# Mid-Holocene wet optimum and Early-Late Holocene arid phases shaped steppe-forest vegetation and human societies of Uzbekistan: Multi-proxy evidences from Lake Fazilman

Lucas Dugerdil, Guillemette Ménot, Odile Peyron, Dilfuza Egamberdieva, Isabelle Jouffroy-Bapicot, Boris Vannière, Jakhongir Alimov, Elise Luneau, Johanna Lhuillier, Sébastien Joannin

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Lucas Dugerdil, Guillemette Ménot, Odile Peyron, Dilfuza Egamberdieva, Isabelle Jouffroy-Bapicot, et al. Mid-Holocene wet optimum and Early-Late Holocene arid phases shaped steppe-forest vegetation and human societies of Uzbekistan: Multi-proxy evidences from Lake Fazilman. CATENA, 2026, 264, pp.109759. 10.1016/j.catena.2025.109759. hal-05451691

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# Mid-Holocene wet optimum and Early-Late Holocene arid phases shaped steppe-forest vegetation and human societies of Uzbekistan: Multi-proxy evidences from Lake Fazilman

Lucas Dugerdil<sup>a,b ID,*</sup>, Guillemette Ménot<sup>a ID</sup>, Odile Peyron<sup>b ID</sup>, Dilfuza Egamberdieva<sup>c,d ID</sup>, Isabelle Jouffroy-Bapicot<sup>e ID</sup>, Boris Vannière<sup>f ID</sup>, Jakhongir Alimov<sup>d,g ID</sup>, Elise Luneau<sup>h,i ID</sup>, Johanna Lhuillier<sup>j ID</sup>, Sébastien Joannin<sup>b ID</sup>

## ARTICLE INFO

Dataset link: https://doi.org/10.1594/PANGAE
A.987394

Keywords:

pollen

brGDGT

Ensemble models

Arid central asia

Plant functional traits

Climate reconstructions

Human impact

## ABSTRACT

The lack of well-dated, high-resolution records from the Uzbekistan piedmont limits our understanding of Holocene climate dynamics, land surface processes, and human-environment interactions in drylands. Although simulations suggest future increased precipitation, models also show considerable discrepancies. In this context, Holocene climate and vegetation reconstructions become crucial to improve model reliability and access climate change consequences on human societies. This study presents the first Holocene-scale, multi-proxy palaeoenvironmental reconstructions from Lake Fazilman, located in the Uzbekistan piedmont area, at the ecotone between steppe and forest.

By combining sediment (X-ray fluorescence, magnetic susceptibility, spectrocolorimetry), pollen, non-pollen palynomorphs, functional trait reconstructions and lipid biomarker analyses, we track hydro-climatic variability, vegetation dynamics, and land-use patterns over the past 10,000 years. Based on quantitative reconstructions locally calibrated, climate ensemble model captures both long-term aridification trends and centennial-scale climate events, including two tipping points at 8.2 ka and 4.2 ka BP driving steppe/juniper woodland alternations under moisture control. These climatic fluctuations are assessed in the context of the Bronze Age socio-environmental transition and the spatial overlap of the proto-urban Bactria-Margiana Archaeological Complex and pastoral Andronovo cultural zones.

Evidence of early human impact appears after 4000 cal yr BP, marked by disturbance-adapted taxa and geochemical shifts, followed by an increase in pastoral indicators around 650 cal yr BP. Despite a precipitation decline during the Bronze Age, climate alone cannot fully explain the decline of the Bactria-Margiana Archaeological Complex. By combining climate-sensitive proxies and land-use indicators, our findings offer new insights into drylands ecosystem resilience and the vulnerability of past societies, informing future responses to environmental change.

<footnote><sup>a</sup> ENS de Lyon, Université Lyon 1, CNRS, UMR 5276 LGL-TPE, Lyon, France</footnote>

<footnote><sup>b</sup> ISEM, University of Montpellier, CNRS, IRD, Montpellier, France</footnote>

<footnote><sup>c</sup> Medical School, Central Asian University, Tashkent 111221, Uzbekistan</footnote>

<footnote><sup>d</sup> Faculty of Biology, National University of Uzbekistan, Tashkent 100174, Uzbekistan</footnote>

<footnote><sup>e</sup> Laboratoire Chrono-environnement, UMR 6249, CNRS, Université Marie et Louis Paster, Besançon, France</footnote>

<footnote><sup>f</sup> Institute of Plant Sciences, Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland</footnote>

<footnote><sup>g</sup> International Agriculture University, Kibray district, 100164, Tashkent, Uzbekistan</footnote>

<footnote><sup>h</sup> Deutsches Archäologisches Institut, Eurasien-Abteilung, Berlin, Germany</footnote>

<footnote><sup>i</sup> CNRS UMR 8215, Trajectoires, Paris, France</footnote>

<footnote><sup>j</sup> CNRS, Laboratoire Archéorient UMR5133, Université Lumière Lyon 2, France</footnote>

<footnote>* Corresponding author at: ISEM, University of Montpellier, CNRS, IRD, Montpellier, France.
E-mail addresses: lucas.dugerdil@ens-lyon.fr (L. Dugerdil), sebastien.joannin@umontpellier.fr (S. Joannin).</footnote>
# 1. Introduction

Although climate forecasting continues to improve in accuracy, the Arid Central Asian (ACA<fnref n="1" />) region remains a challenging case study with unresolved issues (Groisman et al., 2018; Rodda et al., 2019; Li et al., 2019b). Climate models driven by anthropogenic CO₂ emissions predict an increase in precipitation and limited biome migration in ACA under both 1.5 °C and 2 °C global warming scenarios (Intergovernmental Panel on Climate Change, 2018). In contrast, some studies suggest that the region may undergo further aridification and expansion of drylands (Huang et al., 2017). The discrepancy between model climate simulations for ACA (Intergovernmental Panel on Climate Change, 2018) and theoretical expectations for subtropical drylands, where warming is expected to intensify aridification in subtropical regions (Park et al., 2018), can be explained by several factors. First, continental and rain-shadowed geography of ACA plays a major role in regional climate variations (Sato, 2009). Second, increased precipitation may result from enhanced local moisture recycling (Rodda et al., 2019), driven by rising temperatures and agricultural evaporation (Lioubimtseva et al., 2005). Although precipitation responses to warming remain uncertain (Lioubimtseva et al., 2005; Groisman et al., 2018; Chen et al., 2019), climate change already impacts the region. Accelerated glacier melt in the Tian Shan and Pamir-Alai ranges (Aizen et al., 2016) is yet altering river run-off (Sorg et al., 2012), and snow cover is expected to decline under a 1.5 °C scenario across Central Asia (Li et al., 2019b) and the Tibetan Plateau (You et al., 2020), affecting river flow, glacier input, and regional climate systems. These uncertainties are critical as ACA, especially the lowlands and piedmonts of Tajikistan and Uzbekistan, faces high vulnerability and food insecurity. A large population (71.3 million in Central Asia, including 35 million in Uzbekistan; Belolipov et al., 2013; Nunez et al., 2020) depends on ancient irrigated oases along rivers like the Zerafshan, Amu Darya, and Syr Darya (Gintzburger, 2003; Bendezu-Sarmiento and Lhuillier, 2020). Changes in run-off could worsen food insecurity (Rakhmatullaev et al., 2013), especially as regional livelihoods depend on steppe grazing and cereal farming (Christensen et al., 2004), and drylands under strong human pressure are particularly climate-sensitive (Christensen et al., 2004).

To disentangle the complex interactions between climate change and human pressures on ecosystems, palaeoenvironmental studies offer powerful tools for temporally reconstruct socio-ecosystem dynamics. Archaeological remains provide insights into agricultural practices (Spengler, 2020; Lyonnet and Dubova, 2020), irrigation systems (Francfort and Lecomte, 2002; Fouache et al., 2012; Ra'no, 2021), and the relationships between climate (Luneau, 2019), landscapes (Frachetti, 2008), and human societies. In Uzbekistan, questions around ecosystem services and steppe degradation (Wesche et al., 2016; Török et al., 2016) are particularly relevant in the context of nomadic pastoralism (Luneau et al., 2024), and early agricultural societies (Bendezu-Sarmiento and Lhuillier, 2020; Zhou et al., 2025). However, vegetation and climate representations at archaeological sites

are often fragmentary and biased by the nature of the archives. A coupled approach that integrates environmental archives is therefore essential (Spate et al., 2022). While this study focuses on the whole Holocene period, particular attention is devoted to the Bronze Age, as it provides a critical lens through which to explore early interactions between human communities and their environments, especially in relation to land use and agropastoral expansion (Fouache et al., 2020). This period offers a rich archaeological record and encapsulates long-term socio-economic and environmental dynamics that have significantly shaped our understanding of key historical and anthropological issues. These include, notably, the development and decline of the Oxus Civilization (or Bactria-Margiana Archaeological Complex, BMAC) and the expansion of Andronovo communities, often associated with the intensification of pastoral practices. Among the various hypotheses proposed to explain the major societal transformations of the Oxus Civilization (Luneau, 2014, 2020; Bendezu-Sarmiento and Lhuillier, 2020), an episode of abrupt aridification has been proposed as a key factor (Koshelenko et al., 1994; Mousavi, 2008), though Luneau (2019) argue that there is no strong evidence for a significant climatic disruption during the early 4th millennium BP that could be directly linked to the decline of the Oxus Civilization, emphasizing the lack of reliable local reconstructions near the BMAC. An alternative hypothesis considers the transformative impact of contacts with (nomadic) populations migrating from the northern regions (Vinogradova and Kuz'mina, 1996; Luneau, 2014; Rouse and Cerasetti, 2018; Luneau, 2020). Well dated environmental archives from the region may help clarify these competing hypotheses.

Holocene climate reconstructions for Central Asia are sparse due to limited archives and past political instability. Available records from the Aral and Caspian seas (Sorrel et al., 2006, 2007; Leroy et al., 2013a,b), loess (e.g., Obi-Rakhmat, Rudak; Welc et al., 2016; Zhang et al., 2020), and glacial lakes (e.g., Karakol, Karakul, Issyk-Kul; Beer and Tinner, 2008; Aichner et al., 2019; Leroy and Giralt, 2021), suggest a generally dry Early Holocene followed by a wetter Mid-Holocene and gradual Late Holocene aridification, likely linked to weakening (Chen et al., 2008) or southward migration of the Westernlies (Fig. 1E; Chen et al., 2019). Seasonal precipitation regimes varied, but winter-dominated systems remained stable in the Turan depression (Herzschuh et al., 2022). Centennial oscillations along the Holocene are evident, including the 8.2, 4.2 and 3.5 ka BP droughts and later periods aligned with the Roman Warm Period, Dark Ages Cold Period, Medieval Warm Period, and Little Ice Age (respectively RWP, DACP, WMP and LIA; Chen et al., 2019; Rodda et al., 2019). However, Central Asia remains largely understudied in this context (Groisman et al., 2018), with only a few investigations into Holocene climate variability in ACA (Chen et al., 2008; Tan et al., 2024), and a near-total absence of records from southern Central Asia (Afghanistan, Uzbekistan, Turkmenistan, Tajikistan). Past archives from the Central Asian piedmonts are essential to describe the regional climate sensitivity of this complex transitional zone between westerly- and monsoon-influenced domains (Chen et al., 2019, 2022a). However, piedmont lake records are still lacking, although they are crucial for tracing human activity signals and assessing regional climate variability along elevation gradients.

A wide range of stratigraphical and palaeoecological approaches have been applied globally to reconstruct past climate and vegetation dynamics in Eastern Europe and Asia. Among them, pollen enable the reconstruction of plant community successions (Joannin et al., 2022), human impact history (Robles et al., 2022), and climate parameters via pollen-based approaches (Herzschuh et al., 2022). Given the complexity of environmental signals in ACA, accurate climate and vegetation reconstructions depend on robust modern calibrations for pollen data (Tian et al., 2020), plant traits (van der Sande et al., 2021) and vegetation cover (Cao et al., 2018b). Plant functional traits characterize vegetation responses to climate conditions (Garnier et al., 2004), and their reconstruction from pollen assemblages allows the

<footnote n="1">¹ Abbreviations: ACA, Arid Central Asia; AI, Aridity Index; BIT, Branched and Isoprenoid Tetraether index; BMAC, Bactria-Margiana Archaeological Complex; BP, Before Present; BRT, Boosted Regression Trees; CWP, Current Warm Period; CWM, Community Weighted Mean; CMIP, Coupled Model Intercomparison Project; DACP, Dark Ages Cold Period; iso and brGDGT, isoprenoid or branched Glycerol Dialkyl Glycerol Tetraether; LIA, Little Ice Age; CB-LS, Combined Locally Scaled; MAT, Modern Analogue Technique; MAAT, Mean Annual Air Temperature; MAF, Mean Temperature of Months Above Freezing; MAP, Mean Annual Precipitation; MBT, Methylation index of Branched Tetraethers; MI, Methane Index; MR, Multiple linear Regression; MS, Magnetic Susceptibility; MWP, Medieval Warm Period; NAP, Non-Arboreal Pollen; NPP, Non-Pollen Palynomorph; PCA, Principal Component Analysis; RWP, Roman Warm Period; SAR, Sediment Accumulation Rate; SLA, Specific Leaf Area; SSD, Stem Specific Density; SC, Spectrocolorimetry; WAPLS, Weighted Averaging Partial Least Squares and XRF, X-ray Fluorescence</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overview of the study area. (A) Landscape picture of the Lake Fazilman highlighting lake spring, former outlet and geological settings of the coring site. (B) Regional map showing the location of Lake Fazilman and surface samples (TUSDB) used for climate calibration, elevation data from worldclim2.1 (Fick and Hijmans, 2017). The maximum extent of the Andronovo and Bactria-Margiana Archaeological Complex are shown in blue and red areas, respectively. (C) Aerial view of Lake Fazilman (from QGIS 3.26.1, CNES/Airbus 2023; source: earth.google.com, accessed February 2023). (D) Watershed context within the Nuratau Range. The map (B) presents the location of the archaeological sites discussed in the study (blue dots from, e.g., Lhuillier et al., 2013; Luneau, 2014; Mutin, 2023). The speleothems from Tonnel'naya (Cheng et al., 2016) and Talisman (Tan et al., 2024) caves, as well as pollen sequences from the Aral Sea (Sorrel et al., 2006), Lake Bakaly (Beer et al., 2008), and Lake Kichikol (Beer et al., 2007a), are also presented. Ogshagil wetlands studied in Barhoumi et al. (2000) provides local fire history.</figcaption></figure>

reliability of past climate reconstructions to be assessed (Birks, 2020). Yet, using single statistical methods to reconstruct past climate changes have limitations (Ter Braak et al., 1993; Salonen et al., 2019), prompting a growing trend toward multi-method approaches that mitigate individual model biases (Brewer et al., 2008; Peyron et al., 2013; Robles et al., 2022; d'Oliveira et al., 2025; Geng et al., 2025). Despite these methodological advances, challenges remain that constrain the reliability of pollen assemblages as a stand-alone proxy. Consequently, the integration of multiple proxies remains essential for refining pollen-based interpretations (Lotter, 2014). A robust chronological framework, combined with a precise understanding of sedimentological processes enhances reliability. Geochemical analyses help identify these processes, particularly erosion in the watershed and authigenic production within the lake. Micro X-ray Fluorescence (XRF) allows for tracking relative elemental changes along the core (Liu et al., 2013; Croudace

and Rothwell, 2015), while Magnetic Susceptibility (MS; Mathis et al., 2014) and Spectrocolorimetry (SC; Debret et al., 2011) further help in interpreting sediment dynamics and environmental shifts.

To reduce the possible error linked to the human impact on pollen-based climate reconstructions, independent climate proxies are essential for comparison (Robles et al., 2022; Cartapanis et al., 2022; d'Oliveira et al., 2023). Over recent decades, organic geochemistry has proven a powerful tool, particularly the use of lipid biomarkers called glycerol dialkyl glycerol tetraethers (GDGTs; Weijers et al., 2007; De Jonge et al., 2014). Two GDGT subfamilies are commonly studied: isoprenoid GDGTs (isoGDGTs) and branched GDGTs (brGDGTs), the latter produced by bacteria present in soil samples, likely from the *Acidobacterium* genus (Chen et al., 2022b). The relationship between brGDGT distributions and environmental parameters, especially the temperature, is well documented (Weijers et al., 2007; Raberg et al.,
2021). However, brGDGTs-based temperature reconstructions often exhibit biases in drylands, particularly in ACA (Lin et al., 2024). As for pollen and other proxies calibration in these regions is critical, as brGDGT signatures are influenced not only by temperature but also by soil pH (Xie et al., 2015) and precipitation (Duan et al., 2022). These confounding factors may lead to large prediction errors. Thus, careful selection and validation of regional calibrations are essential.

In this context, our study presents the first comprehensive Holocene-scale palaeoenvironmental reconstruction for Uzbekistan, based on sediment record from Lake Fazilman in the Nuratau Range. Our approach integrates (1) the history of human impact, vegetation, and plant functional traits, and (2) quantitative climate reconstructions. This is achieved through a multi-proxy framework that captures key environmental, climatic, and anthropogenic signals. Human impact is assessed using NPPs such as dung fungal spores, anthropogenic pollen indicators, and XRF-derived pollution markers. Vegetation changes are inferred from raw pollen data and pollen-based biome-scores, while plant functional traits are reconstructed using Community Weighted Means derived from modern trait databases and fossil pollen assemblages. Climate reconstructions are obtained from brGDGT fractional abundances and pollen assemblages. Although several biases may influence proxy-environment relationships – such as aridity, sample type, and pH for brGDGT-based climate reconstructions (Xie et al., 2015; Duan et al., 2022), human activity for pollen (Huang et al., 2018a; Robles et al., 2022), taphonomic biases for NPPs (Shumilovskikh and van Geel, 2020), and dilution or moisture-related effects on XRF measurements (Coudace and Rothwell, 2015) – the Lake Fazilman record benefits from locally developed calibrations for plant traits (Dugerdil et al., 2025e), pollen assemblages (Dugerdil et al., 2025d), and brGDGTs (Chen et al., 2021; Dugerdil et al., 2025a,b). These efforts enable the most reliable palaeoenvironmental reconstructions for the Holocene in the piedmonts of Uzbekistan. We therefore applied a rigorous calibration selection to build *ensemble* climate reconstructions which are as unbiased as possible. This study is therefore structured around the following key research questions:

1. Can lacustrine archives reliably capture Holocene environmental changes in Uzbekistan?

2. From which historical period does human impact become detectable in Uzbekistan?

3. How accurate and reliable are the multi-proxy climate reconstructions for Lake Fazilman?

4. How might the Bronze Age climate have influenced the decline of the BMAC civilization?

5. How did plant community functional traits shift throughout the Holocene in response to climate fluctuations?

To address these key research questions, the manuscript follows a structured workflow. First, we assess the suitability of the Lake Fazilman sediment core for reconstructing Holocene environmental change using multiple proxies that track lacustrine dynamics (e.g., inorganic geochemistry, sediment accumulation rates, macro-remains, and algal assemblages). Variations in the hydrology feeding the lake are examined alongside sedimentological changes, water-level fluctuations, and geomorphological setting. Second, using pollen assemblages, we reconstruct past vegetation and biomes after evaluating the reliability and potential biases of key pollen types. We then investigate the history of human impact by comparing regional archaeological and palaeoenvironmental data with human-related proxies recovered from the core. Subsequently, we evaluate the accuracy of climate reconstructions using a multi-proxy, multi-method, and multi-calibration approach that integrates pollen- and brGDGT-based *ensemble* models. Finally, we discuss how climate variability influenced the rise and decline of Bronze Age societies and shaped vegetation functional responses. The manuscript concludes with a critical appraisal of the study's limitations and avenues for improving our understanding of Holocene climate and environmental change in Central Asia.

## 2. Regional setting

Lake Fazilman (Fazilman kul, 40° 33'1.46"N, 66° 35'21.70"E; 1637 m a.s.l., Fig. 1A) is a piedmont lake located in the Nuratau range at the junction of Navoi and Samarkand provinces (Fig. 1B). The piedmont location of Lake Fazilman is particularly valuable for understanding macro-regional climate changes during the Holocene, as most sedimentary archives in the region are located in high mountain settings (Chen et al., 2019). Piedmont zones, representing transitional areas between high mountain ranges and lowland basins, have so far been primarily documented in ACA through loess-palaeosol sequences, which are often limited in terms of proxy diversity (Li et al., 2024). The Fazilman regional geology is shaped by Precambrian metamorphics and Paleozoic sediments from the former Turkestan Ocean, later folded during Late Carboniferous-Early Permian orogeny and reactivated by the Cenozoic Indian-Asian collision (Nurtaev et al., 2013). The lake lies in a structurally complex area with Permo-Carboniferous rocks and intrusive plutonic granites, facilitating groundwater flow (Toth, 2009). Lake Fazilman is fed by seasonal run-off and a nearby perennial spring, profiting of this rich fold network in the craton (spring dashed ellipse on Fig. 1A). The watershed area is ca. 120 ha. Lake Fazilman has no current surface outlet but shows geomorphic traces of past drainage paths (former outlets dashed ellipse on Fig. 1A). Some other landscape evidences attest the presence of other outlets on the north-east part of the lake, at lower elevation (Fig. S1). The lake area varies seasonally between 6.6 and 9 ha (Fig. 1C), with a shallow water column (1–2 m), and is sparsely covered by floating mats and settled vegetated islands mainly composed by *Hippuris vulgaris*, *Schoenoplectus litoralis* and *Phragmites australis*, with open water hosting Characeae and *Potamogeton lucens*.

The lake lies within the Alai-Western Tian Shan steppe ecoregion, dominated by Poaceae, Asteraceae, and *Artemisia* spp., shaped by arid climate and sparse loess deposits. Surrounding vegetation includes *Acantholimon nuratavicum*, *Astragalus bactrianus*, and *Carex pachystylis*, with grazing by livestock. Rocky slopes and piedmonts host *Ephedra intermedia*, *Ferula* spp., *Eremurus* spp. and *Oxytropis* spp., while riparian zones support *Elaeagnus angustifolia*, *Juglans regia*, *Salix* spp., *Populus alba*, and *Celtis caucasica* species (Fig. 1D). The northern slopes and rock screes in the piedmont also host mosaic steppe-shrubland ecotones with *Rosa* spp., *Pistacia vera* and *Rhamnus* spp.. Lake Fazilman sits at the ecotone between lowland *adyr* steppe and mid-mountain *tau* forest-steppe (Gintzburger, 2003), with relict stands of *Juniperus seravschanica* above 1800 m and scattered *Platycladus orientalis* in canyons (Beshko, 1999).

The distinct geography of the Nuratau range results in a continental climate characterized by sharp seasonal temperature contrasts with freezing winters (Lioubimtseva et al., 2005) and extremely hot summers (Belolipov et al., 2013). Most of the Mean Annual Precipitation (MAP) falls during the spring months and remains below 350 mm yr⁻¹ (worldclim2.1; Fick and Hijmans, 2017; Belolipov et al., 2013). The current Mean Annual Air Temperature (MAAT) is ca. 8.2 °C. Human presence in the region dates back to the Lower Palaeolithic, with Upper Palaeolithic tools identified in Nuratau and Neolithic evidence from ca. 8950 cal yr BP (Nishiaki et al., 2022). During the Bronze Age, the lake was situated within the Andronovo cultural sphere (Lyonnet and Dubova, 2020). The Fazilman region remains populated today, with extensive pastoral practices still maintained, reflecting a long-standing tradition of herding in the area (Luneau et al., 2024).

## 3. Methods

### 3.1. *Approach and objectives*

This study relies on a novel, integrative methodological workflow combining multi-proxy, multi-method, and multi-calibration approaches, graphically illustrated in Fig. 2. It builds upon recent modern
calibration studies to ensure the most reliable Holocene reconstructions from Lake Fazilman. In Fig. 2, grey boxes represent the overall sampling strategy, including the extraction of global databases (climate parameters, plant traits, regional plant checklists), global modern datasets used for pollen- and brGDGT-based calibrations, and local modern sampling around Lake Fazilman. Brown boxes indicate the modern calibration studies that underpin both semi-quantitative and quantitative reconstructions: brGDGT-climate calibrations (Sun et al., 2011; De Jonge et al., 2014; Yang et al., 2014; Chen et al., 2021; Dugerdil et al., 2025a,b), pollen-based climate and vegetation reconstructions (Dugerdil et al., 2025d), and pollen-based functional trait reconstructions (Dugerdil et al., 2025e).

Red boxes summarize the methodological steps implemented in the present study, focusing on the past application of these calibrations: (1D) sediment coring of Lake Fazilman; (2C) multi-proxy analyses on the sediment core (pollen, GDGTs, NPPs, MS, XRF); (3C) quantitative climate reconstructions using multiple statistical methods trained on multiple calibration datasets; (4A) selection of the most reliable reconstructions based on model performance, comparison with modern patterns, and modern-past analogue assessment; (4B) development of ensemble climate models combining the selected reconstructions; (4C) reconstruction of semi-quantitative variables that do not require modern calibration datasets (e.g., biome scores, functional traits, human-impact indicators); (4D) evaluation of sediment quality, chronology, and lacustrine dynamics to assess the reliability of the Fazilman archive. Together, these steps structure the workflow of the study, linking modern calibration research with past proxy-based reconstructions.

## 3.2. Lake Fazilman settings and coring

The sediment core was collected in July 2021 at Lake Fazilman. A 227 cm-long core was retrieved from a stable vegetated island using a 50 cm Russian corer. Two twin boreholes, spaced 60 cm apart, yielded nine 50 cm core sections. A composite master sequence was constructed through field-based lithological correlation, supported by XRF and MS cross-referencing (see Fig. S2). Sediment descriptions were visually determined from colour, texture, and average grain size, and the main boundaries between sedimentary units were refined using major variations in the XRF and MS data (Fig. 3). Subsamples of 1 cm³ were extracted using a die-cut for geochemical and palynological analyses. Soil surface samples were also collected from four sites within the Fazilman watershed (green dots on Fig. 1D and Dugerdil et al., 2025d).

## 3.3. AMS-radiocarbon sediment dating

Fifteen samples from the Fazilman core were analysed for AMS-¹⁴C dating (Table S1 and Barhoumi et al., 0000). For each sample, 3 cm³ of sediment were extracted using a die-cut, washed with distilled water, and sieved at 250 µm. Organic or biogenic carbonate materials were hand-picked under 100× magnification using a binocular microscope. The upper three samples contained selected organic remains – mainly Schoenoplectus litoralis seeds (Bojňanský and Fargašová, 2007), wood, charcoal, and peat – nine others consisted of biogenic carbonate remains (snail shells and ostracods), and the final two were bulk sediment, sampled at the same depth as carbonate samples to assess potential reservoir effects. Identified molluscs include freshwater Lymnaeidae and Planorbidae (Gyraulus spp. and G. cf. crista, Vinarski et al., 2017). One additional sample was a macro-charcoal fragment (1 cm × 3 mm × 3 mm) from unidentified angiosperm wood. All samples except the macro-charcoal were dated at the LMC14 lab, Gif-sur-Yvette, using standard chemical pretreatment protocols (Dumoulin et al., 2017) and AMS via ARTEMIS (Moreau et al., 2020); the macro-charcoal was dated at the Poznań Radiocarbon Laboratory. Given that radiocarbon dates from bulk sediment are approximately 1500 years

older than those from fossil material at the same sediment depth (cf. Table S1), we infer a strong reservoir effect in the bulk sediment. Consequently, the bulk samples were excluded from the age-depth model (Fig. 3).

Dates were calibrated using the IntCal20 curve (northern hemisphere terrestrial; Reimer et al., 2020) and modelled in R 4.2.2 using the rbacon package (version 3.3.1; Blaauw and Christen, 2011). Age-depth modelling employed a Bayesian approach, with accumulation rates and uncertainty modelled via Markov Chain Monte Carlo. For the rbacon parameters, the prior accumulation rate mean was set to 20 with a shape of 1.5, the section thickness to 5, and the prior memory mean and strength to 0.5 and 10, respectively, following the default settings recommended by the algorithm. A hybrid model combining BACON and clam (version 2.6.2; Blaauw, 2022) was used to better fit the age-depth structure prior to a rapid increase in accumulation Sediment Accumulation Rate (SAR). The age-depth model displays a 95% confidence interval. Along the manuscript, dates are presented in calibrated years Before Present (hereafter cal yr BP).

## 3.4. Inorganic geochemistry

The inorganic geochemistry analyses were conducted on bulk sediments in order to retain as much material as possible for subsequent palaeoenvironmental studies. X-ray fluorescence (XRF) analysis was conducted at EDYTEM Laboratory (Chambéry) using an XRF Core Scanner 3 (Jansen et al., 1992). A Rhodium X-ray source and Ultralene film were used, with measurements at 10 kV (light elements) and 30 kV (heavy elements). Scans were taken every 5 mm along the sediment with a 10-second retention time. Of 18 scanned elements, 13 with sufficient signal-to-noise ratios were retained: Br, Ca, Fe, K, Mn, P, Pb, Rb, S, Sr, Ti, Zr, and Zn. Raw data are expressed in counts per second (cps). To reduce dilution effects, each element was normalized using either a resistant element (e.g., Ti) or established geochemical ratios (Croudace and Rothwell, 2015). Incoherent (Compton) and coherent (Rayleigh) scatter signals were also recorded. The Inc/Coh ratio is typically used as a proxy for organic matter but may also reflect carbonate content and should be interpreted cautiously (Liu et al., 2013). The analytical error was estimated from six repeated measurements each ten cm along the sediment. The variance obtained from these replicates was then extrapolated to neighbouring samples to estimate the 95% confidence intervals of the XRF data.

Magnetic Susceptibility (MS), gamma density (γD), and Spectrocol- orimetry (SC) were measured using a Geotek Multi Sensor Core Logger at the Chronoenvironnement laboratory in Besançon. The scanning resolution is of 5 mm along the sediment cores. MS values are expressed in 10⁻⁵ SI. For SC, optical lightness (L*), expressed as a percentage and reflecting sediment brightness, was used as a proxy for carbonate content (Mix et al., 1995). The 700/400 nm wavelength ratio (Q7/4), covering the visible light spectrum, was used as a proxy for oxidation versus clay content. These SC signals were jointly interpreted using a Q7/4 diagram to characterize sediment composition (Debret et al., 2011). Relative Absorption-Band Depth between 660 and 670 nm was calculated following the method of Rein and Sirocko (2002) displayed in Eq. (1).

$$ \text{RABD}_{660-670} = \left[ \frac{(6 \times \text{R}_{590} + 7 \times \text{R}_{730})}{13} \right] / \text{R}_{\min(660; 670)} \quad (1) $$

with R_n the reflectance for the wavelength n and R_min(660; 670) the minimum reflectance between wavelength 660 or 670 nm. RABD_{660-670} is interpreted as a proxy of the sedimentary chlorophyll content and by extension a proxy of lake productivity (Zhao et al., 2022).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Methodological workflow followed to accurately reconstruct human, vegetation and climate Holocene variations from the Lake Fazilman sequence. The process relies on five steps from the initial data sampling to the final key research questions raised in the present study. Grey boxes display sampling strategy, brown boxes show the modern studies used to calibrate past signals (De Jonge et al., 2014; Chen et al., 2021; Dugerdil et al., 2025e,d,a,b), while red boxes present the past application of these calibrations (the present study).</figcaption></figure>

## 3.5. Lipid biomarkers

### 3.5.1. GDGT extraction and analysis

Glycerol dialkyl glycerol tetraethers (GDGTs) were extracted from the Fazilman sediment samples following the laboratory protocol fully detailed in Dugerdil et al. (2021a). Mainly, each of the 60 GDGT samples of one cm³ of sediment, after a 24 h lyophilization and weighing, was crushed. The total lipid content (TLC) was twice extracted from the sediment with a dichloromethane (DMC)/methanol (MeOH) mixture (3:1, *v*/*v*) solvent extraction catalyzed by microwave (MARS 6 CEM) at 70 °C and under pressure. The extraction program increases the temperature to 70 °C over 5 min, maintains it at 70 °C for 15 min, and then activates the cooling fans to reduce the temperature for 20 min. Then, to separate GDGTs from other lipids, the TLC was filtered on solid-phase extraction (SPE) cartridges. External C₄₆ GDGTs were added as internal standard in order to estimate the Fazilman GDGTs absolute concentration (Huguet et al., 2006). The TLE was then separated into polar and apolar fractions on a silica column with 5 mL of hexane/DCM (1:1, *v*/*v*) and 10 mL of DCM/MeOH (1:1, *v*/*v*) respectively, and finally concentrated in vials.

The analysis of GDGTs was performed by Ultra-High Performance Liquid Chromatography (UHPLC) using an Agilent 1290 Infinity coupled to an Agilent 6125 single quadrupole mass detector from the LGLTPE, ENS de Lyon laboratory. Analytical separation of GDGTs

was achieved using two UHPLC silica columns (BEH HILIC columns, 2.1 × 150 mm, 1.7 µm). The elution of GDGTs was achieved in three phases: a first phase of isocratic elution using hexane/iso-propanol (99.8:0.2, *v*/*v*) for 25 min during which isoGDGTs were eluted, then a linear gradient to 3.5% hexane in 50 min for brGDGTs elution and a last linear gradient to 10% hexane in 80 min for H-GDGTs and OH-GDGTs elution. After each analysis, columns were cleaned by back-flushing and they were finally equilibrated during 20 min. Flow rate was 0.2 mL min⁻¹ during all the analysis.

Six samples from the main lithological units of the core were analysed, each with five repeated measurements to assess the reproducibility of the analytical setup. The variance obtained from these replicates was then extrapolated to neighbouring samples to estimate the 95% confidence intervals of the GDGT indices and reconstructions. Then, each compound was manually integrated using the *m/z* ratio (i.e., 1302, 1300, 1298, 1296, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1022, 1020 and 1018 for C₄₆ internal standard), in order to identify isoGDGTs (Liu et al., 2012), brGDGTs 5-, 6- (De Jonge et al., 2014) and 7-methyls (Ding et al., 2016). Following De Jonge et al. (2014), the Roman numerals represent different GDGT structures.

### 3.5.2. Indices and fractional abundance calculations

From these raw integrations, the absolute concentrations expressed in mg g⁻¹ gₜ[sed] were estimated following Huguet et al. (2006) and the
<figure><img src="image_5.png" /><figcaption>Fig. 3. Age model and sedimentological summary of the Fazilman core. (A) Age-depth model based on 14 AMS-¹⁴C dates (see Table S1 and Barhoumi et al., 2000), built using the BACON Bayesian model (Blaauw and Christen, 2011) and the IntCal20 calibration curve (Reimer et al., 2020). The dashed blue line indicates the mean age-depth model, the grey shaded area represents the probability density, and the grey dashed ribbon shows the 95% confidence interval. (B) Principal Component Analysis (PCA) of XRF core geochemistry. (C) Sedimentation Accumulation Rate (SAR), Magnetic Susceptibility (MS), and PCA axes of XRF data. (D) Distribution of key macrofossils along the core: Cyperaceae seeds, Characeae gyrogonites and oosporangia, Characeae stems, ostracods, and gastropods (Gyraulus cf. crista and Gyraulus spp.). (E) Lithological profile combining in-situ sediment descriptions and Spectrocolorimetry (SC). (F) SC plot showing sediment dispersion between lightness (L*) and Q7/4 ratio (700/400 nm wavelength). Eight sedimentological units were identified using CONISS cluster analysis on geochemical data from panel C (details in Fig. S3). Unit boundaries are indicated on panel C and correspond to colour-coded lithology, consistent across panels B and E.</figcaption></figure>

fractional abundances (De Jonge et al., 2014) were calculated using Eq. (2).

$$[brGDGT_i]_k = \frac{Area_{i,k}}{\sum_{j=1}^{N_{tot,k}} Area_{j,k}} \qquad (2)$$

with integrated peak area (Area) from HPLC-APCI-MS used to calculate concentrations [brGDGTᵢ]ₖ for each compound i in sample k. These values were used to compute the relative abundance of tetra- (Ia, Ib, Ic), penta- (IIa, IIb, IIc), and hexamethylated brGDGTs (IIIa, IIIb, IIIc). Derived indices included MBT'₅Me, MBT'₆Me, CBT' (De Jonge et al., 2014), isomeric ratios IR₆Me and IR₇Me (Wang et al., 2021), and the Community Index (CI; De Jonge et al., 2019, 2021). IsoGDGT-based indices such as Crenarchaeol abundance (%Cren; Wang et al., 2021) or the Methane Index (MI; Zhang et al., 2011) were also calculated. To track variation in GDGT sources within the Lake Fazilman, we used different brGDGT indices such as the Ib/Ia ratio (with Ib more abundant in soil samples than in lake water; Zhu et al., 2021) or the IIIa/IIa ratio (Xiao et al., 2016). Some isoGDGT ratios also give information about the sources variations such as the BIT index (high BIT value indicates enhanced soil proportion in the GDGT influx; Hopmans et al., 2004), and the mixed iso-/brGDGT ratio (Rᵢ/b; Xie et al., 2012; Yang

et al., 2014). All formulas, references, and proxy interpretations are summarized in Table 1.

## 3.6. Pollen analysis

Pollen was extracted from 53 sediment samples using the standard protocol of Faegri and Iversen (1989) at the Chrono-Environnement laboratory: (1) carbonate removal with 10% HCl, (2) silicate removal with 10% HF, (3) heavy liquid separation using ZnCl₂ and HCl (density 1.1:1.3), (4) filtration with 250 and 10 µm sieves after NaOH, and (5) acetolysis to eliminate cellulose and residual organic matter. All residues were concentrated in glycerol and mounted between a microscope slide and cover slip. Then, samples were stored in glycerin and counted using a BRESSER Science Infinity microscope at 40x and 100x magnification. Full details follow Dugerdil et al. (2025d).

Pollen identification used atlases and keys from temperate environments (Reille, 1999), Mongolia (Murad, 2012), Central Asia (Lu et al., 2018), and the Caucasus (Connor et al., 2018), as well as the pollen reference collection from the ISEM, University of Montpellier. Pollen counts were recorded using PolSais 4.1 (developed in Python 3.0 by Dugerdil et al., 2021a). Total pollen counts were estimated based on the rarefaction curves which evaluate the pollen diversity
<caption>**Table 1**</caption>

Main GDGT indices discussed in this study with their formula, proxy interpretation and references. Performance statistics for all climate reconstruction models are provided in Table S2.

<table><thead><tr><th>Index</th><th>Formula</th><th>Proxy interpretation</th><th>References</th></tr></thead><tbody><tr><td>MBT'<sub>5Me</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ia + Ib + Ic</div><div>Ia + Ib + Ic + IIa + IIb + IIc + IIIa</div></div></td><td>Temperature, precipitation<br>thought soil moisture</td><td>De Jonge et al. (2014)</td></tr><tr><td>MBT'<sub>6Me</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ia + Ib + Ic</div><div>Ia + Ib + Ic + IIa' + IIb' + IIc' + IIIa'</div></div></td><td>Temperature, independent of pH,<br>better in drylands</td><td>De Jonge et al. (2014), Yang et al.<br>(2015), Dang et al. (2016)</td></tr><tr><td>CBT'</td><td>= -log(<div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ic + IIa' + IIb' + IIc' + IIIa' + IIb' + IIIc'</div><div>Ia + IIa + IIIa</div></div>)</td><td>pH</td><td>De Jonge et al. (2014)</td></tr><tr><td>R<sub>i/b</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">IV + IV' + V + VI + VII + VIII</div><div>Ia + Ib + Ic + IIa + IIb + IIc + IIIa + IIIb + IIIa</div></div></td><td>Aridity</td><td>Xie et al. (2012), Yang et al. (2014)</td></tr><tr><td>BIT</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ia + IIa + IIIa + IIa' + IIIa' + IIIa'' + IIa''' + IIIa'''</div><div>Ia + IIa + IIIa + IIa' + IIIa' + IIIa'' + IIa''' + IIIa'' + Cren</div></div></td><td>Lake/Basin, OM production</td><td>Hopmans et al. (2004)</td></tr><tr><td>MI</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">GDGT1+GDGT2+GDGT3</div><div>GDGT1+GDGT2+GDGT3+Cren+Cren'</div></div></td><td>Methanotrophic archaea, anoxia</td><td>Zhang et al. (2011)</td></tr><tr><td>CI</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ia</div><div>Ia + IIa + IIIa</div></div></td><td>Exclude pH sensitive compounds<br>brGDGT-producing bacterial<br>community</td><td>De Jonge et al. (2019, 2021)</td></tr><tr><td>Ib/Ia</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Ib</div><div>Ia</div></div></td><td>brGDGT source (soil vs. lake),<br>seasonality</td><td>Zhu et al. (2021)</td></tr><tr><td>IIla/IIa</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">IIla</div><div>IIa</div></div></td><td>brGDGT source (soil vs. lake)</td><td>Xiao et al. (2016)</td></tr><tr><td>%<sub>Cren</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Cren</div><div>Σ isoGDGT<sub>i</sub></div></div></td><td>Water table</td><td>Wang et al. (2021)</td></tr><tr><td>IR<sub>6Me</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Σ X<sub>6Me</sub></div><div>Σ X<sub>5Me</sub> + Σ X<sub>6Me</sub></div></div></td><td>pH, salinity</td><td>De Jonge et al. (2015)</td></tr><tr><td>IR<sub>7Me</sub></td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">IIIa'' + IIa''</div><div>IIa + IIIa' + IIIa'' + IIa + IIa' + IIa''</div></div></td><td>Salinity</td><td>Wang et al. (2021)</td></tr><tr><td>MAAT<sub>mr-Yang</sub></td><td>= 20.9 - 13.4 × IIa' - 17.2 × IIIa' - 17.5 × IIb' + 11.2 × Ib</td><td>Soil, local from arid China</td><td>Yang et al. (2014)</td></tr><tr><td>MAAT<sub>Tjk-Chen</sub></td><td>= 1.1 + 19.2 × MBT'<sub>5Me</sub></td><td>Soil, local from Tajikistan</td><td>Chen et al. (2021)</td></tr><tr><td>MAAT<sub>L-Sun</sub></td><td>= 3.949 - 5.593 × CBT + 38.213 × MBT'<sub>5Me</sub></td><td>Lake, global</td><td>Sun et al. (2011)</td></tr><tr><td>MAAT<sub>mr-DJ</sub></td><td>= 7.17 + 17.1 × Ia + 25.9 × Ib + 34.4 × Ic - 28.6 × IIa</td><td>Soil, global</td><td>De Jonge et al. (2014)</td></tr><tr><td>MAAT<sub>mrs-DJ</sub></td><td>= 5.58 + 17.91 × Ia - 18.77 × IIa</td><td>Soil, global</td><td>De Jonge et al. (2014)</td></tr><tr><td>MAAT<sub>5Me-DJ</sub></td><td>= -8.57 + 31.45 × MBT'<sub>5Me</sub></td><td>Soil, global</td><td>De Jonge et al. (2014)</td></tr></tbody></table>

saturation using a logarithmic asymptote threshold of 0.05 (Birks and Line, 1992). The terrestrial pollen counts are comprised between 213 and 1086 grains, with an average value of ca. 430 grains per slide. Absolute concentrations were calculated from exotic *Lycopodium* spp. spores added to each sample (batch 140119321). Pollen percentages were computed following Eq. (2).

## 3.7. Non-Pollen Palynomorphs

Non-Pollen Palynomorphs (NPPs), analysed from the same slides as pollen, include cyanobacteria, fungal remains (e.g., arbuscular mycorrhizal fungi, smut fungi spore balls, sporanges, and fruiting bodies), testate amoebae, and aquatic microfossils such as micro-algae and remains of hydrophilous plants. NPP processing followed the same protocol as for pollen, including chemical treatment, counting, and data plotting.

### 3.7.1. Ecological assemblages

NPPs serve as indicators of various ecological processes, including grazing activity (e.g., coprophilous fungi; Gauthier and Jouffroy-Bapicot, 2021), wood decomposition (e.g., saprophytic spores; Ellis and Ellis, 1985; Cugny, 2011), fire events (Van Geel, 1978; Bakker and Van Smeerdijk, 1982), wetland conditions (linked to plant parasitism or soil moisture tolerance; Bakker and Van Smeerdijk, 1982; Montoya et al., 2010), and erosion (Kolaczek et al., 2013). Some fungal parasites are pathogens to grasses or cereals, indicating grassland or crop presence (Wei et al., 2021). While key taxa such as *Sporormiella*, *Sordaria*, and *Glomus* spp. are commonly used to infer past pastoral pressure, fire, water level, and erosion (Shumilovskikh and van Geel, 2020; Wei et al., 2021), we defined eight broader ecological assemblages to improve interpretative reliability. The functional NPP assemblages used

in the present study are: aquatic, wetland, aridity and erosion markers; saprophytic and lignicolous spores, dung fungal spores, plant pathogen, and ubiquitous or indeterminable markers.

### 3.7.2. NPP influx

The composition of algal taxa provides insights into lake water levels and trophic status (Jankovská and Komárek, 2000). Identification of NPPs from Tajikistan and Uzbekistan followed keys from Cugny (2011) and Murad (2012), supported by reference plates from Ghosh et al. (2017) and Wei et al. (2021). Surface pollen samples from the region (Dugerdil et al., 2025d) were also used to assess modern NPP assemblages around Lake Fazilman. NPP abundance was calculated as influx (Fx) for each taxon *i*, based on its concentration ([NPP]<sub>*i*</sub>), following Eq. (3). These values were ranked using SAR and gamma density of the core to obtain influx values in particles.cm<sup>-2</sup> yr<sup>-1</sup> [Eq. (4)]. Influx permit to avoid dilution effect.

$$ [NPP]_i = \frac{N_{NPP_i}}{N_{Lyco-counted}} \times N_{Lyco-total} \quad (3) $$

and

$$ Fx(NPP_i) = [NPP]_i \times SAR \times \gamma D \quad (4) $$

The algae spectrum is presented in the pollen diagram as fractional abundances expressed on the total of pollen and algal remains (%TP+NPP):

$$ f(NPP)_i = \frac{N_{NPP_i}}{\sum N_{NPP}} \times \sum N_{pollen} \times 100 \quad (5) $$
## 3.8. Pollen-based palaeoenvironmental reconstructions

### 3.8.1. Semi-quantitative reconstructions

Pollen assemblages from Lake Fazilman were used to reconstruct multiple palaeoclimate and palaeoenvironmental parameters. The complete calibration procedure applied for quantitative climate reconstructions is detailed in Dugerdil et al. (2025d). Pollen from aquatic taxa (e.g., Potamogeton lucens, Typha/Sparganium-type) were included as indicators of aquatic vegetation and removed from biome, trait and climate reconstructions.

Pollen ratios. Ratios between dominant pollen taxa are widely used as semi-quantitative indicators of past climate, especially in drylands (Herzschuh et al., 2006; Zhao et al., 2012). Commonly used taxa include Artemisia spp. (Ar), Amaranthaceae (Am; previously Chenopodiaceae), Poaceae (Po), and Cyperaceae (Cy), which are dominant across ACA. In this study, we tested Ar/Am (aridity; El-Moslimany, 1990; Herzschuh et al., 2006; Zhao et al., 2012), Ar/Cy (summer temperature; Herzschuh et al., 2006), and Po/(Am+Ar) (aridity; Fowell et al., 2003), though the latter may be biased by overgrazing (Murad, 2012). For clarity, only the two aridity ratios are shown in the pollen diagram.

Biomization. Pollen assemblages were converted to biome scores using a R-based workflow for biomization, adapted from Prentice et al. (1996). Pollen types were assigned to Plant Functional Types (PFTs) using classical pollen to PFTs assignation matrices (Peyron et al., 1998; Tarasov et al., 1998), which were then aggregated into biome scores. Fourteen biomes can be predicted, including Cold Deciduous Forest (CLDE), Cold Mixed Forest (CLMX), Cold Conifer Forest (COCO), Cold Desert (CODE), Cool Mixed Forest (COMX), Cold Steppe (COST), Hot Desert (HODE), Pioneer forest (PION), Taiga forest (TAIG), Temperate Deciduous Forest (TEDE), Tundra (TUND), Warm Mixed Forest (WAMX), Warm Steppe (WAST) and Xerophytic Shrubland (XERO).

### 3.8.2. Pollen-based plant functional traits reconstructions

Pollen percentages were used to infer changes in plant community functional structure over time (Van Der Sande et al., 2019; Adeleye et al., 2023). Reconstructing functional traits from fossil pollen is a novel approach in palaeoecology. We followed the workflow described in Dugerdil et al. (2025e) and applied in a modern Tajikistan-Uzbekistan context by Dugerdil et al. (2025d). Trait data were extracted from the TRY (Kattge et al., 2020), BIEN (Maitner et al., 2018), and GIFT (Weigelt et al., 2020) databases and filtered to retain only species occurring in Tajikistan and Uzbekistan, based on the regional plant checklist detailed in Dugerdil et al. (2025d). Trait values were then aggregated by pollen type using the fine aggregation scheme (Dugerdil et al., 2025e), whereby traits of all species from the same family or genus (when pollen is identified to these levels) are averaged to derive a representative trait value for the pollen type. For pollen types identified at sub-family level (e.g., Asteroideae-type, Cichorioideae-type, Cardueae-type, Ephedra distachya-type, E. fragilis-type), sub-genus level (e.g., Pinus Haploxylon-type, P. Diploxylon-type), or at species groups combining multiple species (e.g., Calligonum-type, Cousinia-type, Echinops-type, Plantago major-type, or P. lanceolata-type), a correspondence table linking pollen types to plant species was established based on plant taxonomy, phylogeny, and pollen morphology (Dugerdil et al., 2025d). Finally, Community Weighted Mean (CWM) trait values were calculated from the pollen assemblages following Garnier et al. (2016). CWM traits allow investigation of how plant communities functionally respond to climatic or human-induced changes (Garnier et al., 2004). Trait values for pollen types are combined with their relative abundances to calculate CWM values (Eq. (6)).

$$CWM_{j,k} = \sum_{i}^{n_k} p_{i,k} \times t_{i,j} \qquad (6)$$

with pollen sample k, j each trait, and n the total number of pollen type for the sample k. $p_{i,k}$ is the pollen percentage i in sample k and $t_{i,j}$ the

average value of trait j and species i. See Dugerdil et al. (2025e) for further details. The traits values were z-scored in order to facilitate the CWM calculation and comparison.

The reconstructed plant traits are the plant height (average adult plant height in metre), the leaf area (in mm²), the N<sub>Leaf</sub> (N content per unit leaf mass, mg g⁻¹), the seed mass (diaspore mass in mg), specific leaf area (SLA, the ratio of leaf area to oven-dry mass, in mm² mg⁻¹), stem specific density (SSD in mg mm⁻³). These plant traits were selected since they are the most commonly measured in the trait databases and since they are important to describe the different plant strategies to adapt in front of climate and environmental conditions (Díaz et al., 2016; Garnier et al., 2016; Bruelheide et al., 2018).

## 3.9. Multi-proxy and multi-method climate reconstructions

### 3.9.1. Climate parameters selection

Following Salonen et al. (2019), only statistically independent climate parameters should be reconstructed from climate proxies. Typically, primary and secondary independent parameters yield the most robust results. Using interpolated data from worldclim2.1 (Fick and Hijmans, 2017) and Aridity Index (AI) values from the CGIAR database (version 2; Trabucco and Zomer, 2018), PCA and correlation matrix identified the most reliable parameters for Uzbekistan: Mean Precipitation and Temperature of the Coldest Quarter (MPCOQ and MTCOQ, respectively; see Fig. 2 in Dugerdil et al., 2025d). However, for comparability with other regional studies and across proxy, we focused on MAAT and MPCOQ, as MAAT strongly correlates with MTCOQ ($r$ = 0.95). Additionally, Mean Annual Temperature above Freezing (MAF) was included and tested, as it is commonly used in brGDGT-based reconstructions (Dearing Crampton-Flood et al., 2020; Raberg et al., 2021).

### 3.9.2. Pollen transfer functions and calibrations

Several approaches allow to convert pollen data into climate parameters (Chevalier et al., 2020). These methods require to be calibrated on a high quality modern pollen dataset (training set). Moreover, this modern dataset use to train the models have a strong influence on the climate reconstruction outputs (Dugerdil et al., 2021a). After testing several methods and training dataset performances on the Aral 86 sequence, it was shown that the Boosted Regression Trees (BRT; Salonen et al., 2012), Modern Analogue Technique (MAT; Guiot, 1990) and Weighted Averaging Partial Least Squares (WAPLS; Ter Braak et al., 1993) have the best performances in the present study area (Dugerdil et al., 2025d). For calibration datasets, the most suitable are the Tajikistan-Uzbekistan Surface pollen Data Base (TUSDB, 95 surface samples from Tajikistan and Uzbekistan; Dugerdil et al., 2025d) and the Arid Central Asian pollen Database (ACADB, 2620 samples from the Caucasus to Mongolia compiled from numerous studies, e.g., Zheng et al., 2014; Peyron et al., 2017; Dugerdil et al., 2021a; d’Oliveira et al., 2023). In Lake Fazilman, only these combinations (BRT, MAT and WAPLS trained with the TUSDB and ACADB) were retained to reconstruct the climate variations along the Holocene.

### 3.9.3. brGDGT-based climate reconstructions

To reconstruct past temperature, we tested eleven brGDGT-based calibrations varying by database size (global vs. regional; De Jonge et al., 2014; Dugerdil et al., 2021a; Chen et al., 2021), sample type (soil or lake sediment; Sun et al., 2011; Yang et al., 2014), and method, i.e., Multiple linear Regression (MR), or MBT<sub>5Me</sub>-based linear regression (Dearing Crampton-Flood et al., 2020; Dugerdil et al., 2021a). Calibrations were assessed to determine the most reliable model for Lake Fazilman. The formulas and references for the brGDGT-based temperature calibrations are presented in Table 1, and the corresponding model performances are reported in Table S2.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Geochemical reconstruction of watershed and lake system evolution at Lake Fazilman. (A) PCA of XRF elements (axes 1 and 2) and key geochemical ratios: Sr/Ca (salinity), Br/Ti and Inc/Coh (organic matter), S/Fe (anaerobic conditions), Fe/Mn (redox), Si/Ti (sand input), Ca/Mg (biogenic carbonates), and Ca/Ti (total carbonates). (B) Optical lightness, magnetic susceptibility (MS), and spectrocolorimetric indices: Absorption Band Depth at 660–670 nm as a proxy for organic matter, and the 700/400 nm wavelength ratio as a proxy for oxidation. (C) iso- and brGDGT-based indices reflecting GDGT source and environmental conditions, including the Branched/Isoprenoid Tetraether (BIT), Ib/la (soil input), 6-methyl and 7-methyl isomerization ratios (linked to pH and salinity, respectively). Calibrated $^{14}$C dates and lithological units from Fig. 3A are shown at right. Colour code for the lithological units follows Fig. 3. Colour codes and directional arrows at the bottom of each panel summarize proxy interpretations based on established literature. For the GDGT indices and the XRF data, the 95% confidence intervals were estimated by extrapolating the analytical variance calculated from replicate measurements performed on representative samples.</figcaption></figure>

Complementary to linear brGDGT calibrations, machine learning methods are increasingly used for regression and classification, mainly because linear classification do not suitably predict past temperature when applied on particular environment such as drylands (O'Beirne et al., 2023). Boosted Regression Trees (BRT), developed for ACA by Dugerdil et al. (2025b), were applied to the Lake Fazilman brGDGT sequence. For the full methodology, see Fig. 1 in their study. The model was trained using the Arid Central Asian brGDGT surface Data Base (ACADB; Dugerdil et al., 2025a). In parallel, unsupervised classification (following the approach developed in O'Beirne et al., 2023) identified two subsets within ACADB: K-cold/wet and K-warm/arid (Dugerdil et al., 2025b). A random forest classifier was then used to estimate the probability of Fazilman samples belonging to either cluster.

Among the different methods and calibrations tested to reconstruct climate parameters from brGDGT compounds, the most reliable ones were selected using statistical criteria including $R^2$, Root Mean Square Error (RMSE) and Moran's I to access spatial auto-correlation (Telford and Birks, 2005). Performance statistics for all climate reconstructions inferred from pollen and brGDGT are provided in Table S2. Combining models (CB) can improve accuracy, thus we applied a weighted model approach as in Dugerdil et al. (2025b). For brGDGT-based reconstructions using the K-cold/wet and K-warm/arid calibration subsets, we used the following mixing model [Eq. (7)]:

$$CP_{i,k} = \hat{p}_{K1,k} \cdot \text{BRT}(CP_{i,k})_{K1,k} + \hat{p}_{K2,k} \cdot \text{BRT}(CP_{i,k})_{K2,k} \quad (7)$$

Here, $CP_{i,k}$ is the climate parameter i for sample k, and $\hat{p}_{K1,k}$ and $\hat{p}_{K2,k}$ are the probability estimates for clusters K1 (K-cold/wet) and K2 (K-warm/arid), respectively. The combined result is compared to the standard ACADB-based model using the randomTF() function (Telford and Birks, 2011; Chen et al., 2017).

### 3.9.4. BRT brGDGT local rescaling

To reduce regional bias in BRT-based brGDGT calibration, a Local Scaling (LS) was applied using surface samples within a 250 km radius

of Lake Fazilman. The local RMSE was evaluated based on the temperature errors between observed and BRT-reconstructed temperature. The linear regressions inferred from the temperature errors was, then, used to corrected the offset and errors at the temperature extrema. Locally scaled reconstructions were applied on both, the full ACADB dataset and the weighted combined models obtain from Eq. (7). These two locally scaled reconstructions are called ACA-LS and CB-LS later.

### 3.9.5. Ensemble model aggregation

An ensemble model is a technique that combines predictions from multiple individual models to produce a single, more accurate and robust prediction. Ensemble averaging is increasingly used with machine learning to improve prediction accuracy by reducing variability of different single models (An et al., 2024). For both pollen- and brGDGT-based reconstructions, significant reconstructions were aggregated using a weighted ensemble approach, similar to Eq. (6) using the normalized inverse of RMSE as weight. Confidence intervals were estimated using a quadratic weighted variance:

$$Var_i = \sum_{m=1}^{n} w_m^2 \cdot (x_{i,m} - \hat{x}_i)^2 \quad (8)$$

where $Var_i$ is the weighted variance, $x_{i,m}$ is the predicted value for model m and time i, and $\hat{x}_i$ is the weighted mean. Model weights $w_m$ for model m are based on inverse RMSE:

$$w_m = \frac{\text{RMSE}_m^{-1}}{\sum_{m=1}^{n} \text{RMSE}_m^{-1}} \quad (9)$$

The confidence interval CI(95%) at each time i is:

$$\text{CI}(95\%)_i = \hat{x} \pm 1.96 \cdot \sqrt{\text{Var}_i} \quad (10)$$

### 3.10. Statistical analysis

All statistical analyses were conducted in R (version 4.4.2; Team, 2020). Principal Component Analyse (PCA) is an unconstrained ordination that reduces data dimensionality by identifying axes (principal
components) capturing the most variance. PCAs were performed for XRF, MS and brGDGTs using the package FactoMineR (version 2.11; Lê et al., 2008) after data transformation. To define sedimentological units (based on PCA of XRF and MS) and pollen-algal assemblage zones, constrained hierarchical clustering (CONISS) was applied via the rioja package (version 1.0-7; Juggins and Juggins, 2019). The optimal number of groups was determined using the incremental sum of squares method (Grimm, 1987). Model performance for brGDGTs (BRT, RF) and pollen (BRT, RF, MAT, WAPLS) was assessed using $R^2$ and RMSE. To compare surface calibration datasets with the Lake Fazilman core, the *randomTF()* function from the *palaeoSIG* package (version 2.1-3; Telford and Birks, 2011) was used. Correlations between proxies were assessed using Pearson's *r* and *p*-values via the *corrr* package (version 0.4.4; Kuhn et al., 2022). When sampling intervals differed (e.g., between XRF and pollen), the *corit* package (version 0.9; Reschke et al., 2019) was used to apply linear interpolation and Gaussian filtering to reduce temporal bias before correlation. Plots were produced using *ggplot2* (version 3.5.1; Wickham, 2016) and *tidypaleo* (version 0.1.3; Dunnington et al., 2022), except for the pollen diagram, made with rioja.

# 4. Results

## 4.1. Fazilman lacustrine system

### 4.1.1. Lake Fazilman age model and sedimentation

AMS-$^{14}$C dating on organic remains (seeds, leaves), macro-charcoal, and Planorbidae shells produced consistent ages (Fig. 3A). To test

for reservoir effects – possibly affecting carbonates and aquatic vegetation such as *Potamogeton lucens* and *Schoenoplectus litoralis* – two bulk sediment samples were dated (135 and 174 cm depth, Table 2 in Barhoumi et al., 2000). They yield ages 700 to 2500 years older than the corresponding biogenic carbonate samples. Biogenic carbonate dates show no reservoir bias, likely due to the small size of the lake, while bulk samples were excluded from the age–depth model. The Lake Fazilman sediment core spans approximately 9960 ± 200 cal yr BP. An initial phase up to 6300 cal yr BP shows high Sediment Accumulation Rate (SAR between 0.15 and 0.30 cm.yr$^{-1}$), followed by a sharp decrease below 0.01 until 3700 cal yr BP. A transitional phase then occurs (SAR between 0.03 and 0.05 cm.yr$^{-1}$) around 1000 cal yr BP, followed by an important rise up to ≈ 0.60 cm.yr$^{-1}$ (Fig. 3C). Despite strong SAR variations, the age model assumes a temporal continuity, even between 126 and 142 cm.

### 4.1.2. Core lithology and sediment variations

CONISS cluster analysis on PC1$_{XRF}$, PC2$_{XRF}$, and MS divides the Fazilman core into eight units aligned with lithological and macrofossil observations (Fig. 3C–F; CONISS details in Fig. S3). From bottom to top: bluish clay-silt with black layers and vivianite, rich in lithogenic, carbonate, and magnetic elements (L1, 157–227 cm); grey clays becoming increasingly lithogenic with redox changes (L2, 142–157 cm); blue clay-silts with black layers low SAR, while MS and XRF spike without any evidences of sediment discontinuity (L3, 126–142 cm); grey clays similar to L2, with still low SAR (L4, 113–126 cm); bluish clay-silt with more organic matter, fewer Planorbidae and Lymnaeidae, replaced by Cyperaceae and Characeae gyrogonites (L5, 75–113 cm);

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Synthetic pollen diagram from the Lake Fazilman sediment core. (A) Samples are plotted by calibrated age (cal yr BP) using the age–depth model from Fig. 3. From left to right: major Holocene climate events in Central Asia (Chen et al., 2015; Rodda et al., 2019); dominant pollen taxa; algal remains; key fungal spores and their ecological groups; pollen ratios based on Poaceae, *Artemisia* spp., and Amaranthaceae; and CONISS clusters. Pollen assemblages are expressed in fractional abundances (%TP), algae are presented as percentage of total pollen plus Non-Pollen Palynomorphs (%TP+NPP), and fungal spores are shown as influx (particles per cm³ yr$^{-1}$). These proxies provide insights into vegetation dynamics, land-use intensity, and ecological responses to climatic changes. The full algal and pollen diagrams are available in Figs. S4 and S5. (B) Detailed view of fungal spore influx and ecological indicator diversity for the last 1500 years, highlighting recent human impact and ecosystem variability at high resolution. (C) Uzbekistan anthropogenic land use through croplands and rangelands surface estimation (in km$^2$) from the HYDE database (version 3.2; Klein Goldewijk et al., 2017).</figcaption></figure>
clay-silt with numerous black organic-rich layers, similar to L5 (L6, 52–75 cm); degraded organic matter with metal pollution indicators (L7, 36–52 cm); and macrophytes and peat from the current vegetated island (L8, 0–36 cm).

The eight lithological units are well differentiated in PCA space, with PC1<sub>XRF</sub> and PC2<sub>XRF</sub> explaining 53% and 27% of variance, respectively (Fig. 3B, C). PC1<sub>XRF</sub> reflects a gradient from authigenic to lithogenic input, with positive values indicating clay-rich mineral influx (Ti, K, Fe) and carbonate production (Ca, Sr). PC2<sub>XRF</sub> contrasts clays and carbonates. Units L1 to L5 show positive PC1<sub>XRF</sub> values, while L6 to L8 are negative. From L1 to L5, PC2<sub>XRF</sub> increases steadily; from L6 to L8, both PC1 and PC2 decrease. L1 is Sr- and Ca-dominated; L2 shows increased P and Mn (Fig. 4A). Ca/Mg, Ca/Ti, and L* have highest values in these units, while GDGT indices (BIT, Ib/la, %Cren, IR<sub>6Me</sub>) are highly fluctuating (Fig. 4B, C). A lithogenic spike in L3 marks peaks of K, Ti, Fe, and Zr, with distinct Fe/Mn, PC2<sub>XRF</sub>, Ca/Mg, Ca/Ti, MS, and RABD<sub>660–670</sub> values defining its boundaries. Though L2 and L4 share similar PC1<sub>XRF</sub>, MS, and SC values, differences in Ca, Sr, and GDGTs indicate a major transition. From L4 to L8, lithogenic input declines, replaced by organic matter. L5 and L6 show increasing oxidation (Fe/Mn, Q<sub>7/4</sub>) and alkalinity (IR<sub>6Me</sub>). L7 and L8 are rich in organic proxies (Br/Ti, Inc/Coh, RABD<sub>660–670</sub>), with rising coarse sediment input (Si/Ti, Zr/Ti).

### 4.1.3. Lake ecology

Algal macro-remains are preserved along the Fazilman core (Fig. 3D). Numerous gyrogonites, oosporangia and carbonated stems of Characeae were observed, mainly in units L1, L2, L6 and L7; while Cyperaceae seeds (e.g., *Schoenoplectus litoralis*) and plant remains (*Potamogeton lucens*, *Hippuris vulgaris* and *Phragmites australis*) pre-dominate in L7 and L8. Pollen slides also contained aquatic plant pollen (*Myriophyllum* spp., *P. lucens*, *Typha/Sparganium*-type) and algae, dominated by Volvocaceae indet., *Botryococcus braunii*, HdV-66, and *Zygnema* spp. (Table 2 and Fig. S4). In total, 13 algal taxa were identified, including Zygnemataceae (*Mougeotia* spp., *Spirogyra* spp., *Zygnema* spp.; van Geel and Grenfell, 1996) and colonial forms like *Botryococcus braunii* and *Pediastrum* spp. (*P. boryanum var. brevicorne*, *P. integrum*; Komárek and Jankovská, 2001). CONISS analysis was performed on pollen and algal assemblages (Fig. 5A). The incremental sum of squares identified six optimal groups: P1 (9960–8450 cal yr BP), P2 (8450–5950), P3 (5950–4450), P4 (4450–3400), P5 (3400–150), and P6 (150 to –72 cal yr BP).

During phase P1 (9960–8450 cal yr BP), algae are dominated by *B. braunii* (34%), *Zygnema* spp. (27%), Volvocaceae indet. (22%), and HdV-66 (13%; Figs. 5A, S4). *Zygnema* initially lead before being overtaken by *B. braunii*. In P2 (8450–5950 cal yr BP), *B. braunii* peak (41%), while Volvocaceae indet. (23%) and HdV-66 (20%) increase. After 7000 cal yr BP, *B. braunii* decline and HdV-66 peak at the P2–P3 boundary. In P3 (5950–4450), Volvocaceae continue rising, while *Zygnema* stabilize (5%–10%). In P4 (4450–3400), Volvocaceae remain dominant (39%) and new taxa like *Pediastrum* spp. and *Typha/Sparganium*-type appear. P5 (3400–150) is again dominated by Volvocaceae (53%) with a shift to a new assemblage including *P. lucens*, *Tetraedron* cf. *minimum*, and *Spirogyra* spp.. In the modern-like phase P6 (150 to -72), *P. lucens* (40%) and *Spirogyra* spp. (16%) dominated, with Volvocaceae still prominent (19%).

## 4.2. Vegetation variations

### 4.2.1. Plant community variations

The Fazilman pollen diagram (Fig. 5A) is dominated by Non-Arboreal Pollen (NAP), primarily *Artemisia* spp. (24%), Poaceae (20%), Cyperaceae (18%), Amaranthaceae (10%), and limited arboreal pollen, mainly *Juniperus* spp. (9%). Common but minor taxa (<1%) include Ranunculaceae, Asteroideae, *Plantago lanceolata*-type, Cichorioideae, Brassicaceae, Lamiaceae, Fabaceae, and *Cousinia* spp.. *Ephedra distachya* and *Ephedra fragilis* types are also sporadically present. Full details are available in Fig. S5. Pollen shifts align with the CONISS-defined zones:

1. **P1** (9960–8450 cal yr BP): Poaceae (35%) dominates, followed by *Artemisia* spp. (20%), *Juniperus* spp. (12%), Cyperaceae (9%) and Amaranthaceae (8%). Average diversity is of 16 types.

2. **P2** (8450–5950 cal yr BP): Low pollen influx (<300.10³ particles.cm⁻².yr⁻¹), marked by Poaceae drop, and *Juniperus* spp. peak (both ≈ 27%). *Juniperus* spp. maximum is around 7000 cal yr BP. Diversity falls to 10–12 types.

3. **P3** (5950–4450 cal yr BP): *Juniperus* spp. and Poaceae decrease (up to 13 and 23% respectively), while Cyperaceae and *Artemisia* spp. rise (up to 17 and 23%). Minor taxa include Fabaceae, *Cousinia* spp., and *Plantago lanceolata*-type. The pollen diversity and influx are similar to P1.

4. **P4** (4450–3400 cal yr BP): Cyperaceae maximum (32%), Poaceae and *Juniperus* spp. reach minimum values (15 and 6% respectively). The relatively high percentage of Ranunculaceae (≈ 5%) have to be noticed.

5. **P5** (3400–150 cal yr BP): *Artemisia* spp. dominates (30%), Amaranthaceae peaks (14%). Cyperaceae declines (up to 20%), Poaceae and *Juniperus* spp. remain constant. Pollen influx and diversity reach maxima (≈ 2000.10³.particles.cm⁻².yr⁻¹ and 20 types).

6. **P6** (150 to –72 cal yr BP): Similar to P4 with Cyperaceae (32%), *Artemisia* spp. (19%), Poaceae (16%), Amaranthaceae (9%) and notable contributions from *Juniperus* spp., *Asteroideae*, *Plantago lanceolata*-type, and *Ranunculaceae*.

### 4.2.2. Biome reconstructions

Lake Fazilman sequence is dominated by warm (WAST) and cold (COST) steppes, with mean scores of 14.3 and 13.2, respectively (Figs. 6A, S6). These are followed by cold (CODE) and hot deserts (HODE, both 5.1), and tundra (TUND, 4.8). COST and WAST dominate throughout the record, but minor biome variations remain notable. Before 9000 cal yr BP, deserts (CODE, HODE) and tundra outweigh forest types. Between 9000 and 4200 cal yr BP, forest biome scores increase slightly. Around 4200 cal yr BP, steppes reach a peak (scores ≈ 18) and become dominant again. From that point onward, only short-term oscillations (200–500 years) are visible between steppe, desert, and tundra biomes. From 500 to 50 cal yr BP, the environment is marked by a spike in desert biomes, followed by a steppe resurgence in the last century.

### 4.2.3. Functional traits variations

Biome changes are reflected in shifts in Community Weighted Mean (CWM) trait values, although *biomization* and CWM analyses rely on different statistical treatments of pollen data. Among the six traits of the global plant functional spectrum (Díaz et al., 2016), strong correlation are observable (Figs. 6B, S7 and S8 A). Particularly, seed mass is strongly correlated with SSD (*r* = 0.97) and plant height (*r* = 0.95), and is negatively correlated with SLA, leaf area, and N<sub>leaf</sub> (*r* = –0.94, –0.89, and –0.85, respectively). Due to the trait strong correlations in the data structure, we further focus only on the leaf area, plant height, and seed mass. In the Early Holocene, leaf area is moderate, while plant height and seed mass remain low. During the Mid-Holocene, these traits increase – likely linked to *Juniperus* spp. rise – before returning to earlier values after 4200 cal yr BP. The Late Holocene shows relatively stable trait patterns with slight increases in plant height and seed mass.

### 4.2.4. Fungal spores and other NPP remains

All observed NPP types from Lake Fazilman are listed in Table 2. Only major fungal spores with ecological relevance are shown in Fig. 5A. Fungal influx and diversity remained low before 2500 cal yr BP (about 5 types), then rose sharply from 1400 cal yr BP onward. A detailed NPP diagram for this period is shown in Fig. 5B. The dominant taxa, ranked by average influx (Table 2), include HdV-200, *Phaeosphaeria* spp., *Cercophora* spp., *Sordaria* spp., *Sporormiella* spp., *Curvularia* spp., *Arnium* spp., and *Coniochaeta* cf. *lignaria*. Minor but
<caption>**Table 2**</caption>

Non-Pollen Palynomorphs present in the Lake Fazilman sequence with their commonly used NPP ID, taxonomic type, references in the literature, ecological indication and average influx values along the core.

<table><thead><tr><th>Taxa</th><th>ID</th><th>Taxonomic types</th><th>References</th><th>Proxy</th><th>F<sub>x</sub><sub>Fur</sub></th></tr></thead><tbody><tr><td>Botryococcus braunii</td><td>HdV-766</td><td>Algae</td><td>Komárek and Marvan (1992), Batten et al. (1996), Jankovská and Komárek (2000)</td><td>Aquatic markers</td><td>2556.42</td></tr><tr><td>Gloeotrichia-type</td><td>HdV-146</td><td>Cyanobacteria</td><td>Shumilovskikh and van Geel (2020)</td><td></td><td>22.34</td></tr><tr><td>Mougeotia spp.</td><td>HdV-133</td><td>Algae</td><td>Van der Wiel (1982)</td><td></td><td>29.61</td></tr><tr><td>Pediastrum spp.</td><td></td><td>Algae</td><td>Batten et al. (1996), Jankovská and Komárek (2000), Komárek and Jankovská (2001)</td><td></td><td>316.74</td></tr><tr><td>Penium/Inocybe spp.</td><td>HdV-66</td><td>Algae/Spore ball of smut fungi</td><td>Van Geel (1978), Shumilovskikh et al. (2015)</td><td></td><td>1076.91</td></tr><tr><td>Spirogyra spp.</td><td>HdV-130</td><td>Algae</td><td>McCarthy et al. (2021)</td><td></td><td>305.69</td></tr><tr><td>Tetraedron cf. minimum</td><td>HdV-371</td><td>Algae</td><td>Bakker and Van Smeerdijk (1982), Jankovská and Komárek (2000)</td><td></td><td>98.23</td></tr><tr><td>Tetraedron cf. trilobatum</td><td></td><td>Algae</td><td>Bakker and Van Smeerdijk (1982), Jankovská and Komárek (2000)</td><td></td><td>14.17</td></tr><tr><td>Volvocaceae indet.</td><td>HdV-128</td><td>Algae</td><td>Pals et al. (1980), Bakker and Van Smeerdijk (1982), Tsvirko (2023)</td><td></td><td>3342.6</td></tr><tr><td>Zygnema spp.</td><td></td><td>Algae</td><td>McCarthy et al. (2021)</td><td></td><td>1318.46</td></tr><tr><td>Bullinaria spp.</td><td></td><td>Testate Amoebae</td><td>Magnan et al. (2021)</td><td>Wetland markers</td><td>0.08</td></tr><tr><td>Cephalodella exigua</td><td>HdV-70</td><td>Rotifera remains</td><td>Van Geel (1978), McCarthy et al. (2021)</td><td></td><td>8.21</td></tr><tr><td>Chironomidae indet.</td><td>HdV-509</td><td>Insect remains</td><td>McCarthy et al. (2021)</td><td></td><td>0.23</td></tr><tr><td>HdV-722</td><td>HdV-722</td><td>Spore fungi</td><td>Bakker and Van Smeerdijk (1982)</td><td></td><td>1.54</td></tr><tr><td>Xylomyces chlamydosporus</td><td>KNG-7</td><td>Spore fungi</td><td>Shumilovskikh et al. (2016, 2017b,a)</td><td></td><td>4.52</td></tr><tr><td>HdV-200</td><td>HdV-200</td><td>Spore fungi</td><td>Kuhry (1997), van Asch et al. (2025)</td><td>Aridity marker</td><td>165.62</td></tr><tr><td>Glomus spp.</td><td>HdV-207</td><td>Arbuscular mycorrhizal fungi</td><td>Kołaczek et al. (2013)</td><td>Erosion marker</td><td>3.17</td></tr><tr><td>Brachysporium spp.</td><td>TM-014</td><td>Spore fungi</td><td>Cugny (2011)</td><td>Saprophytic</td><td>3.45</td></tr><tr><td>Cercophora spp.</td><td>HdV-112</td><td>Spore fungi</td><td>Cugny (2011), Montoya et al. (2010), Shumilovskikh and van Geel (2020)</td><td>& lignicolous spores</td><td>79.41</td></tr><tr><td>Chaetomium spp.</td><td>HdV-7a</td><td>Spore fungi</td><td>Cugny et al. (2010), Gauthier and Jouffroy-Bapicot (2021)</td><td></td><td>0.44</td></tr><tr><td>Coniochaeta cf. lignaria</td><td>HdV-172</td><td>Spore fungi</td><td>Cugny et al. (2010), Gauthier and Jouffroy-Bapicot (2021)</td><td></td><td>11.83</td></tr><tr><td>Coniochaetaceae indet.</td><td>HdV-6</td><td>Spore fungi</td><td>Cugny (2011), Murad (2012)</td><td></td><td>6.09</td></tr><tr><td>Microthyrium-type</td><td>HdV-8B</td><td>Fruit body</td><td>Van Geel (1978), Blackford and Innes (2006)</td><td></td><td>2.94</td></tr><tr><td>Pleospora-type</td><td>HdV-3B</td><td>Spore fungi</td><td>Cugny (2011), Wei et al. (2021)</td><td></td><td>1.69</td></tr><tr><td>Savoryella cf. lignicola</td><td>UG-1118</td><td>Spore fungi</td><td>Gelorini et al. (2011), Wei et al. (2021)</td><td></td><td>0.07</td></tr><tr><td>Valsaria spp.</td><td>HdV-140</td><td>Spore fungi</td><td>Montoya et al. (2010), Cugny (2011)</td><td></td><td>0.07</td></tr><tr><td>Xylariaceae indet.</td><td>TM-301</td><td>Spore fungi</td><td>Cugny (2011)</td><td></td><td>1.1</td></tr><tr><td>Delitschia spp.</td><td>TM-23A-B</td><td>Spore fungi</td><td>Baker et al. (2013), Wei et al. (2021), Gauthier and Jouffroy-Bapicot (2021)</td><td>Dung fungal spores</td><td>0.22</td></tr><tr><td>Podospora spp.</td><td>HdV-368</td><td>Spore fungi</td><td>Cugny (2011), Baker et al. (2013)</td><td></td><td>1.91</td></tr><tr><td>Sordaria spp.</td><td>HdV-55</td><td>Spore fungi</td><td>Cugny (2011), Baker et al. (2013), Wei et al. (2021)</td><td></td><td>27.21</td></tr><tr><td>Sporormiella spp.</td><td>HdV-113</td><td>Spore fungi</td><td>Djamali et al. (2016), Cugny (2011), Baker et al. (2013), Wei et al. (2021)</td><td></td><td>19.57</td></tr><tr><td>Arnium spp.</td><td>HdV-261</td><td>Spore fungi</td><td>Cugny (2011), Gauthier and Jouffroy-Bapicot (2021)</td><td>Plant pathogens</td><td>17.34</td></tr><tr><td>Arthrinium spp.</td><td>TM 4070</td><td>Spore fungi</td><td>Cugny (2011), Ellis and Ellis (1985), van Geel and Aptroot (2006)</td><td></td><td>0.23</td></tr><tr><td>Curvularia spp.</td><td>TM-R</td><td>Spore fungi</td><td>Gelorini et al. (2011)</td><td></td><td>17.81</td></tr><tr><td>Inonotus spp.</td><td></td><td>Spore ball of smut fungi</td><td>Ghosh et al. (2017)</td><td></td><td>4.07</td></tr><tr><td>Meliola niessleana</td><td>HdV-14</td><td>Spore fungi</td><td>Van Geel (1978), Cugny (2011), Wei et al. (2021)</td><td></td><td>4.09</td></tr><tr><td>Nigrospora spp.</td><td>UY-33</td><td>Spore fungi</td><td>Leal et al. (2021), Wei et al. (2021)</td><td></td><td>1.11</td></tr><tr><td>Phaeosphaeria spp.</td><td>HdV-981</td><td>Spore fungi</td><td>Miola (2012), Murad (2012), Gelorini et al. (2011)</td><td></td><td>150.34</td></tr><tr><td>Thecaphora spp.</td><td>HdV-364</td><td>Spore ball of smut fungi</td><td>Murad (2012), Shumilovskikh and van Geel (2020)</td><td></td><td>2.44</td></tr><tr><td>Tilletia spp.</td><td>HdV-27</td><td>Spore ball of smut fungi</td><td>Cugny (2011), Murad (2012), Shumilovskikh and van Geel (2020)</td><td></td><td>1.41</td></tr><tr><td>Urocystis spp.</td><td>UG-1079</td><td>Spore ball of smut fungi</td><td>Gelorini et al. (2011), Wei et al. (2021)</td><td></td><td>2.53</td></tr><tr><td>Basidiomycota</td><td>TM-B</td><td>Spore ball of smut fungi</td><td>Cugny (2011)</td><td>Ubiquitous or indeterminable</td><td>3.12</td></tr><tr><td>cf. Entophyctis lobata</td><td>HdV-13</td><td>Sporange</td><td>Van Geel (1978)</td><td></td><td>6.68</td></tr><tr><td>Nematoda eggs-type</td><td>HdV-531</td><td>Eggs</td><td>van Geel et al. (1983)</td><td></td><td>0.22</td></tr><tr><td>Persiciospora spp.</td><td>HdV-124</td><td>Spore fungi</td><td>Bakker and Van Smeerdijk (1982), van Geel and Aptroot (2006), Cugny (2011)</td><td></td><td>0.15</td></tr><tr><td>Alternaria spp.</td><td>HdV-1034</td><td>Spore fungi</td><td>van Geel et al. (2011)</td><td></td><td>2.46</td></tr></tbody></table>

ecologically informative types include cf. *Entophyctis lobata*, *Xylomyces chlamydosporus*, *Glomus* spp., *Urocystis* spp., and *Podospora* spp. (Wei et al., 2021).

Prior to 2500 cal yr BP, NPPs were rare, limited to *Glomus* spp., testate amoebae, and rotifers, with no dung fungi. After 2500 cal yr BP, sporadic appearances of HdV-200 and *Arnium* spp. mark the start of ecological change. Total NPP influx increased from 2500 to 100 cal yr BP, peaking at 800.10³ particles.cm⁻².yr⁻¹, and declined over the past 170 years. A marked HdV-200 peak appears around 1400–1300 cal yr BP, indicating aridity rise. Between 1100–1000 cal yr BP, diversity rises with dominance of HdV-200, *Arnium* spp., and *Cercophora* spp.. Proper dung fungal spores (e.g., *Sporormiella* and *Sordaria* spp.) emerge between 700–600 cal yr BP and remain high until 300 cal yr BP. After a brief decline, dung spores increase again from 150 cal yr BP to the present, now with higher diversity including

*Delitschia* spp., and non-exclusive dung fungal spores, however, indicating grazing (*Chaetomium* spp., *Pleospora*-type, and *Urocystis* spp.; Wei et al., 2021).

### 4.3. GDGTs

#### 4.3.1. *brGDGT source variations*

The brGDGT distribution in Lake Fazilman reflects mixed sources, both soil input and *in-situ* production (Fig. 7A, B). Although the lake is narrow and shallow (1–2 m depth, 6–9 ha area), its small watershed (ca. 120 ha), combined with spring influx, makes source tracking essential. Common sourcing indices include IIIa/IIa and BIT (Hopmans et al., 2004; Xiao et al., 2016), while Ib/la is more relevant in arid settings (Zhu et al., 2021). The PCA1<sub>XRF</sub> interpreted as authigenic vs. lithogenic proxy correlates with Ib/la (r = 0.66), while PCA2<sub>XRF</sub> associated to carbonate/OM vs. redox conditions gradient aligns with BIT (r = 0.65) in Figs. 3C, 4 and S8B. IIIa/IIa shows no significant
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Pollen-based landscape reconstruction from Lake Fazilman. (A) Biome scores derived from pollen functional types (PFTs) using the biomization method (Peyron et al., 1998; Tarasov et al., 1998), showing eight major biomes: Cold Steppe (COST), Warm Steppe (WAST), Cold Desert (CODE), Hot Desert (HODE), Tundra (TUND), Cold Conifer Forest (COCO), Cold Mixed Forest (CLMX), and Cool Mixed Forest (COMX). (B) Reconstruction of Community Weighted Mean (CWM) plant traits, including leaf area, plant height, and stem specific density (Garnier et al., 2016, SSD); based on trait-pollen assemblages (Dugerdil et al., 2025e). Blue bars at the bottom indicate the relative difference between plant height and SSD, providing insight into ecological strategy shifts over time. The complete CWM traits and biome scores are shown in Figs. S6 and S7.</figcaption></figure>

correlation (*p*-value >0.001). Scatter plots show that lithologies L1–L3 have low BIT and high Ib/la, L4 shows moderate values, and L5–L8 exhibit high BIT and low Ib/la. The %Cren highlighting lake water level is high in L1, constantly decreases in L2–L3 and remains low afterward. These patterns inform shifting in soil input vs. *in-situ* lacustrine GDGT production; this shift has to be taken into account in the brGDGT-based climate reconstructions.

### 4.3.2. brGDGT distribution and indices variation

BrGDGT distribution in Lake Fazilman shows distinct patterns across lithological units. PCA on the 15 brGDGT compounds explains 63 and 10% of the variance along the first two axes (Fig. 7C). Unit L1 has positive PCA1<sub>brGDGT</sub> and PCA2<sub>brGDGT</sub>, dominated by IIIc, IIIC', IIIB, IIIB', Ia and Ib. Units L2–L4 show negative PCA2<sub>brGDGT</sub> linked to IIb, Ic, IIc

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Evaluation of brGDGT sourcing and potential lithological and aridity-related biases affecting temperature reconstructions. (A, B) Scatter plots of lithological unit distributions based on three sourcing indices: BIT, Ia/la, and IIIa/IIa ratios (Hopmans et al., 2004; Xiao et al., 2016; Zhu et al., 2021). These metrics assess the relative contribution of terrestrial vs. in-lake brGDGT production. (C) Principal Component Analysis (PCA) of the 15 brGDGT compounds, providing an overview of variance in compounds composition across the core. (D) Linear relationship between MBT'<sub>5Me</sub> and MBT'<sub>6Me</sub> indices. (E) Temporal difference between these two ratios, interpreted as an indicator of aridity influence on brGDGT distributions (Dugerdil et al., 2025a). Vertical axis of panel E is reversed to ease comparison with AI reconstructions.</figcaption></figure>

and Ib. The upper units L5–L8 display negative PCA1<sub>brGDGT</sub>, dominated by IIIa, IIa, IIIa' and IIa'.

This distribution leads to a gradual decline in MBT'<sub>5Me</sub> and MBT'<sub>6Me</sub> over the Holocene (Fig. 8A), with sharp increases from 500 to 150 cal yr BP. The two indices diverge between 10,000–9000 cal yr BP (L1, MBT'<sub>5Me</sub> > MBT'<sub>6Me</sub>) and around 7000–5000 cal yr BP (L4, inverse pattern). Hexamethylated compounds drive this trend, while pentamethylated brGDGTs rise slowly, averaging 50% (Fig. 8B). pH proxies (CBT', IR<sub>6Me</sub>) remain relatively stable (Fig. 8C), suggesting neutral waters, but show mild alkalinity before 9000 and ca. 4200 cal yr BP, and increased acidity after the LIA, in line with the organic-rich
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** GDGT-based environmental proxies from Lake Fazilman. From top to bottom: (A, B) Methylation ratios and associated methyl percentages. (C) Physicochemical indicators, including relative fractional abundances of Ib and Ila compounds, CBT', and the IR<sub>6Me</sub> index. (D) Bacterial community variation tracked by the Cyclization Index (CI) and the R<sub>i/b</sub> ratio. Reconstructed MAAT derived from BRT models (E, upper part; Dugerdil et al., 2025b) and linear calibrations (E, lower part; Sun et al., 2011; Yang et al., 2014; De Jonge et al., 2014; Chen et al., 2021). (F) Aridity Index (AI) estimates from BRT. The BRT predictions are BRT ACA-LS (whole calibration dataset, locally scaled), CB-LS (combination between the K-cold/wet and K-warm/arid sub-calibrations locally scaled), and BRT Ensemble (ensemble models from the significant calibrations listed in Table S3). The 95% confidence intervals for GDGT indices and linear calibrations were derived from replicate measurements, whereas those for the ensemble models were computed using the quadratic variance approach from Eqs. (8)–(10).</figcaption></figure>

sediment in L7 and L8 (Br/Ti, Inc/Coh and RADB<sub>660-670</sub> in Fig. 4A, B). Bacterial community indices (CI and R<sub>i/b</sub>) vary similarly (Fig. 8D). Both rise between 10,000 and 9000, stabilize after 8200, then diverge after 3500 cal yr BP: CI declines while R<sub>i/b</sub> holds steady, peaking again after 700 cal yr BP.

## 4.4. Lake Fazilman climate reconstructions inferred from pollen and brGDGTs data

### 4.4.1. Climate parameters assessment

The most accurate climate parameters for both proxies were selected using randomTF() p-values (Table 3 and Fig. S9). For pollen, the precipitation parameter MPCOQ shows the strongest performance (eight calibrations with *p* <0.1), followed by other precipitation and temperatures parameters (AI, MAF, MAP, MAAT, MTCOQ). For brGDGTs, AI, MAF, MAAT, and MPCOQ are most reliable (Fig. S10). These results guide the choice of calibrations for the climate reconstruction based on the ensemble models. Based on surface calibration metrics (R<sup>2</sup>, RMSE, Moran's I), the best pollen-based reconstructions are MPCOQ, AI, and MTCOQ (Dugerdil et al., 2025d), while for brGDGTs they are MAAT, MAF, AI, and MPCOQ (Dugerdil et al., 2025b).

For brGDGTs, several recent studies based on global modern datasets suggest reconstructing the MAF rather than the MAAT, because brGDGT production is largely driven by summer bacterial activity (Dearing Crampton-Flood et al., 2020; Raberg et al., 2021). However, in drylands, the brGDGT signal may instead reflect the rainy season when soil moisture availability and temperature are not synchronous (De Jonge et al., 2014). Precipitation regimes in ACA also vary strongly along a west-east gradient, resulting in contrasting summer- versus winter-dominated rainfall patterns (Tan et al., 2024). In such a context, MAAT is likely to be reconstructed more reliably than MAF. We therefore selected MAAT for Lake Fazilman, also because a regionally relevant calibration is available for Tajikistan (Chen et al., 2021), and because using MAAT facilitates comparison between pollen- and brGDGT-based reconstructions. AI is retained as it combines temperature and precipitation via the ratio between MAP and annual evapotranspiration (Trabucco and Zomer, 2018). Afterward, climate reconstructions for AI, MAAT and MPCOQ are discussed.

### 4.4.2. Climate trends based on the brGDGT linear calibrations

The main brGDGT linear calibrations, based on MR or the MBT'<sub>5Me</sub> index, show broadly consistent temperature trends (Fig. 8E). Some rapid events are detectable about 9700, 8200, 4000 and 2500 cal yr BP. The temperature peak occurring around 9700 cal yr BP, is followed by four distinct phases: rapid cooling from 9700 to 7300 cal yr BP (≈ 2 °C in 2400 years), slight warming from 7300 to 4200 (≈ 1 °C in 3100 years), a pronounced cooling from 4200 to the pre-industrial period (down to 4 °C), and a sharp rise during the last 200 years. Calibration differences are mainly related to (1) the offset between observed and reconstructed MAAT, where calibrations from De Jonge et al. (2014) and Chen et al. (2021) yield smaller errors compared to ones from Yang et al. (2014) and Sun et al. (2011); and (2) the reconstructed temperature range, which is wider in MAAT<sub>5Me-DJ</sub> and MAAT<sub>L-Sun</sub>.

### 4.4.3. Climate trends based on the brGDGT machine learning calibration

Applying brGDGT cluster classification for Lake Fazilman, most samples are classified as K-cold/wet with a probability >90%, except for the past 500 years, where few samples fall into the K-warm/arid cluster (Fig. S10). The weighted combined model closely follows the K-cold/wet model, showing a general temperature decline across the Holocene with three warm phases (10,000–9400, 7500–6500, and 4200–2800 cal yr BP). The BRT trends are consistent with linear calibrations, except during 7300–4200 cal yr BP, where they show a cooling trend followed by stability, rather than a warming (Fig. 8E).
<caption>**Table 3**</caption>

Proportion of variance for each climate reconstructions based on pollen transfer functions. These statistical results come from the randomTF() significance test developed to estimate the reliability of MAT and WAPLS climate reconstruction for Fazilman based on the different calibration sets (TUSDB and ACADB) compared to transfer function trained on random calibration sets (for 999 permutations, Telford and Birks, 2011). The complete histograms of the randomTF() can be found on Fig. S9.

<table><thead><tr><th>Proxy</th><th>Database</th><th>Model</th><th>AI</th><th>MAAT</th><th>MAF</th><th>MAP</th><th>MPCOQ</th><th>MTCOQ</th><th>Threshold<sup>a</sup></th><th>PCA<sub>1</sub>(var.%)<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">Pollen</td><td rowspan="3">TUSDB</td><td>MAT</td><td>0.06</td><td>0.05</td><td>0.07</td><td>0.07</td><td>0.09</td><td>0.05</td><td>0.14</td><td>0.39</td></tr><tr><td>WAPLS</td><td><strong>0.24</strong><sup>*</sup></td><td>0.18</td><td><strong>0.25</strong><sup>**</sup></td><td><strong>0.26</strong><sup>**</sup></td><td><strong>0.25</strong><sup>*</sup></td><td>0.09</td><td>0.21</td><td>0.39</td></tr><tr><td>BRT</td><td>0.29</td><td>0.3*</td><td>0.31*</td><td>0.31*</td><td>0.3*</td><td>0.12</td><td>0.32</td><td>0.65</td></tr><tr><td rowspan="3">ACADB</td><td>MAT</td><td>0.05</td><td><strong>0.25</strong><sup>**</sup></td><td><strong>0.25</strong><sup>**</sup></td><td><strong>0.14</strong><sup>*</sup></td><td><strong>0.27</strong><sup>**</sup></td><td><strong>0.22</strong><sup>**</sup></td><td>0.12</td><td>0.36</td></tr><tr><td>WAPLS</td><td>0.11</td><td>0.17</td><td>0.17</td><td>0.18</td><td><strong>0.28</strong><sup>*</sup></td><td>0.16</td><td>0.26</td><td>0.36</td></tr><tr><td>BRT</td><td>0.15</td><td>0.06</td><td>0.28*</td><td>0.12</td><td>0.27*</td><td>0.05</td><td>0.28</td><td>0.64</td></tr><tr><td rowspan="2">brGDGT</td><td>ACADB</td><td>BRT</td><td>0.38</td><td>0.29</td><td>0.37</td><td></td><td>0.04</td><td></td><td>0.56</td><td>0.73</td></tr><tr><td>ACADB-combined</td><td>BRT</td><td>0.39</td><td>0.26</td><td>0.28</td><td></td><td>0.31</td><td></td><td>0.63</td><td>0.73</td></tr></tbody></table>

<sup>a</sup> This threshold correspond to the 95% interval values for the 999 permutation of randomly-based transfer functions, i.e., climate reconstructions with a proportion of variance above the proportion of variance of 95% of the randomly made transfer functions (Chen et al., 2017).

<sup>b</sup> This percentage of variance (PCA₁) theoretically corresponds to the maximum of explainable past pollen variance.

* displays predictions with *p*-values < 0.1.

** displays predictions with *p*-values < 0.01.

Bold values highlights the predictions above the 95% threshold.

For aridity, BRT-based reconstructions indicate a hyper-arid phase from 10,000 to 8200 cal yr BP, increasing moisture until 500 cal yr BP, and slight aridification afterward (Fig. 8F). This is supported by the offset between MBT'_{5Me} and MBT'_{6Me} (Figs. 7D, 8A). Their correlation ($R^2$ = 0.75) weakens during 10,000–8800 and after 200 cal yr BP. The $\Delta(MBT'_{5Me}; MBT'_{6Me})$ highlights arid control on brGDGT (Dugerdil et al., 2025a), confirming the early arid conditions, a wetter Mid-Holocene, and renewed aridification after 4200 cal yr BP, with a peak during the LIA–CWP transition and at the RWP beginning.

### 4.4.4. Local rescaling of the BRT-brGDGT calibrations

Using brGDGT surface samples within a 250 km radius around Lake Fazilman (Fig. 9A), we evaluated the temperature errors of several linear and machine learning calibrations (Fig. 9B, C). This buffer radius was chosen as a compromise: large enough to provide a substantial modern dataset, yet still representative of the climatic conditions and brGDGT distribution around Lake Fazilman. For sites closest to the lake (<10 km), calibrations from De Jonge et al. (2014) yielded the lowest errors. For more distant sites, MAAT_{Tjk-Chen} and MAAT_{BRT ACA} remained the least biased. Linear Regressions (Fig. 9D) show that MAAT_{mrt-DJ} is the most biased ($R^2$ = 0.17; residuals between 0 and 8 °C), underestimating temperatures. MAAT_{5Me-DJ} and MAAT_{Tjk-Chen} show similar $R^2$ of 0.50, but the latter has tighter residuals (–5 to 5 vs. –5 to 10 °C). The best fit is from MAAT_{BRT ACA} ($R^2$ = 0.61), with a regression near the 1:1 slope and residuals between –4 and 8 °C. A local rescaling based on this regression was applied, producing the adjusted MAAT_{BRT ACA-LS} model. While maintaining the same regression, the rescaled version reduces the temperature bias (residuals between –4 and 4 °C, RMSE of 2.4).

### 4.4.5. Climate trends based on the pollen data

Pollen ratios of key herbaceous taxa (Artemisia spp., Amaranthaceae and Poaceae) provide semi-quantitative climate reconstruction. Ar/Am and Po/(Am+Ar) are indicators of aridity, each influenced differently by human activity and local vegetation (Fig. 5A and Fowell et al., 2003). These ratios diverge during the Early Holocene (P1), with Po/(Am+Ar) indicating a stable period of high moisture, while Ar/Am reflects high aridity. They converge during the Mid- and Late Holocene showing moist to arid transition (P2–P5), before diverging again at the end of the Late Holocene (P5), with Po/(Am+Ar) showing a more rapid increase in moisture. Quantitative reconstructions shows that (1) local calibrations yield smaller offsets from present-day climate values (dashed line on Fig. 10), (2) reconstructions based on local calibrations

show narrower Holocene value ranges, and (3) overall climate trends depend more on the modern pollen calibration dataset than the method itself (e.g., similar trends for BRT-TUSDB, MAT-TUSDB, and WAPLS-TUSDB). For temperature (Fig. 10A), local calibrations indicate a warm Early and Late Holocene bracketing a slightly milder Mid-Holocene, with a transition at ca. 8200 cal yr BP. Macro-regional calibrations also show a warm Early Holocene, but with stronger Mid-Holocene cooling (up to 2 °C) and a generally colder Late Holocene, despite a mild warming trend from 4000 to 1500 cal yr BP. These trends are consistent across methods. For precipitation and aridity (Fig. 10B, C), results are broadly consistent across methods and calibrations. The Early Holocene appears semi-arid, followed by a moisture increase from 8200 to 7000/5000 cal yr BP, depending on the method. After 4200 cal yr BP, a drying trend develops until 3000 cal yr BP. Local calibrations show a strong precipitation increase from the last 500 years.

### 4.4.6. Toward the best climate reconstructions: ensemble models

Ensemble models technique allows to produce a single, more accurate and robust climate prediction. We have applied this new method to the Lake Fazilman climate reconstructions derived from pollen and brGDGTs to increase the reliability of our climate reconstruction (An et al., 2024). The results inferred from our multi-method approach (for pollen: MAT, BRT, WAPLS, local and regional modern pollen datasets; for brGDGTs: classic and machine-learning calibrations locally rescaled) are integrated in the ensemble model, valorizing strengths of local and regional calibration datasets (Dugerdil et al., 2021a). Pollen-based ensemble models selection relied on randomTF() outputs (Table 3) and trend inspection (Figs. 10, S9 and S10). Reconstruction displaying high offsets, unreliable climate range, and insignificant statistical performances were removed. A similar selection was applied to brGDGT models. Details of selected reconstructions are in Table S3. The pollen ensemble MAAT reconstruction shows an Early Holocene warming followed by a Mid-Holocene cooling and a minimum at 3500 cal yr BP (Fig. 11A). A warming trend extends to 500 cal yr BP, declining slightly thereafter. For aridity, the Early Holocene is reconstructed as semi-arid with maximal moisture values during the Mid-Holocene followed by a strong aridification trend from 6500 to 3500 cal yr BP, progressively replace by slow aridification up to 1000 cal yr BP (Fig. 11B, C). The last millennium shows rising moisture and precipitation. The brGDGT ensemble reveals long-term cooling from 9500 cal yr BP to the pre-industrial, followed by a sharp modern rise. While MPCOQ and AI trends differ slightly, both suggest an arid Early Holocene with precipitation >100 mm yr⁻¹, yet low AI due to high evapotranspiration. AI rises from 8200 to 500 cal yr BP. MPCOQ peaks in the Mid-Holocene then declines until 500 cal yr BP.
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Local rescaling of the BRT-based brGDGT calibration for Lake Fazilman. (A) Map of surface brGDGT sampling sites within a 250 km radius of the lake. (B) Differences between observed and reconstructed Mean Annual Air Temperature (MAAT) for each site. (C) Box-plots illustrate prediction errors from five brGDGT calibrations: De Jonge et al. (2014), Chen et al. (2021), and Dugerdil et al. (2025b). (D) Scatter-plots of observed vs. predicted MAAT for each calibration, with associated residuals. (D4) Linear regression applied to rescale the ACA BRT calibration locally. (D5) Resulting local-scale calibration (BRT ACA-LS), used to generate improved MAAT reconstructions for Lake Fazilman.</figcaption></figure>

### 4.4.7. Pollen vs. brGDGT

The climate signals depicted by both pollen and brGDGT reconstructions are overall consistent. Using Gaussian-filtering and linear interpolation, global Pearson's *r* values between pollen and brGDGT ensemble reconstructions are 0.52 for MPCOQ, 0.34 for MAAT, and -0.49 for AI (Fig. 11). Phase-by-phase *r*-value comparisons (10,000–3500, 3500–2000, 2000–50, and 50 cal yr BP to present) show positive and strong correlations during the 10,000–3500 and 2000–50 cal yr BP phases. Only phases 3500–2000 and 50 cal yr BP to present, present inverse trend for MAAT and AI; while MPCOQ reconstructions correlate positively in all phases. The strongest proxy divergence occurs for MAAT and AI between 3500 and 2000 cal yr BP (highlighted in Fig. 11A, C). Moreover, ensemble-weighted variance, reflecting the spread among model predictions, indicates narrower confidence intervals for brGDGT-based reconstructions across all climate parameters. MAAT confidence is slightly lower during the Mid-Holocene, while MPCOQ shows consistently stable uncertainty. AI confidence is most variable during the Late Holocene, especially for pollen. The calibration offset is generally lower for MAAT and AI inferred by brGDGTs, while MPCOQ shows better results with pollen.

## 5. Discussion

### 5.1. Assessment of the Fazilman sequence

#### 5.1.1. Hydrological shifts inferred from carbonate precipitation

The Ca/Ti ratio, a standard proxy for carbonate content in lacustrine sediments across ACA (e.g., Qinghai Lake; Liu et al., 2013), closely

tracks optical lightness (L\*, also linked to carbonates; Debret et al., 2011), revealing an overall decline in carbonate content through Fazilman Holocene sequence, with a sharp drop after 7300 yr cal BP (Fig. 4). Carbonates dominate from 10,000 to 7300 cal yr BP, and dramatically reduce afterward.

Three hypotheses could explain these changes before and after 7300 cal yr BP: (1) in-situ biogenic carbonate production (mollusc shells, ostracods or Characeae), (2) watershed run-off carrying carbonates, and (3) evaporite formation under arid conditions. To assess this, the Ca/Mg ratio (a proxy for biogenic carbonate production; Liu et al., 2013) was compared to Ca/Ti and L\* (Fig. 4). Their covariation suggests that biogenic sources dominate the carbonate pool. Between 10,000 and 7300 cal yr BP, carbonate-rich Characeae stems correlate with high Ca/Mg and L\* values (Fig. S8B). Post-7300 cal yr BP, only non-carbonated gyrogonites remain, marking a decline in stem calcification. Carbonate mineralization of Characeae happens along the stems due to photosynthesis (Soulié-Märsche and García, 2015). These calcite encrustations, driven by photosynthesis, depend on light availability, water alkalinity, and Mg/Ca levels (Anadón et al., 2002). Given the relatively stable alkalinity (IR\_6Me in Fig. 4C), likely stable Characeae photosynthesis (marked by the Characeae continuity along the whole Holocene), and water transparency, the reduction in calcification likely reflects reduced Mg and Ca supply, suggesting a hydrological shift around 7300 cal yr BP.

#### 5.1.2. Hydrological shifts recorded inferred from aquatic and littoral vegetation successions

The aquatic ecological succession in Lake Fazilman is well defined (Figs. 3D, 5). From 10,000 to 9400 cal yr BP, Zygnema spp. acts as a
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Quantitative climate reconstructions from Lake Fazilman pollen data. (A) Mean Annual Air Temperature (MAAT), (B) Mean Precipitation of the Cold Quarter (MPCOQ), and (C) Aridity Index (AI). Results are shown for three reconstruction methods – Boosted Regression Trees (BRT), Modern Analogue Technique (MAT), and Weighted Averaging Partial Least Squares (WAPLS) – using the two more significant surface calibration datasets: ACADB (red) and TUSDB (blue). Dashed lines indicate present-day climate values at the site, with MAAT and MAP derived from worldclim2.1 (Fick and Hijmans, 2017) and AI from the CGIAR global aridity database (Trabucco and Zomer, 2018). Vertical bars on the right of each panel indicate the confidence interval, calculated from the mean values ± the RMSE for each calibration. Holocene climate phases referenced from other ACA studies are highlighted to contextualize long-term trends in temperature, precipitation, and aridity (Chen et al., 2019). Abbreviations for centennial-scale climate oscillations: LIA, Little Ice Age; MWP, Medieval Warm Period; DACP, Dark Ages Cold Period and RWP, Roman Warm Period. Yellow-blue arrows display the AI evolution from arid to wet conditions.</figcaption></figure>

pioneer aquatic algal taxon (van Geel and Grenfell, 1996), indicating a shallow marsh environment (Innes and Zong, 2021). This initial wetland stage likely represents a pond near a spring, with clear water and high abundances of Characeae, Planorbidae, and low authigenic element input (Soulié-Märsche and García, 2015; Vinarski et al., 2017). From 9400 to 6500 cal yr BP, *Botryococcus braunii* becomes dominant, especially between 8600 and 7300 cal yr BP, replacing *Zygnema* spp. in the competitive niche (van Geel and Grenfell, 1996), and reflecting deeper open water and possibly warmer conditions (Montoya et al., 2010). This productive phase includes high OM (Inc/Coh, Br/Ti), biogenic carbonates (Ca/Mg), abundant Lymnaeidae, Planorbidae, Characeae stems, and ostracods (Croudace and Rothwell, 2015; Vinarski et al., 2017).

A major shift around 7300 cal yr BP is marked by declining Characeae mineralization and ostracods, indicating hydrological changes (Anadón et al., 2002). The rise of NPP HdV-66 – possibly *Penium* spp. (Van Geel, 1978) or *Inocybe* spp. (Shumilovskikh et al., 2015) – suggests drier, oligotrophic peatland conditions, corroborating the hydrological change hypothesis. Synchronously, biomarkers also record dramatic water level decrease (%Cren in Fig. 4C and Wang et al., 2021). The numerous floating mats currently located in the lake centre may have been produced at this period due to water level fluctuations (Azza et al., 2006).

From 4200 cal yr BP, sedimentation stabilizes but with altered water chemistry (no more Characeae stems mineralisation; Anadón et al., 2002). Volvocaceae indet. dominate in open water (Innes and Zong, 2021), likely shallow stagnant waters (Tsvirko, 2023). This mosaic wetland (connected ponds surrounding floating mats) may explain the increasing diversity in the aquatic vegetation associated to the Volvocaceae constant increasing. From 4200 to 3400 cal yr BP, the aquatic assemblages are marked by the presence of *Pediastrum boryanum* var. brevicorne, *P. integrum*, and *Myriophyllum* spp., which indicate an oligotrophic and dystrophic swamp environment (Komárek and Jankovská, 2001). The decline in salinity after 4200 cal yr BP, as inferred from $IR_{7Me}$ and Sr/Ca, likely facilitated the development of these taxa over *Botryococcus braunii* (Komárek and Jankovská, 2001; El Atfy et al., 2024).

After 3400 cal yr BP, new taxa such as *Typha/Sparganium*-type, *Potamogeton lucens*, *Schoenoplectus litoralis*, for hyrophilous and aquatic flora, *Tetraedron* cf. *minimum*, and *Spirogyra* spp. for algae, begin to colonize the lake. The transformation of floating mats into bottom-rooted vegetated islands likely promoted the expansion of emergent vegetation (Azza et al., 2006), including *Typha* spp., *Schoenoplectus litoralis*, *Hippuris vulgaris*, and *Phragmites australis* in the case of Lake Fazilman.
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Comparison of pollen- and brGDGT-based ensemble reconstructions for Mean Annual Air Temperature (A), Mean Precipitation of the Colder Quarter (B), and Aridity Index (C). The shaded ribbons highlight the 95% confidence interval calculated from the ensemble quadratic variance using Eqs. (8)–(10). Upper panels show a synthetic timeline of major Holocene climate phases in ACA and the evolution of key societies in present-day Uzbekistan. Central bar plots display Gaussian-filtered Pearson's correlation coefficients ($r$) between pollen and brGDGT reconstructions. The upper bars show correlation across the full sequence, while the lower bars divide the timeline into four phases based on climatic shifts: 10,000–3500, 3500–2000, 2000–50, and 50 cal yr BP to present. Dashed frames highlight negative correlations between pollen and brGDGT. Abbreviations for centennial-scale climate oscillations: CWM, Current Warm Period; LIA, Little Ice Age; MWP, Medieval Warm Period; DACP, Dark Ages Cold Period and RWP, Roman Warm Period.</figcaption></figure>

Until 450 cal yr BP, Volvocaceae remained dominant, but afterward, Characeae (indicated by Gyrogonites), *Potamogeton lucens* in open water, and emergent *Schoenoplectus litoralis* (seeds and macro-remains) regained importance. Characeae are sensitive to eutrophication, carbon, and light availability (Soulié-Märsche and García, 2015). Their competition with *P. lucens* suggests a trophic shift around 450 cal yr BP (Scheffer and Van Nes, 2007). Characeae thrive in clear, oligotrophic waters, while *Potamogeton* spp. favour turbid, nutrient-rich conditions (Van Den Berg et al., 1999), implying both eutrophication due to increased grazing in Nuratau (Luneau et al., 2024). Over the last 150 years, *P. lucens* became dominant, likely reflecting pastoral decline, as supported by dung fungal spores and the HYDE model (Fig. 5B, C).

### 5.1.3. *Hiatus or sedimentation slowdown?*

Significant discontinuities in XRF signals and abrupt changes in SAR are often interpreted as hiatuses (Messager et al., 2021). In Lake Fazilman, strong SAR and MS fluctuations between ca. 7200–3200 cal yr BP, raise this question (Fig. 3A, C). Hiatuses are also claimed whenever important SAR changes are observed before and after the potential hiatus phase (Burr et al., 2019). In Lake Fazilman, it is not the case as the SAR shows gradual breaks at 200, 142, 126 and 100 cm, suggesting SAR variability throughout the Holocene, likely linked to fluctuating spring input. Moreover, no hiatus is evidenced by sedimentation transitional facies (Fig. 3E).

Climatically, this phase shows cooling and high precipitation, unfavourable conditions for drought induced hiatuses. While water presence does not guarantee sediment preservation, erosional surfaces from wave action typically affect larger lakes than Fazilman such as the Aral Sea (Nutz et al., 2018). Moreover, the aquatic succession and isoGDGT indices support lower water level and suggest reduced rather than interrupted sediment influx. Thus, the L2–L3 boundary lacks lithological evidence of a hiatus: no desiccation features or erosion surfaces are observed (Dugerdil et al., 2021b). Instead, L3 shows a gradual transition from grey clays with brown layers to bluish silts (Fig. 3E), more indicative of oxido-reduction dynamics than drying.

Mainly, L3 is marked by a strong increase in PC1<sub>XRF</sub>, indicating high lithogenic input (Croudace and Rothwell, 2015). The dominance of Fe, Ti, K and Zr, typically found in clays, may suggests an increased dust influx rather than fluvial sediment from the watershed. Then, this lithogenic signal may suggest an increase of wind strength, which is a common interpretation in drylands (Sharifi et al., 2015). However, combined with the SAR decline, this rather supports a hydrological shift. Moreover, the rise in Fe/Ti shows that the iron accumulation is not solely aeolian but also linked to early diagenetic enrichment (Fig. S11 A and Richter et al., 2006). While Ti is stable (inert to diagenetic processes), Fe may be remobilized in pore-waters, enhancing its concentration. The Fe/Mn ratio indicates reducing conditions early in L3 (135–140 cm), while later (128–135 cm), a drop in this ratio and a mismatch between Fe/Ti and MS suggest magnetic mineral formation (e.g., greigite, magnetite, green rust) under suboxic/anoxic conditions. Although S/Ti gradually increases, the S vs. Fe plot shows that L3 has the lowest S and highest Fe in the core (Fig. S11B and Richter et al., 2006). This may reflect a mix of magnetite, vivianite and green rust more than greigite, explaining the colour change from grey to bluish clay and increased MS (greigite is more magnetic than vivianite; Li et al., 2019a), in a sediment poor in S and organic matter (low S/Ti and Br/Ti; Rothe et al., 2014). Despite known diagenetic effects on some biomarkers, no relevant studies have shown such bias on GDGT-based palaeoenvironmental proxies (Naafs et al., 2019). Sulphur as well, seems to be not impacted by diagenetic remobilization, as it correlates well with the isoGDGT Methane Index (MI, Figs. S8B and S11 A). Fe and Mn XRF signals, impacted post-deposition, are unreliable for assessing redox conditions in the water column from 7200–4200 cal yr BP. Instead, %Cren, MI, Br/Ti, Inc/Coh, and S/Ti suggest a shallow water column, low organic matter, and suboxic to oxic conditions (Zhang et al., 2011; Wang et al., 2021). The clay-rich, low-SAR sediment between 7300 and 5700 yr cal BP, thus created ideal conditions for iron diagenesis under anoxic pore-water, despite suboxic lake conditions.
### 5.1.4. Linkages between sedimentation rate and water table fluctuations

The multi-proxy analysis allows us to hypothesize Holocene water table variations at Lake Fazilman summarized in Fig. S12. Geomorphological features indicate a past lake level higher than today, with an ancient shoreline 2 m above the current water level and a former outlet (Fig. 1A). From 10,000 to 9700 cal yr BP, sediment characteristics – sand and silt deposits (Fig. 3E, Si/Ti in Fig. 4) and high soil input indicators (BIT index, PC2<sub>XRF</sub>, Si/Ti) – suggest a recent or ongoing lake formation phase. This interpretation is supported by the presence of pioneer algae *Zygnema* spp. (van Geel and Grenfell, 1996; Innes and Zong, 2021) and sediments rich in vivianite and Fe/Mn crusts (visible as discontinuous black layers). The Fe/Mn signal and sediment colour imply a ferric hydroxide crust, possibly a green layer, resulting from well-oxygenated, shallow, and dynamic waters influenced by spring input and wind action (Fagel et al., 2005).

We propose that maximum in water level was reached at the Early-Mid-Holocene transition, with a continuous rise from 8500 to 7800 cal yr BP. This is supported by the maximum in Crenarchaeol percentage (Wang et al., 2021), and by high Ca/Mg ratios indicative of in-lake biogenic carbonate production (Liu et al., 2013), likely sourced from freshwater taxa like *Gyraulus* spp. and Characeae (Anadón et al., 2002; Vinarski et al., 2017). Around 7000 cal yr BP, the lake experienced a drop in water table, accompanied by a sharp rise in SAR, persisting until 3700 cal yr BP. This phenomenon explains the iron early diagenesis process. Beyond this period, proxy records suggest a slow and gradual rise in water level, up to the present 2 m water depth during spring.

### 5.1.5. Sedimentation slowdown and the fault reactivation hypothesis

A discrepancy emerges between the inferred lake water level and the precipitation reconstructed from both pollen and brGDGT proxies (Fig. 11). As illustrated in Fig. S12, the highest lake levels (9300–7300 cal yr BP) occur prior to the 8.2 ka wet event, while the subsequent drop in water level (7300–4200 cal yr BP) precedes the precipitation decline around 6300 cal yr BP. A parsimonious explanation is that groundwater dynamics, and thus lake level, are also influenced by orogenic reactivation. Wet resurgences north-east of Fazilman align with the current spring feeding the lake and wet resurgences located east-northward in the valley (Fig. 1A, C, D). The two currently-dry resurgences located just 2 m a.s.l. below the lake elevation are also spatially aligned (Fig. S1). These geomorphological and hydrological observations may be linked to the North Nuratau fault system (Nur-taev et al., 2013). This zone marks the contact between the Kyrgyz-Kazakh microcontinent and the Turkestan palaeo-ocean (Movlanov and Koloskova, 2020), where fault reactivation could have diverted spring flow and lowered the lake level around 7300 cal yr BP. This fault is known to be seismically active (Ullah et al., 2015). Seismic activity can disturb sediment integrity and SAR (Messager et al., 2021), and influence hydrological systems (Toth, 2009). In the Tibetan Plateau, Holocene earthquake frequency has been linked to sediment rate variations, with quiescence observed from 8000 to 5500 cal yr BP (Zhou et al., 2024). Although no similar records exist for the Pamir-Alai range, it is plausible that tectonic reactivation of Nuratau folds around 7300 cal yr BP altered groundwater circulation, affecting Lake Fazilman (Toth, 2009). Particularly, a Holocene earthquake with a magnitude of 6.7–7.4 was recorded in the southern Tian Shan (about 600 km from Lake Fazilman) at 5100 ± 1000 or 6500 ± 1500 cal yr BP, depending on the dating model (Patyniak et al., 2017). This event likely coincides with the low SAR and iron diagenetic processes event recorded in Lake Fazilman ca. 7300–5700 yr cal BP (Fig. S12). Although the considerable distance (ca. 600 km) weakens the likelihood of a direct causal link between this earthquake and the hydrological changes at Lake Fazilman, Ullah et al. (2015) demonstrated that the Nuratau fault system, located closer to the study site, is a seismically active, though understudied, structure. This observation makes the hypothesis of local fault reactivation a plausible explanation, even though another earthquake event is more likely responsible.

Despite significant Holocene water level fluctuations, the Fazilman sediment sequence is continuous, although a major decrease in SAR from 7300 to 3000 cal yr BP reduces temporal resolution for this interval. Nevertheless, the record before and after this phase remains sufficiently detailed to improve understanding of Holocene vegetation–climate interactions in ACA, a poorly documented region (Sorrel et al., 2007; Luneau, 2019), and their relation to the human societies as BMAC expansion and decline (Sorrel et al., 2007; Luneau, 2019; Bendezu-Sarmiento and Lhuillier, 2020; Lyonnet and Dubova, 2020).

## 5.2. Holocene plant ecology and vegetation history

### 5.2.1. Aquatic and grassland cyperaceae

Cyperaceae is a key taxon in Arid Central Asia (ACA) with high pollen percentages in the Fazilman pollen sequence, (ca. 18% and peaks reaching 38% ca. 4000 cal yr BP). Surface pollen samples from Tajikistan and Uzbekistan (Dugerdil et al., 2025d) also show significant Cyperaceae presence across all vegetation types, from *chul* warm deserts (ca. 5%, primarily *Carex physodes*) to mountain *tau* steppes (ca. 15%, largely *Carex pachystylis*). In the broader ACA region, Cyperaceae remains ecologically significant, from Manchurian steppes (Xu et al., 2014) to Tibet (Shen et al., 2021) and Mongolian mountain meadows (Ma et al., 2008; Dugerdil et al., 2021a). Thus, Cyperaceae in ACA is not exclusively indicative of local wetland vegetation.

Nevertheless, in Lake Fazilman, the local component – mainly *Schoenoplectus litoralis* on floating mats – plays a clear role. Macroscopic remains (seeds and fibres) of *S. litoralis* are present from the top core to approximately 95 cm depth (ca. 500 cal yr BP), supporting a substantial historical presence. Disentangling local aquatic vs. watershed grassland contributions remains challenging but critical, both for ecological interpretation and the accuracy of pollen–climate transfer functions. Local and aquatic taxa are not taken into account by the climate reconstruction methods (d’Oliveira et al., 2023), due to their sensitivity to local hydrology, rather than regional climate (Lacoul and Freedman, 2006). Differentiating between *S. litoralis*, *C. pachystylis*, and *C. physodes* pollen types could help. Morphological distinctions (grain size, shape, and ornamentation) have been observed, particularly in wetland vs. soil samples (Dugerdil et al., 2025d). However, these differences may stem from taphonomic processes rather than taxonomy. Cyperaceae pollen is highly sensitive to oxidation and pH, which vary widely in arid settings (Li et al., 2005); and in Fazilman sediments, fluctuations in Fe/Mn, S/Fe, Q<sub>7/4</sub>, and IR<sub>6Me</sub> ratios indicate significant redox and pH shifts throughout the Holocene which may affect the Cyperaceae taphonomy (Fig. 4).

Cyperaceae in Lake Fazilman is negatively correlated with open woodlands pollen such as Poaceae (R<sup>2</sup> = 0.53) and *Juniperus* spp. (R<sup>2</sup> = 0.42), and with hydrophytes like *Botryococcus braunii* (R<sup>2</sup> = 0.44) and HdV-66 (R<sup>2</sup> = 0.33, Fig. S8C). They are positively associated with taxa indicating swampy, submerged vegetation such as *Potamogeton lucens* (R<sup>2</sup> = 0.33), *Pediastrum* spp. (R<sup>2</sup> = 0.29), and *Typha/Sparganium*-type (R<sup>2</sup> = 0.27, Fig. S8C). This suggests both a competitive dynamic between *C. pachystylis* grassland and steppe-forest vegetation, and within-lake interactions among hydrophytes (*Komárek and Jankovská*, 2001; *Tsvirko*, 2023). Then, the Cyperaceae signal in Lake Fazilman reflects a mixed contribution from both local aquatic and regional grassland sources. Despite recent efforts, it remains difficult to isolate aquatic pollen from *S. litoralis* from that of *C. pachystylis* in the *tau* steppes or *C. physodes* in the *chul* desert (Gintzburger, 2003; Dugerdil et al., 2025d). This uncertainty is particularly critical around the 4.2 event, when Cyperaceae peaks (Fig. 5A). Cyperaceae is generally linked with cold conditions in ACA (Ma et al., 2008; Shen et al., 2021) and is often used to reconstruct summer temperatures (*Herzschuh et al.*, 2006), although it also thrives in desert and semi-desert habitats of the Tibetan Plateau (*Herzschuh et al.*, 2010) and Qilian Shan (Huang et al., 2018a). This broad ecological range may influence the temperature decrease between 4200 and 2500
cal yr BP, when Cyperaceae reaches its maximum. Transfer functions based on ACADB, which is rich in high-altitude Cyperaceae, tend to predict colder MAAT than the TUSDB, which better represents open woodlands and steppes of the tau range (Dugerdil et al., 2025d). Given the likelihood that Fazilman Cyperaceae signal originates from its watershed rather than local aquatic production, the TUSDB appears more reliable. Environmental DNA analysis of sediments could help clarify this question (Liu et al., 2021).

### 5.2.2. *Juniper migrations*

*Juniperus* spp. is a key taxon in pollen-based climate and traits reconstructions (Salonen et al., 2019; Dugerdil et al., 2025e). It dominates the Central Asian forest-steppe ecotone (Erdos et al., 2018), and is the main arboreal component of tau ecosystems (i.e., mountains and piedmonts; Sennikov et al., 2016), where forests are limited and increasingly threatened by climate change (Egamberdieva and Öztürk, 2018). Its sensitivity during the Holocene has been documented in various records (Beer et al., 2007a; Mathis et al., 2014). In Lake Fazilman, however, pollen identification is difficult due to poor preservation and low pollen influx, particularly during its peak (8200–6500 yr cal BP, Figs. 5A, 3C). During these phases, abundant HdV-66 and Volvocaceae also hinder identification.

The presence of conifer stomata could confirm the reliability of *Juniperus* spp. pollen signal (Sweeney, 2004). Local occurrence of junipers near Lake Fazilman between 8400 and 5000 cal yr BP is supported by stomata and high pollen percentages (Figs. 5 and S13). Similar synchrony between pollen and stomata has been also shown in Kyrgyz Tian Shan records (Beer et al., 2007a,b). Before and after this interval, *Juniperus* pollen type remains below 10%, reflecting regional pollen rain from sparse tau juniper woodlands (Beer et al., 2007b). Due to its long-distance dispersal, *Juniperus* spp. pollen is widespread in Uzbekistan (Dugerdil et al., 2025d). Other ACA records also show Mid-Holocene juniper maxima. In Kichikol, open tau juniper woodlands expand around 5000 cal yr BP then decline post-4000 cal yr BP (Beer et al., 2007a). In Son-Kul, decline begins at 4500 cal yr BP (Mathis et al., 2014), and Bakaly shows peaks between 6400–5000 and 3500–2000 cal yr BP (Beer et al., 2008). Further north, moist conditions favour *Picea* spp. instead (e.g., Karakol, Issyk-Kul; Beer and Tinner, 2008; Leroy and Giralt, 2021). While Fazilman and Bakaly share synchronous juniper peaks, sites in Tian Shan show a 500–1000 year lag, possibly due to northward or upward migration with elevation (1637 m in Fazilman, 1897 m in Bakaly, 2541 m in Kichikol and up to 3010 m a.s.l. in Son-Kul).

### 5.2.3. *Woodlands characterization*

The forest biomes in Fazilman are indistinguishable: similar scores are recorded for Cold Mixed Forest (CLMX), Cold Conifer Forest (COCO), Cool Mixed Forest (COMX), and Temperate Deciduous Forest (TEDE). All mainly reflect Arboreal Pollen, dominated by *Juniperus* spp., with secondary contributions from *Betula* spp. and *Salix* spp., which are not ecologically distinct enough to separate these forest biomes. In light of the current vegetation communities of Uzbekistan and Tajikistan, these biomes likely reflect the presence of tau open woodlands (Cl. *Junipero-Pinetea sylvestris* Rivas-Mart. 1965) and *yailau* cryophilous open woodlands (Cl. *Juniperetea pseudosabinae* Mirkin et al. 1986). Their are primarily differentiated by elevation and dominant species, i.e., *J. seravschanica* or *J. semiglobosa* for tau, and *J. semiglobosa* or *J. pseudosabina* for *yailau* (Dugerdil et al., 2025d). Tau woodlands dominated by *Juniperus* spp. and Poaceae steppe from 8200 to 6000 cal yr BP likely expanded across the Nuratau. Comparable expansions are also inferred in Bakaly for tau woodlands, and in Kichikol and Son-Kul for *yailau* types, reflecting differences in elevation.

### 5.2.4. *Cold and warm steppes fluctuations*

Similarly to the forest biomes, the *biomization* approach cannot distinguish Cold (COST) from Warm Steppes (WAST), as their biome-scores follow nearly identical trends along the Holocene (Fig. 6A). WAST likely dominated before 8200 cal yr BP, while COST prevailed after 4200 cal yr BP, in line with the brGDGT-based temperature reconstructions. Although steppe types are distinguishable along strong temperature gradients (e.g., Mediterranean to Kazakhstan; Tarasov et al., 1998), such differentiation is harder in Middle Asia, where steppe climate varies by elevation and region (Wesche et al., 2016; Gintzburger, 2003). Additionally, steppe and pseudosteppe pollen (*Artemisia* spp., Poaceae, Amaranthaceae) are widely dispersed (Dugerdil et al., 2025d), further complicating Fazilman interpretations. This makes steppe history in Uzbekistan, which is located between cold and warm steppe zones, both complex and essential. Functional trait responses differ under warm-arid vs. cold-arid climates (Frenette-Dussault, 2013; Nunes et al., 2017), but the coarse pollen identification level hinders this distinction (Dugerdil et al., 2025e). Improved species-level identification using detailed pollen keys (Lu et al., 2018), or using environmental DNA (Liu et al., 2021) could help refine past steppe reconstructions.

After 4200 cal yr BP, steppe composition fluctuates. From 4200–3400 cal yr BP, tau steppes dominate, with high Poaceae and Cyperaceae (Gintzburger, 2003). From 3400–2000 cal yr BP, declining Cyperaceae and rising Amaranthaceae and *Ephedra distachya*-type indicate expansion of *adyr* pseudosteppes in drier conditions (Rodda et al., 2019). Between 2000–1000 cal yr BP, *adyr* steppes remain dominant, before a return of tau steppes and a brief juniper resurgence (1000–500 cal yr BP) which is also asynchronously recorded in Kyrgyzstan (Beer et al., 2008; Mathis et al., 2014). These dynamics are echoed by the desert/steppe biome-score ratio (Fig. 6A).

## 5.3. Human impact Holocene history

### 5.3.1. *Human impact recorded in Lake Fazilman*

Andronovo settlement was recently discovered in Pangat, 30 km closed to Lake Fazilman, by the State University of Samarkand. The lake is located near pre-BMAC and BMAC proto-urban sites in the Zeravshan valley, such as Sarazm and Dzham (Mutin, 2023), and the Surkhan-Darya province, such as Molali, Dzharkutan, Tilla Bulak, and Kattabulak (Fig. 1B; Bendezu-Sarmiento and Lhuillier, 2020; Lyonnet and Dubova, 2020). The Andronovo-BMAC interactions around 4200–3450 cal yr BP have shaped regional cultural transformations (Luneau, 2020; Vinogradova and Kuz’mina, 1996). However, traces of anthropogenic influence from the Bronze Age in Lake Fazilman remain sporadic, with pollen of fruit/nut trees: *Juglans regia*, *Cerealia*-type, *Morus* spp. and Rosaceae indet. (from 4200, 2500, 1450 and 450 cal yr BP, respectively), possibly linked to riparian gardens maintained by Tajik sedentary communities in northern Nuratau (Luneau et al., 2024). These pollen types may indicate early human impact (Shumilovskikh et al., 2023), however they may also be produced by wild species as well. A clearer human signal appears around 650 cal yr BP with the rise of dung fungal spores (e.g., *Sordaria* spp., *Sporormiella* spp.) associated to human-induced pollen types (e.g., Asteroideae-type and *Plantago lanceolata*-types), followed by an exponential NPP increase from 450 cal yr BP, indicating a strong ecological threshold in land use (Huang et al., 2018a; Gauthier and Jouffroy-Bapicot, 2021; Spate et al., 2022). A summary of human-impact related proxy is given in Fig. S14. Trace elements indicative of pollution (Pb and Cu from XRF data) appear around 650 cal yr BP and show an exponential increase after 450 cal yr BP, in line with NPPs data.

Later, the region witnessed successive population movements, including those of the Saka, Scythians, Turks, and Mongols, reflecting long-standing steppe-oasis dynamics and contributing to Central Asia’s socio-environmental evolution (Curtis, 1997; Frachetti, 2008). Despite this continuous human presence, intensified land impact becomes apparent only around 500 cal yr BP in Lake Fazilman. This may reflect
either (1) a significant threshold in regional pastoral pressure deeply shaping landscapes or (2) a stable, sustained human presence near the lake. The Fazilman eutrophication and anoxia signals (e.g., Br/Ti, S/Fe, Q7/4, MI, Fig. 4 and Zhang et al., 2011; Debret et al., 2011; Joannin et al., 2022) further support increased local pastoral activity (Ghosh et al., 2017; Gauthier and Jouffroy-Bapicot, 2021; McCarthy et al., 2021). Archaeological evidence, such as nomadic remains at Kokkovga (12 km from Fazilman), suggests local settlements persisted into the modern era (Luneau et al., 2024).

Multi-proxy analyses from Lake Fazilman indicate an initial phase of human impact beginning around 4200 cal yr BP, characterized by the cultivation of fruit and nut trees and the onset of human-related fire regimes in the region (Barhoumi et al., 0000). A more pronounced tipping point in human influence occurs at ca. 650 cal yr BP, followed by a rapid intensification after 450 cal yr BP. This pattern raises the question of a threshold effect in the detectability of human impact: although archaeological evidence shows well-established societies in the area since 4200 cal yr BP, their activities may have remained below the ecological or sedimentary threshold required to leave a strong proxy signal. Alternatively, the 650 cal yr BP shift may reflect a spatial reorganization of human activity, with land use moving closer to Lake Fazilman. A further possibility is that human-related proxies are affected by taphonomic biases that obscure earlier signals.

The rise in Cyperaceae and *Potamogeton lucens* remains alongside lithological change (organic-rich peat layers ca. 300–100 cal yr BP) complicates dung fungal spores attribution solely to anthropogenic factors since peat sediment enhanced fungal spore preservation (Shumilovskikh and van Geel, 2020). It is especially evident in the last 150 years, and may partly explain the apparent human impact spike likely due to taphonomic effects. The conclusions raised from Lake Fazilman about human impact variability along the Mid- and Late Holocene have to be taken carefully due to the NPP taphonomic biases, as well as the sediment variability (i.e., SAR variations, organic matter content and spore preservation; Shumilovskikh and van Geel, 2020).

### 5.3.2. Lake Fazilman record in the macro-regional Holocene human impact synthesis

The Fazilman record is giving the first insights from the southern Middle Asian low-lands and piedmonts, and fits broader trends of Holocene human-environment interactions in Middle Asia:

* **Lower Paleolithic to Early Neolithic (2.1 Ma–9000 cal yr BP):** human presence established (Shnaider et al., 2023; Nishiaki et al., 2022), but no clear human impact on landscapes in the Early Holocene recorded in Lake Fazilman.

* **Neolithic (7000–4250 cal yr BP):** first human impact on the landscape is recorded in Middle Asia (Spate et al., 2022), but it remains limited to high-elevation sites as mostly investigated, like Chatyr-Kul or Issyk-Kul in the Tian Shan (Schroeter et al., 2020; Leroy and Giralt, 2021), not in low-lands.

* **Proto-urban BMAC phase (4250–3650 cal yr BP):** agropastoral activity flourished in the context of the cultural developments associated with the BMAC and Andronovo complexes (Tengberg, 2013; Mashkour, 2013; Rouse et al., 2025), yet sedimentary evidence of disturbance in the Fazilman sequence is minimal and limited to nuts tree gardening. Larger-scale human impacts growth continuously, mostly in hilly and mountainous areas (Spate et al., 2022), while human-induced fires become predominant over climate-driven ones (Barhoumi et al., 0000). Increased human impact is recorded in the lowlands after 4000 cal yr BP, likely resulting from population migrations from higher elevations in the Altai and Tian Shan ranges (Huang et al., 2021; Li et al., 2022; Xiang et al., 2023).

* **Iron Age to Middle Age (3650–650 cal yr BP):** successive phases of urban decline and renewal, associated with diverse societies, such as the Andronovo, Saka, Yaz 1 cultures, Achaemenids, Greco-Bactrians, Kushans, Sogdians, or Turco-Mongols, coincide with increased fire use and intensified pastoral activity (Barhoumi et al., 0000).

* **Mongol Empire (650–450 cal yr BP):** first major dung fungal spore influx in Fazilman postdates the Mongol conquest of Middle Asia (Curtis, 1997), marking pastoral intensification similar to patterns in Tian Shan, Pamir-Alai and Mongolian Plateau from 2000 to 1000 cal yr BP (Dugerdil et al., 2021b; Spate et al., 2022).

* **Timurid period (450–300 cal yr BP):** maximum human impact recorded in terms of dung fungal spore influx in line with land-use intensification through Timurid complex agriculture, garden design, and fertilization reshaping landscapes (Subtelny, 1997).

* **Uzbek khanates (300–100 cal yr BP):** relative decline in impact due to political instability, waning Silk Road trade, and conflicts between Persians and Uzbeks associated to intern wars among khanates (mainly Bukhara and Khorazm; Curtis, 1997).

* **Russian conquest to Soviet era (post-1865):** rapid Soviet land-use expansion and irrigation-based intensification (mainly due to crops and rangeland collectivization and increasing cotton quotas; Curtis, 1997) are captured by Fazilman NPPs and HYDE model trends (Klein Goldewijk et al., 2017). The stagnation post-1980 and landscape abandonment after 1991 (less subservient Uzbek administration to Russian central power, ethnic animosities and Soviet collapse) is recorded in Fazilman NPPs, HYDE model and Middle Asian studies (Zhang et al., 2022) and likely facilitated steppe recovery (Török et al., 2016; Zhang et al., 2022).

## 5.4. Toward a reliable climate reconstruction from Lake Fazilman

### 5.4.1. A clear precipitation history

The precipitation reconstruction for Lake Fazilman shows a strong agreement across proxies, calibration datasets and methods (Figs. 5, 6, 8, and 10). Overall, the Early Holocene (before 8200 cal yr BP) was dry. BrGDGT-BRT reconstructions suggest wetter conditions during 10,000–9000 cal yr BP, but are likely biased, as $\Delta(MBT'_{5Me} - MBT'_{6Me})$ signals enhanced aridity during this period (Dugerdil et al., 2025a), likely due to favoured distribution of 6- over 5-methyls in arid soil (Guo et al., 2021). From 8200 to 4200 cal yr BP, all proxies indicate maximum precipitation, consistent with regional trends across ACA (Chen et al., 2010) from the Altai (Huang et al., 2018b; Leroy et al., 2021) to the Caucasus range (Joannin et al., 2014; Robles et al., 2022). In Uzbekistan, this wet period is known as the Liavliakan pluvial, culminating around 6000 cal yr BP (Lioubimtseva et al., 2005). This trend is particularly evidenced by the speleothem records from Tonnel'Naya and Talisman caves (Cheng et al., 2016; Tan et al., 2024). In Lake Fazilman, the 4.2 ka event is marked by a decrease in precipitation; however, it does not result in drier conditions (McKay et al., 2024). This reduction in rainfall was likely offset by synchronous cooling, which maintained stable soil water evapotranspiration rates (Fig. 11A, C). No particular drop appears in Tonnel'Naya at the 4.2 ka event, while it is well marked, earlier ca. 5100 cal yr BP in Talisman cave (Cheng et al., 2016; Tan et al., 2024), suggesting either a spatio-temporal lag within the region or the influence of local environmental conditions. Following the 4.2 ka event, rainfall declines at Lake Fazilman until the Dark Age Cold Period, around 1800–1600 cal yr BP (Aichner et al., 2015), similarly to Lake Karakul, Kyrgyzstan. After this, the signal weakens, possibly due to grazing-induced vegetation shifts disrupting pollen-based climate reconstructions (Robles et al., 2022; Huang et al., 2018a). Even if, human control on landscape in Lake Fazilman reach its tipping point only from 650 cal yr BP. It is also noteworthy that the inter-models variability with the pollen-based ensemble models for the precipitation and aridity index are more important within the Late Holocene (Fig. 11B and C).
Simple pollen ratios permit to ease the understanding of quantita-
tive climate reconstruction (El-Moslimany, 1990; Fowell et al., 2003).
The aridity patterns produced by quantitative reconstructions are con-
firmed and explained by them. Pollen ratios show increasing aridity up
to the LIA, and a rebound in moisture after 450 cal yr BP. Similarly,
HdV-200 influx supports the aridification trend between 2500 and 450
cal yr BP, reflecting wetland drying (Mikhailova et al., 2021). In this
context, the BRT-based reconstructions, both for brGDGT and pollen,
are the most in line with the pollen ratios. However, Po/(Am+Ar) is
the only ratio indicating wet conditions before 8200 cal yr BP, though
this may be misleading due to over-representation of Poaceae in arid
zones and its sensitivity to grazing (Fowell et al., 2003; Ma et al.,
2008). Poaceae are abundant in both deserts and mesic grasslands and
often dominate due to poor preservation of other taxa (Ma et al., 2008;
Murad, 2012). Thus, high Poaceae and Amaranthaceae abundances
before 8200 cal yr BP likely reflect pollen input from nearby lowland
chul deserts and upland adyr steppes (Gintzburger, 2003; Dugerdil
et al., 2025d), as also supported by high desert and steppe biome-scores
(CODE, HODE, COST, WAST; Fig. 6A).

5.4.2. *Temperature reconstruction assessment*

The temperature increase between 10,000 and 9500 cal yr BP should be interpreted with caution, as it coincides with the initial lake settlement phase, marked by low water levels (Figs. 4, 8). Specific environmental conditions – such as elevated sand input, soil influx, and higher alkalinity – likely influenced GDGT production (Fig. 7; De Jonge et al., 2021; Duan et al., 2022). As a result, brGDGT-based reconstructed MAAT values during this period may reflect these confounding factors rather than actual temperature changes (Dugerdil et al., 2025a). Enhanced aridity, supported by independent proxies (Fig. 11C; Guo et al., 2021), may have further compromised brGDGT reliability by altering soil water content, which is known to affect brGDGT distributions (Chen et al., 2021; Duan et al., 2022).

For the pollen-based temperature, the local calibration shows lim-
ited reliability due to low statistical performance (R² of 0.13, 0.28,
0.40 for WAPLS, MAT, and BRT, respectively; Dugerdil et al., 2025d).
Even the BRT approach is mitigated here, as the TUSDB dataset is
reduced (n < 100; Salonen et al., 2019), which can enhance strong lin-
ear responses between the proxy and the climate parameter (Schonlau,
2005). Consequently, its weight in the ensemble model is reduced (due
to low RMSE), aligning pollen-based trends more closely with brGDGT,
since ACADB trend is closer to brGDGT one. Overall, both proxies yield
consistent temperature reconstructions over the Holocene (Fig. 11),
though key discrepancies arise, particularly between 3000 and 2000
cal yr BP. A warming has been documented in ACA between 4000 and
3000 cal yr BP (Beer and Tinner, 2008); in agreement with the brGDGT
signal. This proxy responds rapidly to temperature shifts (Qian et al.,
2019), recording almost synchronous signal up to several decades (Cao
et al., 2018a), allowing near-synchronous signal capture. In contrast,
pollen records often lag due to vegetation response times (Webb,
1986), typically around 300 years during the Holocene (Marsicek et al.,
2018). Even after vegetation equilibrium with climate, pollen produc-
tivity may still be climate-influenced, especially by temperature effects
on the pollen season (Schramm et al., 2021). In this context, the
warming trend during the 4000–3000 cal yr BP period, may maintain
the previous vegetation composition (i.e., Cyperaceae dominance in
tau steppes, Fig. 5A and Dugerdil et al., 2025d) in the pollen signal
due to compensation of their spatial decline by an enforced pollen
production. Also, this may result from events of high production of
masting-sporadic pollen, lasting over extended periods due to stochastic
triggers (Geburek et al., 2012). This lag also reflects life-history traits of
constituent taxa (Lacourse, 2009): fast vs. slow trade-off differ between
a set of plant functional traits involved in the persistence, regeneration
and dispersal functions of plants (e.g., plant growth form and height,
life span, seed mass; Kleyer et al., 2008).

Finally, both proxies depict an Early Holocene thermal optimum
followed by long-term cooling toward the pre-industrial era. Two brief
warming phases appear in the Mid-Holocene and between 3500–2500
cal yr BP. For short-term temperature fluctuations, brGDGT offers
higher reliability than pollen in such environments.

5.4.3. *Mismatch in aridity reconstruction*

The AI was reconstructed from both pollen and brGDGT proxies: for the pollen ACADB BRT calibration, models yield an R² of 0.86 with RMSE = 1370 (Dugerdil et al., 2025d), while brGDGT models reach R² of 0.69 with RMSE = 1300 (Dugerdil et al., 2025b). Both models return strong randomTF() values (Table 3) and consistent ensemble reconstructions, with narrow confidence intervals suggesting minimal disagreement among individual models (Fig. 11C). However, the Holocene trends are anti-correlated (Pearson's r = -0.49), with particular negative correlation between 3500 and 2000 cal yr BP. Outside this window, both proxies agree: moisture increases after 2000 cal yr BP and during the Early Holocene, particularly following the 8.2 ka event, consistent with records from the Altai (Zhang and Feng, 2018). The divergence may result from changes in brGDGT sources. Before 4200 cal yr BP, the lake system had high water levels with a stable balance between in-situ brGDGT production and soil influx. The shift post-4200 cal yr BP is marked by a change in the bacterial CI (De Jonge et al., 2019), and the Δ(MBT'_{5Me}; MBT'_{6Me}) index, which is a reliable aridity marker (Dugerdil et al., 2025a), in line with the pollen-based slight and continuous aridification trend from 4200 to 1000 cal yr BP. While the BRT AI model integrates all brGDGT compounds (potentially sensitive to confounding factors; Guo et al., 2021), Δ(MBT'_{5Me}; MBT'_{6Me}) specifically targets aridity influence on -5 and -6 isomers (Chen et al., 2021; Duan et al., 2022). Mainly, the warm period evidenced in ACA between 4000 and 3000 cal BP by Beer and Tinner (2008) may be responsible for this confounding factor effect.

5.5. *Societal and ecophysiological response to climate variations*

5.5.1. *Consequences for the BMAC decline theory*

From the Fazilman multi-proxy record, a clear climatic transition
marks a decrease in precipitation from the Holocene optimum around
6000 cal yr BP to about 1800 cal yr BP, aligning with the DACP in
ACA (Aichner et al., 2015). This Mid-to-Late Holocene aridification
trend is well documented across western ACA (Joannin et al., 2014;
Wang et al., 2025), and in the Pamir-Alai piedmont (Cheng et al., 2016;
Tan et al., 2024). The decline of the Oxus Civilization, which some
researchers associate with an episode of abrupt climate change (Koshe-
lenko et al., 1994; Mousavi, 2008), falls within this phase. Luneau
(2019) note the absence of solid palaeoenvironmental evidence in the
immediate vicinity of BMAC sites, highlighting a gap that is partially
addressed by records from Lake Fazilman and Tonnel'naya Cave (Cheng
et al., 2016). Both archives indicate a trend of long-term aridification
but lack the resolution necessary to confirm abrupt climatic shifts.

Nevertheless, our data shows a sharp drop in summer precipitation between 4200 and 4000 cal yr BP (Fig. 10B), matching with the early phase of the BMAC. A similar event occurs about 3500 cal yr BP, with stronger aridity effect than precipitation drop (Fig. 11B, C). When accounting for age–depth uncertainties (Blaauw and Christen, 2011; Chevalier et al., 2019), these drops could align with the 3950–3750 cal yr BP window, which corresponds to the period just before the final transformations of the Oxus Civilization. While a direct chronological correlation between events is difficult to establish, it should be considered that a change unfolding over a long period may have had a lasting impact. Several centuries of (gradual) aridification may have exerted cumulative pressure on economic and social structures. Although the timespan and temporal resolution of the Fazilman aridification phase are presently insufficient to draw firm conclusions about the BMAC decline, our results support a significant aridification trend from 6000 to 1800 cal yr BP, with sharp intensifications around 4200 and 3500 cal
yr BP, and provide additional evidence for climate change during the lifespan of the Oxus Civilization. The *ensemble* models show moderate but existent inter-model variability (ensemble 95% confidence interval from Fig. 11). Nonetheless, these new data strongly encourage consideration of environmental shifts as a major factor in the development of the Oxus Civilization, possibly contributing – alongside other potential causes – to its rise (around 4200 cal yr BP), but also its eventual decline (after 3750 cal yr BP). Synchronous climate and societal variations during the Late Holocene are documented in several ACA case studies, including the Altai and Tian Shan ranges, where climate is thought to have influenced human settlement patterns and migrations (Huang et al., 2021; Li et al., 2022; Xiang et al., 2023).

At present, establishing direct causality between climatic events and the BMAC decline remains challenging. On one hand, regional settlement patterns display considerable complexity, with evidence for a reduction in site size and an increase in site abandonment from approximately 3700 cal yr BP. However, these developments occurred alongside the appearance of new sites and a possible territorial expansion (Luneau, 2014). On the other hand, multiple hypotheses – economic and social for the most convincing – remain plausible explanations for the disappearance of the BMAC (Luneau, 2020). Current archaeological evidence therefore points to a more nuanced scenario in which resilience and adaptability played significant roles, and in which shifts in settlement patterns were shaped by a multifaceted interplay of environmental factors, techno-economical change, and socio-cultural dynamics. A causal relationship between climatic fluctuations and societal collapse thus remains uncertain.

### 5.5.2. Vegetation functional response to climate through time

Plant community functional response to water stress. Trait-based approaches are increasingly used in environmental sciences to link ecosystem structure (e.g., pollen-inferred plant communities) with ecophysiological functions (An et al., 2024). In Lake Fazilman, thought based on pollen data (Van Der Sande et al., 2019; van der Sande et al., 2021), plant height and seed mass show a strong temporal correlation (r = 0.95, Fig. S8 A), reflecting a common strategy along the global plant spectrum related to size and the r/K life-history gradient (Díaz et al., 2016). This continuum also emerges at the plant community level (Bruelheide et al., 2018). Surprisingly, stem specific density (SSD) values in Fazilman are also highly correlated with plant height and seed mass (r = 0.96 and 0.97, Fig. S8 A), despite SSD not being part of the plant size axis in global trait spectrum. SSD may have important independent variation at any given plant height (Díaz et al., 2016). Regarding functional adaptation to abiotic stress, plant height is typically positively correlated with precipitation (Frenette-Dussault, 2013; Nunes et al., 2017). Arid conditions induce water stress, increasing cavitation risk in plant vessels, which promotes shorter statures to mitigate hydraulic failure (Enquist, 2002). Aridity also favours herbs and shrubs over trees, lowering community mean height. SSD is likewise linked to aridity (Mo et al., 2024), particularly through potential evapotranspiration (Bruelheide et al., 2018), as higher SSD helps avoid drought-induced mortality (Díaz et al., 2016). In this context, arid conditions (e.g., MAP below 400 mm·yr⁻¹) may favour higher SSD values (i.e., negative SSD/precipitation correlation). However, SSD also supports structural stability, especially in tall trees (Chave et al., 2009), creating a secondary correlation with plant height, and, indirectly, precipitation. Under this assumption, SSD and precipitation may be positively correlated. As no modern study on plant trait responses to climate currently exists in Central Asia, the Fazilman record provides a unique opportunity to explore this interplay by reconstructing community strategies tied to plant size, growth rate, drought tolerance, and stem structure. It helps clarify whether the co-variation of plant height, seed mass, and SSD reflects functional coordination under climate constraints or distinct ecological responses.

The reconstructed plant height in Fazilman reflects a gradient from tall *Juniperus* spp. to intermediate Amaranthaceae, *Artemisia* spp.,

Poaceae, and finally to short Cyperaceae. This aligns with the SSD values, which are high for conifers and low for Cyperaceae and Amaranthaceae (Díaz et al., 2016; Mo et al., 2024). Both traits track the precipitation trends reconstructed by pollen and GDGT. A pronounced drop in plant height and seed mass between 5000–4000 cal yr BP exceeds the gradual precipitation decline, underscoring the vulnerability of woody vegetation to water stress (Bruelheide et al., 2018), particularly in drylands (Frenette-Dussault, 2013; Nunes et al., 2017). This shift, mainly affecting *Juniperus* spp., exemplifies the collapse of a slow life-history strategy (e.g., tall woody plant) after the 4.2 ka event (Lacourse, 2009). Based on this, a precipitation threshold for sustaining woody plants in Uzbekistan is estimated at 400±50 mm yr⁻¹, consistent with modern *Juniperus* spp. climatic distributions (Gintzburger, 2003; Erdos et al., 2018).

A synchronous drop in plant height and SSD emphasizes the SSD strong correlation with precipitation. While this contrasts with typical functional responses to aridity (Nunes et al., 2017; Bruelheide et al., 2018), it raises the question of whether the SSD-precipitation link reflects drought tolerance or biomechanical stability. This decline illustrates the dominance of a slow life-history strategy during the Mid-Holocene, marked by low growth rates (Lacourse, 2009). Traits like SSD and wood density (Díaz et al., 2016; Chave et al., 2009) govern growth, and their co-variation with plant height and seed mass suggests a consistent ecological strategy. The *tau* open woodland, composed of *Juniperus seravschanica*, *J. semiglobosa*, *Acer* spp., and *Crataegus* spp. (Dugerdil et al., 2025d), thrived in wetter Mid-Holocene conditions. Taller plants likely had competitive advantages for light (Grime, 1974), while higher SSD minimized biomechanical risks and supported larger seeds for regeneration (Garnier et al., 2016), all reinforcing a conservative slow life-history strategy (Lacourse, 2009).

To clarify the SSD-plant height-precipitation relationship, we examined the relative percentage difference between SSD and plant height (bottom panel in Fig. 6). A smaller difference – where SSD declines less than height – is most evident during the Mid-Holocene, intermediate in the Early and Late Holocene, and elevated during drought phases such as the 4.2 ka event and the LIA. This pattern suggests the drought-tolerance function of SSD becomes prominent under arid stress. These variations correspond to shifts in vegetation types: (1) *tau* open woodlands with *Juniperus* spp. during wetter phases, (2) *adyr* steppes with *Carex pachystylis*, and (3) *adyr* pseudosteppes with *Artemisia* spp. and ephemeral Amaranthaceae (e.g., *Gamanthus gamocarpus*, *Climacoptera lanata*). This functional succession recurs during both the Mid-to-Late Holocene transition and the LIA-post-industrial period (less intensively). It aligns with the SSD distribution among dominant pollen types: *Juniperus* spp., Cyperaceae, Poaceae, *Artemisia* spp., and Amaranthaceae (Fig. S15). While it seems surprising that graminoids show higher SSD than some woody taxa, many Amaranthaceae in Uzbekistan are ephemeral, turgescent herbs with low SSD, and it could be biased by the SSD uncertain estimation for non-woody plants (Dugerdil et al., 2025e). In the post-industrial period, the SSD-plant height relationship shifts. Whether due to increasing aridity or anthropogenic pressure remains unclear. In arid rangelands like Uzbekistan (Gintzburger, 2003), both drivers shape similar functional traits: precipitation (Nunes et al., 2017), and grazing jointly influence plant height, with shorter plants favoured under grazing pressure (Adler et al., 2004).

*Leaf economic spectrum variation to temperature changes.* The leaf economic spectrum represents a global gradient of plant strategies, from fast to slow returns on nutrient and dry mass investment in leaves (Wright et al., 2004). In ACA surface samples, leaf area strongly shapes plant functional space (Dugerdil et al., 2025e). Leaf size and shape are known to reflect climate, especially temperature and precipitation, and are used in palaeoclimate reconstructions (Peppe et al., 2011). In Lake Fazilman, leaf area positively correlates with spring temperature (R² = 0.36), as do SLA and Nleaf (R² = 0.40 and 0.48), aligning with known leaf trait responses to temperature (An et al., 2024). However, these
traits are also influenced by precipitation regimes (Griffin-Nolan et al., 2018), complicating interpretation. Their dual control may explain weaker but still significant correlations with spring precipitation ($R^2$ = 0.45, 0.28, and 0.18, respectively), suggesting the need for caution when linking functional traits to specific climatic drivers. Wright et al. (2017) showed that the relationship between leaf area, summer temperature, and precipitation reverses between dry and wet climates. Under wet conditions, modelled leaf area declines with decreasing MTCOQ. For instance, this pattern observed in Europe along the Holocene (Veeken et al., 2022), contrasts with findings at Fazilman, where decreasing MTCOQ occurs under arid conditions. Our CWM leaf area reconstruction shows an increase with decreasing MTCOQ, likely reflecting the dominance of stress-tolerant, nutrient-rich species, consistent with Wright et al. (2017).

## 5.6. Perspectives

**Methodological limits.** Despite significant efforts to improve the palaeoclimate and palaeoenvironmental framework in ACA, numerous limitations and uncertainties persist. First, the Lake Fazilman record relies on a single site, which limits the assessment of spatio-temporal variability. Regional syntheses such as Chen et al. (2022a) and Tan et al. (2024) partially address this gap, but data remain scarce across southern Central Asia. The present study is also subject to proxy-specific biases, including the influence of confounding factors – such as salinity and aridity – on brGDGT-climate relationships (Dugerdil et al., 2025a), human impact and pollen representativeness affecting pollen-climate calibrations (Dugerdil et al., 2025d), and the differential preservation of spores across sediment types (Shumilovskikh and van Geel, 2020), among others. However, these limitations are partly mitigated by the multi-proxy approach, as overlapping interpretations between proxies help compensate for their individual biases (Lamentowicz et al., 2025).

Chronological uncertainties also persist due to strong variations in SAR and potential temporal changes in the intensity of the $^{14}$C reservoir effect. Quantitative reconstructions are further affected by methodological biases: the performance estimators (e.g., RMSE, $R^2$) for both pollen- and brGDGT-based climate reconstructions are slightly lower than those typically obtained in temperate mid-latitude regions (d'Oliveira et al., 2025). For trait reconstructions, the limited trait coverage of modern plant taxa, still poorly documented in Uzbekistan, may influence the accuracy of past reconstructions (Kattge et al., 2020). Nevertheless, aggregating individual trait values at the pollen-type level substantially improves trait coverage within pollen assemblages (Dugerdil et al., 2025e). In any case, further methodological developments are required to enhance our understanding of Holocene climate and vegetation history in Uzbekistan, as outlined in the following subsections.

**Functional trend in palaeoecology.** The CWM reconstruction from Lake Fazilman, integrating pollen and plant functional traits, offers promising insights for palaeoecology, though further work is needed to bridge gaps with plant ecology (Reitalu et al., 2014). In his pioneering work, Grime (1974) proposed that vegetation composition is shaped by three main forces, i.e., competition, stress, and disturbance, corresponding to Competitor (C), Stress-tolerant (S), and Ruderal (R) strategies. Species positions in CSR space can be estimated using an algorithm based on three leaf traits: leaf area, specific leaf area, and leaf dry matter content (Pierce et al., 2013). This enables the reconstruction of temporal changes in C, S, and R strategy proportions to evaluate ecological drivers over time (Connor et al., 2018). Additionally, CSR profiles have been defined for each biome (Pierce et al., 2017), offering a potential tool to assess biomization accuracy. Applying CSR in palaeoecology may clarify the drivers of pollen variability by revealing vegetation responses to climate, competition, and environmental filtering (Bruelheide et al., 2018).

**Climate reconstruction technical improvement.** Although the reliability of climate reconstructions depends on modern and fossil dataset quality, on local pollen-climate relationships, and calibration statistics,

it can also benefit from technical advances. Most Holocene climate syntheses rely on a single method and calibration dataset (Herzschuh et al., 2023), favouring a homogenized approach for comparability across sites. However, this often overlooks local and methodological specificities, partly due to the absence of robust statistical frameworks for merging reconstructions, unlike those used in future climate modelling (Intergovernmental Panel on Climate Change, 2018). While the value of multi-proxy approaches is recognized to enhance the reliability of climate reconstructions (Lotter, 2014), similar efforts are needed for integrating multiple methods and calibrations. In this context, refining calibration selection (Chen et al., 2017; Liang et al., 2020) and employing ensemble models (An et al., 2024) represent major advancements. Seasonal biases in climate parameter interpolation significantly impact pollen-based climate reconstructions and deserve greater attention (Rehfeld et al., 2016). Similar concerns apply to brGDGT-based reconstructions, where seasonal bias remains debated (Cao et al., 2018a; Dearing Crampton-Flood et al., 2020). This issue is central to unravel the Holocene Conundrum – the mismatch between Holocene temperature reconstructions and climate models – in ACA (He, 2011; Braconnot et al., 2012).

## Conclusions

The Lake Fazilman record appears to be a key palaeoclimate and palaeoenvironmental sequence in the western part of the Arid Central Asia where the scarcity of environmental archives make the understanding of the Quaternary interplay between landscape, vegetation, climate and human society, very weak. Our multi-proxy (lipid biomarkers, Magnetic Susceptibility, Non-Pollen Palynomorphs, pollen, Spectrocolorimetry and XRF) study, supported by local surface calibrations, and covering the whole Holocene permits to reconstruct biomes, vegetation, plant functional distribution and climate changes using transfer functions, linear models and machine learning. Mainly, it was demonstrated that:

* Locally calibrated brGDGT-based temperature reconstructions underscore the necessity of region-specific calibration for accurate palaeoclimate inference in dryland contexts. The ensemble models, which combine the strengths of individual reconstruction outputs for both pollen and brGDGTs, enhance the accuracy and reliability of climate reconstructions. Both of these methodological advances can be applied across past archives in ACA to deepen our understanding of this vast and still poorly understood region.

* Climatically, the Lake Fazilman accurately records the Holocene precipitation history with an arid phase in the Early Holocene (before 8200 cal yr BP), an optimal precipitation phase associated with a spreading of tau open woodlands dominated by Juniperus spp. and Poaceae steppes during the Mid-Holocene (from 8200 to 6000 cal yr BP); followed by an arid Late Holocene (from 4200 to 650 cal yr BP) and a more contrast precipitation pattern after the Dark Age Cold Period (i.e., around 1800 cal yr BP in ACA). Our results on the Lake Fazilman are in line with the broader context of the western ACA climate zone.

* Centennial-scale climatic events, including the 4.2 and 3.5 ka BP dry phases, are distinctly recorded and align with broader regional climate instability during the Bronze Age.

* The climate-vegetation-human society interplay in Western Uzbekistan is well recorded in the Fazilman multi-proxy sequence with (1) a synchronous aridification and Bactrio-Margian Archeological Complex development, without being able to confirm the role of aridification in the decline processes of the Oxus Civilization and (2) a more marked human impact on vegetation though grazing, biomass burning and tau woodland control beginning around 650 cal yr BP. The human impact is maximum from 450 to 300 cal yr BP and from 100 cal BP to the present.
* Functional trait analysis of pollen data reveals major shifts in vegetation strategies over time, linked to both climate variability and increasing human influence from the Mid-Holocene onward. The traits adapted to water-stress are among the most important to understand the vegetation history in steppe-forest ecotone.

The Fazilman watershed provides a valuable reference for understanding long-term interactions between climate, vegetation, and human land use in a vulnerable semi-arid landscape. These results have implications for interpreting future climate and land-use changes in Central Asian drylands, where food and water security are increasingly threatened. However, multiplication of sediment archive investigations remain necessary to ensure that the conclusions raised from Lake Fazilman reflect regional trend over local filtering.

### CRediT authorship contribution statement

**Lucas Dugerdil:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Guillemette Ménot:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Formal analysis, Conceptualization. **Odile Peyron:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Formal analysis, Conceptualization. **Dilfuza Egamberdieva:** Writing – review & editing, Resources, Project administration. **Isabelle Jouffroy-Bapicot:** Writing – review & editing, Data curation. **Boris Vannière:** Writing – review & editing, Data curation. **Jakhongir Alimov:** Writing – review & editing, Resources, Project administration. **Elise Luneau:** Writing – review & editing, Validation, Conceptualization. **Johanna Lhuillier:** Writing – review & editing, Validation, Funding acquisition. **Sébastien Joannin:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Code availability

All R scripts used for data analysis and figure production are available on GitHub (https://github.com/LucasDugerdil/Paleo_Fazilman), with the first release archived on Zenodo (https://doi.org/10.5281/zenodo.17381814). A ready-to-use script for applying brGDGT BRT calibrations in Arid Central Asia is provided in a dedicated GitHub repository (https://github.com/LucasDugerdil/ACADB_brGDGT_calibrations), with its first release archived on Zenodo (https://doi.org/10.5281/zenodo.16679065).

### Funding sources

This research was funded, in whole or in part, by ANR STEPABIL-ITY, Grant ANR-22-CE27-0018. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. The ENS de Lyon financed the PhD scholarship of L. Dugerdil. This research was conducted in cooperation framed by a memorandum of understanding (MoU) between the CNRS/University of Montpellier and the National University of Uzbekistan.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

We thank the LMC14 staff (Laboratoire de Mesure du Carbone-14), ARTEMIS national facility, (LSCE (CNRS-CEA-UVSQ)-IRD-IRSN-MC) for the results obtained with the accelerator mass spectrometry method associated with the DYNACA INSU project. We extend special thanks to Salomé Ansanay-Alex and Mathilde Ghirardi for their involvement in the GDGT acquisition. We are also grateful to Vivien Mai Yung Sen and Alice Molard for their support with the XRF analyses at the Chambéry laboratory, Fanny Canone from the PEA²t platform (Université Marie et Louis Pasteur, CNRS, Chrono-environnement, F-25000 Besançon, France) for performing the physico-chemical preparation of pollen samples, Isabel Figueiral for macro-charcoal and Characeae identification, Céline Gasser for assistance with ¹⁴C selection, Julien Didier for access to the geotech bank, Mareike Wieczorek for the PANGAEA process, and Eric Garnier and Cyrille Violle for their expertise in plant functional traits. We thank members of ResearchGate for their helpful responses to specific questions: particularly Bruno Granier, Marc Philippe, and Mohammed el Hassi. We would also like to acknowledge Behruz Toshbadalov and Abdurakhman Zaripov, whose contributions to the fieldwork were essential to the success of this study. Our gratitude also goes to La Tendresse, Le BIB, Sarah Millet-Amrani, and Zoé Hernandez for their invaluable support. Portions of this manuscript were edited using a generative AI tool to improve clarity and readability. This work is an ISEM contribution ISEM 2025-302. We also gratefully acknowledge the thoughtful comments of the four anonymous reviewers and the editor, whose efforts significantly improved the manuscript.

### Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.catena.2025.109759.

### Data availability

The raw pollen, NPPs, GDGTs and geochemical (XRF, MS and SC), as well as the main reconstruction outputs (pollen- and GDGT-based climate reconstructions from ensemble models, CWM traits and biome-scores), data are available in PANGAEA in Dugerdil et al. (2025c) and downloadable at the following link: https://doi.org/10.1594/PANGAEA.987394.

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