# Changing Movements in a Changing World: Modelling Early Pleistocene and Early Middle Pleistocene Climatic and Ecological Environments and Influences on Hominin Dispersal in Eurasia

Kamilla Laustsen Lomborg, Carolina Cucart-Mora, Jan-Olaf Reschke, Christine Hertler, Matt Grove, Benoit Gaudou, Mehdi Saqalli, Marie-Hélène Moncel

## ► To cite this version:

Kamilla Laustsen Lomborg, Carolina Cucart-Mora, Jan-Olaf Reschke, Christine Hertler, Matt Grove, et al.. Changing Movements in a Changing World: Modelling Early Pleistocene and Early Middle Pleistocene Climatic and Ecological Environments and Influences on Hominin Dispersal in Eurasia. Journal of Computer Applications in Archaeology, 2026, 9, pp.66 - 88. 10.5334/jcaa.212. hal-05530666

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Submitted on 27 Feb 2026

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Changing Movements in a
Changing World: Modelling
Early Pleistocene and Early
Middle Pleistocene Climatic
and Ecological Environments
and Influences on Hominin
Dispersal in Eurasia

<figure><img src="image_3.png" /></figure>

Journal of
Computer Applications
in Archaeology

REVIEW

KAMILLA L. LOMBORG ID

CAROLINA CUCART-MORA ID

JAN-OLAF RESCHKE ID

CHRISTINE HERTLER ID

MATT GROVE ID

BENOIT GAUDOU ID

MEHDI SAQALLI ID

MARIE-HÉLÈNE MONCEL ID

[u] ubiquity press

ABSTRACT

In a world of drastic climatic and ecological changes, our knowledge of how the environment influenced hominin behaviour is of the utmost importance. Archaeology plays a key role in this domain, as it is the only discipline that studies empirical evidence of past societies' responses to environmental change. Computational models generating predictions about past climatic and ecological conditions are vital for understanding the archaeological record and how these factors shaped the dispersal of hominins out of Africa and into Eurasia during the Early and early Middle Pleistocene. In this paper, various models for past reconstructions of climatic and ecological conditions and simulation techniques are presented to provide an overview of the diverse approaches, possibilities, advantages and constraints of using computational reconstructions in archaeological research. Focusing on studies of hominin dispersals out of Africa and into Eurasia during the Early and early Middle Pleistocene, this paper discusses the links between environmental factors and hominin dispersal behaviour. The use of simulation techniques to represent hominin populations, such as cellular automata or agent-based modelling, can contribute to connecting small-scale environment-induced influences on hominins to large-scale patterns, supported by ecological theories of species survival and spatial behaviour. Collectively, these approaches provide an elaborate foundation for understanding environmental influences on past hominin dispersals.

CORRESPONDING AUTHOR:

Kamilla L. Lomborg

CNRS UMR 7194 HNHP,
Muséum National d'Histoire
Naturelle, Institut de
Paléontologie Humaine, FR
kamillalomborg@gmail.com

KEYWORDS:

Environmental Reconstruction;
Climate; Early Pleistocene;
Hominin Dispersal;
Computational Archaeology

TO CITE THIS ARTICLE:

Lomborg, KL, Cucart-Mora,
C, Reschke, J-O, Hertler, C,
Grove, M, Gaudou, B, Saqalli,
M, Moncel, M-H. 2026.
Changing Movements in a
Changing World: Modelling
Early Pleistocene and Early
Middle Pleistocene Climatic and
Ecological Environments and
Influences on Hominin Dispersal
in Eurasia. *Journal of Computer
Applications in Archaeology*,
9(1): 66-88. DOI: https://doi.
org/10.5334/jcaa.212

<footnote>*Author affiliations can be found in the back matter of this article</footnote>
# 1 INTRODUCTION

Reconstructions of past climate and environment are widely used in the study of past ecosystems and species evolution, including the human lineage. Archaeology plays a key role in investigating deep-time processes (d'Alpoim Guedes et al. 2016; Rick and Sandweiss 2020; Smith et al. 2012), as the discipline studies the relationships between hominin behaviour and environmental factors (Boivin et al. 2016; Burke et al. 2017, 2021; Carrión, Rose and Stringer 2011). Climate, ecology and species interactions have influenced the evolution of species over the past 5 million years, as evidenced in palaeoclimatic and faunal records, and have also profoundly shaped palaeodemographic dynamics (deMenocal 2011; Svenning et al. 2015). Hunter-gatherer groups were closely linked to their environments in terms of habitats, raw materials and nutritional resources. Ethnographic and anthropological studies of hunter-gatherer societies confirm strong relationships between technological developments and environmental conditions, thereby helping to understand how societal structures adapted to environmental fluctuations (Fuentes 2016; Grove 2009, 2018; Kelly 1983; Zhu et al. 2021; Zonker, Padilla-Iglesias and Djurdjevac Conrad 2023).

The interplay between climatic and environmental conditions on one hand, and hominin behaviour on the other, is thus key to understanding the first hominin dispersals into Eurasia. The archaeological record shows that the first waves of hominin dispersal occurred at the gates of Europe at 1.8 Ma in the southern Caucasus, but the earliest evidence of dispersals into Western Eurasia currently dates to between 1.4 Ma and 1.2 Ma (Abbate and Sagri 2012; Garcia et al. 2010; Jöris 2013; Michel et al. 2017). The earliest signs of occupation in eastern Asia are older, from 2.42 to 1.6 Ma in China, from 1.5 to 0.1 Ma in Java, and later by Denisovans from 200 ka onwards in Siberia and throughout Asia (Sawafuji et al. 2024). Marine isotope analyses of lake and sea deposits from the Mediterranean Sea identified more than 21 marine isotope stages (MIS) during the Early and early Middle Pleistocene (MIS36 to MIS15), with repeated climatic changes (Head and Gibbard 2005; Li et al. 2008). As a result of the climatic periodicity shift during the Middle Pleistocene Transition (MPT), cyclicity changed from 41,000 to 100,000 years, triggering glacial events in the Northern Hemisphere, and increasing aridification and winter monsoon activity in Asia. These events led to changes in the landscape (vegetation and animals) and in food resource availability, both of which are crucial for hominin dispersal opportunities (Ao et al. 2023; Suc and Popescu 2005; Zan et al. 2024; Zhou et al. 2023). Correlations between isotopic stages and the terrestrial records from archaeological sites confirm that these shifts resulted in changing environmental conditions for hominin occupation (Ashton and Lewis 2012; Clark et al. 2006; Dennell and Roebroeks 1996; Moncel et

al. 2021), and have given rise to hypotheses on how they affected population expansions, contractions and regional extinctions (Abbate and Sagri 2012; Bermúdez de Castro and Martinón-Torres 2013; Dennell, Martinón-Torres and Bermúdez de Castro 2011). Climate proxies such as pollen, plant microfossils (Kennedy et al. 2008; Messager et al. 2011; Reale and Monechi 2005; Suc and Popescu 2005), molluscs and vertebrates (Kahlke et al. 2011), illustrate the strong impact of climate changes on ecosystems (Head and Gibbard 2005; Mildenhall and Byrami 2003).

Correlations between patterns in archaeological and climatic data have engendered theories on the causalities between environmental conditions and occupation dynamics through extended or restrained dispersal options for hominin populations, especially in the northern hemisphere (Gibert et al. 2022; Grove 2009; Leroy, Arpe and Mikolajewicz 2011; Moncel 2010; Stewart and Stringer 2012; Szymanek and Julien 2018; Zeller et al. 2023). Hominin occupation in Europe during the Early Pleistocene is mainly documented by lithic artefacts, with sparse hominin fossils at several sites: for instance, Pirro Nord (1.7–1.3 Ma) in Italy (Arzarello et al. 2007; Cheheb et al. 2019), Sima del Elefante-Atapuerca (1.2 Ma) in Spain (Carbonell et al. 2008), and Pont-de-Lavaud (1.1 Ma) and Vallonnet Cave (1.2–1.1 Ma) in France (Michel et al. 2017). Questions have arisen as to whether hominin occupation was continuous or not, with supporting arguments pointing to favourable environmental conditions, and physiological and cultural adaptability (Davis et al. 2025; Foister et al. 2023; Hosfield and Cole 2018; MacDonald 2018; Zeller et al. 2023). On the contrary, some authors posit discontinuous occupation, highlighting the vulnerability of isolated groups (French 2021), scarce resource availability and environmental obstacles (Bailey 2010; Bailey and King 2011; Dennell 2017; O'Connor et al. 2017; Prat 2018). As these hypotheses demonstrate, the sparse archaeological records throughout this long period challenge the construction of consistent explanations regarding the role of environmental factors in population dynamics. Therefore, alternative analytical methods are required.

Given that the archaeological record is challenged by long-term taphonomic processes (Domínguez-Rodrigo and Fernández-López 2011; Giusti and Arzarello 2016), palaeoenvironmental reconstructions and simulations of population dynamics are useful for simplifying complex real systems, and as frameworks for understanding the responses of past populations to environmental changes (Burke et al. 2021; d'Alpoim Guedes et al. 2016; Kleinhans 2023; Madear and Madear 2021; Nikulina et al. 2024; Scherjon 2019). Twenty-four years ago, the first "Out of Africa" simulation was proposed (Mithen and Reed 2002) and has since been expanded, putting greater emphasis on the environmental factors affecting dispersal behaviour. Subsequent models, e.g., Hughes et al. (2007) and Romanowska et al. (2017), argue that
environmental conditions influenced early hominin dispersals out of Africa (Hughes et al. 2007; Mithen and Reed 2002; Romanowska et al. 2017). These models have proven useful in addressing specific research questions, but all the authors point towards high-resolution climatic and ecological data as the key to obtaining the best possible results, an opinion shared by other researchers (d'Alpoim Guedes et al. 2016; Haywood et al. 2002; Zeller et al. 2023). Since these modelling attempts, the spatial and temporal resolution of global-scale climatic and environmental data has expanded, and now extends further back in time. Therefore, it is timely to reconsider modelling options for an updated "Out of Africa" simulation. This paper provides a state-of-the-art overview of the available palaeoenvironmental reconstructions and emulators, as well as suitable simulation approaches for investigating the conditions of continental-scale hominin dispersals from Africa into Eurasia during the Early and early Middle Pleistocene. The aim of the paper is to highlight how reconstructions and simulation tools can enhance our understanding of

past large-scale population dynamics by presenting an overview of available research and data, and providing a conceptual step-by-step workflow linking environmental data with a dispersal simulation.

## 2 RECONSTRUCTING PALAEOENVIRONMENTS AND SIMULATING HOMININ BEHAVIOUR

Several options are available for climatic and environmental reconstructions, with varying spatiotemporal resolutions. Models, with their inherent possibilities and constraints, can generate temporally and spatially continuous simulations for global-scale archaeological enquiries, otherwise beyond the reach of the archaeological record (Birks 2019; Curran et al. 2021; Heiri et al. 2014; Kennedy et al. 2008; Saqalli and Baum 2016; Zarza et al. 2023). The overview of models presented here focuses on global climatic (Table 1) and environmental emulators and reconstructions (Table 2). Emulators

<table><caption>**Table 1** Overview of the climatic models presented in this paper, emphasising the parameters, spatial and temporal resolution and time reach of the models. The column to the far right shows the sources of information for the model.</caption><thead><tr><th></th><th>CLIMATIC PARAMETERS</th><th>SPATIAL RESOLUTION (LATITUDE × LONGITUDE)</th><th>TEMPORAL RESOLUTION</th><th>PERIOD REACH</th><th>SOURCE</th><th>AVAILABLE SITE</th></tr></thead><tbody><tr><td>CESM2</td><td>Atmosphere, ocean, land, river run-off, land ice, sea ice</td><td>1° × 1° or 2° × 2°</td><td>1 kyr</td><td>-</td><td>Bacmeister et al. (2020), Rocha (2023), Cesm.ucar.edu</td><td>Community Earth System Model 2 (CESM2) | Community Earth System Model</td></tr><tr><td>HadCM3</td><td>Atmosphere and ocean temperature, sea ice quantity</td><td>2.5° × 3.75°</td><td>1 kyr</td><td>800 ka</td><td>Gordon et al. (2000), Krapp et al. (2021)</td><td>BRIDGE - Paper: Valdes et al 2017</td></tr><tr><td>Paleo-PGEM</td><td>Ocean-atmospheric model (AOGCM) & ice sheet quantity</td><td>1° × 1°</td><td>1 kyr</td><td>5 Ma</td><td>Holden et al. (2016, 2019), Barreto et al. (2023)</td><td>PALEO-PGEM-Series: a spatial time series of the global climate over the last 5 million years (Plio-Pleistocene)</td></tr><tr><td>Paleo-Clim</td><td>Surface temperature & precipitation estimates (AOGCM)</td><td>0.04° × 0.04°</td><td>Snapshots</td><td>130 ka, 787 ka, 3.264–3.025 Ma, 3.3 Ma</td><td>Brown et al. (2018)</td><td>PaleoClim.org</td></tr><tr><td>Oscillayers</td><td>19 bio-climatic parameters of mean and extreme temperatures and precipitation</td><td>0.04° × 0.04°</td><td>10 kyr</td><td>5.4 Ma</td><td>Gamisch (2019)</td><td>Dryad | Data -- Oscillayers: a data set for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution</td></tr><tr><td>PMIP4</td><td>Air temperature, precipitation, sea surface temperature, ocean heat, snow cover & depth, soil moisture, ice sheet extent, vegetation fraction</td><td>0.5° × 0.5° to 2.5° × 2.5°</td><td>Snapshots</td><td>Last millennium, LGM, mid-Holocene, last glacial, mid-Pliocene</td><td>Taylor et al. (2012), Kageyama et al. (2018)</td><td>Model database | PMIP</td></tr></tbody></table>
<table><caption>**Table 2** Overview of the environmental reconstructions presented in this paper, emphasising the parameters, spatial and temporal resolution, and chronological range of the models. The column to the far right shows the sources used for the reconstructions.</caption><thead><tr><th></th><th>CLIMATIC PARAMETERS</th><th>OUTPUTS</th><th>SPATIAL RESOLUTION</th><th>TEMPORAL RESOLUTION</th><th>PERIOD REACH</th><th>SOURCE</th><th>AVAILABLE SITE</th></tr></thead><tbody><tr><td>LOVECLIM/ iLOVECLIM</td><td>Ocean general circulation model (AOGCM) & thermodynamic sea ice model</td><td>Vegetation cover following climatic variables</td><td>1° × 1°</td><td>1-year</td><td>125 ka</td><td>Goosse et al. (2010), Roche et al. (2014)</td><td>Loveclim 784K | Climate Data Overview - iLOVECLIM ESM</td></tr><tr><td>PRISM4</td><td>Palaeogeography, sea level, ocean temperature, land and sea ice</td><td>Terrestrial vegetation, soil, lakes</td><td>0.25° × 0.25°</td><td>Marine isotope stages</td><td>2.6–3.6 Ma</td><td>Dowsett et al. 2016</td><td>PRISM4: Data</td></tr><tr><td>BIOME4</td><td>Atmospheric CO₂ (CESM)</td><td>Vegetation cover, divided into 28 biomes from PFTs</td><td>0.5° × 0.5°</td><td>1000-year</td><td>LGM</td><td>Harrison & Prentice (2003), Zeller et al. (2023)</td><td>GitHub - jedokaplan/BIOME4: The BIOME4 equilibrium global vegetation model</td></tr><tr><td>CARAIB</td><td>Air temperature, precipitation, sunshine hours, air humidity, wind speed</td><td>Soil hydrology, surface energy budget, GPP, NPP, LAI, biomass, BAGs cover fraction, fire module</td><td>1° × 1°</td><td>1-year</td><td>LGM, Mid Holocene, Miocene</td><td>François et al. (1998), Dury et al. (2011)</td><td>UMCCB: Research: Projects: BIOSERF</td></tr><tr><td>ORCHIDEE</td><td>Atmospheric CO₂, air temperature, humidity</td><td>Soil carbon, soil temperature and hydrology, vegetation divided into 13 PFT groups, river and floodplain scheme</td><td>1° × 1°</td><td>1-year</td><td>Input dependent</td><td>Krinner et al. (2005), Guimberteau et al. (2018)</td><td>Documentation/ Forcings - ORCHIDEE</td></tr></tbody></table>

are models that transpose large-scale modelling processes to 'simple' versions for further applications (Castelletti et al. 2012). The word *emulator* in this paper refers to a generator of data, and reconstructions are the output result. Correspondingly, *estimates* refer to results from calculations, while *estimation* refers to the process of producing estimates. The emulators presented are accessible to a wide range of researchers, ensuring that studies are accessible, reproducible, and transparent. Data and emulators can be processed and run on average computer power available to most researchers, and the methodology is communicated in an understandable format for non-modellers. This focus therefore excludes models requiring high-performance computing power and high-level modelling expertise (e.g., (Govett et al. 2024; Timmermann et al. 2022; Timmermann, Wasay and Raia 2024). Here, the selection of models focuses on the applicability of the data sets or emulators to the studied spatiotemporal range, along with constraints related to literature accessibility, work limits and the disciplinary focus of the authors (Barry, Merkebu and Varpio 2022). It is important to be aware of these limitations in order to define the scope of the investigation and any possible inherent biases. A short statement on how the literature review was carried out

can be found in the supplementary material, Appendix A: Literature search strategy.

## 2.1 CLIMATE

Climate is the most important factor for the functioning of and variations in ecosystems (Magri and Palombo 2013; Malhi et al. 2020; Svenning et al. 2015), and is thus essential in environmental reconstructions. A range of climatic sources (see Head and Gibbard 2005) reveal changes in the Early Pleistocene with severe glacial and interglacial transitions (Carrión, Rose and Stringer 2011; Ravelo et al. 2004). Climatic data show a shift in the frequency of climatic oscillations, from a periodicity of 41,000 years to 100,000 years between 1.25 Ma and 0.7 Ma, caused by an increase in CO₂ (Clark et al. 2006; Clark and Pollard 1998; Lisiecki and Raymo 2005). These cyclic changes induced changes in vegetation (Popescu et al. 2010; Rodríguez et al. 2016; Sanchez Goñi 2024; Sitch et al. 2003; Suc and Popescu 2005; Zan et al. 2024; Zhou et al. 2023), which could have played a major role in hominin dispersals (Bar-Yosef and Belmaker 2011; Berto et al. 2024; Carrión, Rose and Stringer 2011; van der Made 2011; Szymanek and Julien 2018). A large set of climate simulation models has been built since the first IPCC report (1992), and improved climate reconstructions
have increased our understanding of the influences of climate change on hominin demography (Berger 2021). Climatic emulators and reconstructions are presented below, providing an overview of the options available for palaeoclimatic reconstructions. This section presents and discusses the emulators and the data generated by them, focusing on those that provide climate data sets from the Early and early Middle Pleistocene, or which have previously been applied to studies of that period.

### 2.1.1 Climatic reconstructions

Polvani et al. (2017) and Rocha (2023) presented the emulator **Earth System Models (ESM)** to simulate the Earth's climate through biological, chemical and physical processes. Due to the heavy data load of such complex simulations, simpler climatic reconstructions have been produced by the Community Earth System Models (CESM) to facilitate applicability to analyses (Polvani et al. 2017; Rocha 2023). The latest version of **CESM2** generates outputs from the atmosphere (i.e., precipitation, cloud forcing, land, ocean, wave models, river run-off, land and sea ice components), which can be combined depending on the research questions and software. The time step follows orbital sequences and can be down-scaled to 30-minute steps. The spatial resolution is approximately 1° × 1°, with varying resolutions across components. For pre-industrial periods, the reconstruction has been calibrated with two control simulations, respectively going back in time 500 and 1,200 years (Danabasoglu et al. 2020), and with PaleoCalibr, e.g., in the work of Zhu et al. (2022). The reconstructions have not been scientifically validated, but they include control simulation to ensure the model's reproducibility (Yeager et al. 2006). The estimates show increasing uncertainties the further the model goes back in time (Zhu et al. 2022). However, as demonstrated by Lemmen (2007) and Zhu et al. (2017, 2022), they can be useful in studies of past environmental conditions (Lemmen 2007; Zhu et al. 2017, 2022).

Another model extending back to the Last Glacial Maximum (LGM) is the **Hadley Centre coupled model (HadCM3)**, developed by Gordon et al. (2000). It emulates a 19-layer atmospheric component, a 20-layer ocean component with sea bottom topography, and sea ice estimations of ice concentrations and movements. It has monthly time steps and a spatial resolution of 3.75° × 2.5° for the atmospheric model, and 1.25° × 1.25° for the ocean model. The reconstruction has been validated by comparisons with observed ocean heat transport dynamics and temperature classes from the Atlantic and the Pacific. These generally concur, yet with a slightly overturning element in the HadCM3 emulator, causing overestimations of heat transported northwards by western boundary wind-driven components and southwards by interior components (Gordon et al. 2000). However, with the high spatial resolution of the

ocean, the authors do not expect realistic boundary currents (Gordon et al. 2000; Valdes et al. 2017). The fact that the emulator was defined on the basis of post-industrial climatic data presents a challenge, and complicates its use in pre-industrial periods. However, Gordon et al. (2000) showed that the difference between reconstructions and real-world climate was 0.3 °C, which only represents minor uncertainty in long-term global estimates (Gordon et al. 2000).

**Paleo-Clim** by Brown et al. (2018) focuses on the Pleistocene climate, is complemented by atmospheric-ocean general circulation models (AOGCMs) from HadCM3, and simulates annual mean temperature, temperature of the coldest and warmest months, annual precipitation, the extremes of the wettest and driest quarters and the effects of topography. This model differs from other reconstructions by applying a snapshot approach, generating chronological windows at MIS19 (787 ka), during the mid-Pliocene warm period (3.264-3.025 Ma), and MIS M2 (3.3 Ma), with a pixel-to-pixel resolution of 0.04° × 0.04°. These reconstructions have been validated by comparing raw differences and anomaly ratios of precipitation data with corresponding values from HadCM3 reconstructions. Pearson correlations between the reconstructions, global visualisations and raw climate model values were generally high, although using raw differences may result in underestimated outputs. However, the authors argue that the snapshots offer detailed insights into certain moments in time, functioning as precise cases for the exploration and validation of climatic drivers of ecological change (Brown et al. 2018).

The **Paleo-PGEM** climate emulator by Holden et al. (2019) generated the repository **Paleo-PGEM-series** (Barreto et al. 2023), presenting dynamic spatiotemporal climate estimates. The emulator generates detailed resolutions for detecting climate variability forced by orbital changes with PLASIM-GENIE, an AOGCM containing a 10-layered vertical climatology (Holden et al. 2019), BIOGEM, an ocean model (Ridgwell et al. 2007) and an efficient numerical terrestrial scheme (ENTS) for terrestrial carbon cycle modules (Holden et al. 2018). The simulation runs in orbit cycles, with varying concentrations of atmospheric CO₂ and ice sheet coverage, monthly temperature and precipitation estimates, and 17 bioclimatic variables. The model is validated by comparison with observation-based global temperature reconstructions for the LGM, the Mid-Holocene and the Last Interglacial, and proxy data from different periods and regions, showing a minor deviation in the spatial pattern of temperature anomalies around 127 ka (Barreto et al. 2023). The 1,000-year resolution yielded uncertainties in the feedback between atmospheric circulation and topography due to incomplete computational methods or a lack of knowledge on climatic variables. For this reason, the
authors emphasise that the model should be considered
as a realistic reconstruction for distinguishing spatial
variabilities, and not as a complete high-resolution
reconstruction (Holden et al. 2019).

Gamisch (2019) presents Oscillayers data to achieve
high-resolution spatial and temporal climate estimates.
Using 19 bioclimatic parameters, such as mean and
extreme values of temperature and precipitation,
Oscillayers provides proxy indicators of terrestrial
climates and sea level changes. The spatial resolution
of the model is 0.4° × 0.4°, with 10,000-year time steps,
extending back to 5.4 Ma. After comparisons with
general circulation models (GCMs), the reconstructions
correlate with other Holocene, LGM and Pliocene climatic
estimates. The author argues that reconstructions
are useful for testing eco-evolutionary hypotheses
when accompanied by ecological niche models. The
main advantage of Oscillayers over dynamic climatic
reconstructions is its detailed spatial resolution beyond
800 ka (Gamisch 2019). However, Barreto et al. (2023)
express concerns about limits and strong assumptions
on data, especially with regard to extrapolating changes
linked to the extensions and contractions of the LGM ice
sheet during the Pleistocene (Barreto et al. 2023).

PMIP is the palaeoclimatic segment of the Coupled
Model Intercomparison Project (CMIP5), which generates
high-resolution reconstructions of past climate. The
current version of the emulator (PMIP4) encompasses
atmospheric processes, ocean circulation, sea surface
temperatures, sea and land ice, soil moisture and
hydrology, carbon cycles and vegetation, and generates
outputs of air temperature, precipitation, soil moisture,
snow cover and depth, sea surface temperature, ocean
heat and currents, the extent of sea ice and vegetation
fraction. The spatial resolution of the reconstruction can
be as low as 0.5° × 0.5 °, but for most reconstructions,
resolutions are between 1° × 1° or 2.5° × 2.5°, depending
on the resolution of the input data. As with Oscillayers,
the PMIP4 emulator provides time-specific snapshots,
which for now have been produced for the LGM (21 kyr),
the mid-Holocene (6 kyr), and a transient simulation from
850-1850 CE (Kageyama et al. 2018; Taylor, Stouffer and
Meehl 2012). The reconstructions have been validated
against proxy reconstructions from ice cores, marine
sediments, pollen and speleothems by direct comparison
and forward modelling, and through benchmarking
tools and standardised control simulations (Kageyama
et al. 2018). However, the model struggles to generate
more complex climate systems, such as the relationship
between ice sheet volume, ocean heat transport, and
cloud properties in the Arctic (Kageyama et al. 2021).
This results in challenges for estimating accurate cooling
in the northern and southern Arctic areas (Ohgaito et al.
2021).

All the reconstructions and emulators presented
above (Table 1) combine global-scale temporal and

spatial applicability, the ability to estimate past climates
and potential for combination, calibration, and validation
against climate proxies (Chevalier et al. 2020; Heiri et
al. 2014; Krapp et al. 2021). Proxy records are routinely
calibrated with instrumental records, thereby increasing
their reliability and extending their range forward and
backwards in time (Frank et al. 2010). However, extending
back as far as the Early Pleistocene can be challenging for
some proxies. To ensure reliable global reconstructions of
the Early Pleistocene, isotopic data from ice core samples
are often used for validation (e.g., Berends et al. 2021;
Grant et al. 2025; Kageyama et al. 2018), while more
regional proxies include pollen and faunal records (Bacon
et al. 2023; Berto et al. 2024; Birks 2019; Herzschuh et al.
2023; van der Made, Morales and Montoya 2006). Having
established the model's reliability, the specific purpose
of the reconstruction must be considered. Modifications
to temporal limits, resolution, or the addition of other
climatic variables highlight how the choice of emulators
must be linked to the research question. Moreover, adding
environmental elements to climatic reconstructions –
such as net primary productivity variables, as shown by
the work of Krapp et al. (2021), or vegetation feedback
represented by leaf area index (LAI), as shown by O'ishi
et al. (2021) – creates a broader impression of past
environmental conditions. For this reason, the next section
introduces emulators for environmental reconstructions
that capture ecological variations. It is fitting to use such
emulators alongside climatic reconstructions.

## 2.2 ENVIRONMENT

Investigations of ecological conditions at archaeological
sites have enhanced our knowledge of the environmental
conditions of past societies (Curran et al. 2021; Jackson and
Overpeck 2000; Leroy, Arpe and Mikolajewicz 2011; Magri
and Palombo 2013; Messager et al. 2011). Specifically,
it is argued that the change to a 100-ka climatic cycle
in the MPT caused considerable environmental changes
(Legrain, Parrenin and Capron 2023; Li et al. 2008),
leading to a global increase in open vegetation, greater
regularity in environmental phases, increased aridity and
colder temperatures. Environmental changes have been
linked to changes in hominin behaviour. Behrensmeyer
(2006) further proposes that such changes caused
environmental stress, resulting in selective pressure and
developments in the genus *Homo*. The same author
underlined how environmental data and proxies are vital
to the quality, resolution, coverability and level of detail
of the reconstructions (Behrensmeyer 2006). There
is arguably a clear interdependency between climate
and environment (Leroy, Arpe and Mikolajewicz 2011;
McClymont et al. 2023; Pearce et al. 2025; Prentice et al.
1992; Schmidt et al. 2025; Strandberg et al. 2022; Zhou et
al. 2023), as practically demonstrated with environmental
emulators using climate data as input for estimations
(Bertrix et al. 2025; Kaplan et al. 2003; Krinner et al. 2005;
Nikulina et al. 2024; Zapolska et al. 2023). However, using only climate-driven environmental simulations and their global resolution cannot guarantee vegetation reconstructions corresponding to observable data. Other factors may influence vegetation conditions, such as anthropogenic activities (Nikulina et al. 2024; Zapolska et al. 2023), as well as factors that are too small to be captured at continental resolutions. Therefore, emulators focusing on detailed processes of climatic effects on soil properties, plant functions, etc., are an important addition to climatic reconstructions. The section below presents environmental or coupled climate-environment emulators for global-scale reconstructions. Other data sets and emulators of climatic and environmental reconstructions do exist, but are not included here due to their unsuitable resolution for continental-scale research (e.g., SEIB Sato et al. 2007).

### 2.2.1 Environmental emulators

LOVECLIM, by Goosse et al. (2010), is a coupled climate and environmental emulator, useful for both climatic and basic environmental estimates. It generates glacial temperatures, climatic anomalies (such as Heinrich and millennial-scale Dansgaard-Oeschger events), hydroclimatic variabilities, and includes a general ocean circulation model (CLIO model), a thermodynamic sea ice model (Goosse and Fichefet 1999), and a terrestrial vegetation model following annual mean values of climatic variables (VECODE by Brovkin et al. 1997). The emulator has a grid-size of 3° × 3°, but can be downscaled to a 1° × 1° resolution, with yearly timesteps. The reconstructions are validated against 63 proxy data reconstructions of the period 140–20 ka. The comparison showed a general correlation with other reconstructions, with minor underestimates of the reconstruction for the Last Interglacial Ocean warming. Otherwise, the estimated surface temperatures matched the ice-core data and the estimated anomalies in rainfall ratios. However, the reconstructions present biases linked to model formulation, and cannot be expected to simulate the same details as a GCM. Still, Goosse et al. (2010) argue for the emulator’s utility for the environmental reconstruction of past periods (Goosse et al. 2010).

A further development of this model is iLOVECLIM, which retains the same core elements (atmosphere, ocean, vegetation), in addition to the water oxygen isotope cycle, an oceanic carbon model, an iceberg trajectory module, and the interactive ice sheet model GRISLI. The spatial resolution of the emulator is the same as for LOVECLIM and has, for now, been used for reconstructions of the LGM. Estimates were evaluated against an observation-derived data set of mean annual surface temperatures and long-term mean climatologies of annual precipitation. In general, latitudinal cooling is reflected in the estimates, while estimations of mean temperatures for North America, Greenland and Western

Europe are too warm (Quignet et al. 2018; Roche et al. 2014).

The PRISM4 emulator by Dowsett and colleagues (2016) includes various variables to generate climate and vegetation reconstructions, including palaeogeography, sea levels, ocean temperatures, land and sea ice cover estimations, lakes and soil. The resulting output represents terrestrial vegetation using biome classes. It is a conceptual model of environmental conditions and landmass changes during the mid-Piacenzian (3.6–2.6 Ma), with a spatial resolution of 0.25° × 0.25°. The time-steps follow the chronology of marine isotopic stages by Lisiecki and Raymo (2005), with 1,000-year steps until 0.6 Ma, 2,000-year steps between 0.6 and 1.5 Ma, and 2,500-year steps between 1.5 and 3 Ma. Since the model is intended as a conceptual model for boundary conditions for other environmental reconstructions, it has not been scientifically validated. However, PRISM4 has proven useful when used to verify other models, with orbital forcing correlating with present-day forcing and isotope peaks. The authors argue that the multiproxy chronology is useful. However, it is still important to consider calibration uncertainties and the orbital-scale chronological resolution, which are more suited to regional than global-scale analyses (Dowsett et al. 2016).

The ORCHIDEE model, presented by Guimberteau et al. (2018) and Krinner et al. (2005), is a land-surface emulator which represents spatial variation in environments through different outputs: 13 plant functional type (PFT) classes, gross primary productivity (GPP), NPP and biomass values. The outputs are calculated from the internal dynamics of soil properties and feedback on water and atmospheric CO₂ concentrations. The reconstruction has a spatial resolution of 4° × 2.5° for global simulations, with 1-year time steps, or determined by the resolution of the climatic input data (Krinner et al. 2005). Simulation outputs were validated against real temperature gradients, basin-scale averages for the dominating waterbodies, other topsoil moisture models, and forest site data. These indicate that the model has a slight tendency to overestimate carbon density in some regions, including North-Western Europe. Still, the outputs of vegetational units are argued to be reliable representations of arctic vegetation, due to their separate calibrated parameters (Guimberteau et al. 2018).

The environmental emulator BIOME4 by Harrison and Prentice (2003) uses atmospheric CO₂, temperatures, mean monthly precipitation, soil physical properties, and solar radiation at different latitudes to infer changes in PFTs grouped into 28 biome categories. The model has a spatial resolution of 0.5° × 0.5°, and 1,000-year time steps going back to the LGM. For validation purposes, the reconstruction is accompanied by a site-based vegetation map, derived from pollen and plant macrofossil data and divided into PFT groups. Overall, the reconstruction matches the observed forest cover with minor
overestimates in higher latitudes and underestimates in the tropics (Harrison and Prentice 2003). Even though the time range of the emulator does not correspond to the chronology of this paper, it is included because of its use in Early Pleistocene studies, e.g., in Romanowska et al (2017). BIOME4 was the only available emulator for dynamic environmental reconstructions in 2017, despite the fact that the emulator was constructed on the basis of LGM data (Romanowska et al. 2017). However, other climatic and environmental emulators have been published since then, such as the Paleo-PGEM Series, enabling the use of suitable chronological reconstructions.

The Carbon Assimilation in the Biosphere (CARAIB) model is a process-based dynamic vegetation emulator used to study global vegetation and its role in the past and present carbon cycle. The emulator contains modules for hydrological budget, heterotrophic respiration, soil carbon dynamics, photosynthesis and stomatal regulation, carbon allocation, plant growth, plant competition, biogeography and fire disturbance. Combined, these modules produce outputs of soil hydrology, biomass, GPP, NPP, LAI and biome distribution by bioclimatic affinity groups (BAGs). The model can produce output down to a spatial resolution of 0.5° × 0.5°. However, this is determined by the resolution of climatic data, which also determines temporal resolution (Dury et al. 2011). Still, given the spatiotemporal scope of this paper, the emulator is problematic, namely in terms of the resolution requirements of input data. The emulator requires climatic input data (air temperature, precipitation, relative humidity, relative sunshine hours, wind speed) at a daily resolution (François et al. 1998), which can be challenging to obtain for the Pleistocene. However, reconstructions have been generated back in time, for example by François et al. (1998, 2006) for the LGM and the late Miocene. These reconstructions were generated with climatic data from present GCMs, and their outputs have been validated against other simulated reconstructions and real CO₂ measurements (François et al. 1998, 2006).

Environmental emulators and reconstructions, with different resolutions, variables, and outputs, clearly provide various levels of insights into past conditions (Table 2). Reconstructions vary in terms of temporal resolution, time range and output data, which are important aspects to consider when choosing a suitable reconstruction.

Although Timmermann and Friedrich (2016) argue that climate is the dominant driver of hominin dispersal, the influence of vegetation must not be overlooked. A comparison of the study by Timmermann and Friedrich (2016) with that of Zeller et al. (2023), which adds the biome factor, shows how additional information influences the dispersal options of hominin populations. The latest models of environmental impacts on hominin dispersals out of Africa have demonstrated that high-

resolution environmental data could enhance our understanding of the influence of vegetation on dispersal behaviour (Hughes et al. 2007; Romanowska et al. 2017).

Yet, linking climatic and environmental reconstructions does not satisfactorily reflect their influence on hominin behaviour, and further theoretical and methodological tools are necessary to connect them. The next section presents different theories and simulation techniques for analysing the hominin-environment relationship in detail.

## 2.3 THE LINK BETWEEN ENVIRONMENT AND HOMININS

### 2.3.1. Static models

In addition to palaeoenvironmental reconstructions, another important element is missing when investigating early hominin dispersals into Eurasia during the Early Pleistocene: the hominins themselves. Different hominin species are known to have dispersed outside of Africa. However, various obstacles arise when we attempt to address hominin diversity. Firstly, there are limited hominin fossils in the Early and early Middle Pleistocene archaeological record, which complicates precise species divisions (Arzarello et al. 2007; Gabunia et al. 2000; Gibert et al. 1998; Huguet et al. 2025; Larick et al. 2001; Lorenzo et al. 2015; Palmqvist 1997; Swisher et al. 1994). Secondly, there are no clear definitions of physiological and cultural differences between species and thus no insights into how such differences influenced behaviour (Antón and Josh Snodgrass 2012; Duda and Zrzavý 2013). Therefore, the approach taken in this paper assumes that the hominin population is homogeneous.

**Niche modelling, or ecological niche modelling (ENM),** is a computational tool for analysing species' distribution potential and actual geographical distributions. These algorithms derive from the discussion centring on Grinnellian niches and the principles of behavioural ecology, and use climatic or environmental data and presence/absence data to predict the geographical distribution of a species or population (Araújo and Guisan 2006; Austin and Smith 1989; Fitzhugh et al. 2019; Pearson and Dawson 2003; Soberón 2007).

Examples of these concepts are **species distribution models (SDMs)**, which predict the presence or abundance of a species depending on climatic and/or geophysical characteristics, substrates, and nutrients. Simulations have been used to identify ecological variables that predict species distribution probability and spatial preferences, trained on environmental and species data in either absence, presence/absence or abundance data sets (Elith and Leathwick 2009; Guisan and Thuiller 2005; Kearny and Porter 2009; Simoes et al. 2020; Soberón 2007). The **MaxEnt** simulation tool is used to generate SDMs from presence-only data and environmental parameters relevant to habitat suitability. Such studies show that presence-only data provide a foundation for
distribution analysis, which can be compared to other case studies (Hijmans and Graham 2006; Valavi et al. 2022). Furthermore, SDMs can be improved owing to the growing number of occurrence records and high-resolution spatial environmental data sets (Phillips, Anderson and Schapire 2006). Another tool for simulating SDMs is **tidysdm**, an R package containing correlative SDMs which benefit from the existing functions of *tidymodels* in an easy-to-adapt modelling framework. With the tidysdm package, it is possible to use climatic data from several periods, either in conjunction with presence data or using the *pastclim* tool, and it is thus ideal for addressing archaeological questions (Leonardi et al. 2024). Franklin et al. (2015) emphasise how SDMs can accompany niche models to discover and validate environmental parameters when location data are missing (Franklin et al. 2015). Despite the complex interplay between scale, modelling method and data quality, SDMs have been used for large-scale species distribution mapping (Elith et al. 2006, 2011), and in archaeological research on hominin dispersal and habitat preferences (Franklin et al. 2015; Padilla-Iglesias et al. 2022; Yaworsky, Nielsen and Nielsen 2024). Close relatives to SDMs are **habitat suitability models (HSMs)**, which identify influential environmental parameters limiting a species' spatial range using environmental and presence data (Guisan and Thuiller 2005; Thuiller 2024). However, neither HSMs nor SDMs allow for the observation of the temporal evolution of the system. Without a temporal perspective, it is impossible to observe how population dynamics (i.e., population structure), dispersal, abundance and local extinction evolve depending on their adaptation or preference for a set of climatic or environmental variables. To overcome this limitation, authors such as Thuiller and Münkemüller (2010) suggest linking HSMs with individual-based modelling methods, such as ABMs (Thuiller and Münkemüller 2010).

### 2.3.2. Dynamic models

Since population dispersal is a dynamic process evolving over time, static insights from niche models and SDMs are not suitable for studying the causal mechanisms between climatic and environmental changes and the process of hominin dispersal. Instead, this question demands dynamic models (Romanowska, Wren and Crabtree 2021). One of these simulation methods is **cellular automata (CA)**, which uses a grid-based world where a numerical state value is attributed to each cell, often ranging within a scale or a Boolean value. Spatial scale is determined by the number of cells, cell-size, and interaction-reachability of the agent within the cell-neighbourhood. In dispersal models, the numerical value of the cell represents the probability of occupation, and the Boolean value indicates the presence or absence of a population. This type of simulation is appropriate for large-scale studies, where the population is viewed as a

single coherent entity, and less suitable for investigating inter-population dynamics (Bithell and Macmillan 2007; Romanowska 2015), as shown through the use of CA models in studies of global-scale dispersals of hominin populations.

The first Out of Africa CA by Mithen and Reed (2002), named *Stepping out*, featured a landscape arranged in hexagons, a climate following glacial-interglacial cycles, two sea-level heights, and a single hominin population with various dietary and environmental preferences. At each time step, the population could stay and adapt to the current cell and/or colonise a neighbouring one, starting their dispersal in East Africa. Simulations showed how adaptive specialisation and the colonisation rate influenced dispersals and thereby how environmental factors could evaluate dispersal routes, conditions, and chronology (Mithen and Reed 2002).

Nikitas and Nikita (2005) expanded *Stepping out* with three landscape types and three population dispersal modes, the first with a stable total population size, the second with population growth based on movement, extinction and colonisation rates, and the third as a combination of both scenarios. In this way, they tested the impact of natural obstacles and dispersal dynamics on early dispersal timing and routes into Eurasia. The results demonstrated that dispersal dynamics had a greater impact on dispersal timing than environmental barriers, and that a stable population size was more probable than a high population growth rate (Nikitas and Nikita 2005). Hughes et al. (2007) added environmental elements from BIOME4 to *Stepping Out* and modified the simulation into a null-hypothesis model by testing the influence of the environment on the dispersal pattern of a simplified non-developing hominin population. They observed an increase in the chronological predictability of occupation, thus arguing for a greater focus on ecological niches in hominin dispersal (Hughes et al. 2007).

This perspective was included in the latest modelling attempt by Romanowska et al. (2017), who investigated whether dispersal and population dynamics could explain the distribution of archaeological assemblages in relation to the Movius line. Cell biome values were generated using an equation-based modelling technique, with an environmental setup including temperature, sea-level curve, and eight biome categories connected to these variables. The environment was built with the structures of a CA, with grids containing biome-specific values for carrying capacity, population dispersal probability and population growth rates. The simulations emphasised that local environmental conditions and levels of hominin adaptations were more relevant to hominin population density than an increased distance from the population centre (Romanowska et al. 2017).

**Agent-based modelling (ABM)** offers inter-population insights by adding a third component; agents, along
with a spatially structured environment and rules of
behaviour defining the interactions between the two.
Agents and the environment possess different attributes,
making it possible to observe changing properties as the
result of their interplay, thereby providing a bottom-up
insight into large-scale patterns (Romanowska, Wren
and Crabtree 2021; Wilensky and Rand 2015). Used in
a number of disciplines, the impact of this method in
archaeology has steadily increased over the last couple
of decades (Cegielski and Rogers 2016; Chliaoutakis
and Chalkiadakis 2016; Davies et al. 2019; Graham
2020; Lake 2015; Reeves et al. 2023; Romanowska et al.
2019). ABM's bottom-up approach inspired the study of
hominin dispersals, since it allows for the observation
and testing of different scenarios, a rare opportunity in
archaeological research (Hölzchen et al. 2016; Hughes et
al. 2007; R. Vahdati et al. 2019; Romanowska et al. 2017).
Agent-based models have been used to test different
drivers of dispersal, such as environmental conditions
(d'Alpoim Guedes et al. 2016; Hughes et al. 2007; Perry
et al. 2016), demographic dynamics (Coto-Sarmiento et
al. 2023; Cucart-Mora, Lozano and Fernández-López de
Pablo 2018; R. Vahdati et al. 2022; Romanowska et al.
2017), and cultural and cognitive abilities (Barton et al.
2011; Reeves et al. 2023; Wren et al. 2014).

Bioco et al. (2022) presented SDSim, a simulation approach connecting the principles of SDM and ABM. Based on the use of important variables for species ecology, the SDSim generates a raster habitat suitability map and combines it with agents’ birth, reproductive, death, and distribution rates (Bioco et al. 2022).

The growing number of simulation studies reveals
increasing confidence in the methodology and its
potential for investigating climatic and environmental
effects on dispersal dynamics. However, in the same way
as for palaeoenvironmental reconstructions, simulated
dispersal patterns require validation. Given the limited
number of sites, it is vital to take stock of the risk of
overfitting the dispersal simulation to the available
data. This is often a challenge when working with
archaeological data, which is why various approaches
have been developed to evaluate the proximity between
simulation results and archaeological data, e.g., matching
the model to stylised facts (i.e., generalised patterns, e.g.,
Romanowska 2014; Romanowska et al. 2017), scenario
testing (e.g., Griffin and Stanish 2007; Wren et al. 2014),

and parameter sweeping (e.g., Hughes et al. 2007; Nikitas
and Nikita 2005). These examples show how to heighten
awareness of biases in the archaeological record,
thereby strengthening the use of data. In addition to
comparisons to archaeological data, simulation results
can be compared to the output of previous simulations,
such as those presented above (Hughes et al. 2007;
Romanowska et al. 2017). Comparisons with simulations
covering the same chronology and scale would reveal
whether finer resolution data increase or decrease the
precision of the simulation's predictive accuracy. Another
intercomparison could be made with equation-based
models, comparing results across different modelling
approaches (Callegari et al. 2013).

2.3.3 The optimal method for modelling hominin
dispersals out of Africa

Given the various options for climatic and environmental
reconstructions, emulators, and simulation techniques,
the next question is which combination is the most
suitable for the research question examined here:
how and which climatic and environmental factors
influenced spatiotemporal hominin dispersal patterns
during the Early and early Middle Pleistocene? Here,
a conceptual workflow proposal presents the general
steps of connecting environmental data with a dispersal
simulation (Figure 1). Detailed tutorials of building
a simulation can be found in Romanowska (2015),
and instructions for specific software for building
reconstructions are often provided on accompanying
webpages or documents, e.g., Paleo-PGEM Series (PALEO-
PGEM-Series: a spatial time series of the global climate
over the last 5 million years (Plio-Pleistocene)) or BIOME4
(GitHub - jedokaplan/BIOME4: The BIOME4 equilibrium
global vegetation model).

As demonstrated in Figure 1, the process starts with
the best suited climatic or environmental reconstruction
to the research question, generated from the emulator
and data set. In Tables 3 and 4, the options are listed along
with considerations on the suitability and combinations
of data and emulators for answering this question.

Factors limiting the use of the presented data sets
or emulators are low spatial or temporal resolution or
the model's temporal limits. In terms of our research
question, a temporal range back to the Early Pleistocene
is indispensable. This criterion rules out HadCM3 for

<figure><img src="image_4.png" /><figcaption>**Figure 1** Flow diagram displaying the workflow for producing a raster layer applicable to a hominin dispersal simulation.</figcaption></figure>
<table><caption>**Table 3** List of climatic emulators and reconstructions addressed in this paper, advantages and limitations for investigating global hominin dispersal patterns during the Early Pleistocene and validation methods.</caption><thead><tr><td>EMULATOR</td><td>ADVANTAGES</td><td>LIMITATIONS</td><td>VALIDATION</td></tr></thead><tbody><tr><td>CESM2</td><td>High spatial resolution<br>High temporal resolution</td><td>Lack of Pleistocene studies/data</td><td>No, but comes with control simulations</td></tr><tr><td>HadCM3</td><td>High temporal resolution</td><td>Lower spatial resolution<br>Timescale limited to 800 ka</td><td>Observed ocean data</td></tr><tr><td>Paleo-PGEM</td><td>High spatial resolution<br>High temporal resolution<br>Time reach: 5 Ma</td><td>Computing limitations, accuracy of<br>represented elements/dynamics in the<br>downscaling</td><td>Observed temperature reconstructions<br>& proxy data</td></tr><tr><td>Paleo-Clim</td><td>Very high spatial resolution</td><td>Snapshot approach</td><td>Inter-model comparison</td></tr><tr><td>Oscillayers</td><td>Very high spatial resolution<br>Bioclimatic parameters<br>Time reach: 5.4 Ma</td><td>Low temporal resolution</td><td>Inter-model comparison</td></tr><tr><td>PMIP4</td><td>Very high spatial resolution<br>Detailed output</td><td>Snapshot approach<br>Requires detailed input data</td><td>Proxy reconstructions, control<br>simulations, benchmark tools</td></tr></tbody></table>

<table><caption>**Table 4** List of environmental emulators and reconstructions addressed in this paper, advantages and limitations for investigating global hominin dispersal patterns during the Early Pleistocene and validation methods.</caption><thead><tr><td>EMULATOR</td><td>ADVANTAGES</td><td>LIMITATIONS</td><td>VALIDATION</td></tr></thead><tbody><tr><td>LOVECLIM/<br>iLOVECLIM</td><td>High spatial resolution<br>High temporal resolution<br>Vegetation cover<br>Sea Ice</td><td>So far only tested back to 125 ka</td><td>Proxy data & inter-model<br>comparison</td></tr><tr><td>PRISM4</td><td>Very high spatial resolution<br>Vegetation cover<br>Land and sea ice<br>Lake element<br>Time reach: 2.6-3.6 Ma</td><td>Low temporal resolution</td><td>No, functions as a conceptual<br>model</td></tr><tr><td>BIOME4</td><td>Very high spatial resolution<br>High temporal resolution<br>Biome-divided vegetation cover</td><td>Limited to the LGM</td><td>Proxy data divided into PFT groups</td></tr><tr><td>CARAIB</td><td>Very high spatial resolution<br>Variety of output data: soil hydrology,<br>biomass, GPP, NPP, LAI & BAGs</td><td>Input data requirements</td><td>Inter-model comparisons, CO<sub>2</sub><br>observations</td></tr><tr><td>ORCHIDEE</td><td>High spatial resolution<br>Very high temporal resolution<br>PFT-class divided vegetation cover,<br>including GPP and NPP values<br>River and floodplain scheme<br>Applicable to any climate forcing</td><td>Very detailed, heavy to run<br>Based on present measurements</td><td>Observed temperature gradients,<br>basin-scale averages, topsoil<br>moisture models, forest data</td></tr></tbody></table>

climate estimates and BIOME4 as an ecological emulator. Studying environmental influences on global-scale hominin spatiotemporal dispersal and comparability to archaeological arrival points requires a high resolution. Here, the resolution of climate estimates from the CESM2, iLOVECLIM, and Paleo-PGEM, and reconstruction from ORCHIDEE are the most suitable. A combination of any of the three climatic reconstructions and the land-surface emulator ORCHIDEE would be the optimal solution for a palaeoenvironmental reconstruction applicable to the current research question. It should be noted that combining different data sets and emulators entails formatting data, which can be challenging depending on the systems in use. However, most systems offer support

desks or have online communities, which can provide help and solutions.

When working with the Early and early Middle Pleistocene, caution should be paid to glacial and interglacial periods, which comprise substantially different climatic and environmental conditions (Clark et al. 2006; Herbert 2023; Lisiecki and Raymo 2005). This is important when establishing a habitat suitability map through niche modelling. Niche modelling has been used in a number of simulation studies to indicate population presence probability based on environmental conditions (i.e., Hughes et al. 2007; Romanowska et al. 2017; Yaworsky et al. 2024), and software tools have been developed to perform these calculations, further
increasing the scope of application of the method (Elith et al. 2011; Merow, Smith and Silander Jr 2013; Muscarella et al. 2014). Since niche models provide static representations based on climatic and environmental data from a given point in time, it is essential to generate two independent habitat suitability maps; one projecting hominin presence in glacial periods and another for interglacial periods (see Figure 1). In this way, differences between glacial and interglacial periods, which may influence hominin dispersal, are highlighted.

The final step is defining how hominin populations react to the habitat suitability value of the cell. In previous modelling attempts of hominin dispersals out of Africa (Hughes et al. 2007; Mithen and Reed 2002; Nikitas and Nikita 2005; Romanowska et al. 2017), this reaction was tailored to the characteristics of the data and the research question. However, hominin reaction patterns overlapped with similar demographic aspects, such as the colonising rate, extinction rate, ecological preferences, and population growth, all of which depend on the environmental condition of the cell. Population growth is vital for the population's large dispersal movements, when accompanied by a movement strategy, especially for a continental dispersal model. Therefore, the main aim of this process is to link climatic or environmental variables to population growth, so that each cell contains a value representing the suitability of the variable-defined environment for population prosperity. This approach has been applied in other studies to estimate population dynamics (Schmidt et al. 2020, 2025; Wren and Burke 2019), and is useful for studying past populations when empirical indications of population dynamics are limited (French 2016, 2021; Riede 2019). The output of this modelling approach thus provides a raster layer with each cell containing a value of population growth, based on the climatic/environmental variable chosen at the beginning of the process.

The above section presents the conceptual steps necessary for data and emulator selection, data formation, and linking environmental variables to population dynamics. The workflow presented here is merely one possible approach to linking environmental conditions with hominin dispersal behaviour. Depending on the data, scale, methodological preferences, and disciplinary backgrounds, other workflows can be chosen (Hölzchen et al. 2016). However, a workflow as presented here integrate approaches from multiple disciplines, providing a robust research foundation for addressing various concerns and aspects.

## 3 CONCLUSION

In the face of the equifinality of the archaeological record, archaeologists endeavour to assess the material with appropriate methodological and theoretical tools. Investigations of early hominins in the Palaeolithic are challenged by a fragmented and biased archaeological

record (Abbate and Sagri 2012). However, as some scholars have pointed out (French 2016, 2021; Riede 2014; Silva et al. 2022), the archaeological record is the only empirical context for long durée processes of palaeodemography. Using palaeoenvironmental reconstructions and simulations of hominin populations, the gaps in the archaeological record can be approached in a flexible and quantitatively testable environment. However, it is important to align the research question with the scale of analysis and data availability, to ensure viable analyses of hominin-environment interactions (Faith et al. 2021). Global-scale reconstructions and simulations, as presented here, are valuable for observing large-scale patterns and developments in populations across generations (Hölzchen et al. 2016). This is the focus of the ERC Lateurope project (2023–2027), which investigates the different factors influencing the later occupation of Western Europe, in comparison to eastern Eurasia, during the Early and early Middle Pleistocene. Part of the investigation builds on a palaeoenvironmental reconstruction of Eurasia and Africa to test hominin dispersal scenarios. For this purpose, different options of climatic and environmental emulators, reconstructions, and simulation techniques are considered.

As direct observations are inaccessible, model-generated scenarios are indispensable tools. The precision and time ranges of models have increased in recent years as a result of improved data and computational techniques. It is thus possible to analyse the environmental drivers of the first hominin dispersals in the Early Pleistocene, as shown by Mithen and Reed (2002), Nikitas and Nikita (2005), Hughes et al (2007), and Romanowska et al. (2017). The Pleistocene comprises radical climatic changes, and is thus interesting for understanding the impact of environmental influences. Presently, humanity in the Anthropocene also faces substantial climatic changes. Knowledge of past climatic and environmental dynamics is imperative to enhance our understanding of present and future conditions (Jackson 2007; Tierney et al. 2020), and to evaluate how they influence human societies (Jackson, Dugmore and Riede 2017; Riede 2018). As of now, deep-time insights into human responses to environmental changes are of the utmost relevance. Archaeology plays a key role in this matter, as it analyses data on past societies, enriched by theoretical and methodological contributions from neighbouring disciplines. Modelling does not hold all the answers to questions regarding past dispersal dynamics, and environmental reconstructions should not be misconstrued as true representations of past environments. However, given the limitations of other methods' access to detailed global environmental information, computational models are promising tools, if they are appropriately calibrated and carefully validated. A growing understanding of modelling by archaeologists, open access to codes and software, and communication of methodological approaches to non-modellers all help to 'unblackbox' this method and increase its applicability to archaeology.
## ADDITIONAL FILE

The additional file for this article can be found as follows:

* **Appendix A.** Literature search strategy. DOI: https://doi.org/10.5334/jcaa.212.s1

## FUNDING INFORMATION

This project is supported by the ERC Adv. Grant LATEurope, funding from the European Research Council (ERC, Horizon Europe), with grant agreement ID: 101052653.

## COMPETING INTERESTS

One of the authors, Mehdi Saqalli, is a member of the Editorial Board at the *Journal of Computer Applications in Archaeology*. The author has not been involved in the peer-review process or the decision-making for accepting this manuscript.

## AUTHOR CONTRIBUTIONS

The first author reviewed the models and wrote the text. The co-authors provided enriching discussions and important feedback for the final outcome of the paper.

## AUTHOR AFFILIATIONS

**Kamilla L. Lomborg** [ID] orcid.org/0009-0001-0667-2997

CNRS UMR 7194 HNHP, Muséum National d'Histoire Naturelle, Institut de Paléontologie Humaine, FR

**Carolina Cucart-Mora** [ID] orcid.org/0000-0001-7716-292X

CNRS UMR 7194 HNHP, Muséum National d'Histoire Naturelle, Institut de Paléontologie Humaine, FR

**Jan-Olaf Reschke** [ID] orcid.org/0000-0003-0526-3267

CNRS UMR 7194 HNHP, Muséum National d'Histoire Naturelle, Institut de Paléontologie Humaine, FR; ROCEEH Research Center, Senckenberg Research Institute, DE

**Christine Hertler** [ID] orcid.org/0000-0002-8252-9674

ROCEEH Research Center, Senckenberg Research Institute, DE

**Matt Grove** [ID] orcid.org/0000-0002-2293-8732

Department of Archaeology, Classics and Egyptology, University of Liverpool, UK

**Benoit Gaudou** [ID] orcid.org/0000-0002-9005-3004

Institut de Recherche en Informatique de Toulouse, Université Toulouse 1 Capitol, FR

**Mehdi Saqalli** [ID] orcid.org/0000-0001-5405-466X

CNRS UMR 5602 GEODE Géographie de l'Environnement, Maison de la Recherche, Université Toulouse 2 Jean Jaurès, FR

**Marie-Hélène Moncel** [ID] orcid.org/0000-0002-0398-2316

CNRS UMR 7194 HNHP, Muséum National d'Histoire Naturelle, Institut de Paléontologie Humaine, FR

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**TO CITE THIS ARTICLE:**

Lomborg, KL, Cucart-Mora, C, Reschke, J-O, Hertler, C, Grove, M, Gaudou, B, Saqalli, M, Moncel, M-H. 2026. Changing Movements in a Changing World: Modelling Early Pleistocene and Early Middle Pleistocene Climatic and Ecological Environments and Influences on Hominin Dispersal in Eurasia. *Journal of Computer Applications in Archaeology*, 9(1): 66-88. DOI: https://doi.org/10.5334/jcaa.212

Submitted: 05 March 2025

Accepted: 05 September 2025

Published: 27 February 2026

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*Journal of Computer Applications in Archaeology* is a peer-reviewed open access journal published by Ubiquity Press.