quaternary
Meeting Report
State of the Art in Paleoenvironment Mapping for
Modeling Applications in Archeology—Summary,
Conclusions, and Future Directions from the
PaleoMaps Workshop
Christian Willmes 1,2, * , Kamil Niedziółka 3 , Benjamin Serbe 4 , Sonja B. Grimm 5 ,
Daniel Groß 5 , Andrea Miebach 2,6 , Michael Märker 7 , Felix Henselowsky 2,8 ,
Alexander Gamisch 9 , Masoud Rostami 2,10 , Ana Mateos 11 , Jesús Rodríguez 11 ,
Heiko Limberg 2,6 , Isabell Schmidt 2,12 , Martin Müller 2,12 , Ericson Hölzchen 13,14,15 ,
Michael Holthausen 16 , Konstantin Klein 2,10 , Christian Wegener 2,10 , Bernhard Weninger 12 ,
Trine Kellberg Nielsen 2,12,17 , Taylor Otto 2,12,17 , Gerd-Christian Weniger 2,12,17 ,
Olaf Bubenzer 2,8 and Georg Bareth 1,2
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*
Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany;
g.bareth@uni-koeln.de
Collaborative Research Centre 806, University of Cologne, Bernhard-Feilchenfeld-Str. 11,
50969 Cologne, Germany; a.miebach@uni-bonn.de (A.M.); felix.henselowsky@uni-heidelberg.de (F.H.);
Masoud.Rostami@uni-koeln.de (M.R.); heiko_limberg@gmx.de (H.L.); isabell.schmidt@uni-koeln.de (I.S.);
martinmue@netcologne.de (M.M.); konstantin.klein@uni-koeln.de (K.K.); c.wegener@uni-koeln.de (C.W.);
tkn@moesgaardmuseum.dk (T.K.N.); otto@neanderthal.de (T.O.); weniger@neanderthal.de (G.-C.W.);
olaf.bubenzer@uni-heidelberg.de (O.B.)
Institute of Archaeology, Cardinal Stefan Wyszyński University in Warsaw, Wóycickiego 1/3,
01-938 Warsaw, Poland; k.niedziolka@wp.pl
Cluster of Excellence ROOTS, Kiel University, Leibnizstraße 3, 24118 Kiel, Germany;
bserbe@roots.uni-kiel.de
CRC 1266, ZBSA, Centre for Baltic and Scandinavian Archaeology, Schloss Gottorf,
24837 Schleswig, Germany; sonja.grimm@zbsa.eu (S.B.G.); daniel.gross@zbsa.eu (D.G.)
Institute of Geosciences, University of Bonn, Nussallee 8, 53115 Bonn, Germany
Department of Earth and Environmental Sciences, Pavia University, Via Ferrata 1, 27100 Pavia, Italy;
michael.maerker@unipv.it
Institute for Geography, Heidelberg University, Berliner Straße 48, 69120 Heidelberg, Germany
Department of Biosciences, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria;
AlexanderGamisch@gmx.at
Institute for Geophysics and Meteorology, University of Cologne, Pohligstraße 3, 50969 Cologne, Germany
National Research Center on Human Evolution (CENIEH), Paseo Sierra de Atapuerca, 3,
09003 Burgos, Spain; ana.mateos@cenieh.es (A.M.); jesus.rodriguez@cenieh.es (J.R.)
Department of Prehistoric Archaeology, University of Cologne, Weyertal 125, 50923 Cologne, Germany;
b.weninger@uni-koeln.de
Senckenberg Research Institute, ROCEEH, Senckenberganlage 25, 60325 Frankfurt am Main, Germany;
ericson.hoelzchen@senckenberg.de
ROCEEH, Heidelberg Academy of Sciences, Karlstraße 4, 69117 Heidelberg, Germany
Department of Paleobiology and Environment, Goethe University Frankfurt, Max-von-Laue-Straße 13,
60438 Frankfurt am Main, Germany
terrestris GmbH, Kölnstr. 99, 53111 Bonn, Germany; holthausen@terrestris.de
Neanderthal Museum, Talstraße 300, 40822 Mettmann, Germany
Correspondence: c.willmes@uni-koeln.de
Received: 23 January 2020; Accepted: 21 April 2020; Published: 8 May 2020
Abstract: In this report, we present the contributions, outcomes, ideas, discussions and conclusions
obtained at the PaleoMaps Workshop 2019, that took place at the Institute of Geography of the
Quaternary 2020, 3, 13; doi:10.3390/quat3020013
www.mdpi.com/journal/quaternary
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University of Cologne on 23 and 24 September 2019. The twofold aim of the workshop was:
(1) to provide an overview of approaches and methods that are presently used to incorporate
paleoenvironmental information in human–environment interaction modeling applications, and
building thereon; (2) to devise new approaches and solutions that might be used to enhance the
reconstruction of past human–environmental interconnections. This report first outlines the presented
papers, and then provides a joint protocol of the often extensive discussions that came up following
the presentations or else during the refreshment intervals. It concludes by adressing the open
points to be resolved in future research avenues, e.g., implementation of open science practices, new
procedures for reviewing of publications, and future concepts for quality assurance of the often
complex paleoenvironmental data. This report may serve as an overview of the state of the art in
paleoenvironment mapping and modeling. It includes an extensive compilation of the basic literature,
as provided by the workshop attendants, which will itself facilitate the necessary future research.
Keywords: paleoenvironment reconstruction; paleoenvironment modeling; paleoclimate modeling;
open science; human–environment interaction; archeological modeling
1. Introduction
Data describing paleoenvironment reconstructions are heterogeneous and diverse, in form or
format, in method of creation, in terminology, in spatial scale, in semantics, in statistical significance or
validity and many further dimensions. We can distinguish between observational measurements and
inferred or modelled paleoenvironmental reconstructions. Data of the observational kind, for example,
a palynological sediment core analysis and its results, are mostly included in a spatially implicit/tacit
form into a common study on human–environment interaction, by describing the paleoenvironment
of the “surrounding” of a site which, however is not explicitly defined. Consequently, it is difficult to
include this kind of paleoenvironment reconstruction into a geospatial explicit and discrete modeling
application. In contrast, quantitatively modelled geospatial data, for example, a paleoclimate model,
or a digital elevation model, can be included in a spatial explicit modeling application without much
additional technical effort, because this kind of data is already modelled in the same domain of
describing space, i.e., spatially explicit.
A spatial explicit (geospatial) paleoenvironmental model which contains information describing
objects, events or phenomena that have an explicit location in space and time, in a consistently
defined spatiotemporal reference frame and data format that can be processed by specialized software
(i.e., geographic information system (GIS)). In contrast, an implicit or tacit paleoenvironmental model
would be a (textual) description of a paleoenvironment, not bound to an explicit model of space-time,
i.e., coordinates on earth surface and in time, thus not given in a strictly defined data format. The latter
kind of paleoenvironment data would be for example a description of a paleolandscape, for example,
as a qualitative interpretation of geoscientific information from a sedimentological interpretation, of a
geologic or gemorphologic land formation process, described in a traditional geoscientific study [1–3].
This kind of information is, of course, very valid, useful and important for the understanding of
specific paleoenvironments, but it can not directly be used in spatially explicit archeological modeling
applications. An example for an explicit model of a paleoenvironment, would be the Paleoclimate
Modelling Intercomparison Project (PMIP) paleoclimate simulations and its publicly available data
sets [4] or the widely re-used PMIP derived WorldClim paleo data sets [5].
Following this argument, PaleoMaps is about creating geospatial explicit paleoenvironmental
data from the vast amount of published implicit paleoenvironmental information and knowledge,
by compiling and creating paleoenvironmental reconstructions in reusable digital (preferably GIS)
data format [6]. The general contribution of this approach to the human–environment interaction
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modeling community is to make tacit or implicit paleoenvironment information explicitly available as
geospatial data, for re-use in archeological, geospatial, human–environment modeling applications.
The idea and basic first maps of the PaleoMaps project were developed as a side project of the
Collaborative Research Centre (CRC) 806 data management and data services project [7–9]. The CRC
806 (www.sfb806.de) is an interdisciplinary collaborative research project, concerning the history
of human mankind and in particular human–environment-interaction, with about 80 participating
researchers from the Universities of Cologne, Bonn and Aachen [10].
2. Workshop Summary
The workshop was set out as a two half-days event, compare the program (Figure 1), or
the workshop website (https://crc806db.uni-koeln.de/paleomaps/intro/), which remains online
including all abstracts for documentation.
The first day of the workshop dealt with the question of how to compile spatiotemporal data
sets (i.e., PaleoMaps), that can be applied in modeling applications such as agent-based models
(ABMs) [11–13], species distribution models (SDMs) [14–16] or any other geospatial modeling
applications within the paleoenvironmental and archeological domain [17,18]. On the second day,
this was followed by presentations addressing specific aspects of how to make use of explicit
paleoenvironmental models and data (i.e., PaleoMaps) in exemplary human-environment interaction
modeling applications.
In the following short summaries of the presentations are provided, in the order they were held at
the workshop (see Figure 1 for an overview of the program schedule). The presenters will summarize
their projects independently and afterwards in Section 3 the interrelations and possible connections of
these projects are discussed.
Figure 1. The program of the PaleoMaps Workshop, available online https://crc806db.uni-koeln.de/
paleomaps/program/.
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2.1. Day 1—Monday, 23 September
On the first day, eight talks were scheduled that dealt with the reconstruction and modeling of,
as well as the preparation of maps of spatially explicit pleoenvironments.
2.1.1. Introduction to the Paleomaps Workshop by Christian Willmes
The workshop started with a talk by Christian Willmes (the workshop host), outlining the aims
of the PaleoMaps idea (see Figure 2 for an example). The first formulation of the PaleoMaps idea
was formulated and published already 3 years ago [6], still mainly focused on the infrastructure and
data model aspects, but not yet named PaleoMaps. This idea was afterwards developed further,
to generally include all aspects of gathering data and information, as well as methods and techniques
for modeling these information as GIS based representations of paleoenvironments. The following two
questions will be addressed: What exactly is the idea behind the project? What are the key challenges
for compiling comprehensive PaleoMaps, i.e., a collection of paleoenvironmental spatial data for a
given spatiotemporal context?
Figure 2. A PaleoMap is a geographic information system (GIS) based model of a paleoenvironment
reconstruction, consisting of several data sources, for example a paleo digital elevation model (DEM),
a paleo climate model, a paleo hydrology model, and glaciation extends. Example PaleoMap-LGM
Paleoenvironment of Europe [19].
2.1.2. GIS, Archeology and Paleoenvironment by Kamil Niedziółka
Currently, GIS tools are widely used at various stages of archeological investigations [20]. One of
their biggest advantages is the ability to integrate various types of data, not just archeological ones.
This gives great opportunity to modern exploitation of environmental and paleoenvironmental data
and its comparison with archeological data sets. Due to that, three case studies from the area of
contemporary Poland (see Figure 3) which are based–however in a different manner–on the utilization
of archeological and environmental/paleoenvironmental data with the support of GIS tools, were
discussed. These case studies are connected with: comparison of archeological data and recently
obtained pollen data in specific part of Polish Central Pomerania where numerous archeological sites
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are known (1); investigation of settlement transformations in the area of Polish Eastern Pomerania
during the end of the Bronze Age and at the Early Iron Age. In this case archeological data was
compared with chosen data sets of environmental data in order to verify statements present in
current archeological literature and related with settlement conditions at that periods (2); archeological
researches of the Białowieża Forest based on the analysis of airborne laser scanning (ALS) data of the
whole woodland complex with additional use of environmental datasets (3). Particular emphasis was
put on the main problems related with the utilisation of different sorts of data and its quality (especially
in case of archeological data). Similarly, the issues connected with cooperation between different
disciplines, both in the field and while the elaboration of the research effects, were also discussed.
Figure 3. Research locations associated with the three case studies mentioned in the text: 1—Central
Pomerania (Wierzchowo Lake micro-region); 2—Eastern Pomerania; 3—The Białowieża Forest.
2.1.3. EPHA–European Prehistoric and Historic Atlas by Benjamin Serbe
On behalf of his work-group, Benjamin Serbe presented the latest state of the “European
Prehistoric and Historic Atlas” (EPHA) that was compiled at the “Centre for Baltic and Scandinavian
Archeology” (ZBSA) in Schleswig. The general idea was that mapping prehistoric sites on
contemporaneous maps ignores the different reality people faced in former times and bears the
chance of misinterpreting patterns or relations of dots on maps. The research of the ZBSA focuses
particularly on the North Sea and Baltic Sea area where the topography changed significantly over
time. Considering this problem, EPHA was developed to provide base layers for distribution maps.
Similar maps of different time slices were previously published for the North Sea [21] and for the Baltic
Sea [22] to show the development of ice sheets and coastlines of the inspected areas during the Late
Weichelian and Early Holocene. The direct predecessor of our project, the “Maps of Late Glacial NW
Europe”, was developed by Grimm [23] and later updated [24]. In 2016, the idea of these base layer
maps was renewed with the aim to provide a set of maps which were also useable in a GIS. During the
development of the project, we decided to define the geographic boundary to cover most of continental
Europe except the Mediterranean (which will be covered in future steps of the project) and further
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defined a timeframe from the last glacial maximum (LGM) up until today. Since 2016, a total of nine
maps (Figure 4) were published online (www.epha.zbsa.eu). As the project is ongoing, additional
time slices and updates of the already existing ones are constantly in preparation. All the maps are
published as Open Access (CC-BY 4.0 Licence) and the workgroup invites interested colleagues to
contribute by sharing new data or corrections.
Figure 4. One of the prerendered maps (Younger Dryas) as provided by the European Prehistoric and
Historic Atlas (EPHA)-project (www.epha.zbsa.eu).
2.1.4. Biome Modeling and Climate Reconstruction Using Proxy Data by Andrea Miebach
After the first coffee break, Andrea Miebach reported on the latest approaches and recent
publications of the B3 project of the CRC 806. The project uses different methods to reconstruct
paleoenvironments in the Near East and Europe during the last 200,000 years to get a better
understanding of human–environment interaction in the past (see Figure 5). The applied methods
include: (I) Spatial biome modeling for different climate scenarios using a biome-climate transfer
function [25]. Here, the response of vegetation to changing climate parameters is explored and biome
probabilities are mapped. (II) Diachronic local biome and climate reconstruction based on pollen data
of sediment cores (e.g., [26–28]). With the help of biome-climate transfer functions, fossil pollen data is
used to reconstruct time-series of climate parameters in a probabilistically way. (III) Spatial climate
reconstruction using paleo proxy data (e.g., [27,29,30]). The third method combines a set of botanical
fossil data with climate model simulations to get a spatially explicit climate reconstruction. The results
of these different approaches provide new insights into the environmental history on different scales.
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Figure 5. The described methods of the Collaborative Research Centre (CRC) 806 B3 project provide
spatial and diachronic reconstructions of climate and vegetation in the past. Here we show examples
of the three methods: (I) Probable biome distribution in the Eastern Mediterranean region during
Marine Isotope Stage 3. Modeled probabilities for each biome (color range) differ from today’s
biome distribution (grey grids). Modified after [25]. (II) Diachronic climate reconstruction (winter
temperature and annual precipitation) based on fossil pollen data from a Holocene sediment core
from the Dead Sea. Marginal probability density of reconstruction (color range) with interdecile range
(outer full lines), interquartile range (hatched area), mode (black line), expectation value (thick white
line), and partial linear trends (thin white lines). Reprinted from [26] with permission from Elsevier.
(III) Difference between assimilated temperatures (using paleo proxy data combined with paleo
climate model simulations from the PMIP3 ensemble) and the PMIP3 multi-model mean, for winter
temperatures during the Mid-Holocene. Modified after [27].
2.1.5. Integration of Information Derived by Terrain Analysis, Geo-Physical Applications and Field
Work to Reconstruct Paleo-Environmental Landscape Features: Examples from Italy and Africa by
Michael Märker
The reconstruction of paleo-landscape features for archeological and/or paleontological purposes
is primarily based on available environmental data and additional site information (e.g., [31]). Hence,
site location analysis is usually a combination of multi-variate statistics and spatial continuous datasets
that have been prepared by advanced GIS applications [31,32]. For example, digital elevation data of
different origin and on different scales are utilized to derive topographic indices describing certain
processes or characteristics of geomorphologic, geologic, climatic, hydrologic, vegetation or strategic
circumstances [33–38]. In three case studies from Italy (see Figure 6) and Eastern Africa, we illustrate
what DEMs, remotely sensed data, detailed terrain analysis, data mining technologies and geophysical
methods tell us about Landscape pattern and how their integration might help to understand, and to
reconstruct paleo-landscapes [34,39–42].
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Figure 6. Pre-79 AD environmental reconstruction of the Sarno River plain, Campania, Italy according
to [41].
2.1.6. Geomorphometry in Paleo-GIS Applications by Felix Henselowsky
Abtiotic landscape parameters such as a very smooth or rough topography are also highly
affecting the way in which humans have lived in a given landscape. However, the modeling and
mapping of paleoenvironments in context of Pleistocene human behaviour tend to focus on parameter
such as climate, vegetation or associated biomes. They represent mainly direct and indirect biotic
parameters. As an additional important abiotic parameter, the impact of topography at various
scales in the spatial analysis of a given landscape with regard to geoarcheological questions has been
discussed with case studies from Northeast Africa and the Eastern Desert of Egypt [43]. Two issues
in particular are of major importance in geomorphometry: the spatial resolution of a DEM, and the
spatial scale of landforms under consideration. A clear identification of various scales is important,
as geomorphometry operates on a wide spectrum of scales ranging from a few millimetres up to several
hundreds of kilometres, resulting in a wide range of landforms. It is discussed what the topography
can contribute to the characterisation of a given landscape at various scales, where hunter-gatherers
have lived and how this can be integrated into the characterisation of paleolandscapes with particular
focus on human behaviour at different scales (see Figure 7).
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Figure 7. Synthesis of spatial scales based on geomorphological relief units and their link to human
behavior as classification in geomorphometric analysis in Paleo-GIS Applications, source [43].
2.1.7. Oscillayers: A Dataset for the Study of Climatic Oscillations over Plio-Pleistocene Time-Scales at
High Spatial-Temporal Resolution by Alexander Gamisch
Alexander Gamisch presented his work about the Oscillayers data set (see Figure 8).
The presentation started with a short introduction about spatial explicit climatic data, including the
biologically relevant bioclim variables derived from observational data (e.g., WorldClim, [5]) as well as
simulations of large scale general circulation models (GCM). Then, he gave a short overview of the
available sources for paleo-bioclimatic data for different time points (https://www.worldclim.org; [5];
http://chelsa-climate.org; [44]; http://ecoclimate.org; [45]; https://github.com/GlobalEcologyLab/
PaleoView/releases/tag/v1.3; [46]; http://paleoclim.org; [47]) while simultaneously pointing out the
general lack of a global paleo-climatic dataset spanning the entire Plio-Pleistocene. To fill this gap he
introduced Oscillayers, a global-scale climatic dataset, representing the 19 bioclim variables for the last
5.4 million years at high spatial (2.5 arc-minutes = 4.65 × 4.65 = 21.62 km2 at the equator) and temporal
(10 kyr time periods) resolution [48]. This dataset builds upon interpolated anomalies (∆ layers)
between two end member climates (here the present and the LGM) that were scaled relative to a spatial
implicit global mean temperature curve, derived from benthic stable oxygen isotope ratios (cf. [49]).
After a walkthrough of the methodological steps required and the corresponding data validation
steps the utility and potential applications in macroecology, macroevolution, paleobiology and human
evolution science etc. for this and similar approaches (cf. [50]), was discussed. The Oscillayers dataset
is available at the Dryad digital repository [51].
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Figure 8. Example layer (Bio1; the first out of 539 time points) of the Oscillayers paleo-bioclim dataset
(Bio1–Bio19) spanning the Plio-Pleistocene (the last 5.4 Myr) in steps of 10 kyr plotted in GoogleEarth.
2.1.8. Modeling Macro Scale Paleo Climates during Heinrich Events by Masoud Rostami
A Heinrich event (HE) is a natural phenomenon that occurred during the last glacial periods. It is
assumed that the broken off large amounts of icebergs traversed the North Atlantic at these periods.
As it is hypothesized that the HEs may indicate an extreme global climate change, so paying enough
attention to these phenomena is important. The precise evolution of these glacial events is still under
debate. Although there is no consensus, some mechanisms for the periodic iceberg release during HEs
have been proposed, e.g., a multi-millennial buildup collapse (binge–purge) cycle of the Laurentide ice
sheet (LIS) by [52]. Based on this classical hypothesis, a steady, time-independent snow accumulation
rate without any variation in external climate is the only requirement of an oscillatory behavior from
the LIS. Some prominent Heinrich layers, H1, H2, H4, and H5, appear are accompanied by massive
discharges of large amounts of icebergs with a major source area from Hudson Strait into the North
Atlantic. Old versions of the Earth system models (ESMs) were based on this assumption that the extent
and elevation of the Greenland and Antarctic ice sheets are time invariant without any interaction with
other parts of the climate system, while it has been established that they have mutual interaction with
atmospheric and ocean currents on time scales of a decade or less. After introducing improved ice
sheet dynamics of HEs in a ESM model, we colleagues simulate HEs by applying the LGM boundary
conditions under different freshwater and sea surface temperature (SST) forcing scenarios over the
northern North Atlantic. By means of reproduced climate variables for stadial and interstadial cycles,
a human existence potential (HEP), and archaeological data, we estimate the HEP over Europe for
some Upper Palaeolithic technocomplexes (see Figure 9).
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Figure 9. Estimation of the human existence potential (HEP) of the cold stadials during the Aurignacian
technocomplex. Excavation sites are presented as white squares.
2.2. Day 2—Tuesday, 24 September
For the second day of the workshop, eight talks concerning modeling applications in the
archeological context were held. The different approaches should shed light into the questions
of how paleoenvironmental models are applied and re-used by archeological modeling applications.
2.2.1. Carrying Capacity (CC) and Species Diversity in the Pleistocene by Jesús Rodríguez
Carrying capacity (CC) is a theoretical concept that represents the maximum biomass that an
ecosystem can sustain over the long term. Many past ecosystems differ in ecological structure
from their recent counterparts, especially in the diversity and ecological characteristics of the large
mammal fauna [53,54]. Since carrying capacity (CC) is directly related to the maximum sustainable
population density of each species in the ecosystem [50], estimating the CC of large mammals in
those past ecosystems is essential to understand their functioning. As an example, the number of
large herbivores (weighing more than 450 kg) was much higher in Europe during the Pleistocene
than in the present [55,56]. Similarly, carnivore diversity was extremely high at the end of the early
Pleistocene [57] in Mediterranean Europe. In his presentation, J. Rodríguez adressed the question of
whether that high diversity was related to an increased CC in those past ecosystems. Most archeologist
agree that ungulates were a major food resource for the Paleolithic hunter-gatherers [58–61]. Thus,
estimating ungulate abundance in the past provides key information to evaluate the availability of
that key resource for humans in those environments. It is well known that ungulate carrying capacity
(UCC) is ultimately determined by net primary production (NPP) [62,63] and that the main factors
determining NPP are temperature and rainfall [64]. Thus, Rodríguez et al. [65] developed a simple
model to predict maximum UCC in recent ecosystems from mean Annual Temperature and Annual
Rainfall. That model was eventually used by [50] to map the variation in UCC in Mediterranean
Europe during the Late Early Pleistocene and the Early Middle Pleistocene. Annual Rainfall and Mean
Annual Temperature for those periods were obtained from interpolated paleoclimate maps, following
the same interpolation methodology applied by other authors [48,49]. Using the interpolated UCC
maps [50] (see Figure 10) showed that carnivore species richness was as high in the Early Pleistocene
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as it is in the richer recent African ecosystems. In contrast carnivore CC, which depends on UCC, was
much lower. Consequently carnivores necessarily occurred in the late Early Pleistocene ecosystems
of Europe at low population densities, and this undoubtedly affected ecosystem functioning and the
survival opportunities for hominins.
Figure 10. Example of the estimation of ungulate carrying capacity (Ungulate CC) in southern
Europe for the period 1.6–1.07 Ma. from interpolated maps of annual rainfall (Annual P) and mean
anual temperature.
2.2.2. Spatial Clustering of Archeological Data by Heiko Limberg
Most techniques for spotting regions of higher site densities are of spatial nature and do not
take the uncertainty of the radiocarbon dating into account. This requires the archeologist to split
datasets of site distributions into epochs in the first place. We develop a density-based clustering
algorithm, which extends the very flexible (in terms of ability to cluster arbitrary shaped patterns)
OPTICS algorithm [66]. It interprets distance to the nearest points in a probabilistic sense and can
incorporate the entire temporal information from the calibrated radiocarbon data. We thereby translate
the mutual reachability distance developed for the OPTICS algorithm into probabilistic information.
Consequently, transition probabilities from site to site are defined such that the transition tends to be
towards higher density regions. Different density regions can then be identified with the help of the
Analysis of absorbing reducible Markov chains.
2.2.3. Using Core Areas and Extended Areas as Informed Scales in Modeling Human-Environment
Interaction by Isabell Schmidt and Martin MüLler
Core Areas and Extended Areas are archeologically informed scale levels, introduced in the
framework of the Cologne Protocol [67,68] to obtain population estimates for Prehistoric mobile
foraging societies in well-studied regions of Europe [69,70]. They form part of the scalar model
presented in Figure 11 left. Their informative value in the field of modeling human–environment
interaction is discussed.
The Cologne Protocol uses a site-density based geostatistical procedure to discern Core Areas of
dense site distribution from “empty areas” of less dense and thus less intensive occupation (Figure 11
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right; [17,69,71,72]). Since archeological sites are prone to numerous biases, our approach requires
explicit data on positive as well as negative evidence for observed patterns. We argue that Core Areas
then provide a necessary mean to adjust the temporal scale of archeological data prior to integration
into and comparisons with environmental models.
Figure 11. Hierarchical scale model of the Cologne Protocol for the estimation of population densities
during the Upper Paleolithic in Europe (left, after [70]), and a map on central Europe, with Core Areas
(red lines), Extended Areas (dotted lines), and the Total Area of Calculation (dashed line) for a case
study of the early Upper Paleolithic (right, after [17]).
Modeling approaches run the risk of integrating parameters with different temporal and spatial
resolution and relevance in the model set-up. Data on site distribution (as a proxy for human
presence) and climatic/environmental conditions are frequently used in this context—and are a
case in point. While commonly used climatic models reconstruct conditions of a single moment in
time, archeological data frequently subsume evidence from a large temporal scale, covering several
environmental conditions and shifts. To reduce distortions caused by the short-term outliers in site
distributions, Core Areas are proposed as a meaningful way to address the problem [73].
“Extended Areas” constitute the next scale level (Figure 11), describing an interconnected
social/economic area of past societies, obtained through superimposition of Core Areas and evidence
on lithic raw material transport (for more details see [68]). Other archeological correlates for economic
or social contact might be added at this scale level. Since Extended Areas—by definition—include
information on human mobility and interaction, they provide meaningful spatial data to model cultural
adaptation to environmental conditions (see [74] and references therein for a similar application).
The use of Core Areas in modeling context has been subject to a study characterising humans’
relationship with Europe’s main river systems [75]. The relationship was investigated diachronically
throughout the Upper and Final Paleolithic as well as Neolithic. The results show that the percentage
distances of the Core Areas to the rivers aligned across two spatial scales (Europe and Central Europe)
and two culture-economic phases (the Upper Paleolithic and the Neolithic) (ibid.).
2.2.4. Simulating Early Hominin Expansions: Integration of Hominin Behavior and Paleoenvironment
Reconstructions with Agent-Based Modeling by Ericson HöLzchen
Ericson Hölzchen discussed applications of agent-based modeling, to examine hominin
expansions at different scales (see Figure 12). To approach the complex history of hominin expansions
the expertise from different disciplines, such as archeology, paleoanthropology, geology etc. is
required. The advantage of agent-based modeling is that it allows integrating data from fossil and
archeological finds and behavioural models from various disciplines to explicitly test hypotheses on
hominin expansions in dynamic simulations. In combination with GIS-environmental reconstructions
it provides a virtual laboratory, where the behaviour of hominins can be examined on reality-oriented
landscapes. Nonetheless, good scientific practice is required to obtain valuable results from the
simulation experiments. Therefore, a substantial amount of effort needs to be invested in extensive
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sensitivity analyses, good documentation and interdisciplinary discussions when formulating the
conceptual model. When considering GIS-data, technical aspects such as resolution and projection will
affect the simulation results. Therefore, to reduce the sources of errors when integrating GIS-data and
for the sake of efficiency, the collaboration with geographers is strongly suggested. To illustrate the
application of agent-based modeling with GIS-data, ongoing modeling projects within the frame of the
ROCEEH project and the METHOD IFG were presented. Future directions point to address hominin
expansion ABMs at different scales and the transition between scales.
Figure 12. Agent-based models (ABMs) that integrate GIS maps as agent-based modeling environment
with which the agents interact. Shown are example visualizations of a continental-scale model (left)
and a regional scale model (right).
2.2.5. Distribution Modeling of Paleofauna in the Western Mediterranean between the Heinrich Events
(HEs) H5 and H4 by Michael Holthausen
The master‘s thesis [76] from which Michael Holthausens talk emerged, deals with SDM for eight
selected prey animals of the Neanderthals and the anatomically modern human within the framework
of the Collaborative Research Center 806 (“Our way to Europe”). This research was realized for three
methods in three climatically different time slices during the Late Pleistocene. One profile method,
one regression method and one machine learning method were used. A model was developed that
performs these three methods in order to obtain a potential distribution of the paleofauna in the Late
Pleistocene and to link it to the dispersal of humans in this region. The results show that all three
methods predict conditions for the presence of the species (see Figure 13) which may have hunted
from both Neanderthals and anatomically modern humans. However, there are differences in the
predicted regions between the individual methods for each species. Another task was to determine the
best performing method. Based on this work, MaxEnt [77], a machine learning method, emerged as
the best performing method among the applied methods.
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Figure 13. Exemplary result maps for the species Equus caballus for the methods Bioclim, GLM and
Maxent, and the time slices H4, GI11 and H5. Green areas indicate a presence or suitable area and gray
indicates a predicted absence of the species. Source: [76].
2.2.6. Modeling Human Existence Potential (HEP) and Dispersal by Konstantin Klein
Archeological records indicate that human population experienced frequent decline and growth
as humans were on their way to populate the whole planet. These fluctuations can be seen in the
development of population patterns and techno-cultures. Despite of the diverse setbacks, humans
adapted to various environments and improved their technology. Climate, together with other
environmental factors, profoundly influenced the development of human population. Our hypotheses
is that climate and environment were the main drivers for human existence and dispersal. In the
workshop, we presented a case study of HEP in Europe during the Last Glacial Maximum [73]. HEP
for hunter-gatherer existence is estimated using climate/environment data, supported by archeological
evidences. In Klein et al., we introduced the concept of Environmental Human Catchment and Best
Potential Path to investigate the social and technological interactions between hunter-gatherer groups
and identified HEP barriers which prevented the interactions. We have shown that the Solutrean
population in western Europe adapted to different environmental conditions than the Epigravettian
population in eastern Europe, as the contact between them was prevented by an HEP barrier. In the
second part of the talk, two human dispersal models were presented. The first model simulates the
large scale changes of human population density over time. Our main assumptions are that humans
disperse in the direction of high HEP and the population growth and decay can be estimated by
solving the population density conservation equation. Preliminary results of human dispersal of an
initially arbitrary population distribution in Europe during the Last Glacial Maximum were shown.
The second model, the Constraint Agent Based Model, simulates the movement of individual humans
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and estimates human mobility based on the statistical behavior of a large ensemble of individuals.
In contrast to some existing ABMs, the movement of the individuals is influenced by random factors
but constrained by a drift term which depends on the HEP. Processes such as reproduction, cooperation
and conflicts are being included in the model (see Figure 14).
Human Existence
Potential (HEP)
Applications
Environmental HEP
Enviromental Human Catchment
Forced by climate and
environment, estimation of resources
Implicates the influential areas
of local potential maxima,
similar to hydrological basins
Accessible HEP
Best Potential Path
Modified by technology
and social constraints,
limiting the use
of the resources
Calculates the best travel path
between two points with the
potential as a cost function
Dispersal and
Mobility model
Human Dispersal Model
Available HEP
Dispersal simulation
Considers the distribution of humans,
shows the influence
of population pressure
Dynamic dispersal or mobility
model on different scales
Large scale dispersal
model including advection, diffusion and
birth-death terms, simulates the change of
human density over time
Constraint Agent
Based Model
Small scale migration
model that simulates
individuals or groups of
individuals, including a
HEP dependent drift term
Figure 14. Overview and description of the components of our modeling framework.
2.2.7. Auto-Amplification and Climatic Resonance as Major Drivers of the Abrupt
Wild-Domestic-Transition (WDT) at 10.2 Ka Calbp in the Emed and Its Equivalent in the Wmed by
Bernhard Weninger
In contemporary archeological research, the domestication of plants and animals in the Near
East during the Early Holocene is alternatively interpreted as an overall slow and gradual, or else
as rapid process. Both positions are supposedly supported by 14C-radiometric data, but which is of
generally low quality. A recent chronological re-analysis [78] of the archeological, archeobotanical
and archeozoological data confirmed that the wild-domestic-transition (WDT) was indeed initially
slow (millennial scale), but terminated at 10.2 ± 0.2 ka calBP with an abrupt switch to herding
and agriculture. Interestingly, the WDT is itself synchronous with an abrupt climatic switch to
higher precipitation, as documented in marine and terrestrial climate records in the Mediterranean
and adjacent regions (e.g., Levant, Iran). The WDT is immediately associated with the onset of
Neolithic dispersal out of the Fertile Crescent into Central Anatolia, and from the Northern into the
Southern Levant. From the viewpoint of Complex System Theory (Niche Construction, Punctuated
Equilibrium), it appears possible to understand the rapidity of WDT as due to amplification with
feedback (i.e., resonance) for a small number of causal factors, abbreviated: human agency tightly
coupled with the genetic properties of domesticates and enhanced water availability. In the present
paper, we compare the eMed (Neolithic) response to the precipitation increase at 10.2 ka calBP
(Sapropel S1) with the response of contemporaneous Hunter-gatherer (Epipaleolithic) societies in the
wMed (Iberian Peninsula (IP), North Africa, Saharan Regions). The method applied is large-scale
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combination of paleoclimate records with archeological 14C-data using the method of Barcode Seriation
(see Figure 15).
Figure 15. Comparison of Epipaleolithic and Neolithic (14C-based) Demography between the Levante
(without Syria) and North Africa (without Egypt). The results of Barcode Seriation are compared with
Summed Calibrated Probability Distributions of the same data. Interpretation: (1) in both regions,
the demographic development is strongly dependent on water availability; (2) Neolithic (farming)
demography is more sensitive to climatic extremes (RCC = rapid climate change) than Epipaleolithic
(hunting/gathering). (North African Precipitation Index data from [79]).
2.2.8. Modeling Late Pleistocene Human Behaviour in the Western Mediterranean: Past and Future
Perspectives by Trine Kellberg Nielsen
The IP is a particularly interesting region for the research of hunter-gatherer behaviour in Europe.
There has been and continues to be a long discussion about the asynchronous patterns of human
occupation and adaptation between the Northern and Southern regions of the IP. Studies such
as [18,80] show that climate events impacted the lives of the hunter-gatherer societies of the Western
Mediterranean, but oftentimes the exact nature of this influence and its effect on human mobility is not
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known. This is why, especially in the past few years, there has been an increase in the application of
modeling approaches to the archeological data record of the IP, in order to describe in more detail the
effects of climate change on human societies.
Especially HEs 4 to 1 seem to have impacted the human population histories on the IP, leading to
bottlenecks and subsequent local and/or regional extinction events [81]. An example of this is the
decrease in precipitation during HE 1 in the Southeast of the IP [80], which may have led to a
local population breakdown [18]. Related to this approach—comparing models of climate history
with settlement history—is the modeling of habitat suitability [82,83], which helps us to not only
understand human dispersal and distribution, but also human adaptive flexibility and allows us to
identify important climate parameters influencing human dispersal range.
Especially concerning Neanderthals, the IP has been in the focus of Paleolithic research for its
possible role as a refugium. A particularly well-known discussion deals with the arrival time in the
North and subsequent distribution to the South of the IP by the first Homo sapiens populations spreading
in to the European Western Mediterranean. This debate, which has its roots in the “Ebro frontier”
hypothesis [84], has recently resurfaced again by the controversial claim that early Homo sapiens
with Aurignacian artifacts were already present at Bajondillo cave in the South of the IP already at
around 45–43 ka BP [85], and see discussion in [86,87]. However, climate models combined with
chronostratigraphic Bayesian modeling seems to confirm that Neanderthals disappeared between the
cold periods of H5 and H4 [88,89].
On a more regional scale, the IP is the perfect test case to analyse human–environment interactions.
Our research group has been working on linking archeological sites back to the landscape, which can
tell us how the human groups interacted with the landscape as well as each other [18] (see Figure 16).
This can then be connected to climate changes. Least Cost Path and Site Catchment analysis are
two classical examples of this, employed to analyse human mobility and resource selection and how
these may have changed throughout time [90,91]. Site pattern analysis can also tell us a lot about
settlement histories of hunter-gatherers. Kernel Density Estimation and Ripley’s K analysis results
allow us to identify settlement hubs; the distribution of these tells us which areas in the landscape
were particularly suitable for human settlement, such as the Southern and Northern coastlines of the
IP during the Last Glacial Maximum [18]. These analyses also allow the identification of settlement
breakdowns, as we can see for Heinrich 1 in the Southeast of the IP. Using the site distribution as
a basis for social network modeling, we can also discuss long-term stability of a society. Applying
Wobst’s mating network model [92], we could see clear breakdowns of social networks in this same
Southeastern Iberian region during HE 1. This breakdown was apparently so devastating that the
human groups could not bounce back after the climate got better in Greenland Interstadial 1.
Ultimately, these models all rest on the quality of the input data. Data comes in many different
forms and different scales, from the intra-site scale (assemblage data) to global climate models. Each of
these data sources has their own set of biases which must be accounted for during modeling, e.g.,
the inherent problem of only having presence data for many archeological complexes, determined
by preservation and research history. Therefore, we need to rigorously test our models against all
different types of data and especially against new data appearing. modeling human–environment
interaction is such an important aspect of archeological research, reflecting the more dynamic aspects
of hunter-gatherer lifeways, that we must continue to critically assess and apply the multitude of
different modeling approaches to archeological datasets.
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Figure 16. Archaeological sites with Solutrean, Magdalenian, or Iberomaurusian occupation layers on
an ombrotype map of the Worldwide Bioclimatic Classification System (1996–2015) for Iberia (original
scale 1:1 Mio.) and Africa (original scale 1:25 Mio.). Source: [18].
3. Discussion, Conclusions, Future Directions
The thematically diverse set of talks, given by researchers from archeological, paleontological and
geoscientific backgrounds, presented a wide spectrum of different approaches to create and re-use
digital paleoenvironment reconstructions in the domain of human–environment modeling applications.
The talks of the first day dealt with approaches, methods and implementations to gather, collect,
compile and create explicit paleoenvironmental models and datasets. The diversity in approaches
and in how the resulting data and models are formalized or stored and published, showed how
compilations such as the PaleoMaps idea can help to integrate these quite heterogeneous information
and data into spatially explicit modeling applications. The diversity also showed how very interesting
and hetereogenous paleoenvironmental information can be included in spatial archeological and
paleontological modeling applications.
In the field of human–environment interaction modeling, like ABM or SDM for example, it is
visible in the scientific record so far, that most applications depend on few published paleoclimate
models. Mostly the well known climate model implementations, for the time slices Pre-Industrial,
Mid-Holocene, and LGM of the (Paleo Model Intercomparison Project) PMIP 2 & 3 projects
(i.e., CMIP/PMIP [4] and its derivates like WorldClim [5], ecoClimate [45] or PaleoClim [47]). PMIP 4
is currently under way and will add two additional time slices [93]. These models sometimes also
apply additionally paleoenvironmental features like according sea levels or lake levels, but if it
comes to the integration of granular site specific geoscientific paleoenvironment reconstructions from
sediments and drill cores, in an explicit paleoenvironmental model, this is seen quite seldom in the
published record. These latter kinds of reconstructions are mostly included implicitly, for evaluating
explicit paleoclimate models, or for testing non-spatial hypotheses. An interesting example of a quite
ambitious and comprehensive project to create explicit GIS data for Quaternary glaciation features,
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was the collection on “Quarternary Glaciations” [94] including very comprehensive freely available
GIS data sets (https://booksite.elsevier.com/9780444534477/). Another very positive study in this
direction is the Loessmap project [95]. This kind of research studies and approaches for creating GIS
data sets and publishing them for reuse needs to be promoted and established more widely in the
geoscientific, paleoenvironmental and archeological domains.
A further important point that was discussed at the workshop, was how to ensure
reproducibility [96] of archeological models in the scientific record. Like many programs that are
implemented in code, ABMs may be a “black box” where it is difficult to understand what happens
during a simulation and in the code. Therefore good documentation is required to understand and to
reproduce the code. The Overview, Design concepts, and Details protocol (ODD protocol) is such a
standardized description of ABMs [97]. The questions of how the data of the paleoenvironment
reconstructions are published, emerged in many of the talks and discussions. In this regard,
many authors already try to publish the underlying data with its publications, but it is far from
being the standard. Analyses made with R often have a role model character (e.g., [98–100]. During
the closing discussion of the workshop, the implementation of Open Science paradigms to ensure
reproducibility was emphasized. This particularly means the publication of data and code that was
used or produced in a study. In general the principles of Open Science [101–103], like open access
publication, open licenses and access for the underlying data and also an open peer review process, is
the best way to go forward.
Each discipline concerning paleoenvironment reconstruction has very particular approaches to
data formats and how to publish underlying data of scientific studies. For example, many scientists
and working groups publish paleoenvironmental reconstructions and models in a scientific paper that
describe the reasoning behind the modeling in prose, and some explicit mathematical formulation of
some aspects of the model. If lucky, the code of the model will be published as supplement of the study.
But what you will see rather seldom in this domain, is the publication of the modeled output itself
or even more rare, a map in a digital geospatial (GIS) data format. In reality, to access these results
and data, it is common to get in contact with the authors and ask for a copy or how to access this
data. But the audience agreed that both major approaches (in the form of explicit and implicit spatial
information) of including paleoenvironmental information into an archeo-paleontological modeling
application are valid and useful depending on the methodological approach and design of the specific
study. It was also clear, that the PaleoMaps idea should be developed further and implemented by
producing more PaleoMaps. An open question for future development of the idea is, if a central
infrastructure or a decentralized platform can be developed. There are very good arguments in favor
of a centralized website hosting PaleoMaps datasets, but there are also very good arguments to keep it
decentralized, meaning no central data repository. But a collaborative metadata collection, for example
via Wikidata, by describing PaleoMaps datasets in Wikidata [104] could be a very promising way
forward. This would provide a central data store for paleoenvironmental data for the application in
forthcoming human–environment interaction studies. These data sets are favorably spatiotemporally
as explicit as possible and thus would facilitate potential for re-usability.
In summary, the workshop was a success, many interesting topics were addressed and
good discussions took place. The ideas and results presented and discussed in this report can
contribute to improve the representation of paleoenvironmental models and data in archeological and
human–environment interaction modeling applications.
Funding: The work for compiling and publishing this report and the workshop itself was funded by German
Research Foundation (DFG) through the Collaborative Research Centre 806 (www.sfb806.de), Data Management
and Data Services Project Z2 (DFG project number 57444011). B.S., S.B.G. and D.G. as members of the EPHA
project were funded by the Collaborative Research Centre 1266 “Scales of Transformation— human–environmental
interaction in prehistoric and archaic societies” of the German Research Foundation (DFG, DFG project number
2901391021-SFB 1266). The work of A.G. was funded by the FWF grant (P29371) to Hans Peter Comes (University
of Salzburg).
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Acknowledgments: Thanks are due to the Institute of Geography of the University of Cologne for providing the
workshop location, and to the student research assistants Johanna Steiner and Alexander Hoffmann for helping
with the organization and the logistics of facilitating the workshop.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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