# Reconstructing the palaeoclimate of the Cradle of
# Humankind in South Africa over the last 3.5 million
# years using machine learning and mammalian fossil
# assemblages

Pierre Linchamps, Raphaël Hanon, Emmanuelle Stoetzel, Christine Steininger, Jean-Tristan Brandenburg, Bernhard Zipfel, Sandrine Prat, Dominic Stratford, Raphaël Cornette, Pierre Latouche

► To cite this version:

Pierre Linchamps, Raphaël Hanon, Emmanuelle Stoetzel, Christine Steininger, Jean-Tristan Brandenburg, et al.. Reconstructing the palaeoclimate of the Cradle of Humankind in South Africa over the last 3.5 million years using machine learning and mammalian fossil assemblages. Quaternary Science Reviews, 2025, 370, pp.109655. 10.1016/j.quascirev.2025.109655. hal-05347123

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3

4 **Reconstructing the palaeoclimate of the Cradle of Humankind in South Africa**
5 over the last 3.5 million years using machine learning and mammalian fossil
6 assemblages

7

8 Pierre Linchamps<sup>a,b,c,*</sup>, Raphaël Hanon<sup>c,d,e</sup>, Emmanuelle Stoetzel<sup>c</sup>, Christine Steininger<sup>f</sup>, Jean-Tristan

9 Brandenburg<sup>g,h</sup>, Bernhard Zipfel<sup>e</sup>, Sandrine Prat<sup>c</sup>, Dominic Stratford<sup>a,i</sup>, Raphaël Cornette<sup>j</sup>, Pierre
10 Latouche<sup>k,l</sup>

11

12 <sup>a</sup> School of Geography, Archaeology and Environmental Studies (GAES), University of the Witwatersrand,
13 Johannesburg WITS, Private Bag 3, 2050, South Africa

14 <sup>b</sup> GENUS, University of the Witwatersrand, Johannesburg WITS, Private Bag 3, 2050, South Africa

15 <sup>c</sup> NHNP UMR 7194, CNRS, Mus'eum National d'Histoire Naturelle, UPVD, Paris, France

16 <sup>d</sup> PACEA UMR 5199, Universit'e de Bordeaux, France

17 <sup>e</sup> Evolutionary Studies Institute (ESI), University of the Witwatersrand, Johannesburg WITS, 2050, South Africa

18 <sup>f</sup> Machine Intelligence Neural Discovery (MIND) Institute, University of the Witwatersrand, Johannesburg WITS,
19 2050, South Africa

20 <sup>g</sup> Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand,
21 Johannesburg WITS, 2050, South Africa

22 <sup>h</sup> Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand,
23 Johannesburg WITS, 2050, South Africa

24 <sup>i</sup> Department of Anthropology, Stony Brook University, Stony Brook, NY, 11794, USA

25 <sup>j</sup> ISYEB UMR 7205, CNRS, Mus'eum National d'Histoire Naturelle, UPMC, EPHE, Universit'e des Antilles, Paris,
26 France

27 <sup>k</sup> LMBP UMR 6620, Universit'e Clermont Auvergne, CNRS, Aubi'ere, France

28 <sup>l</sup> Institut Universitaire de France (IUF), Paris, France

29

30
31 **Abstract**

Hypotheses linking climate change to hominin evolution are based on the observation that many events of speciation, extinction, and morphological and behavioural evolution coincided with periods of increased climatic variability and habitat instability. Fossil fauna recovered from palaeoanthropological sites can be used to reconstruct past climatic and environmental conditions associated with hominin assemblages and to explore correlative changes through time. However, temporal correlations between global climate change patterns and local environmental proxies from hominin-bearing deposits are not always verifiable. This study explores the use of machine learning algorithms for faunal-based palaeoenvironmental reconstructions in the Cradle of Humankind (CoH) over the past 3.5 million years.

We develop a new method based on random forest models using the presence/absence data of rodents and bovids, two clades commonly employed in palaeoenvironmental reconstructions, to assess the evolution of six temperature and precipitation variables in the region. In contrast to continental-scale trends, we found no evidence for a gradual transition toward more arid conditions. Instead, all reconstructions indicate lower precipitation levels accompanied by more moderate interannual precipitation variability in the past. Although interpretations differ slightly when faunal indicators are analysed separately, the combined evidence from rodents and bovids shows that habitats, despite considerable climatic variability through time, consistently occupied the ecotone between grassland and savanna, closely resembling present-day conditions in the CoH. These findings challenge hypotheses that propose a progressive shift from closed, mesic environments to open, arid landscapes during the Pliocene and Pleistocene in the region.

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52 **Keywords:** random forests, palaeoenvironments, hominin evolution, Plio-Pleistocene, Sterkfontein

53 Valley

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## 1. Introduction

The reconstruction of past climate and environments remains a central challenge in palaeontology. The range of available proxies and methods underscores their importance, while also reflecting the difficulty of producing definitive, coherent and correlating interpretations. In the context of hominin evolution, the reconstruction of past climates and landscapes aims primarily to understand the pressures that may have shaped clade diversification and contributed to the acquisition of new behaviours (e.g., Behrensmeyer, 2006; deMenocal, 2011; Maslin et al., 2014; Faith et al., 2021).

Hominins diverged from the chimpanzee lineage in Africa, where much of their evolutionary history unfolded. Their evolution is therefore closely intertwined with the continent's climatic history. The earliest hominins are found in Africa about seven million years ago (Ma), during the Late Miocene (Haile-Selassie, 2001; Senut et al., 2001; Brunet et al., 2002). This period coincides with the spread of C4-dominated ecosystems in South Africa (Dupont et al., 2013; Hoetzel et al., 2013), starting around 10 Ma in North and East Africa (Feakins et al., 2013; Uno et al., 2016; Polissar et al., 2019), and the onset of a gradual cooling trend that resulted in several phases of Arctic and Antarctic ice-sheet expansion until the Pliocene warmth at 5.3 Ma (Cook et al., 2013; Wang et al., 2024). The Pliocene was a period of global warmth, marked by atmospheric CO2 concentrations comparable to modern levels (~400 ppm), significantly higher global temperatures (Haywood et al., 2020), and generally wetter conditions across most continents (Salzmann et al., 2011). The interval is divided into two stages: the Zanclean (5.3–3.6 Ma), marked by warm and climatically unstable conditions reflected in sea surface temperature (SST) records (Athanasiou et al., 2017; Beltran et al., 2021), and the Piacenzian (3.6–2.58 Ma), which was generally cooler and more stable than the preceding stage, aside from two pronounced cooling events around 3.6 and 3.3 Ma (Tan et al., 2017; 2018). Climate deterioration began around 3.6 Ma and progressively intensified through the Pleistocene (2.58–0.01 Ma), driven by interacting processes such as atmospheric and oceanic reorganization (Dowsett and Caballero Gill, 2010; Etourneau et al., 2010; Trauth et al., 2021; van der Lubbe et al., 2021), tectonic activity (Cane and Molnar, 2001; Sepulchre et al., 2006; Petrick et al., 2019), and orbital forcing (Trauth et al., 2009; Nutz et al., 2017; Lepre and Quinn, 2022).

These long-term climatic and environmental changes have been proposed to coincide with major events in hominin evolution and behavioural development (e.g., Vrba, 1985; 1995; Reed, 1997; Trauth et al., 2007; deMenocal, 2011; Donges et al., 2011; Shultz and Maslin, 2013; Maslin et al., 2014; Owen et al., 2015).
2018). Some such events include the appearance and extinction of *Australopithecus* (Alemseged, 2023) and *Paranthropus* (e.g., Broom, 1938; Leakey, 1959; Arambourg and Coppens, 1968; Wood and Strait, 2004; Herries et al., 2009; Wood and Schroer, 2013), the appearance and diversification of the genus *Homo* (e.g., Leakey et al., 1964; Antón and Snodgrass, 2012; Villmoare et al., 2015; Prat, 2022), the development of Oldowan and Acheulean industries (Klein, 2000; Plummer, 2004), the earliest evidence for controlled use of fire (Brain, 1993; Berna et al., 2012; Pickering and Bunn, 2012; MacDonald et al., 2021), a significant increase in butchering marks on animal bones (Heinzelin et al., 1999; Plummer, 2004; Domínguez-Rodrigo et al., 2005; Pobiner, 2020; Hanon et al., 2022a; 2025), and the earliest known hominin expansion outside of Africa (e.g., Derricourt, 2005; Carbonell et al., 2008; Agustí and Lordkipanidze, 2011; Prat, 2018).

At a macroevolutionary perspective, a scale as broad as the continental level can only support oversimplified narratives. On one hand, the generalization of a pan-African climatic scenario is contradicted by the heterogeneity of climate responses recorded in archives across the continent, which reflects not only regional differences in Earth's climate system (Trauth et al., 2021) but also inter-proxy functional heterogeneity (Pfalz et al., 2021). On the other hand, the reconstruction of hominin evolutionary history is limited by the spatially biased availability of exposed fossil deposits (Hopley and Maxwell, 2022; Barr and Wood, 2024). Robust tests of hypotheses concerning causal links between evolutionary and climatic change are best conducted with proxy data that match the spatiotemporal scale of the fossils under study, i.e. data derived directly from dated stratigraphic units containing hominin remains

In this study, we present palaeoclimate reconstructions based on fossil faunas from a series of prolific hominin-bearing sites of the Cradle of Humankind (CoH) in Gauteng Province, South Africa. Within these karstic cave infills, fossil fauna provides the most reliable palaeoenvironmental proxy, as key terrestrial palaeovegetation indicators are absent (Carrión and Scott, 1999). The CoH assemblages span the last 4 million years, coinciding with several major events in hominin evolution, and thus offer a unique opportunity to contrast phenotypic (morphology and behaviour) and climatic data across an extended timescale at fine spatial resolution.
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## 1.1. The climate change record of the Cradle of Humankind and South Africa

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The CoH hominin-bearing fossil deposits in the Blaaubank Valley, Gauteng Province, South Africa, provide nearly optimal conditions to investigate these questions: the fossil record has yielded an exceptionally rich collection of faunal and palaeoanthropological remains (representing more than 30% of Africa's pre-Homo sapiens hominin fossils) spanning the last 4 Ma across a confined area of approximately 10 × 15 km (Fig. 1). The relatively small area suggests that climatic changes should be simultaneous over the CoH, which enables meaningful diachronic palaeoenvironmental comparisons.

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Furthermore, environmental changes was likely driven primarily by climatic fluctuations rather than tectonic uplift (Yang et al., 2021), given the region's relative tectonic stability since the Neogene and throughout the Pliocene-Pleistocene (Erlanger et al., 2012; Yang et al., 2021).

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Despite these opportunities, the geomorphological and taphonomic characteristics of the CoH karst systems present significant challenges to the use and interpretability of both the palaeoanthropological and palaeoenvironmental records.

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First, most fossil assemblages are poorly stratigraphically and temporally constrained (Pickering et al., 2019; Pickering & Edwards, 2021), hampering our ability to establish a direct and precise chronology for the earliest hominin fossils and faunal assemblages. Second, key palaeoclimate proxies such as soil carbonates and fossil vegetation (including macrobotanical remains, pollens, spores, phytoliths, carbon isotope data, etc.) are unavailable or poorly preserved in calcareous sites (Scott and Bonnefille, 1986; Carrión and Scott, 1999; Bamford et al., 2010; 2016), which limits our understanding of environmental dynamics in the CoH region.

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In fact, continuous records of Pliocene-Pleistocene environmental change in southern Africa remain sparse despite the region's significance for hominin evolution. The closest available sequences come from two marine core sites, MD96-2048 spanning the last 2.14 Ma (Caley et al., 2018), and International Ocean Discovery Program (IODP) Expedition 361 Site U1478 spanning the last 4 Ma (Koutsodendris et al., 2021), both located offshore of the Limpopo River mouth in the Mozambique Channel, approximately 650 km east of the CoH. Pollen data from Site U1478 core document a trend toward cooler, drier, and more open ecosystems between 4 and 2 Ma (Taylor et al., 2021) but with a lower magnitude than the North and East African records. Vegetation during the mid-Pliocene Warm Period (~3.3–3.0 Ma) may have been more forested than present, corroborating fossil wood evidence from Member 4 of Sterkfontein (Bamford, 1999). After 2 Ma, the MD96-2048 record reveals substantial

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variability in vegetation (Castañeda et al., 2016; Caley et al., 2018), driven not only by hydroclimatic changes but also by fluctuations in sea surface temperatures (SSTs) and atmospheric CO₂ levels (Dupont et al., 2019). This complexity poses challenges for interpreting vegetation proxies such as pollen and plant leaf wax carbon isotopes in marine sediments. While no clear consensus emerges among studies, all suggest that southern African environments were relatively stable during this time interval compared to those of tropical Africa (Castañeda et al., 2016).

Using the TetraEther indeX of 86 carbon atoms (TEX₈₆) of marine archaeal lipids to infer Sea Surface Temperatures (SSTs), Taylor et al. (2021) evidence a trend toward progressively cooler conditions between 4 and 1.8 Ma with some events of temperature increases. Five successive trends are highlighted from this study: (Trend 1) between 4 and 3.7 Ma, SSTs average 29.7°C and arid conditions prevail; (Trend 2) between 3.7 and 3.2 Ma, SSTs cooled by approximately 3.2°C Ma, though the climate remained arid; (Trend 3) between 3.2 and 2.9 Ma, conditions became warmer, with SSTs around 2°C higher than present levels of ~28°C; (Trend 4) between 2.9 and 2.3 Ma, long-term SSTs decrease from 29°C to 25.7°C; (Trend 5) between 2.3 and 1.8 Ma, SSTs increased.

Caley et al. (2024) produced a hydroclimate stack for the Limpopo catchment covering the last 4 Ma by combining two datasets: (i) records from the past 2 Ma (Caley et al., 2018), which integrate multiple proxies including plant-wax δ¹³C and δD, benthic foraminiferal δ¹⁸O, and Fe/Ca ratios; and (ii) records from 4–2 Ma, based on terrigenous input (Ti/Ca; Koutsodendris et al., 2021) and δD₃WAX and TEX₈₆ (Taylor et al., 2021). The stack highlights five successive phases of hydroclimate variability: (Phase 1) between 4 and 2.51 Ma, southern African climate is rather humid; (Phase 2) between 2.51 and 1.57 Ma, rather arid; (Phase 3) between 1.57 and 0.95 Ma, new humid period; (Phase 4) between 950 ka and 400 ka, new phase of aridity; (Phase 5) between 400 ka and the present day, tendency toward slightly more humid conditions.

Today, South Africa experiences both subtropical and temperate climate conditions, moderated by the surrounding oceans along its west, south, and east coasts, as well as the elevation of the interior escarpment. The country has distinct summer and winter rainfall regions, with most precipitation occurring over the interior during the austral summer. Climatic zones range from desert and semi-desert in the drier northwest to sub-humid conditions along the eastern coast, with a Mediterranean climate in the southwest and a cool, wet climate at high elevations in the eastern Drakensberg. Overall, South Africa receives relatively low precipitation, with about half of the country (primarily in the western and
central regions) receiving less than 500 mm of annual rainfall (Fig. 2). The driest areas include the Namib and Kalahari Deserts in the northwest, as well as parts of the Karoo in the interior. In contrast, the eastern regions, particularly along the Drakensberg Mountains, KwaZulu-Natal, and the Lowveld, receive significantly higher rainfall, often exceeding 1,000 mm annually.

This pronounced climatic heterogeneity across the country suggests that, despite geographic proximity, marine sedimentary records from the southeastern coast may not reliably reflect local environmental conditions in the CoH. The latter is situated at the intersection of two climate types defined by the Köppen-Geiger classification system (Peel et al., 2007): a monsoon-influenced humid subtropical climate (Cwa) and a subtropical highland or temperate oceanic climate with dry winters (Cwb). The current landscape is predominantly grassveld, bordered to the north by a bushveld zone that transitions into savannah. Marked by high altitude and pronounced diurnal temperature variations in winter, the CoH may have followed environmental trajectories distinct from those captured in regional marine cores. This spatiotemporal resolution mismatch must therefore be carefully accounted for when reconstructing past climates relevant to hominin evolution, reinforcing the need to prioritize local proxy data wherever possible.

Some authors have used speleothem dating in the CoH caves to correlate phases of enhanced speleothem growth with wetter conditions outside the cave, linked to periods of higher effective precipitation (Pickering et al., 2007; 2019; Herries et al., 2019; Edwards et al., 2020); by contrast, the faunal assemblages, accumulated during the episodes of clastic sedimentation, are thought to reflect drier climate conditions. However, this interpretation is contested by several studies showing that fauna reflects a wide variety of habitats, from open grassland environments to closed woodland (e.g., de Ruiter et al., 2008; de Ruiter, 2022; Hanon et al., 2022b; Hanon and Steininger, 2024), indicative of fluctuating precipitation during clastic sedimentation.

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<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 1.** A, Location of the Cradle of Humankind (CoH) in South Africa, with important fossil sites marked by white dots. B, Timescale of the last four million years showing the published estimated ages in million years ago (Ma) of the faunal fossil assemblages used in this study.</figcaption></figure>

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<figure><img src="image_4.png" /><figcaption>**Figure 2.** Maps of South Africa illustrating temperature (in °C) and precipitation (in mm) for the period 1970-2000, and climatic similarity based on the six variables. A RGB colour space was used to represent the climatic (dis)similarity, which corresponds to the scores of the first three principal components (PC) of a PC analysis performed on the six variables. AMT: annual mean temperature; MTCM: mean temperature of the coldest month; MTWM: mean temperature of the warmest month; AP: annual precipitation; PDM: precipitation of the direst month; PWM: precipitation of the wettest month. Climate data were obtained from WorldClim 2 (Fick and Hijmans, 2017). The Cradle of Humankind (CoH) and Makapansgat hominin-bearing localities are indicated by white circles.</figcaption></figure>

**1.2. Faunal-based palaeoenvironmental reconstructions**

Many approaches have been developed to study climates and environments using the mammalian fossil record, with extensive literature describing the analytical techniques, assumptions, and limitations (e.g., Behrensmeyer, 1992; Bobe et al., 2007; Bradley, 2015; Faith & Lyman, 2019; Lyman, 2017; Reed, 2013; Reed et al., 2013). Faith and Lyman (2019) delineate between methods based on the presence/absence of taxa, methods based on taxonomic abundance, taxon-free methods (i.e. methods that do not rely on taxonomic categories such as community structure analyses, ecomorphology and ecometrics, and palaeodietary reconstruction), methods based on taxonomic diversity (richness, evenness, and heterogeneity), and methods based on size clines and ecogeographic rules (e.g. the Bergmann's rule); all of which have found recent application in interpreting the abundant faunal record of the CoH (e.g., over the last two years: Linchamps et al., 2023b; Foister et al., 2024; Hanon and Steininger, 2024; Linchamps et al., 2025b; 2025a; Lüdecke et al., 2025; Sambo et al., 2025).

Among these methods, those focusing on taxonomic presences and absences provide the most straightforward interpretations and are the most widely used. This approach, grounded in ecological
niche theory and the principle of actualism (Belyea, 2007; Lyman, 2017), interprets changes in faunal taxonomic composition as indicators of shifts in climatic and environmental conditions. The reliability of these methods depends on the accurate identification of taxa, which can be challenging for the Plio-Pleistocene faunas of the CoH, where many taxonomic classifications require revision (Avery, 1998; Hanon and Steininger, 2024; Malherbe et al., 2025).

Most faunal studies in the CoH have provided qualitative interpretations of past environments (focusing on aspects such as vegetation type and structure, landscape openness, water availability, etc.). In contrast, quantitative reconstructions remain rare, with a notable exception being the work of Avery (2001) based on micromammal assemblages. A frequently suggested interpretation for palaeoenvironmental evolution in the area is that of a progressive shift from closed, mesic environments to increasingly open, arid landscapes over time, which has been inferred by isotopic (Luyt and Lee-Thorp, 2003; Hopley et al., 2006), palaeobotanic (Cadman and Rayner, 1989; Bamford, 1999) and faunal evidence (e.g., Vrba, 1975; Reed, 1997; Hanon et al., 2019; Pavia et al., 2022; Sewell et al., 2022; Hanon and Steininger, 2024; Linchamps et al., 2025b). However, this scenario has been challenged by several studies emphasizing instead the environmental heterogeneity inferred from faunal assemblages (Vrba, 1974; de Ruiter et al., 2008; de Ruiter, 2022).

While qualitative (categorical) reconstructions offer evocative insights into past environments, quantitative approaches are increasingly preferred due to their comparability across models and proxies as well as their capacity to reflect continuous variation. Though quantitative data can be converted into discrete environmental categories (e.g., arid, semi-arid, humid), the reverse is challenging: categorisation reduces continuous information into simplified bins, thereby lowering resolution and potentially obscuring finer environmental nuances.

Several limitations have hindered the development of quantitative approaches for the CoH assemblages. First, from the standpoint of the ecological assumptions underlying these reconstructions, a primary challenge lies in the complexity of ecological dynamics, which are influenced by the intricate interaction of geographic and abiotic factors, biotic interactions, and stochastic processes (MacArthur, 1984; Gaston, 2003; Chase and Leibold, 2009). These interactions vary across time and ecosystems, leading to highly context-dependent relationships between species and their environments, a challenge that an actualistic framework struggles to address. Second, from a mechanistic perspective, the complexity of these systems implies nonlinear interactions between species and environmental
variables, necessitating statistically advanced models to accurately account for them. However, the
environmental and species data used to train these models often exhibit strong dependence structures
such as spatial autocorrelation (Legendre, 1993; Telford and Birks, 2005), which increase the risk of
model overfitting and limits a model's ability to generalize effectively to past time periods. In this regard,
machine learning techniques, with their capacity to handle complex structures and nonlinear
interactions, offer a promising solution.

The aim of this study is to reconstruct climate evolution from several published faunal lists compiled
from the numerous fossil assemblages of the CoH. The approach builds on the methodology developed
by Linchamps et al. (2023a), which uses machine learning to generate quantitative climatic
reconstructions based on the structure of mammalian communities, and is here expanded to account
for preservation biases in fossil lists. We further refine the method to include rodents and bovids (either
separately or combined), two clades that have historically received significant attention in CoH research
due to their palaeoenvironmental relevance. Interpreting these assemblages through a unified
framework enables us to assess whether a marked climatic trend can be detected, particularly the
frequently proposed shift toward increasing aridification, which is potentially associated with decreasing
precipitation and rising temperatures over time.

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## 2. Material and Methods

### 2.1. Machine learning for palaeoenvironmental reconstructions

Machine learning (ML) is a subset of artificial intelligence that involves training algorithms to learn
patterns from data and make predictions or decisions without being explicitly programmed. ML has
found numerous diverse applications in the palaeosciences to address tasks that were previously
challenging to compute (Bickler, 2021; Yu et al., 2024). ML algorithms are particularly well-suited for
predictive modelling, and it was anticipated that they would also find fruitful applications for the
reconstructions of past climates and environments. Several commonly used proxies have already
generated fruitful ML-based reconstructions such as pollen (e.g., Salonen et al., 2014, 2019; Sobol et
al., 2019; Sobol & Finkelstein, 2018), tree rings (Helama et al., 2009), or leaf physiognomy (Li et al.,
2016; Wei et al., 2021), but ML applications for faunal-based environmental reconstructions remain rare
288 (e.g., Spradley et al., 2019; Žliobaitė, 2019; Royer et al., 2020; Linchamps et al., 2023a; Chang et al., 2024).

290 Linchamps et al. (2023a) described the analytical methodology used in this study, which was developed
291 from neo-ecological data to capture the non-linearity of climate-species relationships while mitigating
292 the issue of spatial autocorrelation. Following this framework, faunal data (presence/absence of species
293 and higher taxonomic ranks) and associate temperature and precipitation variables from approximately
294 350,000 localities in Africa formed the study's database. Random forest models (Breiman, 2001) were
295 trained to predict each climate variable separately based on faunal community composition. A
296 geographical block validation approach was designed for model validation and selection while
297 accounting for spatial dependence in the data (Dormann et al., 2007). Independent validation using
298 faunal field inventories was used to test and validate the transferability of the models to real-world data.
299 In the analysis presented here, we build upon this methodology and adapt it to account for fossil data.
300 Despite promising potential, the use of machine learning for faunal-based palaeoenvironmental
301 reconstructions faces additional challenges, mainly related to the extent and nature of the data available
302 for training robust models. First, the formation and preservation of fossils are rare events. The scarcity
303 of the vertebrate fossil record appears incompatible with the quantity of data required to develop the
304 learning capabilities of a ML algorithm (Yu et al., 2024). Secondly, the incomplete and fragmentary
305 nature of the fossil record introduces biases in the representation of the faunal community (Kidwell and
306 Holland, 2002; Hopley and Maslin, 2010), which ML models can misinterpret. In some cases, poor
307 preservation of remains also leads to difficulties in taxonomic determination, resulting in many faunal
308 lists lacking species resolution.

309 The framework we use here addresses these issues by training on a large dataset that includes multiple
310 taxonomic ranks and faunal groups and incorporates two mechanisms to introduce false absences that
311 aim to replicate patterns of incompleteness and indeterminacy in the fossil record. It enables the use of
312 fossil fauna from the CoH to track local climate evolution over the last 3.5 million years. The predicted
313 temperature and precipitation variables can then be compared to palaeoclimatic data from the nearest
314 marine records and discussed from an environmental perspective.
315 **2.2. Training dataset**

316 **2.2.1. Neo-ecological training data**

317 The African rodent and bovid databases were assembled from two main sources, following the data
318 collection workflow in Linchamps et al. (2023a). Six climatic variables (annual mean temperature [AMT],
319 maximum temperature of warmest month [MTWM], minimum temperature of coldest month [MTCM],
320 annual precipitation [AP], precipitation of wettest month [PWM], precipitation of driest month [PDM])
321 were retrieved from Worldclim 2.1 spatial raster images at a 5-minute spatial resolution (Fick and
322 Hijmans, 2017), spanning the whole African continent. This dataset provided 357,683 grid-cell records,
323 on which the presence or absence of extant African rodents (order Rodentia) and bovids (family
324 Bovidae) was recorded using species geographical distributions. As primary consumers, both groups
325 respond to vegetation changes driven by climate variations, providing insights into past environments.
326 Their contributions are complementary, reflecting differences in abundance, ecology, and taphonomic
327 processes. While rodents may provide more local indications due to restricted dispersal capabilities
328 (Andrews, 1990; Avery, 2007), bovids have been extensively studied with respect to their physiology,
329 life history, ecology, and evolution, and their fossils are almost ubiquitous in hominin-bearing sites (Bibi,
330 2009; Hanon and Steininger, 2024). These maps were obtained from expert-based range data compiled
331 in Burgin et al. (Burgin et al., 2020) and made available through the Map of Life platform
332 (https://mapoflife.ai; Marsh et al., 2022)). For a few species whose distributions have been recently
333 revised based on molecular evidence, range maps were manually drawn using data and figures
334 provided in the relevant publications. All maps were eventually clipped to the boundaries of the African
335 continent. In total, we compiled distributional data for 464 wild rodent species and 183 wild bovid
336 species with partial or exclusive distributions within the continent. The full set of range maps used in
337 this study is available for download on Figshare (see the ‘Data availability’ section). For rodents, we
338 omitted *Apodemus sylvaticus*, *Mus musculus*, *Rattus norvegicus* and *Rattus rattus* in our dataset, as
339 these species were introduced recently into Africa; for bovids, we treated species identified by Castelló
340 et al. (2016) as *Nyala angasii*, *Strepsiceros chora*, *S. cottoni*, *S. strepsiceros*, and *S. zambeziensis* as
341 all belonging to the genus *Tragelaphus*, following most recent and consensual taxonomic updates (e.g.,
342 Hassanin et al., 2018). We also recorded presence/absence information at the scales of the genus, the
343 tribe, the subfamily, and the family, following Wilson et al. (2016, 2017) for rodent systematics and
344 Castelló et al. (2016) for bovids systematics. Using supra-specific taxa as ecological proxies builds on the principle of niche conservatism, which posits that closely related taxa tend to retain similar ecological tolerances and climatic niches across short to moderate evolutionary timescales (Wiens and Graham, 2005; Peterson, 2011). This approach allows the inclusion of extinct taxa for which direct ecological data are lacking, ensuring that they still contribute ecological information (albeit in a more approximate form) through the climatic envelope of their higher taxonomic rank.

350 We acknowledge that species distribution maps present important limitations for palaeo- and neo-macroecological models, notably, their variable, sometimes coarse resolution, and a general tendency to overestimate species richness for a given area (e.g., Hurlbert and White, 2005; Thonis and Akçakaya, 2025). Despite such issues, range-based data offer several advantages that outweigh the use of occurrence records and species lists from protected natural areas. First, the volume of data obtainable using distribution maps far exceeds that of localized observations, which is critical for training machine learning models that require extensive input data. Second, alternative sources including occurrence records and faunal lists also suffer from inherent biases, such as under-representation of specific taxonomic groups, taxonomic incompleteness, potentially outdated systematics, and uneven sampling efforts, issues especially acute for African rodents. Third, the aggregation of ecological information at the community level drawn from the individual contributions of each species helps buffer the noise introduced by overprediction at the species level. Using both bovids and rodents further enhances model stability by integrating specific ecological sensitivity. Finally, ecological information at supra-specific levels can be extracted without the risk of biases associated with taxonomically unrepresentative species lists.

### 2.2.2. Introduction of absences to simulate the incomplete fossil record

365 To simulate the fragmentary nature of the fossil record, we subsequently introduced two types of absences into this dataset, aiming to replicate the two common sources of incompleteness in palaeontological studies: identification issues and incomplete sampling. Identification issues arise when specimens cannot be identified at the species level. For instance, it is common to restrict the identification of African Pleistocene rodents to the genus level, as morphological criteria are lacking to confidently distinguish between different species (Linchamps et al., 2023b). Incomplete sampling leads to the absence of a taxon from the fossil fauna list, even though it was present in the past environment.
366
367
368
369
370
371
372
373 To replicate identification issues, we converted presence data (1) into absence data (0) for species
ranks only, while retaining original presence information at the genus and higher ranks. To replicate
incomplete sampling, we apply the same procedure but adjust the supra-specific variables as well to
account for the absence of taxa. We introduced identification incompleteness to 25% of the
observations in the initial dataset and sampling incompleteness to 25% of the remaining observations,
all while preserving the original dataset's species occurrence frequency distribution. Each community i
in our dataset, composed of a set of species $x_i$, initially possesses a specific number of species $n_i$. To
introduce incompleteness, we transformed the original species composition ($x_i, n_i$) into ($\bar{x}_i, \bar{n}_i$). Here, $n_i$
is determined by summing the presence $x_i$ of species within the community. When converting presence
data to absence data, we set a minimum number of five species to be preserved (below this number,
community palaeoecological analyses may provide too little information about the structure of past
environments) and a maximum percentage of 50% of the species to be potentially randomly removed
after accounting for incompleteness. Beyond these thresholds, the ecological analysis of a faunal
community may provide insufficient information about the environment's structure. For each community,
the number of removed species ($r_i = n_i - \bar{n}_i$) was obtained by computing $\bar{n}_i = [C_i \times n_i]$, that is the
target integer smaller than $C_i \times n_i + 1$. By performing random removal, we refrain from formulating a
rule governing the exclusion of species, despite the fact that differential preservation is known to occur
among organisms based on various factors such as size, abundance, or species lifestyle
(Behrensmeyer et al., 2000; Stoetzel et al., 2021). Given $\bar{n}_i$, the absent species were selected uniformly
at random. This uniformity helps in preserving the overall distribution of communities relative to the
original dataset (Fig. 3). Using this new dataset, which preserves both data independence and the
relative frequency distributions of the initial data, we ultimately trained and tuned models to predict the
six climatic variables (output) based on the presence/absence of rodents, bovids, or both taxonomic
groups (input).

397
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Maps of Africa showing rodent and bovid species richness in the original dataset (left) and after adding incompleteness (center). On the right, frequency histograms of the distribution of species richness in the original dataset (in green) and after the introduction of incompleteness (in purple). Each dataset consists of 357,683 observations. Silhouettes from http://phylopic.org/.</figcaption></figure>

## 2.3. Random forests and geographical block cross-validation

We used random forest algorithms (Breiman, 2001) to predict past climate conditions of several fossil sites based on their faunal list. For model choice and evaluation, we performed a 0.75/0.25 train-test split on the whole dataset (which integrates incompleteness) and held out the test set for later model evaluation. We performed 5-fold cross-validation on the training dataset to assess model performance and optimize the hyperparameters of models over grid searches. Instead of a partitioning into random folds, we applied geographical block cross-validation in order to account for spatial autocorrelation (Dormann et al., 2007; Wenger and Olden, 2012; Le Rest et al., 2014), randomly assigning multiple, non-contiguous 2.1x2.1 degrees geographical blocks to one of the five folds, following the procedure described in Linchamps et al. (2023). Variable importance was calculated as the mean decrease in residual sum of squares (Breiman, 2001). Standard deviation (SD) was calculated as the variability of predictions across all individual trees in the fitted random forest, providing an estimate of prediction uncertainty.
## 2.4. Application of the models to interpret fossil data from palaeoanthropological sites

We compiled a list of all identifiable bovid and rodent fauna from hominin-bearing fossil assemblages within the CoH, encompassing the last 3.5 Ma (Table 1). The selected assemblages have yielded at least five distinct taxa of rodents or bovids. Some published lists were not retained due to uncertain stratigraphic provenance of the fossils, such as the micromammals from Kromdraai A and Sterkfontein Member 4. Concise overviews of each assemblage are provided as supplementary information SI1, which include details on the age of the deposits, stratigraphy, fossil discoveries, and dominant palaeoenvironmental reconstructions.

<table><caption>**Table 1.** South African deposits used for predicting past climatic variables, with chronological framework (mean, minimum and maximum ages in Ma), associated hominins, and number of bovids or rodent taxa published in faunal lists (the numbers in brackets indicate the number of extinct taxa). Further details on taxonomic composition and references of faunal lists are provided in Supplementary SI2.</caption><thead><tr><th>Assemblage</th><th>Abbrev.</th><th>mean</th><th>min</th><th>max</th><th>Hominins</th><th>n bov.</th><th>n rod.</th></tr></thead><tbody><tr><td>Sterkfontein post-Member 6</td><td>STP6</td><td>0.1</td><td></td><td></td><td></td><td>—</td><td>18 (0)</td></tr><tr><td>Swartkrans Member 3</td><td>SK3</td><td>1.0</td><td>1.1</td><td>0.9</td><td><i>P. robustus</i></td><td>11 (0)</td><td>20 (1)</td></tr><tr><td>Sterkfontein Member 5 East Acheulean</td><td>ST5EA</td><td>1.0</td><td></td><td></td><td><i>P. robustus</i></td><td>—</td><td>17 (5)</td></tr><tr><td>Cooper's Cave Member D</td><td>CD</td><td></td><td>1.4</td><td></td><td><i>P. robustus</i></td><td>13 (2)</td><td>19 (2)</td></tr><tr><td>Swartkrans Member 2</td><td>SK2</td><td>1.4</td><td>1.7</td><td>1.1</td><td><i>P. robustus</i>, early <i>Homo</i></td><td>13 (1)</td><td>20 (1)</td></tr><tr><td>Malapa</td><td>Mala</td><td>1.9</td><td>2.0</td><td>1.8</td><td><i>A. sediba</i></td><td>19 (0)</td><td>—</td></tr><tr><td>Gondolin 2</td><td>GD2</td><td>1.9</td><td>2.0</td><td>1.8</td><td><i>P. robustus</i></td><td>10 (1)</td><td>21 (6)</td></tr><tr><td>Drimolen Main Quarry</td><td>DMQ</td><td>1.97</td><td>2.0</td><td>1.95</td><td><i>P. robustus</i>, early <i>Homo</i></td><td>8 (2)</td><td>15 (4)</td></tr><tr><td>Swartkrans Member 1 Hanging Remnant</td><td>SK1HR</td><td>2.05</td><td>2.3</td><td>1.8</td><td><i>P. robustus</i>, early <i>Homo</i></td><td>14 (3)</td><td>—</td></tr><tr><td>Gladysvale</td><td>Glad</td><td>2.1</td><td>2.5</td><td>1.7</td><td><i>A. cf. africanus</i></td><td>27 (8)</td><td>16 (1)</td></tr><tr><td>Haasgat</td><td>Haas</td><td>2.15</td><td>2.3</td><td>2.0</td><td></td><td>11 (2)</td><td>—</td></tr><tr><td>Sterkfontein Name Chamber</td><td>NCH</td><td>2.18</td><td></td><td></td><td></td><td>—</td><td>19 (1)</td></tr><tr><td>Swartkrans Member 1 Lower Bank</td><td>SK1LB</td><td>2.2</td><td>2.3</td><td>2.1</td><td><i>P. robustus</i>, early <i>Homo</i></td><td>11 (2)</td><td>24 (1)</td></tr><tr><td>Sterkfontein Member 5 East Oldowan</td><td>ST5WO</td><td>2.2</td><td>2.4</td><td>2.0</td><td>early <i>Homo</i></td><td>—</td><td>18 (1)</td></tr><tr><td>Kromdraai Unit P</td><td>KP</td><td></td><td></td><td>2.2</td><td><i>P. robustus</i>, early <i>Homo</i></td><td>20 (6)</td><td>—</td></tr><tr><td>Bolt's Farm X Cave</td><td>BXC</td><td></td><td>2.5</td><td></td><td></td><td>11 (1)</td><td>—</td></tr><tr><td>Sterkfontein Member 4</td><td>ST4</td><td>3.5</td><td>3.7</td><td>3.3</td><td><i>A. africanus</i>/prometheus</td><td>19 (8)</td><td>—</td></tr></tbody></table>

We considered two scenarios to address open taxonomic determinations (e.g., "cf." and "aff."). In
scenario 1, which is the more inclusive approach, we lumped the "cf." and "aff." identifications with the
presumed taxon (e.g., *Steatomys cf. pratensis* > *Steatomys pratensis*). In the more conservative
scenario 2, we dropped the "cf." and "aff." identifications (e.g., *Steatomys cf. pratensis* > *Steatomys*).
In the manuscript, the predictions correspond to the average of the values from both scenarios, as the
two sets of predictions were highly correlated (Pearson's *r* = 0.99 for the three faunal groups).

Furthermore, some previously published identifications are now considered obsolete based on
systematic advancements, and specialists have made more likely reassignments of each faunal group.

For example, the genus *Tatera* is reassigned to the genus *Gerbilliscus* (Pavlinov, 2008), and the species *Tatera leucogaster* is split into *Gerbilliscus brantsii* and *G. leucogaster* as the two are sympatric and morphologically indistinguishable.

A spreadsheet compiling all fossil data used for each site, along with the corresponding references and
taxonomic lists generated under the two proposed scenarios, is available as supplementary information
in SI2.

## 2.5. Interpretation of predicted climate variables

We generate six temperature and precipitation predictions for each faunal group available at the various
fossil sites and each taxonomic treatment applied (scenario 1 and scenario 2). All results presented in
this manuscript correspond to the average of the predictions from both scenarios, but individual
predictions for each scenario are provided as supplementary information in SI3.

To track the evolution of each variable over time and detect potential climate trends while accounting
for the inherent age uncertainty of faunal records, we applied a Monte Carlo simulation approach. For
each faunal group, we used predicted temperatures and precipitations and simulated 1,000 possible
age values per fossil occurrence, randomly sampling within the provided minimum and maximum age
ranges (see Table 1). When only a mean age was available, we used it directly.

Using these simulated ages, we fit two regression models: a linear model (LM) and a generalized
additive model (GAM). The LM assumes a linear relationship between one climatic variable and the age
of the fossil deposits, while the GAM allows for non-linear trends by fitting a smoothing function. The
LM provides a unimodal representation of climate evolution from the oldest deposit to the most recent,
while the GAM may allow for capturing trends over shorter, continuous time spans. We generated
climate predictions across the entire age range and computed the mean predicted values across all
simulations for both model types. Additionally, we quantified the uncertainty in these estimates by
calculating 95% confidence intervals based on the distribution of simulated predictions. Furthermore,
for LM models, we computed correlation coefficients (r) to determine the strength and direction of the
relationship between age and climate. We did not report r for GAM models, as their non-linear nature
makes correlation coefficients more challenging to interpret meaningfully.

The results of these analyses, including the comparison between LM and GAM response curves,
provide insights into climate trends over time while partly incorporating the inherent uncertainty in fossil
age estimates. However, we acknowledge that this approach simplifies the geological and stratigraphic
complexity of the deposits, which can sometimes exhibit significant dating discrepancies and large
confidence intervals.

For the environmental interpretation of the six climatic variables, we combined the palaeopredictions
based on mixed assemblages with present-day climatic values from all localities in Southern Africa
situated south of 15°S latitude. We then performed a Principal Component Analysis (PCA) on
normalized data to examine the distribution of fossil samples within the space defined by the first two
principal components and discuss their affinities with the biomes (after Dinerstein et al., 2017) of modern
reference localities.

3. Results

3.1. Fossil rodents and bovids as palaeoclimate proxies

The performance of the models for the six climate variables using the three explanatory taxonomic
groups (bovids, rodents, and the combination of both) is shown in Table 2. The geographically cross-
validated performance metrics indicate that the six climate variables are not predicted with equal
effectiveness. Overall, precipitation is predicted more accurately than temperatures: annual
precipitation is the most accurately predicted variable based on performance metrics (mean aR² = 0.97),
whereas annual mean temperature is the least accurately predicted (0.83). There are also differences
in accuracy based on the taxonomic group used for predictions: rodents yield better predictions (mean
aR² = 0.93) than bovids (0.84), while the combined use of both groups of mammals provides the best
results (0.96).

488
<table><caption>**Table 2.** Performance metrics of random forest algorithms for temperature and precipitation predictions using bovids, rodents, and combined datasets. Abbreviations: aR² adjusted coefficient of determination, RMSE root mean square error, MAE mean absolute error.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Bovids</th><th colspan="3">Rodents</th><th colspan="3">Bovids & Rodents</th></tr><tr><th>aR<sup>2</sup></th><th>RMSE</th><th>MAE</th><th>aR<sup>2</sup></th><th>RMSE</th><th>MAE</th><th>aR<sup>2</sup></th><th>RMSE</th><th>MAE</th></tr></thead><tbody><tr><td>Annual mean temperature (AMT)</td><td>0.70</td><td>1.9</td><td>1.3</td><td>0.90</td><td>1.1</td><td>0.8</td><td><strong>0.92</strong></td><td><strong>1.0</strong></td><td><strong>0.6</strong></td></tr><tr><td>Mean temp. of the coldest month (MTCM)</td><td>0.85</td><td>2.2</td><td>1.5</td><td>0.94</td><td>1.3</td><td>0.9</td><td><strong>0.96</strong></td><td><strong>1.2</strong></td><td><strong>0.8</strong></td></tr><tr><td>Mean temp. of the warmest month (MTWM)</td><td>0.77</td><td>2.3</td><td>1.6</td><td>0.91</td><td>1.5</td><td>1.0</td><td><strong>0.93</strong></td><td><strong>1.3</strong></td><td><strong>0.9</strong></td></tr><tr><td>Annual precipitation (AP)</td><td>0.96</td><td>123</td><td>73</td><td>0.98</td><td>96</td><td>50</td><td><strong>0.98</strong></td><td><strong>82</strong></td><td><strong>43</strong></td></tr><tr><td>Precipitation of the driest month (PDM)</td><td>0.85</td><td>6</td><td>2</td><td>0.88</td><td>5</td><td>2</td><td><strong>0.93</strong></td><td><strong>4</strong></td><td><strong>1</strong></td></tr><tr><td>Precipitation of the wettest month (PWM)</td><td>0.94</td><td>27</td><td>16</td><td>0.96</td><td>21</td><td>11</td><td><strong>0.98</strong></td><td><strong>17</strong></td><td><strong>9</strong></td></tr></tbody></table>

The introduction of sampling bias helps mitigate the issue of overfitting and enhances the robustness of predictions against faunal impoverishment. As a corollary, it also allows for the assessment of performance degradation as a function of species removal (Fig. 4). The curves presented below were constructed for faunal communities from South African cells, as the behaviour of each model is conditioned by species community compositions, which are spatially constrained. Overall, we can observe that temperature variables, although less well predicted when the whole test set is taken into account, maintain more stable performance than precipitation variables during random species removal. Predictions based on bovids appear to be the most sensitive to species removal: performance estimates fall below an aR² = 0.5 threshold for all variables except MTCM when seven species are removed. Rodent-based models are less sensitive to species depletion; their performance remains relatively stable until seven or eight species are removed, except for PDM prediction, which deteriorates sooner. As expected, the combination of rodents and bovids yields the most reliable predictions in impoverished or undersampled assemblages. In South Africa, for mixed assemblages, the models tolerate up to 10 missing species before the aR² falls below 0.8.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Response curves comparing the scores of adjusted coefficients of determination (aR²) for South African faunal communities in response to the removal of species for each of the six climate variables, according to the taxonomic group used for predictions (left: bovids; middle: rodents; right: rodents + bovids). Darker dashed line: aR² = 0.9; lighter dashed line: aR² = 0.8. The beige bar plots represent the frequency of observations (training + test sets) for the South African communities (top, total of 25,672 observations) and for the African continent as a whole (bottom, total of 360,329 observations).</figcaption></figure>

**3.2. Palaeoclimate reconstructions of the hominin-bearing fossil localities at the Cradle of Humankind**

Palaeotemperatures (AMT, MTCM, and MTWM) and palaeoprecipitation regimes (AP, PDM, PWM) were predicted for multiple CoH sites based on fossil assemblages of bovids, rodents, and combined faunal datasets (Table 3). Fig. 5 shows predictions grouped by the hominins associated with each fossil assemblage, while Fig. 6 shows the evolution of climatic conditions over time for each indicator taxa. Generally, bovids exhibit higher temperatures compared to rodents at sites where both taxa coexist. For instance, the mean predicted AMT is 19.2 ± 2.6°C for bovids, 15.7 ± 1.3°C for rodents, and 16.7 ± 1.5°C for mixed faunas. In contrast, the influence of taxa on precipitation predictions is less pronounced (mean AP is 624 ± 192mm for bovids, 627 ± 132mm for rodents, and 639 ± 172mm for mixed faunas). It is, however, interesting to note that despite potential discrepancies between predictions from different indicator groups, the overall trends (as shown by the slope of the linear models on Fig. 6) are generally
consistent. Predictions based on combined rodent and bovid assemblages do not always yield intermediate values between those obtained using each taxonomic group independently.

AMT shows a marked decrease with bovids, with all assemblages exhibiting warmer conditions than today, while rodents and the combined faunas do not evidence such clear trend. MTCM fluctuates widely, with almost all sites exceeding today's 1.4 °C. When rodents are considered, either alone or in combination with bovids, the predictions indicate a significantly marked decrease in winter temperatures over time. MTWM shows an overall slight decrease over time, but with considerable variability in the reconstructed temperatures. Reconstructed AP shows substantial variability across sites, with SK1 and SK3 among the driest, but indicates an overall increase in precipitation over time. PDM exhibits similarly high variability regardless of the indicator taxa, though present-day conditions appear to be the driest. Finally, reconstructed PWM suggests an overall increase in precipitation over time, though with significant differences between sites.

<table><caption>**Table 3.** Predictions of six climatic variables related to temperatures and precipitation based on bovids (B), rodents (R) and mixed faunas (M) using RF algorithm, compared with the values recorded at Sterkfontein today. AMT annual mean temperature, MTCM mean temperature of the coldest month, MTWM mean temperature of the warmest month, AP annual precipitation, PDM precipitation of the driest month, PWM precipitation of the wettest month.</caption><thead><tr><th rowspan="2">Abbrev.</th><th rowspan="2">Age</th><th colspan="3">AMT</th><th colspan="3">MTCM</th><th colspan="3">MTWM</th><th colspan="3">AP</th><th colspan="3">PDM</th><th colspan="3">PWM</th></tr><tr><th>B</th><th>R</th><th>M</th><th>B</th><th>R</th><th>M</th><th>B</th><th>R</th><th>M</th><th>B</th><th>R</th><th>M</th><th>B</th><th>R</th><th>M</th><th>B</th><th>R</th><th>M</th></tr></thead><tbody><tr><td>Today</td><td>0</td><td>16.5</td><td>16.5</td><td>16.5</td><td>1.4</td><td>1.4</td><td>1.4</td><td>26.9</td><td>26.9</td><td>26.9</td><td>715</td><td>715</td><td>715</td><td>3</td><td>3</td><td>3</td><td>133</td><td>133</td><td>133</td></tr><tr><td>STP6</td><td>0.1</td><td>-</td><td>16</td><td>-</td><td>-</td><td>1.4</td><td>-</td><td>-</td><td>28</td><td>-</td><td>-</td><td>612</td><td>-</td><td>-</td><td>24</td><td>-</td><td>-</td><td>109</td><td>-</td></tr><tr><td>SK3</td><td>1.0</td><td>18.9</td><td>16.4</td><td>17</td><td>8.9</td><td>4.2</td><td>2.8</td><td>34.7</td><td>27.5</td><td>27.5</td><td>563</td><td>625</td><td>554</td><td>11</td><td>21</td><td>10</td><td>113</td><td>105</td><td>104</td></tr><tr><td>ST5EA</td><td>1.0</td><td>-</td><td>12.5</td><td>-</td><td>-</td><td>3.7</td><td>-</td><td>-</td><td>22.6</td><td>-</td><td>-</td><td>638</td><td>-</td><td>-</td><td>26</td><td>-</td><td>-</td><td>110</td><td>-</td></tr><tr><td>CD</td><td>1.4</td><td>17.1</td><td>15.7</td><td>16</td><td>0.2</td><td>2.9</td><td>0.2</td><td>32.6</td><td>28.6</td><td>29.4</td><td>639</td><td>557</td><td>652</td><td>8</td><td>9</td><td>7</td><td>169</td><td>90</td><td>108</td></tr><tr><td>SK2</td><td>1.4</td><td>21.8</td><td>16.4</td><td>16.9</td><td>8.8</td><td>4.2</td><td>3.3</td><td>32.2</td><td>27.5</td><td>27.5</td><td>710</td><td>625</td><td>721</td><td>8</td><td>21</td><td>10</td><td>149</td><td>105</td><td>122</td></tr><tr><td>Mala</td><td>1.9</td><td>17.3</td><td>-</td><td>-</td><td>4.7</td><td>-</td><td>-</td><td>29.2</td><td>-</td><td>-</td><td>773</td><td>-</td><td>-</td><td>22</td><td>-</td><td>-</td><td>71</td><td>-</td><td>-</td></tr><tr><td>GD2</td><td>1.9</td><td>21.8</td><td>15.6</td><td>16.8</td><td>4.8</td><td>1.4</td><td>2.8</td><td>32.5</td><td>28.5</td><td>29</td><td>564</td><td>600</td><td>623</td><td>5</td><td>10</td><td>6</td><td>129</td><td>93</td><td>116</td></tr><tr><td>DMQ</td><td>1.97</td><td>21.6</td><td>15.2</td><td>15.4</td><td>7.9</td><td>6.5</td><td>4.2</td><td>30.9</td><td>26.9</td><td>27</td><td>591</td><td>635</td><td>640</td><td>3</td><td>9</td><td>9</td><td>115</td><td>110</td><td>132</td></tr><tr><td>SK1HR</td><td>2.05</td><td>17.4</td><td>-</td><td>-</td><td>1.4</td><td>-</td><td>-</td><td>35.4</td><td>-</td><td>-</td><td>617</td><td>-</td><td>-</td><td>10</td><td>-</td><td>-</td><td>51</td><td>-</td><td>-</td></tr><tr><td>Glad</td><td>1.7</td><td>17.9</td><td>17.9</td><td>18.4</td><td>5</td><td>7.4</td><td>3.8</td><td>35.3</td><td>28.7</td><td>29.9</td><td>655</td><td>771</td><td>719</td><td>14</td><td>10</td><td>10</td><td>107</td><td>185</td><td>98</td></tr><tr><td>Haas</td><td>2.15</td><td>19.5</td><td>-</td><td>-</td><td>3.3</td><td>-</td><td>-</td><td>34.7</td><td>-</td><td>-</td><td>539</td><td>-</td><td>-</td><td>22</td><td>-</td><td>-</td><td>186</td><td>-</td><td>-</td></tr><tr><td>NCH</td><td>2.18</td><td>-</td><td>16.8</td><td>-</td><td>-</td><td>5.2</td><td>-</td><td>-</td><td>28.6</td><td>-</td><td>-</td><td>607</td><td>-</td><td>-</td><td>8</td><td>-</td><td>-</td><td>104</td><td>-</td></tr></tbody></table>
<table><tr><td>SK1LB</td><td>2.2</td><td>16.7</td><td>16.6</td><td>16.8</td><td>2.8</td><td>7.3</td><td>4.1</td><td>35</td><td>27.5</td><td>27.6</td><td>650</td><td>610</td><td>570</td><td>6</td><td>21</td><td>10</td><td>40</td><td>98</td><td>95</td></tr><tr><td>ST5WO</td><td>2.2</td><td>-</td><td>13.5</td><td>-</td><td>-</td><td>4.5</td><td>-</td><td>-</td><td>27.4</td><td>-</td><td>-</td><td>622</td><td>-</td><td>-</td><td>-</td><td>26</td><td>-</td><td>-</td><td>101</td><td>-</td></tr><tr><td>KP</td><td>2.2</td><td>19.6</td><td>-</td><td>-</td><td>6.7</td><td>-</td><td>-</td><td>32.1</td><td>-</td><td>-</td><td>607</td><td>-</td><td>-</td><td>10</td><td>-</td><td>-</td><td>151</td><td>-</td><td>-</td><td>-</td></tr><tr><td>BXC</td><td>2.5</td><td>18.4</td><td>-</td><td>-</td><td>4.5</td><td>-</td><td>-</td><td>29.2</td><td>-</td><td>-</td><td>621</td><td>-</td><td>-</td><td>12</td><td>-</td><td>-</td><td>136</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ST4</td><td>3.5</td><td>22.4</td><td>-</td><td>-</td><td>3</td><td>-</td><td>-</td><td>31.3</td><td>-</td><td>-</td><td>579</td><td>-</td><td>-</td><td>8</td><td>-</td><td>-</td><td>62</td><td>-</td><td>-</td><td>-</td></tr></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Climatic predictions for six climate variables grouped according to the hominin associated with the fossil assemblages. The numbers in grey represent the number of corresponding fossil assemblages for each predictor. AMT: mean annual temperature, MTWM: mean temperature of the warmest month; MTCM: mean temperature of the coldest month; AP: annual precipitation; PDM: precipitation of the direct month; PWM: precipitation on the warmest month.</figcaption></figure>

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<figure><img src="image_8.png" /><figcaption>**Figure 6.** Evolution of the predicted temperatures in °C (AMT: mean annual temperature, MTCM: minimum temperature of the coldest month, MTWM: maximum temperature of the warmest month) and precipitation in mm (AP: annual precipitation, PDM: precipitation of the driest month, PWM: precipitation of the wettest month) over the last 3.5 million years (x-axis). Climate curves were obtained using both linear models (LM, dashed grey lines) and generalized additive models (GAM, solid black lines), with shaded areas representing 95% confidence intervals derived from Monte Carlo simulations.</figcaption></figure>
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## 3.3. Palaeoenvironmental interpretation

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Nine biomes are recognized in South Africa (Dinerstein et al., 2017), each characterized by specific temperature ranges and levels of precipitation. Using the six predicted variables for each fossil assemblage, it is therefore possible to determine the most-likely dominant biome. Fig. 7 presents the results of a PCA projecting the six predicted variables derived from CoH faunas into the climatic space of southern Africa (any location located south of 15°S latitude), enabling the comparison between the reconstructed environmental conditions of fossil assemblages with those of the region's modern biomes. PC1, which explains 44% of the variance, primarily reflects temperature gradients, with strong negative loadings for AMT and MTCM. PC2 (34.3% of the variance) is mainly associated with precipitation. The projections of scores of PC3 (12.1%) and PC4 (7.1%), which together account for a minor proportion of the total variance, are provided as supplementary information SI4.

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Each faunal indicator set generates a distinct palaeoenvironmental scenario. Bovids induce a moderately wide climatic spread, with pronounced climatic differences among some assemblages, though overall they point predominantly toward savanna environments. Rodent-based predictions, by contrast, are more tightly clustered around present-day conditions, but with some notable outliers such as ST5EA and ST5WO. Mixed faunas cluster closely around present-day conditions in the Sterkfontein Valley, at the ecotone between high-elevation grasslands and savanna. Based on mixed fauna, SK1LB and SK3 align most closely with modern conditions, whereas DMQ and Glad show more distinctive climatic signatures: DMQ points to comparatively wetter conditions, likely consistent with savanna environments, while Glad suggests a warmer and drier setting.

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Thus, whereas bovids suggest considerable variability in environmental conditions, independent of chronological sequence, the clustering of mixed faunas points instead to relative stability through time.

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As mixed faunas provide the most reliable reconstructions, they are therefore preferred for inferring long-term environmental changes in the CoH.
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Principal Component Analyses (PCA) of climate data for all locations from Southern Africa located south of 15°S latitude and the predicted climate conditions at the CoH fossil sites using bovid, rodent, and mixed faunas. Each location was coloured according to the dominant biome in which it is included, following the biome's classification from Dinerstein et al. (2017).</figcaption></figure>

# 4. Discussion

## 4.1. Faunal-based palaeoclimate reconstructions

### 4.1.1. "All models are wrong, but some are useful"

It is important to recognize that models by their very nature simplify reality and always fall short of its complexities, and one must remain aware of the limitations of palaeoclimate reconstructions (Harrison et al., 2016). Faunal-based reconstructions involve an even higher level of abstraction, as they use the responses of primary consumer communities as indirect measures of climate (Faith and Lyman, 2019), which are further influenced by various fossilization filters and biases not directly related to some environmental conditions. For instance, in rodent assemblages, both the predator(s) of the fossil assemblage (Andrews, 1990) and the sampling strategies employed for the recovery of the fossil (particularly the mesh size) (Lyman, 2012) have a strong influence on the species spectrum and relative abundance. Presence/absence methods help mitigate these effects. Other limitations are inherent to the functional modelling paradigm, which in this study correlates species distribution data to environmental predictors. Species biogeography is shaped by a combination of ecological and evolutionary processes, as well as historical and stochastic factors (e.g., Elith & Leathwick, 2009; Guisan & Thuiller, 2005), with the latter two regarded as undesirable noise in our predictive models. Although it is likely impossible to isolate their influence, we sought to minimize it by including a large number of species to counterbalance idiosyncratic trajectories that could skew the models. However,
while species presence/absence seems to be a reliable proxy for predicting present-day climate, can it
be equally justified for environments 3.5 million years ago? Our approach does not properly address
the fundamental issue of no-analog assemblages and ecosystems in palaeontology (Williams and
Jackson, 2007; Faith et al., 2019), and the more recent the fossil fauna is, the more reliable the results
tend to be. In contrast, our models are robust to potential factors that may lead to faunal
impoverishment, whether they be preservation biases, identification issues, or taxonomic
indeterminacy. Where faunal lists contain extinct taxa (i.e., taxa not represented in the neo-ecological
dataset used to train the models), we used the higher taxonomic rank as a substitute for the explanatory
variable. This approach assumes that closely related taxa generally maintain comparable ecological
preferences and climatic tolerances over short to moderate evolutionary periods. By using the broader
taxonomic group, we consider its ecological envelope to approximate that of the extinct species,
allowing us to capture meaningful ecological information even when direct ecological data are
unavailable. However, this remains partly hypothetical, as it cannot be ruled out that some species went
extinct precisely because the climatic conditions defining their niche no longer existed. Scientific
approaches such as stable isotope analysis for assessing trophic positions (e.g., Steininger, 2011;
Leichliter et al., 2017; Lüdecke et al., 2025), dental microwear and mesowear for dietary inferences
(e.g., Schubert et al., 2006; Ulbricht et al., 2015), or ecometrics/ecomorphology for habitat preferences
(e.g., Kovarovic and Andrews, 2007; Schap et al., 2024, Sambo et al., 2025) hold promise for
addressing some of these questions in the future. However, such data remain limited for most extinct
taxa, particularly rodents.

Despite these biases, this method offers several advantages over other approaches. First, the models
are grounded in a large number of training data, so the mechanistic relationship is empirically grounded
rather than intuitive, which allows a high level of repeatability. Range-map-derived data allows for the
generation of numerous occurrences across the African continent, addressing the influence of uneven
sampling intensity. The prediction of quantitative variables allows for the tracking of climate change
over time and integrates faunal data within a set of proxies that can be associated with hominins
remains. This approach also enables faunal lists from different publications to be processed using a
unified, integrative methodology, and predictions can be discussed in the light of the taxonomic
identifications. It would of course be possible to incorporate additional variables that might impact model
performance, such as accumulation type or the presence of anthropogenic occupation markers;
however, introducing these would increase the number of uncontrolled variables, challenging the actualistic paradigm that is foundational to this approach. By focusing solely on the relationship between species distribution and climate, this method minimizes extrapolation biases and reduces the potential misguidances associated with incorporating information derived from subjective interpretations.

### 4.1.2. Which taxa? How many?

There is an evocative debate in palaeontology and zooarchaeology that remains unsettled; it is the question of which taxonomic group provides the most accurate palaeoenvironmental information (Avery, 2007; Faith and Lyman, 2019). There is likely no definitive answer, as each group offers distinct and complementary insights based on its specific ecological characteristic. An integrative approach is likely the most effective, yet it is complicated by the increased specialisation of palaeontologists and zooarchaeologists who tend to focus on specific taxonomic groups. There is also the possibility that opposing trends detected across taxonomic groups may cancel each other out in a mixed model (Hurlbert, 1971; Cruz-Uribe, 1983), so it is useful to present the results of each predictor both separately and combined.

In Southern Africa, fossil bovid lists are much more numerous than rodent lists (Avery, 2019), providing a strong advantage for conducting large-scale climatic syntheses. However, our models trained on rodent data outperform those based on bovids and are also more robust to faunal impoverishment. Those outperformances can be attributed to various reasons. First, small mammals are widely considered more reliable ecological indicators than large mammals (Andrews, 1990; Falk and Semken, 1998; Yalden et al., 2001; Avery, 2007; Reed, 2007; Royer et al., 2020). The main arguments put forth are that smaller habitat home ranges, in contrast to those of larger mammals, make them highly sensitive to changes in climate and environment at a local scale. Additional factors include a reduced capacity for dispersion or migration, more precise ecological requirements, and in some contexts the presence of their remains in significant quantities, among others. Another important, data-related reason for the higher performance of models based on rodents is the greater quantity, and secondarily the higher resolution, of distribution data used in constructing the training dataset. Specifically, the rodent dataset includes 464 distribution variables, while the bovid dataset contains only 183, allowing for a more comprehensive identification of underlying patterns and interactions between faunal and climatic variables, thereby improving predictive accuracy. Although many predictor variables may lead to overfitting and affect the understandability of a model, the random forests algorithm can handle high
dimensional data effectively through feature selection and randomization techniques (Dietterich, 2000; Breiman, 2001), which makes them valuable for complex datasets. However, some issues remain regarding the identification of fossil rodents from the CoH, which require a major taxonomic revision in light of new systematic knowledge enabled by molecular phylogenies (Linchamps et al., 2025b). Some identifications will likely be revised in the coming years, especially for deposits older than 2 Ma where the systematic identification of modern species is questionable.

What is the minimum sampling required for robust palaeoenvironmental predictions? In this regard, behaviour of our models and their performances for communities with distinct species richness provide insights that can guide recommendations for the theoretical palaeoenvironmental interpretation of a fossil assemblage based on its faunal composition. Fig. 8 displays model performance metrics when predicting the six-climate variable for South African faunal communities. One must note that the interpretation of these curves is not straightforward, as multiple factors influence the prediction quality (including the frequency of each observation, the relative importance of variables, the degree of species ubiquity or endemism within South Africa, its current climatic variability, etc.). The initial threshold of five distinct taxa, which we selected as an arbitrary prerequisite, is a minimum requirement above which performance significantly improves. For bovids, temperature prediction curves show notable gains of performance with the inclusion of at least seven species; the precipitation curves offer less information regarding minimum sampling, as it rather reflects the models' difficulty in accurately predicting precipitation variables in regions of high species richness, especially along the Indian Ocean coastline, where precipitation patterns shift sharply while bovid community composition remains relatively similar. For rodents, a minimum of 10 species is needed to achieve reliable predictions for both temperature and precipitation. When both clades are considered together, a minimum of 15 species is required to obtain satisfactory predictions for temperature and precipitation with our models.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Response curves comparing the scores of adjusted coefficients of determination (aR²) for South African faunal communities in response to species richness for each of the six climate variables, according to the taxonomic group used for predictions (left: bovids; middle: rodents; right: rodents + bovids). Darker dashed line: aR² = 0.9; lighter dashed line: aR² = 0.8. The beige bar plots represent the frequency of observations (training and test sets) for the South African communities (top, totalling 25,672 observations) and for the African continent (bottom, totalling 360,329 observations).</figcaption></figure>

**4.2. Climate and environmental changes in the CoH over the last 3.5 million years**

The climatic predictions from this study can be compared to the large scale palaeoclimatic reconstructions derived from the nearby marine sequences MD96-2048 and IODP 361 U1478 (Fig. 9). It is important to first note a clear discrepancy between the temporal resolution of the CoH fossil assemblages and the highly resolved absolute timescale of the core sequences, which prevents testing any statistically significant correlation. The humid and arid phases described by Caley et al. (2024) at the regional scale do not coincide with major deviations in the precipitation values reconstructed locally in this study. Similarly, the regional increasing and decreasing trends in SSTs highlighted by Taylor et al. (2021) are not reflected in the temperature variables reconstructed in this study.

In fact, comparison of syntheses based on numerous proxies used to reconstruct climate and terrestrial environments from marine sediment cores reveals that it is difficult to identify a marked long-term directional climatic trend over the past 3.5 million years in southern Africa. Shorter cycles can be detected within this time interval, but it is sometimes challenging to reconcile results from different vegetation proxies (Maslin et al., 2012; Castañeda et al., 2016; Caley et al., 2018; Dupont et al., 2019),
715 geochemical proxies (Koutsodendris et al., 2021; Taylor et al., 2021), or geomorphological proxies
716 (Erlanger et al., 2012; Stollhofen et al., 2014; Hearty et al., 2020; Yang et al., 2021). Overall, it emerges
717 that environmental conditions remained more stable at the regional scale in southern Africa than in
718 eastern Africa (Castañeda et al., 2016), a conclusion supported by the results of our study using mixed
719 faunas.

720 In the CoH valley, speleothem formation dating has provided another line of palaeoclimatic evidence,
721 interpreted as marking periods of increased effective precipitation (Pickering et al., 2019). Following
722 this hypothesis, faunal assemblages deposited during phases of clastic sedimentation should reflect
723 drier conditions. However, our precipitation reconstructions (AP, PDM, PWM) do not support this view;
724 instead, they exhibit substantial variability across assemblages, suggesting that these fossil
725 assemblages record a wider range of environmental conditions than would be expected if they reflected
726 only dry phases. The consistently higher PDM values compared to present-day also indicate more
727 moderate interannual precipitation variability.

728 Faunal analyses in the CoH have produced numerous palaeoenvironmental reconstructions over the
729 past 80 years, yet surprisingly little quantitative palaeoclimate information. Avery's (2001)
730 reconstructions based on micromammals from Swartkrans and Sterkfontein suggested minimal climatic
731 differences between samples, with lower AP (310-550 mm) and higher MTCM (above freezing)
732 compared to present-day conditions. Our study yields the same indications, although the range of
733 predicted values in our reconstructions is considerably broader, regardless of the faunal predictor.

734 Regarding the evolution of environmental conditions in the Sterkfontein Valley, the most commonly
735 proposed scenario is a gradual transition from closed, mesic environments to increasingly open, arid
736 landscapes over time, which has been inferred by isotopic (Luyt and Lee-Thorp, 2003; Hopley et al.,
737 2006), palaeobotanic (Cadman and Rayner, 1989; Bamford, 1999) and faunal evidence (e.g., Vrba,
738 1975; Reed, 1997; Hanon et al., 2019; Pavia et al., 2022; Sewell et al., 2022; Hanon and Steininger,
739 2024; Linchamps et al., 2025b). These studies typically focus on a single site, with the inferred
740 environmental conditions then placed within a broader chronological framework through comparisons
741 with other fossil deposits. The issue with such an approach is the absence of a standardized analytical
742 framework for objectively comparing reconstructions derived from different proxies, increasing the risk
743 of selective data interpretation and confirmation bias. This also overlooks the fact that most deposits
744 have yielded varied, sometimes contradictory, environmental reconstructions depending on the fauna
745

and methodology used. A good example is the Hanging Remnant of Member 1 of Swartkrans, where
the numerous habitat reconstructions range from a closed, woodland habitat (Benefit and McCrossin,
1990) to an open, xeric environment (Grine, 1981), with a wide range of intermediate interpretations
(summarized in de Ruiter et al., 2022).

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Our temperature and precipitation reconstructions do not indicate a progressive shift toward
environmental aridification, which would be characterized by a decrease in AP (and a potential increase
in MAT); instead, we observe an opposite pattern, with an increase in AP and a reduction in MAT over
time, but the substantial variability in the outcomes precludes the identification of a statistically
significant trend.

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Several studies have challenged the notion of directional environmental shifts, instead emphasizing
persistent habitat heterogeneity throughout the temporal sequence (Vrba, 1974; de Ruiter et al., 2008;
de Ruiter, 2022). Our results are consistent with this perspective. A key outcome of this study is that
interpretations vary depending on whether bovids, rodents, or both combined are used as indicator
taxa. Although all groups register marked climatic variability, the PCA of bovid-based inferences tends
to point toward predominantly savanna environments in the past—albeit without a clear temporal
directionality—whereas the mixed-fauna results suggest that past habitats were not substantially
different from those of today. Given their higher predictive reliability, mixed faunas are taken as the
most trustworthy indicator, suggesting that local environments have remained relatively stable over the
past 3.5 Ma, with most sites situated at the ecotone between grassland and savanna habitats.

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The apparent lack of directional ecological change may reflect to some extent limitations in the
resolution of the faunal record rather than true environmental stasis. One contributing factor may be the
palimpsestic nature of CoH deposits, which produces time-averaged signals due to the mixing of fossil
material accumulated over extended periods (Hopley and Maslin, 2010). Such mixing can smooth or
mask temporal dynamics on an ecologically significant scale. Artificial time-averaging may also result
from excavation and fossil recovery methods, such as collection from dumps or poorly controlled
stratigraphic contexts, which hinder the reconstruction of precise chronostratigraphic frameworks.

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The chronology of CoH fossil faunas remains the main limitation for their use in high-resolution
palaeoclimatic syntheses, although it is expected to improve in the coming years with the advancement
and increase of dating methods for each site. The dating of sites selected in this study has a direct
influence on the behaviour of the linear and additive models used to track climate evolution over time.

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Yet, some dating methods, such as U-Pb dating, determine the age of geological formations rather than the fossil assemblages themselves, potentially leading to significant discrepancies between the age of speleothems and that of fossils within the same level (Bruxelles et al., 2014; Granger et al., 2015; Kuman et al., 2021). Additionally, the contemporaneity of large mammal and micromammal assemblages, which are accumulated by different agents, is not always demonstrated; for example, Linchamps (2023) cautioned that the micromammals from SK3, estimated to be around 1 Ma, might be older than the other fossils from this member and could actually be contemporaneous with those from SK1LB at 2.2 Ma. The chronostratigraphy of these assemblages also highlights a significant representational bias in certain time periods, including the Upper Pleistocene (0.129–0.012 Ma), the Chibanian (0.77–0.129 Ma) and the Piacenzian (3.60–2.58 Ma), while most assemblages date between 2.3 and 1.8 Ma.

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<figure><img src="image_11.png" /></figure>

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**Figure 9.** Graphical synthesis of the palaeoenvironmental data discussed in this study. From top to bottom: In yellow, stacked record of sea surface temperatures (SSTs, in °C) derived from the tetraether index (TEX86_{86}86) using marine archaeal lipids at IODP Site U1478, as calculated by Taylor et al. (2021), with five temperature-

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791 change trends highlighted in yellow. In blue, stacked record of hydroclimate variability from the Limpopo region
792 (Mozambique Channel, SW Indian Ocean), calculated by Caley et al. (2025) based on principal component 1 (PC1)
793 from records at Site MD92-2048 (Caley et al., 2018) and IODP Site U1478 (Koutsodendris et al., 2021; Taylor et
794 al., 2021), with five alternating dry and wet phases indicated by blue shaded areas. In green, Cyperaceae pollen
795 percentages from Caley et al. (2018). In red and blue, temporal evolution of PC1 and PC2 scores from the PCA of
796 reconstructed climate variables projected into the climatic space of southern Africa. At the bottom, timescale of the
797 fossil assemblages considered in this study, with uncertainty margins for the estimated dates.

798 **4.3. Climatic niches of *Paranthropus robustus* and early *Homo***

799 *Paranthropus robustus*, characterized by its heavily buttressed craniofacial morphology, notably large
800 and flat postcanine dentition, and robust mandible, has traditionally been interpreted—much like the
801 *Paranthropus* genus as a whole—as a dietary specialist (e.g., Vrba, 1988; O’Brien et al., 2023).
802 According to some researchers (Caley et al., 2018), this proposed dietary specialization may have
803 contributed to the species’ extinction during increasingly arid and seasonally variable environments that
804 prevailed around 1 Ma, in contrast to the more generalist ecological strategies observed in early *Homo*.
805 Wood and Strait (2004), utilizing a broad dataset encompassing both southern and eastern Africa and
806 spanning a temporal range from 2.9 to 1.4 million years ago, assessed eleven criteria—including dietary
807 breadth, habitat preference, and paleoecological reconstructions—and concluded that both
808 *Paranthropus* and early *Homo* were ecological generalists. Supporting this, paleoecological analyses
809 of *P. robustus* (e.g., Hanon et al., 2022a), as well as strontium isotope data, corroborate its eurytopic
810 nature. Further evidence from dental microwear and macrowear patterns, alongside stable isotope
811 analyses, indicates that *P. robustus* exhibited considerable dietary flexibility, with hard-object feeding
812 likely occurring only episodically as a potential fall-back strategy (e.g., Sponheimer et al., 2006; Lüdecke
813 et al., 2018). Both early *Homo* and *P. robustus* appear to have been dietary opportunists capable of
814 exploiting a range of paleoenvironmental contexts (Lüdecke et al., 2018). While *P. robustus* relied more
815 heavily on plant-based resources than early *Homo* (Balter et al., 2012), its diet included approximately
816 25% C4/CAM plant resources (such as tropical grasses and sedges) and 75% C3 plant resources (e.g.,
817 fruits and leaves from trees and shrubs) (Lee-Thorp and van der Merwe, 1993; Lee-Thorp et al., 1994;
818 Sponheimer et al., 2023).

819 Our climatic reconstructions do not indicate differentiated climatic niche for these two taxa. However,
820 this pattern mostly reflects their co-occurrence within fossil assemblages. For instance, with the
exception of the ST5WO assemblage, all *Homo*-bearing sites included in this study also contained remains of *P. robustus*. Consequently, the frequent co-occurrence of these taxa may lead to environmental reconstructions based on associated faunal assemblages to underestimate ecological differences between sympatric species.

Despite marked morphological differences—particularly in the masticatory apparatus—that distinguish *Paranthropus* from the earliest representatives of the genus *Homo*, other domains such as technological behaviour (Prat, 2024), habitual terrestrial bipedal locomotor behaviour (Pickering et al., 2025), manual dexterity (Prat, 2023), and dietary adaptability reveal less pronounced distinctions. These aspects are intimately connected to broader patterns of social organization, which warrant further investigation in order to better understand the divergent evolutionary trajectories of *Homo* and *Paranthropus* and the eventual extinction of *P. robustus*. Our results suggest that this extinction may have been less directly driven by climatic factors than is commonly postulated.

## 5. Conclusion

Machine learning algorithms offer significant advantages for faunal-based palaeoenvironmental reconstructions by capturing complex underlying patterns that influence species distributions in relation to specific climatic conditions. These methods have the potential to enhance the accuracy of previous reconstructions in the Cradle of Humankind in South Africa. The reconstruction of six temperature and precipitation variables over the last 3.5 Ma do not show a progressive shift toward environmental aridification such as evidenced at a broader continental scale. Instead, the data suggest a local increase in annual precipitation and a decrease in mean annual temperature over time, although the important climate variability precludes the identification of statistically significant trends.

The study challenges the assumption of a gradual environmental shift from closed mesic to open arid landscapes; instead, local environmental conditions in the CoH may have remained relatively stable over the past 3.5 million years, with most reconstructions falling at the ecotone between grassland and savannah habitats. This research also highlights the resolution limits of palaeoenvironmental reconstructions based on faunal data. Despite continuous methodological improvements in quantitative modelling, faunal-based approaches remain fundamentally dependent on the quality of the fossil record.

In the Cradle of Humankind, key challenges include the uncertain chronostratigraphic framework and resolution of the deposits, potential time-averaging effects, and the outdated taxonomy of several faunal
assemblages, which underscore the critical role of fieldwork in expanding faunal datasets and the necessity of taxonomic expertise to refine fossil identifications. These limitations are significant but are expected to be addressed in the coming years as archaeological and palaeontological research in the region advances.

854

## Data availability

All datasets used in this study are publicly available. Species distribution maps derived from expert-based range data can be accessed via the Map of Life (https://mol.org/) and the IUCN Red List (https://www.iucnredlist.org/) websites (accessed on 1 August 2025). Bioclimatic variables were obtained from WorldClim (https://www.worldclim.org/data/bioclim.html; accessed on 1 August 2025).

The R and Python code used for the analyses, along with the distribution maps generated in this study, are archived and available on Figshare at: https://doi.org/10.6084/m9 figshare.28806740.v3 (accessed on 1 August 2025).

863

## References

865

Agustí, J., Lordkipanidze, D., 2011. How “African” was the early human dispersal out of Africa? Quaternary Science Reviews, Early Human Evolution in the Western Palaearctic: Ecological Scenarios 30, 1338–1342. doi:10.1016/j.quascirev.2010.04.012

Alemseged, Z., 2023. Reappraising the palaeobiology of Australopithecus. Nature 617, 45–54. doi:10.1038/s41586-023-05957-1

Andrews, P., 1990. Owls, Caves and Fossils: Predation, Preservation and Accumulation of Small Mammal Bones in Caves, with an Analysis of the Pleistocene Cave Faunas From Westbury-Sub-Mendip, Somerset, U.K. University of Chicago Press.

Antón, S., Snodgrass, J., 2012. Origins and Evolution of Genus Homo: New Perspectives. Current Anthropology 53. doi:10.1086/667692

Arambourg, C., Coppens, Y., 1968. Decouverte D'un Australophecien Nouveau Dans Les Gisements De Lomo (Ethiopie). South African Journal of Science 64, 58. doi:10.10520/AJA00382353_4440

Athanasiou, M., Bouloubassi, I., Gogou, A., Klein, V., Dimiza, M.D., Parinos, C., Skampa, E., Triantaphyllou, M.V., 2017. Sea surface temperatures and environmental conditions during the “warm Pliocene” interval (~ 4.1–3.2 Ma) in the Eastern Mediterranean (Cyprus). Global and Planetary Change 150, 46–57. doi:10.1016/j.gloplacha.2017.01.008
886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

Avery, D.M., 1998. An assessment of the Lower Pleistocene micromammalian fauna from Swartkrans members 1–3, Gauteng, South Africa. Geobios 31, 393–414. doi:10.1016/S0016-6995(98)80022-3

Avery, D.M., 2001. The Plio-Pleistocene vegetation and climate of Sterkfontein and Swartkrans, South Africa, based on micromammals. Journal of Human Evolution 41, 113–132.

Avery, D.M., 2007. Micromammals as palaeoenvironmental indicators of the southern African Quaternary. Transactions of the Royal Society of South Africa 62, 17–23. doi:10.1080/00359190709519193

Avery, D.M., 2019. A Fossil History of Southern African Land Mammals. doi:10.1017/9781108647243

Bamford, M., 1999. Pliocene Fossil Woods from an Early Hominid Cave Deposit, Sterkfontein, South Africa. South African Journal of Science 95, 231–237.

Bamford, M.K., Neumann, F.H., Pereira, L.M., Scott, L., Dirks, P.H.G.M., Berger, L.R., 2010. Botanical remains from a coprolite from the Pleistocene hominin site of Malapa, Sterkfontein Valley, South Africa.

Bamford, M.K., Neumann, F.H., Scott, L., 2016. Pollen, charcoal and plant macrofossil evidence of Neogene and Quaternary environments in southern Africa. In: Knight, J., Grab, S.W. (Eds.), Quaternary Environmental Change in Southern Africa: Physical and Human Dimensions. Cambridge University Press, Cambridge, pp. 306–323. doi:10.1017/CBO9781107295483.019

Barr, W.A., Wood, B., 2024. Spatial sampling bias influences our understanding of early hominin evolution in eastern Africa. Nature Ecology & Evolution 8, 2113–2120. doi:10.1038/s41559-024-02522-5

Behrensmeyer, A.K., 1992. Terrestrial Ecosystems Through Time: Evolutionary Paleoecology of Terrestrial Plants and Animals. University of Chicago Press.

Behrensmeyer, A.K., 2006. Climate Change and Human Evolution. Science 311, 476–478.

Behrensmeyer, A.K., Kidwell, S.M., Gastaldo, R.A., 2000. Taphonomy and Paleobiology. Paleobiology 26, 103–147.

Beltran, C., Sicre, M.-A., Ohneiser, C., Sainz, M., 2021. A composite Pliocene record of sea surface temperature in the central Mediterranean (Capo Rossello composite section – South Sicily). Sedimentary Geology 420, 105921. doi:10.1016/j.sedgeo.2021.105921

Belyea, L.R., 2007. Revealing the Emperor's new clothes: niche-based palaeoenvironmental reconstruction in the light of recent ecological theory. The Holocene 17, 683–688. doi:10.1177/0959683607079002

Benefit, B.R., McCrossin, M.L., 1990. Diet, species diversity and distribution of African fossil baboons. Kroeber Anthropological Society Papers 71, 77–93.

Berna, F., Goldberg, P., Horwitz, L., Brink, J., Holt, S., Bamford, M., Chazan, M., 2012. Microstratigraphic evidence of in situ fire in the Acheulean strata of Wonderwerk Cave, Northern Cape province, South Africa. Proceedings of the National Academy of Sciences of the United States of America 109, E1215-20. doi:10.1073/pnas.1117620109

Bibi, F., 2009. The fossil record and evolution of Bovidae: state of the field. Palaeontologia Electronica 12, 1–11.

Bickler, S.H., 2021. Machine Learning Arrives in Archaeology. Advances in Archaeological Practice 9, 186–191. doi:10.1017/aap.2021.6
934 Bobe, R., Alemseged, Z., Behrensmeyer, A.K., 2007. Approaches to the analysis of
935 faunal change during the East African Pliocene. In: Bobe, R., Alemseged, Z.,
936 Behrensmeyer, A.K. (Eds.), Hominin Environments in the East African
937 Pliocene: An Assessment of the Faunal Evidence. Springer Netherlands,
938 Dordrecht, pp. 1-24. doi:10.1007/978-1-4020-3098-7_1

939 Bradley, R.S., 2015. Paleoclimatology: Reconstructing Climates of the Quaternary,
940 third edition, Elsevier. ed.

941 Brain, C.K., 1993. Swartkrans: a cave's chronicle of early man. Transvaal Museum
942 Monograph, Pretoria.

943 Breiman, L., 2001. Random Forests. Machine Learning 45, 5-32.
944 doi:10.1023/A:1010933404324

945 Broom, R., 1938. The Pleistocene Anthropoid Apes of South Africa. Nature 142, 377-
946 379. doi:10.1038/142377a0

947 Brunet, M., Guy, F., Pilbeam, D., Mackaye, H.T., Likius, A., Ahounta, D., Beauvilain,
948 A., Blondel, C., Bocherens, H., Boisserie, J.-R., De Bonis, L., Coppens, Y.,
949 Dejax, J., Denys, C., Duringer, P., Eisenmann, V., Fanone, G., Fronty, P.,
950 Geraads, D., Lehmann, T., Lihoreau, F., Louchart, A., Mahamat, A., Merceron,
951 G., Mouchelin, G., Otero, O., Campomanes, P.P., De Leon, M.P., Rage, J.-C.,
952 Sapanet, M., Schuster, M., Sudre, J., Tassy, P., Valentin, X., Vignaud, P., Viriot,
953 L., Zazzo, A., Zollikofer, C., 2002. A new hominid from the Upper Miocene of
954 Chad, Central Africa. Nature 418, 145-151. doi:10.1038/nature00879

955 Bruxelles, L., Clarke, R.J., Maire, R., Ortega, R., Stratford, D., 2014. Stratigraphic
956 analysis of the Sterkfontein StW 573 Australopithecus skeleton and
957 implications for its age. Journal of Human Evolution 70, 36-48.
958 doi:10.1016/j.jhevol.2014.02.014

959 Burgin, C.J., Wilson, D.E., Mittermeier, R.A., Rylands, A.B., Lacher, T., Sechrest, W.,
960 2020. Illustrated checklist of the mammals of the world: Vol. 1: Monotremata to
961 Rodentia. Lynx Edicions.

962 Cadman, A., Rayner, R.J., 1989. Climatic change and the appearance of
963 Australopithecus africanus in the Makapansgat sediments. Journal of Human
964 Evolution 18, 107-113.

965 Caley, T., Extier, T., Collins, J.A., Schefuß, E., Dupont, L., Malaizé, B., Rossignol, L.,
966 Souron, A., McClymont, E.L., Jimenez-Espejo, F.J., García-Comas, C.,
967 Eynaud, F., Martinez, P., Roche, D.M., Jorry, S.J., Charlier, K., Wary, M.,
968 Gourves, P.-Y., Billy, I., Giraudeau, J., 2018. A two-million-year-long
969 hydroclimatic context for hominin evolution in southeastern Africa. Nature 560,
970 76-79. doi:10.1038/s41586-018-0309-6

971 Caley, T., Souron, A., Uno, K.T., Macho, G.A., 2024. Climate and Human Evolution:
972 Insights from Marine Records. doi:10.1146/annurev-marine-032223-031306

973 Cane, M.A., Molnar, P., 2001. Closing of the Indonesian seaway as a precursor to east
974 African aridification around 3-4 million years ago. Nature 411, 157-162.
975 doi:10.1038/35075500

976 Carbonell, E., Bermúdez de Castro, J.M., Parés, J.M., Pérez-González, A., Cuenca-
977 Bescós, G., Ollé, A., Mosquera, M., Huguet, R., Made, J. van der, Rosas, A.,
978 Sala, R., Vallverdú, J., García, N., Granger, D.E., Martinón-Torres, M.,
979 Rodríguez, X.P., Stock, G.M., Vergès, J.M., Allué, E., Burjachs, F., Cáceres, I.,
980 Canals, A., Benito, A., Díez, C., Lozano, M., Mateos, A., Navazo, M.,
981 Rodríguez, J., Rosell, J., Arsuaga, J.L., 2008. The first hominin of Europe.
982 Nature 452, 465-469. doi:10.1038/nature06815
983

984

985

Carrión, J.S., Scott, L., 1999. The challenge of pollen analysis in palaeoenvironmental studies of hominid beds: the record from Sterkfontein caves. Journal of Human Evolution 36, 401–408. doi:10.1006/jhev.1998.0276

986

987

988

989

Castañeda, I.S., Caley, T., Dupont, L., Kim, J.-H., Malaizé, B., Schouten, S., 2016. Middle to Late Pleistocene vegetation and climate change in subtropical southern East Africa. Earth and Planetary Science Letters 450, 306–316. doi:10.1016/j.epsl.2016.06.049

990

991

992

Castelló, D.J.R., Huffman, B., Groves, C., 2016. Bovids of the World: Antelopes, Gazelles, Cattle, Goats, Sheep, and Relatives, Illustrated edition. ed. Princeton University Press, Princeton.

993

994

995

Chang, M., Ruan, Q., Shao, Q., Liu, J., Li, Q., Ni, X., 2024. Late Middle Pleistocene micromammals of the Bianfudong paleolithic site, Heqing, western Yunnan. Quaternary International 697, 49–63. doi:10.1016/j.quaint.2024.05.010

996

997

Chase, J.M., Leibold, M.A., 2009. Ecological Niches: Linking Classical and Contemporary Approaches. University of Chicago Press.

998

999

Cruz-Uribe, K., 1983. The Mammalian Fauna from Redcliff Cave, Zimbabwe. The South African Archaeological Bulletin 38, 7–16. doi:10.2307/3888210

1000

1001

deMenocal, P.B., 2011. Climate and Human Evolution. Science (New York, N.Y.) 331, 540–2. doi:10.1126/science.1190683

1002

1003

1004

Derricourt, R., 2005. Getting “Out of Africa”: Sea Crossings, Land Crossings and Culture in the Hominin Migrations. Journal of World Prehistory 19, 119–132. doi:10.1007/s10963-006-9002-z

1005

1006

1007

1008

Dietterich, T.G., 2000. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, 139–157. doi:10.1023/A:1007607513941

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N.D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E.C., Jones, B., Barber, C.V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J.E.M., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., Souza, N. de, Pintea, L., Brito, J.C., Llewellyn, O.A., Miller, A.G., Patzelt, A., Ghazanfar, S.A., Timberlake, J., Klöser, H., Shennan-Farpón, Y., Kindt, R., Lillesø, J.-P.B., Breugel, P. van, Graudal, L., Voge, M., Al-Shammari, K.F., Saleem, M., 2017. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 67, 534–545. doi:10.1093/biosci/bix014

1019

1020

1021

1022

Domínguez-Rodrigo, M., Rayne Pickering, T., Semaw, S., Rogers, M.J., 2005. Cutmarked bones from Pliocene archaeological sites at Gona, Afar, Ethiopia: implications for the function of the world's oldest stone tools. Journal of Human Evolution 48, 109–121. doi:10.1016/j.jhevol.2004.09.004

1023

1024

1025

1026

Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J., 2011. Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108, 20422–20427. doi:10.1073/pnas.1117052108

1027

1028

1029

1030

1031

Dormann, C., McPherson, J., Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., Kühn, I., Ohlemüller, R., R. Peres-Neto, P., Reineking, B., Schröder, B., Schurr, F., Wilson, R., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628. doi:10.1111/j.2007.0906-7590.05171.x
1032

Dowsett, H.J., Caballero Gill, R.P., 2010. Pliocene Climate. Stratigraphy 7, 105–110.

1033

doi:10.29041/strat.07.2.01

1034

Dupont, L.M., Rommerskirchen, F., Mollenhauer, G., Schefuß, E., 2013. Miocene to

1035

Pliocene changes in South African hydrology and vegetation in relation to the
expansion of C4 plants. Earth and Planetary Science Letters 375, 408–417.

1036

1037

doi:10.1016/j.epsl.2013.06.005

1038

Dupont, L.M., Caley, T., Castañeda, I.S., 2019. Effects of atmospheric CO₂ variability

1039

of the past 800&thinsp;kyr on the biomes of southeast Africa. Climate of the
Past 15, 1083–1097. doi:10.5194/cp-15-1083-2019

1040

1041

Edwards, T.R., Pickering, R., Mallett, T.L., Herries, A.I.R., 2020. Reconstructing the

1042

depositional history and age of fossil-bearing palaeokarst: A multidisciplinary
example from the terminal Pliocene Aves Cave Complex, Bolt's farm, South
Africa. Results in Geophysical Sciences 1–4, 100005.

1043

1044

1045

doi:10.1016/j.ringps.2020.100005

1046

Elith, J., Leathwick, J.R., 2009. Species Distribution Models: Ecological Explanation

1047

and Prediction Across Space and Time. Annual Review of Ecology, Evolution,
and Systematics 40, 677–697. doi:10.1146/annurev.ecolsys.110308.120159

1048

1049

Erlanger, E.D., Granger, D.E., Gibbon, R.J., 2012. Rock uplift rates in South Africa

1050

from isochron burial dating of fluvial and marine terraces. Geology 40, 1019–
1022. doi:10.1130/G33172.1

1051

1052

Etourneau, J., Schneider, R., Blanz, T., Martinez, P., 2010. Intensification of the

1053

Walker and Hadley atmospheric circulations during the Pliocene–Pleistocene
climate transition. Earth and Planetary Science Letters 297, 103–110.

1054

1055

doi:10.1016/j.epsl.2010.06.010

1056

Faith, J.T., Lyman, R.L., 2019. Paleozoology and Paleoenvironments: Fundamentals,

1057

Assumptions, Techniques. Cambridge University Press, Cambridge.
doi:10.1017/9781108648608

1058

1059

Faith, J.T., Rowan, J., Du, A., 2019. Early hominins evolved within non-analog

1060

ecosystems. Proceedings of the National Academy of Sciences 116, 21478–
21483. doi:10.1073/pnas.1909284116

1061

1062

Falk, C., Semken, H.A.Jr., 1998. Taphonomy of rodent and insectivore remains in

1063

North American archaeological sites: Selected examples and interpretations.
In: Saunders, J.J., Styles, B.W., Baryshnikov, G.F. (Eds.), Quaternary
Paleozoology in the Northern Hemisphere. pp. 285–321.

1064

1065

1066

Feakins, S.J., Levin, N.E., Liddy, H.M., Sieracki, A., Eglinton, T.I., Bonnefille, R., 2013.

1067

Northeast African vegetation change over 12 m.y. Geology 41, 295–298.
doi:10.1130/G33845.1

1068

1069

Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: new 1-km spatial resolution climate

1070

surfaces for global land areas. International Journal of Climatology 37, 4302–
4315. doi:10.1002/joc.5086

1071

1072

Foister, T.I.F., Liu, L., Saarinen, J., Tallavaara, M., Zhang, H., Žliobaitė, I., 2024.

1073

Quantifying heterogeneity of hominin environments in and out of Africa using
herbivore dental traits. Quaternary Science Reviews 337, 108791.
doi:10.1016/j.quascirev.2024.108791

1074

1075

1076

Fourvel, J.-B., Thackeray, J.F., 2022. Paleoenvironments and the Context of Plio-

1077

Pleistocene Hominins at Kromdraai, South Africa. In: Reynolds, S.C., Bobe, R. (Eds.), African Paleoecology and Human Evolution. Cambridge University Press, pp. 82–85. doi:10.1017/9781139696470.008

1078

1079
1080 Gaston, K.J., 2003. The Structure and Dynamics of Geographic Ranges. Oxford
1081 University Press.

1082 Granger, D.E., Gibbon, R.J., Kuman, K., Clarke, R.J., Bruxelles, L., Caffee, M.W.,
1083 2015. New cosmogenic burial ages for Sterkfontein Member 2 Australopithecus
1084 and Member 5 Oldowan. Nature 522, 85-88. doi:10.1038/nature14268

1085 Guisan, A., Thuiller, W., 2005. Predicting species distribution: offering more than
1086 simple habitat models. Ecology Letters 8, 993-1009. doi:10.1111/j.1461-1087
1087 0248.2005.00792.x

1088 Haile-Selassie, Y., 2001. Late Miocene hominids from the Middle Awash, Ethiopia.
1089 Nature 412, 178-181. doi:10.1038/35084063

1090 Hanon, R., Steininger, C., 2024. Fossil Bovidae (Mammalia: Artiodactyla) from
1091 southern Africa: Biochronology and palaeoenvironments. Quaternary
1092 Environments and Humans 2, 100010. doi:10.1016/j.qeh.2024.100010

1093 Hanon, R., Patou-Mathis, M., Péan, S., Prat, S., 2019. Paleobiodiversity and large
1094 mammal associations during the late Pliocene and the Early Pleistocene in
1095 South Africa. Quaternaire. Revue de l'Association française pour l'étude du
1096 Quaternaire 243-256. doi:10.4000/quaternaire.12131

1097 Hanon, R., Patou-Mathis, M., Péan, S., Prat, S., Cohen, B., Steininger, C., 2022a. Early Pleistocene hominin subsistence behaviors in South Africa: Evidence from the hominin-bearing deposit of Cooper's D (Bloubank Valley, South Africa). Journal of Human Evolution 162, 103116. doi:10.1016/j.jhevol.2021.103116

1102 Hanon, R., Péan, S., Patou-Mathis, M., Prat, S., Rector, A., Steininger, C., 2022b. Fossil Bovidae from the Hominini-bearing site of Cooper's D (Bloubank Valley, South Africa): implications for Paranthropus robustus Broom, 1938 and early Homo Linnaeus, 1758 habitat preferences. Comptes Rendus Palevol. doi:10.5852/cr-palevol2022v21a21

1107 Hanon, R., Val, A., Sambo, R., Stratford, D., 2025. Butchery activities associated with
1108 Member 5 at Sterkfontein, South Africa. Archaeological and Anthropological
1109 Sciences 17, 49. doi:10.1007/s12520-024-02135-w

1110 Harrison, S.P., Bartlein, P.J., Prentice, I.C., 2016. What have we learnt from
1111 palaeoclimate simulations? Journal of Quaternary Science 31, 363-385.
1112 doi:10.1002/jqs.2842

1113 Hassanin, A., Houck, M.L., Tshikung, D., Kadjo, B., Davis, H., Ropiquet, A., 2018. Multi-locus phylogeny of the tribe Tragelaphini (Mammalia, Bovidae) and species delimitation in bushbuck: Evidence for chromosomal speciation mediated by interspecific hybridization. Molecular Phylogenetics and Evolution 129, 96-105. doi:10.1016/j.ympev.2018.08.006

1118 Haywood, A.M., Tindall, J.C., Dowsett, H.J., Dolan, A.M., Foley, K.M., Hunter, S.J.,
1119 Hill, D.J., Chan, W.-L., Abe-Ouchi, A., Stepanek, C., Lohmann, G., Chandan,
1120 D., Peltier, W.R., Tan, N., Contoux, C., Ramstein, G., Li, X., Zhang, Z., Guo,
1121 C., Nisancioglu, K.H., Zhang, Q., Li, Q., Kamae, Y., Chandler, M.A., Sohl, L.E.,
1122 Otto-Bliesner, B.L., Feng, R., Brady, E.C., Heydt, A.S. von der, Baatsen, M.L.J.,
1123 Lunt, D.J., 2020. The Pliocene Model Intercomparison Project Phase 2: large-
1124 scale climate features and climate sensitivity. Climate of the Past 16, 2095-
1125 2123. doi:10.5194/cp-16-2095-2020

1126 Hearty, P.J., Rovere, A., Sandstrom, M.R., O'Leary, M.J., Roberts, D., Raymo, M.E., 2020. Pliocene-Pleistocene Stratigraphy and Sea-Level Estimates, Republic of
1128

1129

1130

1131

1132

1133

1134

1135

1136

1137

1138

1139

1140

1141

1142

1143

1144

1145

1146

1147

1148

1149

1150

1151

1152

1153

1154

1155

1156

1157

1158

1159

1160

1161

1162

1163

1164

1165

1166

1167

1168

1169

1170

1171

1172

1173

1174

1175

1176

South Africa With Implications for a 400 ppmv CO2 World. Paleooceanography and Paleoclimatology 35, e2019PA003835. doi:10.1029/2019PA003835

Heinzelin, J. de, Clark, J.D., White, T., Hart, W., Renne, P., WoldeGabriel, G., Beyene, Y., Vrba, E., 1999. Environment and Behavior of 2.5-Million-Year-Old Bouri Hominids. Science 284, 625–629. doi:10.1126/science.284.5414.625

Helama, S., Makarenko, N.G., Karimova, L.M., Kruglun, O.A., Timonen, M., Holopainen, J., Meriläinen, J., Eronen, M., 2009. Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms. Annales Geophysicae 27, 1097–1111. doi:10.5194/angeo-27-1097-2009

Herries, A.I.R., Curnoe, D., Adams, J.W., 2009. A multi-disciplinary seriation of early Homo and Paranthropus bearing palaeocaves in southern Africa. Quaternary International, Great Arc of Human Dispersal 202, 14–28. doi:10.1016/j.quaint.2008.05.017

Herries, A.I.R., Adams, J.W., Joannes-Boyau, R., Armstrong, B., Baker, S., F. Blackwood, A., Boschian, G., V. Caruana, M., Penzo-Kajewski, P., Murszewski, A., S. Rovinsky, D., 2019. Integrating palaeocaves into palaeolandscapes: An analysis of cave levels and karstification history across the Gauteng Malmani dolomite, South Africa. Quaternary Science Reviews 220, 310–334. doi:10.1016/j.quascirev.2017.02.007

Hoetzel, S., Dupont, L., Schefuß, E., Rommerskirchen, F., Wefer, G., 2013. The role of fire in Miocene to Pliocene C4 grassland and ecosystem evolution. Nature Geoscience 6, 1027–1030. doi:10.1038/ngeo1984

Hopley, P.J., Maslin, M.A., 2010. Climate-averaging of terrestrial faunas: an example from the Plio-Pleistocene of South Africa. Paleobiology 36, 32–50. doi:10.1666/0094-8373-36.1.32

Hopley, P.J., Maxwell, S.J., 2022. Environmental and Stratigraphic Bias in the Hominin Fossil Record: Implications for Theories of the Climatic Forcing of Human Evolution. In: Reynolds, S.C., Bobe, R. (Eds.), African Paleoecology and Human Evolution. Cambridge University Press, pp. 15–23.

Hopley, P.J., Latham, A.G., Marshall, J.D., 2006. Palaeoenvironments and palaeodiets of mid-Pliocene micromammals from Makapansgat Limeworks, South Africa: A stable isotope and dental microwear approach. Palaeogeography, Palaeoclimatology, Palaeoecology 233, 235–251. doi:10.1016/j.palaeo.2005.09.011

Hurlbert, A.H., White, E.P., 2005. Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecology Letters 8, 319–327. doi:https://doi.org/10.1111/j.1461-0248.2005.00726.x

Hurlbert, S.H., 1971. The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology 52, 577–586. doi:10.2307/1934145

Kidwell, S.M., Holland, S.M., 2002. The Quality of the Fossil Record: Implications for Evolutionary Analyses. Annual Review of Ecology, Evolution, and Systematics 33, 561–588. doi:10.1146/annurev.ecolsys.33.030602.152151

Klein, R., 2000. The Earlier Stone Age of Southern Africa. South African Archaeological Bulletin 27, 107–122. doi:10.2307/3888960

Koutsodendris, A., Nakajima, K., Kaboth-Bahr, S., Berke, M.A., Franzese, A.M., Hall, I.R., Hemming, S.R., Just, J., LeVay, L.J., Pross, J., Robinson, R., 2021. A Plio-Pleistocene (c. 0–4 Ma) cyclostratigraphy for IODP Site U1478 (Mozambique
1177

1178

1179

1180

1181

1182

1183

1184

1185

1186

1187

1188

1189

1190

1191

1192

1193

1194

1195

1196

1197

1198

1199

1200

1201

1202

1203

1204

1205

1206

1207

1208

1209

1210

1211

1212

1213

1214

1215

1216

1217

1218

1219

1220

1221

1222

1223

1224

Channel, SW Indian Ocean): Exploring an offshore record of paleoclimate and ecosystem variability in SE Africa. Newsletters on Stratigraphy 159–181. doi:10.1127/nos/2020/0608

Kovarovic, K., Andrews, P., 2007. Bovid postcranial ecomorphological survey of the Laetoli paleoenvironment. Journal of Human Evolution 52, 663–680. doi:10.1016/j.jhevol.2007.01.001

Kuman, K., Granger, D.E., Gibbon, R.J., Pickering, T.R., Caruana, M.V., Bruxelles, L., Clarke, R.J., Heaton, J.L., Stratford, D., Brain, C.K., 2021. A new absolute date from Swartkrans Cave for the oldest occurrences of Paranthropus robustus and Oldowan stone tools in South Africa. Journal of Human Evolution 156, 103000. doi:10.1016/j.jhevol.2021.103000

Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J., Bretagnolle, V., 2014. Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation. Global Ecology and Biogeography 23, 811–820. doi:10.1111/geb.12161

Leakey, L.S.B., 1959. A New Fossil Skull From Olduvai. Nature 184, 491–493. doi:10.1038/184491a0

Leakey, L.S.B., Tobias, P.V., Napier, J.R., 1964. A New Species of The Genus Homo From Olduvai Gorge. Nature 202, 7–9. doi:10.1038/202007a0

Lee-Thorp, J.A., Merwe, N.J. van der, 1993. Stable carbon isotope studies of Swartkrans fossils. Transvaal Museum Monographs 8, 251–256.

Lee-Thorp, J.A., Merwe, N.J. van der, Brain, C.K., 1994. Diet of Australopithecus robustus at Swartkrans from stable carbon isotopic analysis. Journal of Human Evolution 27, 361–372. doi:10.1006/jhev.1994.1050

Legendre, P., 1993. Spatial Autocorrelation: Trouble or New Paradigm? Ecology 74, 1659–1673. doi:10.2307/1939924

Leichliter, J.N., Sandberg, P., Passey, B., Codron, D., Avenant, N.L., Paine, O.C.C., Codron, J., Ruiter, D. de, Sponheimer, M., 2017. Stable carbon isotope ecology of small mammals from the Sterkfontein Valley: Implications for habitat reconstruction. Palaeogeography, Palaeoclimatology, Palaeoecology 485, 57–67. doi:10.1016/j.palaeo.2017.06.003

Lepre, C.J., Quinn, R.L., 2022. Aridification and orbital forcing of eastern African climate during the Plio-Pleistocene. Global and Planetary Change 208, 103684. doi:10.1016/j.gloplacha.2021.103684

Li, S.-F., Jacques, F.M.B., Spicer, R.A., Su, T., Spicer, T.E.V., Yang, J., Zhou, Z.-K., 2016. Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy. Palaeogeography, Palaeoclimatology, Palaeoecology 442, 1–11. doi:10.1016/j.palaeo.2015.11.005

Linchamps, P., 2023. Diversity and evolution of Quaternary micromammal faunas from South Africa: novel approaches for reconstructing the palaeoenvironments of early hominins (Thèse de Doctorat). Muséum national d'Histoire naturelle, Paris.

Linchamps, P., Stoetzel, E., Robinet, F., Hanon, R., Latouche, P., Cornette, R., 2023a. Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies. Frontiers in Ecology and Evolution 11.

Linchamps, P., Stoetzel, E., Cornette, R., Denys, C., Hanon, R., Maringa, N., Matthews, T., Steininger, C., 2023b. Fossil micromammals and the
1225

palaeoenvironments of the Paranthropus robustus site of Cooper's Cave.

1226

Historical Biology.

1227

Linchamps, P., Hanon, R., Stoetzel, E., Cohen, B., Steininger, C., 2025a. Medium and

1228

large-sized Glires (Rodents and Lagomorphs) from the early Pleistocene

1229

Cooper's D locality in the Cradle of Humankind, Gauteng, South Africa.

1230

Quaternary International 715, 109590. doi:10.1016/j.quaint.2024.11.008

1231

Linchamps, P., Stoetzel, E., Amberny, L., Steininger, C., Clarke, R.J., Caruana, M.V.,

1232

Kuman, K., Pickering, T.R., 2025b. New modern and Pleistocene fossil

1233

micromammal assemblages from Swartkrans, South Africa: paleobiodiversity,

1234

taphonomic and environmental context. Journal of Human Evolution 200,

1235

103636.

1236

Lubbe, H.J.L. van der, Hall, I.R., Barker, S., Hemming, S.R., Baars, T.F., Starr, A.,

1237

Just, J., Backeberg, B.C., Joordens, J.C.A., 2021. Indo-Pacific Walker

1238

circulation drove Pleistocene African aridification. Nature 598, 618–623.

1239

doi:10.1038/s41586-021-03896-3

1240

Lüdecke, T., Kullmer, O., Wacker, U., Sandrock, O., Fiebig, J., Schrenk, F., Mulch, A.,

1241

2018. Dietary versatility of Early Pleistocene hominins. Proceedings of the

1242

National Academy of Sciences 115, 13330–13335.

1243

doi:10.1073/pnas.1809439115

1244

Lüdecke, T., Leichliter, J.N., Stratford, D., Sigman, D.M., Vonhof, H., Haug, G.H.,

1245

Bamford, M.K., Martínez-García, A., 2025. Australopithecus at Sterkfontein did

1246

not consume substantial mammalian meat. Science 387, 309–314.

1247

doi:10.1126/science.adq7315

1248

Luyt, C.J., Lee-Thorp, J.A., 2003. (PDF) Carbon isotope ratios of Sterkfontein fossils

1249

indicate a marked shift to open environments c. 1.7 Myr ago. South African

1250

Journal of Science 99.

1251

Lyman, R., 2012. The influence of screen mesh size, and size and shape of rodent

1252

teeth on recovery. Journal of Archaeological Science 39, 1854–1861.

1253

doi:10.1016/j.jas.2012.01.027

1254

Lyman, R.L., 2017. Paleoenvironmental Reconstruction from Faunal Remains:

1255

Ecological Basics and Analytical Assumptions. Journal of Archaeological

1256

Research 25, 315–371.

1257

MacArthur, R.H., 1984. Geographical Ecology: Patterns in the Distribution of Species.

1258

Princeton University Press.

1259

MacDonald, K., Scherjon, F., Van Veen, E., Vaesen, K., Roebroeks, W., 2021. Middle

1260

Pleistocene fire use: The first signal of widespread cultural diffusion in human

1261

evolution. Proceedings of the National Academy of Sciences 118,

1262

e2101108118. doi:10.1073/pnas.2101108118

1263

Malherbe, M., Pickering, R., Stynder, D., Haeusler, M., 2025. The large mammal fossil

1264

fauna of the Cradle of Humankind, South Africa: a review. PeerJ 13, e18946.

1265

doi:10.7717/peerj.18946

1266

Marsh, C.J., Sica, Y.V., Burgin, C.J., Dorman, W.A., Anderson, R.C., Toro Mijares, I.

1267

del, Vigneron, J.G., Barve, V., Dombrowik, V.L., Duong, M., Guralnick, R., Hart,

1268

J.A., Maypole, J.K., McCall, K., Ranipeta, A., Schuerkmann, A., Torselli, M.A.,

1269

Lacher Jr, T., Mittermeier, R.A., Rylands, A.B., Sechrest, W., Wilson, D.E.,

1270

Abba, A.M., Aguirre, L.F., Arroyo-Cabrales, J., Astúa, D., Baker, A.M., Braulik,

1271

G., Braun, J.K., Brito, J., Busher, P.E., Burneo, S.F., Camacho, M.A., Cavallini,

1272

P., Almeida Chiquito, E. de, Cook, J.A., Cserkész, T., Csorba, G., Cuéllar Soto,

1273

E., Cunha Tavares, V. da, Davenport, T.R.B., Deméré, T., Denys, C., Dickman,
1274 C.R., Eldridge, M.D.B., Fernandez-Duque, E., Francis, C.M., Frankham, G., Franklin, W.L., Freitas, T., Friend, J.A., Gadsby, E.L., Garbino, G.S.T., Gaubert, P., Giannini, N., Giarla, T., Gilchrist, J.S., Gongora, J., Goodman, S.M., Gursky-Doyen, S., Hackländer, K., Hafner, M.S., Hawkins, M., Helgen, K.M., Heritage, S., Hinckley, A., Hintsche, S., Holden, M., Holekamp, K.E., Honeycutt, R.L., Huffman, B.A., Humle, T., Hutterer, R., Ibáñez Ulargui, C., Jackson, S.M., Janecka, J., Janecka, M., Jenkins, P., Juškaitis, R., Juste, J., Kays, R., Kilpatrick, C.W., Kingston, T., Koprowski, J.L., Kryštufek, B., Lavery, T., Lee Jr, T.E., Leite, Y.L.R., Novaes, R.L.M., Lim, B.K., Lissovsky, A., López-Antoñanzas, R., López-Baucells, A., MacLeod, C.D., Maisels, F.G., Mares, M.A., Marsh, H., Mattioli, S., Meijaard, E., Monadjem, A., Morton, F.B., Musser, G., Nadler, T., Norris, R.W., Ojeda, A., Ordóñez-Garza, N., Pardiñas, U.F.J., Patterson, B.D., Pavan, A., Pennay, M., Pereira, C., Prado, J., Queiroz, H.L., Richardson, M., Riley, E.P., Rossiter, S.J., Rubenstein, D.I., Ruelas, D., Salazar-Bravo, J., Schai-Braun, S., Schank, C.J., Schwitzer, C., Sheeran, L.K., Shekelle, M., Shenbrot, G., Soisook, P., Solari, S., Southgate, R., Superina, M., Taber, A.B., Talebi, M., Taylor, P., Vu Dinh, T., Ting, N., Tirira, D.G., Tsang, S., Survey, S.T., Valdez, R., Van Cakenberghe, V., Veron, G., Wallis, J., Wells, R., Whittaker, D., Williamson, E.A., Wittemyer, G., Woinarski, J., Zinner, D., Upham, N.S., Jetz, W., 2022. Expert range maps of global mammal distributions harmonised to three taxonomic authorities. Journal of Biogeography 49, 979–992. doi:10.1111/jbi.14330

1296 Maslin, M.A., Pancost, R.D., Wilson, K.E., Lewis, J., Trauth, M.H., 2012. Three and a half million year history of moisture availability of South West Africa: Evidence from ODP site 1085 biomarker records. Palaeogeography, Palaeoclimatology, Palaeoecology 317–318, 41–47. doi:10.1016/j.palaeo.2011.12.009

1300 Maslin, M.A., Brierley, C.M., Milner, A.M., Shultz, S., Trauth, M.H., Wilson, K.E., 2014. East African climate pulses and early human evolution. Quaternary Science Reviews 101, 1–17. doi:10.1016/j.quascirev.2014.06.012

1303 Nutz, A., Schuster, M., Boës, X., Rubino, J.-L., 2017. Orbitally-driven evolution of Lake Turkana (Turkana Depression, Kenya, EARS) between 1.95 and 1.72 Ma: A sequence stratigraphy perspective. Journal of African Earth Sciences 125, 230–243. doi:10.1016/j.jafrearsci.2016.10.016

1307 O'Brien, K., Hebdon, N., Faith, J.T., 2023. Paleoenvironmental evidence for environmental specialization in *Paranthropus boisei* compared to early *Homo*. Journal of Human Evolution 177, 103325. doi:10.1016/j.jhevol.2023.103325

1310 Owen, R.B., Muiruri, V.M., Lowenstein, T.K., Renaut, R.W., Rabideaux, N., Luo, S., Deino, A.L., Sier, M.J., Dupont-Nivet, G., McNulty, E.P., Leet, K., Cohen, A., Campisano, C., Deocampo, D., Shen, C.-C., Billingsley, A., Mbuthia, A., 2018. Progressive aridification in East Africa over the last half million years and implications for human evolution. Proceedings of the National Academy of Sciences of the United States of America 115, 11174–11179.

1316 Pavia, M., 2020. Palaeoenvironmental reconstruction of the Cradle of Humankind during the Plio-Pleistocene transition, inferred from the analysis of fossil birds from Member 2 of the hominin-bearing site of Kromdraai (Gauteng, South Africa). Quaternary Science Reviews 248, 106532. doi:10.1016/j.quascirev.2020.106532

1321 Pavia, M., Val, A., Carrera, L., Steininger, C.M., 2022. Fossil birds from Cooper's D aid in reconstructing the Early Pleistocene paleoenvironment in the Cradle of
1323

Humankind (Gauteng, South Africa). Journal of Human Evolution 167, 103185.

1324

doi:10.1016/j.jhevol.2022.103185

1325

Pavlinov, I., 2008. A review of phylogeny and classification of Gerbillinae (Mammalia:

1326

Rodentia), Moscow University Publishing. ed. Moscow.

1327

Peel, M.C., Finlayson, B.L., McMahon, T.A., 2007. Updated world map of the Köppen-

1328

Geiger climate classification. Hydrology and Earth System Sciences 11, 1633–

1329

1644. doi:10.5194/hess-11-1633-2007

1330

Peterson, A.T., 2011. Ecological niche conservatism: a time-structured review of

1331

evidence. Journal of Biogeography 38, 817–827. doi:10.1111/j.1365-

1332

2699.2010.02456.x

1333

Petrick, B., Martínez-García, A., Auer, G., Reuning, L., Auderset, A., Deik, H.,

1334

Takayanagi, H., De Vleeschouwer, D., Iryu, Y., Haug, G.H., 2019. Glacial

1335

Indonesian Throughflow weakening across the Mid-Pleistocene Climatic

1336

Transition. Scientific Reports 9, 16995. doi:10.1038/s41598-019-53382-0

1337

Pfalz, G., Diekmann, B., Freytag, J.-C., Biskaborn, B.K., 2021. Harmonizing

1338

heterogeneous multi-proxy data from lake systems. Computers & Geosciences

1339

153, 104791. doi:10.1016/j.cageo.2021.104791

1340

Pickering, R., Edwards, T.R., 2021. Factors controlling age quality in UPb dated Plio-

1341

Pleistocene speleothems from South Africa: The good, the bad and the ugly.

1342

Chemical Geology 579, 120364. doi:10.1016/j.chemgeo.2021.120364

1343

Pickering, R., Hancox, P.J., Lee-Thorp, J.A., Grün, R., Mortimer, G.E., McCulloch, M.,

1344

Berger, L.R., 2007. Stratigraphy, U-Th chronology, and paleoenvironments at

1345

Gladysvale Cave: insights into the climatic control of South African hominin-

1346

bearing cave deposits. Journal of Human Evolution, African Paleoclimate and

1347

Human Evolution 53, 602–619. doi:10.1016/j.jhevol.2007.02.005

1348

Pickering, R., Herries, A., Woodhead, J., Hellstrom, J., Green, H., Paul, B., Ritzman,

1349

T., Strait, D., Schoville, B., Hancox, J., 2019. U–Pb-dated flowstones restrict

1350

South African early hominin record to dry climate phases. Nature 565.

1351

doi:10.1038/s41586-018-0711-0

1352

Pickering, T., Bunn, H., 2012. Another take on meat-foraging by Pleistocene African

1353

hominins: Tracking behavioral evolution beyond baseline inferences of early

1354

access to carcasses. Stone Tools and Fossil Bones: Debates in the

1355

Archaeology of Human Origins 152–173.

1356

doi:10.1017/CBO9781139149327.007

1357

Pickering, T.R., Cazenave, M., Clarke, R.J., Heile, A.J., Caruana, M.V., Kuman, K.,

1358

Stratford, D., Brain, C.K., Heaton, J.L., 2025. First articulating os coxae, femur,

1359

and tibia of a small adult *Paranthropus robustus* from Member 1 (Hanging

1360

Remnant) of the Swartkrans Formation, South Africa. Journal of Human

1361

Evolution 201, 103647. doi:10.1016/j.jhevol.2024.103647

1362

Plummer, T., 2004. Flaked stones and old bones: biological and cultural evolution at

1363

the dawn of technology. American Journal of Physical Anthropology Suppl 39,

1364

118–164. doi:10.1002/ajpa.20157

1365

Pobiner, B.L., 2020. The zooarchaeology and paleoecology of early hominin

1366

scavenging. Evolutionary Anthropology: Issues, News, and Reviews 29, 68–

1367

82. doi:10.1002/evan.21824

1368

Polissar, P.J., Rose, C., Uno, K.T., Phelps, S.R., deMenocal, P., 2019. Synchronous

1369

rise of African C4 ecosystems 10 million years ago in the absence of

1370

aridification. Nature Geoscience 12, 657–660. doi:10.1038/s41561-019-0399-2

1371

2
1372 Potts, R., 1998. Environmental hypotheses of hominin evolution. American Journal of Physical Anthropology 107, 93-136. doi:10.1002/(SICI)1096-8644(1998)107:27+<93::AID-AJPA5>3.0.CO;2-X

1375 Prat, S., 2018. First hominin settlements out of Africa. Tempo and dispersal mode: Review and perspectives. Comptes Rendus Palevol, Hominins and tools. Expansion from Africa towards Eurasia / Homininés et outils. Expansions depuis l'Afrique vers l'Eurasie 17, 6-16. doi:10.1016/j.crpv.2016.04.009

1379 Prat, S., 2022. Emergence of the genus Homo: From concept to taxonomy. L'Anthropologie, Paléoanthropologie 126, 103068. doi:10.1016/j.anthro.2022.103068

1382 Prat, S., 2023. Beyond the genus stereotype. Who were the first toolmarkers in Africa? Crossed views between archaeology and anatomy. L'Anthropologie, Comportement de l'Homme préhistorique en zones tropicales 127, 103187. doi:10.1016/j.anthro.2023.103187

1386 Prat, S., 2024. Cognitive abilities and identity of Early Pleistocene (2.6-0.8 million years) stone-tool makers, an overview. Lesedi: Carnets de terrain= Lesedi: Field notes 26, 38-42.

1389 Reed, D.N., 2007. Serengeti micromammals and their implications for Olduvai paleoenvironments. In: Bobe, R., Alemseged, Z., Behrensmeyer, A.K. (Eds.), Hominin Environments in the East African Pliocene: An Assessment of the Faunal Evidence, Vertebrate Paleobiology and Paleoanthropology Series. Springer Netherlands, Dordrecht, pp. 217-255. doi:10.1007/978-1-4020-3098-7_9

1395 Reed, K.E., 1997. Early hominid evolution and ecological change through the African Plio-Pleistocene. Journal of Human Evolution 32, 289-322. doi:10.1006/jhev.1996.0106

1398 Reed, K.E., 2013. Multiproxy Paleoecology: Reconstructing evolutionary context in paleoanthropology. In: A Companion to Paleoanthropology. John Wiley & Sons, Ltd, pp. 203-225. doi:10.1002/9781118332344.ch11

1401 Reed, K.E., Spencer, L., Rector, A.L., 2013. Faunal approaches in early hominin paleoecology. In: Sponheimer, M., Lee-Thorp, J.A., Reed, K.E., Ungar, P.S. (Eds.), Early Hominin Paleoecology. Boulder, pp. 3-34.

1404 Reed, K.E., Kuykendall, K.L., Herries, A.I.R., Hopley, P.J., Sponheimer, M., Werdelin, L., 2022. Geology, Fauna, and Paleoenvironmental Reconstructions of the Makapansgat Limeworks Australopithecus africanus-Bearing Paleo-Cave. In: Bobe, R., Reynolds, S.C. (Eds.), African Paleoecology and Human Evolution. Cambridge University Press, Cambridge, pp. 66-81. doi:10.1017/9781139696470.007

1410 Royer, A., García Yelo, B.A., Laffont, R., Hernández Fernández, M., 2020. New bioclimatic models for the quaternary palaearctic based on insectivore and rodent communities. Palaeogeography, Palaeoclimatology, Palaeoecology 560, 110040. doi:10.1016/j.palaeo.2020.110040

1414 Ruiter, D.J. de, 2022. Swartkrans: A Record of Paleoenvironmental Change in the Cradle of Humankind. In: Bobe, R., Reynolds, S.C. (Eds.), African Paleoecology and Human Evolution. Cambridge University Press, Cambridge, pp. 102-119. doi:10.1017/9781139696470.011

1418 Ruiter, D.J. de, Sponheimer, M., Lee-Thorp, J.A., 2008. Indications of habitat association of Australopithecus robustus in the Bloubank Valley, South Africa. Journal of Human Evolution 55, 1015.
1421 Salonen, J.S., Luoto, M., Alenius, T., Heikkilä, M., Seppä, H., Telford, R.J., Birks, H.J.B., 2014. Reconstructing palaeoclimatic variables from fossil pollen using boosted regression trees: comparison and synthesis with other quantitative reconstruction methods. Quaternary Science Reviews 88, 69–81. doi:10.1016/j.quascirev.2014.01.011

1426 Salonen, J.S., Korpela, M., Williams, J.W., Luoto, M., 2019. Machine-learning based reconstructions of primary and secondary climate variables from North American and European fossil pollen data. Scientific Reports 9, 15805. doi:10.1038/s41598-019-52293-4

1430 Salzmann, U., Williams, M., Haywood, A.M., Johnson, A.L.A., Kender, S., Zalasiewicz, J., 2011. Climate and environment of a Pliocene warm world. Palaeogeography, Palaeoclimatology, Palaeoecology, Special Issue: Climate and seasonality in a Pliocene warm world 309, 1–8. doi:10.1016/j.palaeo.2011.05.044

1435 Sambo, R., Hanon, R., Maringa, N., Steininger, C., Zipfel, B., Braga, J., Linchamps, P., 2025. Ecomorphological analysis of bovid remains from the Plio-Pleistocene hominin-bearing deposit of Unit P at Kromdraai, South Africa. Journal of Archaeological Science: Reports 61, 104871. doi:10.1016/j.jasrep.2024.104871

1440 Schubert, B.W., Ungar, P.S., Sponheimer, M., Reed, K.E., 2006. Microwear evidence for Plio-Pleistocene bovid diets from Makapansgat Limeworks Cave, South Africa. Palaeogeography, Palaeoclimatology, Palaeoecology 241, 301–319. doi:10.1016/j.palaeo.2006.04.004

1444 Scott, L., Bonnefille, R., 1986. Search for pollen from the hominid deposits of Kromdraai Sterkfontein and Swartkrans: some problems and preliminary results. Search for pollen from the hominid deposits of Kromdraai Sterkfontein and Swartkrans: some problems and preliminary results 82, 380–384.

1448 Senut, B., Pickford, M., Gommery, D., Mein, P., Cheboi, K., Coppens, Y., 2001. First hominid from the Miocene (Lukeino Formation, Kenya). Comptes Rendus de l'Académie des Sciences - Series IIA - Earth and Planetary Science 332, 137–144. doi:10.1016/S1251-8050(01)01529-4

1452 Sepulchre, P., Ramstein, G., Fluteau, F., Schuster, M., Tiercelin, J.-J., Brunet, M., 2006. Tectonic Uplift and Eastern Africa Aridification. Science 313, 1419–1423. doi:10.1126/science.1129158

1455 Sewell, L.C., Kibii, J.M., Reynolds, S.C., 2022. The Paleoenvironments of Sterkfontein: Old Questions, New Approaches. In: Bobe, R., Reynolds, S.C. (Eds.), African Paleoecology and Human Evolution. Cambridge University Press, Cambridge, pp. 92–101. doi:10.1017/9781139696470.010

1459 Shultz, S., Maslin, M., 2013. Early Human Speciation, Brain Expansion and Dispersal Influenced by African Climate Pulses. PLOS ONE 8, e76750. doi:10.1371/journal.pone.0076750

1462 Sobol, M.K., Finkelstein, S.A., 2018. Predictive pollen-based biome modeling using machine learning. PLOS ONE 13, e0202214. doi:10.1371/journal.pone.0202214

1465 Sobol, M.K., Scott, L., Finkelstein, S.A., 2019. Reconstructing past biomes states using machine learning and modern pollen assemblages: A case study from Southern Africa. Quaternary Science Reviews 212, 1–17. doi:10.1016/j.quascirev.2019.03.027
1469 Sponheimer, M., Passey, B.H., Ruiter, D.J. de, Guatelli-Steinberg, D., Cerling, T.E., 1470 Lee-Thorp, J.A., 2006. Isotopic evidence for dietary variability in the early 1471 hominin Paranthropus robustus. Science (New York, N.Y.) 314, 980–982. 1472 doi:10.1126/science.1133827

1473 Sponheimer, M., Daegling, D.J., Ungar, P.S., Bobe, R., Paine, O.C.C., 2023. Problems 1474 with Paranthropus. Quaternary International, Multiple Scales of Stable Isotope 1475 Palaeoecology (Papers in Honour of Prof. Julia Lee-Thorp) 650, 40–51. 1476 doi:10.1016/j.quaint.2022.03.024

1477 Spradley, J.P., Glazer, B.J., Kay, R.F., 2019. Mammalian faunas, ecological indices, 1478 and machine-learning regression for the purpose of paleoenvironment 1479 reconstruction in the Miocene of South America. Palaeogeography, 1480 Palaeoclimatology, Palaeoecology 518, 155–171. 1481 doi:10.1016/j.palaeo.2019.01.014

1482 Steininger, C.M., 2011. The Dietary Behaviour of Early Pleistocene Bovids from 1483 Cooper's Cave and Swartkrans, South Africa (Ph.D.). University of the 1484 Witwatersrand, Johannesburg (South Africa), South Africa.

1485 Stoetzel, E., Bochaton, C., Bailon, S., Cochard, D., Gala, M., Laroulandie, V., 2021. 1486 Multi-Taxa Neo-Taphonomic Analysis of Bone Remains from Barn Owl Pellets 1487 and Cross-Validation of Observations: A Case Study from Dominica (Lesser 1488 Antilles). Quaternary 4, 38. doi:10.3390/quat4040038

1489 Stollhofen, H., Stanistreet, I.G., Hagke, C. von, Nguno, A., 2014. Pliocene–Pleistocene 1490 climate change, sea level and uplift history recorded by the Horingbaai fan- 1491 delta, NW Namibia. Sedimentary Geology 309, 15–32. 1492 doi:10.1016/j.sedgeo.2014.05.008

1493 Tan, N., Ramstein, G., Dumas, C., Contoux, C., Ladant, J.-B., Sepulchre, P., Zhang, 1494 Z., De Schepper, S., 2017. Exploring the MIS M2 glaciation occurring during a 1495 warm and high atmospheric CO2 Pliocene background climate. Earth and 1496 Planetary Science Letters 472, 266–276. doi:10.1016/j.epsl.2017.04.050

1497 Tan, N., Ladant, J.-B., Ramstein, G., Dumas, C., Bachem, P., Jansen, E., 2018. 1498 Dynamic Greenland ice sheet driven by pCO2 variations across the Pliocene 1499 Pleistocene transition. Nature Communications 9, 4755. doi:10.1038/s41467- 1500 018-07206-w

1501 Taylor, A.K., Berke, M.A., Castañeda, I.S., Koutsodendris, A., Campos, H., Hall, I.R., 1502 Hemming, S.R., LeVay, L.J., Sierra, A.C., O’Connor, K., Scientists, E. 361, 1503 2021. Plio-Pleistocene Continental Hydroclimate and Indian Ocean Sea 1504 Surface Temperatures at the Southeast African Margin. Paleoceanography and 1505 Paleoclimatology 36, e2020PA004186. doi:10.1029/2020PA004186

1506 Telford, R.J., Birks, H.J.B., 2005. The secret assumption of transfer functions: 1507 problems with spatial autocorrelation in evaluating model performance. 1508 Quaternary Science Reviews 24, 2173–2179. 1509 doi:10.1016/j.quascirev.2005.05.001

1510 Thonis, A., Akçakaya, H.R., 2025. Perils of Using Range Maps in Macroecological 1511 Models of Species Richness. Journal of Biogeography 52, e15072. 1512 doi:10.1111/jbi.15072

1513 Trauth, M.H., Maslin, M.A., Deino, A.L., Strecker, M.R., Bergner, A.G.N., Dühnforth, 1514 M., 2007. High- and low-latitude forcing of Plio-Pleistocene East African climate 1515 and human evolution. Journal of Human Evolution 53, 475–486. 1516 doi:10.1016/j.jhevol.2006.12.009
1517 Trauth, M.H., Larrasoaña, J.C., Mudelsee, M., 2009. Trends, rhythms and events in Plio-Pleistocene African climate. Quaternary Science Reviews 28, 399–411. doi:10.1016/j.quascirev.2008.11.003

1520 Trauth, M.H., Asrat, A., Cohen, A.S., Duesing, W., Foerster, V., Kaboth-Bahr, S., Kraemer, K.H., Lamb, H.F., Marwan, N., Maslin, M.A., Schäbitz, F., 2021. Recurring types of variability and transitions in the ~620 kyr record of climate change from the Chew Bahir basin, southern Ethiopia. Quaternary Science Reviews 266, 106777. doi:10.1016/j.quascirev.2020.106777

1525 Ulbricht, A., Maul, L.C., Schulz, E., 2015. Can mesowear analysis be applied to small mammals? A pilot-study on leporines and murines. Mammalian Biology 80, 14–20. doi:10.1016/j.mambio.2014.06.004

1528 Uno, K.T., Polissar, P.J., Jackson, K.E., deMenocal, P.B., 2016. Neogene biomarker record of vegetation change in eastern Africa. Proceedings of the National Academy of Sciences 113, 6355–6363. doi:10.1073/pnas.1521267113

1531 Villmoare, B., Kimbel, W., Seyoum, C., Campisano, C., DiMaggio, E., Rowan, J., Braun, D., Arrowsmith, R., Reed, K., 2015. Early Homo at 2.8 Ma from Ledi-Geraru, Afar, Ethiopia. Science. doi:10.1126/science.aaa1343

1534 Vrba, E.S., 1974. Chronological and ecological implications of the fossil Bovidae at the Sterkfontein Australopithecine site. Nature 250, 19–23. doi:10.1038/250019a0

1536 Vrba, E.S., 1975. Some evidence of chronology and palaeoecology of Sterkfontein, Swartkrans and Kromdraai from the fossil Bovidae. Nature 254, 301–304. doi:10.1038/254301a0

1539 Vrba, E.S., 1985. Ecological and adaptive changes associated with early hominid evolution. In: Delson, E. (Ed.), Ancestors: The Hard Evidence. Alan R. Liss, New York, pp. 63–71.

1542 Vrba, E.S., 1988. Late Pliocene Climatic Events and Hominid Evolution. In: Evolutionary History of the Robust Australopithecines. Routledge, pp. 405–426.

1544 Vrba, E.S., 1995. On the connections between paleoclimate and evolution. In: Vrba, E.S., Denton, G.H., Partridge, T.C., Burckle, L.H. (Eds.), Paleoclimate and Evolution, with Emphasis on Human Origins. Yale University Press, New Haven, pp. 385–424.

1548 Wei, G., Peng, C., Zhu, Q., Zhou, X., Yang, B., 2021. Application of machine learning methods for paleoclimatic reconstructions from leaf traits. International Journal of Climatology 41, E3249–E3262. doi:10.1002/joc.6921

1551 Wenger, S., Olden, J., 2012. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution 3, 260–267.

1554 Wiens, J.J., Graham, C.H., 2005. Niche Conservatism: Integrating Evolution, Ecology, and Conservation Biology. Annual Review of Ecology, Evolution, and Systematics 36, 519–539. doi:10.1146/annurev.ecolsys.36.102803.095431

1557 Williams, J.W., Jackson, S.T., 2007. Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment 5, 475–482. doi:10.1890/070037

1560 Wilson, D.E., Lacher, T.E., Mittermeier, R.A., 2016. Handbook of the Mammals of the World – Vol. 6, Lagomorphs and Rodents I, Lynx Edicions. ed.

1562 Wilson, D.E., Lacher, T.E., Mittermeier, R.A., 2017. Handbook of the Mammals of the World – Vol. 7, Rodents II, Lynx Edicions. ed.

1564 Wood, B., Schroer, K., 2013. Paranthropus. In: A Companion to Paleoanthropology. John Wiley & Sons, Ltd, pp. 457–478. doi:10.1002/9781118332344.ch24
1566

Wood, B., Strait, D., 2004. Patterns of resource use in early Homo and Paranthropus.

1567

Journal of Human Evolution 46, 119–162. doi:10.1016/j.jhevol.2003.11.004

1568

Yalden, D., Yalde, D.W., Brothwell, D.R., Pollard, A.M., 2001. Mammals as Climatic

1569

Indicators. In: Handbook of Archaeological Sciences. John Wiley & Sons Ltd.

1570

Yang, J., Nie, J., Garzanti, E., Limonta, M., Andò, S., Vermeesch, P., Zhang, H., Hu,

1571

X., Wang, Z., Zhao, B., Ncube, L., Stevens, T., Li, M., Li, H., Chen, T., Miao, Y.,

1572

Pan, B., 2021. Climatic Forcing of Plio-Pleistocene Formation of the Modern

1573

Limpopo River, South Africa. Geophysical Research Letters 48,

1574

e2021GL093887. doi:10.1029/2021GL093887

1575

Yu, C., Qin, F., Watanabe, A., Yao, W., Li, Y., Qin, Z., Liu, Y., Wang, H., Jiangzuo, Q.,

1576

Hsiang, A.Y., Ma, C., Rayfield, E., Benton, M.J., Xu, X., 2024. Artificial

1577

intelligence in paleontology. Earth-Science Reviews 252, 104765.

1578

doi:10.1016/j.earscirev.2024.104765

1579

Zachos, J., Pagani, M., Sloan, L., Thomas, E., Billups, K., 2001. Trends, Rhythms,

1580

and Aberrations in Global Climate 65 Ma to Present. Science 292, 686–693.

1581

doi:10.1126/science.1059412

1582

Žliobaitė, I., 2019. Concept drift over geological times: predictive modeling baselines

1583

for analyzing the mammalian fossil record. Data Mining and Knowledge

1584

Discovery 33, 773–803. doi:10.1007/s10618-018-0606-6

1585