ArticlePDF Available

Mapping past human land use using archaeological data: A new classification for global land use synthesis and data harmonization

Authors:
  • University Pompeu Fabra & IMF-CSIC

Abstract and Figures

In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database. The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a wide utility for creating a common language between research and policy communities, linking archaeologists with climate modelers, biodiversity conservation workers and initiatives.
Content may be subject to copyright.
RESEARCH ARTICLE
Mapping past human land use using
archaeological data: A new classification for
global land use synthesis and data
harmonization
Kathleen D. Morrison
1
, Emily HammerID
2
*, Oliver Boles
1
, Marco Madella
3,4
,
Nicola WhitehouseID
5,6
, Marie-Jose Gaillard
7
, Jennifer BatesID
1
, Marc Vander Linden
8
,
Stefania Merlo
4
, Alice YaoID
9
, Laura Popova
10
, Austin Chad Hill
1
, Ferran Antolin
11
,
Andrew Bauer
12
, Stefano BiagettiID
13,14
, Rosie R. Bishop
15
, Phillip Buckland
16
,
Pablo Cruz
17
, Dagmar Dreslerova
´
18
, Gerrit Dusseldorp
19
, Erle EllisID
20,21
,
Dragana Filipovic
22
, Thomas FosterID
23
, Matthew J. Hannaford
24
, Sandy P. Harrison
25
,
Manjil Hazarika
26
, Hajnalka HeroldID
27
, Johanna Hilpert
28
, Jed O. Kaplan
29
,
Andrea KayID
30
, Kees Klein Goldewijk
31
, Jan Kola
´řID
32,33
, Elizabeth KyazikeID
34
,
Julian Laabs
35,36,37
, Carla LancelottiID
38
, Paul Lane
39,40
, Dan LawrenceID
41
, Krista Lewis
42
,
Umberto Lombardo
43
, Giulio LucariniID
44,45
, Manuel Arroyo-Kalin
46
, Rob Marchant
47
,
Francis Mayle
48
, Meriel McClatchieID
49
, Madeleine McLeester
50
, Scott MooneyID
51
,
Magdalena Moskal-del Hoyo
52
, Vanessa NavarreteID
53
, Emmanuel Ndiema
54
,
Eduardo Go
´es Neves
55
, Marek Nowak
56
, Welmoed A. OutID
57
, Cameron Petrie
38,58
, Leanne
N. PhelpsID
59,60
, Zsolt Pinke
61
, Ste
´phen RostainID
62
, Thembi RussellID
4
,
Andrew SluyterID
63
, Amy K. Styring
64
, Eduardo TamanahaID
65
, Evert Thomas
66
,
Selvakumar VeerasamyID
67
, Lynn Welton
40
, Marco Zanon
22
1Department of Anthropology, University of Pennsylvania, Philadelphia, Pennsylvania, United Statesof
America, 2Department of Near East Languages and Civilizations and the Price Lab for the Digital
Humanities, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 3ICREA–
CaSEs–Department of Humanities, Universitat Pompeu Fabra, Barcelona, Spain, 4School of Geography,
Archaeology and Environmental Studies, The University of the Witwatersrand, Johannesburg, South Africa,
5School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth, United Kingdom,
6Department of Archaeology, University of Glasgow, Glasgow, United Kingdom, 7Department of Biology
and Environmental Sciences, Linnaeus University, Va
¨xjo
¨, Sweden, 8Institute for the Modelling of Socio-
Environmental Transitions, Bournemouth University, Bournemouth, United Kingdom, 9Department of
Anthropology, University of Chicago, Chicago, Illinois, United States of America, 10 Barrett Honors College,
Arizona State University, Tempe, Arizona, United States of America, 11 Integrative Prehistory and
Archaeological Science (IPNA/IPAS), University of Basel, Basel, Switzerland, 12 Department of
Anthropology, Stanford University, Stanford, California, United States of America, 13 Department
d’Humanitats, Universitat Pompeu Fabra, Barcelona, Spain, 14 School of Geography, Archaeology and
Environmental Studies, University of the Witwatersrand, South Africa, 15 Museum of Archaeology, University
of Stavanger, Stavanger, Norway, 16 Department of Historical, Philosophical and religious Studies, Umeå
University, Umeå, Sweden, 17 UE CISOR CONICET UNJu, Argentine National Science Council (CONICET),
Argentina, 18 Institute of Archaeology of the Czech Academy of Sciences, Academy of Sciences Prague,
Czech Republic, 19 Faculty of Archaeology, Leiden University, Leiden, The Netherlands, 20 Palaeo-
Research Institute, University of Johannesburg, Johannesburg, South Africa, 21 Department of Geography
and Environmental Systems, University of Maryland Baltimore County, Maryland, United States of America,
22 Institute of Pre- and Protohistoric Archaeology, Kiel, Germany, 23 College of Arts & Sciences,
Anthropology, University of Tulsa, Tusla, Oklahoma, United States of America, 24 School of Geography,
University of Lincoln, Lincoln, United Kingdom, 25 School of Archaeology, Geography and Environmental
Science, University of Reading, Reading, United Kingdom, 26 Department of Archaeology, Cotton University,
Guwahati, India, 27 Department of Archaeology, University of Exeter, Exeter, United Kingdom, 28 Institute
for Prehistoric Archaeology, Universitat zu Koln, Cologne, Germany, 29 Department of Earth Sciences, The
University of Hong Kong, Hong Kong, Hong Kong, 30 Max Planck Institute for the Science of Human History,
Jena, Germany, 31 Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The
Netherlands, 32 Institute of Botany of the Czech Academy of Sciences, Prague, Czech Republic, 33 Institute
of Archaeology and Museology, Masaryk University, Brno, Czech Republic, 34 Department of History and
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 1 / 38
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Morrison KD, Hammer E, Boles O,
Madella M, Whitehouse N, Gaillard M-J, et al.
(2021) Mapping past human land use using
archaeological data: A new classification for global
land use synthesis and data harmonization. PLoS
ONE 16(4): e0246662. https://doi.org/10.1371/
journal.pone.0246662
Editor: Jacob Freeman, Utah State University,
UNITED STATES
Received: June 25, 2020
Accepted: January 24, 2021
Published: April 14, 2021
Copyright: ©2021 Morrison et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Middle East land use
data for 6 kya have been exported as a shapefile
and are available through the PANGAEA Data
Publisher for Earth & Environmental Science (doi.
pangaea.de/10.1594/PANGAEA.922243). A blank
copy of the land use geodatabase with the land use
classification programmed into it is available as a
Supporting Information file.
Funding: This study was undertaken as part of the
Past Global Changes (PAGES) project (and its
Political Science, Kyambogo University, Kampala, Uganda, 35 Institute for Archaeolgical Scienes, Bern
University, Bern, Switzerland, 36 Oeschger Centre for Climate Change Research, Bern University, Bern,
Switzerland, 37 Institute of Pre- and Protohistoric Archaeology, Kiel University, Keil, Germany, 38 ICREA–
Department of Humanities, Universitat Pompeu Fabra, Barcelona, Spain, 39 Department of Archaeology,
University of Cambridge, Cambridge, United Kingdom, 40 School of Geography, Archaeology and
Environmental Studies, University of the Witwatersrand, Witwatersrand, South Africa, 41 Department of
Archaeology, Durham University, Durham, United Kingdom, 42 Department of Sociology and Anthropology,
University of Arkansas at Little Rock, Little Rock, Arkansas, United States of America, 43 Institute of
Geography, University of Bern, Bern, Switzerland, 44 Institute of Heritage Science, National Research
Council of Italy, Montelibretti, Rome, Italy, 45 Department of Asian, African and Mediterranean Studies,
University of Naples L’Orientale, Naples, Italy, 46 Institute of Archaeology, University College London,
London, United Kingdom, 47 York Institute for Tropical Ecosystems, Department of Environment and
Geography, University of York, York, United Kingdom, 48 Department of Geography and Environmental
Science, University of Reading, Reading, United Kingdom, 49 School of Archaeology, University College
Dublin, Dublin, Ireland, 50 Department of Anthropology, Dartmouth College, Hanover, New Hampshire,
United States of America, 51 School of Biological, Earth and Environmental Sciences, UNSW Sydney,
Sydney, Australia, 52 W. Szafer Institute of Botany, Polish Academy of Sciences, Warsaw, Poland,
53 Department of Prehistory, Universitat Autònoma de Barcelona, Bellaterra, Spain, 54 Department of Earth
Sciences, National Museums of Kenya, Nairobi, Kenya, 55 Laborato
´rio de Arqueologia dos Tro
´picos, Museu
de Arqueologia e Etnologia, Universidade de São Paulo, São Paulo, Brazil, 56 Institute of Archaeology,
Jagiellonian University, Krako
´w, Poland, 57 Department of Archaeological Science and Conservation,
Moesgaard Museum, Højbjerg, Denmark, 58 McDonald Institute for Archaeological Research, University of
Cambridge, Cambridge, United Kingdom, 59 Tropical diversity, Royal Botanic Garden Edinburgh, Edinburgh,
United Kingdom, 60 School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom,
61 Department of Physical Geography, Eo
¨tvo
¨s Lora
´nd University, Budapest, Hungary, 62 Centre national de
la recherche scientifique, Nanterre, France, 63 Department of Geography and Anthropology, Louisana State
University, Baton Rouge, Louisiana, United States of America, 64 School of Archaeology, University of
Oxford, Oxford, United Kingdom, 65 Instituto de Desenvolvimento Sustenta
´vel Mamiraua
´, Amazonas, Brazil,
66 The Alliance of Bioversity International and CIAT, Lima, Peru, 67 Department of Maritime History and
Marine Archaeology, Tamil University, Tanjore, India
*ehammer@sas.upenn.edu
Abstract
In the 12,000 years preceding the Industrial Revolution, human activities led to significant
changes in land cover, plant and animal distributions, surface hydrology, and biochemical
cycles. Earth system models suggest that this anthropogenic land cover change influenced
regional and global climate. However, the representation of past land use in earth system
models is currently oversimplified. As a result, there are large uncertainties in the current
understanding of the past and current state of the earth system. In order to improve repre-
sentation of the variety and scale of impacts that past land use had on the earth system, a
global effort is underway to aggregate and synthesize archaeological and historical evi-
dence of land use systems. Here we present a simple, hierarchical classification of land use
systems designed to be used with archaeological and historical data at a global scale and a
schema of codes that identify land use practices common to a range of systems, both imple-
mented in a geospatial database. The classification scheme and database resulted from an
extensive process of consultation with researchers worldwide. Our scheme is designed to
deliver consistent, empirically robust data for the improvement of land use models, while
simultaneously allowing for a comparative, detailed mapping of land use relevant to the
needs of historical scholars. To illustrate the benefits of the classification scheme and meth-
ods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000
BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES)
LandCover6k working group, an international project comprised of archaeologists,
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 2 / 38
working group LandCover6k), which in turn
received support from the U.S. National Science
Foundation, Swiss Academy of Sciences and the
Chinese Academy of Sciences. The work of global-
scale coordination, database development, and the
work of land use groups around the world was
supported with workshop grants from Past Global
Change (PAGES) and the Human and Biosphere
Commission of INQUA (the Global Holocene Land
Use - HoLa - International Focus Group and related
projects). Additional funding was provided by the
University of Chicago, the University of
Pennsylvania, and the many institutional and
personal sources of support for travel and time that
allowed the many participants in this project to
contribute their expertise and enthusiasm to this
critical effort. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a
wide utility for creating a common language between research and policy communities, link-
ing archaeologists with climate modelers, biodiversity conservation workers and initiatives.
Land transformation has been the primary driving force of human alteration of terrestrial
ecosystems,strongly interacting with most other aspects of global environmental change. . .
Weindl et al.2017
Introduction: Earth systems models, land cover, and the past
Although earth system models are often seen as tools for exploring the future, they rely in part
on understandings of the past, including models of land cover change through time. It is the
aim of the LandCover6k working group, formed in 2015 as a collaboration between archaeolo-
gists, historians, geographers, paleoecologists, and modelers, to improve the basis for incorpo-
rating past land cover change into earth system models (http://pastglobalchanges.org/science/
wg/landcover6k/intro). We seek to do this by producing reconstructions of past vegetation
and human land use through time that are grounded in paleoenvironmental and archaeolog-
ical data.
In order to explain how paleoenvironmental and archaeological data can improve earth sys-
tem models, it is necessary to review these models and the limitations of their approach to land
cover. The land surface is a central component of the earth system. Changes in land cover
affect a range of earth system processes including biodiversity, water resources, and air quality.
Land cover also influences climate through interactions between land and atmosphere. These
may be broadly partitioned into biogeochemical feedbacks, including sources and sinks of
greenhouse gases and aerosols, and biogeophysical feedbacks, including surface reflectance
(albedo), evapotranspiration, and momentum transfer from wind [1]. Some land-atmosphere
feedbacks are positive, amplifying ongoing climate change, while others are negative, attenuat-
ing climatic trends. Land-atmosphere interactions currently constitute an area of uncertainty
in climate projections, and it is a priority in the scientific community to improve earth system
models by incorporating land cover change into simulations.
Understanding land cover change requires information on vegetation and human land use
as well as interlinked changes between the two. In earth system models, this information
comes from dynamic vegetation schemes and anthropogenic land cover change scenarios.
Recent earth system models contain a dynamic vegetation scheme that simulates the distribu-
tion and properties of potential natural vegetation, i.e., vegetation that would be predicted to
grow under specific climatic and soil conditions [2,3]. These models allow land cover to
change with climatic change and simulate how land cover and climate interact through posi-
tive or negative feedbacks. Dynamic vegetation schemes do not necessarily reflect actual land
cover, because human agency often leads to modification of land cover that cannot be pre-
dicted on the basis of environmental change alone. Scenarios used to make future climate pro-
jections therefore employ a representation of anthropogenic land cover change, including
deforestation and expansion and abandonment of cropland, as an essential boundary condi-
tion [48]. While there are numerous proxies for paleoenvironmental change, their distribu-
tion in space and time is highly heterogenous and at present the only way to produce a
spatially and temporally continuous picture of past environmental change is to use models.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 3 / 38
While changes in natural vegetation cover can be simulated directly by many earth system
models, as noted above, information on past land use in these models is currently provided by
what are called anthropogenic land cover change (ALCC) scenarios [913]. The models
behind these scenarios generally combine estimates of historic human population figures with
model-specific algorithms, based on land use per capita figures, to estimate the total magnitude
and spatial distribution of land use. The result of most ALCC models are maps (scenarios)
quantifying a general metric of land use such as crop or pasture area fraction.
While ALCC scenarios have been widely applied in earth system modeling studies, all exist-
ing ALCC models are subject to major limitations. Most ALCC models are not directly based
on proxy data for past land cover, they do not use observations of past vegetation, and they do
not incorporate evidence for the variable impacts of past populations on their environments,
variability tied to specific land use practices and to social and political factors such as past sur-
plus production, capital accumulation, and trade [2,cf. 14]. Instead, these models presume a
parameterized representation of per capita land use that may be constant, time dependent, or
dependent on other factors such as population density. All current ALCC scenarios lack the-
matic information on the effects of past human land use, such as those created by large-scale
burning, plowing, irrigation, and livestock management. Finally, ALCC scenarios differ signif-
icantly from one another and compare poorly with independent reconstructions [15]. In spite
of these inherent limitations, ALCC models are used as part of Land Use Harmonization Mod-
els [10]. It is therefore of crucial importance that ALCC models are improved, as ALCC sce-
narios provide data essential for earth system modeling of the past, present, and future.
A major current research challenge crosscutting the social, biological, and physical sciences
is thus to improve our understanding of the scope of early human land use, resultant changes
in land cover, and consequent feedbacks to both cultural and climatic systems. Archaeologists
and environmental historians have generated a large amount of data on past human land use;
they therefore have a central role to play in the improvement of ALCC scenarios. By bringing
together this significant repository of archaeological and historical data on how human activi-
ties have affected the earth system, archaeologists and historians can contribute to the develop-
ment of ALCC models that better reflect the timing, magnitude, and nature of human
influence on the earth system over time.
The exclusion of archaeological and historical data from ALCC models is not an oversight.
Archaeologists and historians have rarely attempted to generate global data in a format that
would be useful for a comparative world history of land-use systems or for incorporation into
the models of the earth systems science community. Generating such data is a difficult task,
requiring the synthesis of heterogeneous qualitative and quantitative datasets into general
regional narratives of historical land use through time and then translating such narratives
into spatial form within a digital map and database. Additionally, such work should aggregate
data on the effects of past land use systems, including changes in land cover and perturbations
to biogeochemical cycling through water management, changes in species composition, and
disturbance such as fire. Taking on this enormous task, however, is essential both to improve
ALCC models and to empower archaeologists and historians to play a larger role in the writing
of critical narratives of human-environmental interaction.
In this paper, we lay the groundwork for archaeological and historical synthesis of data on
past land use by defining a land use classification scheme. The land use classification is the out-
come of more than three years of consultation with groups of archaeologists, historians, and
geographers from all parts of the world who specialize in time periods covering the entire
Holocene. While a preliminary version of our classification was presented by Morrison et al.
[2], here we provide more detail on the classification, its application, its implementation in a
geospatial database, and the research processes required to start a global mapping effort.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 4 / 38
Holocene land use and its significance: LandCover6k
The degree to which pre-industrial land use and consequent land cover change affected the
global climate is disputed [1620]. Some human-induced changes were dramatic, such as
large-scale forest clearance and management; the domestication of plant and animal species,
the establishment of associated agricultural livelihoods, and the redistribution of these across
the planet; and the reshaping of entire environments via terracing, irrigation, and urban
expansion. Other transformations such as the management of wild plants, hunting, and the
long-term use of fire on a regional scale [21] are less evidently consequential but may, in aggre-
gate, have also contributed to earth system-level changes. These pre-industrial anthropogenic
influences on land cover and biogeochemical cycling may have affected climate through both
biogeophysical and biogeochemical feedbacks [22].
There is little doubt that the effects of pre-industrial human land use on terrestrial ecosys-
tems were profound at local to regional scales, e.g., with the advent of agricultural societies in
Southwest Asia, South Asia, China, and Mesoamerica [for summary of discussions, see 23],
but global scale effects are more debated. Several existing ALCC models suggest that vegetation
modification was fairly minor at global scales prior to the Industrial Revolution [9,22,24],
with correspondingly small greenhouse gas releases and minimal effects on the global carbon
cycle and climate system [25]. Others have argued that early agricultural and pastoral activities
triggered significant releases of greenhouse gases (CO
2
, CH
4
) to the atmosphere [e.g., 11,26,
27]. Kaplan et al. [11] estimated that Holocene ALCC could have resulted in 84 to 102 Pg C
released to the atmosphere by 3000 BP, equivalent to a substantial rise in atmospheric CO
2
of
7 ppm. These estimates are consistent with measurements of the isotopic composition of CO
2
recovered from high-resolution Antarctic ice-cores [25] because for much of the Holocene
anthropogenic CO
2
emissions were offset by the long-term sequestration of carbon due to
peatland expansion [35].
In many places, the intensity of land use practices increased over time through the Holo-
cene [e.g., 28,29], although this process was by no means uniform across time and space. Land
use choices and their consequences, such as effects on vegetation, soils, and wildlife, are path-
dependent, meaning that they are contingent upon complex and recursive sets of prior condi-
tions, including past human land use itself. While extreme environments historically limited
land use options, those limits also fluctuated as a result of technological change and past cli-
mate change. Thus, land use at any one time is influenced by land cover, precipitation, soil fer-
tility, and other ‘natural’ situations that create suitable conditions, but technological
developments can potentially transform those conditions. Over the 12,000 years of the pre-
industrial Holocene, human populations expanded their distribution to permanently settle on
all of the continents except Antarctica, and on most oceanic islands. While increasing popula-
tion did play an important role in the expansion of human impact, numbers alone are mislead-
ing, as factors such as wealth accumulation, inequality of consumption, and cultural demands
for specific goods and produce also fueled significant land use changes [30]. Anthropogenic
effects on land cover therefore cannot be reduced to population variables alone; instead, levels
and forms of consumption have also played a significant role. That is, not all people have, or
had, identical impacts on the environment [31]. Differential forms and rates of consumption
are made possible by land use practices (e.g., farming) and by other forms of production (e.g.,
metal working). Land use, the mechanism by which resources such as food, fuel, and other
goods (e.g., prestige items, technological items) are produced, emerges as a critical mediator
between ‘raw’ population human numbers and realized environmental impacts [32].
A great deal of uncertainty still surrounds the Holocene CO
2
record, and this uncertainty is
fostered by the lack of high-quality data-based syntheses of global land use and of
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 5 / 38
anthropogenic land cover change for the Holocene. Syntheses of historical land use are less
developed than those of land cover, in part because of higher data heterogeneity in the archae-
ological and historical record and the larger size and disciplinary diversity of the scholarly
communities involved. While numerous regional-scale syntheses exist [e.g. 33,34], even world
prehistory or global history textbooks generally do not attempt to integrate land use informa-
tion using a consistent format for all periods and regions. Significant efforts to aggregate and
synthesize archaeological and historical information in publicly available databases exist for
North America (DINAA–Digital Index of North American Archaeology; CARD–Canadian
Archaeological Radiocarbon Database) and Europe (e.g., The Cultural Evolution of Neolithic
Europe EUROEVOL, [35]) but these are typically limited in spatial and temporal scope. These
important databases of site-level records are not available for all world regions. A recent effort
at global-scale synthesis from archaeological data [29] using a preliminary version [2] of the
classification presented here established the value of such efforts, but did not produce a fine-
grained, empirically consistent data set, nor did it attempt to integrate historical and archaeo-
logical evidence of past land use with information from other proxies such as pollen records
[36].
These lacunae are addressed through the LandCover6k project, which is working to pro-
duce global maps of land use and land cover based on synthesized archaeological and paleo-
ecological data pertaining to designated time slices throughout the Holocene, from the advent
of farming to the industrial revolution [37]. Mapping both human land use and land cover
allows us to reconstruct anthropogenic land cover change (ALCC) directly, using the rich
empirical records of both paleoecology and archaeology. LandCover6k consists of three inter-
related efforts: (1) land cover synthesis and mapping [e.g., 38], using pollen records and pollen-
vegetation models such as REVEALS [39] and others [40]; (2) land use synthesis and mapping
[2and this paper]; and (3) modeler-paleoscientist coordination and co-design [8, see [41] for a
general discussion of the LandCover6k project]. Empirically independent records of land use
and land cover are needed in order to better understand the complex relationships between
them. If we are to resolve the debate over the longer-term impact of humans on the earth sys-
tem, the forms, timing, extent, severity, and significance of human action on land cover and
other processes such as carbon cycling cannot be assumed; they must be empirically demon-
strated. This paper outlines our approach to land use synthesis and mapping, designed specifi-
cally to be integrated with the work of the land use and earth system modeling efforts that are
part of LandCover6k [8] and to integrate with pollen-based land cover syntheses [see [42] for
an example set in Ireland].
Classifying past land use
The goals of the LandCover6k project required the development of a bespoke land use classifi-
cation scheme. Many existing classifications of anthropogenic land use focus more on the out-
comes or presumed outcomes of these activities than on the activities themselves. Sauer, for
example, defined five land utilization categories [43 pp. 48]: “(1) barrens, (2) woodlands, (3)
permanent pastures and meadows, (4) cultivated lands, [and] (5) town sites.” While Sauer’s
categories have found broad application, e.g., in the concept of “anthromes”, the specific goals
of the LandCover6k project required a purpose-built classification that separates cultural activ-
ities from possible outcomes and that specifies human land use practices more explicitly. This
is required because more than one form of land use can result in the same land cover. For
example, a woodland could be created through land use practices as different as foraging and
arboriculture. Because a fundamental goal of LandCover6k is to better understand land use
and land cover changes and their interconnections [37], we developed a land use classification
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 6 / 38
focused on cultural practices, i.e., the ‘uses’ people made of the land, rather than on the pre-
sumed outcomes of those practices [cf. 44].
Although related, land use is distinct from land cover [45], but many existing land use clas-
sifications do not fully separate the two [46,47]. The conflation of the two concepts creates
both ontological and epistemological issues with consequences for the classification and its
usability [45]. Data from satellite and aerial imagery, for example, provide information on the
biophysical properties of a land unit from which land use is then inferred. Classification struc-
tures derived from remote sensing often employ terminology applicable to both land use and
land cover, such as ‘cropland’ or ‘forest’ [e.g. 10,48]. However, forests are a land cover category
which can be used in many ways such as recreation, hunting, foraging, or forms of agrofor-
estry. Thus, to understand how past land use affected forests, we need independent evidence
for both the land use (in the form of archaeological or historical data) and the land cover
(from paleoecological data such as pollen analysis).
Ethnographic descriptions of traditional land use systems are an important basis for under-
standing past land use, but they are not sufficient. There are past land use systems that lack
modern or ethnographic analogues. Further, the global scope of LandCover6k requires col-
lapsing regionally and temporally specific vocabulary into fewer and more generic categories.
For example, the category of swidden/shifting cultivation is variously known as slash-and-
burn, ladang,milpa,jhum and other terms [49]. These terms have only local salience and are
not used with any consistency on a larger spatial scale. There is variability within swidden/
shifting forms of cultivation with respect to cultigens, the period of active cultivation and of
fallow, and the extent to which woody plants are integral to cropping regimes, but these land
use systems are similar enough to be united for large-scale classification of land use effects on
land cover. Although our classification uses terms current in the literature, we have favored
generic rather than temporally or regionally specific terms, and a major effort has gone into
defining these.
Our first task was the development of a uniform terminology for a single land use classifica-
tion that could be used for all time periods and regions. Historical and archaeological data on
past land use are extensive, but widely scattered, based on a diverse range of indicators, and
interpreted using multiple classification systems. Definitions of land use categories change
from context to context, and between different archaeological and historical traditions. These
challenges may make scholars reluctant to commit to definitive land use categories. However,
the detailed and nuanced understandings of past land use developed for individual regions
must be simplified in some way to be used for global-scale description of land use changes.
Our classification is designed to build connections between the terms, procedures, and forms
of knowledge produced by highly diverse scholarly communities. Using archaeological and
historical data in ALCC models requires harmonization of land use categories, but existing
ALCC schema [913,50,51] use a terminology that was not designed to incorporate archaeo-
logical data. Bridging the gap between the data-rich but classification-diverse world of histori-
cal scholarship and the ALCC community is important if the latter are to benefit from the long
history of scholarship on past land use.
Our classification of agricultural types shares some features with the work of Widgren and
colleagues [52,53; see also 54,55] who produced global maps of the dominant agricultural sys-
tems between CE 1000 and 1800, but it also differs in significant ways. Widgren and colleagues
used a limited number of categories since the aim was to produce global maps of dominant
land use systems. LandCover6k instead produces a database, which means that it can include
more information than might be legible on a map. We also opted to analytically separate sys-
tems of production into components. For example, the co-occurrence of domesticated plants
and animals in an agrarian system is often defined as ‘mixed farming’ [52], but there is little
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 7 / 38
agreement on how much emphasis on livestock (and of what kind) is needed to qualify a sys-
tem as mixed. Numerical thresholds (e.g. “50% of the income produced on farm should come
from livestock”) are not something archaeologists can calculate with any degree of certainty
[though see 5658]. We therefore code livestock-keeping separately from the cultivation of
domestic plants in the database, with the understanding that these often co-occur. Our data
structure allows us to track these correlations without needing to define them a priori.
We also built on prior classifications of hunting and gathering and pastoralism [5961] but
in a way that is adapted to the possibilities and limitations of archaeological evidence and
accommodates a database structure that includes numerous additional variables. Khasanov’s
[62] categorization of pastoralism, for example, is a useful framework to break down livestock
management practices according to levels of mobility; spatial and temporal patterns of move-
ment between resource zones are clearly a significant factor in the potential land cover impact
of pastoralism. However, archaeological data cannot necessarily identify levels of mobility in
the past [63]; where this is possible, it requires datasets that scholars have only just begun to
collect [64,65].
Past land use is inferred from multiple forms of archaeological and historical data [66]. Pas-
toral practices with or without concomitant forms of agriculture, for example, may be identifi-
able on the basis of settlement sizes, distributions, and duration [65,67,68]; faunal remains
[showing exploitation of wild taxa, husbandry practices of domesticates, including the use of
secondary products such as milk, traction, and manure; e.g., 6972]; botanical remains
[macro- and micro-remains showing crops grown, commensal weeds, sowing, cultivation,
processing and storing strategies, taxonomic signatures of wood assemblages from fuel; 73
77]; coprophilous fungal spores, dung and pasture/meadow fossil beetles and dung residues
indicative of large grazers [e.g., 7882]; landscape features [relict fields, terraces, canals, reser-
voirs, check-dams, 8388]; geoarchaeological evidence [soil micromorphology, buried soil
profiles, evidence of erosional regimes; e.g., 8992]; and isotopic evidence for human and ani-
mal diet and cultivation practices [e.g., 64,75,9396]. Archaeological data are unevenly dis-
tributed in space and time and subject to different taphonomic pressures. Building on various
typologies previously assembled under the auspices of LandCover6k [e.g., 2,9799], the system
presented here harmonizes and synthesizes the literature on human subsistence practices and
other land use activities.
The LandCover6k land use classification and variables
The LandCover6k land use classification scheme has five principal features. First, it is scale-
and source-independent. Second, it uses uniform terminology for all world regions and peri-
ods, terminology that was agreed upon in LandCover6k workshops involving diverse partici-
pants. Third, it is hierarchical and flexible, incorporating both categories of varying specificity
and variables that are relevant across land use systems. The categories and variables are defined
with the limitations of archaeological data in mind, specifically the variability in data coverage
and data quality across regions and time periods. Variables also include assessments of data
coverage and quality. Fourth, it relies upon expert assessment of dominant forms of land use
in an 8 x 8 km area at a particular point in time (see Implementation of the Classification in a
Geospatial Database,” below). Fifth, the classification takes the perspective of land rather than
people. We explain and justify each of these features below.
According to the Food and Agriculture Organization of the United Nations (FAO), land
cover classifications should be “scale-independent, meaning that the classes at all levels of the
system should be applicable at any scale or level of detail; and source-independent, implying
that it is independent of the means used to collect information” [100]. Following these
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 8 / 38
principles, our database classifies global land use according to predefined categories at varying
levels of detail. Within the hierarchical structure of the classification (discussed further below),
all categories are scale-independent and independent of the different data sources used.
Source-independence is especially important for classifications of human land use practices
since there are so many ways to study them, as described above. Because not all forms of infor-
mation are available or relevant for all times and places, it is not feasible to build categories a
posteriori by recording huge amounts of primary data. Instead, our categories are a priori,
using terms widespread in the literature, modified and refined by an extensive process of con-
sultation and workshops, and harmonized into a single globally applicable system.
The classification employs consistent language for describing historical and ancient land
use. As discussed previously, a frequent obstacle to the global synthesis of past data on land use
has been historians’ and archaeologists’ tendency to use terminology specific to their region
and time period of interest. This is further complicated by the wide range of disciplinary tradi-
tions concerned with the collection of land use data and the diverse forms of information each
can contribute. We have attempted to adopt language that preserves some of the complexity
and nuance contained within existing terminology but also simplifies enough to facilitate
global comparisons.
The structure of the classification is hierarchical. Categories are divided into levels termed
‘LU’ (land use) levels 1–3. The highest level of classification, LU1, is the most general and is
designed to facilitate broad global analyses, while second- and third-order categories, LU2 and
LU3, provide the opportunity to record increasingly detailed information suitable for more
nuanced studies. The LU1 classification applied to a grid cell is the dominant type of land use
at that grid cell location and is the most relevant for modelling purposes. The more specific lev-
els of classification (and the potential expandability of the classification at LU3), allow scholars
to pursue regionally or temporally specific analyses but also have their data included in global-
scale studies.
While we do not explicitly account for the intensity of land use activity, in areas where
human beings were present to only a limited extent, for example only through transit routes or
access to restricted areas, we propose a classification of ‘extensive or minimal land use’. While
this category implies minimal impact of human activity, it is important to distinguish such
zones from areas with ‘no human land use’, such as unpopulated islands and high-altitude
zones.
A challenge of historical land use mapping is that the boundaries between or among land
use classes can be ambiguous. There is, for example, a robust debate in the archaeological liter-
ature about the definition of agriculture, with many scholars suggesting that food procurement
and food production be viewed as a continuum [101103]. We cover this middle ground with
‘Low Level Food Production,’ a LU2 category that can be used to highlight times and places
where land use practices straddled the divide between these subsistence strategies, recognizing
that neither is necessarily exclusive [103].
Further to our hierarchical land use classification, we define additional variables that are
relevant across land use categories. These variables are vital to understanding the relationship
between land use and land cover and are designed to assist climate modelers in addressing spe-
cific concerns such as the history of landscape burning, livestock, soil turnover, wood harvest,
and other issues. Again, extensive consultation between archaeologists and climate modelers
was necessary to develop categories and measures that are both useful for earth systems scien-
tists but also amenable to archaeological research. As noted, recording variables as well as cate-
gories allows us to make some land use categories more generic. For example, the term
‘agropastoralism’ is widely used to describe land use systems that integrate both farming and
animal husbandry, especially those with ‘high’ levels of dependence on domesticates such as
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 9 / 38
ovicaprids and/or bovids. However, the term is not used consistently around the world and
there is no agreed-upon standard for what balance of farming and herding constitutes ‘agro-
pastoralism’ and what is simply ‘agriculture’ with some domestic animals. Accordingly, we
coded for animal (and plant) domesticates as variables distinct from the classification of agri-
cultural practices, allowing for a higher degree of variability to be recorded. The variables
recorded across land use systems include assessments of data coverage and quality in order to
facilitate identification of times and places needing additional research.
Human land use is often heterogeneous, integrating multiple forms and strategies of pro-
duction [49]; our scheme is structured to capture some of that diversity, but of necessity
requires that decisions about dominant land use practices be made, sacrificing detail for scale,
especially in the higher levels of classification. Our use of a relatively fine-grained 8 x 8 km spa-
tial grid (discussed below in “Implementation of the Classification in a Geospatial Database”) is
also meant to allow land use mosaics to be more faithfully represented than they are at coarser
spatial resolutions. In some cases, archaeological and historical data are capable of making
even finer spatial distinctions than can be captured by the grid, but in other cases the data are
not yet adequate to this task. Our analytical practices are thus a compromise–without sacrific-
ing the primary goal of producing empirically-grounded land use maps for climate modelers,
we also worked to develop recording strategies that would be of value to archaeologists, histori-
ans, human geographers, environmentalists, and others.
It is important to stress that this land use classification pertains specifically to land rather
than people. We are mapping the forms of past human land use that took place in specific loca-
tions and times based on assessments drawing from multiple lines of evidence, as noted above.
The specific cultural identity or identities of people in the past who practiced some form of
land use is not at issue here. For example, our ‘pastoralism’ category is restricted to areas
where very little or no land is under cultivation. Where crops and livestock are present,
whether or not these are managed by single or multiple social groups, classification falls under
the agriculture category, with a specification of which domestic animals are present, as noted
above. This “land, not people” distinction is important since most archaeological data is col-
lected and analyzed with reference to cultural or political groupings, and scholars work hard to
differentiate the presence of multiple cultural groups in the same place at the same time. Our
database does not take these distinctions into account, focusing instead on the aggregate effects
of land use practices in one time and place.
Although the focus of the classification is necessarily on land rather than people, we recog-
nize that all types of societies up to the present engage in some use of wild resources, often
alongside the use of agricultural and/or pastoral resources, and have built this fact into the def-
inition of our land use categories. Our hunting-gathering-foraging-fishing (HGFF) class only
applies if it is the dominant form of land use in a particular area. We preserve this category for
land inhabited by people for whom wild resources were/are their principal economic resource
[104,105]. No global map exists of specialized HGFF land use in the past. By adding proxies
into our database, such as settlement mode and fire, we can better understand the role of
HGFF land use on land cover throughout time. We wish to make the important point that
HGFF societies have impacted the landscapes they inhabit–sometimes considerably so [e.g.,
106], and we aim to better assess the nature of these impacts. For example, we use the ‘fire:
landscape-burning’ variable to signify land management using fire, which was one of the most
widespread methods of deliberate land management used by HGFF societies in the past and
present [104,107].
In (S2 File), we describe each upper level (LU1) classification and then, for those categories
with LU2 and LU3 distinctions, we explain how those distinctions were made and provide spe-
cific examples of that category or subcategory. Listed examples are illustrative rather than
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 10 / 38
exhaustive. Fig 1 shows the land use classification as a hierarchy, with coded variables in list
form. Another way to visualize the classification is as a nested series of categories. Schematic
figures of each LU1 and its nested subcategories, if any, are included here in the main text to
provide illustrations of some of the basic distinctions between them [Figs 27].
The classification system has been through various iterations, and the version presented
here is the full, final version of the concept note previously published by our working group
[2]. While the rationale behind the categories remains the same and we hope to have preserved
the allowances for regional examples expressed by the LandCover6k membership, the current
system has been modified in accordance with issues raised during testing of the database itself.
Implementation of the classification in a geospatial database
The LandCover6k project is committed to producing a global land use database for several
time slices requested by the climate modelers in joint meetings: 12 kya, 6 kya, 4 kya, and CE
1500. Some continental-scale subgroups are also working on additional time slices. Towards
this goal, we have implemented the global land use classification scheme described above and
in the (S2 File) in a GIS database, the structure of which is discussed in SI-2. The classification
system has been set in the geodatabase in the form of drop-down menus containing all of the
valid options for each level of the land use classification hierarchy (LU1, LU2, LU3) and all of
the valid options for each of the variables and data assessments relevant across land-use
systems.
Fig 1. The PAGES LandCover6k land use classification system.
https://doi.org/10.1371/journal.pone.0246662.g001
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 11 / 38
In order to ensure global coverage and maximum standardization of our land-use maps,
regional groups will enter data for 8 x 8 km squares in a vector data format (polygon GIS fea-
ture class). The size of spatial units represents a compromise between the needs of the model-
ing, pollen, and archaeological communities involved in the LandCover6K project. While the
LandCover6k pollen group is producing land cover rasters with a resolution of 1 degree [e.g.
108], we elected to use a much smaller grid. The 8 x 8 km grid squares are already much larger
than archaeologists and historians are typically comfortable with (as they often spend their
whole careers studying individual locales), but we had to compromise in the interest of achiev-
ing global maps in a reasonable time frame. The smallest grids used by the ALCC modelers are
5 minutes (1/12 of a degree) so we chose a grid that closely matches this (Fig 8). The task of the
regional land use groups will be to enter classifications and to code variables for land that falls
within the existing polygon squares.
Each regional subgroup within LandCover6k works with an identical copy of the database
to record assessments for squares and time periods falling within the range of their collective
expertise. A blank version of the geodatabase with the 8 x 8 km global grid is included in the
supplementary material. Because the nature and quality of archaeological data vary by region,
Fig 2. Uninhabited island representing LU1-“no human land use”. Created with BioRender.com, under a CC BY license, with permission from Biorender,
original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g002
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 12 / 38
different groups develop and pursue their own intermediate strategies of land-use mapping to
populate the LandCover6k database. For example, some regional groups are able to rely on
pre-existing site databases, while others must first generate them or perform land-use assess-
ments in a more general way. Some groups pursue a strategy of having collaborators draw land
use polygons on paper maps or in Google Earth before using the spatial intersection between
these polygons and the squares of the global grid to insert classifications into the database.
Each regional group selects, assembles, and uses its own body of 1) background environ-
mental information, 2) spatial archaeological data, and 3) decision rules on which to base their
classification. Background environmental information commonly includes raster and vector
datasets representing elevation, slope, hydrological basins, soil types, soil depth, past rainfall
patterns, and past coastlines for the time slice in question. Regional-group-provided base maps
are particularly important for dealing with the important issue of sea level change, which
affects which sets of squares will require land-use assessments at each time period. Spatial
archaeological data commonly include site locations with information on paleobotanical and
zooarchaeological identifications (if available), radiocarbon dates, paleoenvironmental recon-
structions, and maps of land use from published regional archaeological syntheses. Decision
Fig 3. Mountain ranges with access routes or evidence of exploration but not inhabitation representing LU1-“extensive-minimal”. Created with
BioRender.com, under a CC BY license, with permission from Biorender, original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g003
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 13 / 38
rules on which to apply classifications include, but are not limited to, spatial assessments such
as buffers representing hinterlands around known sites, elevations above which people did not
live or practice a certain type of land use, and paleoenvironmental zones associated with a par-
ticular type of land use. The generalized processes of translating regional archaeological data
into a spatial format using the classification scheme and the accompanying geospatial database
are set out in the flowchart [Fig 9].
It is unfeasible to record metadata and references within the spatial database because the
database is structured around individual 8 x 8 km squares and most references and data deci-
sions apply to broader areas. Additionally, the recording of references in the spatial database
would provide limited space for reviewing arguments in existing literature and does not
directly facilitate publication of the resulting maps. Instead, regional groups record references
and data decisions in an essay format. Regional groups prepare one metadata essay for their
Fig 4. LU1-“agriculture” shown as several LU2 categories: LU2-“herbaceous/ground crops” is shown by agriculture of maize and millets (upper left).
LU2-“swidden/shifting cultivation” is shown by forest clearance and cultivation of small crops in that space (upper right). LU2- “wet cultivation” (lower left)
includes LU3 categories: LU3-“rice paddy/taro pond fields” represented by rice paddies, LU3-“raised fields/chinampas” shown by chinampa agriculture and
LU3-“wetland cultivation” shown by cereal and pulse cultivation on river floodplains. LU2-“agroforestry/arboriculture” (lower right) includes LU3
categories: LU3-“arboriculture (tree crops)” shown with an apple orchard, LU3-“agroforestry” without soil enrichment shown through a woody perennial
management system with undifferentiated soils from unexploited areas, and LU3-”agroforestry with soil enrichment of woody perennial management” with
a Amazonian dark earth soil. Created with BioRender.com, under a CC BY license, with permission from Biorender, original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g004
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 14 / 38
region for each time slice. The essays identify published sources of maps and information used
in preparing the LandCover6k digital land-use map and specify spatial “rules” or other logic
that were used in assigning grid squares and groups of grid squares to particular land-use cate-
gories and land-use variables. Below, we provide an example of this approach for the Middle
East at 6 kya.
Archaeological land use mapping: Mesopotamia and Arabia at 6 kya
Here we provide a brief preliminary example of the application of the classification system for
Mesopotamia and Arabia at 6 kya (Fig 10). Chronological precision of archaeological data vary
significantly, and for this time -slice, addition of a temporal buffer means that evidence from
4250–3750 BCE is included within the 6 kya slice. In terms of modern geopolitical boundaries,
the example covers the countries of Iraq, Syria, Jordan, Kuwait, Saudi Arabia, Qatar, Bahrain,
the United Arab Emirates, Oman, and Yemen. For the purposes of keeping the example brief,
Fig 5. LU1-“hunter-gatherer-fisher-forager” visualized as several LU2 categories: LU2-“hunter-gatherer-forager” is represented by wild resources
gazelle and wild gathered nuts, fruits, seeds and berries. LU2-“broad-based and aquatic resources” is shown by a line of caught fish, shells and a collection
of gathered nuts. LU2-“low-level food production” is represented by gazelle and wild gathered nuts, fruits, seeds and berries, and a small number of
domesticated resources including deer and weedy/semi-domesticated pulses and cereals. LU2-“specialized fish production” is shown through ponded
resources in which fish have been collected and placed for future use. Created with BioRender.com, under a CC BY license, with permission from Biorender,
original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g005
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 15 / 38
we explicitly exclude Iran, Turkey, Lebanon, and Israel, as the coastal Levant and the Taurus
and Zagros Mountain zones to the west, north, and east are complex zones that will be
addressed in a later paper. The Mesopotamian portion of this exercise shows an example of
land-use classification in a zone that is relatively well-studied archaeologically, while the Ara-
bia portion shows an example of land-use classification in a less well-known zone with patchy
data coverage and areas that are relatively archaeologically unknown at this time period.
Mesopotamia land use data. Data sources for southern Mesopotamia are based on the
regional-scale archaeological surveys by Adams [109111] and Wright [112], as well as
paleoenvironmental data. Data sources for northern Mesopotamia are based on regional-scale
surveys by several teams working in northern Syria, and more recently, northern Iraq. Syrian
data are frequently systematic and well-synthesized in review publications [13,111,113], but
more recent data from Iraqi Kurdistan have been collected from preliminary reports [114
120]. For southern surveys, the 6 kya time slice corresponds to the Early Uruk period (c. 4000–
3500 BCE), when irrigation agriculture and animal husbandry had been well established in the
area for more than two millennia. For northern surveys, this time slice corresponds to the Late
Chalcolithic 2 and 3 period (c. 4200–3900, 3900–3600 BCE). Many regions of the north were
Fig 6. LU1-“pastoralism” shown as several LU2 categories: LU2-“anchored pastoralism” shown as cattle and sheep in proximity to a settlement. LU2-
“ranching” shows cattle enclosed in pasture land away from wild/unmanaged lands. LU2-“mobile–regular” shows sheep and goats being led along a specific
path. LU2-“mobile–irregular” shows sheep and goat being moved along in a less regular pattern along a less well trodden path. Created with BioRender.com,
under a CC BY license, with permission from Biorender, original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g006
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 16 / 38
among the early centers of plant and animal domestication in the Neolithic and therefore had
a 3000-4000-year history of food production by this time.
The landscape of southern Mesopotamia during this period differed considerably from the
present. The Persian Gulf and its associated marshes extended further to the north at this time,
reaching their greatest extent around 4550 BCE [121125]. The land use patterns likely associ-
ated with coastline and marsh areas have been mapped according to reconstructions of the
spatial extent of these environments in Algaze [126 Fig 1, pp. 202,based on 127]. The Tigris
and Euphrates rivers have continuously modified their courses through time via avulsion.
Since the advent of irrigation agriculture, human communities have both slowed and acceler-
ated this water course change in different areas [128]. In the third millennium BCE and later, a
denser recoverable settlement pattern and surface topography makes it easier to reconstruct
the location of river channels and canals [111,129]. For earlier periods, the reconstruction of
watercourses is more hypothetical. We have used hypothesized major watercourses of the late
fifth and early fourth millennium BCE as reconstructed in [126, Fig 1, pp. 202; based on 121],
the location of known Early Uruk sites from major survey areas, and models of early irrigation
Fig 7. LU1-“Urban/Extractive industries” shown as several LU2 categories: LU2-“dispersed urban/peri-urban” shows a spread-out settlement with
houses beyond the wall/edge of the settlement, and agriculture space within the settlement limits. LU2-“dense urban” shows closely packed houses/
buildings with little green/agricultural spaces. LU2-“mining/quarrying” is represented by a stone mine. Created with BioRender.com, under a CC BY license,
with permission from Biorender, original copyright 2020.
https://doi.org/10.1371/journal.pone.0246662.g007
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 17 / 38
agriculture as a basis for assigning 6 kya irrigated agricultural land. Wilkinson et al. [130]
argue that early irrigated agriculture in southern Mesopotamia was not widespread, but
instead only took place on river levees, which are on average 5 to 6 km wide. It is widely recog-
nized that the major southern Mesopotamian surveys mainly focused on areas in the center of
the Tigris-Euphrates alluvium that were largely outside the boundaries of twentieth century
agriculture, as ancient sites were more visible in these areas. Ancient settlement patterns nearer
the modern courses of the rivers, especially the Tigris, remain poorly documented in grey liter-
ature and in local languages, and are, for the purposes of this example, not included here [131,
132]. Additionally, remains of early periods like the Uruk period tend to be deeply buried in
some parts of the plain, either as a result of later occupation or alluviation. Thus, our use of
known Early Uruk sites in the land use classification surely underestimates the extent of irri-
gated agricultural land at 6 kya.
In northern Mesopotamia, the river courses have been more stable through time and pres-
ent-day courses are close to those of several thousand years ago. Rainfed agriculture has been
practiced across non-riverine steppe areas receiving more than 200–300 mm of rain per year;
irrigation is much rarer than in the south and was almost certainly absent in the 6 kya
Fig 8. The effect of grid size on data visibility. Modern 30 x 30 m landcover data from the GAP/LANDFIRE National Terrestrial Ecosystems data set (courtesy of
the U.S. Geological Survey - https://doi.org/10.5066/F7ZS2TM0) (A), aggregated via majority rule to 8 x 8 km (B), .5˚ x .5˚ (C), and 1˚ x 1˚ (D) grids.
https://doi.org/10.1371/journal.pone.0246662.g008
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 18 / 38
timeframe [128]. Changes in rainfall, and therefore changes in the spatial limits of where
rainfed agriculture is possible, are the main environmental differences for consideration in
mapping past land use. We have used the location of Late Chalcolithic sites, and especially the
Fig 9. Land Use classification flowchart showing the generalized processes LandCover6k is using to translate regional archaeological data into a
spatial format using the classification scheme and the accompanying geospatial database described in the paper.
https://doi.org/10.1371/journal.pone.0246662.g009
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 19 / 38
mapping of Late Chalcolithic ‘core’ agricultural areas by Wilkinson et al. [113 Fig 17, pp. 77],
as the basis for assigning 6 kya rainfed agricultural land. Such ‘core’ areas have been defined in
opposition to a ‘zone of uncertainty’ located to the south and east with lower rainfall. While
Fig 10. Mesopotamia and Arabia land use at 6 kya, an example using the classification scheme, geodatabase, and classification processes
outlined in the paper. Explanations for how the classification has been applied and citations for archaeological data used in this example are
discussed throughout the text.
https://doi.org/10.1371/journal.pone.0246662.g010
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 20 / 38
this zone of uncertainty became an area of settlement and pastoral activity in the Late Chalco-
lithic 4–5 and especially the third millennium BCE Early Bronze Age, Late Chalcolithic 1–3
settlement was sparse or absent. The rainfed parts of Syria are among the best-surveyed zones
of the Middle East, and some additional agricultural zones beyond those identified by Wilkin-
son et al. exist, for example around the western Syrian site of Ebla [133]. Additionally, recent
survey in Iraqi Kurdistan has documented dense Late Chalcolithic settlement in a number of
areas that were also ‘core’ agricultural zones at this time, including the Eastern Upper Tigris
region (Erbil, greater Zab, Mosul, and Dohuk plains) and intermontane valleys of the Zagros
(Shahrizor and Rania Plains) [114116,118120,134136]. It seems likely that future research
will demonstrate that significant Late Chalcolithic rainfed agricultural settlement extended
over most of the major plains in Iraqi Kurdistan, and thus our classification includes them,
even though not all have yet been investigated.
Zooarchaeological and paleobotanical data is virtually non-existent for the south for this
period because almost no Early Uruk sites have been excavated and political conflict in the
region over the last several decades has hindered the application of scientific approaches to
sites of all periods. Zooarchaeological and paleobotanical studies in northern Mesopotamia
typically focus on the later phases of the Late Chalcolithic period (beginning c. 3700 BCE),
when Uruk populations and material culture spread into the north from the south. Based on
these studies it seems that some indigenous Late Chalcolithic communities in the upper
Euphrates and Tigris basins may have focused on cattle and pig pastoralism, and that intensive
sheep/goat pastoralism was introduced later with the Uruk Expansion [137]. However, a recent
meta-analysis of zooarchaeological data in the region concludes that sheep and goat were the
dominant taxa during both the fifth and fourth millennia, with herd composition tied to pre-
cipitation patterns [138]. On the basis of studies in northern Mesopotamia and analogy to later
periods in the south, we assume that irrigated cereal, legume, and date-palm agriculture as well
as sheep/goat, pig, and cattle pastoralism were practiced in various environmental zones in the
south and that rainfed cereal and legume agriculture as well as sheep/goat, pig, and cattle pas-
toralism were practiced in the north. In the absence of empirical data for southern marsh life-
ways at the time, we rely on ethnographic analogy to suggest that communities living within
marsh environments practiced some pastoralism and perhaps flood-recession agriculture, but
primarily relied on fishing, birding, hunting, and gathering for subsistence [139].
Mesopotamia classification. Using the LU classification outlined here, in the area of
Southern Mesopotamia (south of Baghdad), the land use for the 8 km pixels surrounding Early
Uruk sites and hypothesized late fifth-early fourth millennium BCE river courses have been
assigned as LU1 agriculture with LU2 herbaceous/ground crops and canals/channels for water
modification. Residents of this region kept domestic sheep/goat, cattle, and pig and grew
wheat, barley, pulses, and date palms; these are recorded in the crop and animal variables. In
the reconstructed marsh areas, LU1 hunting-gathering-fishing-foraging with LU2 low-level
food production has been assigned. People living here may have kept cattle as their main ani-
mal domesticate, since pigs and sheep/goat do not thrive in wetland environments. As the
extended Gulf area was at this time underwater, it was coded LU1 as no human land use. In
the areas outside the marshes and riverine areas, LU1 Extensive/Minimal was assigned;
although there is little archaeological evidence for occupation, we presume that people did
move across this region.
In northern Mesopotamia (north of Baghdad) we have assigned LU1 agriculture with LU2
herbaceous/ground crops, flood for water modification, and sheep/goat, cattle, pig, wheat, bar-
ley, and pulses to squares in the modern floodplains of the Tigris and Euphrates rivers and
major tributaries that flow year-round. An assignment of LU1 agriculture with LU2 herba-
ceous/ground crops, focusing on sheep/goat, cattle, pig, and wheat, barley, and pulses was
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 21 / 38
given to the steppes outside the modern floodplains of the Tigris and Euphrates and in areas
interpreted to be Late Chalcolithic agricultural cores on the basis of settlement patterns and
excavations (these areas would have received at least 200-300mm of rain per year, and were
coded “rainfed” for water modification). This form of agriculture does not necessarily extend
into the mountain foothills at the edge of Mesopotamia in Turkey, Northern Iraq, and Iran,
though as noted, some Zagros valleys in Iraqi Kurdistan do have dense agricultural settlement
at this time. The intermontane valleys within Iraq were defined according to topographic slope
using the GTOPO30 digital elevation model. For the purposes of this paper, our mapping
example ends here.
Ceramic production was well-established in both northern and southern Mesopotamia by
this time (having begun during the Late Neolithic or Pottery Neolithic, after c. 6000 BCE in
the north and c. 5400 BCE in the south), and was accomplished on a large scale in cities and
towns. Accordingly, ceramic production is recorded in the pyrotechnology variables.
Arabia land use data. In Arabia, we have relied on data synthesized by Magee [140],
McCorriston and Martin (for southwest Arabia) [141], and Petraglia et al. (for northern Ara-
bia) [142]. The available data are much more unevenly distributed than in Mesopotamia, and
the land mass is much larger and more variable. The land use mapping therefore often relies
on evidence from individual sites and a concise general assessment is more difficult to pro-
duce. Arabia and Mesopotamia show several broad differences in terms of subsistence and
land-use leading up to 6 kya. First, Arabia was not within the native range of the wild progeni-
tors of major animal and plant domesticates. These domesticates had to be introduced from
elsewhere, especially Mesopotamia (via boat trade in the Gulf, which began by the late sixth or
fifth millennium BCE), the Levant, and perhaps (in the case of cattle) east Africa. Agriculture
and pastoralism therefore developed later in Arabia. Faunal remains and rock art indicate that
sheep and goat were present from at least the late seventh millennium and cattle were present
from the early sixth millennium BCE [143145]. Second, unlike the Fertile Crescent, of which
Mesopotamia is a part, agriculture followed pastoralism. In highland south Arabia, domesti-
cated plants are not evidenced until a millennium later than domesticated animals. Many Ara-
bian populations adopted herding practices in the absence of agriculture and apparently also
in the absence of a tradition of wild plant collection and cultivation. In most places, pastoral-
ism appears as part of a broader subsistence strategy focused on hunting and/or marine
resources. Third, the Neolithic in Arabia was aceramic.
Declining climate conditions during the late fifth millennium BCE Arabian Late Neolithic
led to a “Dark Millennium” c. 4000–3000 BC, during which there were major shifts in subsis-
tence strategies and occupation patterns throughout Arabia [146]. Although there was a brief
period of slightly greater humidity at 6 kya, this was sandwiched between two significantly
more arid phases [142]. Environmental proxy data from Al-Qunf and Hoota caves indicate
that the Indian Ocean monsoon system migrated south to its current position by c. 4000 BCE,
ending the Holocene Moist Phase [147,148]. As a result, most of Arabia no longer received
the summer rainfall it had in previous millennia. The effects of this aridity varied by region.
In southeast Arabia, archaeological site distributions indicate that there was a near absence
of occupation in the eastern province of Saudi Arabia and in the south along the shores and
interior of the UAE [140]. The exception to this is Akab, located on a lagoon in Umm al
Quwain [149]. Unlike earlier periods, which showed material connections with contemporary
inland material culture at places like Buhais 18, the Akab artefactual material is very different
from the earlier, Neolithic material, suggesting that in the fourth millennium there was a fun-
damental break in the coastal to inland pastoral exchange system seen in the Neolithic. These
major changes and a new focus on coastal resources may be related to the region’s generally
sparse groundwater resources [142].
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 22 / 38
More sites were occupied along the coastline of the Gulf of Oman. For example, the ceme-
tery at Suwayh I has material from the end of the fifth millennium [150] and the region has
three different lithic facies dating to fourth millennium [151]. Charpentier [152] also found
over 50 sites dating to 3700–3000 in the Ja’alan, most in the coastal zone, accompanied by
large shell middens. Uerpmann [146] attributes intensification of settlement on the Omani but
not Gulf coast to environmental and geomorphological differences—coastal areas in UAE are
rarely fed by waters from the Hajjar mountains, but the Omani coast has many large wadis car-
rying water; that area also still received some limited, scattered summer rainfall. Zooarchaeolo-
gical data from sites like Ras al-Hamra 5 indicate that sheep, goat, and cattle were herded,
though it is unclear whether the pastoral system was mobile or sedentary [146,153]. The Gulf
of Oman coast appears to have provided a “refuge” where populations could mainly exploit
marine resources and continue to practice some herding during the climatic deterioration;
inland areas seem to be largely empty of occupation at this time [140].
The interior of southwest Arabia did not suffer as much from the climatic deterioration
because it provided a greater diversity of exploitable economic niches that were used more
intensively for pastoralism and agriculture [154]. Some indications of irrigated agriculture
appear in highland Yemen [155,156]. Barley, wheat, chickpea, and possibly millet were present
in late fourth millennium BCE Hayt al-Saud and Jubabat al Juruf, slightly later than the time
slice considered here, in areas where terrace agriculture may date back to the fourth millen-
nium BCE [128,157]. Traces of runoff irrigation are found in lowland eastern Yemen at Wadi
Sana, a tributary of Hadramawt, dating to the mid-fifth through mid-fourth millennium BCE.
Irrigation here may have developed in response to decreasing precipitation, but the major
crops are not known [158]. Overall, Harrower [158] and McCorriston and Martin [141] char-
acterize the lowland inhabitants of Southwest Arabia at this time as “pastoralist irrigators,”
using agriculture and cattle pastoralism with seasonal transhumance. Pastoralism was also
important in the highlands, primarily cattle but also sheep/goat herding [156].
More detailed data exist for the lowlands of southwest Arabia at this time. McCorriston and
Martin [141] argue that between 7–5.5 kya there is evidence of cattle ownership with accompa-
nying tribal markings, perhaps suggesting grazing rights in particular territories. The site of
Khawlan, north Yeman, shows evidence for cattle and caprines and Kheshiya, Wadi Sana, has
evidence for specialized cattle pastoralism in the form of cattle skull rings and collective feast-
ing events. In the Wadi Sana, there is evidence of landscape-scale burning events, perhaps
linked to more intensive pastoralism, around 5880 BP [159].
The number of known sites in the Tihama (Red Sea Coast) is small; shell midden sites posi-
tioned 5–10 kilometers inland at local environmental interfaces radiocarbon date from the late
seventh to the late fourth millennium BCE and therefore extend into the 6 kya time slice. One
site in the region had faunal remains of cattle and sheep/goat in contexts with a single radio-
carbon date (3600–3180 BCE) falling just later than the time range considered here (SRD-1).
This is the earliest recovered evidence for herding in the Tihama [156].
There is substantively less data for northern Arabia and the interior of the peninsula,
though recent projects are now providing more detailed pictures of northwestern Saudi Arabia
and eastern and southern Jordan [142]. The few sites known from the vast stretches of interior
Arabia demonstrate a significant decline in human activity around 6 kya, compared to earlier
Neolithic pastoral activity, which had often been concentrated around paleolakes and oases.
Surveys in the northern region show significant occupation by the Neolithic from the Saudi
highlands through eastern Jordan [140,160163] including in and around the Nafud desert
[164,165]. However, many surveyed sites are not directly dated, and it is assumed that they
correspond to occupations during more climatically optimal periods than 6 kya. Where dates
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 23 / 38
are available, evidence for continued occupation of the northern region by 6 kya is patchy, and
likely significantly less intense in comparison to earlier and later periods.
Settlement may have continued during this time period at the important north Arabian
basins of Tayma and Jubbah, around the Nafud desert. For example, the continued presence of
the pollen of domesticated plants at Tayma from 6.6 kya [166] onward suggests continued
human activity, and a carnelian bead manufacturing site at Tayma dates to this period [167].
Sites in the Jubbah basin mostly remain undated. Petraglia et al. [142] argue that the presence
of large, shallow aquifers, and the possibility of rainfall from the winter westerly storm systems
at northern oases like Tayma and Jubbah, resulted in more continuity through arid periods
than is seen in southeastern Arabia.
Further to the north, in the portions of north Arabia that fall within Jordan and Syria, there
is abundant survey data for “late prehistoric” periods that suffers from the same problems
noted for the Arabian interior and the Nafud. Sites and features of the Neolithic/Chalcolithic/
Early Bronze Age are not precisely dated, but likely mostly date to periods when climatic con-
ditions were more optimal, and few or no radiocarbon dates fall in the 6 kya range. However,
there is general consensus among scholars that there was still some low level of continued
human occupation of these areas [168]. Betts and Martin [169] have suggested that Chalco-
lithic sites exist in the harra (basalt desert) and hamad (limestone desert) of Jordan but that
these have frequently gone unrecognized in earlier surveys due to a lack of good diagnostic
material. Recent work has, however, identified several sites from northern, southeastern, and
eastern Jordan that demonstrate continued pastoralist use of the landscape into the Chalco-
lithic period encompassing the 6 kya time slice [170173]. This includes sites like Tell al-Hibr
[169], with faunal evidence for a potential mixed subsistence strategy of herding and hunting
as in earlier periods. Additional sites like Tulul al-Ghusayn, Khirbet Abu al-Husayn, and
Khirbet al-Ja’bariya may have been inhabited year-round [174,175], the latter of which have
radiocarbon dates that overlap the 6 kya time slice [176]. Mu¨ller-Neuhof [175] has identified,
via systematic survey, evidence for continued seasonal pastoral use, along with flint mining, of
the harra through this period. While there is good evidence for terrace agriculture in eastern
Jordan, at Jawa and other sites, as early as the middle to late fourth millennium BCE [174], this
was likely not possible earlier because of the increased aridity in the region. Environmental
proxy data for this more northern area is inferred from precipitation-induced changes in the
water levels in the Dead Sea [177], speleothem data from the Soreq cave near Jerusalem [178],
and pollen cores from the Sea of Galilee [179]. North of the harra, there are a few agropastoral
villages identified as Chalcolithic/Early Bronze, including Qarassa, Sharaya, Tell el-Baharia,
and Tell el-Khazzimi [180185], that may have been occupied at the 6 kya time slice, but in the
absence of radiocarbon dates these sites have not been included. The situation is different in
the Jordan Valley, with evidence from large sites on both sides of the river, and significant
quantities of radiocarbon dates identifying villages inhabited across the 6 kya time slice [186,
187]. In Jordan, this includes sites like Tuleilat Ghassul, Abu Hamid, Tell esh-Shuna, [187,
188]. Sites in the Jordan Valley utilized mixed farming and pastoral subsistence strategies that
included sheep/goat, pig, and cattle with einkorn and emmer wheat, six-row and two-row bar-
ley, lentils, chickpeas, and olive [188192].
Arabia classification. Building from this patchy information, the 6 kya LU classifications
for Arabia are as follows. Bahrain, Qatar, the coast and interior of UAE and the eastern areas
of Saudi Arabia were assigned LU1 Extensive/Minimal. These areas had been inhabited in the
Neolithic, but evidence for inhabitation is mostly lacking in the fourth millennium. The coastal
plains of Oman and Yemen (including the Ja’alan and the Tihama) are assigned LU1 hunting-
gathering-fishing-foraging with LU2 low-level food production; residents here had a primary
reliance on shellfish, but also kept sheep, goat, and cattle, and these are coded in the animal
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 24 / 38
variables. There is disagreement about the extent to which the coastal communities in Oman
like Ras al-Hamra 5 were sedentary or mobile in a coastal-winter inland-summer pattern
attested in more climatically optimal periods. However, it should be noted that no inland sites
have been found [140], and therefore the interior of Oman has also been assigned to LU1
Extensive/Minimal.
Given the limited research in highland Yemen, potential agricultural areas have been
mapped based on the characteristics of the two known (slightly later) sites discussed above, the
dating of terrace systems, and general reconstruction of pre-industrial agricultural methods by
altitude as presented in [128 pp. 187, Fig 9.2]. Following this reconstruction, which shows the
altitudinal limits of different sorts of agricultural systems for the Tihama (Red Sea, west) versus
Rub al-Khali (east) side of the Yemeni highland, we have assigned the following classifications:
1) squares with significant areas above 2500m ASL (on the east side) and above 2000 m ASL
(on the west side) were assigned LU1 agriculture with LU2 herbaceous/ground crops with
rainfed in the water modification variable. Terrace systems are found in these areas, but unlike
lower altitudes, irrigation was not required. 2) squares with terrain 1500–2500 m ASL on the
west slopes were assigned to LU1 agriculture with LU2 herbaceous/ground crops with terrace
water modification. 3) squares below 1500 m ASL between the highlands and the Tihama
coastal plain were assigned to LU1 minimal-extensive because 6 kya sites are not known from
this zone (though this could just be due to a lack of research). 4) squares 2000–2500 m ASL on
the east slopes were assigned to LU1 agriculture with LU2 herbaceous/ground crops with ter-
race-based water modification. 5) squares below 2000 m ASL on the east slopes were assigned
to LU1 minimal-extensive; in later periods this was an agriculturally productive area under
episodic flood irrigation, but evidence from oases like Ma’rib seem to indicate such irrigation
agriculture began in the third millennium BCE. Elevations were derived from the GTOPO30
digital elevation model. For all of the LU1 agriculture zones of highland Yemen, we coded cat-
tle, sheep/goat, wheat, barley, and chickpea in the animal and crop variables, following very
limited botanical data. Lowland and interior parts of southwest Arabia within 1–8 km square
from major drainages not in the Ramlat as-Sabatayn/Rub al-Khali sand deserts (primarily the
Wadi Hadramawt and its tributaries) have also been assigned to LU1 agriculture with LU2 her-
baceous/ground crops with dam water modification (following Harrower’s [158] identification
of runoff irrigation in Wadi Sana), with the same animal and plant variables recorded as for
highland Yemen (in the absence of crop data). For these wadi areas, the presence of seasonal
mobility and landscape-scale burning are noted. Areas within 2–8 km squares from these
wadis are assigned to LU1 Pastoralism with cattle and sheep/goat coded into the animal
variables.
All of interior Arabia was assigned LU1 Minimal/Extensive. This classification may become
more spatially nuanced in the future with more research, but it also may not, as 6 kya was a dif-
ficult period to live in this region.
For northern Arabia, areas around most of the few known sites dating to this period have
been assigned LU1 hunting-gathering-fishing-foraging with LU2 low-level food production.
This includes the Tayma and Jubbah basins as well as 3 x 3 square (24 x 24 km) neighborhoods
around known sites east of Jawa and other sites in Jordan noted above. Communities in these
areas may have practiced some limited cultivation of crops like the six-row hulled barley, ein-
korn, bread wheat, and emmer found at slightly later sites [174,193]. They commonly herded
sheep/goat and possibly cattle [174,194]. However, they also relied significantly upon highly
mobile hunting and gathering, and in places like the Nafud they did not leave architectural
remains. Given the research difficulties discussed above, this mapping may under-represent
the extent of human activity in the area at 6 kya. Ongoing and future work in the region may
clarify this. Areas outside of those surrounding these known sites have been classified as LU1
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 25 / 38
Minimal/Extensive. For the Jordan Valley, squares immediately adjacent to the Jordan River
between Teleilat Ghassul (just north of the Dead Sea) and Tell esh-Shunah (at the confluence
of the Yarmuk and Jordan rivers) have been assigned LU1 agriculture with LU2 herbaceous/
ground crops with rainfed in the water modification and wheat, barley, pulses, olive, sheep/
goat, pig, and cattle coded in the plant and animal variables.
Conclusion
There is a critical need for data-based global assessments of past human land use. Past land use
practices transformed land cover in complex and variable ways and with differing degrees of
intensity. Although we know that regional-scale transformations in vegetation and even land-
forms were sometimes very dramatic, it is not yet clear how significant the aggregate of the
many local records of landscape transformation documented by archaeologists, paleoecolo-
gists, and historians might be on a global scale. Archaeology and other historical disciplines
have generated vast quantities of information over the last century or more, but until now
these data have not been made commensurate, nor have they been aggregated at a global scale.
Data harmonization of this sort requires a common analytical language, shared categories, and
shared data formats, requirements addressed by the development of this classification and the
accompanying database. As discussed above, the classification, land use variables, and database
reported here are the outcome of an extensive process of consultation and co-design [8]
between climate modelers, paleoecologists, and archaeologists. Although purpose-built for the
objective of improving climate models [37], this database also has significant potential to
inform historical research. Indeed, in some world regions, the syntheses we are building are
the first and often the most systematic effort to integrate existing large but scattered and incon-
sistent archaeological data sets.
Classifying and documenting past land use practices is only one step in understanding the
impact of our species on the earth system. While data on land use practices such as plowing,
large-scale burning, or flooded-field farming that directly affect chemical cycles, such as car-
bon and methane, may be immediately relevant to climate models, the impacts of land use his-
tories on anthropogenic land cover change (ALCC) are importantly mediated by climate,
prior conditions, and other factors. We therefore adopt multiple approaches to improving
ALCC estimates for the past. First, we aim to link pollen-based vegetation reconstructions
with archaeologically-based land use data [37]. Pollen based reconstructions, though produced
at lower resolution, provide an important test of the land use database [8]. Second, we aim to
improve existing ALCC models, which differ significantly from one another [15].
This work is already underway. Harrison et al. [8] outlines a protocol for evaluating the
land use and land cover models created from the various archaeological data used in ALCC
modeling. It suggests ways that the reconstruction created from archaeological data can be
implemented into global land use and land cover scenarios, and how these should be evaluated
using independent pollen-based reconstructions of land cover and climate. As part of this, the
improved models can then be used in paleoclimate simulations. Within the PMIP (Palaeocli-
mate Modelling Intercomparison Project) the improved models are being utilized to quantify
the magnitude of anthropogenic impacts on climate through time, and in doing so the Land-
Cover6k project is playing a vital role in improving the realism of Holocene climate simula-
tions [8].
While there are multiple approaches ongoing to improve ALCC models [29], LandCover6k
is novel in important ways. The structure of our database ensures that data is recorded in rela-
tively fine-scale, comparable spatial units. The classification scheme resulted from an extended
workshop process in which archaeologists came together to develop a shared understanding
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 26 / 38
that is built into the detailed descriptions and definitions we have provided. This facilitated the
consistent use of classification terms and the assessment of variables in comparable ways.
Finally, the classification scheme, database, and resulting products are explicitly designed to
both facilitate the work of modelers and to serve as a resource for archaeologists.
Existing efforts to quantify ALCC rely on estimates of past population to inform projections
of potential land cover impact [9,11], and a recent contribution compiled published regional
estimates of per capita land use for cropland and pasture as an additional parameter for popu-
lation-based models [10]. While population is clearly an important factor in the area of land
needed to meet resource requirements, there are multiple strategies for obtaining specific
resources and thus many different vegetation-change outcomes that may result. Further, popu-
lation effects are mediated by varied forms and levels of consumption [195]. Demography with
an assumption of constant per capita cropland use ignores factors such as surplus production,
waste, and agricultural intensification [2]. Existing ALCC models rely on algorithms to, for
example, distribute population evenly across the landscape or to distribute past population
through linear extrapolation from modern demographic patterns; both of these approaches
miss well-known locational preferences of groups practicing different forms of land use as well
as ways these locational preferences have changed through time with technological, environ-
mental, and cultural change. Our datasets can thus provide empirically-based corrections and
constraints on ALCC models, enhancing their value to earth system modelers.
The classification scheme and accompanying database outlined here is a critical part of this.
The classification scheme is a simplification of what were complex systems of land use, but
such simplification is necessarily for global-scale aggregation. By coding some variables
(domesticates, burning, tillage, etc.) apart from the land use categories, we attempt to both cap-
ture commonly-recorded archaeological variables significant to environmental change and
also to keep open the possibility of analyzing associations among classes and variables. For
example, raised fields are often found in areas under maize cultivation, but our data structure
allows this association to be tested and for new combinations to be identified if they exist. We
thus highlight the value of this exercise to archaeology and history as well as to earth system
science. Archaeological analyses of past land use rely on a very large range of indicators, and
data coverage and quality vary significantly across space and time. Our database is designed to
be public, iterative, and correctable, able to integrate new data and understandings. As a first
step in the harmonization of archaeological land use data, it is necessarily preliminary.
Although we begin with a limited number of time slices, there is the potential for more power-
ful transient time series analyses in the future. The LandCover6k research will be able to iden-
tify areas or times of rapid land use change, areas of contention that require further work, and
areas that are lacking data but clearly are importantly for land use and land cover research.
Applied research and modeling groups associated with ecosystems services such as ARIES
(Artificial Intelligence for Ecosystems Services) will also benefit from the LandCover6k work,
and from the consistent language used in this classification scheme.
Human impact on the earth system has a long history, but we cannot accurately assess its
significance without global-scale synthesis. Past human populations levels, while important, do
not directly index past human impact; just as in the present, some groups of people consumed
more and/or different resources than others, complicating demographic effects. Historical
land cover ‘footprints’ were mediated in part by forms of land use, from gathering and hunting
to agriculture and industry. Although the significance of anthropogenic land cover change is
widely recognized, existing efforts to model these changes on a global scale are problematic,
with competing models varying significantly. Despite the existence of significant archives of
archaeological and historical data, these data have not, to date, been systematically used to cor-
rect or constrain ALCC models. We have developed a common language for land use
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 27 / 38
classification, a database for recording land use assessments, and strategies for data manage-
ment and coordination as a first step toward using these important but scattered, uneven, and
regionally-focused historical archives to contribute to a better understanding of the earth
system.
Figs 27were created using Biorender.com. The authors are grateful to Ka Ki Jacqueline
“Jacky” Chan for her help in creating Fig 9.
Supporting information
S1 File.
(ZIP)
S2 File.
(DOCX)
Acknowledgments
This study was undertaken as part of the Past Global Changes (PAGES) project (and its work-
ing group LandCover6k). The classification presented here is the result of extensive discussion,
argument, and compromise. Archaeological and historical synthesis of this type, and on this
scale, is unprecedented, and we acknowledge the range and depth of intellectual contributions
to this work. We would like to thank in particular: Jonathan Aleru, William Balee, Tilman
Baum, Carla Jaimes Betancourt, Amy Bogaard, Michelle Chaput. Joanne Clarke, Charles R.
Clement, Anne Casile, Anne Dallmeyer, Bamidele Sunday Dennis, William Doolittle, Anneli
Ekblom, Laure Emperaire, Xiuqi Fang, Dan Gavin, Amy Goldberg, Jane Humphries, Nestor
Ignacio Gasparri, Jose
´Iriarte, Christian Isendahl, Nadim Jayaraju, Nadia Khalaf, Carla Klehm,
Sjoerd Kluiving, Julian Laabs, Terri Lacourse, Emma Loftus, Munyaradzi Manyanga, Katie
Manning, Andrew Martindale, Nicolas Maughan, Sathaporn Monprapussorn, Claide P. Mor-
aes, Janken Myrdal, Malebogo Mvimi, Cecelia Pallo, Shilpa Pandey, Markus Reichstein, Felix
Riede, Philip Riris, Neil Roberts, Mark Robinson, Rick Schulting, Maria Saña Seguı
´, Daryl
Stump, Federica Sulas, Hikaru Takahara, Enric Tello, Richard Thomas, LuAnn Wandsnider,
Jietao Wang, Zhao Wanyi, Josh Wells, and Mats Widgren for their work on the LandCover6k
LU classification workshops and the ongoing regional workshops.
Author Contributions
Conceptualization: Kathleen D. Morrison, Emily Hammer, Oliver Boles, Marco Madella,
Nicola Whitehouse, Marie-Jose Gaillard, Jennifer Bates, Marc Vander Linden, Stefania
Merlo, Alice Yao, Laura Popova, Austin Chad Hill.
Data curation: Emily Hammer.
Investigation: Kathleen D. Morrison, Emily Hammer, Oliver Boles, Marco Madella, Nicola
Whitehouse, Marie-Jose Gaillard, Jennifer Bates, Marc Vander Linden, Stefania Merlo,
Alice Yao, Laura Popova, Austin Chad Hill.
Methodology: Kathleen D. Morrison, Emily Hammer, Oliver Boles, Marco Madella, Nicola
Whitehouse, Marie-Jose Gaillard, Jennifer Bates, Marc Vander Linden, Stefania Merlo,
Alice Yao, Laura Popova, Austin Chad Hill, Ferran Antolin, Andrew Bauer, Stefano Bia-
getti, Rosie R. Bishop, Phillip Buckland, Pablo Cruz, Dagmar Dreslerova
´, Gerrit Dussel-
dorp, Erle Ellis, Dragana Filipovic, Thomas Foster, Matthew J. Hannaford, Sandy P.
Harrison, Manjil Hazarika, Hajnalka Herold, Johanna Hilpert, Jed O. Kaplan, Andrea Kay,
Kees Klein Goldewijk, Jan Kola
´ř, Elizabeth Kyazike, Julian Laabs, Carla Lancelotti, Paul
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 28 / 38
Lane, Dan Lawrence, Krista Lewis, Umberto Lombardo, Giulio Lucarini, Manuel Arroyo-
Kalin, Rob Marchant, Francis Mayle, Meriel McClatchie, Madeleine McLeester, Scott
Mooney, Magdalena Moskal-del Hoyo, Vanessa Navarrete, Emmanuel Ndiema, Eduardo
Go
´es Neves, Marek Nowak, Welmoed A. Out, Cameron Petrie, Leanne N. Phelps, Zsolt
Pinke, Ste
´phen Rostain, Thembi Russell, Andrew Sluyter, Amy K. Styring, Eduardo Tama-
naha, Evert Thomas, Selvakumar Veerasamy, Lynn Welton, Marco Zanon.
Supervision: Kathleen D. Morrison.
Writing – original draft: Kathleen D. Morrison, Emily Hammer, Oliver Boles, Marco
Madella, Nicola Whitehouse, Marie-Jose Gaillard, Jennifer Bates, Austin Chad Hill.
Writing – review & editing: Kathleen D. Morrison, Emily Hammer, Oliver Boles, Marco
Madella, Nicola Whitehouse, Marie-Jose Gaillard, Jennifer Bates, Marc Vander Linden, Ste-
fania Merlo, Alice Yao, Laura Popova, Austin Chad Hill, Ferran Antolin, Andrew Bauer,
Stefano Biagetti, Rosie R. Bishop, Phillip Buckland, Pablo Cruz, Dagmar Dreslerova
´, Gerrit
Dusseldorp, Erle Ellis, Dragana Filipovic, Thomas Foster, Matthew J. Hannaford, Sandy P.
Harrison, Manjil Hazarika, Hajnalka Herold, Johanna Hilpert, Jed O. Kaplan, Andrea Kay,
Kees Klein Goldewijk, Jan Kola
´ř, Elizabeth Kyazike, Julian Laabs, Carla Lancelotti, Paul
Lane, Dan Lawrence, Krista Lewis, Umberto Lombardo, Giulio Lucarini, Manuel Arroyo-
Kalin, Rob Marchant, Francis Mayle, Meriel McClatchie, Madeleine McLeester, Scott
Mooney, Magdalena Moskal-del Hoyo, Vanessa Navarrete, Emmanuel Ndiema, Eduardo
Go
´es Neves, Marek Nowak, Welmoed A. Out, Cameron Petrie, Leanne N. Phelps, Zsolt
Pinke, Ste
´phen Rostain, Thembi Russell, Andrew Sluyter, Amy K. Styring, Eduardo Tama-
naha, Evert Thomas, Selvakumar Veerasamy, Lynn Welton, Marco Zanon.
References
1. Claussen M. Late Quaternary vegetation-climate feedbacks. Clim Past. 2009; 5: 203–216. https://doi.
org/10.5194/cp-5-203-2009
2. Morrison K, Hammer E, Popova L, Madella M, Whitehouse N, Gaillard M-J. Global-scale comparisons
of human land use: developing shared terminology for land-use practices for global change. PAGES
Mag. 2018; 26: 8–9. https://doi.org/10.22498/pages.26.1.8
3. Levis S. Modeling vegetation and land use in models of the Earth System: Modeling vegetation and
land use in models of the Earth System. WIREs Clim Change. 2010; 1: 840–856. https://doi.org/10.
1002/wcc.83
4. Luyssaert S, Jammet M, Stoy PC, Estel S, Pongratz J, Ceschia E, et al. Land management and land
cover change have impacts of similar magnitude on surface temperature. Nature Clim Change. 2014;
4: 389–393. https://doi.org/10.1038/nclimate2196
5. Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, et al. Overview of the Coupled Model
Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev.
2016; 9: 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
6. He F, Vavrus SJ, Kutzbach JE, Ruddiman WF, Kaplan JO, Krumhardt KM. Simulating global and local
surface temperature changes due to Holocene anthropogenic land cover change: CLIMATIC
EFFECTS OF HOLOCENE ALCC. Geophys Res Lett. 2014; 41: 623–631. https://doi.org/10.1002/
2013GL058085
7. Smith MC, Singarayer JS, Valdes PJ, Kaplan JO, Branch NP. The biogeophysical climatic impacts of
anthropogenic land use change during the Holocene. Clim Past. 2016; 12: 923–941. https://doi.org/
10.5194/cp-12-923-2016
8. Harrison SP, Gaillard M-J, Stocker BD, Vander Linden M, Klein Goldewijk K, Boles O, et al. Develop-
ment and testing scenarios for implementing land use and land cover changes during the Holocene in
Earth system model experiments. Geosci Model Dev. 2020; 13: 805–824. https://doi.org/10.5194/
gmd-13-805-2020
9. Klein Goldewijk K, Beusen A, Van Drecht G, De Vos M. The HYDE 3.1 spatially explicit database of
human-induced global land-use change over the past 12,000 years. Global Ecology and Biogeogra-
phy. 2011; 20: 73–86. https://doi.org/10.1111/j.1466-8238.2010.00587.x
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 29 / 38
10. Klein-Goldewijk K, Beusen A, Doelman J, Stehfest E. Anthropogenic land use estimates for the Holo-
cene—HYDE 3.2. Earth System Science Data. 2017; 9: 927–953. https://doi.org/10.5194/essd-9-927-
2017
11. Kaplan JO, Krumhardt KM, Ellis EC, Ruddiman WF, Lemmen C, Goldewijk KK. Holocene carbon
emissions as a result of anthropogenic land cover change. The Holocene. 2011; 21: 775–791. https://
doi.org/10.1177/0959683610386983
12. Le Que
´re
´C, Moriarty R, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, et al. Global Carbon Bud-
get 2015. Earth Syst Sci Data. 2015; 7: 349–396. https://doi.org/10.5194/essd-7-349-2015
13. Lawrence DM, Hurtt GC, Arneth A, Brovkin V, Calvin KV, Jones AD, et al. The Land Use Model Inter-
comparison Project (LUMIP) contribution to CMIP6:rationale and experimental design. Geosci Model
Dev. 2016; 9: 2973–2998. https://doi.org/10.5194/gmd-9-2973-2016
14. Hughes R, Weiberg E, Bonnier A, Finne
´M, Kaplan J. Quantifying Land Use in Past Societies from Cul-
tural Practice and Archaeological Data. Land. 2018; 7: 9. https://doi.org/10.3390/land7010009
15. Gaillard M-J, Sugita S, Mazier F, Trondman a.-K, Brostro
¨m a., Hickler T, et al. Holocene land cover
reconstructions for studies on land cover-climate feedbacks. Climate of the Past. 2010; 6: 483–499.
https://doi.org/10.5194/cp-6-483-2010
16. Li X, Dodson J, Zhou J, Zhou X. Increases of population and expansion of rice agriculture in Asia, and
anthropogenic methane emissions since 5000BP. Quaternary International. 2009; 202: 41–50. https://
doi.org/10.1016/j.quaint.2008.02.009
17. Fuller DQ, van Etten J, Manning K, Castillo C, Kingwell-Banham E, Weisskopf A, et al. The contribu-
tion of rice agriculture and livestock pastoralism to prehistoric methane levels: An archaeological
assessment. The Holocene. 2011; 21: 743–759. https://doi.org/10.1177/0959683611398052
18. Lewis SL, Maslin MA. Defining the Anthropocene. Nature. 2015; 519: 171–180. https://doi.org/10.
1038/nature14258 PMID: 25762280
19. Summerhayes C, Charman D. Introduction to Holocene Climate Change: new perspectives. Journal
of the Geological Society. 2015; 172: 251–253. https://doi.org/10.1144/jgs2014-113
20. Rostain S. Islands in the rainforest: landscape management in pre-Columbian Amazonia. Walnut
Creek, CA: Left Coast Press, Inc; 2012.
21. Bliege Bird R, Bird DW, Codding BF, Parker CH, Jones JH. The “fire stick farming” hypothesis: Austra-
lian Aboriginal foraging strategies, biodiversity, and anthropogenic fire mosaics. Proceedings of the
National Academy of Sciences. 2008; 105: 14796–14801. https://doi.org/10.1073/pnas.0804757105
PMID: 18809925
22. Pongratz J, Reick CH, Raddatz T, Claussen M. Biogeophysical versus biogeochemical climate
response to historical anthropogenic land cover change. Geophys Res Lett. 2010;37. https://doi.org/
10.1029/2010GL043010
23. Ellis EC, Kaplan JO, Fuller DQ, Vavrus S, Klein Goldewijk K, Verburg PH. Used planet: A global his-
tory. Proceedings of the National Academy of Sciences. 2013; 110: 7978–7985. https://doi.org/10.
1073/pnas.1217241110 PMID: 23630271
24. Pongratz J, Reick C, Raddatz T, Claussen M. A reconstruction of global agricultural areas and land
cover for the last millennium. Global Biogeochem Cycles. 2008; 22: n/a-n/a. https://doi.org/10.1029/
2007GB003153
25. Elsig J, Schmitt J, Leuenberger D, Schneider R, Eyer M, Leuenberger M, et al. Stable isotope con-
straints on Holocene carbon cycle changes from an Antarctic ice core. Nature. 2009; 461: 507–510.
https://doi.org/10.1038/nature08393 PMID: 19779448
26. Ruddiman WF. The Anthropogenic Greenhouse Era Began Thousands of Years Ago. Climatic
Change. 2003; 61: 261–293. https://doi.org/10.1023/B:CLIM.0000004577.17928.fa
27. Ruddiman WF. The early anthropogenic hypothesis: Challenges and responses. Rev Geophys. 2007;
45. https://doi.org/10.1029/2006RG000207
28. Scarre C, editor. The human past: world prehistory and the development of human societies. Fourth
edition. London: Thames & Hudson; 2018.
29. Stephens L, Fuller D, Boivin N, Rick T, Gauthier N, Kay A, et al. Archaeological assessment reveals
Earth’s early transformation through land use. Science. 2019; 365: 897–902. https://doi.org/10.1126/
science.aax1192 PMID: 31467217
30. Morrison KD. Fields of victory: Vijayanagara and the course of intensification. Berkeley: Univ. of Cali-
fornia, Archaeological Research Facility; 1995.
31. Guha R. How much should a person consume? Berkeley: University of California Press; 2006.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 30 / 38
32. Morrison KD. Provincializing the Anthropocene: Eurocentrism in the Earth System. In: Cederlo
¨f G,
Rangarajan M, editors. At Nature’s Edge: The Global Present and Long-term History. Oxford: Oxford
University Press; 2018. pp. 1–18. https://doi.org/10.1093/oso/9780199489077.003.0001
33. Shennan S. The First Farmers of Europe: An Evolutionary Perspective. 1st ed. Cambridge University
Press; 2018. https://doi.org/10.1017/9781108386029
34. Barker G, Gilbertson D, Mattingly DJ, editors. Archaeology and desertification: the Wadi Faynan Land-
scape Survey, southern Jordan. Oxford: Council for British Research in the Levant: Oxbow Books;
2007.
35. Manning K, Colledge S, Crema E, Shennan S, Timpson A. The Cultural Evolution of Neolithic Europe.
EUROEVOL Dataset 1: Sites, Phases and Radiocarbon Data. Journal of Open Archaeology Data.
2016; 5: e2. https://doi.org/10.5334/joad.40
36. Roberts N. How humans changed the face of Earth. Science. 2019; 365: 865–866. https://doi.org/10.
1126/science.aay4627 PMID: 31467209
37. Gaillard M, Whitehouse N, Madella M, Morrison K, Gunten LV, editors. Special Issue: Past Land Use
and Land Cover. PAGES Magazine. 2018; 26.
38. Dawson A, Cao X, Chaput M, Hopla E, Li F, Edwards M, et al. Finding the magnitude of human-
induced Northern Hemisphere land cover transformation between 6 and 0.2 ka BP. PAGES Mag.
2018; 26: 34–35. https://doi.org/10.22498/pages.26.1.34
39. Hellman S, Gaillard M-J, Brostro
¨m A, Sugita S. The REVEALS model, a new tool to estimate past
regional plant abundance from pollen data in large lakes: validation in southern Sweden. J Quaternary
Sci. 2008; 23: 21–42. https://doi.org/10.1002/jqs.1126
40. Fyfe RM, Woodbridge J, Roberts N. From forest to farmland: pollen-inferred land cover change across
Europe using the pseudobiomization approach. Glob Change Biol. 2015; 21: 1197–1212. https://doi.
org/10.1111/gcb.12776 PMID: 25345850
41. Gaillard M-J, Morrison K, Whitehouse N. Past anthropogenic land use and land cover change at the
global scale for climate modelling studies: PAGES LandCover6k Working Group. Quaternary Per-
spectives. 2015; 22: 25–27.
42. Whitehouse NJ, Bunting M, McClatchie M, Barratt P, McLaughlin R, Schulting R, et al. Prehistoric land
cover and land-use history in Ireland at 6000 BP. PAGES Mag. 2018; 26: 24–25. https://doi.org/10.
22498/pages.26.1.24
43. Sauer CO. Mapping the Utilization of the Land. Geographical Review. 1919; 8: 47–54.
44. Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, et al. Joint Deep Learning for land cover and land
use classification. Remote Sensing of Environment. 2019; 221: 173–187. https://doi.org/10.1016/j.rse.
2018.11.014
45. Fisher P, Comber AJ, Wadsworth R. Land use and land cover: contradiction or complement. Re-pre-
senting GIS. 2005; 85–98.
46. Anderson JR. A land use and land cover classification system for use with remote sensor data. US
Government Printing Office; 1976.
47. Loveland TR, Giri CP. History of land cover mapping. Remote sensing of land use and land cover:
Principles and applications. Boca Raton: CRC Press; 2012.
48. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, et al. High-Resolution
Global Maps of 21st-Century Forest Cover Change. Science. 2013; 342: 850–853. https://doi.org/10.
1126/science.1244693 PMID: 24233722
49. Brookfield HC. Exploring agrodiversity. New York: Columbia University Press; 2001.
50. Klein Goldewijk K, Beusen A, Janssen P. Long-term dynamic modeling of global population and built-
up area in a spatially explicit way: HYDE 3.1. The Holocene. 2010; 20: 565–573. https://doi.org/10.
1177/0959683609356587
51. Hurtt GC, Chini LP, Frolking S, Betts RA, Feddema J, Fischer G, et al. Harmonization of land-use sce-
narios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood har-
vest, and resulting secondary lands. Climatic Change. 2011; 109: 117–161. https://doi.org/10.1007/
s10584-011-0153-2
52. Widgren M. Towards a global history of agricultural systems. PAGES Mag. 2018; 26: 18–19. https://
doi.org/10.22498/pages.26.1.18
53. Widgren M. Mapping Global Agricultural History. In: Kinda A, Komeie T, Mnamide S, Mizoguchi T,
Uesugi K, editors. Proceedings of the 14th International Conference of Historical Geographers, Kyoto
2009. Kyoto: Kyoto University Press; 2010. pp. 211–212.
54. Whittlesey D. Major agricultural regions of the earth. Annals of the Association of American Geogra-
phers. 1936; 26: 199–240.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 31 / 38
55. Grigg D. The Agricultural Regions of the World: Review and Reflections. Economic Geography. 1969;
45: 95–132. https://doi.org/10.2307/143367
56. Bogaard A, Arbogast R-M, Ebersbach R, Fraser RA, Knipper C, Krahn C, et al. The Bandkeramik set-
tlement of Vaihingen an der Enz, Kreis Ludwigsburg (Baden-Wu¨rttemberg): an integrated perspective
on land use, economy and diet. Germania: Anzeiger der Ro
¨misch-Germanischen Kommission des
Deutschen Archa
¨ologischen Instituts. 2017; 1–60 Seiten. https://doi.org/10.11588/GER.2016.39068
57. Gross E, Jacomet S, Schibler J. Stand und Ziele der Wirtschaftsarcha
¨ologischen Forschungen an
Neolithischen Ufer- und Inselsiedlungen im Unteren Zu¨richseeraum (Kt. Zu¨rich, Schweiz). In: Schibler
J, Sedlmeier J, Spycher HP, editors. Festschrift fu¨r Hans R Sta
¨mpfli. Basel: Helbing Lichtenhahn;
1990. pp. 77–100.
58. Wendt KP, Hilpert J, Zimmermann A. Landschaftsarcha
¨ologie IV. Bericht der Ro
¨misch-Germanischen
Kommission. 2019; 9–218. https://doi.org/10.11588/BERRGK.2015.0.59354
59. Binford LR. Constructing frames of reference: an analytical method for archaeological theory building
using hunter-gatherer and environmental data sets. Berkeley: University of California Press; 2001.
60. Price TD, Brown JA, editors. Prehistoric hunter-gatherers: the emergence of cultural complexity.
Orlando: Academic Press; 1985.
61. Rowley-Conwy P, Piper S. Hunter-Gatherer Variability: Developing Models for the Northern Coasts.
ARCTIC. 2017; 69: 1. https://doi.org/10.14430/arctic4623
62. Khazanov AM. Nomads and the outside world. Cambridge: Cambridge University Press; 1984.
63. Cribb R. Nomads in Archaeology. 1st ed. Cambridge University Press; 1991. https://doi.org/10.1017/
CBO9780511552205
64. Miller ARV, Makarewicz C, editors. Isotopic investigations of pastoralism in prehistory. Abingdon,
Oxon; New York, NY: Routledge; 2018.
65. Bernbeck R. An Archaeology of Multi-Sited Communities. In: Wendrich W, Barnard H, editors. The
Archaeology of Mobility Old World and New World Nomadism. Los Angeles: The Cotsen Institute of
Archaeology Press; 2008. pp. 43–77.
66. Mayle FE, Iriarte J. Integrated palaeoecology and archaeology–a powerful approach for understanding
pre-Columbian Amazonia. Journal of Archaeological Science. 2014; 51: 54–64. https://doi.org/10.
1016/j.jas.2012.08.038
67. Miller NF, Gleason KL, editors. Archaeology of garden and field. Philadelphia: Univ Of Pennsylvania;
1994.
68. Marcus J, Stanish C. Agricultural Strategies. Los Angeles: Cotsen Institute; 2006.
69. Miller NF, Zeder MA, Arter SR. From Food and Fuel to Farms and Flocks: The Integration of Plant and
Animal Remains in the Study of the Agropastoral Economy at Gordion, Turkey. Current Anthropology.
2009; 50: 915–924. https://doi.org/10.1086/606035
70. Greenfield HJ. The Secondary Products Revolution: the past, the present and the future. World
Archaeology. 2010; 42: 29–54. https://doi.org/10.1080/00438240903429722
71. Lancelotti C, Madella M. The ‘invisible’ product: developing markers for identifying dung in archaeolog-
ical contexts. Journal of Archaeological Science. 2012; 39: 953–963. https://doi.org/10.1016/j.jas.
2011.11.007
72. Forbes Hamish. Off-Site Scatters and the Manuring Hypothesis in Greek Survey Archaeology: An Eth-
nographic Approach. Hesperia: The Journal of the American School of Classical Studies at Athens.
2013; 82: 551. https://doi.org/10.2972/hesperia.82.4.0551
73. Jones G, Bogaard A, Charles M, Hodgson JG. Distinguishing the Effects of Agricultural Practices
Relating to Fertility and Disturbance: a Functional Ecological Approach in Archaeobotany. Journal of
Archaeological Science. 2000; 27: 1073–1084. https://doi.org/10.1006/jasc.1999.0543
74. Jones G, Bogaard A, Halstead P, Charles M, Smith H. Identifying the intensity of crop husbandry prac-
tices on the basis of weed floras. Annu Br Sch Athens. 1999; 94: 167–189. https://doi.org/10.1017/
S0068245400000563
75. Bogaard A, Fraser R, Heaton THE, Wallace M, Vaiglova P, Charles M, et al. Crop manuring and inten-
sive land management by Europe’s first farmers. Proceedings of the National Academy of Sciences.
2013; 110: 12589–12594. https://doi.org/10.1073/pnas.1305918110 PMID: 23858458
76. van der Veen M. Formation processes of desiccated and carbonized plant remains–the identification
of routine practice. Journal of Archaeological Science. 2007; 34: 968–990. https://doi.org/10.1016/j.
jas.2006.09.007
77. Antolı´n F, Jacomet S, Buxo
´R. The hard knock life. Archaeobotanical data on farming practices during
the Neolithic (5400–2300 cal BC) in the NE of the Iberian Peninsula. Journal of Archaeological Sci-
ence. 2015; 61: 90–104. https://doi.org/10.1016/j.jas.2015.05.007
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 32 / 38
78. Whitehouse NJ, Smith D. How fragmented was the British Holocene wildwood? Perspectives on the
“Vera” grazing debate from the fossil beetle record. Quaternary Science Reviews. 2010; 29: 539–553.
https://doi.org/10.1016/j.quascirev.2009.10.010
79. Smith D, Nayyar K, Schreve D, Thomas R, Whitehouse N. Can dung beetles from the palaeoecologi-
cal and archaeological record indicate herd concentration and the identity of herbivores? Quaternary
International. 2014; 341: 119–130. https://doi.org/10.1016/j.quaint.2013.11.032
80. Anderson RS, Ejarque A, Rice J, Smith SJ, Lebow CG. Historic and Holocene Environmental Change
in the San Antonio Creek Basin, Mid-coastal California. Quat res. 2015; 83: 273–286. https://doi.org/
10.1016/j.yqres.2014.11.005
81. Ejarque A, Anderson RS, Simms AR, Gentry BJ. Prehistoric fires and the shaping of colonial trans-
ported landscapes in southern California: A paleoenvironmental study at Dune Pond, Santa Barbara
County. Quaternary Science Reviews. 2015; 112: 181–196. https://doi.org/10.1016/j.quascirev.2015.
01.017
82. Shahack-Gross R. Herbivorous livestock dung: formation, taphonomy, methods for identification, and
archaeological significance. Journal of Archaeological Science. 2011; 38: 205–218. https://doi.org/10.
1016/j.jas.2010.09.019
83. Scarborough VL. Flow of power: ancient water systems and landscapes. Santa Fe: SAR Press;
2003.
84. Morrison KD. Archaeologies of flow: Water and the landscapes of Southern India past, present, and
future. Journal of Field Archaeology. 2015; 40: 560–580. https://doi.org/10.1179/2042458215Y.
0000000033
85. Miller H. Water supply, labor requIrements, and land ownershIp In Indus floodplain agricultural sys-
tems. In: Stanish C, Marcus J, editors. Agriculture and Irrigation in Archaeology. Los Angeles: Cotsen
Intstitute of Archaeology Press; 2006. pp. 92–128.
86. Stump D. Intensification in Context: Archaeological Approaches to Precolonial Field Systems in East-
ern and Southern Africa. African Studies. 2010; 69: 255–278. http://tandfprod.literatumonline.com/doi/
abs/10.1080/00020184.2010.499201
87. Chang C, Koster HA. Beyond Bones: Toward an Archaeology of Pastoralism. Advances in Archaeo-
logical Method and Theory. Elsevier; 1986. pp. 97–148. https://doi.org/10.1016/B978-0-12-003109-2.
50006–4
88. Hammer E. Local landscape organization of mobile pastoralists in southeastern Turkey. Journal of
Anthropological Archaeology. 2014; 35: 269–288. https://doi.org/10.1016/j.jaa.2014.06.001
89. French CAI. Geoarchaeology in action: studies in soil micromorphology and landscape evolution. Lon-
don: Routledge; 2003.
90. Macphail RI, Courty MA, Gebhardt A. Soil micromorphological evidence of early agriculture in north-
west Europe. World Archaeology. 1990; 22: 53–69. https://doi.org/10.1080/00438243.1990.9980129
91. Bauer AM, Morrison KD. Assessing anthropogenic soil erosion with multi-spectral satellite imagery:
An archaeological case study of long-term land use in Koppal District, northern Karnataka. In: Frenez
D, Tosi M, editors. South Asian Archaeology 2007, Proceedings of the 19th International Conference
of The European Association of South Asian Archaeology, Volume I: Prehistoric Periods. Oxford:
Archaeopress-BAR International Series; 2013. pp. 67–75.
92. Boles OJC, Lane PJ. The Green, Green Grass of Home: an archaeo-ecological approach to pastoral-
ist settlement in central Kenya. Azania: Archaeological Research in Africa. 2016; 51: 507–530. https://
doi.org/10.1080/0067270X.2016.1249587
93. Webb EA, Schwarcz HP, Healy PF. Detection of ancient maize in lowland Maya soils using stable car-
bon isotopes: evidence from Caracol, Belize. Journal of Archaeological Science. 2004; 31: 1039–
1052. https://doi.org/10.1016/j.jas.2004.01.001
94. Wallace M, Jones G, Charles M, Fraser R, Halstead P, Heaton THE, et al. Stable carbon isotope anal-
ysis as a direct means of inferring crop water status and water management practices. World Archae-
ology. 2013; 45: 388–409. https://doi.org/10.1080/00438243.2013.821671
95. Rosen A. Phytolith indicators of plant and land use at C¸atalho
¨yu¨k. In: Hodder I, editor. C¸atalho
¨yu¨k Proj-
ect Volume IV: Inhabiting C¸atalho
¨yu¨k. Cambridge: MacDonald Institute, Cambridge University;
2005. pp. 203–212.
96. Petrie CA, Singh RN, Bates J, Dixit Y, French CAI, Hodell DA, et al. Adaptation to Variable Environ-
ments, Resilience to Climate Change: Investigating Land, Water and Settlement in Indus Northwest
India. Current Anthropology. 2017; 58: 1–30. https://doi.org/10.1086/690112
97. Kay AU, Kaplan JO. Human subsistence and land use in sub-Saharan Africa, 1000BC to AD1500: A
review, quantification, and classification. Anthropocene. 2015; 9: 14–32. https://doi.org/10.1016/j.
ancene.2015.05.001
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 33 / 38
98. Phelps LN, Kaplan JO. Land use for animal production in global change studies: Defining and charac-
terizing a framework. Glob Change Biol. 2017; 23: 4457–4471. https://doi.org/10.1111/gcb.13732
PMID: 28434200
99. Kay AU, Fuller DQ, Neumann K, Eichhorn B, Ho
¨hn A, Morin-Rivat J, et al. Diversification, Intensifica-
tion and Specialization: Changing Land Use in Western Africa from 1800 BC to AD 1500. J World
Prehist. 2019; 32: 179–228. https://doi.org/10.1007/s10963-019-09131-2
100. Di Gregorio A, Jansen LJM. Land cover classification system (LCCS): classification concepts and user
manual; for software version 1.0. Rome: FAO; 2001.
101. Price TD, Bar-Yosef O. The Origins of Agriculture: New Data, New Ideas: An Introduction to Supple-
ment 4. Current Anthropology. 2011; 52: S163–S174. https://doi.org/10.1086/659964
102. Smith BD. The Cultural Context of Plant Domestication in Eastern North America. Current Anthropol-
ogy. 2011; 52: S471–S484. https://doi.org/10.1086/659645
103. Bird-David N. Hunting and Gathering Societies: Anthropology. International Encyclopedia of the Social
& Behavioral Sciences. Elsevier; 2015. pp. 428–431. https://doi.org/10.1016/B978-0-08-097086-8.
12090–2
104. Gammage B. The biggest estate on earth: how Aborigines made Australia. Nachdr. Sydney: Allen &
Unwin; 2012.
105. Anderson K. Tending the wild: Native American knowledge and the management of California’s natu-
ral resources. 2013.
106. Kershaw AP. Climatic change and Aboriginal burning in north-east Australia during the last two glacial/
interglacial cycles. Nature. 1986; 322: 47–49. https://doi.org/10.1038/322047a0
107. Mellars P. Fire Ecology, Animal Populations and Man: a Study of some Ecological Relationships in
Prehistory. Proc Prehist Soc. 1976; 42: 15–45. https://doi.org/10.1017/S0079497X00010689
108. Cao X, Tian F, Li F, Gaillard M-J, Rudaya N, Xu Q, et al. Pollen-based quantitative land cover recon-
struction for northern Asia covering the last 40 ka cal BP.Clim Past. 2019; 15: 1503–1536. https://doi.
org/10.5194/cp-15-1503-2019
109. Adams RM. Land behind Baghdad: a history of settlement on the Diyala plains. Chicago: Univ. of Chi-
cago Pr.; 1965.
110. Adams RM. Settlement and Irrigation Patterns in Ancient Akkad. The city and area of Kish. Miami:
Field Research Projects; 1972. pp. 182–208.
111. Adams RM. Heartland of cities: Surveys of Ancient Settlement and Land Use on the Central Flood
Plain of the Euphrates. Chicago: University of Chicago Press; 1981.
112. Wright HT. The southern margins of Sumer: Archaeological survey of the area of Eridu and U. In:
Adams RM, editor. Heartland of Cities: Surveys of ancient settlement and land use on the central
floodplain of the Euphrates,. Chicago: University of Chicago Press; 1981. pp. 295–338.
113. Wilkinson TJ, Philip G, Bradbury J, Dunford R, Donoghue D, Galiatsatos N, et al. Contextualizing
Early Urbanization: Settlement Cores, Early States and Agro-pastoral Strategies in the Fertile Cres-
cent During the Fourth and Third Millennia BC. J World Prehist. 2014; 27: 43–109. https://doi.org/10.
1007/s10963-014-9072-2
114. Altaweel M, Marsh A, Mu¨hl S, Nieuwenhuyse O, Radner K, Rasheed K, et al. New Investigations in
the Environment, History, and Archaeology of the Iraqi Hilly Flanks: Shahrizor Survey Project 2009–
2011. Iraq. 2012; 74: 1–35. https://doi.org/10.1017/S0021088900000231
115. Iamoni M, editor. Trajectories of complexity: socio-economic dynamics in Upper Mesopotamia in the
Neolithic and Chalcolithic periods. Wiesbaden: Harrassowitz Verlag; 2016.
116. Kolinski R. An Archaeological Reconnaissance in the Greater Zab Area of the Iraqi Kurdistan
(UGZAR) 2012–2015. In: Salisbury RB, Ho
¨flmayer F, Bu¨rge T, Horejs B, Schwall C, Mu¨ller V, et al.,
editors. Proceedings of the 10th International Congress on the Archaeology of the Ancient Near East
Vol 2. Wiesbaden: Harrassowitz Verlag; 2018.
117. Kopanias K, MacGinnis J, editors. The Archaeology of the Kurdistan Region of Iraq and Adjacent
Regions. Archaeopress Publishing Ltd; 2016. https://doi.org/10.2307/j.ctvxrq0m8
118. Bonacossi DM, Iamoni M. Landscape and Settlement in the Eastern Upper Iraqi Tigris and Navkur
Plains: The Land of Nineveh Archaeological Project, Seasons 2012–2013. Iraq. 2015; 77: 9–39.
https://doi.org/10.1017/irq.2015.5
119. Pfa
¨lzner P, Sconzo P, Beutelscheiß R, Edmonds A, Glissmann B. The Eastern H
˘abur Archaeological
Survey in Iraqi Kurdistan. A preliminary report on the 2014 Season. Zeitschrift fu¨r Orient-Archa
¨ologie.
2016; 9: 10–69.
120. Ur J, Babakr N, Palermo R, Soroush, M, Ramand S, Nova
´ček K. The Erbil Plain Archaeological Sur-
vey: Preliminary Results, 2012–2018. Iraq. in press.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 34 / 38
121. Pournelle J. Marshland of Cities: Deltaic Landscapes and the Evolution of Early Mesopotamian Civili-
zation. University of California, San Diego. 2003. Available: http://core.tdar.org/document/380824
122. Pournelle J. Physical Geography. In: Crawford HEW, editor. The Sumerian World. London: Rout-
ledge; 2013. pp. 13–32.
123. Aqrawi A. Stratigraphic signatures of climatic change during the Holocene evolution of the Tigris–
Euphrates delta, lower Mesopotamia. Global and Planetary Change. 2001; 28: 267–283. https://doi.
org/10.1016/S0921-8181(00)00078-3
124. Bru¨ckner H. Uruk–a Geographic and Palaeo-Ecologic Perspective on a Famous Ancient City in Meso-
potamia. Geoo
¨ko. 2003; 24: 229–248.
125. Sanlaville P. The deltaic complex of the lower Mesopotamian plain and its evolution through millennia.
In: Nicholson E, Clark P, editors. The Iraqi Marshlands. London: Politicos Publishing; 2003. pp. 133–
150.
126. Algaze G. Initial Social Complexity in Southwestern Asia: The Mesopotamian Advantage. Current
Anthropology. 2001; 42: 199–233. https://doi.org/10.1086/320005
127. Pournelle J. The littoral origins of Near Eastern civilization. MS, Department of Anthropology, Univer-
sity of California, San Diego. 2000.
128. Wilkinson TJ. Archaeological landscapes of the Near East. Tucson: University of Arizona Press;
2003.
129. Gasche H, Tanret M, editors. Changing watercourses in Babylonia: towards a reconstruction of the
ancient environment in lower Mesopotamia. Chicago, IL: Oriental Institute of the University of Chi-
cago; 1998.
130. Wilkinson TJ, Rayne L, Jotheri J. Hydraulic landscapes in Mesopotamia: the role of human niche con-
struction. Water Hist. 2015; 7: 397–418. https://doi.org/10.1007/s12685-015-0127-9
131. Hritz C. Tracing Settlement Patterns and Channel Systems in Southern Mesopotamia Using Remote
Sensing. Journal of Field Archaeology. 2010; 35: 184–203. https://doi.org/10.1179/
009346910X12707321520477
132. Salman I. Atlas of the archaeological sites in Iraq. Baghdad: Al-Huria Printing House; 1976.
133. Mantellini S, Micale MG, Peyronel L. Exploiting diversity: the archaeological landscape of the Eblaite
Chora. In: Matthiae P, Marchetti N, editors. Ebla and its landscape: early state formation in the ancient
Near East. Walnut Creek: Left Coast Press; 2013. pp. 238–256.
134. Skuldbol T, Colantoni C. Tracking early urbanism in the hilly flanks of Mesopotamia–three years of
Danish archaeological investigations on the Rania Plain. In: Kopanias K, MacGinnis J, editors. The
archaeology of the Kurdistan region of Iraq and adjacent regions. Oxford: Archaeopress; 2016. pp.
411–416.
135. Nieuwenhuyse O, Odaka T, Mu¨hl S, Kopanias K, MacGinnis J. Halaf Settlement in the Iraqi Kurdistan:
the Shahrizor Survey Project. The archaeology of the Kurdistan region of Iraq and adjacent regions.
Oxford: Archaeopress; 2016. pp. 257–266.
136. Peyronel L, Vacca A. Northern Ubaid and Late Chalcolithic 1–3 Periods in the Erbil Plain: New Insights
from Recent Researches at Helawa, Iraqi Kurdistan. Origini. 2015; 37: 89–127.
137. Arbuckle BS, Hammer EL. The Rise of Pastoralism in the Ancient Near East. J Archaeol Res. 2019;
27: 391–449. https://doi.org/10.1007/s10814-018-9124-8
138. Gaastra JS, Greenfield TL, Greenfield HJ. Constraint, complexity and consumption: Zooarchaeologi-
cal meta-analysis shows regional patterns of resilience across the metal ages in the Near East. Qua-
ternary International. 2019; S1040618218310334. https://doi.org/10.1016/j.quaint.2019.03.013
139. Salīm SM. Marsh dwellers of the Euphrates Delta. London: Athlone Press; 1962.
140. Magee P. The Archaeology of Prehistoric Arabia: Adaptation and Social Formation from the Neolithic
to the Iron Age. Cambridge: Cambridge University Press; 2014.
141. McCorriston J, Martin L. Southern Arabia’s early pastoral population history: some recent evidence.
In: Petraglia MD, Rose J, editors. The evolution of human populations in Arabia. Heidelberg: Springer;
2010. pp. 237–250.
142. Petraglia MD, Groucutt HS, Guagnin M, Breeze PS, Boivin N. Human responses to climate and eco-
system change in ancient Arabia. Proc Natl Acad Sci USA. 2020; 117: 8263–8270. https://doi.org/10.
1073/pnas.1920211117 PMID: 32284422
143. Boivin N, Fuller DQ. Shell Middens, Ships and Seeds: Exploring Coastal Subsistence, Maritime Trade
and the Dispersal of Domesticates in and Around the Ancient Arabian Peninsula. J World Prehist.
2009; 22: 113–180. https://doi.org/10.1007/s10963-009-9018-2
144. Makarewicz CA. The adoption of cattle pastoralism in the Arabian Peninsula: A reappraisal. Arab Arch
Epig. 2020; 31: 168–177. https://doi.org/10.1111/aae.12156
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 35 / 38
145. Drechsler P. The dispersal of the Neolithic over the Arabian Peninsula. Oxford: Archaeopress; 2009.
146. Uerpmann M. The dark millennium: Remarks on the final stone age in the Emirates and Oman. In:
Potts DT, Hellyer P, Al Naboodah H, editors. Archaeology of the United Arab Emirates: proceedings of
the First International Conference on the archaeology of the UAE. London: Trident Press; 2003. pp.
74–81.
147. Fleitmann D. Holocene Forcing of the Indian Monsoon Recorded in a Stalagmite from Southern
Oman. Science. 2003; 300: 1737–1739. https://doi.org/10.1126/science.1083130 PMID: 12805545
148. Fleitmann D, Matter A. The speleothem record of climate variability in Southern Arabia. Comptes
Rendus Geoscience. 2009; 341: 633–642. https://doi.org/10.1016/j.crte.2009.01.006
149. Me
´ry S, Charpentier V, Auxiette G, Pelle E. A dugong bone mound: the Neolithic ritual site on Akab in
Umm al-Quwain, United Arab Emirates. Antiquity. 2009; 83: 696–708. https://doi.org/10.1017/
S0003598X00098926
150. Charpentier V, Marquis P, Pelle
´E
´. La ne
´cropole et les derniers horizons V e mille
´naire du site de Gor-
bat al-Mahar (Suwayh, SWY-1, Sultanat d’Oman): premiers re
´sultats. Proceedings of the Seminar for
Arabian Studies. 2003; 33: 11–19.
151. Uerpmann M. Structuring the Late Stone Age of Southeastern Arabia. Arab Arch Epigraphy. 1992; 3:
65–109. https://doi.org/10.1111/j.1600-0471.1992.tb00032.x
152. Charpentier V. Hunter-gatherers of the “empty quarter of the early Holocene” to the last Neolithic soci-
eties: chronology of the late prehistory of south-eastern Arabia (8000–3100 BC). Proceedings of the
Seminar for Arabian Studies. 2008; 38: 93–116.
153. Salvatori S. Death and ritual in a population of food foragers in Oman, in The Prehistory of Asia and
Oceania. In: Afanas’ev GE, Cleuziou S, Lukas JR, Tosi M, editors. The Prehistory of Asia and Ocea-
nia. Forli: UISPP; 1996. pp. 205–222.
154. Le
´zine A-M, Robert C, Cleuziou S, Inizan M-L, Braemer F, Saliège J-F, et al. Climate change and
human occupation in the Southern Arabian lowlands during the last deglaciation and the Holocene.
Global and Planetary Change. 2010; 72: 412–428. https://doi.org/10.1016/j.gloplacha.2010.01.016
155. Edens C. Exploring early agriculture in the highlands of Yemen. In: Sholan AM, Antonini S, Arbach M,
editors. Sabaean Studies: archaeological, epigraphical, and historical studies. Naples: Università
degli Studi di Napoli; 2005. pp. 185–211.
156. Edens C, Wilkinson TJ. Southwest Arabia during the Holocene: Recent archaeological developments.
Journal of World Prehistory. 1998; 12: 55–119. https://doi.org/10.1023/A:1022449224342
157. Ekstrom H, Edens C. Prehistoric agriculture in highland Yemen: New results from Dhamar. Bulletin of
the American Institute of Yemeni Studies,. 2003; 45: 23–35.
158. Harrower MJ. Hydrology, Ideology, and the Origins of Irrigation in Ancient Southwest Arabia. Current
Anthropology. 2008; 49: 497–510. https://doi.org/10.1086/587890
159. McCorriston J, Oches EA, Walter DE, Cole Kl. Holocene Paleoecology and Prehistory in Highland
Southern Arabia. paleo. 2002; 28: 61–88. https://doi.org/10.3406/paleo.2002.4739
160. Parr PJ, Zarins J, Ibrahim M, Waechter J, Garrard A, Clarke C, et al. Comprehensive archaeological
survey program: preliminary report on the second phase of the Northern Province. Atlal. 1978; 2: 29–
50.
161. Ingraham ML, Johnson TD, Rihani B, Shatla I. Preliminary report on a reconnaissance survey of the
northwestern province. Atlal. 1981; 5: 59–84.
162. Gilmore M, al-Ibrahim M, Murad AS. Preliminary report on the northwestern and northern region sur-
vey 1981 (1401). Atlal. 1982; 6: 9–23.
163. Betts AVG, editor. The later prehistory of the Badia. Oxford: Oxbow; 2013.
164. Zarins J, Rahbini A, Kamal M. Preliminary report on the archaeological survey of the Riyadh area.
Atlal. 1982; 6: 25–38.
165. Breeze PS, Groucutt HS, Drake NA, Louys J, Scerri EML, Armitage SJ, et al. Prehistory and palaeoen-
vironments of the western Nefud Desert, Saudi Arabia. Archaeological Research in Asia. 2017; 10: 1–
16. https://doi.org/10.1016/j.ara.2017.02.002
166. Dinies M, Neef R, Kuerschner H. Early to Middle Holocene vegetational development, climatic condi-
tions and oasis cultivation in Tayma. In: Hausleiter A, Eichmann R, al-Najem M, editors. Tayma:
Archaeological Exploration, Palaeoenvironment, Cultural Contacts. Oxford: Archaeopress; 2018. pp.
128–143.
167. Hausleiter A, Eichmann R. The archaeological exploration of the oasis of Tayma. In: Hausleiter A,
Eichmann R, al-Najem M, editors. Tayma: Archaeological Exploration, Palaeoenvironment, Cultural
Contacts. Oxford: Archaeopress; 2018. pp. 2–59.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 36 / 38
168. Akkermans PM, Huigens HO, Bru¨ning ML. A landscape of preservation: late prehistoric settlement
and sequence in the Jebel Qurma region, north-eastern Jordan. Levant. 2014; 46: 186–205.
169. Betts AVG, Martin L. Excavations at Tell al-Hibr. In: Betts AVG, Martin L, McCartney C, editors. The
Later Prehistory of the Badia Excavations and Surveys in Eastern Jordan: Vol 2. Oxford: Oxbow
Books; 2013. pp. 143–155.
170. Abu-Azizeh W. The Copper Age. The Chalcolithic period (4500–3600). In: Ababsa M, editor. Atlas of
Jordan—History, Territories, and Society. Beyrouth: Presses de l’Ifpo; 2013. Available: https://doi.
org/10.4000/books.ifpo.4885
171. Abu-Azizeh W. The South-Eastern Jordan’s Chalcolithic-Early Bronze Age Pastoral Nomadic Com-
plex: Patterns of Mobility and Interaction. Pale
´orient. 2013; 39: 149–176.
172. Bradbury J, Braemer F, Sala M. Fitting upland, steppe, and desert into a ‘big picture’ perspective: a
case study from northern Jordan. Levant. 2014; 46: 206–229. https://doi.org/10.1179/0075891414Z.
00000000042
173. Akkermans P, Huigens HO. Long-term Settlement Trends in Jordan’s Northeastern Badia: The Jabal
Qurma Archaeological Landscape Project. Annual of the Department of Antiquities of Jordan. 2019;
59: 503–515.
174. Meister J, Krause J, Mu¨ller-Neuhof B, Portillo M, Reimann T, Schu¨tt B. Desert agricultural systems at
EBA Jawa (Jordan): Integrating archaeological and paleoenvironmental records. Quaternary Interna-
tional. 2017; 434: 33–50. https://doi.org/10.1016/j.quaint.2015.12.086
175. Mu¨ller-Neuhof B. A ‘marginal’ region with many options: the diversity of Chalcolithic/Early Bronze Age
socio-economic activities in the hinterland of Jawa. Levant. 2014; 46: 230–248. https://doi.org/10.
1179/0075891414Z.00000000043
176. Mu¨ller-Neuhof B, Abu-Azizeh W. Milestones for a tentative chronological framework for the late prehis-
toric colonization of the basalt desert (north-eastern Jordan). Levant. 2016; 48: 220–235.
177. Weninger B, Clare L, Rohling E, Bar-Yosef O, Bo
¨hner U, Budja M, et al. The Impact of Rapid Climate
Change on Prehistoric Societies during the Holocene in the Eastern Mediterranean. Doc praeh. 2009;
36: 7–59. https://doi.org/10.4312/dp.36.2
178. Bar-Matthews M, Ayalon A. Mid-Holocene climate variations revealed by high-resolution speleothem
records from Soreq Cave, Israel and their correlation with cultural changes. The Holocene. 2011; 21:
163–171. https://doi.org/10.1177/0959683610384165
179. Schiebel V, Litt T. Holocene vegetation history of the southern Levant based on a pollen record from
Lake Kinneret (Sea of Galilee), Israel. Veget Hist Archaeobot. 2018; 27: 577–590. https://doi.org/10.
1007/s00334-017-0658-3
180. Bradbury J. Landscapes of Burial? The Homs Basalt, Syria in the 4th-3rd millennia BC. Ph.D. disserta-
tion, Durham University. 2011.
181. Braemer F. Badia and Maamoura,. syria. 2011; 31–46. https://doi.org/10.4000/syria.891
182. De Contenson H. Rapport pre
´liminaire sur les fouilles de Tell al-Khazami en 1967. Annales Arche
´olo-
giques Arabes Syriennes. 1968; 18: 55–62.
183. Godon M, Baldi JS, Ghanem G, Iba
´ñez JJ, Braemer F. Qarassa North Tell, Southern Syria: The Pot-
tery Neolithic and Chalcolithic sequence. A few lights against a dark background. paleo. 2015; 41:
153–176. https://doi.org/10.3406/paleo.2015.5660
184. Nicolle, C, al-Maqdissi M. Sharaya: un village du Bronze ancien IA en Syrie du Sud. Pale
´orient 32(1):
125–136. Pale
´orient. 2006;32: 125–136.
185. Sulaiman G. Tell al-Baharia: an important site in the Damascus basin. In: Borell Tena F, Bouso Garcia
M, Gomez Bach A, Tornero Dacasa C, Vicente Campos O, editors. Broadening Horizons 3: confer-
ence of young researchers working in the Ancient Near East. Barcelona: Universitat Autonoma de
Barcelona; 2012. pp. 113–122.
186. Bourke S, Lawson E, Lovell J, Hua Q, Zoppi U, Barbetti M. The Chronology of the Ghassulian Chalco-
lithic Period in the Southern Levant: New
14
C Determinations from Teleilat Ghassul, Jordan. Radiocar-
bon. 2001; 43: 1217–1222. https://doi.org/10.1017/S0033822200038509
187. Burton M, Levy TE. The Chalcolithic Radiocarbon Record and Its Use in Southern Levantine Archaeol-
ogy. Radiocarbon. 2001; 43: 1223–1246. https://doi.org/10.1017/S0033822200038510
188. Rowan YM, Golden J. The Chalcolithic Period of the Southern Levant: A Synthetic Review. J World
Prehist. 2009; 22: 1–92. https://doi.org/10.1007/s10963-009-9016-4
189. Bourke SJ. The Chalcolithic period. In: Macdonald B, Adams R, Bienkowski P, editors. The archaeol-
ogy of Jordan. Sheffield: Sheffield Academic Press; 2001. pp. 107–163.
190. Bourke SJ. The Late Neolithic/Early Chalcolithic Transition at Teleilat Ghassul: Context, Chronology
and Culture. Paleorient. 2007; 33: 15–32.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 37 / 38
191. Hill AC. Specialized Pastoralism and Social Stratification—Analysis of the Fauna from Chalcolithic Tel
Tsaf, Israel". Ph.D. dissertation, University of Connecticut. 2011. Available: https://opencommons.
uconn.edu/dissertations/AAI3504774
192. Graham P. Archaeobotanical remains from late 6th/early 5th millennium BC Tel Tsaf, Israel. Journal of
Archaeological Science. 2014; 43: 105–110. https://doi.org/10.1016/j.jas.2013.12.018
193. Willcox G. Appendix D. Plant Remains. In: Helms SW, editor. Jawa: Lost City of the Black Desert.
New York: Cornell University Press; 1981. pp. 247–248.
194. Ko
¨hler I, Helms SW. Appendix E. Animal Remains. Jawa: Lost City of the Black Desert. New York:
Cornell University Press; 1981. pp. 249–252.
195. Morrison KD. From Millets to Rice (and Back Again?): Cuisine, Cultivation and Health in Early South
India. In: Schug GR, Walimbe S, editors. A Companion to South Asia in the Past. New York: Wiley-
Blackwell; 2016. pp. 358–373.
PLOS ONE
Mapping past human land use using archaeological data
PLOS ONE | https://doi.org/10.1371/journal.pone.0246662 April 14, 2021 38 / 38
... Spatially explicit datasets and maps based on this second generation of REVEALS reconstructions are currently being produced within PAGES LandCover6k and used to evaluate and revise the HYDE (Klein Goldewijk et al., 2017) and KK10 (Kaplan et al., 2009) ALCC scenarios. Moreover, LandCover6k archaeology-based reconstructions of past land-use change (Morrison et al., 2021) will be integrated with the datasets of REVEALS land cover. Besides the uses listed above, the second generation of REVEALS reconstruction for Europe offers great potential for use in a large range of studies on past European regional vegetation dynamics and changes in biodiversity over the Holocene as well as the relationship between regional plant cover, land use and climate over millennial and centennial timescales. ...
... All studies used the ERV model to calculate RPPs, and all but one study used modern pollen assemblages and vegetation; only Nielsen (2004;Denmark) used historical pollen and vegetation data. A total of 11 studies used pollen assemblages from moss pollsters, and 5 studies used pollen assemblages from lake sediments. ...
... Figure C1. Location of the selected studies of relative pollen productivities (RPPs) in Europe: 1 -Britain (Bunting et al., 2005), 2 -Czech Republic (Abraham and Kozáková, 2012), 3 -Denmark (Nielsen, 2004), 4 -Estonia (Poska et al., 2011), 5 -Finland (Räsänen et al., 2007, 6 -France (Mazier et al., unpublished), 7 -Germany (Matthias et al., 2012), 8 -Germany (Theuerkauf et al., 2012), 9 -Norway (Hjelle, 1998, 10 -Poland (Baker et al., 2016), 11 -Romania (Grindean et al., 2019), 12 -Sweden (von Stedingk et al., 2008, 13 -Sweden (Sugita et al., 1999), 14 -Sweden (Broström et al., 2004), 15 -Switzerland (Soepboer et al., 2007), 16 -Switzerland . Table C1. ...
Article
Full-text available
Quantitative reconstructions of past land cover are necessary to determine the processes involved in climate-human-land-cover interactions. We present the first temporally continuous and most spatially extensive pollen-based land-cover reconstruction for Europe over the Holocene (last 11 700 cal yr BP). We describe how vegetation cover has been quantified from pollen records at a 1 • × 1 • spatial scale using the "Regional Estimates of VEgetation Abundance from Large Sites" (REVEALS) model. REVEALS calculates estimates of past regional vegetation cover in proportions or percentages. REVEALS has been applied to 1128 pollen records across Europe and part of the eastern Mediterranean-Black Sea-Caspian corridor (30-75 • N, 25 • W-50 • E) to reconstruct the percentage cover of 31 plant taxa assigned to 12 plant functional types (PFTs) and 3 land-cover types (LCTs). A new synthesis of relative pollen productivities (RPPs) for European plant taxa was performed for this reconstruction. It includes multiple RPP values (≥ 2 values) for 39 taxa and single values for 15 taxa Published by Copernicus Publications. 1582 E. Githumbi et al.: European pollen-based REVEALS land-cover reconstructions for the Holocene (total of 54 taxa). To illustrate this, we present distribution maps for five taxa (Calluna vulgaris, Cerealia type (t)., Picea abies, deciduous Quercus t. and evergreen Quercus t.) and three land-cover types (open land, OL; evergreen trees, ETs; and summer-green trees, STs) for eight selected time windows. The reliability of the REVEALS reconstructions and issues related to the interpretation of the results in terms of landscape openness and human-induced vegetation change are discussed. This is followed by a review of the current use of this reconstruction and its future potential utility and development. REVEALS data quality are primarily determined by pollen count data (pollen count and sample, pollen identification, and chronology) and site type and number (lake or bog, large or small, one site vs. multiple sites) used for REVEALS analysis (for each grid cell). A large number of sites with high-quality pollen count data will produce more reliable land-cover estimates with lower standard errors compared to a low number of sites with lower-quality pollen count data. The REVEALS data presented here can be downloaded from https://doi.org/10.1594/PANGAEA.937075 (Fyfe et al., 2022).
... Spatially explicit datasets and maps based on this second generation of REVEALS reconstructions are currently being produced within PAGES LandCover6k and used to evaluate and revise the HYDE (Klein Goldewijk et al., 2017) and KK10 (Kaplan et al., 2009) ALCC scenarios. Moreover, LandCover6k archaeology-based reconstructions of past land-use change (Morrison et al., 2021) will be integrated with the datasets of REVEALS land cover. Besides the uses listed above, the second generation of REVEALS reconstruction for Europe offers great potential for use in a large range of studies on past European regional vegetation dynamics and changes in biodiversity over the Holocene as well as the relationship between regional plant cover, land use and climate over millennial and centennial timescales. ...
... All studies used the ERV model to calculate RPPs, and all but one study used modern pollen assemblages and vegetation; only Nielsen (2004;Denmark) used historical pollen and vegetation data. A total of 11 studies used pollen assemblages from moss pollsters, and 5 studies used pollen assemblages from lake sediments. ...
... Figure C1. Location of the selected studies of relative pollen productivities (RPPs) in Europe: 1 -Britain (Bunting et al., 2005), 2 -Czech Republic (Abraham and Kozáková, 2012), 3 -Denmark (Nielsen, 2004), 4 -Estonia (Poska et al., 2011), 5 -Finland (Räsänen et al., 2007, 6 -France (Mazier et al., unpublished), 7 -Germany (Matthias et al., 2012), 8 -Germany (Theuerkauf et al., 2012), 9 -Norway (Hjelle, 1998, 10 -Poland (Baker et al., 2016), 11 -Romania (Grindean et al., 2019), 12 -Sweden (von Stedingk et al., 2008, 13 -Sweden (Sugita et al., 1999), 14 -Sweden (Broström et al., 2004), 15 -Switzerland (Soepboer et al., 2007), 16 -Switzerland . Table C1. ...
Article
Full-text available
Quantitative reconstructions of past land cover are necessary to determine the processes involved in climate–human–land-cover interactions. We present the first temporally continuous and most spatially extensive pollen-based land-cover reconstruction for Europe over the Holocene (last 11 700 cal yr BP). We describe how vegetation cover has been quantified from pollen records at a 1∘ × 1∘ spatial scale using the “Regional Estimates of VEgetation Abundance from Large Sites” (REVEALS) model. REVEALS calculates estimates of past regional vegetation cover in proportions or percentages. REVEALS has been applied to 1128 pollen records across Europe and part of the eastern Mediterranean–Black Sea–Caspian corridor (30–75∘ N, 25∘ W–50∘ E) to reconstruct the percentage cover of 31 plant taxa assigned to 12 plant functional types (PFTs) and 3 land-cover types (LCTs). A new synthesis of relative pollen productivities (RPPs) for European plant taxa was performed for this reconstruction. It includes multiple RPP values (≥2 values) for 39 taxa and single values for 15 taxa (total of 54 taxa). To illustrate this, we present distribution maps for five taxa (Calluna vulgaris, Cerealia type (t)., Picea abies, deciduous Quercus t. and evergreen Quercus t.) and three land-cover types (open land, OL; evergreen trees, ETs; and summer-green trees, STs) for eight selected time windows. The reliability of the REVEALS reconstructions and issues related to the interpretation of the results in terms of landscape openness and human-induced vegetation change are discussed. This is followed by a review of the current use of this reconstruction and its future potential utility and development. REVEALS data quality are primarily determined by pollen count data (pollen count and sample, pollen identification, and chronology) and site type and number (lake or bog, large or small, one site vs. multiple sites) used for REVEALS analysis (for each grid cell). A large number of sites with high-quality pollen count data will produce more reliable land-cover estimates with lower standard errors compared to a low number of sites with lower-quality pollen count data. The REVEALS data presented here can be downloaded from https://doi.org/10.1594/PANGAEA.937075 (Fyfe et al., 2022).
... Comprehending past land use strategies in Ifugao therefore requires an interdisciplinary approach that draws upon history, ethnography, archaeology, anthropology, linguistics, ecology, and geography (Wilson, 1998;Izdebski et al., 2016;Rick and Sandweiss, 2020). Recently, an interdisciplinary community effort has emerged (Kay and Kaplan, 2015;Morrison et al., 2018;Kay et al., 2019;Morrison et al., 2021) to synthesize and quantify the longstanding interconnectivity of human and climate history first expounded by Ladurie (1971) and Lamb (1997; see also Harrison et al., 2018;Gaillard et al., 2018;Widgren, 2018). ...
... Lastly, our models may be further developed using GIS to map our diagrams onto physical landscapes to create complete land use maps that visualize how specific villages and towns could have developed over time. Alternatively, an adjusted form of this model might be applied through GIS to chart the impact of anthropogenic land use on forest cover throughout Ifugao and the southeast Cordillera, akin to work performed by Kay et al. (2019) and the LandCover6k project (Morrison et al., 2021). ...
Article
Full-text available
Land use modelling is increasingly used by archaeologists and palaeoecologists seeking to quantify and compare the changing influence of humans on the environment. In Southeast Asia, the intensification of rice agriculture and the arrival of European colonizers have both been seen as major catalysts for deforestation, soil erosion, and biodiversity change. Here we consider the Tuwali-Ifugao people of the Cordillera Central (Luzon, Philippines), who resisted Spanish colonial subjugation from the 16th to the mid-nineteenth century, in part through the development of a world-renowned system of intensive wet-rice terrace agriculture. To quantify changes in how the Tuwali-Ifugao used their environment, we model land use in Old Kiyyangan Village, a long-inhabited settlement, at two timepoints: circa 1570 CE, prior to the Spanish arrival in Luzon, and circa 1800 CE, before the village was sacked by Spanish military expeditions. Our model demonstrates that between 1570 and 1800 the adoption of rice as a staple and the corresponding expansion in terrace agriculture, along with a general diversification of diet and land use, enabled the village’s population to double without increasing total land use area. Further, this major intensification led to the solidification of social hierarchies and occurred without a proportional increase in deforestation.
... Regional to continental scale land-use data has mainly been in the form of model-derived anthropogenic land-cover change scenarios (ALCCs), based on estimates of per capita land use and past population density (Kaplan et al., 2011;Klein Goldewijk et al., 2017). Efforts to produce past land-use estimates based on archaeological and historical data have led to the development of a new land-use classification for global syntheses (Morrison et al., 2021). The latter is part of the Past Global Change (PAGES) LandCover6k working group initiative to upscale archaeological information on land use across the world and produce landuse maps for selected time slices of the Holocene, which has previously not been possible due to large differences in terminology. ...
Article
Full-text available
Realistic and accurate reconstructions of past vegetation cover are necessary to study past environmental changes. This is important since the effects of human land-use changes (e.g. agriculture, deforestation and afforestation/reforestation) on biodiversity and climate are still under debate. Over the last decade, development, validation, and application of pollen-vegetation relationship models have made it possible to estimate plant abundance from fossil pollen data at both local and regional scales. In particular, the REVEALS model has been applied to produce datasets of past regional plant cover at 1° spatial resolution at large subcontinental scales (North America, Europe, and China). However, such reconstructions are spatially discontinuous due to the discrete and irregular geographical distribution of sites (lakes and peat bogs) from which fossil pollen records have been produced. Therefore, spatial statistical models have been developed to create continuous maps of past plant cover using the REVEALS-based land cover estimates. In this paper, we present the first continuous time series of spatially complete maps of past plant cover across Europe during the Holocene (25 time windows covering the period from 11.7 k BP to present). We use a spatial-statistical model for compositional data to interpolate REVEALS-based estimates of three major land-cover types (LCTs), i.e., evergreen trees, summer-green trees and open land (grasses, herbs and low shrubs); producing spatially complete maps of the past coverage of these three LCTs. The spatial model uses four auxiliary data sets—latitude, longitude, elevation, and independent scenarios of past anthropogenic land-cover change based on per-capita land-use estimates (“standard” KK10 scenarios)—to improve model performance for areas with complex topography or few observations. We evaluate the resulting reconstructions for selected time windows using present day maps from the European Forest Institute, cross validate, and compare the results with earlier pollen-based spatially-continuous estimates for five selected time windows, i.e., 100 BP-present, 350–100 BP, 700–350 BP, 3.2–2.7 k BP, and 6.2–5.7 k BP. The evaluations suggest that the statistical model provides robust spatial reconstructions. From the maps we observe the broad change in the land-cover of Europe from dominance of naturally open land and persisting remnants of continental ice in the Early Holocene to a high fraction of forest cover in the Mid Holocene, and anthropogenic deforestation in the Late Holocene. The temporal and spatial continuity is relevant for land-use, land-cover, and climate research.
Article
Full-text available
Charting the long-term trends in European wheat and maize yields and harvested areas and the relation of yields to climatic and economic drivers, two profound spatial processes become apparent. One consequence of the relatively late modernization of Eastern Europe has been to shift the focus of grain production from West to East. The warming trend prevailing over the past decades in the summer and winter seasons has been accompanied by a South to North shift in the harvested areas. The combination of these two processes has meant that the north-eastern sector of the European grain chessboard has emerged as the main benefciary. There, the relatively low sensitivity of cereals to climatic change plus high economic growth rates have been accompanied by the most dynamic increases in cereal yields on the continent. As a result, a modern version of the 3000-year-old grain distribution system of the Ancient World is being restored before our eyes. One noteworthy finding is that increasing January–March temperatures have had a significant positive impact on wheat yields from Northern to South-Eastern Europe, and this is, at least in part, compensating for the negative impact of summer warming.