Holzmaar (50°7^′ N, 6°53^′ E; 425 m a.s.l.) is a maar lake located in the Westeifel Volcanic Field, Germany Fig. A,. The catchment is characterized by iron-rich Devonian bedrock and known for its iron deposits associated with volcanism . The lake basin exhibits a circular morphology with a small surface area of 58 000 m² (5.8 ha) relative to its maximum depth of 20 m . Under modern conditions, due to this great depth and steep basin slopes (Fig. B), deep-water mixing is typically inhibited below 10–15 m . While this specific crater morphometry naturally predisposes the lake to meromixis and a high sensitivity to climatic forcing cf., Holzmaar operates primarily as a monomictic meso- to eutrophic lake, or dimictic in years with winter ice cover . It experiences seasonal thermal stratification from spring (April/May) to late autumn or winter (October/December) , with a steep thermocline reaching a maximum depth of approximately 6 m . The resulting physical isolation of the hypolimnion drives oxygen depletion and solute accumulation, establishing a distinct metalimnetic chemocline roughly between 6 and 8 m depth . This seasonal hypolimnetic anoxia ultimately favors the preservation of the annually laminated (varved) sediments utilized in this study .

Four adjacent sediment cores (Fig. B, HZM19-07, HZM19-08, HZM19-10, HZM19-11) were recovered in August 2019 from the centre of Holzmaar at 18–19 m water depth using a UWITEC piston corer and compiled to a composite sediment record . The X-ray fluorescence (XRF) core scanning for elemental composition with a Cr X-ray tube, high-resolution logging of magnetic susceptibility, and determination of total organic carbon (TOC), total nitrogen (TN), and biogenic silica (BSi) data at a relatively low resolution of 16 cm were complemented by hyperspectral scanning data acquired at the University of Bern and higher resolution diatom stratigraphy (every 4 cm) established at the University of Bremen.

Sedimentary pigments were quantified non-destructively using hyperspectral imaging with a Specim PFD-CL-65-V10E line scan camera operating in the visible and near-infrared range (VNIR, 400–1000 nm). The camera set-up (frame rate: 8 Hz, exposure: 120 ms) delivered data with a spectral resolution of 1.56 nm and a spatial resolution of 80 µm (pixel size). The hyperspectral data were normalized using a white reference (BaSO4) and dark reference (closed camera aperture).

Hyperspectral data post processing was performed using napari-sediment , an open-source Python plugin for the napari image viewer, specifically designed for interactive analysis of hyperspectral sediment core imagery. napari-sediment provides an integrated workflow that combines visualization, preprocessing, dimensionality reduction, and spectral analysis in a single graphical user interface following the data processing proposed in and further described in the Appendix . Relative Absorption Band Depth (RABD) indices were calculated to quantify absorption troughs associated with specific photopigments. Following established protocols by , and , absorption trough minima were identified at the following wavelengths:

619 nm – Phycocyanin from cyanobacteria

RABD index values were calculated using the formula of : 1RABDλ=Xright⋅Rleft+Xleft⋅RrightXright+XleftRλ where Rλ is the reflectance at the absorption minimum (λ), X stands for the number of bands from the left side of the trough to λ (Xleft), or from the right side to the λ (Xright), and Rleft and Rright are the reflectance values at start and end of the reflectance trough.

RABD index values from hyperspectral imaging were calibrated to absolute pigment concentrations measured by spectrophotometry using linear regression models. Calibration samples were selected to represent the full range of pigment concentrations present in the core following a normal distribution.

The chronology for the Holzmaar composite profile is based on the Bayesian age-depth model established by . This model integrates the original high-resolution varve chronology of with radiometric dating to optimize the age transfer to the new HZM19 sediment cores. The age model is stratigraphically anchored by the Laacher See Tephra (LST) , a distinct isochron visible in the lithology (Cluster IV, HClust, dark red in Fig. ), which serves as a critical time marker for the Allerød. This varve-based framework provides annual to sub-decadal temporal resolution, allowing for precise correlation of biogeochemical changes during the Late Glacial period (ca. 14 690–11 700 cal yr BP).

The biostratigraphy for the Holzmaar record follows the regional pollen stratigraphy . In this study, we adopt the term Pleniglacial to describe the period corresponding to the Oldest Dryas in Swiss and South German biostratigraphies, e.g., . Similarly, the period referred elsewhere as the Meiendorf is here termed the Bølling. The subsequent sequence follows the regional standard: the Older Dryas stadial, the Allerød interstadial with the Laacher See Tephra, the Younger Dryas stadial. The exact dates of their stratigraphic boundaries are from .

To combine the advantages of rapid, non-destructive, and ultra-high-resolution hyperspectral imaging (HSI) with the precise, compound-specific quantification of traditional wet-chemistry methods, we calibrated the continuous HSI indices against discrete pigment concentrations. These independent pigment measurements were performed using UV-visible absorption spectrophotometry (Shimadzu UV-1800) and high-performance liquid chromatography (HPLC). Pigment extractions were performed on 0.5 to 1.5 g wet and homogenized sediment subsamples using 100 % HPLC-grade acetone following the protocol of . In short, we extracted pigments by adding 5 to 13 mL acetone in several steps (2–3 mL per step), keeping the samples in dark and at 4 °C conditions over night, or 30 min in -4 °C between the extraction steps, until the supernatant was visually colourless. The samples were always centrifuged (3500 rpm, 10 min), the supernatant was decanted and filtered through hydrophobic PTFE 0.22 µm filters (13 mm syringe filters) and stored at -20 °C in darkness until analysis.

For spectrophotometric analysis, pigment extracts were measured using a UV-visible spectrophotometer (wavelength range 350–900 nm, spectral resolution 0.1 nm) with 1 cm path length PP cuvettes. Total chlorophyll a and derivatives (pheophytins, pheophorbides) were quantified spectrophotometrically using absorption at 663 and 665 nm and translated to concentration using the Lambert-Beer law and the extinction coefficient for bulk green pigments (chlorophylls, pheophytins and pheophorbides) in 90:10 Acetone:Water solution (ϵ=80.8⋅10-3 L mg⁻¹ cm⁻¹, adjusted after ). Total bacteriopheophytin a concentration was quantified using the absorption peak at 745 nm in 90 % acetone (±10 % of water originating from the wet sediment), and applying the specific absorption coefficient ϵ=52.9⋅10-3 L mg⁻¹ cm⁻¹, .

Pigment extracts (100 µL aliquots) were analysed using a Dionex Ultimate 3000 series reverse-phase HPLC system equipped with a C18-ODS column (Agilent Omnisphere-5) and a Diode Array Detector (DAD-3000RS), following the method of described in detail in . Chromatogram peaks were integrated using Chromeleon 7.2 (ThermoFischer Scientific^®) with a Gaussian fit approach. Individual pigments were identified based on retention times and absorption spectra, and quantified using pre-determined linear regression coefficients for authentic standards .

To calibrate hyperspectral imaging (HSI) data with pigment concentrations measured by spectrophotometry, we employed a proxy-proxy linear regression model. After testing for normality using Shapiro-Wilk tests and screening for outliers using Rosner's test, we established linear relationships between RABD indices and pigment concentrations. For total chlorophyll (TChl), we fitted the model: 2TChl(µgg-1)=467.6×RABD670-467.1 with R2=0.92, p=4.6⋅10-19, RMSEP = 3.25 µg g⁻¹, (Fig. ).

For bacteriopheophytin a (Bphe a), we fitted: 3Bphea(µgg-1)=131.3×RABD845-129.2 with R2=0.82 p=4.6⋅10-13, RMSEP = 0.97 µg g⁻¹ (Fig. ). These calibration equations were then applied to the entire HSI dataset to generate spatially-resolved pigment concentration maps.

Diatom samples were prepared from 50 freeze-dried subsamples collected at discrete 4 cm intervals along the composite sediment profile. An aliquot of each sample was oxidized with 30 % H2O2 and heated in a water bath at approximately 80 °C for 2–5 min to eliminate organic matter, following standard methods . Permanent slides were mounted using Naphrax mounting medium. A minimum of 400 diatom valves per slide were counted using light microscopy (Olympus CX40 with Plan Ach 100X/1.25 immersion objective and Zeiss Axioplan with Plan Neofluar 100X/1.30 immersion objective equipped with differential interference contrast) to calculate relative abundances and absolute concentrations (valves per gram dry sediment) using a modified (square cover slides) evaporation tray method . Diatom identification followed standard European floras, and diatom accumulation rates were calculated by multiplying total diatom abundance by sediment accumulation rates derived from the varve chronology.

Sequential extraction was performed using a modified protocol from and to differentiate sedimentary P, Fe, and Mn into chemically distinct pools. The extraction scheme defined four operational fractions: (1) F1 (labile/porewater), extracted with 0.46 M NH4Cl; (2) F2 (redox-sensitive), extracted with 0.11 M Bicarbonate-Dithionite (BD); (3) F3 (Al-bound/organic/hydroxides), extracted with 1 M NaOH; and (4) F4 (calcium-bound/residual), extracted with 0.5 M HCl. The extracts were analysed using ICP-MS (Agilent Series 7600) in a 1 % HNO3 matrix-matched solution at the University of Bern. The concentrations of Fe, Mn and P were calculated based on external calibration curves from IPC-MS multi-element standard.

We acknowledge that strict anaerobic integrity could not be maintained during core transport and subsampling. Consequently, labile reduced species (e.g., porewater Fe2+, authigenic vivianite) susceptible to rapid oxidation may have shifted from their target fractions (F1 or F3) into the reducible fraction (F2). Therefore, we adopt a conservative operational definition, where we interpret Fraction 2 not as specific oxides, but as the total reactive pool, encompassing both original oxides and secondary oxidation products of authigenic minerals. To infer the original mineralogical hosts of phosphorus, we evaluate the molar Fe:P stoichiometry of this reactive pool, rather than relying on the operational separation of F1 and F2 alone.

To find the link of our in-lake biogeochemical data with the external drivers, we compiled existing pollen data of Holzmaar from the literature and databases . To synchronize these records with our 2019 chronology, individual core sections from the 1992 campaign (HZM92 1a–6u, 2b–5o, 2b–5u) were correlated to the 2019 composite depth using visual lithostratigraphic tie-points and then stacked with the 1996 composite record (see Appendix  for full methodological details). The Holzmaar high-resolution pollen record covers only the period from 15 000 to 13 250 cal yr BP, and thus the trends for Betula, Pinus, and non-arboreal pollen (NAP) from 13 250 to 11 750 cal yr BP were modelled using the high-resolution record from the nearby Meerfelder Maar . Missing values were reconstructed by applying scaling factors derived from the linear relationship between the two lakes during their overlapping stratigraphy (code is available as a part of the workflow at GitHub/Renku .

Moreover, we generated the annual, July and January insolation curve for the location of Holzmaar, with the resolution of 50 years from -50 to 15 950 cal yr BP using the model of . To further constrain the temperature proxy, we used the chironomid-based July temperature reconstruction for Gerzensee as well as for Egelsee . We also utilized the regional GDGT-based temperature reconstruction from the Eifel, including Holzmaar . Lastly, we included the North GRIP δ18O and ice-core dust records into our set of external variables. These datasets demonstrate a clear seasonal pattern in dust concentrations that correlate with climatic variations.

The first step was to evaluate relationships among external environmental variables and reduce dimensionality to minimize collinearity and identify the most informative drivers. Initial pre-selection of predictor variables was based on Pearson correlation matrices and Principal Component Analysis (PCA) of z-scored data (Fig. ). Prior to redundancy analysis (RDA), we performed Variance Inflation Factor (VIF) assessment to quantify multicollinearity among the remaining candidate variables using the threshold VIF > 10 indicating critical multicollinearity,. This VIF-based filtering removed seven variables with high collinearity (annual insolation, January insolation, July insolation, Holzmaar Betula, Pinus, non-arboreal pollen, and arboreal pollen), retaining nine variables with VIF ≤ 10 for the final RDA model. We added passively on top of the RDA three additional variables (Holzmaar Betula, Pinus and January insolation), which did not pass the VIF just for the purpose of easier interpretation.

As a quality control measure, three GDGT-derived temperature samples with known analytical issues (ages 13 128, 13 340, 13 875 yr b2k) were excluded from all analyses. The final subset of retained environmental variables comprised: GDGT-derived temperature, chironomid-inferred July temperature from Gerzensee, chironomid-inferred July temperature from Egelsee, δ18ONGRIP, NGRIP dust data, XRF-derived Ca and Ti variation, and regional pollen abundances (Juniperus, Salix from Holzmaar). This refined set of non-redundant variables was used for RDA analysis with pigment data.

High-resolution XRF core scanning data were normalized using centred log-ratio (CLR) transformation to address the closed-sum constraint inherent to compositional data and minimize matrix effects . Hyperspectral imaging indices were calibrated against discrete spectrophotometer bulk pigment measurements using linear regression, as described above, enabling the quantification of sedimentary pigment concentrations at high spatial resolution throughout the core. The XRF and hyperspectral imaging (HSI) datasets, acquired at native sub-millimetre resolution (HSI: 80 µm pixel size, see Sect. 2.2), were co-registered and resampled to a common 1 mm depth resolution prior to further analysis, whereas pollen, diatom, and external climate proxy records were resolved at coarser, decadal-to-centennial resolution. To facilitate multi-proxy comparison, including the external datasets, all datasets were harmonized and interpolated to a common (100 years) temporal resolution using Gaussian kernel smoothing.

Hierarchical cluster analysis (Ward's D2 method with Euclidean distance) and principal component analysis (PCA) were applied to the CLR-transformed XRF data to identify geochemical facies, with optimal cluster numbers determined through silhouette analysis. HPLC-derived pigments were grouped based on Pearson correlation coefficients of their downcore concentration profiles, and taxonomic assignments to algal and bacterial producer groups (e.g., cyanobacteria, green algae, diatoms, cryptophytes, purple sulphur bacteria) were made following established pigment biomarker literature (see Table ).

Redundancy analysis (RDA) was performed to quantify the relationship between pigment community composition and environmental drivers. The RDA model was constructed using scaled CLR-transformed pigment data as response variables and the VIF-selected environmental variables as predictors. Statistical significance of the overall RDA model was assessed using permutation tests (999 unrestricted permutations) at a significance level of α=0.050. Permutation testing was chosen as it is more robust for multivariate data and does not assume normality. An ecological variable grouping approach was used to ensure that major environmental drivers (vegetation proxies: Betula and Pinus; winter insolation) were represented in the analysis even if they were excluded by strict VIF filtering (VIF > 10). The full workflow is available on Renku/GitHub and fully linked to the dataset at Zenodo (10.5281/zenodo.18429717, ).

The unconstrained hierarchical clustering of XRF data resulted in five distinctive clusters (Figs.  and ) separating more allochthonous sediment (Ti, K, Zr, Al) from autochthonous sediment (Ca, S, inc/coh). In combination with the hyperspectral data, we interpret the clusters as follows (Fig. ): Cluster I (lime-green) corresponds to clayey silts with low pigment abundance at the bottom of the core, followed by cluster II inter-layered with cluster III (orange, dark blue) representing the varved section of diatoms (Si/Ti) and organic matter (inc/coh, RABD620, the total chlorophylls, bacteriopheophytin a). Cluster IV (dark red) is specific for the LST followed by organic rich section of cluster III (dark blue). The core top is classified as cluster V (light blue), which carries similarities with cluster I and is mainly minerogenic.

Similar separation is observed in the PCA (Fig. ), where PC1 (45.3 %, Figs.  and ) separates the XRF data between allochthonous (positive) and autochthonous (negative) sediment sources. PC2 (11.5 %) represents the division between minerogenic (positive) vs organic matter and redox sensitive (negative) elements in the sediment record.

Lastly, CONISS clustering was used to explore the clusters constrained with depth. Similarly to the unconstrained hierarchical clustering, five clusters were defined, which coincide well with the previously published lithology .

The sedimentary pigment data were examined using a heatmap, where individual pigments were clustered based on their time-series similarities using correlation (Fig. ). This clustering resulted in three distinct primary producer assemblages that closely track the stratigraphic zones: (1) the Pleniglacial assemblage, (2) the transitional Bølling assemblage, and (3) the stratified Allerød assemblage (Fig. ). The statistical separation of these communities and their distinct stratigraphic evolution, including the return to mixed conditions during the Younger Dryas, is further visualized in the PCA trajectory (Fig. ).

The Pleniglacial assemblage (pigment group 1, Figs. , and ) is characterized by high abundances of pigments associated with cyanobacteria (echinenone, canthaxanthin) and cryptophytes (alloxanthin). This early assemblage also includes green algae (α-carotene, lutein) alongside diatoms and chrysophytes. Notably, this assemblage features strong signals of physiological stress markers, including diatoxanthin (a light-stress indicator in diatoms) and astaxanthin. The astaxanthin indicates contributions from freshwater chlorophytes such as Haematococcus pluvialis or dinoflagellates (e.g., the Peridinium or Glenodinium). The presence of benthic species of diatoms, such as Staurosira construens, supports low lake-level, well-mixed (holomictic) conditions. Additionally, the early presence of the planktonic diatom Pantocsekiella ocellata aligns with the detection of specific pigment for green sulphur bacteria (isorenieratene, Fig. ), suggesting low-light conditions and anoxygenic niches. Further, we do not observe any significant formation of the redox-sensitive fractions of Mn (Fig. ), P, or Fe (Fig. ), indicating generally oxygenated bottom waters. This very diverse aquatic primary producer community occupied the lake during the relatively colder Pleniglacial period and was gradually replaced in the early Bølling by deeper water column community.

The transitional Bølling assemblage developed in the lake at the onset of the Bølling warming (14 443 cal yr BP, ), indicated by a sharp peak in total oxygenic biomass (chlorophyll a and its derivatives, pigment group 2, Fig. ) alongside cyanobacteria (zeaxanthin). This expansion was accompanied by the appearance of purple non-sulphur bacteria (OH-spheroidine, Fig. ) and green sulphur bacteria (bacteriochlorophyll a). While the main diatom and chrysophyte community is represented by the continuous presence of diatoxanthin and diadinoxanthin, we also observe a singular, isolated peak of fucoxanthin. The phase is further characterized by a distinct turnover in the diatom community. Initially, the Pleniglacial benthic dominant, Staurosira construens, completely disappears. The benthic and periphytic niches abandoned by this species are rapidly occupied by a new shallow-water assemblage including Amphora indistincta, Pseudostaurosira brevistriata, Pseudostaurosira robusta, and Staurosirella lapponica (Fig. ). As lake levels rose , A. indistincta gradually declined while the planktonic diatom Stephanodiscus minutulus rose in high abundances. By the mid-Bølling, the open-water assemblage further diversified with the appearance of Pantocsekiella ocellata. The specific composition of this transitional community suggests that the lake was partly nutrient limited, likely in nitrogen. However, the gradually increasing reactive manganese pool (F2 + F3) indicates that despite rapidly increasing primary production, the lake bottom waters remained at least seasonally oxygenated (Fig. ).

The stratified Allerød assemblage is dominated by specific marker pigments for purple sulphur bacteria (okenone, bacteriopheophytin a) co-occurring with the continued high abundance of planktonic diatoms (S. minutulus and P. ocellata). The high concentrations of okenone are particularly significant, as they strictly indicate the development of stable, meromictic conditions where the chemical density gradient (chemocline) reached into the photic zone. This permanent stratification separated the actively circulating upper water column (mixolimnion) from the stagnant, euxinic deep water (monimolimnion). Crucially, this community structure remained remarkably stable throughout the Allerød, showing no substantial shifts in composition even following the deposition of the Laacher See Tephra (LST). Although the LST event introduced a significant amount of iron and silica, as well as abrupt changes in light penetration, the established meromictic stability buffered the ecosystem against this disturbance. The persistent euxinia below the chemocline was sufficient to maintain the robust bacterial plate of okenone-producing Chromatiaceae without interruption.

The Younger Dryas assemblage is defined by a sudden and profound restructuring of the phototrophic community, initiated by the sharp decline and eventual complete disappearance of the purple sulphur bacteria marker pigment okenone (Fig. ). This total collapse of the anoxygenic bacterial plate is accompanied by a severe reduction in bacteriopheophytin a and total chlorophyll a concentrations. Diatom data for this interval are sparse, likely reflecting poor diatom preservation and a near-absence of siliceous primary production during this predominantly minerogenic, low-productivity interval, combined with the 4 cm sampling resolution of the diatom record; together these preclude a detailed reconstruction of the community turnover at this transition. For the subsequent re-establishment of the diatom community in the following Holocene, we refer readers to the higher-resolution diatom stratigraphy of Holzmaar reported by . Despite the sparse diatom data during this phase, the high-resolution pigment stratigraphy (Fig. ) demonstrates a clear multivariate departure away from the stable Allerød cluster, reflecting a return toward a lower-biomass, mixed-community space.

The sequential extraction data differentiates the available/reactive pools of Mn, P and Fe under changing redox conditions and anoxia. All extracted fractions are shown in the supplementary data (Fig. ). Based on these profiles, we focus on Fraction 2 (F2) to represent the total reactive pool of Mn, Fe and P available for remobilization from the sediment during lake stratification.

Overall, the most abundant fraction for all three elements was the residual/carbonate-bound fraction (F4), which is dissolved only at low pH. The second most abundant fraction was the reactive F2, which represents phases readily dissolvable under reducing conditions.

Total manganese concentrations were highest in the Pleniglacial sediments. With the onset of the Bølling and the increasing concentrations of okenone during the Allerød, the reactive fraction of Mn (F2) shows a decreasing trend (Fig. ), consistent with release from the sediment to the anoxic water column (reductive dissolution). Only after the LST (LST) we observe a distinct increase in the Mn reactive pool sequestered in sediments (Fig. ).

Reactive iron (F2) and phosphorus (F2) display strong stratigraphic covariance throughout the record. Fe is closely coupled with P within this reactive fraction, with the molar Fe:P ratio of F2 stabilizing at ∼ 1.5 throughout the Bølling and Allerød (Fig. ). This value corresponds to the 3:2 stoichiometry of authigenic vivianite (Fe3(PO4)2, ). In classic stratified systems, the onset of meromixis and deep-water anoxia typically drives internal nutrient loading: Fe(III)-(oxy)hydroxides undergo reductive dissolution, releasing their adsorbed phosphorus back into the water column. However, the stoichiometric coupling in our F2 pool indicates that in Holzmaar, the initial physical sequestration of P by meromixis was immediately followed by a post-depositional chemical reaction. The abundant availability of dissolved ferrous iron (Fe2+) under strict anoxia allowed this newly liberated phosphorus to be rapidly re-precipitated as authigenic vivianite. Therefore, it is this post-depositional mineral formation that permanently sequestered the phosphorus in the sediment, rather than mere adsorption onto iron oxides (which typically yields ratios >2, ). While vivianite is frequently below bulk XRD detection limits, this consistent stoichiometric coupling in the reactive pool is a widely accepted diagnostic tool for identifying its presence and role in phosphorus sequestration in lacustrine settings .

Following the LST, the lake was geochemically enriched by the influx of volcanic material. Fe and Mn concentrations in the reactive fraction (F2) increase significantly while P remains stable, resulting in fluctuating Fe:P ratios and an excess of reactive iron relative to phosphorus. This mineral influx effectively transformed the lake into an even more efficient phosphorus trap. By locking phosphate into stable mineral phases such as vivianite, the sediment acts as an efficient trap that prevents nutrient release back into the water column. This iron-driven mechanism eliminates the internal loading effect common in carbonate-dominated or iron-poor lakes, ultimately preventing the ecosystem from being held in a prolonged eutrophic or anoxic state fueled by legacy phosphorus.

The pollen records from Meerfelder Maar (MFM) and Holzmaar (HZM) reveal a transformation of the catchment during the transition from the Pleniglacial to the Late Glacial (Fig. ). The onset of the Bølling (∼14450 cal yr BP) is marked by an increase in Juniperus concurrent with a relative decline in Pinus. Only in the late Bølling, the gradual establishment of Betula is observed.

At the onset of the Allerød, Betula became the dominant tree taxon in the catchment, although total Arboreal Pollen (AP) abundances remained below the closed forest threshold (<80%, ). This expansion coincides stratigraphically with the onset of preservation of biochemical varves and the appearance of indicators of anoxia.

Progressing through the Allerød, the vegetation shifted from Betula to Pinus. The percentages of Betula pollen gradually declined, while Pinus pollen increased, eventually replacing Betula as the dominant tree species by the late Allerød. The termination of the anoxic phase (disappearance of okenone) at the onset of the Younger Dryas coincided with the decline of Betula. In contrast, regional AP values remained high (∼70%, ) due to the persistence of Pinus, indicating that the recovery of the lake's oxygenation state occurred despite sustained forest cover.

The high-resolution pigment and diatom stratigraphy of Holzmaar reveals distinct ecological shifts that mirror the physical state of the lake. Rather than representing an internal autogenic succession, these communities demonstrate highly sensitive, allogenic responses to climate-driven changes in temperature, wind shielding, and mixing regimes (Fig. ).

The Pleniglacial phase corresponds to a holomictic water column heavily influenced by cold temperatures and high detrital input (Ti). This signal reflects local loess deflation from the Rhine Valley, driven by high winds and limited vegetation cover, and cold mean annual temperatures (insolation, Figs.  and ). The primary producer community during this phase is characterized by abundances of various benthic diatoms, such as Staurosira construens , alongside cryptophytes (alloxanthin) and cyanobacteria (echinenone and canthaxanthine, Fig. ). Cryptophytes traced by the pigment alloxanthin are characteristic of cold, well-mixed, minerogenic glacial lakes adapted to low-light benthic conditions . Further, the presence of these small fragilarioid diatoms infers a low-light environment and a highly unstable landscape. These small benthic taxa are well-documented low-light specialists possessing a distinct ecological advantage that allows them to thrive in highly turbid, minerogenic conditions or under prolonged seasonal ice cover . Consistent with these shallow-water conditions, the ratio of chlorophyll a to pheophytin a, a proxy for water-column photodegradation, shows a significant positive correlation with the abundance of S. construens (r=+0.488, p=0.0014), supporting the hypothesized link between shallower water-column residence time and reduced pigment degradation .

While overall primary production was low during the Pleniglacial, the community structure indicates that nitrogen was a key limiting nutrient. This inference of nitrogen limitation is supported by two concurrent signals: the relative abundance of cyanobacteria, which are highly efficient at operating in nitrogen-poor environments or utilizing nitrogen fixation , and the expansion of Juniperus in the catchment, a terrestrial pioneer taxon known to colonize young, nitrogen-poor soils .

In the open water, the pelagic pigment profile further reveals a community operating under severe environmental and nutritional strain. The prominent signal of astaxanthin points to cold-tolerant microalgae like Haematococcus pluvialis or dinoflagellates. In H. pluvialis, astaxanthin accumulation is a well-documented physiological response to extreme environmental stress, including cold temperatures, high irradiance, and severe nutrient or nitrogen starvation, triggering the formation of protective resting cysts . This is further corroborated by the presence of diatoxanthin, a specific xanthophyll produced by diatoms undergoing light stress . Together, these stress-induced pigments depict a harsh, ultra-oligotrophic water column.

Despite these extremes, the presence of green sulphur bacteria (isorenieratene) highlights survival within highly specific low-light niches. Because green sulphur bacteria are obligate anaerobes and extreme low-light specialists, their co-occurrence with low-light tolerant diatoms suggests they occupied transient, shaded anoxic micro-niches. These habitats likely developed under seasonal ice cover or within the turbid cryolittoral zone, analogous to modern assemblages found in polar lakes or the early developmental stages of Lake Jaczno .

Disentangling the drivers of anoxia requires separating the regional temperature trends from local catchment forcing. While local warm-season temperatures increased moderately by ∼+2.8 °C in the period from 14 642 to 12 846 cal yr BP , the primary driver of the presence of anoxia in the photic zone was likely the extension of the growth season . This shift prevented deep freezing and extended the thermal stratification period into warm-seasons, providing the physical boundary conditions for anoxia. Given the latitudinal position of Holmzaar (∼ 50° N), a permanent, perennial ice cover during the Pleniglacial is highly unlikely, and is further argued against by the continuous, undisturbed deposition of sediments preserved throughout both the Pleniglacial and the Younger Dryas. However, an extended seasonal or unusually severe winter ice cover cannot be excluded and would have reinforced deep-water isolation during these colder intervals. The specific timing of the euxinic phase, which lags the initial warming, suggests an influence of catchment-mediated processes, such as vegetation and soil development, to cross the anoxia threshold.

In addition to regional climate, the onset of anoxia was likely pre-conditioned by the specific basin morphometry of Holzmaar. With a relative depth (Zr, a ratio of maximum depth to lake mean diameter) of 7.4 %, the volcanic maar basin is morphologically predisposed to stable thermal stratification by decoupling the hypolimnion from wind-driven mixing . Global predictive models suggest that a high Zr, combined with rising temperatures, moved the onset of spring stratification earlier in the season .

While direct evidence for lake-level fluctuations at Holzmaar is limited, regional records indicate higher lake levels during the Bølling-Allerød across Central Europe . Such a deepening would have increased Zr and physically primed the system for anoxia. Within this physically sensitive framework, the expansion of Betula in the watershed acted as a reinforcing factor. However, given the high crater rim (<+35 m) and significant relative depth (-20 m), the threshold of arboreal pollen (AP) required to shield the lake from wind was likely much lower than in shallower basins. This implies that the lake was physically predisposed to stability regardless of subtle changes in forest density.

The onset of intense anoxia coincides precisely with the expansion of Betula in the watershed and a reduction in allochthonous sediment yield (from 16 to <2 t km⁻² yr⁻¹), as quantified by . This confirms that vegetation stabilized the crater slopes and its catchment. We propose that Betula afforestation strongly reinforced lake stratification primarily through wind shielding. The forestation of the crater rim acted as a roughness filter, dampening the wind shear required for deep mixing .

At Holzmaar, anoxia disappeared precisely at the onset of the Younger Dryas (YD), although Pinus coverage increased (Fig. ). However, because Pinus pollen is highly susceptible to long-distance aeolian transport, these regional AP values must be interpreted conservatively. This recovery highlights that climatic cooling and a regional shift towards less humid conditions were the decisive drivers for breaking stable chemical stratification. The temperature decline and increased aridity overrode the physical shielding of the crater rim by increasing water density and enhancing convective mixing. Furthermore, despite the highly arid regional climate, the development of deep seasonal soil frost and frozen ground conditions strongly restricted groundwater infiltration , which substantially amplified seasonal surface run-off (elevated Ti). This mechanism explains the sustained delivery of allochthonous minerogenic sediment into the basin despite reduced overall precipitation . Consequently, the shift in forest composition (from Betula to Pinus) is interpreted as a climatically-driven consequence rather than the main cause of re-oxygenation. This abrupt physical and ecological transition is clearly reflected in the stratigraphic trajectory of pigment assemblages, which exhibits a rapid return toward the oligotrophic space (Fig. ).

The geochemical response of Holzmaar reveals a highly efficient nutrient retention mechanism that distinguishes it from purely iron-controlled or carbonate-controlled lakes . Within the reactive pool (Fraction 2), the molar Fe:P ratio stabilizes at ∼1.5 throughout the anoxic phase (Fig. ). This stoichiometry is the diagnostic signature of formation of authigenic ferrous phosphates such as vivianite (Fe3(PO4)2⋅8H2O) in iron-rich sediments . It indicates that the volcanic iron flux provided a sufficient reactive buffer to sequester phosphorus in the sediment throughout the DOE-1, even under reducing conditions .

The sequential extraction data further identify the acid-soluble Fraction 4 (HCl, Fig. ) as the dominant long-term sink for manganese, iron, and phosphorus. We interpret this stable pool as the result of a dual-retention mechanism: initially, the reactive iron pool (F2) traps porewater phosphorus as vivianite ((Fe3(PO4)2⋅8H2O), and subsequently, this phosphorus is buried within or converted into stable mineral phases in Fraction 4 that do not redissolve during redox shifts. By permanently locking P, Mn and Fe into this stable sink, the lake avoided the development of a chemical legacy or an internal phosphorus loading . The biological consequence of this geochemical resilience is a high degree of community reversibility, as the pigment assemblages effectively return to the oligotrophic community once the climatic drivers of the Allerød (warming) were removed (Fig. ).

This geochemical framework provides critical insight into lake ecosystem resilience and rapid high reversibility. We attribute this resilience to a specific decoupling of nutrient cycles under an iron-unlimited regime. While manganese followed the expected reductive dissolution-precipitation pattern, being recycled back into the water column during anoxia (depleted F2) and precipitated during the seasonal mixing (increase F2 + F3), phosphorus behaved differently. In many eutrophic or carbonate-dominated systems, such as Lake Burgäschi, Lake Baldeggersee or Lake Soppensee (Swiss Plateau, ), the release of legacy phosphorus creates a delayed recovery, sustaining anoxia long after external climate forcings are removed . In those systems, sulfate reduction often strips iron from the sediments to form minerals like pyrite, releasing phosphorus back into the water column .

Consequently, regional cooling at the onset of the Younger Dryas allowed the system to re-oxygenate, evidenced by the decline of anoxia followed by the vegetation shift (Betula to Pinus) with a minimal time lag (Fig. ). Ultimately, while climate and vegetation set the boundary conditions, the specific coupling of catchment characteristics and an iron-rich lithogenic buffer determined the resilience of the aquatic ecosystem.