between present-day malaria and environmental factors For the multiple correlation analyses, each of the variables was logarithmized. The results of multiple correlations also provided the starting-point constants (a) and the standardized regression weights (B) which were used in the modelling of falciparum malaria prevalence values for the reference and the past period. The highest regression weights are related to the ambient temperature and the number of months when the development of the parasite is possible in the mosquito vector. The multiple R2 is 0.3227, the standard error of multiple esti­ mates is 0.9775. According to the regression result, p < 0.0001, which means that the result is very significant. Table 1 Show the correlation matrix, Table 2 the ANOVA statistical table of the multiple regressions. Based on the gained constant and the regression weights, the annual falciparum malaria incidence value at a given geographical locality can be calculated according to the following formula:

3.6. Random points and grid selection To test whether the African and Southwest Asia sites differ from the South Asian ones in the aspect of the former climatic and other factors on the distribution of human settlement patterns, two grids were selected. The so-called ‘Africa and Southwest Asia’ grid (or grid A) not only covers the entire Africa continent but also covers the Arab Peninsula and Southwest Asia. The ‘Indian Peninsula’ grid (or grid B) covers most of the territory of the Indian Peninsula. The delimiting coordinates of the ‘Africa’ and ‘Indian Peninsula’ grids were as follows: − 34.91 to 38.00 N, − 17.75 to 63.00 E, and 8.00–36.00 N, 66.50 to 89.00 E. For the statis­ tical analyses, 250 and 197 random points were selected in the ‘Africa and Southwest Asia’ and the ‘Indian Peninsula’ grids. Fig. 3 shows the used grids, the involved Middle Palaeolithic archaeological sites and the 5

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Fig. 2. The modelled incidence patterns of malaria in the Last Interglacial period (A) and in 1970–2000 (B) and the countrywide prevalence of falciparum malaria in Africa in 2017 (C; https://vizhub.healthdata.org/lbd/malariaIHME, 2019). The grey fields show the areas where the annual precipitation is less than 80 mm because the annual minimum rainfall required to support mosquito breeding is more than this value according to Blanford et al. (2003).

Fig. 3. The used grids, the involved Middle Palaeolithic archaeological sites and the position of the random sampling points within the grids. The ‘grid A’ means the common grid of the archaeological sites in Africa and Southwest Asia, and ‘grid B’ is the grid of the Middle Palaeolithic sites in the Indian Peninsula.

position of the random sampling points within the grids (blind pattern). The number of archaeological sites was 355 (‘Africa and Southwest Asia’ grid) and 94 (‘Indian Peninsula’ grid). The number of the random points was 250 (‘Africa and Southwest Asia’ grid) and 126 (‘Indian Peninsula’ grid).

random points of the two-sided Mann–Whitney U test was utilized. To compare the distribution of the two samples, the Kolmogorov-Smirnov test (abbreviated as KS Statistic (Marsaglia 2003; Miller 1956; Massey 1951); analyses of the two samples were also performed using Kolmogorov-Smirnov test to confirm the result of the Mann-Whitney U test. The KS statistic is a non-parametric test which is very robust. Originally it was designed for continuous distributions, but it is also suitable for discrete or scaled values. In this case, however, it is less likely to reject the null hypothesis than in a continuous case. Its great advantage is that it is non-distributional and not only suitable for studying statistics from a normal distribution. Trial statistics follow the same distribution for each continuous distribution and are therefore widely used. In order to determine differences between sites where human set­ tlements were present and absent (blind pattern) in the Middle

3.7. Statistical analyses Because some of the data is not normally distributed or the variances of the two samples may be significantly different, the t-test cannot be used. In this case, the robust Mann–Whitney U test (abbreviated as W statistic; Mann and Whitney, 1947) can be used if all samples are in­ dependent of each other and the data are at least ordinal. Null hypoth­ esis: the two samples are from the same distribution. The analyses of the variance of the relative malaria values of the archaeologic sites and the 6

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Palaeolithic era, two-sided one-sample Kolmogorov-Smirnov tests were performed. The Kolmogorov-Smirnov test is a non-parametric test that can verify whether the cumulative distribution function of the selected data set is the same or not as the hypothesized cumulative distribution function. To analyse the completed malaria incidence model, the test can be written as follows: ⃒) (⃒ ⃒̂ ⃒ D* = max ⃒ F(x (6) i ) − G(yi )⃒

which can be stretched with 62,657 edges. The Indian Peninsula area has 94 nodes with 4324 edges per layer. exi1,2 =

(8)

where the ex1,2 is the weight of the edge between the given two nodes, xi is the value of the variable at node x1, x2 is the value of the variable at node x2, dx1,2 is the distance between the nodes xi1 and xi2. The networks were identified for Africa and Indian Peninsula area as well, including the variables of precipitation (PRC), temperature (TMP), extrinsic (EXT), Thornthwaite Agrometeorological Index (TAI) and modelled malaria incidence (INC), it follows that the networks have five different layers. Thus, this formulation shows the changes in the modelling variables over real spatial distance, which is the basis of the sensitivity analysis in order to identify the key variables of the presented novel modelling approach. The multilayer network variables are compared based on the Pairwise Quantum Jensen-Shannon Distance (De Domenico et al., 2014):

xi

where ̂ F(xi ) is the empirical cumulative distribution function of the human settlements in the sampled points of the modelling variables (xi) and G (yi) is the cumulative distribution function of the blind pattern in the sampled points of the modelling variables (yi). The Mann-Whitney U test statistic was used to test the null hypoth­ esis that blind and settlement point sets originate from the same popu­ lation. to test the null hypothesis that blind and settlement point sets originate from the same population. The test is related to the Wilcoxon rank-sum statistic in the following way according to the paradigms of Gibbons and Chakraborti (2011) and Hollander and Wolfe (1999): nxi (nx i + 1) U=W − 2

|xi1 − xi2 | dx1,2

( ) 1 ) 1 ( JSD xi xj = D(xi M) + D xj M 2 2

(7)

(9)

where M = ½ (xi +xj ) is the mixture distribution, and D is a symmetrized and smoothed version of the Kullback–Leibler divergence.

where U, is the number of times a x precedes an y in an ordered arrangement of the elements in the two independent samples xi and yi. W is the sum of the ranks in sample 1.

4. Results 4.1. Comparison of the modelled malaria and the environmental factors

3.8. Network analysis

Projecting the Middle Palaeolithic archaeological sites to the involved factors, coincidences were observed in the spatial patterns. In the southern Sahara region, West, Central, and East Africa, the extrinsic development of P. falciparum parasites was possible during the entire year. The number of months when malaria parasite development could occur inside the mosquito could be the highest in the northernmost coasts of Africa and south of the Capricorn. In the highest elevations of equatorial Africa and in the Drakensberg Mountains, the annual number of the months when the extrinsic development of P. falciparum parasites was possible was zero in the Last Interglacial Period. From the Bie and Katanga Plateaus to the Cape of Good Hope, this value showed a rapidly decreasing trend. It can also be seen that most of the human settlements in Africa occurred in those areas where the annual precipitation and annual TAI values were fallen in the low or moderate interval, and the annual mean temperature was moderate. In the Indian Peninsula, the above-described coincidences are less visible in the modelled maps. In general, it seemed that the shorter annual extrinsic development period of P. falciparum parasites, the low or medium-low precipitation and annual TAI values and the higher temperature values coincide with the former Middle Palaeolithic site distribution patterns (Fig. 5). The northern border of falciparum malaria in South Africa could be found closer to the equator. The lower elevations of the mountains and highlands could be free of malaria. In Africa and Southwest Asia, the Middle Palaeolithic archaeological sites occurred in those regions where the modelled potential relative falciparum malaria prevalence has been low or medium-level values. In contrast, in the Indian Peninsula, this coincidence cannot be seen (Fig. 6).

A multilayer network has been defined to examine the relationship between malaria incidence and Middle Palaeolithic site locations. The nodes of the multilayer network represent the location of the sites, while the edges show the changes in the values of the different variables as follows. Fig. 4 shows the logic of the patterning of the analyzed vari­ ables, where each layer represents the spatial pattern of a variable in the GIS model. By sampling the different variables at the same points, the spatial pattern of the variables becomes comparable. Thus, it is possible to explore the relationships between temperature, malaria, precipita­ tion, and other variables not only in a point-by-point but also in a spatial pattern, which is one of the main novelties of the proposed methodology. The undirected networks are defined for Africa and the Indian Peninsula. The five layers of the African network contains 355 nodes,

4.2. Statistical evaluation of the variables Significant differences were found between the variances of the five variables at random points and the Middle Palaeolithic sites in the case of the ‘Africa and Southwest Asia’ grid. Excluding the precipitation, also significant differences were found between the variances of the variables at random points and the Middle Palaeolithic sites in the case of the ‘Indian Peninsula’ grid. The comparison of the variances of the five variables between the archaeological sites of both grids consistently

Fig. 4. The schematic illustration of the multilayer network approach (adopted from Boccaletti et al., 2014). 7

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Fig. 5. The Middle Palaeolithic sites projected to the number of the months, when the extrinsic development of P. falciparum parasites was possible (A), the annual Thornthwaite Agrometeorological Index (B), the annual sum of precipitation (C) and the annual mean temperature (D) in the Last Interglacial Period.

Fig. 6. The Middle Palaeolithic sites projected to the relative malaria incidence in the Last Interglacial Period without the depiction of the 80 mm annual pre­ cipitation limit of Anopheles distribution according to Blanford et al. (2003). 8

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showed significant differences according to the Mann-Whitney U and Kolmogorov-Smirnov tests. The dryer, warmer, more arid areas with lower potential malaria suitability conditions were preferred by the Middle Palaeolithic human populations. Those regions which were a particularly suitable area for malaria mosquitoes and falciparum ma­ laria, the former holoendemic, hyperendemic and partly the mesoen­ demic areas, were avoided by the ancient human populations in Africa, Southwest Asia, and the Indian Peninsula. In the Indian Peninsula, human settlements existed under significantly wetter and warmer climate conditions and in potentially more malaria-endemic regions than in Africa and Southwest Asia (Fig. 7 and Table 3). Fig. 7 summa­ rizes the differences in the value set of variables in archaeologic areas and in the value set of other areas. The box plot on the left shows the absent (non-archaeological site) interval of the given variable, while the box plot on the right shows the intervals for the archaeological sites. Differences in the value set of the variables explain the location of the

sites. 4.3. The results of the network analysis The number of the months, when the extrinsic development of P. falciparum parasites was possible, showed the strongest spatial structural overlap (the lowest structural reducibility value) with the modelled malaria incidences according to the occurrence patterns of the Middle Palaeolithic archaeological sites and the weakest spatial structural overlap (the lowest structural reducibility value) with the annual Thornthwaite Agrometeorological Index in the ‘Africa and Southwest Asia’ grid. In the ‘Indian Peninsula’ grid, primarily the annual mean temperature and the annual precipitation sum showed the strongest spatial structural overlap and the number of months when the extrinsic development of P. falciparum parasites was possible the weakest struc­ tural overlap with the modelled malaria incidences according to the

Fig. 7. The box-plot charts of the variances of the number of the months when the extrinsic development of P. falciparum parasites was possible (A), the annual Thornthwaite Agrometeorological Index (B), the annual sum of precipitation (C) and the annual mean temperature (D) and the modelled potential relative malaria prevalence values of the archaeologic sites and the random points (A: ‘Africa and Southwest Asia’ grid, B: ‘Indian Peninsula’ grid, C: the comparison of the ‘Africa and Southwest Asia’ and ‘Indian Peninsula’ grids. Variable 1: random points. variable 2: archaeologic sites). 9

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Table 3 The statistical results of the Mann-Whitney U test and the Kolmogorov-Smirnov test (Var: variable, ‘R–S’: the comparison of the variances of the random and archaeological site points, ‘Africa’- ‘Indian Peninsula’: the comparison of the variances of the archaeological site point values of the ‘Africa and Southwest Asia’ (abbreviated as ‘Africa’) and ‘Indian Peninsula’ grids; iMal is the modelled malaria incidence per 1000 inhabitants, NoEx is the number of months when the devel­ opment of the parasite is possible in the mosquito vector, aTAI is the annual Thornthwaite Agrometeorological Index in mm◦ C− 1, aPsum: the annual sum of precipi­ tation, aTmean: the annual mean temperature). Var Noex aTAI Tmean Psum iMal

mean values 1

2

‘Africa’ R -S ‘Indian Peninsula’ R1-S2 ‘Africa’1- ‘Indian Peninsula Africa’ R1-S2 ‘Indian Peninsula’ R1-S2 ‘Africa’1- ‘Indian Peninsula Africa’ R1-S2 ‘Indian Peninsula’ R1-S2 ‘Africa’1- ‘Indian Peninsula Africa’ R1-S2 ‘Indian Peninsula’ R1-S2 ‘Africa’1- ‘Indian Peninsula Africa’ R1-S2 ‘Indian Peninsula’ R1-S2 ‘Africa’1- ‘Indian Peninsula

‘

2

‘2 ‘2 ‘2 ‘2

Mann-Whitney U test

Kolmogorov-Smirnov test

mean1

mean2

W statistic

p

KS statistic

p

10.27 10.28 5.97 52.05 125.71 36.83 20.42 21.73 18.30 706.53 802.10 462.20 0.3988 0.5140 0.1607

5.97 8.83 8.83 36.83 48.50 48.50 18.30 24.16 24.16 462.20 745.68 745.68 0.1607 0.3747 0.3747

64513.5 3362.5 4683.5 52405.5

< 0.0001 0.0001 < 0.0001 0.0021

7252 58884.5 3211.5 3801 55,423 6117.5 9025 65,063 6175 7979

0.0013 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.8309 < 0.0001 < 0.0001 0.0008 < 0.0001

0.3322 0.2503 0.6385 0.1999 0.3127 0.1119 0.2579 0.2778 0.3060 0.2486 0.1371 0.2031 0.3490 0.2550 0.5392

< 0.0001 0.0042 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0010 < 0.0001 < 0.0001 0.2599 0.0044 < 0.0001 0.0026 < 0.0001

occurrence patterns of the Middle Palaeolithic archaeological sites (Fig. 8). Fig. 8 shows the dissimilarities between the spatial patterns of the variables. Based on Fig. 8, it can be concluded that the incidence of malaria in Africa is mostly related to the number of months, the pattern is least explained by the Thornthwaite agrometeorological index. In the case of the Indian Peninsula, temperature is the dominant explanatory variable of the malaria incidence, followed by the number of months and the least affected by the Thornthwaite agrometeorological index.

5. Discussion The presented findings confirm that humans avoided humid, warm equatorial and tropical climate regions in the Middle Palaeolithic era and preferred the arid and semi-arid and even the hyperarid areas. Primarily those areas were avoided, where the extrinsic development of falciparum parasites could persist for the entire year. This is a very important observation because it’s a long-known fact that the lifespan of the malaria sporozoites under the tropical and subtropical thermal

Fig. 8. The structural networks (A1: ‘Africa’, A2: the ‘Indian Peninsula’) and the structural reducibility matrices of the involved values (B1: ‘Africa’, B2: ‘Indian Peninsula’; TMP: temperature, INC: modelled malaria incidence, EXT: the numbers of months when the extrinsic development of P. falciparum promastigotes in the mosquito vector is possible, TAI: the annual Thornthwaite agrometeorological index, PRC: the annual precipitation sum). 10

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conditions does not exceed the one, one and half months (King, 1917). It was shown that in Africa and Southwest Asia, the combination of those factors is detrimental to mosquito reproduction like the hyperthermal conditions with low precipitation sums and the consequent high aridity indices which showed a coincidence with the high number of Middle Palaeolithic archaeological sites. A difference from this picture is that in the Indian Peninsula, the precipitation did not differ in the archaeological sites and random points. In addition, in the case of the Indian Peninsula, the power (W statistic value) of the Manny-Whitney U test is generally weaker in the comparison of the random and site points than in the case of Africa and Southwest Asia. It was also shown that in Africa and Southwest Asia, the number of months when the extrinsic development of Plasmodium fal­ ciparum parasites was possible showed the strongest structural overlap with the modelled malaria incidences according to the spatial occur­ rence patterns of the Middle Paleolithic archaeological sites. In contrast, in the Indian Peninsula, climatic factors showed the strongest and the number of months when the extrinsic development was possible the weakest structural overlap with the modelled malaria incidences. In other words, in Africa and Southwest Asia, a directly malaria-related factor showed the most coherent structural pattern with the modelled malaria incidences. In the Indian Peninsula, climatic factors played the same role. A possible explanation of this phenomenon is that malaria was not present in the Peninsula at that time. It is possible because different malaria parasites plausibly originated in Africa (Krief et al., 2010). It is plausible that malaria could be a less important health concern for the ancient South Asian populations, with the addition that P. vivax caused malaria may be present in South Asia in the Middle Palaeolithic period. Theory suggests that P. vivax is derived from a Plasmodium species that inhabited macaques in Southeast Asia in the late Pliocenemiddle Pleistocene era (Escalante et al., 2004). However, there are several contra-hypotheses which suggest the African origin of the pro­ tozoa (e.g. Culleton and Carter, 2012; Liu et al., 2014; Loy et al., 2017). If vivax malaria existed in the Middle Palaeolithic era in South Asia, due to the lower mortality rate of this malaria form – compared to the case of P. falciparum malaria – it had not as detrimental impact on the South Asian human populations as P. falciparum malaria had and has even until now on the African societies. It is important to emphasize that falcipa­ rum malaria is not the only important vector-borne disease in tropical and subtropical regions. For example, at least 36 mosquito-borne ar­ boviruses indigenous to Africa that can cause serious infections in humans (Braack et al., 2018). For example, the areas with the risk of the mosquito-mediated Yellow Fever virus transmission in Africa are almost the same when falciparum malaria occurs (compare the maps of CDC 2019 and IHME 2019). However, it is indisputable that Plasmodium falciparum caused malaria, which is the deadliest mosquito-borne dis­ ease in present-day Africa (WHO 2014). In the present times, the African animal trypanosomiasis strongly limits the agricultural production in large areas of the continent decreasing the environmental carrying ca­ pacity of the affected regions (Swallow, 2000). The following can be stated: falciparum malaria itself plays a significant role as the most important causative agent of infectious deaths, and falciparum malaria is the most important malaria form in Africa. Last, but not least, because the environmental preference of the different mosquito vectors seems to be similar due to the common development requirements (e.g. open water for breeding, high air humidity and temperature for the ontogeny and reproduction) and physiology (e.g. poikilothermy, large body surface-body volume ratio) of these arthropods (Silver 2007; Briegel 2003), falciparum malaria can be held as the indicator of equatorial-tropical-subtropical mosquito-borne diseases. Based on the above-mentioned facts, the prevalence of mosquito-borne diseases is generally low in the arid regions of the Earth. It is a crucial question related to the topic of the study when the malaria parasites evolved, but the hypotheses of the literature should be accepted with some caution. As it was mentioned, Escalante et al. (2004)

hypothesized that P. vivax malaria has a South Asian origin and the original hosts could be Macaca species. In contrast, Liu et al. (2014) concluded that this malaria parasite could have an African origin. Loy et al. (2017) also found that Plasmodium vivax originated in African apes and the human P. falciparum lineages originated from gorillas. It is very likely that the malaria parasites coevolved with humans and apes in Central Africa and dispersed later to other continents (Culleton and Carter, 2012). Unfortunately, looking back to the present, it is difficult to uncover the past host transfers of P. falciparum and the time of the transport of the parasite to South Asia. Ayala et al. (1999) found that the closest relative of P. falciparum is Plasmodium reichenowi Sluiter, Swel­ lengrebel & Ihle, 1922, a parasite of chimpanzees. Molecular clock an­ alyses support the hypothesis that P. falciparum appeared at the same time as humans and chimpanzees diverged from each other. Alterna­ tively, Liu et al. (2010) argued that P. falciparum was rather a gorilla and not of a chimpanzee, bonobo or ancient human origin. Phylogenetic analysis indicates that the extant P. falciparum lineages evolved from P. reichenowi, likely by a single host transfer, which may have occurred as early as 2–3 million and/or 10,000 years ago (Rich et al., 2009). Joy et al. (2003) provided genetic evidence for a sudden increase in the African human Plasmodium parasite population about 10,000 years ago. They hypothesize that the emergence of virulent P. falciparum occurred in Africa only within the past 6000 years. Although Joy et al. (2003) found that the presently existing form of P. falciparum could develop in the Mid-Holocene era, the authors also argued that both the world and some regional populations of the falciparum malaria parasites appear to be older (about 50–100 kys old). The authors linked this finding to the early migration waves of humans out of Africa. On the other hand, Hughes and Vierra (2001) found that P. falciparum should exist in the last 300–400 kys and the effective population size of this parasite species has been of the order of at least 105 at this time. The apparent contra­ diction can be resolved by the fact that according to malaria’s Eve hy­ pothesis Rich et al., 1998, P. falciparum underwent a severe population bottleneck about 3–5 (or 6) kys ago, while the most recent common ancestor of the presently existing P. falciparum lineages could have lived about 100–180 kys ago Mu et al., 2002a,b). This time is significantly older than the time of the hypothesized bottleneck. It shows that the evolutionary history of the most abundant and lethal malaria parasite is not known sufficiently, and, in fact, the controversial theories and evi­ dence do not exclude each other. Concerning these facts, it can be concluded that P. falciparum malaria should develop somewhen in the mid-Pleistocene era, and there is no reason to concern this parasite with a serious health risk at least for the African human populations in the Middle Palaeolithic era. It is plausible that early humans suffered from malaria when their adaptation to the open savannah conditions started. P. falciparum orig­ inated in Africa and colonized Southeast Asia and South America separately much later (Conway et al., 2000). Although modern humans later conquered the dense rainforests of Central Africa, the human populations remained higher in the historical times in the drier zones. Malaria must cause strong adaptation stress to human populations in the past. It should not be forgotten that P. falciparum is only one among Plasmodium parasites. Plasmodium malariae Feletti & Grassi, 1889 and Plasmodium ovale Stephens, 1922 are also common causative agents of malaria in Sub-Saharan Africa. Based on these facts, it is plausible that malaria was common diseases among our African ancestors, and there is no reason why their ecological needs would have been different. Regarding India and the Straits of Hormuz-India corridor, malaria may not have been a serious health concern until the Late Holocene era. In such arid and hyperarid regions as the Sahara or a major part of the Arabian Peninsula, precipitation patterns played a very important role in the stability of human life and a longer dry period could struggle vast human migrations. Despite this fact, the importance of climatic, mostly precipitation conditions on the Middle Palaeolithic archaeological site patterns should not be overemphasized and generalized. In other words, not only the climate and the climate-influenced livelihood opportunities 11

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are the sole factor in the archaeological site structure of human pop­ ulations. Modern humans have an African origin (Henn et al. 2011, 2016bib_Henn_et_al_2011bib_Henn_et_al_2016). The well-known ge­ netic evidence for this hypothesis is that the native African populations harbour the greatest human genetic diversity in the world, with special regard to the populations of central and southern Africa (Marks et al., 2014; Lachance et al., 2012; Tishkoff et al., 2009). What is a generally neglected factor in the examination of the former distribution of archaeological sites is that mosquito harm is far from being harmless in Africa. It is striking that 90 percent of malaria cases occur in the present days of this continent (WHO 1999). On the global level, malaria is the fourth leading cause of death of children under the age of five years and pregnant women in the developing countries of Africa, South America and South Asia (Rowe et al., 2006; Martens and Hall 2000). The present relation between severe under-five age malaria morbidity in children and the level of Plasmodium falciparum transmission in Africa is known (Snow et al., 1997). The risk of a child dying before completing five years of age is still highest in Africa (Cowman et al., 2016). For example, in 2017, it was eight times higher than that in Europe, largely due to the infant and childhood malaria. Malaria causes about 200,000 estimated infant deaths in Sub-Saharan Africa per year. 65% of malaria affects children under 15 years old. The highest death rates, between 85 and 51 per 100, 000 inhabitants, were observed in Ivory Coast, Angola, and Burkina Faso, but Mozambique and Mali were also severely affected in the last decade. Based on the above-mentioned facts, it can be hypoth­ esized that in the Middle Palaeolithic era, falciparum malaria was maybe not present in the Indian Peninsula. It is striking that while falciparum malaria could be theoretically mesoendemic and in the Indian Ocean coasts of present-day Iran and Pakistan, the HbS allele frequency in the present day-populations East of the Strait of Hormuz to the Kathiawar Peninsula, West India is zero (Piel et al., 2010). Because the HbS allele frequency is a fingerprint of a long-term malaria impact on human populations, it means that in the last thousand years, malaria was not endemic in his area, which is the most important natural strait between Southwest and Southeast Asia. Concerning the barrier role of the present-day South Asian arid and semi-arid environments, it should be noted that both vivax and falcip­ arum malaria reached Papua New Guinea in the pre-colonization times (Genton et al., 2008). The geography of South and Southeast Asia did not change significantly from the late Miocene era (Selley et al., 2004). It implies that malaria could travel across the coastal areas of South Asia, even the climate of certain regions did not support the presence of autochthonous malaria transmission. It should be noted that during the glacial-interglacial cycles, the precipitation conditions of South Asia could differ from the annual precipitation sum patterns of the present time of the Last Interglacial Period’s climate model suggested values. A similar occurred after the end of the last glacial period in the North­ grippian, when the climate of Africa became significantly more humid than now or during the Last Glacial era (Wang et al., 2019). It was a relatively short transitional period which plausibly also affected posi­ tively the humidity conditions of South Asia. Similar wetter, “green Sahara periods” could also occur in earlier times during the ˜ a et al., Plio-Pleistocene era, influencing the human evolution (Larrasoan 2003). The generalization of the Last Interglacial period for the whole Middle Palaeolithic site analysis should be discussed. In the Middle Palaeolithic era, the climate was not stabile. The temperature and pre­ cipitation conditions showed a notable fluctuation. In an ideal case, it would be the correct method to make a correlation between the climatic variables of the settlement and random points according to the real former climatic data, but it is impossible due to several purposes. At first, although some climate models exist for the last 300,000 years, each site would require its own climate model because the exact age of the set­ tlements is different. On the other hand, the ancient human settlements were inhabited for longer times during the Middle Palaeolithic era. That means that even in the case of one archaeological site, the selection of

the climate model would be difficult working with narrow temporal categories. Furthermore, the age determination often results in a large temporal variance, for example, in the case of the temporal dispersion of the determined age of Jebel Irhoud 1–5 specimens is ±32 kys (Richter et al., 2017). It would be another problem that selecting short periods of time, due to the low number of the included sites, the statistical power would be low. Thanks to these facts, the evaluation of the environmental factors on Middle Palaeolithic settlements is commonly based on longer periods (e.g. the climate model of the Last Interglacial Climate Model; see e.g. Groucutt et al., 2015a, 2015b). As it was discussed above, both vivax and falciparum malaria could have an earlier origin than the Middle Palaeolithic era. It indicates that only the presently existing, most frequent form of falciparum malaria developed in the Holocene. The absence of dense human populations in malaria-affected areas and the presence in the less-affected “islands” is not a modern phenomenon. Because the milder climate of the tropical highlands can stop the extrinsic development of Plasmodium parasites in their mosquito vectors, it causes very low malaria risks in the equatorial mountainous regions. The extrinsic cycle of P. vivax and P. falciparum parasites starts when the ambient mean temperature reaches the 15 and 18 ◦ C values which are the thermal limits which can be handled as the lower threshold of the transmission of the parasites (Patz et al., 2006). Due to these facts, dense human populations in New Guinea have traditionally only lived in the highlands of the island where the annual mean temperatures are lower than on the lowlands. Now, malaria is holoendemic in lowland coastal areas of Papua New Guinea, but almost or completely absent above 1800–2000 m in the highlands (Radford et al., 1975). In the mountainous areas of Colombia or Ethiopia, a similar altitudinal adaptation of the resident human was observed (Siraj et al., 2014). Each of the factors of falciparum malaria transmission and the po­ tential incidence of the mosquito-borne disease was low in South Africa and in the higher mountainous areas of East Africa in the Last Inter­ glacial Period. In East Africa, the patterns showed a heterogenous pic­ ture and, in general, in the higher elevations the development of the parasites was lower, the extrinsic incubation season was shorter and the potential malaria incidence values were lower than in the present times. In contrast, in West and Central Africa, where malaria is a deadly disease nowadays, these values could be like the present situation. It is a very important result because malaria is a very strong population stressor in several countries of Africa (Sachs and Malaney 2002), which could even impact the frequency of genetic polymorphism and genetic diseases humans (Denic et al., 2008; Ohashi et al., 2004; Rihet et al., 2004; Miller 1994). It can also play the role of the strongest negative Malthusian demographic factor. For example, in 2010, in 19 countries of Africa, the cumulative probability of malaria death was more than 1000 per 1000 inhabitants. Around 61% of the deaths were related to children under 15 years (WHO 2019, 2017). The mean cumulative mortality probability value of Benin, Burkina Faso, Burundi, the Central African Republic, Congo, the Democratic Republic of Congo, Gabon, Guinea, Guinea-Bissau, the Ivory Coasts, Liberia, Mozambique, Niger, Nigeria, Rwanda, Sierra Leone, Togo, Uganda, and Zambia was 138 ± 23.4. The proportional mortality rate of children under 15 years was 90 ± 15.2 per 100,000 (Guardian 2012). This means that a significant proportion of new borns are not worth a fertile age. If the mortality of the ancient falciparum malaria were also high, it could decrease the population growth of the ancient Central and West African populations. The cost of the hereditary protection against falciparum malaria is also significant at the population-level: the sickle haemoglobin disease significantly decreases the life expectancy of the sub-Saharan populations. Fifty percent of patients with sickle cell anaemia survive beyond the fifth decade, and in the case of people who are homozygous for sickle hae­ moglobin, the median age at death is between their 40 and 50 years (Platt et al., 1994). The above-mentioned facts raise the possibility of malaria and other mosquito-borne tropical diseases. These were the important elements of natural selection among early humans. It is also 12

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notable that children and pregnant women, who form the most vulnerable population in the case of malaria, formed a major part of human populations, which means that human populations could be more vulnerable to falciparum malaria in the Middle Palaeolithic era than now.

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