Centennial scale sequences of environmental deterioration preceded the end-Permian mass extinction

The study addresses the unresolved question of what drove the end-Permian mass extinction (EPME) and how rapidly environmental stressors unfolded leading up to it. Prior work implicates Siberian Traps Large Igneous Province volcanism as a primary driver, capable of triggering marine anoxia, extreme warmth, toxic metal and gas emissions, ozone destruction, acid rain, and enhanced weathering. However, Siberian Traps activity spanned at least ~900 kyr, whereas the main marine extinction interval lasted ~60 ± 48 kyr, implying shorter, more intense pulses may have been critical. The authors aim to clarify the temporal sequence and causal links among volcanism, terrestrial ecosystem collapse (e.g., wildfires, soil erosion), and marine environmental degradation (e.g., fertilization-driven euxinia) at decadal to centennial scales. By applying ultra-high-resolution molecular biomarker mass spectrometry imaging (MSI) and micro-XRF elemental mapping to the Permian-Triassic GSSP at Meishan, China, they seek to reconstruct centennial-scale wildfire activity, soil input, clastic fluxes, and redox changes immediately preceding and during the onset of the EPME.

The paper situates its approach within several strands of prior research: (1) Extensive evidence links Siberian Traps activity to environmental crises across the Permian-Triassic transition, including anoxia, greenhouse warming, mercury anomalies, and pulsed volcanism. Geochemical proxies such as Hg have been used to infer pulsatile LIP activity at decadal–centennial scales. (2) Sedimentary molecular biomarkers, especially polycyclic aromatic hydrocarbons (PAHs), track combustion from wildfires, volcanism, and impacts; seven-ring PAHs like coronene can indicate higher-temperature volcanogenic combustion. C2-dibenzofuran (C2-DBF), derived from cellulose/furans in soils and litter, serves as a proxy for terrestrial organic matter input and has been used at cm-scale to mark terrestrial ecosystem devastation across the EPME. (3) Recent advances in mass spectrometry imaging enable sub-mm spatial and sub-decadal temporal resolution of biomarker distributions in sediments, opening a path to resolve centennial-scale environmental variability. (4) Prior studies in South China suggest terrestrial crises precede marine extinctions and point to links between increased weathering, nutrient runoff, and marine euxinia, but lacked the ultra-high temporal resolution to define sequences of events. The present study builds on these by combining MSI biomarkers, micro-XRF elemental proxies (Al, S, Fe), and conventional GC-MS to validate and refine interpretations.

Study site and sampling: Sedimentary rock samples were collected from the uppermost Permian Changxing Formation at the Meishan C section (Zhejiang Province, South China; 31°4′36.74″N, 119°41′52.80″E), a mid-slope depositional setting and the GSSP for the Permian–Triassic boundary. Lithologically, the upper Permian consists of laminated limestone overlain by argillaceous Lower Triassic strata. A baked diamond saw blade was used to cut subsections (≤5 × 15 × 50 mm) to minimize contamination. Subsections were mounted on MSI holders with conducting tape and stored desiccated. A total of 20 cm from Bed 24d to Bed 25 was investigated; the top of Bed 24e is set to 0 years (0 m). Age model: The section is constrained by high-precision U-Pb zircon dates. A linear high-resolution age model used a weighted-mean sedimentation rate of 2.6 cm/kyr (min 1.6, max 6.5 cm/kyr) derived via Monte Carlo simulation, yielding temporal resolution of ~3.9 yr (1.5–6.3 yr) for 0.1 mm MSI spacing. The authors note uncertainty from assuming linear rates given lithologic and biotic changes; Bayesian Bchron modeling suggests sedimentation increased toward the top of Bed 24, implying increasing temporal resolution upwards. Ultra-high-resolution mass spectrometry imaging (MSI): A 7 T solariX XR FT-ICR-MS with Smartbeam II laser (Bruker) scanned with 100 µm spot spacing. Positive-ion ESI mode, m/z 150–750, external NaTFA calibration followed by internal lock mass (m/z 179.9995). Targeted PAHs and C2-DBF were identified by monoisotopic mass; clusters up to C60 common. Data were exported (m/z, intensity, S/N, xy-coordinates) and processed in MATLAB (Bruker DataAnalysis script). Quality control included 25% data reduction and S/N ≥ 3 filter; lowess smoothing (span 0.01) performed in Origin 2018. Elemental mapping (micro-XRF): Conducted on a Bruker M4 Tornado micro-XRF (Rh source 50 kV, 600 µA; 25 µm spot) under 20 mbar vacuum; 50 µm/pixel, 60 ms/pixel. Al, Fe, and S count maps exported as xy matrices for further processing. Conventional GC-MS: Seven bulk sediment samples spanning −20 mm to 0 (0–10 mm bins of varying thickness) were ground; ~3 g each was extracted ultrasonically five times with toluene:MeOH (9:1). Extracts were concentrated under N2, fractionated on silica gel into non-polar (n-hexane:DCM 4:1) and polar (DCM:MeOH 4:1) fractions. The non-polar fraction was analyzed on an Agilent 6890 GC with 5975 MSD using a DB-5MS column (60 m × 250 µm × 0.25 µm). Oven: 40 °C 2 min, ramp 4 °C/min to 320 °C, hold 22 min. Targeted PAHs: 4-ring (pyrene, fluoranthene), 5-ring (benzo[e]pyrene, benzo[a]pyrene, benzo[b/j/k]fluoranthene), 6-ring (indeno[1,2,3-cd]pyrene, benzo[ghi]perylene), 7-ring (coronene), plus C2-DBF; identification via mass spectra and retention vs published data. Data analysis: Wavelet analysis (Morlet) in PAST software assessed periodicities in PAH records. Pearson correlations evaluated relationships among biomarkers (MSI and GC-MS); significance assessed by p-values. MSI and GC-MS were cross-validated; MSI provided 0.1 mm spatial (sub-decadal temporal) resolution, micro-XRF provided 0.05 mm resolution for elements. Redox interpretation incorporated existing framboidal pyrite data and S, Fe distributions to infer euxinia and pyrite laminae. Ratios and proxies: PAH7/5 (seven- vs five-ring PAHs) used as combustion temperature/intensity proxy and to distinguish volcanogenic vs wildfire/soil PAH contributions; C2-DBF and C30 hopane βα/(βα + αβ) used as soil input markers; Al as clastic input/weathering/acid leaching proxy; Hg/TOC from prior work used to indicate volcanogenic Hg accumulation on land and transport to ocean.

• High-frequency combustion history: Four millennial-scale intervals of increased PAH accumulation (A–D) were identified (GC-MS and MSI), with MSI resolving eight major combustion episodes (events 1–8) plus numerous minor peaks over ~10 kyr leading to the EPME. Mean duration of major events: 270 ± 150 yr (mean ± s.d.). Intensity and frequency of events increased upward, supported by wavelet analysis. - Strong coupling of wildfire and soil input: C2-DBF (terrestrial OM) and PAHs are positively correlated (MSI and GC-MS), indicating wildfire-driven vegetation loss, enhanced soil erosion, and transport of soil OM and PAHs to the marine realm via runoff. From interval B upwards, PAH peaks coincide with elevated Al, marking enhanced clastic influx and possible acid leaching. - Combustion source characterization: PAH7/5 values are generally low, indicating dominance of lower-temperature wildfire/soil-derived PAHs, except between intervals A and B where PAHs are scarce and PAH7/5 peaks, likely reflecting reduced soil OM contribution. Toward the top (events 7–8), PAH7/5 increases alongside Hg/TOC, suggesting a higher proportion of volcanogenic PAHs and intensified volcanism. - Centennial-scale deterioration sequence in the final ~2 kyr: Focusing on the top 4 cm (Bed 24e into Bed 25), a repeated sequence is resolved: (1) wildfire pulses (PAH maxima), (2) soil OM influx (C2-DBF maxima) and oxic-soil signal (C30 hopane βα/(βα + αβ) highest at event 4), (3) clastic/bedrock weathering peaks (Al), and (4) development of euxinia (S, Fe maxima, framboidal pyrite, pyrite laminae). Event 4 marks a turning point with the largest soil OM pulse and subsequent weakening of C2-DBF peaks as soils thin/collapse; event 7 shows coincident C2-DBF and Al maxima. Events 4 and 7 culminate in euxinia; event 8 shows PAH and PAH7/5 maxima, with expected S/Fe/pyrite signal shifted into the overlying ash bed (Bed 25-1). - Terrestrial vs marine crisis timing: The terrestrial ecosystem collapse in South China preceded the onset of the marine extinction by ~300 yr (120–480 yr; ± 2 s.d.). - Mechanism: Wildfire-driven soil loss and nutrient runoff, amplified by volcanism-related stressors (acid rain, heat, aridity), fertilized coastal oceans, boosted productivity and oxygen demand, and promoted euxinic conditions, contributing to marine biotic collapse. - Validation and robustness: MSI and GC-MS datasets agree on the main intervals and events; statistical correlations and wavelet analysis support pulsed nature and increasing intensity toward the EPME.

The findings delineate a clear, centennial-scale causal chain linking Siberian Traps-influenced environmental stressors to both terrestrial and marine crises. Recurrent wildfire pulses stripped vegetation, accelerating soil erosion and delivering terrestrial organic matter and nutrients to the oceans. This fertilization, combined with climatic warming and shelf exposure, drove productivity spikes and oxygen depletion, culminating in euxinia recorded by S, Fe, and framboidal pyrite. The increase in PAH7/5 and Hg/TOC toward the top indicates an increasing volcanogenic contribution, consistent with intensified volcanic pulses. Critically, the terrestrial ecosystem collapse is resolved to precede marine extinction by approximately 300 years, indicating that terrestrial destabilization and nutrient runoff were pivotal precursors to marine anoxia/euxinia and metazoan demise. The results reconcile prior lower-resolution observations with a decadal–centennial perspective, demonstrating that long-timescale mantle processes (Siberian Traps magmatism) manifested as abrupt, pulsed environmental changes that crossed tipping points in coupled land–ocean systems.

This study provides an ultra-high-resolution, mechanistically coherent sequence of environmental deterioration immediately preceding the end-Permian mass extinction. Using MSI biomarkers and micro-XRF elemental mapping at sub-decadal to decadal resolution, the authors identify eight major combustion events over ~10 kyr, with an intensifying pattern culminating in a repeated wildfire–soil weathering–clastic influx–euxinia sequence. Events 4 and 7 are key: they show the strongest soil inputs and lead directly to euxinia, while event 8 reflects elevated volcanogenic signatures. The terrestrial ecosystem collapse in South China preceded the marine extinction by ~300 years (120–480 years), supporting models in which land-sourced nutrient loading and subsequent marine euxinia were primary conduits from volcanism to marine biotic collapse. Future work should refine regional and global synchrony of terrestrial and marine crises, disentangle contributions from Siberian Traps versus circum-Pangean felsic volcanism, and improve chronologies and multi-proxy integration across diverse paleogeographic settings to assess spatial heterogeneity in timing and mechanisms.

• Age model assumptions: The high-resolution age model assumes linear sedimentation (2.6 cm/kyr) across Beds 22–25, though lithological and biotic changes suggest variable rates; while Bayesian modeling indicates increasing rates toward the top of Bed 24, uncertainties in exact durations remain. - Analytical constraints at low concentrations: Intervals with very low PAH concentrations (e.g., between intervals A and B) approach detection limits, increasing analytical uncertainty and potential artifacts in ratio metrics like PAH7/5; interpretations therefore focus on mean values during well-resolved events. - Proxy specificity and transport: PAH and C2-DBF signals reflect both production and transport processes; disentangling in situ combustion sources (wildfire vs volcanogenic) from reworked/soil-derived inputs carries uncertainties. - Spatial representativeness: The Meishan near-shore setting integrates terrestrial and marine signals locally; extrapolation to global patterns must consider geographic variability. - Regional timing differences: Causes of the disparity between high-latitude and low-latitude extinction timings (e.g., influence of circum-Pangean felsic volcanism) remain uncertain and require further multi-proxy, high-precision geochronology and stratigraphic correlation.