Department of Philosophy, History, Culture and Art Studies
University of Helsinki
Finland
HUMANS UNDER CLIMATE FORCING:
HOW CLIMATE CHANGE SHAPED HUNTERGATHERER POPULATION DYNAMICS IN EUROPE
30,000–4000 YEARS AGO
Miikka Tallavaara
ACADEMIC DISSERTATION
To be presented, with the permission of the Faculty of Arts of
the University of Helsinki, for public examination in auditorium XII,
University main building, on 19th December 2015, at 12 noon.
Helsinki 2015
Supervisors
Doc. Tuija Rankama
Department of Philosophy, History, Culture and Art Studies,
University of Helsinki, Finland
Prof. Mika Lavento
Department of Philosophy, History, Culture and Art Studies,
University of Helsinki, Finland
Reviewers
Prof. Robert L. Kelly
Department of Anthropology, University of Wyoming, USA
Prof. Stephen Shennan
UCL Institute of Archaeology, University College London, United Kindom
Opponent
Prof. Robert L. Kelly
Department of Anthropology, University of Wyoming, USA
© Miikka Tallavaara (Summary paper)
© Elsevier Ltd (Paper I)
© Authors (Paper II)
© SAGE Publications (Paper III)
© Archaeopress (Paper IV)
© National Academy of Sciences of the United States of America (Paper V)
ISBN 978-951-51-1766-3 (pbk.)
ISBN 978-951-51-1767-0 (PDF)
http://ethesis.helsinki.fi
Unigrafia
Helsinki 2015
ABSTRACT
Knowledge of prehistoric human population dynamics and its drivers is
important for the understanding of cultural and social evolution. Working
within the human ecological framework, this study aims to contribute to that
knowledge, by reconstructing hunter-gatherer population dynamics in
Europe 30,000–4000 years ago and by exploring the role of climate change
in population size fluctuations. Archaeological reconstructions of population
dynamics in Pleistocene Europe and Holocene Finland are based on spatiotemporal distributions of archaeological radiocarbon dates, which are taken
as a proxy of human activity in time and place. The reliability and validity of
the population history reconstructions are evaluated by studying potentially
biasing effects of research history, taphonomic loss of archaeological
material, and radiocarbon calibration.
In addition to making use of these archaeological methods, this study
aims to develop and evaluate systematic means to use ethnographic data and
palaeoclimate model simulations to reconstruct prehistoric hunter-gatherer
population dynamics. This climate envelope modelling approach is used to
simulate changes in population size and range in Europe between 30,000
and 13,000 years ago, and also to a lesser extent in Holocene Finland.
The results suggest that archaeological reconstructions based on the
distribution of radiocarbon dates are not determined or strongly affected by
biases related to research history. Instead, the reconstructions appear to
reflect a true demographic signal from the past. However, radiocarbon
calibration introduces high-frequency variation in the reconstructions, which
has to be taken into account before any demographic interpretations are
made. Due to this non-demographic variation, the method may not be able to
reliably detect short-term variation in past population size and it is thus
currently better suited to tracking long-term trends in population history.
The taphonomic loss of archaeological material can potentially have a strong
impact on the distribution of archaeological radiocarbon dates, but the
current methods of taphonomic correction may not sufficiently take into
account the geographical variability in taphonomic factors. In the future, it is
therefore important to further develop taphonomic correction methods.
The ability of the climate envelope model simulation of human population
to replicate archaeological patterns indicates that this novel approach is
suitable for studying long-term hunter-gatherer population dynamics. The
method allows not only the exploration of relative changes in population size,
but also the estimation of absolute population density and size. It may also be
able to detect potential inadequacies in the geographical distribution of
archaeological data.
In the Finnish data, the correlation between the archaeological population
size reconstruction and palaeoclimatic data suggests that the climate was an
3
important driver of long-term hunter-gatherer population dynamics, and
that population appears to have changed in equilibrium with climate. The
impact of the climate on human population was mostly indirect, mediated by
its impacts on environmental production and consequently on food
availability. The important role of climate is also supported by the
correspondence between archaeological population reconstruction and the
climate envelope model simulation of past human population size, which
assumes long-term population dynamics to be in equilibrium with the
climate. This correspondence also suggests that the impact of the climate on
terrestrially adapted hunter-gatherer population dynamics has remained
relatively constant from the Late Pleistocene to the present.
4
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to all of those who have
supported me over the seven (!) years for completing my PhD study. Firstly, I
would like to thank my supervisors, docent Tuija Rankama and professor
Mika Lavento of the University of Helsinki, who have offered me trust,
guidance and encouragement during this work. Tuija has been a close and
valuable mentor already since the beginning of my journey in archaeology,
for which I’m very appreciative.
I would also like to thank my co-authors, professor Heikki Seppä,
professor Miska Luoto, professor Heikki Järvinen, docent Markku Oinonen,
Esa Hertell, Natalia Korhonen, Mikael A. Manninen and Petro Pesonen,
without whom I would not been able to complete my thesis. Esa, Mikael and
Petro are also thanked for their friendship throughout the years we have
known each other. Christopher TenWolde is thanked for revising the
language of my thesis, Wesa Perttola for so willingly advising me in the
numerous MapInfo issues, and my room mates at the University of Helsinki,
Sanna Kivimäki, Satu Koivisto, Teemu Mökkönen and Kati Salo for the joyful
company and stimulating discussions in the office and elsewhere.
I’m grateful to my thesis reviewers, professor Robert L. Kelly and
professor Stephen Shennan for their encouraging comments on my work. It
has been a real honour to have my thesis examined by persons, whose work I
so greatly admire.
I appreciate the financial support from Finnish Graduate School in
Archaeology, Finnish Academy of Science and Letters, and Kone Foundation.
This support made my dissertation possible. Especially, the interest and trust
given to my research by the people at Kone Foundation was very encouraging
at the early stages of this work.
Finally, I would like to thank my parents, Kari and Marja-Sisko, who have
always supported me and my, perhaps a bit odd, choice of career. Most of all,
however, I would like to thank my beloved wife Meri, because of your love,
support, insightful discussions, and especially, for so patiently standing those
numerous occasions when I have been mentally away from you.
5
CONTENTS
Abstract ................................................................................................................... 3
Acknowledgements................................................................................................. 5
Contents ..................................................................................................................6
List of original publications.................................................................................... 7
Author’s contribution ............................................................................................ 8
1
Introduction ............................................................................................. 10
1.1
Human population dynamics and cultural change - a brief
review .................................................................................................. 11
1.2
Ecological causes of population size changes ..................................... 16
1.3
Reconstructing prehistoric population history .................................. 17
1.4
Aims of the thesis ................................................................................22
2
Materials and methods ..........................................................................23
2.1
Distribution of radiocarbon dates as a human population
proxy ....................................................................................................23
2.1.1 Evaluation methods for different biases ......................................... 28
2.1.1.1 Research bias ........................................................................ 28
2.1.1.2 Taphonomic bias ...................................................................32
2.1.1.3 Calibration bias .....................................................................32
2.2
Modelling of prehistoric hunter-gatherer population
dynamics .............................................................................................34
2.2.1 Climate envelope modelling approach ............................................ 35
2.2.1.1 Ethnographic training data .................................................. 38
2.2.1.2 Statistical model ...................................................................39
2.2.1.3 Human population range and density simulation .............. 40
2.3
Palaeoenvironmental data ..................................................................42
2.3.1 Palaeoclimatic and palaeoecological proxy data .............................42
2.3.2 Simulated climate data ................................................................... 44
3
Results........................................................................................................ 45
3.1
Evaluation of archaeological population proxy .................................. 45
3.2
Correspondence between Holocene climate change and longterm population dynamics ................................................................. 48
3.3
Impact of event-like climate change on human population ............. 50
3.4
Climate envelope model simulation of Ice Age population
dynamics in Europe ............................................................................ 51
4
Discussion ................................................................................................. 53
4.1
Methodological considerations ........................................................... 53
4.2
Impacts of climate ............................................................................... 57
4.3
The differing dynamics of foragers and farmers ................................ 61
5
Conclusions ............................................................................................. 64
References ......................................................................................................... 67
Publications I–V
6
LIST OF ORIGINAL PUBLICATIONS
This thesis is based on the following publications:
I
Tallavaara, M., Pesonen, P. and Oinonen, M. (2010). Prehistoric
population history in eastern Fennoscandia. Journal of
Archaeological Science 37: 251–260.
II
Oinonen, M., Pesonen, P. and Tallavaara, M. (2010).
Archaeological Radiocarbon Dates for Studying the Population
History in Eastern Fennoscandia. Radiocarbon 52: 393–407.
III
Tallavaara, M. and Seppä, H. (2012). Did the mid-Holocene
environmental changes cause the boom and bust of huntergatherer population size in eastern Fennoscandia? The Holocene
22: 215–225.
IV
Tallavaara, M., Manninen, M. A., Pesonen, P. and Hertell, E.
(2014). Radiocarbon dates and postglacial colonisation dynamics
in eastern Fennoscandia. In F. Riede and M. Tallavaara (eds.),
Lateglacial and Postglacial Pioneers in Northern Europe,
Archaeopress, Oxford, pp.161–175.
V
Tallavaara, M., Luoto, M., Korhonen, N., Järvinen, H. & Seppä,
H. (2015). Human population dynamics in Europe over the Last
Glacial Maximum. Proceedings of the National Academy of
Sciences of the United States of America 112: 8232–8237.
The publications are referred to in the text by their roman numerals.
7
AUTHOR’S CONTRIBUTION
I
MT initiated the study and MT, PP and MO designed it. PP and
MO provided the archaeological data. MT analysed the data and
wrote the paper with contributions from PP. All authors
provided editorial comments and approved the final manuscript.
II
All authors initiated and designed the study. PP and MO
provided the data. MO analysed the data and wrote the paper
with contributions from PP and MT. All authors provided
editorial comments and approved the final manuscript.
III
MT initiated the study and MT and HS designed it. MT provided
the archaeological data and HS provided the palaeoecological
data. MT analysed the data and wrote the paper with
contributions from HS.
IV
MT and PP initiated the study and MT, PP, MAM designed it.
PP, MAM and EH provided the archaeological data. MT analysed
the data with contributions from MAM. MT wrote the paper. All
authors provided editorial comments and approved the final
manuscript.
V
MT and HS initiated the study and MT, HS and ML designed it.
MT prepared the archaeological and ethnographic data. NK and
HJ provided the model-based climate data. MT and ML built
predictive models, analysed their predictive accuracy and
predicted presence and density values. MT constructed ensemble
forecasts of the models, analysed modelling results, compared
the model to the archaeological data and interpreted the results.
MT wrote the paper. All the authors provided editorial
comments and approved the final version of the manuscript.
8
9
Introduction
1 INTRODUCTION
How many people where there in prehistory? This is a common question
asked by laymen to archaeologists, but archaeologists usually struggle to give
a reasonable estimate. Yet, there is a pressing need to be able to reconstruct
prehistoric population history, because the question is also scientifically
important: recent efforts on model building and testing have reaffirmed the
importance of human population dynamics in our socio-cultural and
linguistic evolution (e.g. Bromham et al. 2015; Derex et al. 2013; Henrich
2004; Kempe and Mesoudi 2014; Kline and Boyd 2010; Powell et al. 2009;
Shennan 2001). The importance of demography in social and cultural change
can be felt in modern society as well. In Finland, for example, the
sustainability gap in public finances caused by changes in population size and
age structure is used as a reason to tear down the structures of the welfare
state. In addition to cultural and societal change, human population size can
potentially have impact on the destiny of other species as well: growing
populations of anatomically and behaviourally modern humans have been
partly responsible for past ecosystem changes such as the extinctions of
Pleistocene megafauna (Lorenzen et al. 2011) and Neanderthal humans
(Mellars and French 2011) and current human population growth is probably
leading towards a state shift in the Earth’s biosphere in the near future
(Barnosky et al. 2012).
If knowing how human population size has changed through time is
important, it is equally important to know what factors have affected human
population size, because these factors are the ultimate causes of the cultural
and ecological changes triggered by human population size. This study
addresses both of these questions: it is about reconstructing how human
population size has varied throughout prehistory, and about exploring the
causes of changes in population size. The main focus of the study is on
hunter-gatherer populations in Europe between c. 30,000 and 4000 years
ago. In addition to reconstructing long-term population dynamics, it aims to
explore whether climate, which is one of the most important determinants of
global ecosystems, has affected hunter-gatherer population dynamics. Such
questions regarding demography and human-environment interactions were
recently listed as part of the 25 grand challenges for archaeology (Kintigh et
al. 2014a,b).
From the theoretical point of view, the approach of the study is ecological,
because insights from human life history theory (Hill and Hurtado 1996),
behavioural ecology (Kelly 2013) and macroecology (Burnside et al. 2012;
Hamilton et al. 2012) provide the best tools to understand hunter-gatherer
population dynamics.
10
1.1 HUMAN POPULATION DYNAMICS AND CULTURAL
CHANGE - A BRIEF REVIEW
Shennan (2000) has argued that human population dynamics is the single
most important factor in understanding cultural change. In archaeology and
anthropology, human population size has indeed featured as an important
explanatory variable from the beginning of the New Archaeology in the 1960s
onwards. These early theories were influenced by Boserup’s (1965) ideas of
the imbalance between a population and its resources being a cause for
economic intensification and change. According to Boserup (1965), economic
intensification is a result of human population growth that will increase
population size to the point to which further population growth would cause
food shortage. This stress or pressure would then lead to economic
innovations and intensification that would allow for a new cycle of growth.
Because such innovation and intensification usually implies increasing
labour input relative to yields, it is assumed not to happen spontaneously
without the pressure to innovate.
Although Boserup’s original population pressure theory considered, first
and foremost, agricultural intensification, it was soon generalised to cover
changes among hunter-gatherers as well. Binford (1968) and Flannery (1969)
suggested that pressure in the marginal environments of the Near East
forced hunter-gatherers to broaden their diet to include previously marginal
food sources, leading eventually to the deliberate tending and cultivation of
plant species. Cohen (1977) generalised even further by arguing that that the
slow but steady population growth and filling up of suitable environments
during the Late Pleistocene and Early Holocene led eventually, if not totally
in sync, to the global imbalance between the hunter-gatherer population and
its resources. This prehistoric food crisis resulted in increased dietary
breadth and the invention and adoption of agriculture. On a more local scale,
Dumond (1972) provided example of population pressure-induced
subsistence changes among Eskimo hunter-gatherers in Alaska.
In addition to subsistence changes, population pressure has been seen as
a cause of social change and especially for the rise of hierarchies. An
important concept here is circumscription, which was featured prominently
for the first time in Carneiro’s (1970) theory of the origins of the state.
According to Carneiro, population growth within an area of circumscribed
agricultural land, set off by mountains, seas, or deserts, leads to competition
between previously autonomous groups. As a result, the strongest group
subjugates others, because in their circumscribed environment the groups do
not have any real option to avoid dominance by voting with their feet. This
creates hierarchies between people and groups, and as the process is
repeated, it results in larger and larger political groups and, in some cases, in
the formation of states. In addition to environmental circumscription,
Carneiro (1970) highlights social or, rather, demographic circumscription
that can occur even in areas where environmental circumscription is not a
11
Introduction
problem. This means that as suitable habitats are filled up, groups are
circumscribed by other groups and therefore their options to avoid the
dominance-seeking group are restricted. The process of demographic
circumscription can also be seen as a more general mechanism leading not
just to state formation, but also to the rise of hierarchies and social and
political inequality within and between groups. Quite early on, population
induced infilling and circumscription was considered as an important factor
behind social changes among hunter-gatherer populations as well (Cohen
1977, 1981).
Another important idea related to the rise of hierarchical social
organisations is scalar stress, as introduced by Johnson (1982). Scalar stress
forms when the number of decision making units, such as households,
increases within the group, and the coordination of cooperation and
information flow becomes increasingly difficult. This stress is relieved either
through group fissioning or the development of hierarchical social
organisation. According to Johnson (1982), egalitarian sequential, or
horizontal, hierarchies form when smaller units (e.g. nuclear families) form
new, higher level units (e.g. extended families) that are larger and fewer in
number than previous units. However, the increasing group size and
consequent scalar stress may bring the sequential hierarchy to its limit, and if
group fissioning is not an option due to circumscription, a new form of nonegalitarian vertical hierarchy can arise to facilitate decision making and
implementation. In such systems, certain individuals or groups are
associated with leadership functions and statuses as well as with some degree
of control over resources, which development, according to Johnson (1982),
is required for the coordination and regulation of their utilisation. The
combination of circumscription and scalar stress has been used to explain the
rise of hierarchical hunter-gatherer societies in north-western North
America, as well as worldwide (Ames 1985; Cohen 1985).
Many of these basic ideas are still very much alive in the current
understanding of the role of human population size and density in
behavioural change. However, due to the rise of the role of evolutionary
theory in anthropology and archaeology since the early 1980s, interaction
between the human population and its resources and forms of social
organisation are nowadays more often explicitly analysed within an
evolutionary ecological framework (e.g. Bayham et al. 2012; Hertell 2009;
Janetski 1997; Jerardino 2010; Kennett et al. 2009; Prentiss et al. 2014;
Stiner et al. 2000).
With the rise of the evolutionary paradigm, the understanding of how
population size can affect cultural change has diversified. Cultural
transmission models, originally adapted from population genetics, help us
understand how and why frequencies of different cultural variants change
through time (Boyd and Richerson 1988). These new models and insights
allow us to analyse cultural evolution in a way roughly analogous to genetic
evolution. Shennan (2001) and Henrich (2004) have built models to analyse
12
the role of population size in cultural transmission and the creation of
cultural variation. Their models indicate that large pools of interacting
individuals can create and maintain adaptive skills more effectively, and are
also capable of faster cumulative cultural evolution than small populations. A
decrease in population size may, in turn, result in a loss of complex cultural
traits. Thus, the effects of population size on cultural variation would be
roughly similar to its effects on genetic variation (Frankham 1996). Shennan
(2001) and Powell et al. (2009) link the punctuated appearance of modern
behavioural traits between 100,000 and 50,000 years ago to changes in
human population size and density, whereas Henrich (2004) has suggested
that the gradual loss of cultural traits observed in the archaeological and
ethnological records of Tasmania was caused by a reduction in population
size. This reduction was a result of the separation and isolation of Tasmanian
populations from the larger Australian metapopulation at the end of the
Pleistocene (Henrich 2004). These models have been tested in experimental
laboratory studies, which found strong support for the idea that the size of
the pool of interacting individuals has an impact on cultural evolution (Derex
et al. 2013; Kempe and Mesoudi 2014; Muthukrishna et al. 2014). In
addition, a test in the natural context using ethnohistorical data from
Oceania found that population size best explains the complexity of fishing
technologies among the island-living populations (Kline and Boyd 2010).
From early on, theories that postulate an important role for human
population size in behavioural and cultural change have been criticised. In
order to evoke technological and social innovations, it has been thought that
population-resource-imbalance has to be relatively severe and represent a
long-lasting state, but this important assumption has been questioned
(Cowgill 1975; Cowgill and Wilmsen 1975). This state of affairs is also
problematic from the population ecological point of view as the relationship
between a population and its resources are usually assumed to be in
equilibrium, even though this can also mean stable oscillation (Hanski et al.
1998). Population pressure arguments have been found especially troubling
with respect to hunter-gatherers who, according to a popular idea, are argued
to actively regulate their population size well below carrying capacity. This
active regulation, for which infanticide is the most effective means, is seen as
a major reason for the near zero population growth assumed to characterise
most of the Pleistocene (Cowgill 1975; Cowgill and Wilmsen 1975; Hassan
1981: 144).
However, assumption of active regulation is problematic. Firstly, as the
tendency to maximise (inclusive) fitness has been under strong selection
throughout the evolution of all species, it is theoretically highly unlikely that
humans would voluntarily restrict their reproductive behaviour for the
common good (Shennan 2002: 102–117). Secondly and more importantly,
there is no empirical evidence that birth control mechanisms, such as
infanticide, would have actively been used to control the population size, or
that these would in fact have had an inhibitive effect on population size.
13
Introduction
Indeed, quite the opposite may be the case. Smith and Smith (1994) analysed
Inuit sex-ratio data that has been used to argue for infanticide and they
indeed found evidence for some preferential female infanticide. Although
Smith and Smith (1994) were not able to directly test the population
regulation hypothesis, they found it unconvincing both theoretically and in
the light of the available data. Instead, Smith and Smith (1994) suggested
that preferential female infanticide among the Inuits is best explained by the
differential payback sons and daughters are able to contribute to their
parents’ inclusive fitness in the socio-ecological context of the Inuits. In
addition, Lee (1972) has noted that the workload of !Kung San mothers
increases significantly if the interbirth interval is shorter than four years,
because shorter intervals require mothers to carry much greater loads of
baby and food on her foraging trips. Thus, the interbirth interval of four
years, by whatever means achieved, is linked to mother’s personal wellbeing
and endurance, not necessarily to any intentional attempts to regulate
population size. Blurton Jones (1986) has further argued that the four years
interbirth interval appears to maximise the reproductive success of !Kung
women, most likely because the strain caused by a shorter interval could lead
to increased offspring mortality (but see Hill and Hurtado 1996: 380–385).
Thus, by increasing mothers reproductive success, birth control among
!Kung does not constrain population growth, but can rather accelerate it.
Due to the above mentioned reasons, the common idea of an extremely
low or even zero population growth rate during most of human prehistory is
also problematic. Ethnographic data indicate no hunter-gatherer groups that
would have had a zero growth rate (Hill and Hurtado 1996: 471; Pennington
2001) and it has been suggested that human life history has evolved to be
much more “r-selected” (population size is governed by maximum
reproductive capacity) than the life histories of other great apes, also
allowing much more rapid population growth (Hill and Hurtado 1996: 472).
If this potential for rapid growth characterises hunter-gatherers, one has to
assume that periods of growth were frequently followed by crashes in order
to explain the inferred slow or zero long-term population growth during the
Pleistocene (Boone 2002; Hill and Hurtado 1996: 471).
Even if humans, like other species, have evolved a tendency to maximise
their reproductive success, and thus are not likely to constrain their
reproductive behaviour for the common good, population pressure
arguments may still appear problematic. If, due to the density-dependent
factors, population tends to be in equilibrium with its resources or oscillate
around some “quasi equilibrium”, a key question becomes: under what
circumstances would the imbalance between a human population and its
resources be sufficiently high to trigger economic innovations such as
agriculture? A stable population at the equilibrium would hardly indicate
population pressure, otherwise it would not be in equilibrium. However,
Winterhalder et al. (1988; Winterhalder and Lu 1997) and Belovsky (1988)
suggest that a common type of dynamics among hunter-gatherers would be
14
stable limit cycle, where the population and its dietary breadth oscillates
around some mean with the wavelength varying between 50 to 100 years.
This kind of density-dependent oscillation results from the interplay between
the density of the human population and its resources. Human population
grows fast when resources are abundant, but at the same time the density of
the resources start to decline. The declining abundance of resources slows
the growth of human population and eventually turns the growth into
decline. As human population declines, resources recover, which eventually
allows new human population growth, and so forth. At the peaks of the cycle
there is, indeed, population pressure and the widest diet, but the pressure
starts to be relieved rather quickly, within less than 50 years. This appears to
be a short time for technological innovations to occur, especially as the
archaeological record usually indicates rather gradual economic changes (see
also Bettinger et al. 2010).
Despite the potential problems related to population-resources-imbalance
arguments, it is still possible that under favourable conditions of resource
abundance, population growth can lead to environmental and/or
demographic circumscription, causing the above-mentioned problems in the
coordination of co-operation and dominance avoidance, and consequent
changes in social organisation, even without an imbalance between a
population and its resources. However, Hayden (2001) has questioned the
role of population size in the rise of non-egalitarian social organisation as
well. According to Hayden, population size and unequal social relations can
be correlated, but they are not causally related, because they are both
affected by resource abundance. Thus, high population size or density would
not be a necessary condition for inequalities to arise and the correlation is
only spurious. Instead, the surplus hypothesis argues that non-egalitarian
social organisation would arise as a result of the action of dominance-seeking
individuals who use abundant resources for their own advantage by creating
alliances, gaining prestige, and creating debts (Hayden 2001). The same
surplus can lead to population increase.
Also, the theories that propose an important role for population size in
cultural transmission have been criticised on theoretical (Querbes et al. 2014;
Read 2008) and empirical (Collard et al. 2005, 2011, 2013a,b; Read 2006,
2008, 2012) grounds. Collard et al. have argued that in the ethnographic
hunter-gatherer data, variation in toolkit complexity is not explained by
population size or mobility, but subsistence-related risk, which is measured
by effective temperature (Collard et al. 2005, 2011, 2013a,b). Perhaps the
most vocal critic of the role of population size in cultural variation has been
Read (Read 2006, 2008, 2012). Using ethnographic data, he has argued that
hunter-gatherer toolkit complexity is explained by the combined effects of
growing season length and residential mobility, not by population size (Read
2006, 2008, 2012). According to Read, this implies that variation in toolkit
complexity is “driven by the response of a hunter-gatherer group to
ecological constrains through its mode of resource procurement” (Read
15
Introduction
2008: 620). Later, Read (2012) has generalised his critique and argued that
the variation in fishing technology in Oceania is also better explained by
subsistence-related risk rather than population size (but see the discussion in
the supplementary material of Read 2012; see also Henrich 2006).
All the above-mentioned strongly indicates that population size and
density have a potentially very important role in cultural change, but that
their relative importance is far from resolved. This means that the role of
demography remains an important and interesting research topic, and that
such research will likely generate new insights into human cultural and
biological variability in time and space.
1.2 ECOLOGICAL CAUSES OF POPULATION SIZE
CHANGES
To fully explain cultural change in cases where population size or density
turns out to be an important factor, one has to search for factors that affect
population size. From the human ecological perspective, the study of these
factors is also an important aim unto itself. Changes in population size are
determined by a complex interplay between the number of births and deaths
and in and out migration. Current theoretical understanding of human
demography is best formulated in life history theory. One of its fundamental
assumptions is that because our decision making facility (brains) has evolved
through natural selection, fitness maximisation is expected to be the goal
(conscious or unconscious) of decision makers. This theory predicts that
resource availability would affect the severity of so called life history tradeoffs, such as the one between the maximum number of offspring that can be
produced and the maximum number that can reach the stage of being
successful parents themselves (Hill and Hurtado 1996: 18–35; Shennan
2009). Increasing the amount of resources would decrease these severities
and eventually lead to population expansion as a result of increased fertility
and higher offspring survival. For humans, this link between resource
availability and reproductive success seems to hold, at least in pre-industrial
settings, where the amount of resources available to parents is indeed shown
to correlate with their fertility and with the survival of their offspring (Hill
and Hurtado 1996: 293–320; Pettay et al. 2007; Rickard et al. 2010). The
availability of resources often dictates migration-related decisions as well. At
the population level, Baumhoff (1963) was able to show that the density of
important food resources was predicative of hunter-gatherer population size
in California.
The issue can also be considered from a more macroecological
perspective. Early on, Bartholomew and Birdsell (1953) argued that on a
long-term basis the population size of human hunter-gatherers is in
thermodynamic equilibrium with the trophic levels below it, because the
energy for growth and reproduction for hunter-gatherers comes from these
16
lower trophic levels. Thus, hunter-gatherer population depends on the
production of primary producers, either directly or via higher-order
producers. In terrestrial environments, primary production is mainly
controlled by temperature and hydrology, i.e., climate: as the climate
changes, primary production changes as well. Therefore, it is reasonable to
assume that, on time scales typically studied by archaeologists, mean huntergatherer population size varies with climate. As climate has a strong
influence on the patterning of plant and animal species distributions and on
ecosystems in general (Anderson-Teixeira and Vitousek 2012; Currie 1991;
Holdridge 1967; Stephenson 1990), it is difficult to understand why things
would be different with humans, who are themselves part of the ecosystem
and depend on other species. It has already been convincingly argued, for
example, that hunter-gatherer mobility patterns and territorial requirements
are linked to climatic factors (Binford 1980; Hamilton et al. 2012; Kelly 2013:
88–94). It is useful to keep in mind that the relationship between climate
and other aspects of an ecosystem may not be linear, because the rates of any
biochemical reactions increase exponentially with temperature (AndersonTeixeira and Vitousek 2012).
However, several archaeological studies have not found a strong link
between climate and hunter-gatherer population size or cultural change (e.g.
Fiedel and Kuzmin 2007; Gamble et al. 2005; Gamble 2005; Jochim 2012;
Meltzer and Bar-Yosef 2012). The idea that human population history and
thus, at least in some cases, cultural change would have been controlled by
environmental factors can also be ideologically hard to accept and is easily
deemed as environmental determinism, which is considered inherently
wrong. Culture, and especially technology, is often thought to liberate
humans from such environmental constrains. During the heydays of postmodern archaeology in the 1980s and 1990s, all kinds of ecological
approaches were dismissed because they were seen to impose modern
capitalistic ideology on prehistoric people (Johnson 1999: 146–148).
However, from the scientific point of view, it is important to study how
integral a part of the ecosystem humans actually are, rather than to assume it
a priori.
1.3 RECONSTRUCTING PREHISTORIC POPULATION
HISTORY
One of the most important challenges in the study of prehistoric population
dynamics is how to reconstruct past population trajectory. The ability to
reliably reconstruct population histories is an obvious prerequisite for the
study of the causes and possible effects of changes in population size and
density. Problems with these reconstructions left the early theories
potentially open for severe criticism. As Renfrew pointed out already in 1973,
in order to avoid circularity, one needs evidence of population change that is
17
Introduction
independent of the type of economies or social organisations that one seeks
to explain with population size (Renfrew 1973). Even today some studies use
measures such as changes in dietary breadth or prey species morphology and
population structure to trace changes in human population size and density
(Klein and Steele 2013; Stiner et al. 2000; Stutz et al. 2009). Such arguments
require that these changes are determined only by human population size or
density, an assumption that is hardly justified.
In 1953, Bartholomew and Birdsell stated that “Some archaeologist have
hoped to reconstruct preagricultural population figures from studying the
temporal and spatial distribution of sites, but the inescapable sampling
errors in this approach render it unreliable. We suggest that an analysis of
the energy relationships and efficiency of the techniques for obtaining food
offer a promising approach” (Bartholomew and Birdsell 1953). Their
suggestion has indeed formed one approach of reconstructing past
population figures in archaeology, either by relating known human energy
requirements to the energy available for human consumption in particular
environments, or by projecting into the past the densities of ethnographically
known populations who are assumed to live in environmentally and
technologically analogous conditions to their prehistoric counterparts. The
former alternative provides estimates of the maximum density or size
(density × area) of human population a given area is able to support (e.g.
Casteel 1972; Hassan 1981). However, it requires highly detailed information
on past environments, which is still difficult to obtain. It also makes farreaching assumptions about the harvestable biomass suitable for human
consumption in different environments. Furthermore, it is difficult to know
how to relate maximum population to actual realised population (Casteel
1972; Kelly 2013: 184–185).
Ethnographic analogy overcomes some of these problems by providing
estimates of realised population densities in certain environmental
conditions. The use of analogies, nevertheless, requires some consideration.
For example, Eller et al. (2009) used a single ethnographically informed
density estimate of 0.4 individuals per 100km2 to estimate the global Middle
Pleistocene human census population size. It is without saying that this is a
totally unreasonable approach. Even when the correspondence between the
environmental conditions of ethnographic and prehistoric populations is
justified (e.g. Gräslund 1974), one cannot be sure how representative the
population density of the chosen ethnographic case(s) is for the given
environmental conditions: at the time of the ethnographic documentation,
the density of the hunter-gatherer population could have been very different
from the long-term mean due to its naturally oscillating dynamics.
In general, the use of ethnographic data in estimating prehistoric
population density and size has been rather unsystematic. The availability of
global databases of ethnographically documented hunter-gatherers (Binford
2001; Kelly 2013) now allows a more systematic approach. Fitting a mean
function of population density over chosen environmental gradients yields
18
the most likely estimate of population density in given environmental
conditions. Such an approach takes into account the fact that individual
ethnographically documented groups could have been at different stages of
their population cycles at the time of the ethnographic documentation.
Actually, this approach is no longer based on ethnographic analogy. Instead,
through inductive model building, it informs us how chosen environmental
variables affect human population density. This information or theory can
then be used to estimate past population densities, if the past states of
environmental variables are known (see also Binford 2001: 447–464).
Despite the scepticism of Bartholomew and Birdsell (1953), the temporal
and spatial distribution of archaeological material has become the most
commonly used measure for prehistoric population size. This might be
simply a result of more and more archaeological research being done: as the
archaeological dataset grows, sampling error becomes less severe. The
number of sites (sometimes together with size of the sites) through time was
already used in the late 1960s and early 1970s as a proxy, i.e. an indirect
measure, of population size (e.g. Flannery 1969; Hole et al. 1969; Renfrew
1972; Sanders 1972). These data were usually based on surveys. Survey data
can be problematic, however, because they do not necessarily yield
temporally diagnostic artefacts, and even when present the diagnostic
artefacts often do not cover the whole period the site was in use. In areas
where postglacial rebound has been strong, it is possible to date sites using
shore displacement chronology independently of diagnostic artefacts. The
temporal distribution of sites dated using shore displacement has also been
used as a proxy for human population size (Siiriäinen 1981), but obviously its
geographical applicability is rather restricted.
Figure 1. The use of the distribution of 14C dates as a proxy for relative human population size is
based on the simple idea that there should be a positive correlation between the number of
people and the amount of waste, i.e. archaeological material, they produce. Large populations
leave more sites, hearths, tools, refuse pits, etc. behind them than smaller populations.
19
Introduction
Rick (1987) was the first to measure changes in prehistoric population size
with the temporal distribution of archaeological radiocarbon dates (14C
dates), thus avoiding the problems related to the lack of sufficiently highresolution artefact typologies. The use of the distribution of 14C dates as a
proxy is based on the same idea as the use of distribution of sites: It is
assumed that there is a positive correlation between the number of people
and the amount of dateable contexts (sites, hearths, refuse pits, graves, etc.)
they produce (Fig. 1). Contrary e.g. to ethnographic analogy, this kind of
proxy can only give relative, not absolute, estimates of population size; i.e., it
can tell when population size was larger and when smaller, but it cannot
assign any actual population size (numbers) to the estimate.
Since Rick’s (1987) pioneer study, the method has been increasingly used,
especially during the last ten years, which can be seen in the development of
citations to Rick’s article (Fig. 2). Figure 2 also shows the number of citations
to some other influential papers. The reasons for the recent popularity of the
method may, again, relate to the increased availability of radiocarbon dates,
not least as a result of some large projects (Van Andel et al. 2003; Gamble et
al. 2004, 2005), and to the increased theoretical interest in population size as
an explanatory variable in cultural change.
Figure 2. The development of citations to six influential studies that use distributions of 14C dates
as a human population proxy. Citation data from the ISI Web of Science.
Despite its current popularity, the method is not without its problems. These
relate to the factors that can disturb the link between the number of
prehistoric people and the strength of their archaeological signal as
measured by the distribution of 14C dates. These biases include, for example,
variable research interests that affect what is dated, taphonomic loss of
20
archaeological material through time, the different rates of archaeological
material produced by mobile and sedentary people, differential cultural
practices dealing with organic (dateable) materials, and the biasing effects of
14C calibration. Rick (1987) was already aware of these biases, and carefully
evaluated their potential impact on the data. With the new research boom,
the amount of studies evaluating the method and developing means to filter
out the impacts of different biases, as well as developing the method in
general, has increased (e.g. Ballenger and Mabry 2011; Bamforth and Grund
2012; Brown 2015; Downey et al. 2014; French and Collins 2015; Grove 2011;
Peros et al. 2010; Rhode et al. 2014; Shennan et al. 2013; Steele 2010;
Surovell et al. 2009; Surovell and Brantingham 2007; Timpson et al. 2014;
Williams 2012). It is likely that the awareness of these biases and the
eagerness to develop the method are the reasons why a couple of attempts to
completely dismiss the method have not been very successful (Contreras and
Meadows 2014; Mökkönen 2014). A large number of potential biases
(Mökkönen 2014) nevertheless ensures that there is a constant need to
evaluate and develop the method.
Bartholomew and Birdsell (1953) were hardly able to envision the most
recent developments in reconstructing prehistoric population dynamics,
namely the ones based on the analysis of variation in human DNA. Skylineplot methods (Atkinson et al. 2008, 2009) and pairwise or multiple
sequentially Markovian coalescent analyses (Li and Durbin 2011; Schiffels
and Durbin 2014) use coalescent theory to infer changes in effective
population size from genetic data. However, effective population size does
not have a straightforward relationship with the actual census population
size (Hawks 2008). In addition, these methods depend on estimates of DNA
mutation rate and molecular clock calibrations, which are still debated (Fu et
al. 2013; Scally and Durbin 2012) and imprecise, leading to poor temporal
resolution. Furthermore, at least skyline-plot methods may not be able to
detect demographic histories that go beyond a major reduction in population
size (Grant et al. 2012). This is evident in the Bayesian skyline-plots of
marine species that show flat demographic curves prior to the Last Glacial
Maximum (LGM), when a distinct population decline is assumed to have
occurred (Grant et al. 2012). Similar flat patterns characterise skyline-plot
curves of pre-LGM human populations (Atkinson et al. 2008; Zheng et al.
2012). All these problems make it difficult to meaningfully compare DNAbased population reconstructions with the records of cultural and
environmental changes. Until significant improvements in the DNA-based
method, reconstructions based on archaeological and ethnographic data
must be considered more reliable.
21
Introduction
1.4 AIMS OF THE THESIS
In his book Genes, Memes and Human History, Shennan (2002: 112) defines
the tasks of archaeology of population as follows: 1) to characterise regional
population patterns through time, 2) to identify the factors affecting these,
and 3) to examine the impact of population size and density on other aspects
of human activity and social institutions. This thesis contributes to the first
two of the suggested tasks. By building on the understanding and
methodological developments described above, and by taking into account
the open questions, this thesis aims to:
1. use archaeological radiocarbon dates to reconstruct the prehistoric
population history of eastern Fennoscandia between 11,000 and
1000 cal BP;
2. evaluate the reconstruction by comparing it with other, spatially
and temporally more restricted population proxies and by studying
the effects of research history, taphonomic loss, and 14C calibration
on the distribution of radiocarbon dates;
3. develop a systematic approach of utilising ethnographic data to
reconstruct hunter-gatherer population history within the climate
envelope modelling approach, and to use this approach together
with model-based climate data to reconstruct long-term population
dynamics in Europe between 30,000 and 13,000 cal BP;
4. study the effect of climate and climate-related environmental
†̆
factors on human population dynamics. This is achieved by:
a) comparing the reconstruction of Fennoscandian population
history with the local palaeoclimatic and palaeoenvironmental
records of long-term and event-like environmental changes
that would have been relevant in terms of human population
dynamics;
b) comparing the climate envelope model simulation of past
human population size, which assumes long-term population
dynamics to be in equilibrium with the climate, with the
archaeological population proxy in order to see how realistic
the simulation is.
The main focus of the thesis will be on hunter-gatherer populations. This
allows to look at extremely long-term dynamics and, thus, to see possible
recurrent features in the data. The study areas in Holocene eastern
Fennoscandia (mainly Finland) and Late Pleistocene Europe are well suited
to these questions: Pleistocene humans in Europe were purely huntergatherers, and eastern Fennoscandia contains one of the longest Holocene
hunter-gatherer records in Europe and the best available palaeoclimatic and
palaeoenvironmental data. This arrangement also allows to study whether
the impact of the climate on human populations has been different under
glacial and interglacial climate regimes.
22
2 MATERIALS AND METHODS
2.1 DISTRIBUTION OF RADIOCARBON DATES AS A
HUMAN POPULATION PROXY
The archaeological radiocarbon analyses performed at the Dating Laboratory
of the Finnish Museum of Natural History (University of Helsinki) and its
predecessors form the backbone (c. 80%) of the radiocarbon dataset that is
used in reconstructing population history in eastern Fennoscandia. The
dataset was also extended to cover, as thoroughly as possible, the published
archaeological radiocarbon measured elsewhere. In addition, the data also
contain unpublished dates that were released for use by many Dating
Laboratory customers. At the time of the first publications (papers I and II),
the whole database consisted of almost 2600 individual 14C dates, extending
from the earliest colonization of the area (c. 11,000 cal BP) to the modern
era. When compiling the database, it was decided not to make any a priori
exclusive selections of dates. This means that the dataset also includes dates
that potentially have no link to human activity due to erroneous sample
selection in the field. However, such erroneous selections are expected to
form a clear minority within the dataset, and their role is merely random, not
systematic. Furthermore, exclusion of dates that do not seem to match the
쨐̆
archaeological material of the dated context can bias the real signal of human
activity. For example, the Hossanmäki site in Lohja (southern Finland)
yielded an archaeological assemblage that can be dated on typological and
technological grounds exclusively to the Stone Age (Pesonen and Tallavaara
2006). However, all the radiocarbon dates from burnt bone and hearths
showed much younger ages, extending from Pre-Roman Iron Age to the
Medieval Period (Pesonen and Tallavaara 2006). The exclusion of these dates
because of the lack of an apparent link between the dates and archaeological
assemblage, would have led to a biased result in terms of the depiction of
human activity in the region (see also Shennan et al. 2013).
In paper V, the archaeological proxy of the European Palaeolithic
population is based on 3718 14C dates from 895 sites. Most of the dates are
extracted from the INQUA Radiocarbon Palaeolithic Europe Database v12
(Vermeersch 2005). The dataset has been expanded by including dates from
recent publications. The dataset was critically evaluated using the
information given in the INQUA database. Dates were excluded according to
several criteria: a) all dates that were qualified as unreliable or contaminated,
b) dates without coordinates or laboratory reference, c) duplicate dates, d)
dates with standard errors greater than 5% of the mean 14C age, e) dates from
gyttja, humus, peat, soil or soil organics, organic sediment, humic acid
fraction of the sediment, and fossil timber, f) dates of marine origin, such as
shell, marine shell, and molluscs, g) dates without a clear link to human
23
Materials and methods
activity, such as terminus ante and post quem, surface, above, up from, top,
below, and beneath the cultural layer(s), minimum or maximum age of the
layer, and beyond site, and h) dates of cave bear (Ursus spelaeus), that may
have no link to human activity. In some cases, coordinates or even ages were
corrected according to the original publication of the date.
The whole Fennoscandian dataset and its suitability for reconstructing
population history was evaluated in paper II, whereas smaller subsets of the
data were used in papers I, III, and IV. To study the prehistoric population
history in eastern Fennoscandia, only dates that are older than 800 14C years
were used. The fact that differing research emphases may cause some sites,
or certain chronological phases of a particular site, to have significantly more
dates relative to other sites or phases, can distort population-related
conclusions based on date distributions (Gamble et al. 2005). Shennan and
Edinborough (2007) tackled this problem by calculating a pooled mean of all
the dates from a single chronological phase and constructing the population
curve using these mean dates. Because there are few stratigraphically distinct
phases in eastern Fennoscandia, a slightly different procedure was used in
this study (papers I, III). First, each site's dates were arranged in an
ascending order. Then the first cluster of dates that fell within an arbitrary
interval of 200 radiocarbon years was combined. After that, it was continued
towards the oldest date and combined the second cluster of dates falling
within the interval, and so on. For example, if a site originally had the
following six dates: 4000 bp ± 80, 4120 bp ± 50, 4200 bp ± 95, 4500 bp ±
⯀́
80, 5100 bp ± 45 and 5250 bp ± 50, the first three and the last two dates
would have been combined. Combinations of dates were carried out using the
weighted average method in CalPal calibration program (Weninger and Jöris
2004). After the combination, the following three dates would represent the
site in a combined database: 4105 bp ± 39, 4500 bp ± 80 and 5167 bp ± 33.
When all sites were handled this way, the combined database contained 1160
individual dates. A similar approach to reduce the effect of site phases with
multiple dates has now been used in other studies as well (Shennan et al.
2013).
When studying the earliest colonization of eastern Fennoscandia, a
slightly different approach was followed (paper IV). Because the studied time
period was narrower, and because the number of Mesolithic 14C dates
between 10,900–8500 cal BP is relatively small (N=107), it was decided to
use the dates without combining them. Since the focus in paper IV is on the
impacts of a short-lived climate event, it was necessary to use archaeological
14C dates that would accurately date human activity on site. Therefore, if a
site yielded charcoal and burnt bone dates, the latter were prioritised in the
reconstruction of spatio-temporal population patterns due to their smaller
own-age effect (see paper II).
However, it is well known that there is an offset between marine and
atmospheric carbon reservoirs that affects radiocarbon dates made on
marine samples, such as seal bones. There is considerable spatial and
24
temporal variation in the marine reservoir effect values in different parts of
the world, and the values for the different stages of the Baltic Sea are
currently unknown or only partly understood (e.g. Eriksson 2004;
Hedenström and Possnert 2001; Lindqvist and Possnert 1999; Olsson 2006).
On the other hand, research on the origin of the carbon source in burnt bone
indicates transfer of CO2 from the fuel material into the bone during
combustion (Hüls et al. 2010; Olsen et al. 2013; Strydonck et al. 2010; Zazzo
et al. 2011). This result has obvious implications for the dating of burnt bone
samples, because it suggests that the marine reservoir effect is
counterbalanced to some degree by the transfer of CO2 from wood used as a
fuel. At the same time, terrestrial bones may yield dates that are too old due
to the “old wood effect” and thus decrease the age difference between
charcoal and bone deriving from the same context. However, unless the
exchange of bone carbonate with CO2 is 100%, burnt bone should give more
accurate dates of human activity than charcoal. Several examples where
calcined bone and charcoal from the same context have been dated show a
clear age correspondence between these two sample materials, so that bone
dates are systematically slightly younger than charcoal dates (e.g. Lanting et
al. 2001; Manninen and Tallavaara 2011; Olsen et al. 2008).
The Fennoscandian radiocarbon database was also geographically divided
into three subsets. The border between the southern and central areas
approximates a cultural border that is observable at some points of
prehistory, and especially in the ethnographic material (Carpelan 1999;
;̇
Sarmela 1994; Vuorela 1976) as well as in the genetic data (Hedman et al.
2004; Lappalainen et al. 2006; Salmela et al. 2008). The border between the
central and northern areas, on the other hand, is more arbitrary, but it
nevertheless approximates the southern limit of the historically known
territories of Sami societies (e.g. Näkkäläjärvi 2003: 115). Paper III used only
the combined dates from the southern and central regions (N=869).
To construct a proxy curve for the relative human population size,
radiocarbon dates were calibrated and individual probability distributions
were summed using CalPal (paper I) and OxCal (paper III) calibration
programs (Bronk Ramsey 2009; Weninger and Jöris 2004) and IntCal04 and
IntCal09 calibration curves (Reimer et al. 2004, 2009). This summing
produces a summed probability distribution (SPD) of calibrated dates. This
method is still widely used in studies of prehistoric population size (Collard
et al. 2010; French and Collins 2015; Gamble et al. 2005; Hinz et al. 2012;
Kelly et al. 2013; Riede 2009; Shennan et al. 2013; Shennan and
Edinborough 2007; Smith et al. 2008; Timpson et al. 2014; Wang et al. 2014;
Williams 2013). However, the summing of individual probability
distributions causes problems if standard errors of 14C measurements vary,
which is usually the case. Measurements with a smaller standard error show
more sharply peaked (higher kurtosis) calibrated probability distribution
than measurements with greater standard error (see also Bamforth and
Grund 2012). This is because the same probability mass is distributed within
25
Materials and methods
a narrower range. Thus, the summing of dates with small and large errors
creates a curve that indicates higher relative population size during periods
of small-error-dates, and lower population size during periods of large-errordates, even if the number of dates is equal during both periods. There is also
the possibility of systematic bias as standard errors tend to grow with age, so
that Holocene and Late Pleistocene patterns in SPDs would appear different
only because of systematic change in standard errors, not because of
different population dynamics.
This problem is illustrated in Figure 3, which shows the SPDs of 21 evenly
spaced (50 14C yrs) simulated radiocarbon dates. In the first set of dates
(blue), the standard error is 80 years for each date. In the second set (red),
14C ages are exactly the same as in the first set, but four dates have a standard
error of 40 years (Table 1). These four dates create a very distinct peak in the
red distribution, even though the number and spacing of 14C dates is the
same as in the grey distribution. Thus, SPD may not be the best way to
illustrate temporal distributions of radiocarbon dates.
;̇
Figure 3. The changes in standard errors affect the shape of summed probability distributions
(SPD), but not the shape of temporal frequency distributions. Narrow standard errors create
peaks in the SPD. In the temporal frequency distribution of calibrated median dates such peaks
do not show up. The data used in the figure is shown in Table 1.
In addition to SPD, Figure 3 shows the kernel density curve of the
distribution of calibrated median dates of the red set. This curve correctly
depicts the distribution without any peak, therefore avoiding the problem
related to varying standard errors. Unlike SPD, the distribution of calibrated
medians, weighted averages or modes produces a distribution of dates based
on temporal frequency, even when it is illustrated as a kernel density plot. It
is thus a better representation of a population proxy, as a higher frequency
(or density) really means a higher number of dates, which can be interpreted
as a higher relative population size. In SPD, high probability density can just
indicate dates with smaller standard errors.
26
Table 1. The dates used to create Figure 3.
Blue set
14
Red set
C Age
Std
5000
14
Black set
Calibrated median
C Age
Std
80
5000
80
5746
5050
80
5050
80
5797
5100
80
5100
80
5830
5150
80
5150
80
5903
5200
80
5200
80
5973
5250
80
5250
80
6043
5300
80
5300
80
6087
5350
80
5350
40
6133
5400
80
5400
40
6221
5450
80
5450
40
6248
5500
80
5500
40
6300
5550
80
5550
80
6352
5600
80
5600
80
6390
5650
80
5650
80
6441
5700
80
5700
80
6497
5750
80
5750
80
6551
5800
80
5800
80
6601
5850
80
5850
80
6663
5900
80
5900
80
6726
5950
80
5950
80
6787
6000
80
6000
80
6847
of the red set
In papers IV and V, the population proxy is based on the distribution of
calibrated median dates, and in this summary paper all shown 14C-based
population reconstructions are based on median dates calibrated using the
IntCal13 calibration curve (Reimer et al. 2013) and clam calibration
algorithm (Blaauw 2010) in R statistical software (R Development Core Team
2014).
In papers IV and V, the spatial distribution of 14C dates is used to make
inferences about the range of human population. The idea is that the
calibrated median age reflects human presence at a certain place at a certain
time. In paper V, dates were binned using 1000 year bins. When evaluating
spatial distributions of dates, one has to keep in mind that such distributions
are relatively prone to research biases (see below).
27
Materials and methods
2.1.1 EVALUATION METHODS FOR DIFFERENT BIASES
There are several factors that may bias interpretations based on the temporal
frequency distributions of 14C dates. The potential impacts of these factors
must be considered before variation in the temporal frequency distribution
can be interpreted in terms of past population history. The most notable
biasing factors are the effects of varying research interests, the taphonomic
loss of dateable archaeological material, and the effect of calibration of 14C
dates.
2.1.1.1 Research bias
One of the most important assumptions in the use of temporal frequency
distribution of archaeological material as a population proxy is that the
known sites represent a random sample of all the existing sites in relation to
their age. In Finland, this situation is more tenable, since most surveys are
carried out by cultural heritage management agencies, mainly the National
Board of Antiquities (NBA). Usually cultural heritage management does not
have a scientific interest or focus on certain periods. The same applies to
excavations as well, which usually are rescue excavations at sites determined
by present land use rather than by scientific questions. In the case of
radiocarbon dates, one has to assume further that the researchers have also
made the radiocarbon determinations randomly in relation to their age. This
⯀к
is not as plausible as the previous assumption,
since it is likely that varying
research emphases will have an effect here. The biasing effect of research
interests is strongest in small samples, whereas in larger samples the effect of
different research interests may cancel each other out. Therefore, the large
European-wide dataset of glacial 14C date used in paper V is not evaluated
against research biases. This kind of evaluation would also have been
difficult, due to the lack of sufficient information easily available for those
dates.
It is likely that the spatial distribution of dates is more biased than the
temporal distribution. This is because archaeological activity is mainly
determined by land use intensity: areas with higher intensity would show a
higher density of archaeological sites. However, this kind of spatial bias
should not affect temporal distributions if archaeological data are a
sufficiently random sample of the temporal dimension of the true
archaeological signal. This is demonstrated in Figure 4. Otherwise, possible
spatial biases are not evaluated in this study.
The possible biasing effects of research interests are studied by comparing
the temporal frequency distribution of 14C dates from eastern Fennoscandia
to other independent archaeological population proxies, by studying how the
distribution might have changed throughout the history of radiocarbon
dating in Finland, and by studying how the distribution might vary between
different date submitter classes.
28
Ceramic site frequency index
The frequency of sites dated typologically using ceramic finds is used as an
alternative measure of population size. From c. 7200 cal BP onwards,
Finnish Stone Age and Bronze Age (c. 3500–2500 cal BP) sites are most
easily recognised via pottery finds, which form the basis for the chronological
and geographical division of the archaeological cultures. It should be noted
that the beginning of pottery use in hunter–gatherer populations did not
indicate a shift towards productive economies. This means that pottery was
adopted and used in Mesolithic conditions. In Finland, this era is often
referred to as “Subneolithic,” contrary to the true farming Neolithic.
豰р
Figure 4. Simulated example showing how modern land use intensity could bias the spatial, but
not temporal distribution of the archaeological sample. In the left panel of A, the true signal is
uniformly distributed over the total area. Because of modern land use, the spatial distribution of
the archaeological sample is systematically biased and does not reflect the true signal. However,
despite this spatial bias, the temporal distribution in the archaeological sample reflects the
bimodal distribution of the true signal. The figure is based on simulated data that have three
variables: age, longitude (x), and latitude (y). In the horizontal panel A, coordinates follow a
uniform distribution (within the defined area) and in B, y follows normal and x follows gamma
distribution. In A and B, age follows the temporal distribution shown in the left panel (true signal
panel). The left panel shows the complete data. For the right panel (archaeological signal), the
data was sampled. In A, the sample was biased so that lower latitudes (smaller y) were more
likely to be included in the sample. In B, the data were randomly sampled.
29
Materials and methods
The ceramic database was constructed by Pesonen during the late 1990s for
educational purposes (Pesonen 1999). The database is a compilation of
information from several sources, the most important being the collections of
the National Museum of Finland. Originally, the database contained c. 6000
catalogue numbers, including Neolithic, Early Metal Age, and Iron Age
ceramics. The database was later modified so that each site has as many
entries as it has recognisable ceramic types. This made it possible to calculate
the number of sites containing each ceramic type. As the Iron Age collections
were not studied, and the number of sites is based only on the published
data, it is obvious that the database cannot be used as a proxy for the Iron
Age population levels. Thus, the frequency distribution is presented only for
its Subneolithic–Bronze Age portion.
Because most ceramic types overlap both chronologically and
geographically, the number of sites could not be used directly in the
alternative proxy. Therefore, the number of sites having a certain ceramic
type was divided by the length of the period of use (in hundred years) of this
type. In this way, an average number of sites per century for each ceramic
period was derived. These average numbers were then summed together for
each century. The proxy, the Ceramic Site Frequency Index (henceforth CSFIndex), is formed from these calculations, and is coarser than the distribution
of radiocarbon dates. For example, because of the averaging procedure, it
does not take into account changes in frequency that might have occurred
within a certain ceramic period.
Other alternative proxies
In addition to the CSF-index, the eastern Fennoscandian 14C date-based
proxy is compared to other published archaeological and non-archaeological
indicators of human population size: Siiriäinen (1981) presented a
distribution of coastal sites dated using shore displacement chronology. He
assumed that the distribution (number of sites per 100 years) reflects
changes in human population size. Siiriäinen’s (1981) curve of site frequency
is presented here so that the time boundaries of different cultural periods are
updated according to the current knowledge.
Recently, Sundell (2014; Sundell et al. 2014) used the number of stone
artefacts and number of stone artefact types in a given period as a human
population proxy. Here, the artefact frequency and number of types in a
given period are divided by the length of the period to account for the varying
durations of different cultural periods.
In his study about the beginning of agriculture in Finland, Hertell (2009)
used the percentage of seal bones in coastal assemblages as a proxy for
human population density. This is based on the fact that in the ethnographic
record there is a positive correlation between hunter-gatherer population
density and the proportion of aquatic resources in the diet (e.g. Binford 2001;
Kelly 2013). In this summary paper the same idea is used, but with a larger
dataset (osteological archives compiled by Pirkko Ukkonen and Kristiina
30
Mannermaa at the Finnish Museum of Natural History and unpublished
osteological reports at the National Board of Antiquities) and a different
periodisation: Early Mesolithic (11,000–8500 cal BP), Late Mesolithic
(8500–7200 calBP), Early Neolithic (7200–6000 cal BP), Middle Neolithic
(6000–5400 cal BP), and Late Neolithic (5400–3500 calBP). Assemblages
were dated using information from the ceramic and 14C date databases
described above. Here the mean percentage of marine mammal bone
fragments per period is used as a proxy (see also Tallavaara et al. 2014).
Genetics provide data on population history that is totally independent of
archaeological data and methods. In this study, archaeological proxies are
compared to the timing of the genetic bottleneck inferred from Finnish
genetic data, and dated using a molecular clock (Sajantila et al. 1996). To put
it simply, a genetic (or population) bottleneck means that before and after
the bottleneck, the effective population size has been larger than at the
bottleneck (for more about prehistoric population bottlenecks see Sundell
2014; Sundell et al. 2014).
Historical changes in research interest
The data produced by the Dating Laboratory of the Finnish Museum of
Natural History and its predecessors (N=1979) includes the most complete
information on, for instance, sample submitters and submission dates within
the total data set, thus allowing to study the variation through time and
between different date submitter classes.
珐п
Radiocarbon dating in Finland started during the late 1960s. To study the
possible changes in research interests through time, uncalibrated 14C dates
were divided according to the decade during which the sample was submitted
to the laboratory (1968–1979, 1980–1989, 1990-1999, 2000–2008). If
research interests or other systematic effects have changes over the years, the
shape of the date distribution should vary over the decades.
Variation between sample submitter classes
The same subset of the data was used to study the possible differences
between different sample submitter classes, by classifying the data into four
mutually inclusive classes. These are: a) Individual NBA-submitted dates not
belonging to any larger date series. These are assumed to form the least
biased distribution, because in their studies NBA should not have major a
priori preferences set over any archaeological period. b) The dates submitted
by the 16 most active submitters. c) The remaining 127 sample submitters
(there are altogether 143 different sample submitters). d) The dates
submitted by the Early in the North project, which was the largest individual
project that had submitted dates prior to 2010.
If there is a marked research bias affecting the temporal frequency
distribution of 14C dates, this should show up as a variation between the
above-mentioned classes.
31
Materials and methods
2.1.1.2 Taphonomic bias
Surovell et al. (2009) argue that younger findings are over-represented
relative to older findings in the archaeological record due to the timedependent influence of destructive processes such as erosion and weathering.
Similar time-dependent loss-processes seem to affect geological and
palaentological data (Surovell and Brantingham 2007), as well as historical
coin records (Peros et al. 2010).
Surovell et al. (2009) propose a model of taphonomic bias that is based on
the observation of the deviation between terrestrial and ice core records of
volcanic activity. Terrestrial record shows a clear time-dependent decrease in
the number radiocarbon dates associated with volcanic deposits, whereas ice
core records of volcanism do not show such a time-dependent pattern. Since
the ice core record does not suffer from taphonomic loss due to constant
accumulation, this difference between records suggests that the long-term
trend in the terrestrial record is governed by taphonomic loss of volcanic
deposits.
The model can be used to make a taphonomical correction to the temporal
frequency distribution of the dates. The model gives the number of
sedimentary deposits preserved from the given time. Dividing the number of
archaeological dates from this time by the number of surviving deposits gives
the taphonomically corrected, unstandardized value for the number of dates.
In paper II, a slightly modified version of this model was used, based only
on the Holocene dates. Williams (2012)
published yet another modified
碰п
model of the taphonomic bias. His version of the model corrects the
increasing deviation in residuals in the most recent and oldest time intervals,
and unstable variance that changed over time. Williams (2012) also suggests
that the original and his version of the model over-corrects the number of
dates older than 25,000 cal BP (see also Williams 2013). Despite this
possible over-correction, Williams’ model was used in paper V to correct the
temporal frequency distribution of dates between 30,000 and 13,000 cal BP.
In this summary paper, Williams’ model is also used to correct the
distribution of dates from eastern Fennoscandia.
2.1.1.3 Calibration bias
Because the rate of radiocarbon production is not constant, the calibration
curve that transfers 14C ages to calendar ages is not linear. This, in turn, has
some problematic consequences: when a uniform distribution of calendar
dates is converted to 14C ages and recalibrated after adding some constant or
random standard error, the resulting distribution of calibrated dates (SPD or
distribution of calibrated median dates) is far from uniform (Fig 5). Instead,
calibration creates some packing of the dates and, consequently, also less
dense parts in the distribution, even though the true distribution of calendar
ages is uniform. It is suggested that sharp declines in the calibration curve
32
create distinct peaks in the probability distributions, whereas plateaus in the
curve even out any peaks (Guilderson et al. 2005; Michczyński and
Michczyńska 2006). However, as Figure 5 shows, the correlation between the
shape of the calibration curve and resulting SPD or distribution of calibrated
medians is not perfect (see also Brown 2015).
;к
Figure 5. The effect of calibration on 14C date distributions. When a uniform distribution of
calendar ages is turned into calibrated radiocarbon ages, the distribution is not uniform anymore.
Nevertheless, all of this indicates that calibration procedure can create
artificial peaks and troughs in the date distribution, which can mistakenly be
interpreted in demographic terms. This is problematic, especially when the
focus is on short-lived population events, such as the ones possibly related to
rapid climate events. The fact that at least some of the rapid cold events
coincide with the anomalies in the atmospheric 14C content (Hughen et al.
2000) makes this even more problematic. Recently, Shennan et al. (2013)
suggested a computationally heavy simulation approach to evaluate the
impact of calibration bias on SPD. In paper IV, a simpler, albeit less effective
and accurate, method is used to evaluate calibration bias when studying the
impact of the so-called 10,300 cal BP cold climate event on the early pioneer
population in eastern Fennoscandia. The calibration bias is studied first by
creating an artificial, evenly spaced (10 14C yrs) set of dates with identical
standard errors (80 yrs) covering the same span as the archaeological data.
After calibrating the simulated set, it is possible to compare the peaks and
33
Materials and methods
troughs of the simulated distribution of median dates with the peaks and
troughs of the archaeological date distribution, and to determine which peaks
and troughs in the archaeological distribution are merely calibration
anomalies.
In this summary paper, calibration bias is studied differently from the
approach used in paper IV, following a slightly simplified version of the
Shennan et al. methodology (2013; see also Brown 2015): first, calendar
dates are sampled (N=N of archaeological sample) from the distribution that
follows the trend in archaeological date distribution (loess smooth with the
span = 1). Such distribution takes into account the long term trend in the
number of dates in the archaeological data, but leaves out all short-term
fluctuations. Next, calendar dates are transferred to 14C age scale and
calibrated after adding measurement error. These steps are repeated 200–
1000 times to account for the sampling variation in calendar date sampling
and in calendar-age-to-14C-age-transfer. Finally, the 5th-to-95th percentile
range is calculated. This range covers 90% of the simulations, excluding the
highest and lowest 5% of the values in a given time bin. Such range,
illustrated e.g. by a polygon, represents an archaeological signal of the truly
monotonic growth pattern, which does not contain any major fluctuations. In
other words, it indicates how the truly smooth distribution would show up in
archaeological data if long-term growth rate, sampling, and calibration would
have been the only factors affecting the shape of the distribution. Negative
and positive deviations between this null model and the observed
archaeological pattern are so rare, if the null model holds that they can be
considered as statistically significant differences. On the other hand, if the
simulated and archaeological distributions show corresponding short-term
fluctuations, archaeological fluctuations may not represent a true
demographic signal, but only bias related to calibration and/or sampling.
All these steps are performed using R statistical software (R Development
Core Team 2014). Calibration is performed using the functions of clam
algorithm in R (Blaauw 2010).
2.2 MODELLING OF PREHISTORIC HUNTERGATHERER POPULATION DYNAMICS
As mentioned in Chapter 1.3, archaeological data can provide information
mainly on the relative changes in past population size, and if quantitative
estimates of population size or density are needed one must turn to other
approaches. Modelling and simulations can provide one solution to this
problem. At the same time, by providing a complementary line of evidence,
they contribute to the fuller and more valid picture of prehistoric population
patterns.
The tradition of predictive modelling of site locations is especially strong
in archaeology, but modelling was also used to study socio-cultural processes
34
already in the 1970s, and during recent decades this has become even more
important as computational power has increased (Lake 2014). Today,
modelling and simulations have a strong foothold especially in the study of
human-environment interaction (e.g. Crabtree and Kohler 2012).
2.2.1 CLIMATE ENVELOPE MODELLING APPROACH
The modelling approach used in this study (paper V, and in a much smaller
and simpler scale in paper III) is a kind of a predictive modelling. It is based
on the simple idea of using information on how climate affects population
densities of ethnographically known hunter-gatherers to predict or hindcast
(or retrodict) prehistoric hunter-gatherer population densities. Basically, the
only information this approach requires about the past is the information
about past climate conditions, which is possible to acquire by using
palaeoclimate model data.
As shown in chapter 1.3, the idea of using ethnographic and/or
environmental information to infer prehistoric population densities is not
novel, but ethnographic data has only rarely been put to systematic use
regarding this issue. Instead, the fields of biogeography and ecology have
developed highly sophisticated quantitative methods of using information
from the present to model future or past changes in species range and
abundance. These climate envelope models, or niche models, are increasingly
used in ecology to predict forthcoming
climate-related changes in species
;к
distribution (Araújo and Peterson 2012; Pearson and Dawson 2003; Virkkala
et al. 2013). They are also used in paleobiology to complement incomplete
and coarse fossil-based species distributions, to provide hypotheses
regarding past communities and their dynamics, and to assess the
determinants of organism distributions (Nogués-Bravo et al. 2008; Svenning
et al. 2011).
The aims of climate envelope modelling in palaeobiology (Svenning et al.
2011) translate easily to archaeology, where such modelling can provide:
a. quantitative and high resolution predictions of past human
distributions
b. testable ecological hypotheses regarding human populations and
their dynamics
c. quantitative assessment of the climatic determinants of human
distribution
In addition to demography, the same approach can be used to model other
aspects of human behaviour that potentially have link to climate, such as
mobility, subsistence and technology.
In series of publications, Banks et al. (2008a,b, 2009, 2011, 2013) apply
what they call eco-cultural niche modelling that aims to elucidate the
influence of environmental factors on social and technical systems. Their
approach is closely related to ecological niche modelling, but it differs from
the modelling strategy used in this study. When modelling human range
35
Materials and methods
during the Last Glacial Maximum in Europe, Banks et al. (2008b) use the
spatial distribution of archaeological sites as a basis for their model, which
they then validate by comparing it to the distribution of archaeological sites
in a test set. This might introduce a risk of circularity, as the training and test
sets are both sampled from the same population of archaeological sites. In
this study (paper V), the model of human population size and range
dynamics is built on ethnographic data, and is thus independent of the
archaeological record, which allows the evaluation of the model against
archaeological population proxy. This must be considered as a clear
advantage.
Climate envelope models belong to the class of species distribution or
niche models. According to classic definition, the fundamental ecological
niche of a species comprises those environmental conditions within which a
species can survive and grow (Hutchinson 1957). Climate envelopes, for their
part, constitute the climatic component of the fundamental niche, and can
therefore also be defined as a climatic niche (Pearson and Dawson 2003).
Figure 6 illustrates the idea of the climate envelope by showing the envelope
of human hunter-gatherers in terms of four climate variables.
;к
Figure 6. Climatic envelope or niche of hunter-gatherers in terms of four climate variables. The
size of the bubble is related to population density. The figure is based on Binford’s (2001) data.
It shows the climate conditions where ethnographic hunter-gatherers are
present, and how abundant they are under different conditions. Climate
envelope modelling uses that kind of information on how climate affects the
current distribution of a species in order to hindcast past (or predict future)
changes in the range and density of the species under the selected climate
change scenario.
36
Climate envelope modelling requires (Araújo and Peterson 2012; NoguésBravo 2009; Pearson and Dawson 2003; Svenning et al. 2011):
1. Training or calibration data that includes information on current
species distribution and current climatic conditions.
2. A statistical model, or several models, that are fitted to the training
data (a model algorithm is trained using the training data). This
model predicts species distribution as a function of chosen climate
variables. It is a calibration model that transfers the values of
climate predictors to population density and presence/absence,
when past (or future) species distribution is simulated.
3. Climate data that describes past (or future) climate conditions.
This climate data is input data to the statistical model, when past
(or future) species distribution is simulated.
4. Independent validation data against which the realism of the
simulation is evaluated. Not always available, but when the focus is
on the past, archaeological or fossil records can provide such data.
These requirements for the model used in this study are described in more
detail below.
All the species distribution models, including climate envelope models,
assume an equilibrium between species distribution and environment. This
means that species occurs everywhere the climate (or the environment in
general) is suitable, and is absent from unsuitable areas. It is well know that
this assumption can easily be violated, as a species’ range and abundance can
be in disequilibrium with the environment because of dispersal limitations
and/or interactions with other species (Nogués-Bravo 2009; Svenning et al.
2011).
It is clear, for example, that the geographical distribution of
ethnographically documented hunter-gatherers does not reflect the area that
is suitable for hunter-gatherers, because large areas previously occupied by
foragers are dominated by agricultural populations from the Mid-Holocene
onwards. However, as Binford (2001: 130–159) has shown, an ethnographic
sample of hunter-gatherers reflects their niche variability reasonably well,
because there is no bias regarding the plant community types or plant
productivity despite the clear geographical bias: there are ethnographic
hunter-gatherer populations living in niches that, in some parts of the world,
are now occupied by agricultural populations.
An even more important assumption for the studies, where information
on a present niche is used to simulate the past or future range and abundance
of a species, is niche stability through time. Any considerable shift in a
species’ niche will reduce model transferability across time (Nogués-Bravo
2009; Svenning et al. 2011). Through technological evolution, humans have
been especially prone to expand their niche by adapting to new environments
and increasing the productivity of already occupied areas. This poses a clear
challenge to bioclimatic envelope modelling of prehistoric humans. However,
in hindcasting studies, it is possible to compare model simulations to the
37
Materials and methods
independent archaeological (or palaeobiological) record. Significant
differences between simulated and archaeological distributions would
indicate niche shift, provided that the archaeological record is not a
completely biased sample of the true distribution.
2.2.1.1 Ethnographic training data
In the model construction process, the ethnographic dataset (N=339) of
hunter-gatherer populations compiled by Binford (2001) was used to extract
calibration data to train the statistical models. Among other things, this
dataset includes information on population density, resource use, mobility,
and climatic and other environmental conditions of hunter-gatherer
populations. In paper III, a subset of the data containing hunter-gatherer
populations from arctic to temperate zones were used as training data
(N=198).
Because isotope studies of human bone collagen indicate that the
Pleistocene hunter-gatherers obtained, at most, 30% of their dietary protein
from aquatic resources (Drucker and Bocherens 2004; Richards et al. 2005),
only populations whose main livelihood comes from terrestrial resources
(SUBSP≠3) were selected for the training data in paper V, where focus is on
the Ice Age human population. Increased use of aquatic resources is an
example of niche expansion that occurred among some hunter-gatherer
populations during the Holocene. Aquatic
;к resources are essential for groups
living in polar environments, and they enable generally higher population
densities among hunter-gatherers (Fig. 7). In addition to the exclusion of
aquatic hunter-gatherers, cases whose subsistence is based on mutualistic
relations with non-hunter-gatherers (SUBPOP=X) and mounted huntergatherers (SYSTATE3=1) were excluded from the training data. Furthermore,
to keep the simulated population densities conservative, populations that
either move into and out of a central location that is maintained for more
than one year, or are completely sedentary (GRPPAT=2) were excluded. Such
groups usually live under high population densities. This exclusion means
that the simulation assumes that the Pleistocene human populations in
Europe were residentially fully mobile, an assumption commonly held by
archaeologists.
Altogether, the training data includes information on 127 hunter-gatherer
populations. Because this dataset gives information only on environments
where the hunter-gatherers have existed in recent historical times, 120
pseudo-absence data points to the climate space, where terrestrially adapted
hunter-gatherers have not existed during the historical period (e.g. extremely
cold or extremely hot and dry), were added to enhance the performance of
the statistical models (paper V). The climate data for these points were
obtained from the WorldClim database (Hijmans et al. 2005). Addition of
pseudo-absence data to presence-only data is a standard procedure in
ecological modelling (Barbet-Massin et al. 2012; Phillips et al. 2009).
38
Figure 7. The adaptation to aquatic resources is an important factor that has enabled the
colonisation of the coldest environments, and also the support of higher population densities.
Data from Binford (2001).
Of the available climate variables, mean annual temperature (CMAT) was
selected as a predictor of population density (DENSITY) in paper III focusing
on the Holocene population history in eastern Fennoscandia. In paper V,
;к water balance (WAB), and mean
potential evapotranspiration (PET),
temperature of the coldest month (MCM) were selected as predictors of the
density and presence (DENSITY>0) / absence (DENSITY=0) of the Ice Age
human population in Europe. PET and WAB were selected because they exert
a strong influence on ecosystem productivity and species richness, and
therefore also on food availability for human foragers, whereas MCM affects
winter mortality (Currie 1991; Franklin 2010; Stephenson 1990). PET and
MCM values are directly available from the ethnographic dataset. WABvalues were calculated as the difference between annual precipitation and
PET.
2.2.1.2 Statistical model
In paper III, the climate envelope modelling approach was used in a
simplified way. A calibration model was constructed simply by fitting a
generalized linear model (McCullagh and Nelder 1989) with gamma error
distribution and log link function to predict population density with annual
mean temperature.
In paper V, significantly more sophisticated methodology was
implemented. To model the distribution and density of the human
population, two frameworks were used: One predicting the range
(presence/absence) of the human population and the other predicting
39
Materials and methods
population density. The human population occurrence was modelled as a
binary response variable, and density as a continuous response variable.
Because different modelling algorithms can give different predictions, six
alternative state-of-the-art techniques were used to relate human
presence/absence and density with the explanatory climatic variables. These
techniques are generalized linear modelling (McCullagh and Nelder 1989),
generalized additive modelling (Hastie and Tibshirani 1990), support vector
machines (Cortes and Vapnik 1995; Drucker et al. 1997), classification tree
analysis (Breiman et al. 1984), random forest (Breiman 2001), and
generalized boosting methods (Elith et al. 2008). The final simulation of Ice
Age population dynamics was achieved by averaging the resulting six
predictions (see below).
In paper V, the models’ predictive accuracy was assessed using cross
validation. This means that the ethnographic training data was randomly
split into training (70%) and validation (30%) subsets 100 times. In each
round, a model was built using the training split and its ability to correctly
predict population density and presence/absence was tested using the
validation split. The predictive power of the binary models was determined
by testing the accuracy of predictions made for the validation dataset (i.e.
data that were not used in model development), calculating the area under
the curve of a receiver operating characteristic plot (AUC) (Fielding and Bell
1997) and the true skill statistic (TSS) (Allouche et al. 2006). For density
models, mean R2 between predicted and true values were calculated.
2.2.1.3 Human population range and density simulation
In paper III, the aim of the simple modelling exercise was to produce a null
model or simulation that would indicate how population density would have
varied throughout the Holocene if it was determined by annual mean
temperature alone. This was achieved by inputting the annual mean
temperature values of a pollen-based temperature reconstruction (see below)
into a calibration model that transferred temperature values into huntergatherer population densities. This simulation, which only includes a
temporal component, was compared to an archaeological population proxy to
determine if other factors than annual mean temperature would also have
influenced long-term hunter-gatherer population dynamics, i.e., whether
there are clear differences between simulated and archaeological patterns.
In paper V, the range of the human population for every 1000 years
between 30,000 and 13,000 cal BP was simulated by predicting the
presence/absence of humans for every 0.375° by 0.25° cell containing land
area in Ice Age Europe. Eustatic changes in the sea level and the consequent
changes in the land area of Europe were taken into account by adjusting the
sea level according to the global sea level change curve (Peltier and Fairbanks
2006). The spatial and temporal resolution of this simulation is determined
by the resolution of the climate model (see below) that was used to obtain the
40
values of climate predictors for the Ice Age Europe. The range simulation was
done by using the above-mentioned six calibration model algorithms and
climate predictor values derived from the climate simulation. The climate
simulation-based monthly average temperature and annual precipitation
values were used to calculate PET and WAB values. WAB was calculated as
the
difference
between
annual
precipitation
and
potential
evapotranspiration. PET was calculated as:
PET=58.93×Tabove 0 °C
where Tabove 0 °C is the average temperature of the months with positive
temperatures (Holdridge 1967).
Because different calibration model algorithms usually give slightly different
predictions, their results were averaged by using ensemble averaging
methods (Araújo and New 2007). For binary models, a majority vote was
used, which is an ensemble forecasting method that assigns a presence
prediction only when more than half of the models (i.e. >3 in this case)
predicts a presence. Next, population density was predicted for every 0.375°
by 0.25° cell inside the modelled range. For density models, to average the
results based on different algorithms, their median was calculated for each
cell.
To calculate the human population size in Europe for every 1000 years,
;к
the land area of each cell was first calculated. Here, the systematic northsouth areal change of the 0.375° by 0.25° cells was taken into account. Next,
the predicted population density (median of six predictions) of the cell was
multiplied by the land area of the cell, and these values were summed to get
the total population size.
To evaluate the uncertainty of the population size estimates, the whole
process from calibration model fitting to the calculation of population size
was repeated 500 times, each time using a random sample (70%) of the
training/calibration data. This enabled the calculation of confidence limits
for the simulated population size estimates. The set of modelling techniques
and climate data was held constant throughout the process.
To evaluate the results of the bioclimatic envelope model simulation, the
simulated range and size of the human population was compared to an
archaeological population proxy based on archaeological 14C dates (see
above). The temporal comparison between the simulated population size and
the relative population size given by archaeological proxy was done
statistically, by calculating correlation of these data between 30,000 and
13,000 cal BP. Spatial comparison was done only by eyeballing. These
comparisons indicate whether the climate envelope of hunter-gatherers has
remained the same from the Pleistocene to the Holocene, and whether the
climate has an impact on hunter-gatherer population dynamics.
41
Materials and methods
2.3 PALAEOENVIRONMENTAL DATA
As has been seen, palaeoclimatic reconstructions play a critical role in this
study. However, because the aim in this study is not to produce new
knowledge on past climate or environments and because reconstructions are
made by experts in palaeoclimatology and climate modelling, these data and
methods are only briefly described here.
The aim was to select climatic and ecological variables that would exert a
strong influence on ecosystem productivity, and thus on food availability for
hunter-gatherers, who depend on natural plant and animal resources for
their survival. In addition, factors that have a more direct impact on human
well-being, such as winter temperature, play an important role in huntergatherer population dynamics.
2.3.1 PALAEOCLIMATIC AND PALAEOECOLOGICAL PROXY DATA
In eastern Fennoscandia, archaeological population proxies are compared to
palaeoenvironmental proxies, of which pollen-based temperature
reconstructions are the most important. These are based on using modern
pollen-climate calibration sets to create transfer functions that can be applied
to obtain quantitative reconstructions of past temperature or, in some cases,
precipitation. The idea is thus similar to the human population model
described above: to use the link between
;к the composition of modern pollen
assemblages and climate to infer past climate. Here, the only information
required from the past is pollen composition obtained from sediment
records.
There are many aspects in quantitative climate reconstructions that can
affect the reconstruction output, such as the selection of the transfer function
techniques and the design of the calibration set used for constructing the
transfer function. These aspects have been discussed by Juggins & Birks
(2012). In paper III, the technique used in the reconstructions is weightedaveraging partial least squares (WA-PLS), one of the most commonly used
reconstruction techniques in quantitative palaeoclimatology. Juggins & Birks
(2012) discuss the many advantages of this technique. One problem with this
method is that the absolute reconstructed values of climate parameters can
be highly sensitive to the characteristics of the calibration dataset (Salonen et
al. 2013b). However, the reconstructed patterns appear to be robust
irrespective of the calibration data selection, which enables the correction of
possibly biased absolute values by fixing the curve with modern observed
values (Salonen et al. 2013b). Furthermore, in this study, the sensitivity of
the absolute reconstructed values to the calibration set design is not a critical
problem, since the focus is on the patterns, i.e. on the relative changes of
climate parameters (papers I, III, IV).
In papers I and IV, temperature reconstructions from single high
resolution pollen records are used (Heikkilä and Seppä 2003; Seppä et al.
42
2002), whereas in paper III, the mean annual temperature reconstruction is
based on the combination (stacking) of four pollen records from southern
Finland (Heikkilä and Seppä 2003; Ojala et al. 2008; Sarmaja-Korjonen and
Seppä 2007; Seppä et al. 2009b).
In addition to temperature, proxies for hydroclimate, the intensity of the
primary production season, and aquatic productivity are used in paper III.
The hydroclimatic conditions of eastern Fennoscandia are tracked with
cellulose-inferred oxygen isotope (δ18O) values from one lake sediment core
(Heikkilä et al. 2010). The oxygen isotope values of the lake sediment core
reflect partly the isotopic composition of the meteoric water, and partly the
general water volume of the lake, which is controlled by the outcome of
precipitation and evapotranspiration. As summer evaporation causes
enrichment of the heavier δ18O in relation to lighter δ18O, increased δ18O
values indicate warmer and drier conditions. The annual accumulation of
organic matter in the annually laminated Lake Nautajärvi reflects the
autochthonous primary production of the lake, while at the same time, and
perhaps more importantly, it can be used as a proxy for the warmth and
length of the primary production season (Ojala and Alenius 2005; Ojala et al.
2008). Another proxy used for aquatic production in paper III is the nitrogen
content of the lake sediment (Heikkilä et al. 2010), which, however, is rather
lake-specific and does not reflect any general pattern in the productivity of
aquatic ecosystems, and therefore is not shown in this summary paper.
The Baltic Sea has been an important resource environment for humans
;к
throughout the Holocene. In addition to temperature, its productivity is
affected by the inflow of saline North Atlantic waters (Möllmann et al. 2000;
Ojaveer et al. 1998; Sohlenius et al. 2001; Tuovinen et al. 2008; Vuorinen et
al. 1998; Westman and Sohlenius 1999). After the beginning of the latest
brackish water Litorina stage at around 8500 cal BP, the inflow has been
controlled by meteorological conditions, freshwater input, and the change in
the inlet cross-section area at the Danish Straits (Gustafsson and Westman
2002), which is the most important non-climatic factor. In this study, it is
assumed that the highest productivity of the Baltic Sea occurs when high
salinity and temperature values coincide. In paper I, the highest salinity
levels are indicated according to several sources (Emeis et al. 2003; Tuovinen
et al. 2008; Westman et al. 1999; Westman and Sohlenius 1999), while in
paper III and in this summary paper the salinity of the sea is tracked with the
continuous proxy data given in Gustafsson and Westman (2002).
Changes in the forest ecosystem from tundra to birch forest (paper IV),
and from temperate mixed forest to spruce dominated boreal forest (paper
III), are tracked with several pollen records forming relevant south-north
(Bondestam et al. 1994; Salonen et al. 2013a; Seppä 1996; Seppä et al. 2002;
Seppä and Hammarlund 2000; Subetto et al. 2002) and east-west (Alenius
and Laakso 2006; Heikkilä and Seppä 2003; Ojala and Alenius 2005; Ojala
et al. 2008) transects over the research area.
43
Materials and methods
2.3.2 SIMULATED CLIMATE DATA
In paper V, monthly average temperature and annual precipitation values for
Europe were generated using a full last glacial cycle simulation (126000
years ago until the present-day) with the CLIMBER-2-SICOPOLIS model
system (Ganopolski et al. 2010), which simulates climate at a temporal
resolution of 1000 years. CLIMBER-2 is a so-called Earth system model of
intermediate complexity (EMIC). It includes model components for the
atmosphere, ocean, sea ice, land surface, terrestrial vegetation, and ice sheets
(Petoukhov et al. 2000). The reason for using an EMIC instead of an Earth
system model of full complexity (ESM), or general circulation models (GCM),
is that the time-period is too long for GCM simulations with adequate spatial
resolution. Therefore, downscaling the low resolution simulation results
produced by the climate model is necessary regardless of the choice of the
simulation model. Climate data was first downscaled statistically to the
resolution of 1.5° (longitude) by 0.75° (latitude) (Korhonen et al. 2014). The
temperature data were then re-gridded to 0.375° by 0.250°. During the regridding process, monthly temperature values were lapsed by the pseudo
adiabatic lapse rate (6.4 °C/km) to account for differences in average
elevation between the fine-scale and coarse-scale grids (Vajda and
Venäläinen 2003).
Although climate models provide more detailed information on past
climates than palaeoclimatic records, they are only models, and as with all
models they are not perfect representations
of reality. Different climate
;к
models would give at least slightly different results. One specific problem
with the current model is that it cannot simulate high-frequency climate
variations. Therefore, for example, some of the Late Pleistocene cold events,
such as Heinrich Stadial 1, do not show up in the model data.
44
3 RESULTS
3.1 EVALUATION OF ARCHAEOLOGICAL POPULATION
PROXY
Figure 8 shows that the patterns in the temporal distribution of 14C dates are
remarkably consistent between different date submitter classes and
throughout the history of 14C dating in Finland. All curves indicate low Early
Holocene frequencies, a distinct period of growth and decline between 6000
and 4000 14C years ago and a new rise in date frequencies starting from 3000
14C years ago. For the Early to Mid-Holocene, the clearest deviance is
produced by the dates of the Early in the North project, because their highest
frequencies are reached slightly earlier than in other curves. The spatial
coverage of these dates is, however, rather restricted (northern Finland)
when compared to the other date submitter classes, and the small differences
may reflect regional peculiarities in the temporal distribution of
archaeological material. These can relate to true differences in prehistoric
population history, or the research situation in sparsely populated northern
Finland. Nevertheless, the Early in the North dates also show the same
general Mid-Holocene pattern of growth and decline as other curves.
Figure 9 shows further that, for the Stone Age (11,000—3500 cal BP), the
;к
pattern in date frequency (Fig. 9 A) is similar to other spatially and/or
temporally more restricted archaeological population proxies (Fig. 9 D-H):
all the records show the same Mid-Holocene rise and decline culminating
slightly after 6000 cal BP. Furthermore, genetic data indicate a Late Stone
Age population bottleneck (Fig. 9 I) occurring around 3900 cal BP (Sajantila
et al. 1996), i.e. at the same time with the lowest post Mid-Holocene peak
levels in date distribution. Based on the characteristics of the archaeological
data, Lavento (2001: 175–177) has also suggested that population size
declined, and may even have gone locally extinct at the end of the Late Stone
Age.
All this strongly suggests that research biases do not have a marked effect
on the shape of the temporal frequency distribution of 14C dates and that the
date distribution truthfully reflects the distribution of known archaeological
material. As suggested in Chapter 2.1.1.1, it is possible to assume that the
distribution of the known archaeological material reflects the real
distribution of archaeological material when a dataset is large and/or most of
the research is done by cultural heritage management without any strong
research interests, as is the case in Finland.
It is also obvious that the Mid-Holocene part shows significantly more,
and the Late Stone Age-Early Bronze Age significantly less, dates than the
null model of monotonic growth pattern would suggest (Fig. 9 B). Thus, the
45
Results
observed long-term pattern is not influenced by calibration and/or (random)
sample variation.
;к
Figure 8. The 14C date dataset produced by the Dating Laboratory/Laboratory of Chronology of
the Finnish Museum of Natural History, divided according to sample submitter class (A) and
decade during which the sample was submitted to the laboratory (B).
Based on the evidence presented so far, it is possible to assume that the
temporal distribution of 14C dates reflects the strength of true archaeological
signal and therefore the size of the human population that produced the
archaeological material. However, when the possible taphonomic bias is
taken into account, the pattern changes (Fig. 9 C). In the taphonomically
corrected distribution, the Late Holocene population remains smaller than
during the Mid-Holocene population peak. This may have interesting
implications regarding the cultural history of Finland (see Discussion).
Nevertheless, taphonomic correction does not markedly change the pattern
for Early to Mid-Holocene hunter-gatherers, which are the main focus of this
study: the distinct period of Mid-Holocene growth and decline remains
unchanged. In contrast, when dealing with the Pleistocene data in paper V,
taphonomic correction exerted a strong influence on the shape of the date
distribution and thus also on the results of the paper (see paper V).
46
Figure 9. Comparison of 14C date-based population proxy to other proxies, and evaluation
against calibration and taphonomic bias. A: Human population proxy based on the temporal
frequency distribution of calibrated median 14C dates from Finland (with 100 years bins). B:
Population proxy evaluated against calibration (and sample) bias. Red areas indicate significantly
higher and blue areas significantly lower frequency of dates than the null model of monotonic
growth pattern (dotted line and grey polygon) would suggest. Archaeological realisation of the
null model (grey polygon) is based on 1000 simulations, and it shows the range that covers 90%
of the simulations. C: Taphonomically corrected distribution of dates. D: Ceramic Site Frequency
Index that shows the distribution of sites dated typologically using ceramic finds (Paper I). E:
Distribution of the number of coastal sites (Siiriäinen 1981), adjusted for the length of each period
(Site Frequency Index). F: Distribution of the absolute number of stone artefacts assigned to a
given period (Sundell et al. 2014), adjusted for the length of each period (Artefact Frequency
Index). G: Distribution of the number of different artefact types associated with each period
(Sundell et al. 2014), adjusted for the length of each period (Type Index). H: Percentage of
marine mammal bone fragments in coastal archaeological bone assemblages. I: Timing of the
genetic bottleneck inferred from Finnish genetic data (Sajantila et al. 1996).
47
Results
3.2 CORRESPONDENCE BETWEEN HOLOCENE
CLIMATE CHANGE AND LONG-TERM POPULATION
DYNAMICS
Figure 10 shows that the outlines of the 14C date-based population proxy and
different indicators of climate and ecosystem productivity are correlated
between 11,000 cal BP and 3500 cal BP in Fennoscandia. Similarly to human
population, temperature as reflected in pollen based reconstruction and
lacustrine δ18O data, warmth and length of primary production season, as
well as Baltic Sea productivity, all show a rise, culminating at or slightly after
6000 cal BP, and a subsequent decline. Similarity between the population
proxy and the simulated population density suggests further that annual
mean temperature alone is an important variable affecting hunter-gatherer
population dynamics. Despite the overall similarity, these two patterns differ
in the relative strength of the population peak, which is more pronounced in
the proxy data than in the model. This difference suggests that the annual
mean temperature does not solely explain the variability in hunter-gatherer
population size. Instead, the pattern of the warmth and length of the primary
production season (Fig. 10 C) is highly similar to that of the human
population, with its clear peak at 6000 cal BP. Together with the salinity of
the Baltic Sea, this indicates that the productivity of the terrestrial and
aquatic ecosystems, and consequently food availability for human huntergatherers, probably was especially high between 6000 and 5000 cal BP.
Figure 10 also explores the possible link between hunter-gatherer
population size and major changes in the forest composition in Finland.
Palaeoecological research has shown that a forest composition change
occurred during the Holocene in Fennoscandia, as Norway spruce gradually
invaded and expanded in the region from the east. Norway spruce eventually
became the dominant species by outgrowing and suppressing the temperate
deciduous taxa, particularly lime, that were important components of the
forests prior to the spruce expansion (Seppä et al. 2009a). It appears that
there is an interesting correlation with the timing of the forest ecosystem
change and population size in Finland, as the population decline coincides
with the rise of the spruce and consequent rise of the boreal forest ecosystem.
However, the positive correlation between human population size and
environmental factors seems to vanish after c. 3500 cal BP, as population
size starts to grow again despite the declining temperature and productivity.
This new rise correlates with the intensification of farming economies that
started from the Bronze Age onwards. There are recent claims of much
earlier cultivation (Alenius et al. 2013), but these are based on single pollen
grains in pollen records and cannot be taken as a conclusive evidence of a
productive economy. The firmest pollen evidence for the earliest cultivation
in the southern part of the research area dates nevertheless to c. 4000 cal BP,
whereas the first macrofossil evidence is c. 500 years younger (Vuorela 1998,
48
1999; Vuorela and Lempiäinen 1988). The earliest signs of dairy farming date
slightly earlier to c. 4500 cal BP (Cramp et al. 2014).
;к
Figure 10. Human population proxy compared to palaeoclimatic and palaeoenvironmental
proxies in eastern Fennoscandia. A: 14C date-based human population proxy together with the
simulation that shows how population density should have varied if it was solely affected by
annual mean temperature. B: Pollen-based reconstruction of annual mean temperature (loess
smoothed). C: Organic varve thickness (loess smoothed) in the annually laminated Lake
Nautajärvi (Ojala et al. 2008). Varve thickness reflects the warmth and length of the primary
production during summer. The Late-Holocene rise is linked to human activity, not to climate
(Ojala and Alenius 2005; Ojala et al. 2008). D: Oxygen isotope (δ18O) values (loess smoothed) of
the sediment of Lake Saarikko (Heikkilä et al. 2010). δ18O tracks changes in hydroclimate. High
values indicate warm and dry conditions. E: Changes in the forest composition indicated by
changes in the pollen percentages of temperate tree taxa and Norway spruce (Alenius and
Laakso 2006; Heikkilä and Seppä 2003; Ojala and Alenius 2005; Ojala et al. 2008; Seppä et al.
2009a). F: Salinity of the Baltic Sea (Gustafsson and Westman 2002). Together with
temperature, salinity affects the productivity of the sea. The figure also highlights the MidHolocene period, when the productivity is assumed to be high and when the hunter-gatherer
population size has also been highest.
49
Results
The first scanty evidence for farming is followed by the increased number of
sample sites with positive Cerealia pollen indications dating to the Bronze
Age, while the beginning of more intense agriculture dates to the Late Bronze
Age, and especially to the Early Iron Age (c. 2500 cal BP onwards). This
intensification is indicated by the continuous presence of Cerealia pollen in
pollen diagrams, as well as by the increased number of cultivars and
increased importance of cattle (Asplund 2008; Edgren 1999; Vuorela 1998).
In the inland, signs of cultivation increase during the Late Bronze Age/Early
Iron Age, even though the beginning of continuous cultivation is dated well
into the historical period (Taavitsainen et al. 1998, 2007). The expansion of
agricultural economies observed at the beginning of the Iron Age is also a
more general phenomenon around the Baltic Sea, which interestingly occurs
in the context of long-term climatic cooling (Asplund 2008: 280).
3.3 IMPACT OF EVENT-LIKE CLIMATE CHANGE ON
HUMAN POPULATION
Figure 11 shows that the so-called 10,300 cal BP cold event coincides with the
sharp decline in the number of 14C dates. This suggests that even short-lived
climate events could have had a profound effect on the small pioneer
population of hunter-gatherers. However, when the distribution of
archaeological 14C dates is compared to the simulated distribution (grey
polygon), it appears that the latter has a similar gap in the distribution. In
general, the archaeological population proxy corresponds well with the
simulation, i.e. the null model that assumes smooth growth over the Early
Holocene. Instead of being a real demographic signal, the gap in the
distribution of archaeological 14C dates between 10,300 and 10,100 cal BP
may thus only be a calibration artefact. Although in paper IV it is argued that
the cold event at 10,300 cal BP had a clear impact on human population size
in Finland, this conclusion now seems premature. It is too early to conclude
whether the cold event had any demographic effect or not, despite the fact
that the decline in the date distribution coincides with the event.
50
Figure 11. Early Holocene population dynamics compared to climate variability and evaluated
;к data (loess smoothed) from Greenland
against calibration bias. A: NGRIP Oxygen isotope
(Rasmussen et al. 2006). B: Pollen-based July temperature reconstruction from Ifjord northern
Norway (Seppä et al. 2002). Ifjord temperature data shows at least three cold events (11,400,
10,800 and 10,300 cal BP) that coincide with the cold events in the Greenland data. C: 14C datebased human population proxy shown as a kernel density plot (black curve). Dark grey curve
shows the null model of smooth growth and the grey polygon shows its archaeological
realisation. The polygon is based on 1000 simulations, and shows a range that covers 90% of the
simulations. Comparison shows that the archaeological proxy follows the model of smooth
growth. The figure also shows the period of reduced vegetation cover in north-western Russia,
linked to the cold event at 10,300 cal BP (Subetto et al. 2002), and two of the episodes of
increased freshwater flux from the melting Laurentide Ice Sheet into the North Atlantic (R1 and
R2), which are assumed to have caused the climatic cooling (Clark et al. 2001).
3.4 CLIMATE ENVELOPE MODEL SIMULATION OF ICE
AGE POPULATION DYNAMICS IN EUROPE
Figure 12 shows that the temporal patterns in the simulated population size
(Fig. 12 A) and relative population size estimates based on archaeological 14C
data (Fig. 12 D) are remarkably consistent (rp= 0.84, p<0.00002), which
supports the validity of the simulation. Because the simulation is based only
on three climate variables, this correspondence between the simulation and
the archaeological record also indicates that climate has been an important
factor driving the long-term population dynamics of humans. Both the
simulation and archaeological proxy show relatively high late Marine Isotope
Stage 3 population size levels, a decline towards the LGM minimum and a
51
Results
rapid growth during the Late Glacial. The simulation suggests that the
human population size in Europe was about 330,000 at 30,000 cal BP, and
130,000 during its minimum at 23,000 cal BP and 410,000 during the
Greenland interstadial 1 (Fig. 12 A).
Figure 12. Climate envelope model simulation of human population dynamics between 30,000
and 13,000 years ago. A: Simulated human population size in Europe. Error bars show the
resampling-based confidence limits (95%). B: Simulated mean density in the inhabited area of
Europe. Error bars show the resampling-based confidence limits (95%). C: Changes in the
percentage of potentially inhabited land area in Europe. D: Archaeological population size proxy
based on the taphonomically corrected number of dates. E: Percentage of time the area has
potentially been inhabited between 30,000 and 13,000 years ago. F: Mean population density
(people/100km2) between 30,000 and 13,000 years ago.
The simulated spatial pattern of human population indicates a population
contraction starting in line with the known ice sheet expansion at 27,000 cal
BP (Fig. 12 C). The post-LGM recolonization of the continent started from
19,000 cal BP onwards (Fig. 12 C). The simulation suggests that the
continuously suitable and inhabited area between 30,000 and 13,000 cal BP
covered 36% of the European land area even during the coldest LGM (Fig. 12
C), stretching to the north of the Alps (Fig. 12 E), a result also supported by
an emerging archaeological picture (Terberger and Street 2002). In addition,
the simulation shows a persistent southwest-northeast gradient of decreasing
population densities, with the densest populations throughout the LGM in
the Iberian Peninsula and the Mediterranean region (Fig. 12 F).
52
4 DISCUSSION
4.1 METHODOLOGICAL CONSIDERATIONS
The evaluation of the population proxy described above shows that datingrelated research biases do not determine or strongly influence the temporal
distribution of 14C dates in eastern Fennoscandia. Larger samples, such as
the one used in paper V, are even less likely affected by these biases. In the
Fennoscandian data, the correlation with other proxies indicates that the
archaeological population proxy based on 14C dates is a reliable indicator of
the temporal distribution of known archaeological material instead of being
biased by e.g. research interests. Elsewhere, Williams (2012) and French and
Collins (2015) have compared SPDs with temporal frequency distributions of
archaeological sites in Australia for the last 40,000 years and in the Upper
Palaeolithic southwestern France respectively. Both studies found clear
positive correlation between proxies. Furthermore, Downey et al. (2014)
analysed proportions of immature skeletons from Mesolithic and Neolithic
cemeteries in Europe and found evidence for an increase in the population
growth rate after the adoption of agriculture. This pattern matches the
picture obtained from the SPDs. All these comparisons in different
geographical and chronological contexts provide support for the general
validity of temporal distributions of 14C dates as a proxy for human
population size.
However, Mökkönen (2011, 2014) has recently strongly criticised the
archaeological population proxies. The most important part of his criticism is
that the temporal distribution of the archaeological material reflects changes
in the settlement pattern and, especially, in the current visibility of
archaeological sites, not in the demography. According to Mökkönen (2011,
2014), the Late Stone Age decline in the frequency of 14C dates (and other
proxies) in the Finnish material is a result of the decline in the number of
highly visible pit house sites, a shift towards smaller and thus less visible
pottery, and reduced residential mobility, which together lead to a smaller
number of identified sites. Should this argument hold, one has to assume
that after the Stone Age, residential mobility and the visibility of
archaeological dwelling sites and pottery increases as the number of 14C dates
increases again. Yet, it is known that after the Stone Age the number of pit
house sites does not increase (Pesonen 2002), pottery becomes less visible
(Ikäheimo 2002) and the settlement pattern likely becomes more sedentary
due to the increasing influence of agriculture on the economy. Furthermore,
the pattern in the temporal distribution of 14C dates has remained the same
throughout the history of radiocarbon dating in Finland. The pattern was the
same in the 1970s and 1980s as it is today (Fig. 2). It is also the same in
Siiriäinen’s (1981) curve, which is based on even older data gathered before
53
Discussion
1969. Thus, the boom in the research of house pit sites in the 1990s did not
have any profound influence on the shape of the distributions of
archaeological material and 14C dates.
It thus seems that Mökkönen’s argument is invalid, and will remain so
until it is shown that there is indeed such a strong correlation between the
current visibility of archaeological remains and the frequency of 14C dates (or
known sites) in Finland. In addition, genetic data on the population
bottleneck and the simple population simulation shown in Figures 9 and 10,
are independent of the archaeological record and yet provide clear support
for the temporal distributions of different archaeological proxies. The fact
that several archaeological and non-archaeological records in the Finnish
material show the same pattern indicates that they are tracking a real
demographic signal from the past (see also Tallavaara et al. 2014). The above
mentioned comparison of two independent proxies by Downey et al. (2014)
further suggests that this holds for other datasets as well.
Recently, Contreras and Meadows (2014) have also criticised the use of
14C date-based population proxies by arguing that even when there is a
perfect correspondence between population size and the quantity of dateable
material produced at any given time, these methods cannot trace the true
population history. However, they fall into a logical fallacy of straw man
argumentation when they show that archaeological methods cannot
accurately reflect the two historical population events (< 200 yrs long) that
they had chosen for comparison: the use of 14C dates for population
;к
reconstruction is not based on the argument that 14C date-based proxies
would trace all demographic events. Furthermore, the simulation approach
of Contreras and Meadows (2014) lacks the essential component of sample
variation, which is another flaw in their study. In addition, comparison of the
distribution of time points (true calendar ages) and the distribution of
probability distributions is like comparing apples and oranges. Summed
probability distributions can smooth out true variation (and create artificial
ones), and consequently conceal small-scale fluctuations in the
archaeological signal. The use of point estimates such as calibrated median
dates may solve this problem. All in all, the attempt of Contreras and
Meadows (2014) fails to provide a relevant critique of 14C date-based
population proxies (see also Timpson et al. 2014).
However, the method is clearly not without its drawbacks. As shown here
and elsewhere (e.g. Bamforth and Grund 2012; Brown 2015), short-term
variation in the temporal distribution of 14C dates is strongly influenced by
14C date calibration. This is probably the biggest weakness of the method,
because it hampers archaeologists reliably detecting short-lived demographic
events. The simulation approach developed by Shennan et al. (2013),
Timpson et al. (2014) and Brown (2015) provides a tool to evaluate the
impact of calibration and sample variation on the distribution of dates, but it
does not remove the problem. In this summary paper, the simulation
approach revealed that despite remarkable fluctuations, the Early Holocene
54
archaeological pattern in Finland corresponds to the pattern simulated under
the null hypothesis assuming smooth growth over the Early Holocene. Thus,
simulations can be considered as an essential tool when the focus is on highfrequency fluctuations in population proxy. Simulation allows a case by case
evaluation of whether the observed pattern really deviates from the pattern
simulated under the null model of smooth demographic signal.
In this summary paper, the simulation approach also showed that the
longer-term fluctuations – the Mid-Holocene boom and bust – are not
calibration artefacts, but represent real fluctuations in the archaeological
signal. The same pattern is observed all over northern Europe, where the
observed pattern also deviates significantly from the null model (Shennan et
al. 2013).
On the other hand, taphonomic bias appears to have a strong impact on
the long-term trend in the distribution of 14C dates. The taphonomic
correction changes the shape of the archaeological population proxy both in
Finland and in Palaeolithic Europe. In the Finnish data, the taphonomically
corrected proxy indicates that Mid-Holocene population levels would have
been higher than Late Holocene levels. This may appear counter-intuitive.
However, during the Mid-Holocene, environmental conditions were more
suitable for the hunter-gatherers (see below) who populated the whole area.
From the late Mid-Holocene onwards environmental conditions started to
deteriorate due to the cooling and the gradual replacement of mixed forest by
coniferous forest (see below). The environmental carrying capacity for
;к
hunter-gatherers would thus have been lower during the Late Holocene than
during the Mid-Holocene. The intensification of the agricultural economy
from the Bronze Age–Early Iron Age onwards induced a new population
growth, but during the Iron Age, the area inhabited by the agricultural
population was still relatively restricted. Consequently, the total population
size in the area may have remained smaller than during the Mid-Holocene,
when environmental conditions were optimal for the whole population. Thus,
the taphonomically corrected proxy may not be so flawed a reflection of the
true population history as it may initially seem.
Nevertheless, the model of taphonomic loss and the derived correction is
a simplification of a rather complex phenomenon. It is clear that there is
regional variation in taphonomic loss processes. On the continental scale, for
example, the interplay of climatic and topographic factors result in higher
erosion rate in the Mediterranean region than in central and northern
Europe (García-Ruiz et al. 2013; Poesen and Hooke 1997; Vanmaercke et al.
2011). This probably also leads to a more severe loss or disturbance of
archaeological contexts in the Mediterranean region than elsewhere in
Europe, which could be useful to take into account when correcting
European-wide datasets, such as the one used in paper V. The ideal situation,
in general, would be to devise a correction factor for each regional dataset,
based on local circumstances. In some cases this may work, but in most of
the cases it is practically impossible.
55
Discussion
Thus, one may argue that temporal frequency distributions should not be
routinely corrected because it applies a theoretical correction that may not be
valid in all situations or because it may over correct the number of very old
dates (Williams 2012). However, since the time-dependent loss of material
seems to be a very real process affecting the temporal frequency distributions
of different types of data, it has to be taken into account somehow. So far, the
model first formulated by Surovell et al. (2009) and later modified by
Williams (2012) is the best tool available to tackle this bias.
Although 14C date-based population proxies are shown to be valid
indicators of prehistoric population size, one may still ask, whether it would
be possible to use some other archaeological proxies to avoid all the potential
biases related to 14C date data. Of all the archaeological population proxies,
14C date-based proxy is the most comprehensive, because its use is not bound
to periods or areas where good typological or shore displacement
chronologies are available. In optimal circumstances, its resolution is also
much higher than what can be achieved by typological dating. However,
comparisons between different proxies are always useful as a means to
evaluate the validity of observed patterns.
Even if archaeological proxies provide a reliable method for studying
long-term population dynamics, archaeological data is still sparse and often
ambiguous. As suggested in Chapter 2.1.1.1., this holds especially for spatial
distributions. One major factor here is the Wallacean shortfall-like effect of
incomplete information on species distribution. While the Wallacean
;к
shortfall is true for current plant and animal species, paleontological and
archaeological records provide an obviously even more incomplete and
coarse reflection of true ranges (Svenning et al. 2011), as one is moving
further and further back in time. Therefore, the study of prehistoric
population dynamics demands multiple lines of evidence. It is shown here
that by using only three climate predictors within climate envelope or niche
modelling approach, it is possible to simulate Pleistocene population pattern
that corresponds with the archaeological pattern. This indicates that such a
modelling approach provides an efficient tool that is a necessary complement
to archaeological and DNA-based methods. Furthermore, with this method,
it is possible to estimate population dynamics even for the areas without a
sufficient archaeological record to create a reliable proxy. However, in such
cases, results must be considered more tentative as model-data comparisons
are not possible.
As with all other methods of prehistory, climate envelope modelling is
also an imperfect one: the resolution of the simulation depends on the
resolution of the climate model and, more importantly, the accuracy of the
simulated density and range estimates depends on the ability of the climate
model to correctly simulate past climate. While climate models are becoming
better due to the intensive development, other critical issues remain to be
solved. One is the assumption of niche stability. The further back in time one
is hindcasting population dynamics, the more likely the potentially
56
confounding effect of non-analogous environments becomes. For example,
during the glacial periods of the Pleistocene, atmospheric CO2 levels were 80
ppm lower than during the Holocene, and almost 200 ppm lower than at
present (Monnin et al. 2001). This may have had a major impact on plant
productivity and ecosystem functioning (Gerhart and Ward 2010). It is thus
possible that similar climatic conditions would have affected humans
differently during the Pleistocene than during the Holocene. However, the
similarity between the simulated pattern and the observed archaeological
pattern indicates that this may not be a serious problem in this case. Of
course, the correlation validates only the simulated pattern, i.e. the relative,
not absolute, population sizes. Estimates of absolute population size will
always remain somewhat speculative no matter what method is used to
estimate them.
4.2 IMPACTS OF CLIMATE
The correlations between archaeological and several environmental proxies,
and between climate envelope model simulations and the archaeological
proxies, support the idea that changes in climate and environment have a
strong impact on hunter-gatherer population dynamics: hunter-gatherer
population size appears to change in sync with climate (see also Kelly et al.
2013; Munoz et al. 2010). Because climate envelope model simulations are
;к
based on information derived from
modern or recent historical huntergatherer populations, the ability of models to replicate archaeological
patterns indicates further that the effect of climate has remained relatively
constant from the last glacial to the present. Millennia of cultural evolution
have not fundamentally changed the constraints on terrestrially adapted
hunter-gatherer populations posed by the climate.
However, these results are mostly valid for long-term trends, and do not
provide unambiguous evidence that short-lived climate events (less than c.
200 yrs long) would have impacted archaeologically visible population
history. The Fennoscandian archaeological record indicates changes that may
be related to the Early Holocene cold events, as Manninen (2014) suggests,
but the impact on population size is not clear, because possible short-term
population fluctuations are masked by variation induced by the 14C
calibration procedure. This does not mean that climate events would not
have had an impact on hunter-gatherer population dynamics: Riede (2009)
and Wicks and Mithen (2014) have suggested that the cold events of the late
glacial and Early Holocene would indeed have influenced hunter-gatherer
populations. However, they have not included extensive resampling-based
simulations in their studies to evaluate the possible calibration bias. The
problems related to the detection of the impacts of rapid climate events are
amplified by the fact that some of the cold events co-occur with shifts in
atmospheric 14C content, as both may be linked to abrupt shifts in ocean
57
Discussion
circulation and deep ocean ventilation (Hughen et al. 2000). This means that
the cold events would show up as troughs in a 14C date-based population
proxy, even when population decline had not truly occurred. The problem is
further illustrated in Figure 13. It shows how difficult it is to accurately
radiocarbon date human activity that occurred during such events and,
consequently, how difficult it is to determine the intensity of human action
during the event.
;к
Figure 13. Simulated example showing how difficult it is to accurately date past human activity
occurring during, e.g., the 10,300 cal BP cold event. Seven true calendar ages between 10,300
and 10,000 cal BP were transformed into calibrated radiocarbon ages using the simulation
approach described above. Calibrated radiocarbon ages were simulated 1000 times for each
calendar ages. A: Distributions of simulated calibrated medians for each calendar age (in each
distribution N=1000). The figure shows that practically only one of the calibrated radiocarbon
ages falls within the period between 10,300 and 10,000 cal BP, despite the fact that all calendar
ages, i.e. occurrences of human action, are within the period. The vertical bars show the means
of each distribution. B: With the calibrated probability distributions the gap is not equally serious,
only because probability distributions are so wide. Calibration nevertheless packs the youngest
four dates on top of each other. The mean of simulated radiocarbon ages for each calendar age
was used in the calibration, with a standard error of 80 years.
In the eastern Fennoscandian data, correlation between long-term human
population dynamics and temperature and, especially, productivity proxy
strongly suggests that the impact of climate on human population was mostly
indirect, mediated by its impacts on environmental production and
consequently on food availability. Successful climate envelope model
58
simulation of the glacial human population in Europe, using potential
evapotranspiration and water balance as two of the predictor variables,
supports this idea. In this context, the negative correlation between effective
precipitation and population size in eastern Fennoscandian data may appear
counterintuitive at the outset. However, in the northern latitudes,
temperature is generally the most important factor affecting primary
productivity (e.g. Zheng et al. 2004), and it seems unlikely that water
availability would have limited production during the Holocene. Instead, in
the cold and dry glacial climate, the availability of water played an important
role, especially in the southern and eastern parts of Europe.
In addition to the warmth and length of the growing season, climate
affects ecosystem composition that may also have an effect on food
abundance. Although not necessarily in complete equilibrium with the
climate (Seppä et al. 2009a), the forest ecosystem change in eastern
Fennoscandia correlates with climate change, which makes it difficult to
determine whether it was the climate-controlled productivity as such, or
changes in the forest composition and structure that had a more profound
effect on food abundance, and consequently on hunter-gatherer population
size. As shown in paper III, there is nevertheless a difference in animal
biomass between coniferous and mixed forests in modern data: the
temperate mixed forest ecosystem sustains a higher animal biomass than the
boreal forest. This is evident for the whole animal biomass as well as for
single species, such as moose. Although these biomass differences partly
relate to climate through differences in growing season length, it is still likely
that the differences in forest structure and soil biochemistry also exert a
strong influence. Coniferous species, especially spruce, effectively shade out
herbaceous, shrub and immature tree growth in the understory, thus
reducing the abundance of important food sources, e.g. for ungulates
(Mellars 1975). The negative effect of shading on the undergrowth is further
enforced by acidic litter, which decreases soil fertility (e.g. Koptsik et al.
2001; Levula et al. 2003; Wallrup et al. 2006). It is thus reasonable to
assume that the ecosystem change from species-rich temperate mixed forests
to typical boreal forests would have had a negative effect on hunter-gatherer
food availability in eastern Fennoscandia.
In addition to its influence on food abundance, climate can have direct
somatic effects that affect human survival. Even in modern Finland,
mortality is higher during cold months than during warm months (Hassi and
Ikäheimo 2013). Inhaled cold air, cooling of the body surface, and cold stress
induced by lowering the core body temperature cause physiological
responses leading to increased mortality risk (Barnett et al. 2007; Conlon et
al. 2011; Mäkinen et al. 2009; Mourtzoukou and Falagas 2007). Cold
environments are also more hazardous, and the risk of dying of hypothermia
is obviously much higher than in temperate environments.
Because of the complex and partly collinear relations between the forcing
variables, it is not possible to fully quantify the relative importance of the
59
Discussion
different environmental factors affecting past hunter-gatherer population
dynamics. The climate envelope modelling approach would allow such
quantification by evaluating the predictive ability of multiple sets of predictor
variables. However, such evaluations have not been done in this study. It is
nevertheless clear that there were several interwoven environmental factors
forcing population size in the same direction. Cultural factors cannot be
totally ruled out, but archaeological data e.g. in eastern Fennoscandia do not
indicate technological changes that would explain variation in population
size. Adoption of pottery occurs c. 7200 cal BP, coinciding with the beginning
of population growth, but pottery continues to be in use also during the late
Mid-Holocene population bust. Thus far it is possible to attribute the main
causality of long-term hunter-gatherer population fluctuations to climate
related changes in food availability.
It is argued here that the long-term changes in hunter-gatherer
population size tracked by archaeological population proxies reflect longterm changes in environmental productivity, i.e., carrying capacity. This
implies that the changes that we see in population size are density
independent, i.e., they do not depend on the density or size of the human
population. Density dependent fluctuations occur when the density of the
population affects its own growth rate (Hanski et al. 1998). For example, as
predator population grows, prey population declines, which slows down the
growth of the predator population and eventually turns it negative. As the
predator population declines, resources recover, which will eventually enable
;к
the new growth of the predator population as well. The question is, can we
see density dependent fluctuations in archaeological population proxies? The
simulation study by Belovsky (1988) suggests that density dependent
fluctuations are common in hunter-gatherer populations (see also
Winterhalder et al. 1988; Winterhalder and Lu 1997). In his simulation, the
amplitude of these fluctuations varies according to the productivity of the
environment, so that the most severe fluctuations occur in the most
productive environments. The length of the cycle (high density-low densityhigh density) is c. 100 years in Belovsky’s (1988) simulation. The historical
data on the number of Saami tax payers from Finnish Lapland (Enbuske
2008: 294) supports this figure, although it is not a direct measure of
population size. Such a length for the cycle is clearly too short to be visible in
an average 14C dates-based archaeological population proxy, especially as
much of the short-term variation in the proxy can be attributed to the 14C
calibration.
It is thus more likely that archaeological population proxy usually reflects
changes in the mean population size (Fig 14). This could explain why
population growth rates calculated from archaeological population proxies
are clearly smaller than growth rates based on ethnographic or historical data
(Kelly et al. 2013; Peros et al. 2010): ethnographic data reflects the true
current population growth rate (r), whereas archaeological population proxy
reflects changes in the long-term mean population size, which is controlled
60
by environmental productivity. High-frequency variation inferred from the
ethnographic data and simulations, and low-frequency variation visible in
the archaeological proxies and climate envelope model simulation, indicate
that hunter-gatherer populations were not stationary, neither in the longnor short-term.
Figure 14. Simulated example showing how hunter-gatherer population size (black curve) would
;к factors. Population size oscillates around its
fluctuate through time due to density dependent
mean (grey curve), which changes as environmental productivity changes, for example, due to
climate changes. The resolution of archaeological population proxies is not usually sufficiently
high to detect the density dependent high frequency fluctuation. It is more likely that
archaeological proxies reflect changes in the mean population size.
4.3 THE DIFFERING DYNAMICS OF FORAGERS AND
FARMERS
Although the main focus of this study is on hunter-gatherers, the results from
Finland also enable comparisons between the population dynamics of
foragers and farmers. As mentioned above, the correlation between
population size and environmental factors ceases from about c. 3500 cal BP
onwards, most likely due to the intensification of the farming economy. The
firmest pollen and macrofossil evidence for the earliest cultivation dates to
4000–3500 cal BP in southern and 3500–3400 cal BP in central and eastern
Finland (Taavitsainen et al. 1998, 2007; Vuorela 1998, 1999; Vuorela and
Lempiäinen 1988), while the oldest dated bone of domesticated animals
dates to c. 4000 cal BP (Bläuer and Kantanen 2013) and the oldest evidence
for dairy economy to c. 4500 cal BP (Cramp et al. 2014).
During the Early Metal Age, indications of farming increase in
palynological and archaeological records around the Baltic Sea, suggesting
the intensification of agricultural economy in the area (Asplund 2008;
61
Discussion
Edgren 1999; Sillasoo et al. 2009; Vuorela 1998). This indicates that
agriculture thrived throughout the cooling late Holocene as well as during
some of the colder anomalies of this cooling trend, such as the Little Ice Age
of the 16th century (Lagerås 2007). Similarly, Munoz et al. (2010) noted that
the rise in the agricultural population in the northeastern USA during the
Late Woodland period appears to have occurred during the period of climatic
cooling.
It therefore seems that the adoption of agriculture weakened the climatic
control over the long-term human population dynamics. Whereas huntergatherer population size changes in equilibrium with climate, agricultural
population can grow independently of climate trends. Since agricultural
populations are not dependent only on natural resources, their food
availability is not controlled solely by environmental productivity. This
allows the population to grow even if environmental productivity is
decreasing.
Short-term, inter-annual or -decadal variability is nevertheless known to
have affected crop yields and consequently the size of historical farming
populations (Eckstein et al. 1984; Pitkänen 1993), and it is thus likely that
crop failures and consequent famines occurred during prehistory as well.
However, historical grain figure data for southern Finland shows only a weak
spatial synchrony, which indicates that crop failures and possible population
effects were usually geographically restricted (Holopainen and Helama
2009). This would have permitted population growth over a larger spatial
scale despite frequent but spatially non-synchronous local declines. Thus,
even when in a marginal economic position, agriculture seemed to offer a
buffer against periodic climate induced shortages, which allowed for more
sustainable long-term population growth (Boone 2002).
To act as a buffer against climate-related periodic shortages implies that
the availability of agricultural products reduces the mortality of agricultural
populations relative to non-agricultural populations during these shortages.
Recently, Helle et al. (2014) have shown, however, that the slightly higher
population growth rate of farmers as compared to foragers in historical
northern Finland was related to differences in fecundity. In fact, they do not
find clear population level mortality differences between farmers and nonfarmers during their study period (AD 1740-1900). Helle et al. (2014) suggest
that the differences in fecundity may not relate to dietary differences, since
diets of farmers and non-farmers in northern Finland would have been
roughly similar. However, this may not be entirely correct: in addition to wild
resources and dairy products, grain products already played a role in the
diets of the earliest farmers of the late 17th century, and certainly from the
1750s onwards (Kehusmaa and Onnela 1995: 147–183). A wider resource
base and consequently better food availability would have allowed a better
nutritional status for farmers in general, and also during lean years. This
likely contributed to their higher reproductive success as well.
62
Observations made from the Finnish archaeological and historical data –
that the long-term population dynamics of farmers are independent from
climate and that their growth rates are higher than the growth rates of nonfarmers living in similar environments – illustrate nicely the competitive or
evolutionary superiority of farmers over foragers even in agriculturally
relatively marginal environments: farming has replaced and will replace
foraging in all but agriculturally unsuitable environments by sheer
outnumbering (see also Richerson et al. 2001).
;к
63
Conclusions
5 CONCLUSIONS
Based on the results described and discussed above, it is argued that:
1. Archaeological human population proxy based on the temporal
frequency distribution of 14C dates is a reliable method for the Finnish
data. Together with positive results of evaluations in different periods
and geographical areas, this supports the general validity of the
method.
2. Instead of using the summed probability distribution of calibrated 14C
dates as a population proxy, it might be better to use the distribution
of calibrated median, mean, or mode 14C dates, since such distribution
better facilitates the demographic interpretation of the data.
3. Due to the bias produced by calibration, the method has to be used
cautiously when studying short term fluctuations. High-frequency
variation may not relate to demographic signal, but to features in the
calibration curve.
4. Therefore, the bias associated with the calibration of the dates has to
be taken into account. This is especially pertinent to studies that focus
on short-lived demographic events. The calibration bias can be
evaluated by comparing observed data to the distribution of simulated
data randomly re-sampled from the null model.
;к
5. Due to the calibration bias and other issues, the temporal resolution of
the 14C dates-based method is usually not sufficiently high to track
density dependent fluctuation in human population size. Instead, the
method tracks changes in the long-term mean population size.
6. The climate envelope modelling approach can be a reliable method for
estimating long-term hunter-gatherer population dynamics, and even
absolute population densities and sizes. Although it is not without its
limitations, the approach provides a necessary complement to
archaeological methods. The method can also be used when
archaeological data is not available, and it can reveal potential
inadequacies in the distribution of archaeological data.
7. The climate explains over 60% of the variation in hunter-gatherer
population densities, demonstrating that climate alone is a major
factor affecting long-term hunter-gatherer population dynamics.
8. The impact of the climate on terrestrially adapted hunter-gatherer
population dynamics has remained relatively constant from the Late
Pleistocene to present.
9. Climate influences hunter-gatherer food abundance, and consequently
population density mainly by affecting the length and warmth of the
growing season (i.e. environmental productivity) and vegetation.
Climate also has an effect on wintering conditions such as winter
mortality.
64
10. Climate controls the mean population size or density (or the carrying
capacity) around which the true population size oscillates because of
density dependent factors. This high-frequency density dependent
oscillation is not possible to track with the methods used in this study.
11. Unlike the clear effects of climate on the long-term population
dynamics of hunter-gatherers, the Early Holocene short-lived cold
events did not have a reliably detectable effect on human population
size in eastern Fennoscandia.
12. The adoption of a farming economy weakens the climate forcing on
long-term human population dynamics.
In the future, one of the greatest challenges in the study of past population
history relates to the reconstruction of short-lived demographic events and
high-frequency variation in population size when calibration-induced
variation is present. Automated methods for filtering out the calibration bias
have already been developed (Timpson et al. 2014). These methods can
remove false positive demographic signals from the data, but they do not
help detect true positive signals that are masked by the resolution of
radiocarbon dating. It may turn out that the accuracy of 14C date-based
population proxies will never be sufficiently high to truly detect short-term
demographic variation.
There is also a need to further develop the taphonomic correction
procedure. The process of taphonomic loss is real and must be taken into
;к
account, but the application of a single correction function may over- or
under-correct archaeological temporal frequency distributions. It might be
possible to improve the correction method by taking the geographic variation
in erosion rates into account. So far, the correction method has been applied
to temporal distributions, but it would be worthwhile to develop the means to
taphonomically correct spatial distributions as well.
Because population proxies based on the distribution of archaeological 14C
dates are best suited to regional or even continental scales of analysis, large
14C date datasets are needed. Therefore, the practical and economical
application, evaluation, and development of the method require access to
online databases containing sufficiently detailed contextual and technical
information on the dates. Such databases already exist or are under
construction, but many geographical areas and periods are still not covered
by these databases. There is, thus, an urgent need for new databases, as well
as discussion about the database conventions and best practises (see also
Kintigh et al. 2015).
Climate envelope or niche modelling tools are well established in
biogeography and ecology. Their application to human populations may still
require evaluation, and the tuning of methods that are best suited to the
analysis of hunter-gatherers as relatively small training sets and, in some
cases, complex non-linear relationships between variables pose problems
that are not so common when the focus is on other species.
65
Conclusions
In addition to methodological issues, there is definitely still a need for the
deeper analysis of the relative importance of different climatic and other
environmental factors on hunter-gatherer population dynamics using both
ethnographic snap-shot and archaeological long-term data.
Despite these challenges, this study has hopefully shown the usefulness of
the described approaches. The model simulations are powerful tools to
analyse past dynamics, but they are not perfect representations of the past
reality. Archaeological data will always have the primary role in the study of
prehistory, but neither do they provide perfect, unbiased representations of
past reality. Therefore, we need multiple lines of evidence and multiple
approaches in the study of prehistory, and especially in the study of
prehistoric population dynamics.
;к
66
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