Ecology

Cranial myologyThe muscular origin and insertion sites interpreted in the cranium and mandible of each species are identified in Fig. 1; the 3D reconstructed cranial adductor muscles are shown in Fig. 2 (Incisivosaurus and Citipati) and Fig. 3 (Khaan and Conchoraptor).Figure 1Locations of reconstructed jaw adductor muscle origin and insertion sites for Incisivosaurus gauthieri (a-c), Citipati osmolskae (d-f), Khaan mckennai (g-i), and Conchoraptor gracilis (j-l). Crania are shown in dorsolateral view (a,d,g,j) with temporal and postorbital bars removed to better show medial regions within supratemporal fenestra. The left sides of the crania are shown in anteroventral view (b,e,h,k) with lower temporal and postorbital bars removed to better show posterior and lateral regions within supratemporal fenestra. Mandibles shown in dorsolateral view (c,f,i,l), lateral muscle insertions sites are shown on the left rami, medial insertion sites on the right rami. Scale bars 50 mm. Muscle abbreviations given in results section.Full size imageFigure 2Reconstructed jaw adductor musculature of Incisivosaurus gauthieri (a-d) and Citipati osmolskae (e–h) shown complete in lateral view (a,e), anterolateral view with mAMES removed (b,f), posterolateral view with mAME complex removed (c,g), and ventral view with only the mPT muscles (mPTv removed on left). Scale bars 50 mm, legend colour coded to identify individual muscles. Muscle abbreviations given in results section.Full size imageFigure 3Reconstructed jaw adductor musculature of Khaan mckennai (a-d) and Conchoraptor gracilis (e–h) shown complete in lateral view (a,e), anterolateral view with mAMES removed (b,f), posterolateral view with mAME complex removed (c,g), and ventral view with only the mPT muscles (mPTv removed on left). Scale bars 50 mm, legend colour coded to identify individual muscles. Muscle abbreviations given in results section.Full size imagem. adductor mandibulae externus medialis (mAMEM)The origin site of the mAMEM is less clear than others of the mAME group31 and we reconstruct it, as others have done, in the posterior portion of the supratemporal fossa12,13,16 where it is constrained anterolaterally and anteromedially by the positions of mAMES and mAMEP (Fig. 1). This region comprises parts of the squamosal and parietal in all four taxa and is generally vertical, concave, and featureless in all apart from Citipati. In this taxon, within the supratemporal fossa, the squamosals and parietals are flattened and orientated to form a deep and concave platform directly perpendicular to the line of action of this muscle (Fig. 1e).
The extent and direction of the mAMEM body are somewhat constrained in all taxa by the anterior, dorsal, and posterior edges of the squamosal, quadrate flange, and epipterygoid respectively.The insertion sites are typically unclear12,31. The surangular dorsomedially forms a shelf that overhangs the adductor fossa in Citipati, Khaan, and Conchoraptor (potentially taphonomically exaggerated in the latter two). Insertion onto the dorsomedial and posterior margin of the coronoid eminence (along with insertion of the mAMEP onto the eminence) has been suggested for the mAMEM12,13,31,38, but the palatal morphology (especially in the oviraptorids) restricts space around the coronoid eminence so that we do not reconstruct both the mAMEM and mAMEP as inserting in this area. Instead, we reconstruct the mAMEM as inserting on the shelf-like upper part of the surangular’s dorsomedial surface, posterior to the more anterior insertion of the mAMEP, allocating roughly half of the available surface to each (Fig. 1c,f,i,l). This insertion surface is unclear and largely reconstructed in Incisivosaurus where there is less well-defined slight convexity on the upper part of the medial surangular surface (Fig. 1c). This area of the retrodeformed mandible model for Conchoraptor uses material from Khaan and the two are thus similar (Fig. 1i,l).It is possible the mAMEM and mAMEP merged along their path or did indeed both insert in relation to the coronoid eminence39 but ultimately this would not change reconstructed bite force results significantly.m. adductor mandibulae externus profundus (mAMEP)The mAMEP generally has a medial and/or anteromedial origin within the supratemporal fenestra. A vertical crest, similar to that interpreted as the anterior border of the origination site in Carcharodontosaurus and Daspletosaurus31, Allosaurus40, Corythosaurus41, and Erlikosaurus12, is also identified in Citipati19 (Fig. 1d). We interpret it as the boundary between the mAMEP and mPSTs origins. A small sharp prominence, perhaps similar, is present on the lateral surface of the braincase in Incisivosaurus (Fig. 1a). The surface is more featureless in Khaan and Conchoraptor (Fig. 1g,j), so the anterior limit of the mAMEP origin is constrained by the origin area of the mPSTs (in turn based on the extent and position of the laterosphenoid).In Citipati, a pneumatic opening in the posterolateral wall of the parietal (visible at the posterior of the mAMEP origin in Fig. 1d), underneath where the squamosal contacts the parietal to form the posteromedial margins of the supratemporal fenestra, seems to limit the mAMEP origin posteriorly, dividing it from the mAMEM. A similar opening is not as large or obvious in the other taxa, but similar limits to the origination sites are constrained by the geometry of the supratemporal fenestra. The dorsal extent of the origin is also clear in Citipati where a sharp lateral edge, running from the frontal-parietal contact posterolaterally to form the posterior boundary of the supratemporal fossa, separates the dorsal surface of the parietals from their lateral surfaces that contribute to the supratemporal fossa (Fig. 1d). This edge may function for muscle attachment similarly as suggested for a parietal ridge in the oviraptorid Osoko2.We reconstruct the mAMEP inserting more anteriorly than mAMEM on the mandible (Fig. 1c,f,i,l), including around the apex of the coronoid elevation itself, along with the mAMES, specifically on the dorsomedial surface of coronoid prominence31,39.m. adductor mandibulae externus superficialis (mAMES)In all taxa, the mAMES can be reliably hypothesised to originate on the supratemporal bar31 (Fig. 1b,e,h,k). In oviraptorosaurs, this is formed by the postorbital and squamosal. The supratemporal bars in all taxa are mediolaterally flattened, with the medial surface directed slightly ventromedially, more so in Citipati than the others (Fig. 1e). The postorbital bars are concave along almost the entire medial surface in Citipati. In the other taxa, only the squamosal contribution is concave, with the postorbital ramus being flat or perhaps weakly convex in Khaan (Fig. 1h). There are no clear osteological signs of the extent of the mAMES origin site so we restrict it to the medial surfaces of the supratemporal bar as the ventral surface is narrow (as the bars are mediolaterally thin) and the medial surface is slightly orientated in the correct muscle direction in all taxa. The mAMES is reconstructed as originating along the full extent of this medial surface with its anterior and posterior limits constrained by the origins of the mPSTs and mAMEM respectively.The main body of the jugal has a trough-like gently concave medial surface in all taxa (especially so in Conchoraptor where the postorbital process of the jugal also has confluent concavity on its posteromedial surface) that appears like its form would neatly wrap over the exterior of the mAMES as it bulged outwards laterally and followed it anteroventrally on its origin-insertion path.The mAMES likely inserts onto the dorsolateral edge and lateral surface of the surangular31,39, on a shelf running from the coronoid process to the articular (Fig. 1c,f,i,l). This shelf is more strongly defined in the later diverging taxa, especially Citipati (Fig. 1f) and Khaan (Fig. 1i). The mandibles of the oviraptorids bear apically triangular coronoid eminences, which are anteriorly displaced compared to those of other herbivorous dinosaurs. This has been hypothesized to increase mechanical advantage and attachment area for the temporal musculature as an adaptation for a stronger crushing bite3,8,38. The anteriorly displaced coronoid eminence in oviraptorids has been hypothesized to indicate a more anteriorly extending mAMES (as suggested for some ornithischians39,42). The mAMES is reconstructed thus here. The insertion site is constrained ventrally by the reconstructed extent of the mPTv insertion site, and dorsomedially by the insertions of the mAMEM and mAMEP, which insert onto the dorsomedial surface of the surangular.m. adductor mandibulae posterior (mAMP)The mAMP is a well-constrained muscle of the adductor chamber, consistently attaching to the lateral surface of the quadrate in an extant phylogenetic bracket31. We reconstruct the origin site as the lateral surface of the pterygoid flange of the quadrate (Fig. 1), covering most of this broad flat wing but not encroaching on the epipterygoid (where the mPSTp is present) and pterygoids (where the mPTd originates). No clear muscle scar is apparent in any of the studied taxa. The mAMP origin may also have extended posterodorsally onto the confluent lateral surface of the squamosal, where a curved ridge may demark an expanded origin site for the mAMP in Conchoraptor (Fig. 1j)5; Khaan has a similar morphology (Fig. 1g). This expansion is not reconstructed in earnest—the organization of the other muscle volumes, particularly the passage of the mAMEM, would only permit a thin sliver of extra volume to be created on the expanded origin site, not significantly increasing overall volume, direction, or morphology of the mAMP.The mAMP inserts in the adductor fossa on the medial mandibular surface (Fig. 1c,f,i,l), occupying most of its main extent and posterior and ventral margins31. The adductor fossa in the oviraptorids is large and anteriorly displaced19,38,39 and much more significant than that of Incisivosaurus (Fig. 1c).m. pseudotemporalis superficialis (mPSTs)In all four taxa, the mPSTs originates on the anterior and/or anteromedial wall of the supratemporal fenestra. In Citipati, the area is formed predominantly by the capitate process of the laterosphenoid and the posterior portions of the frontal (Fig. 1d). This surface is concave and rugose. The lateral surface of the laterosphenoid is also rugose, indicating a muscle attachment19. The site is bounded laterally by the postorbital, and two ridges may constrain the origin site of the mPSTs 19: a sharp ridge runs posteromedially from the capitate process of the laterosphenoid to the epipterygoid contact, forming the ventral boundary, and a vertical ridge on the medial wall of the supratemporal fossa constrains the origin posteromedially, demarking it from the mAMEM. A triangular anterodorsal-posteroventral sloping surface (where a clear frontoparietal fossa has been lost in derived oviraptorids) extends to the dorsotemporal fossa. The anterodorsal extent of the mPSTs origin site on this surface is unclear. The frontoparietal fossa has been argued as a vascular space in dinosaurs rather than a site of muscle attachment43, and we place the mPSTs similarly (43; Fig. 7 therein), extending into this sloping triangular space but not wholly filling it. We do not reconstruct any attachment of the mPSTs extending onto the frontal processes of the postorbitals.In Khaan, the origin site is less well preserved (Fig. 1g). The mPSTs origin is placed in a similar position to Citipati and may extend slightly onto the lateral surface of parietals which contribute to the area. Similarly, in Conchoraptor (Fig. 1j), there is more of a contribution of the parietal to the anterior wall of supratemporal fenestra, but very little or no contribution of the frontal. In Conchoraptor, the whole origin site is more anteromedially positioned, and exhibits a large smooth exposure of the laterosphenoid. There are no obvious scars or ridges in the above-mentioned area of Khaan and Conchoraptor. In Incisivosaurus, the anterior corner of the supratemporal fossa is narrow and the mPSTs is more anteromedially positioned (Fig. 1a). The origin site likely comprises the laterosphenoid and small parts of the frontal and parietal.The insertion of the mPSTs is likely related to the medial aspect of the coronoid elevation and parts of the medial adductor chamber39. As the medial regions of the coronoid elevation are occupied by the mAMEP in our reconstruction we position the mPSTs, as the deepest temporal muscle, inserting into the anterior portion of the medial mandibular fossa31 and its anterodorsal rim (Fig. 1c,f,i,l).m. pseudotemporalis profundus (mPSTp)The mPSTp likely attached to the epipterygoid when present in dinosaurs31. When first described in detail, the epipterygoid of C. osmolskae (Fig. 1d) was noted as the largest of any known theropod, with a unique strongly twisted body and dorsal tip hosting robust muscle scars19. We therefore locate the mPSTp origin site on the epipterygoid of each taxon with confidence and reconstruct its origin along the length of the epipterygoid, which is present in all four taxa (though partially reconstructed in Khaan and Conchoraptor) (Fig. 1g,j).The insertion site is problematic but based on extant taxa the muscle likely inserted along the medial surface of the coronoid process or surangular 31. As the coronoid process is occupied by the insertions of the mAMES and mAMEP, we position the insertion of the mPSTp dorsomedially on the surangular, occupying the dorsal rim of the mandibular adductor fossa, the position being largely constrained dorsally by the insertions of the mAMEM and mAMEP (Fig. 1c,f,i,l).m. pterygoideus dorsalis (mPTd)The origin site of the mPTd is reconstructed as the linear dorsal surface of the pterygoid in all oviraptorids where a longitudinal concavity runs anteriorly along their length anterior of the pterygoid flange (Khaan has a convex dorsal surface but the origin site is modelled similarly (Fig. 1h), and possibly the anteriormost dorsolateral surface of the pterygoid flange. The site is limited anteriorly and anterolaterally by the palatines and ectopterygoids, onto which no attachment was modelled as they are relatively small and delicate. In Incisivosaurus, the anterior extent of the origin site is constrained by the level of the jugal ramus of the ectopterygoid anterolaterally and the main body of the ectopterygoid laterally to around a longitudinal concavity on the dorsal surface of the pterygoid (Fig. 1a)—there seems very little/no origination on the palatine.The mandibular insertion of the mPTd is commonly regarded to be onto the medial surface of the articular and retroarticular process31. We reconstruct the mPTd in this position (Fig. 1c,f,i,l), inserting in the narrow medial surface of the posterior aspect of the mandibular ramus, under the medial facet of the articular glenoid and posteriorly onto the medial surface of the retroarticular process.m. pterygoideus ventralis (mPTv)The mPTv is well constrained through phylogenetic bracketing and we reconstruct it in the oviraptorids as originating along the ventral surface of the pterygoid, probably also extending onto the ventral aspect of the pterygoid flange31 and posteriorly terminating before the contact with the quadrate. The anterior of the origin is reconstructed as the level of the ectopterygoid contact, with the site entering the longitudinal ventral concavity that is anteriorly confluent with the choanae. In Citipati, the pterygoid flange is noted as reduced compared to typically carnivorous theropods, maintaining a roughly consistent width throughout its length (Fig. 1e), as suggested by19 to indicate a relatively small m. pterygoideus. However, the main pterygoid body of oviraptorids is relatively elongate. This may be an adaptation to open space for an expanded mAME group to insert onto the mandible, whilst maintaining volume of the mPT. The pterygoids of Incisivosaurus are also elongate and reduced in width (Fig. 1b), though not as extreme as in the derived oviraptorids 24. The origin of the mPTv on the pterygoid ventral surface is interpreted as running from the posteroventral margin anteriorly into a trough medial to the ectopterygoid, and lateral of a ventral flange termed the accessory ventral flange by Xu et al.22, terminating anteriorly before the palatine contact.In all taxa, the mPTv wraps around the ventral surface of the mandibular rami and inserts on the broad section of the lateral surface of the mandible (Fig. 1c,f,i,l), predominantly comprising the angular.Bite force estimatesMeasurements of the final volumetric muscle reconstructions are given in Table 1 along with the calculated muscle contraction force, resultant force acting on the mandible, and relative contribution of each muscle. The oviraptorid oviraptorosaurians show greater muscle volumes compared to the earlier diverging Incisivosaurus. This is confirmed by greater muscle CSA values relative to cranial surface area in Citipati (1.80 × 10–2), Khaan (1.77 × 10–2), and Conchoraptor (1.37 × 10–2), compared to Incisivosaurus (1.21 × 10–2). Table 2 shows the inlever and outlever measurements used to calculate bite force resulting from each cranial muscle (and their relative contribution) and the total estimated bite force in each species, for three different bite positions. These range from 349–499 N in Citipati down in order of cranial size to 53–83 N in Incisivosaurus. Complete calculations and values for Tables 1 and 2 along with measurements for the cranial models are documented in SI 2.Table 1 Geometric measurements of reconstructed muscles and estimated contraction force (Fmus = (volume / length) × 0.3 N/mm2 × 1.532,34). Insertion angles of muscles measured in the sagittal ((alpha)) and coronal ((beta)) planes used to calculate resultant vertical force acting on mandible ((Fres = Fmus times cosalpha times cosbeta)).Full size tableTable 2 Bite force estimates (newtons) for each species, calculated (Fbite = (Fres × Linlever) ÷ Loutlever) for three points on their primary palate: the anterior tip of the beak/teeth; the middle level of the palate/toothrow; the tooth-like projection in the posterior of the oviraptorid palate/the posteriormost teeth. Percentages in brackets reported next to the bite force estimates for the oviraptorid taxa show how much greater these estimates are compared to values that would be predicted by scaling up the bite force estimates of Incisivosaurus by cranial surface area.Full size tableThe condition of the oviraptorid oviraptorosaurian skull is characterised by an increased volume for adductor musculature and increased mechanical advantage resulting from anteroposterior shortening, compared with the more conventional theropod skull geometry of the earlier diverging Incisivosaurus. Estimated bite forces conserve a greater proportion of the resultant force applied to the mandible (Fbite/Fres) in the oviraptorids compared with Incisivosaurus. This results from greater mechanical advantage in the oviraptorids’ jaw for all bite positions, though the difference relative to Incisivosaurus is greatest anteriorly (see Table 3.) These two factors result in their comparatively stronger estimated bite forces, an increase of 17–84% greater (depending on species and bite position; see Table 2) than would be predicted by scaling by cranial surface area. The increased relative bite force of the oviraptorids is not a result of more beneficial muscle insertion angles; there is no clear difference in the ratio of resultant muscle force acting on the mandible to the actual muscle force produced (Fres/Fmus) between Incisivosaurus (0.894) and the three later diverging taxa (Citipati, 0.856; Khaan, 0.851; Conchoraptor, 0.899).Table 3 Mechanical advantage values for the three different positions of the bite force estimates.Full size tableThe relative contribution of the different cranial muscles to bite force is broadly similar in each species (Fig. 4). The mPTv is typically the largest component, followed closely by the mAMES, then the rest of the mAME complex. Citipati differs from the others with a relatively stronger mAMES and mAMEM, and a relatively low value for the mPTv. The width of the Citipati cranium and mandible make the mPTv less vertically orientated and the reconstruction of the mPTv (in all taxa) is less well constrained by bone and other muscle volumes—its volume could be underestimated in all models. No clear difference emerges between Incisivosaurus and the later diverging oviraptorids in the relative contributions of cranial muscles to bite, apart from a slightly relatively weaker mPSTp and mPSTs—reconstructed muscles are proportionally similar but relatively larger in the oviraptorids. The bite force estimates of the four oviraptorosaurians (including Incisivosaurus) are significantly greater than estimates (from similar digital methods) made for other putatively herbivorous theropods of much larger body mass (Fig. 5) both relatively and absolutely.Figure 4The relative contribution of each cranial muscle to total estimated bite force by species. Note that the condition of Citipati appears the most dissimilar to all others in its comparatively stronger mAMEM, mAMES and weaker mPTv.Full size imageFigure 5Comparison of the estimated bite forces in multiple positions of Incisivosaurus and three oviraptorid oviraptorosaurians with other likely herbivorous theropod taxa that have had estimates made using similar digital volumetric methods12,13 show the oviraptorosaurians (oviraptorids especially) are capable of much stronger bite forces both relative to body mass and absolutely. Body mass values from Zanno and Makovicky11.Full size imageGape analysisThe early diverging oviraptorosaurian Incisivosaurus showed the highest estimates of optimal (25.0°) and maximum gape limit (49.5°) compared with the oviraptorid oviraptorosaurians, though not by much; estimates for gape limit in Khaan were lowest (20.5° and 40.0°), marginally less than Citipati (21.0° and 41.0°). Values for Conchoraptor (23.0° and 46.0°) lie between Incisivosaurus and the others. Figure 6 shows these estimates along with charts of the muscle cylinder strains that they are derived from. The anteriormost cylinder representing the mPTv constrains optimal and maximum gape in all but Citipati, in which it is constrained by the anteriormost regions of the mAMES. In this taxon the postorbital half of the skull is particularly low, sloping posteriorly, and the relatively low upper temporal bar directs the strong mAMES ventromedially to a prominent coronoid process of the surangular of the mandible. This leads to a shorter resting length for this muscle, causing its extension during jaw opening to exceed our tension limits just before the mPTv (which is the next most extended). The mAMEM is also relatively more extended in Citipati. The other three species are more similar in relative muscular strain, reinforcing the finding that relative muscle strength and arrangement in Citipati has more differences compared with other oviraptorids, than between some oviraptorids (Khaan and Conchoraptor) and earlier diverging oviraptorosaurians (Incisivosaurus).Figure 6Estimates of the gape angle limit of optimal tension and the maximum limit of gape for muscle tension in Incisivosaurus gauthieri (a), Citipati osmolskae (b), Khaan mckennai (c), and Conchoraptor gracilis (d) from a muscle resting length at a gape angle of 5°. Bar charts show the strain factors of individual modelled muscle cylinders at optimal and maximum tension limit; anteriormost muscle cylinders suffixed ‘1’, posteriormost suffixed ‘2’. Muscle cylinders (and corresponding bars) are colour coded yellow and red when exceeding 130% and 170% of resting length respectively, otherwise green. Note that the anterior mPTv constrains gape in all species apart from Citipati which is constrained by the anterior mAMES. Scale bars 50 mm. Muscle abbreviations given in results section.Full size imageActing antagonistically to the jaw closing muscles is the m. depressor mandibulae (mDM), primarily responsible for jaw depression (opening). It originates from around the paroccipital processes of the cranium, inserting onto the dorsal aspect of the retroarticular process of the mandible31,39. During the gape analysis, we checked mDM length change (from a shorter state at the maximum and optimal estimated gape angles to an elongated state at the 5° resting jaw angle) was not unrealistic. Strain values of the mDM were all calculated to be below the maximum strain limit (1.7) we modelled for the jaw adductors. From its shortest (maximum gape limit) the mDM in Incisivosaurus was extended by a factor of 1.08 at the estimated optimal gape limit and 1.20 the 5° resting jaw angle, Citipati reached 1.11 and 1.33 respectively, Khaan reached 1.16 and 1.48, and Conchoraptor reached 1.19 and 1.67.The oviraptorosaurians show estimated gape limits much lower than those of carnivorous theropods tested by Lautenschlager12, more like herbivorous theropod Erlikosaurus (optimal tension limit 24.0°; maximum tension limit 49.0°; resting gape of 6°). It is noted that herbivorous species exhibit lower gape angles than carnivorous species23,44, and thus our estimates of gape angle may be further support for a herbivorous diet among oviraptorosaurians (when considered against other theropods). Lautenschlager 12 notes that experimental results document gape angle in modern birds can reach angles up to around 40°. The maximum gape angles estimated for these oviraptorosaurians are similar to experimental results of gape angle in birds among passerines and Galliformes, which can reach around 40°45,46,47,48 (though this can be greater in parrots49)—a functional similarity between the crania of birds and oviraptorids which, beyond superficial beaked appearance, are quite dissimilar. More

Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Murphy, R. R., Kemp, W. M. & Ball, W. P. Long-term trends in Chesapeake Bay seasonal hypoxia, stratification, and nutrient loading. Estuar. Coast. 34, 1293–1309 (2011).CAS 

Google Scholar 
Nixon, S. W. et al. The impact of changing climate on phenology, productivity, and benthic–pelagic coupling in Narragansett Bay. Estuar. Coast. Shelf S. 82, 1–18 (2009).ADS 
CAS 

Google Scholar 
Testa, J. M., Murphy, R. R., Brady, D. C. & Kemp, W. M. Nutrient-and climate-induced shifts in the phenology of linked biogeochemical cycles in a temperate estuary. Front. Mar. Sci. 5, 114 (2018).
Google Scholar 
Scanes, E., Scanes, P. R. & Ross, P. M. Climate change rapidly warms and acidifies Australian estuaries. Nat. Commun. 11, 1–11 (2020).
Google Scholar 
Statham, P. J. Nutrients in estuaries—An overview and the potential impacts of climate change. Sci. Total Environ. 434, 213–227 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Kolber, Z. S. et al. Contribution of aerobic photoheterotrophic bacteria to the carbon cycle in the ocean. Science 292, 2492–2495 (2001).CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. & Vergin, K. L. Seasonality in ocean microbial communities. Science 335, 671–676 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl. Acad. Sci. 110, 2342–2347 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).PubMed 

Google Scholar 
Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl. Acad. Sci. 109, 17633–17638 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Techtmann, S. M. et al. The unique chemistry of eastern Mediterranean water masses selects for distinct microbial communities by depth. PLoS ONE 10, e0120605 (2015).PubMed 
PubMed Central 

Google Scholar 
Han, D. et al. Bacterial communities along stratified water columns at the Chukchi Borderland in the western Arctic Ocean. Deep-Sea Res. Pt. II 120, 52–60 (2015).
Google Scholar 
Han, D. et al. Bacterial communities of surface mixed layer in the Pacific sector of the western Arctic ocean during sea-ice melting. PLoS ONE 9, e86887 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Hernando-Morales, V., Ameneiro, J. & Teira, E. Water mass mixing shapes bacterial biogeography in a highly hydrodynamic region of the Southern Ocean. Environ. Microbiol. 19, 1017–1029 (2017).CAS 
PubMed 

Google Scholar 
Han, D., Kang, H. Y., Kang, C.-K., Unno, T. & Hur, H.-G. Seasonal mixing-driven system in Estuarine-Coastal Zone triggers an ecological shift in bacterial assemblages involved in phytoplankton-derived DMSP degradation. Microb. Ecol. 79, 12–20 (2020).CAS 
PubMed 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Higashi, K., Suzuki, S., Kurosawa, S., Mori, H. & Kurokawa, K. Latent environment allocation of microbial community data. PLoS Comput. Biol. 14, e1006143 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kim, D. et al. Water quality assessment at Jinhae Bay and Gwangyang Bay, South Korea. Ocean Sci. J. 49, 251–264 (2014).CAS 

Google Scholar 
Chen, M., Kim, D., Liu, H. & Kang, C.-K. Variability in copepod trophic levels and feeding selectivity based on stable isotope analysis in Gwangyang Bay of the southern coast of the Korean Peninsula. Biogeosciences 15, 2055–2073 (2018).ADS 
CAS 

Google Scholar 
Lee, J. H. et al. The effects of different environmental factors on the biochemical composition of particulate organic matter in Gwangyang Bay, South Korea. Biogeosciences 14, 1903–1917 (2017).ADS 
CAS 

Google Scholar 
Fine, P. V. & Kembel, S. W. Phylogenetic community structure and phylogenetic turnover across space and edaphic gradients in western Amazonian tree communities. Ecography 34, 552–565 (2011).
Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tripathi, B. M. et al. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 12, 1072–1083 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, Y. et al. Two key features influencing community assembly processes at regional scale: Initial state and degree of change in environmental conditions. Mol. Ecol. 27, 5238–5251 (2018).PubMed 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Micorbiol. 6, 370 (2015).
Google Scholar 
Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl. Acad. Sci. 112, E1326–E1332 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: Deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindemann, S. R. et al. The epsomitic phototrophic microbial mat of Hot Lake, Washington: Community structural responses to seasonal cycling. Front. Micorbiol. 4, 323 (2013).
Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Amaral-Zettler, L. A. et al. Microbial community structure across the tree of life in the extreme Rio Tinto. ISME J. 5, 42–50 (2011).PubMed 

Google Scholar 
Han, D. et al. Survey of bacterial phylogenetic diversity during the glacier melting season in an Arctic Fjord. Microb. Ecol. 81, 579–591 (2021).CAS 
PubMed 

Google Scholar 
Brand, L. E., Campbell, L. & Bresnan, E. Karenia: The biology and ecology of a toxic genus. Harmful Algae 14, 156–178 (2012).
Google Scholar 
Escalera, L., Pazos, Y., Moroño, Á. & Reguera, B. Noctiluca scintillans may act as a vector of toxigenic microalgae. Harmful Algae 6, 317–320 (2007).
Google Scholar 
Tada, K., Pithakpol, S., Yano, R. & Montani, S. Carbon and nitrogen content of Noctiluca scintillans in the Seto Inland Sea, Japan. J. Plankton. Res. 22, 1203–1211 (2000).
Google Scholar 
Hyun, B. et al. Effects of increased CO2 and temperature on the growth of four diatom species (Chaetoceros debilis, Chaetoceros didymus, Skeletonema costatum and Thalassiosira nordenskioeldii) in laboratory experiments. J. Environ. Sci. Int. 23, 1003–1012 (2014).
Google Scholar 
Park, J. S., Lee, S. D. & Lee, J. H. Taxonomic study on the euryhaline Cyclotella (Bacillariophyta) species in Korea. J. Ecol. Environ. 36, 407–409 (2013).
Google Scholar 
Yun, S. M. & Lee, J. H. Morphology and distribution of some marine diatoms, Family Rhizosoleniaceae, in Korean coastal waters: A genus Rhizosolenia. Algae 25, 173–182 (2010).
Google Scholar 
Park, J. S., Jung, S. W., Lee, S. D., Yun, S. M. & Lee, J. H. Species diversity of the genus Thalassiosira (Thalassiosirales, Bacillariophyta) in South Korea and its biogeographical distribution in the world. Phycologia 55, 403–423 (2016).
Google Scholar 
Dupont, C. L. et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 6, 1186–1199 (2012).CAS 
PubMed 

Google Scholar 
Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Müller, A. L. et al. Bacterial interactions during sequential degradation of cyanobacterial necromass in a sulfidic arctic marine sediment. Environ. Microbiol. 20, 2927–2940 (2018).PubMed 
PubMed Central 

Google Scholar 
Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. SAR11 bacteria: The most abundant plankton in the oceans. Annu. Rev. Mar. Sci. 9, 231–255 (2017).ADS 

Google Scholar 
Mühlenbruch, M., Grossart, H. P., Eigemann, F. & Voss, M. Mini-review: Phytoplankton-derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ. Microbiol. 20, 2671–2685 (2018).PubMed 

Google Scholar 
Stock, W. et al. Host specificity in diatom–bacteria interactions alleviates antagonistic effects. FEMS Microbiol. Ecol. 95, 171 (2019).
Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Illumina. 16S Metagenomic sequencing library preparation. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System (2013).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
R Core Team. The R Stats Package (R Core Team, 2002).
Google Scholar 
Oksanen, J. & Blanchet, F. G. Package ‘vegan’ (2017).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. 1 (2017).Roberts, D. W. & Roberts, M. D. W. Package ‘labdsv’. (2016).Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 

Google Scholar 
Gustavsen, J. A., Pai, S., Isserlin, R., Demchak, B. & Pico, A. R. RCy3: Network biology using cytoscape from within R. F1000Research 8, 1774 (2019).PubMed 
PubMed Central 

Google Scholar 
Kahle, D., Wickham, H. & Kahle, M. D. Package ‘ggmap’ (2019). More

DataSpatial gridWe created a grid whose units measure 250 m by 250 m based on the census tract layer for the city of Rio de Janeiro from the Instituto Brasileiro de Geografia e Estatística [Brazilian Institute of Geography and Statistics] (IBGE) website https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais. Uninhabited locations were excluded.Dengue cases on the gridDengue is a disease of compulsory notification in Brazil, and cases are notified at the Sistema de Informação de Agravos de Notificação [Information System on Diseases of Compulsory Declaration] (SINAN). Dengue cases notified in Rio de Janeiro between January 2010 and March 2015 were geocoded according to address of residency, and then counted for each grid unit by the Secretariat of Health of the city. We obtained the monthly dengue cases data aggregated at the grid level.Population on the gridThe population data is obtained from the Census 2010 (IBGE) (https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html) and it is available at the census tract level. The census tract areas vary in size and can be bigger than the unit of the grid, primarily in the least densely populated zones of the city. To overcome this issue, we cropped from the census tract layer the areas classified as non-urbanized (such as water bodies, swamps, agricultural areas, green areas, beaches, rocky outcrops) in 2010 by the City Hall of Rio de Janeiro (layer available at http://www.data.rio/datasets/uso-do-solo-2010). The population of each census tract is distributed randomly (uniformly) in the areas obtained after deleting the non-urban areas. The population within the units is computed by adding the grid layer. To create the grid and edit the census tract layer we used QGIS (version 3.6.3)45, and to obtain the population in the grid we used the R software46 with the packages tidyverse47 and sf48. We verify the accuracy of our estimated population by comparison with the WordPop dataset49 (see detailed description and Supplementary Fig. 12 and Supplementary Note 2). We chose the WorldPop dataset because: (i) the estimates are also calculated based on census data and are available for 2010, (ii) the pixel size is 100 m, smaller than the size of our grid unit, and (iii) it is open access.Since the units are in fact small and most of them conserve their area of 250 m by 250 m (Supplementary Fig. 1A), we consider population density as the population of each unit. For consistency, we do not consider units with small effective areas and/or populations sizes less than, or equal to, 10 in our analysis. In total, 8954/20212 units were so excluded. This choice circumvents the problem of high sensitivity to random population distribution, and urban vs. non-urban classification, in very small and/or sparsely populated areas. It also facilitates model simulation and does not affect the peak ratio pattern (Supplementary Fig. 1B).Peak ratio and spatial aggregationSince units are small, we binned them into G groups and aggregated their times series of reported cases. The groups were generated according to two aspects: (1) the geographical location of the units as determined by the administrative divisions of the city (10 areas, 33 regions, and 160 neighborhoods); and (2) the population of the units based on quantiles in order to obtain equal size groups. We considered specifically four different partition levels, resulting in 12, 25, 50, and 100 groups with about 900, 450, 225, and 100 units, respectively (from a total number of 11,247 units for the whole city). Groups of unequal size can introduce different statistical effects (it is not the same, for example, to calculate a mean value using 1000 or 10 elements). To compare quantities across groups it is therefore prudent to define groups with the same number of elements. In particular, this consideration becomes important for a large number of groups. Since the population density distribution (number of individuals per unit) is not uniform, groups defined with “equidistant” boundaries would exhibit very different numbers of elements.Given a unit u, we define its time series ({{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{c}_{u}({t}_{1}),{c}_{u}({t}_{2}),…,{c}_{u}({t}_{f})}), where ({c}_{u}({t}_{i})) is the number of reported cases of dengue at time ({t}_{i}) (i = 1, 2, …f) (and the bold symbol is used to indicate a vector). Thus, the aggregated time series is given by$${{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}=mathop{sum}limits_{uin g}{{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{C}_{g}({t}_{1})=mathop{sum}limits_{uin g}{c}_{u}({t}_{1}),{C}_{g}({t}_{2})=mathop{sum}limits_{uin g}{c}_{u}({t}_{2}),…,{C}_{g}({t}_{f})=mathop{sum}limits_{uin g}{c}_{u}({t}_{f})},$$with (g=1,2,…,G). Then, for each ({{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}) we computed the ratio between the sizes of the second and first DENV4 peaks, that is$${{{{{rm{peakrati}}}}}}{{{{{{rm{o}}}}}}}_{{{{{{rm{g}}}}}}}=frac{{ma}{x}_{tin {season}2}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}{{ma}{x}_{tin {season}1}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}$$
(1)
(Supplementary Fig. 2).The deterministic SIR modelAlthough dengue is a vector-borne disease, for simplicity we omitted the explicit representation of the dynamics of the mosquito population, and treated vector transmission via the seasonality of the transmission rate26. Thus, for each unit u, the deterministic SIR model is based on the following traditional differential equations:$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-gamma {I}_{u}-mu {I}_{u}$$
(2)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u},$$where ({S}_{u},{I}_{u},{R}_{u}), are, respectively, the number of susceptible, infected, and recovered individuals, and ({N}_{u}) the number of inhabitants, of the spatial unit u. Parameter (mu) is the mortality rate (equal to the birth rate), and (gamma) is the recovery rate. The seasonal transmission rate is specified as (beta (t)={beta }_{0}(1+delta {{sin }},(omega t+phi ))). The units are considered independent of each other, and the initial conditions establish that the whole population of each unit is susceptible to the virus (({S}_{u}(t=0)={N}_{u}) and ({I}_{u}left(t=0right)={R}_{u}left(t=0right)=0forall u)). Transmission begins with one infected individual at a time ({t}_{0u}ge t=0) where ({t}_{0u}) is obtained from the data.Since the goal of this model is to examine the representative dynamics of different population densities, we binned the units according to their population into 12 groups, and computed the mean value of their number of inhabitants ({N}_{g}=langle {N}_{uin g}rangle) and of their arrival times of the infection ({t}_{0g}sim langle {t}_{0uin g}rangle) (where g = 1, …, 12). We then simulated the system considering the 12 sets ({{N}_{g},{t}_{0g}}) as given.The stochastic modelSince units will suffer local extinction of transmission, a major component of a stochastic implementation is the description of the local reintroduction of the virus, namely the arrival of a ‘spark’ or imported infection, in analogy to fire spread. Because space is described by a highly-resolved lattice, we considered that well-mixed transmission applies within each unit. Moreover, in lieu of  explicit spatial coupling between units, we postulated  the importation of infection through the specification of a spark rate.For this purpose, we constructed a binary representation of the time series of cases per month by defining the spatial units either as positive or negative according to whether they reported cases or not (Supplementary Fig. 3). Then, to derive a spark rate we explored the dynamics of the number of positive units as follows,$${U}^{{{mbox{+}}}}(t+{dt})={U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{new}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})-{U}_{{{{{{{mathrm{extinct}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(3)
The number of positive units at time ({t+dt}) is equal to the number of positive units at time t, plus the number of units that have been infected ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) between t and t + dt, minus the number of units that were infected at t but are no longer infected at t + dt (i.e., the number of ‘extinctions’ between t and t + dt, ({{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt}))).Since uninfected units (i.e., negative units) require the arrival of a spark to become positive, the following equation specifies the mean of ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) under the assumption that a small unit is unlikely to receive more than a single spark in a period of time dt$${{langle }}{U}_{{{{{{{mathrm{new}}}}}}}}^{{+}}(t,t+{dt}){{rangle }}simeq {N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{-}}(t)}{U},$$
(4)
where ({N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})) is the number of sparks produced between t and t + dt, ({U}^{{{{-}}}}(t)) is the number of negative units at a time t, and (U) is the total number of units in the city ((U={U}^{{{mbox{+}}}}+{U}^{{{{-}}}})).By introducing Eq. (4) into Eq. (3) we obtain,$${U}^{{{mbox{+}}}}(t+{dt})simeq {U}^{{{mbox{+}}}}(t)+{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{{{-}}}}(t)}{U}-{{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(5)
From Eq. (5) we can now compute the spark rate per unit ({{sigma }_{u}}^{{emp}}(t,t+{dt})) from the high-resolution incidence data as$${{sigma }_{u}}^{{emp}}(t,t+{dt})=frac{{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})}{U}simeq frac{{U}^{{{mbox{+}}}}(t+{dt})-{U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{extinct}}}}}}}}(t,t+{dt})}{{U}^{{{{-}}}}(t)}$$
(6)
In order to address the effects of human density on the spark rate, we binned the spatial units according to their population into G groups. To avoid statistical effects due to group size, we considered population quantiles. Then, by applying Eq. (6) to each of these groups, we obtained an empirical spark rate per unit that depends on human density,$${sigma }_{uin g}^{{emp}}(t,t+{dt})={sigma }_{u}^{{emp}}(t,t+{dt}{{{{{rm{;}}}}}}{N}_{g}),$$
(7)
where ({N}_{g}={{langle }}{N}_{uin g}{{rangle }}) with g = 1, 2, …, G.SimulationsThe associated differential equations of the stochastic model are those shown on Eq. (2) but the transmission component has now an additional term ({sigma }_{u}) to describe the importation of infections.$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-gamma {I}_{u}-mu {I}_{u}$$
(8)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u}$$Since the inferred spark rate from the data (Eq. (7)) is obtained from observed infections, we computed the spark rate ({sigma }_{u}) as:$${sigma }_{uin g}={{{{{{mathrm{Poisson}}}}}}}({{sigma }_{uin g}}^{{emp}}/rho )$$
(9)
where (rho) is the reporting rate.The model shown on Eq. (8) was formulated as stochastic by incorporating demographic noise (with the different events represented as Poisson processes). It was implemented in R with the package pomp50. We also considered measurement error by assuming that the observed number of cases ({{C}_{u}}^{{obs}}) during a period of time T is,$${{C}_{u}}^{{obs}}left(Tright)={{{{{{mathrm{binomial}}}}}}}left(rho ,{C}_{u}left(Tright)right),$$
(10)
where ({C}_{u}(T)) is the number of cases computed in the unit u. We simulated the 11,247 units that compose the city of Rio de Janeiro, and aggregated the resulting time series as for the empirical data (see Peak ratio section).The parameters of the model are given in Supplementary Table 1. We relied on parameters estimated for dengue transmission in Rio de Janeiro by ref. 26. Those estimates were obtained for the aggregated city and for the emergence of DENV1. We use these parameters here as a sufficiently realistic set for illustrating and exploring the behavior of the stochastic model with population density. Moreover, with the exception of the spark rate, the model parameters were considered the same for all units. In particular, we applied a uniform reporting rate because access to the nearest public healthcare clinic does not show a dependency on population density (see Supplementary Note 1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

PlantProbs.net. Nickel in plants and soil https://plantprobs.net/plant/nutrientImbalances/sodium.html (accessed Apr 28, 2021).Guodong Liu, E. H. Simonne, and Y. L. Nickel Nutrition in Plants | EDIS. EDis 2011.Liu, G. D. “A New Essential Mineral Element–Nickel.” Plants Nutr. Fertil. Sci. 2001.Kabata-Pendias, A.; Mukherjee, A. Trace Elements from Soil to Human; 2007.Kasprzak, K. S. Nickel advances in modern environmental toxicology. Environ. Toxicol. 11, 145–183 (1987).CAS 

Google Scholar 
Cempel, M. & Nikel, G. Nickel: A review of its sources and environmental toxicology. Polish J. Environ. Stud. 15, 375–382 (2006).CAS 

Google Scholar 
Bradl, H. B. Chapter Sources and origins of heavy metals. Interface Sci. Technol. 6, 1–27 (2005).CAS 
Article 

Google Scholar 
Von Burg, R. Nickel and some nickel compounds. J. Appl. Toxicol. 17, 425–431 (1997).Article 

Google Scholar 
Freedman, B. & Hutchinson, T. C. Pollutant inputs from the atmosphere and accumulations in soils and vegetation near a nickel–copper smelter at Sudbury, Ontario, Canada. Can. J. Bot. 58(1), 108–132. https://doi.org/10.1139/b80-014 (1980).CAS 
Article 

Google Scholar 
Manyiwa, T. et al. Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana. Environ. Geochem. Health https://doi.org/10.1007/s10653-021-00918-x (2021).Article 
PubMed 

Google Scholar 
Kabata-Pendias. Kabata-Pendias A. 2011. Trace elements in soils and… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Kabata-Pendias+A.+2011.+Trace+elements+in+soils+and+plants.+4th+ed.+New+York+%28NY%29%3A+CRC+Press&btnG= (accessed Nov 24, 2020).Almås, A., Singh, B., Agricultural, T. S.-N. J. of & 1995, undefined. The impact of nickel industry in Russia on concentrations of heavy metals in agricultural soils and grass in Soer-Varanger, Norway. agris.fao.org.Nielsen, G. D. et al. Absorption and retention of nickel from drinking water in relation to food intake and nickel sensitivity. Toxicol. Appl. Pharmacol. 154, 67–75 (1999).CAS 
Article 

Google Scholar 
Costa, M. & Klein, C. B. Nickel carcinogenesis, mutation, epigenetics, or selection. Environ. Health Perspect. 107, 2 (1999).Article 

Google Scholar 
Agyeman, P. C.; Ahado, S. K.; Borůvka, L.; Biney, J. K. M.; Sarkodie, V. Y. O.; Kebonye, N. M.; Kingsley, J. Trend Analysis of Global Usage of Digital Soil Mapping Models in the Prediction of Potentially Toxic Elements in Soil/Sediments: A Bibliometric Review. Environmental Geochemistry and Health. Springer Science and Business Media B.V. 2020. https://doi.org/10.1007/s10653-020-00742-9.Minasny, B. & McBratney, A. B. Digital soil mapping: A brief history and some lessons. Geoderma 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017 (2016).ADS 
Article 

Google Scholar 
McBratney, A. B., Mendonça Santos, M. L. & Minasny, B. On digital soil mapping. Geoderma 117(1–2), 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).ADS 
Article 

Google Scholar 
Deutsch.C.V. Geostatistical Reservoir Modeling,… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=C.V.+Deutsch%2C+2002%2C+Geostatistical+Reservoir+Modeling%2C+Oxford+University+Press%2C+376+pages.+&btnG= (accessed Apr 28, 2021).Olea, R. A. Geostatistics for engineers & earth scientists. Stoch. Environ. Res. Risk Assess. 14(3), 207–209. https://doi.org/10.1007/pl00009782 (2000).Article 

Google Scholar 
Gumiaux, C., Gapais, D. & Brun, J. P. Geostatistics applied to best-fit interpolation of orientation data. Tectonophysics 376(3–4), 241–259. https://doi.org/10.1016/j.tecto.2003.08.008 (2003).ADS 
Article 

Google Scholar 
Wadoux, A. M. J. C., Minasny, B. & McBratney, A. B. Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103359 (2020).Article 

Google Scholar 
Tan, K. et al. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987. https://doi.org/10.1016/j.jhazmat.2019.120987 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sakizadeh, M., Mirzaei, R. & Ghorbani, H. Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran. Neural Comput. Appl. 28(11), 3229–3238. https://doi.org/10.1007/s00521-016-2231-x (2017).Article 

Google Scholar 
Vega, F. A., Matías, J. M., Andrade, M. L., Reigosa, M. J. & Covelo, E. F. Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil. J. Hazard. Mater. 167(1–3), 615–624. https://doi.org/10.1016/j.jhazmat.2009.01.016 (2009).CAS 
Article 
PubMed 

Google Scholar 
Sun, H. et al. Prediction of distribution of soil cd concentrations in Guangdong Province, China. Huanjing Kexue/Environmental Sci. 38(5), 2111–2124. https://doi.org/10.13227/j.hjkx.201611006 (2017).Article 

Google Scholar 
Woodcock, C. E. & Gopal, S. Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int. J. Geogr. Inf. Sci. 14(2), 153–172. https://doi.org/10.1080/136588100240895 (2000).Article 

Google Scholar 
Finke, P. A. Chapter 39 Quality assessment of digital soil maps: producers and users perspectives. Dev. Soil Sci. https://doi.org/10.1016/S0166-2481(06)31039-2 (2006).Article 

Google Scholar 
Pontius, R. G. & Cheuk, M. L. A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. Int. J. Geogr. Inf. Sci. 20(1), 1–30. https://doi.org/10.1080/13658810500391024 (2006).Article 

Google Scholar 
Grunwald, S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152(3–4), 195–207. https://doi.org/10.1016/j.geoderma.2009.06.003 (2009).ADS 
Article 

Google Scholar 
Nelson, M. A., Bishop, T. F. A., Triantafilis, J. & Odeh, I. O. A. An error budget for different sources of error in digital soil mapping. Eur. J. Soil Sci. 62, 417–430 (2011).Article 

Google Scholar 
McBratney, A. B., Minasny, B. & ViscarraRossel, R. Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 136, 272–278 (2006).ADS 
CAS 
Article 

Google Scholar 
Stumpf, F. et al. Uncertainty-guided sampling to improve digital soil maps. CATENA 153, 30–38 (2017).Article 

Google Scholar 
Legates, D. R. & McCabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).ADS 
Article 

Google Scholar 
Sergeev, A. P. et al. High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging. AIP Conf. Proc. 2017, 1836. https://doi.org/10.1063/1.4981963 (2017).CAS 
Article 

Google Scholar 
Subbotina, I. E. et al. Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area. AIP Conf. Proc. https://doi.org/10.1063/1.5045410 (2018).Article 

Google Scholar 
Tarasov, D. A., Buevich, A. G., Sergeev, A. P. & Shichkin, A. V. High variation topsoil pollution forecasting in the Russian subarctic: using artificial neural networks combined with residual kriging. Appl. Geochemistry 88, 188–197. https://doi.org/10.1016/j.apgeochem.2017.07.007 (2018).CAS 
Article 

Google Scholar 
Tarasov, D.; Buevich, A.; Shichkin, A.; Subbotina, I.; Tyagunov, A.; Baglaeva, E. Chromium Distribution Forecasting Using Multilayer Perceptron Neural Network and Multilayer Perceptron Residual Kriging. In AIP Conference Proceedings; American Institute of Physics Inc., 2018; Vol. 1978, p 440019. https://doi.org/10.1063/1.5044048.John, K. et al. Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur. CATENA 206, 2 (2021).Article 

Google Scholar 
Gribov, A. & Krivoruchko, K. Empirical Bayesian Kriging Implementation and Usage. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137290 (2020).Article 
PubMed 

Google Scholar 
Samsonova, V. P., Blagoveshchenskii, Y. N. & Meshalkina, Y. L. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Sci. 50(3), 305–311. https://doi.org/10.1134/S1064229317030103 (2017).ADS 
CAS 
Article 

Google Scholar 
Fabijańczyk, P., Zawadzki, J. & Magiera, T. Magnetometric assessment of soil contamination in problematic area using empirical bayesian and indicator kriging: a case study in upper Silesia, Poland. Geoderma 308, 69–77. https://doi.org/10.1016/j.geoderma.2017.08.029 (2017).ADS 
CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 2, 1–16. https://doi.org/10.1007/s13762-020-03089-x (2021).CAS 
Article 

Google Scholar 
Li, T. et al. Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes. Sci. Total Environ. 628–629, 1446–1459. https://doi.org/10.1016/j.scitotenv.2018.02.163 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114065 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hossain Bhuiyan, M. A., Chandra Karmaker, S., Bodrud-Doza, M., Rakib, M. A. & Saha, B. B. Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM PMF and GIS Methods. Chemosphere https://doi.org/10.1016/j.chemosphere.2020.128339 (2021).Article 
PubMed 

Google Scholar 
Kebonye, N. M. et al. Self-organizing map artificial neural networks and sequential gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils. J. Geochemical Explor. 222, 106680. https://doi.org/10.1016/j.gexplo.2020.106680 (2021).CAS 
Article 

Google Scholar 
Weather Spark. Average Weather in Frýdek-Místek, Czechia, Year Round – Weather Spark https://weatherspark.com/y/83671/Average-Weather-in-Frýdek-Místek-Czechia-Year-Round (accessed Sep 14, 2020).Kozák, J. Soil Atlas of the Czech Republic. 2010, 150.Vacek, O., Vašát, R. & Borůvka, L. Quantifying the pedodiversity-elevation relations. Geoderma 373, 114441. https://doi.org/10.1016/j.geoderma.2020.114441 (2020).ADS 
Article 

Google Scholar 
Krivoruchko, K. Empirical Bayesian Kriging; 2012; Vol. Fall 2012.Vapnik, V. The nature of statistical learning theory. Technometrics 38(4), 409. https://doi.org/10.2307/1271324 (1995).Article 
MATH 

Google Scholar 
Li, Z., Zhou, M., Xu, L. J., Lin, H. & Pu, H. Training sparse SVM on the core sets of fitting-planes. Neurocomputing 130, 20–27. https://doi.org/10.1016/j.neucom.2013.04.046 (2014).Article 

Google Scholar 
Cherkassky, V.; Mulier, F. Learning from Data: Concepts, Theory, and Methods: Second Edition; 2006. https://doi.org/10.1002/9780470140529.John, K. et al. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 9(12), 1–20. https://doi.org/10.3390/land9120487 (2020).CAS 
Article 

Google Scholar 
Vohland, M., Besold, J., Hill, J. & Fründ, H. C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 166(1), 198–205. https://doi.org/10.1016/j.geoderma.2011.08.001 (2011).ADS 
CAS 
Article 

Google Scholar 
Fraser, S. J.; Dickson, B. L. A New Method for Data Integration and Integrated Data Interpretation: Self-Organising Maps; 2007.Melssen, W. J.; Smits, J. R. M.; Buydens, L. M. C.; Kateman, G. Using Artificial Neural Networks for Solving Chemical Problems Part II. Kohonen Self-Organising Feature Maps and Hopfield Networks. Chemometrics and Intelligent Laboratory Systems. Elsevier, Amsterdam, 1, 1994, pp 267–291. https://doi.org/10.1016/0169-7439(93)E0036-4.Kooistra, L. et al. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta 484(2), 189–200. https://doi.org/10.1016/S0003-2670(03)00331-3 (2003).CAS 
Article 

Google Scholar 
Li, L. et al. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica Napus L.) using in situ leaf spectroscopy. Ind. Crops Prod. 91, 194–204. https://doi.org/10.1016/j.indcrop.2016.07.008 (2016).CAS 
Article 

Google Scholar 
Różański, S. Ł, Kwasowski, W., Castejón, J. M. P. & Hardy, A. Heavy metal content and mobility in urban soils of public playgrounds and sport facility areas, Poland. Chemosphere 212, 456–466. https://doi.org/10.1016/j.chemosphere.2018.08.109 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bretzel, F. & Calderisi, M. Metal contamination in urban soils of coastal Tuscany (Italy). Environ. Monit. Assess. 118(1–3), 319–335. https://doi.org/10.1007/s10661-006-1495-5 (2006).CAS 
Article 
PubMed 

Google Scholar 
Jim, C. Y. Urban soil characteristics and limitations for landscape planting in hong kong. Landsc. Urban Plan. 40(4), 235–249. https://doi.org/10.1016/S0169-2046(97)00117-5 (1998).Article 

Google Scholar 
Birke, M.; Rauch, U.; Chmieleski, J. Environmental Geochemical Survey of the City of Stassfurt: An Old Mining and Industrial Urban Area in Sachsen-Anhalt, Germany. In Mapping the Chemical Environment of Urban Areas; John Wiley and Sons, 2011; pp 269–306. https://doi.org/10.1002/9780470670071.ch18.Khodadoust, A. P., Reddy, K. R. & Maturi, K. Removal of nickel and phenanthrene from kaolin soil using different extractants. Environ. Eng. Sci. 21(6), 691–704. https://doi.org/10.1089/ees.2004.21.691 (2004).CAS 
Article 

Google Scholar 
Jakovljevic, M.; Kostic, N.; Antic-Mladenovic, S. The Availability of Base Elements (Ca, Mg, Na, K) in Some Important Soil Types in Serbia; 2003. https://doi.org/10.2298/zmspn0304011j.Orzechowski, M.; Smolczynski, S. IN SOILS DEVELOPED FROM THE HOLOCENE DEPOSITS IN NORTH-EASTERN POLAND*; -, 2007; Vol. 15.Pongrac, P. et al. Mineral element composition of cabbage as affected by soil type and phosphorus and zinc fertilisation. Plant Soil 434(1–2), 151–165. https://doi.org/10.1007/s11104-018-3628-3 (2019).CAS 
Article 

Google Scholar 
Kingston, G.; Anink, M. C.; Clift, B. M.; Beattie, R. N. Potassium Management for Sugarcane on Base Saturated Soils in Northern New South Wales; 2009; Vol. 31.Santo, L. T., Nakahata, M. H., & Schell, V. P. Santo LT, Nakahata MH, Ito GP and Schell VP (2000)…. – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Santo+LT%2C+Nakahata+MH%2C+Ito+GP+and+Schell+VP+%282000%29.+Calcium+and+liming+trials+from+1994+to+1998+at+HC%26S.+Technical+supplement+to+Agronomy+Report+83%2C+Hawaiian+Agricultural+Research+Centre. (accessed May 16, 2021).Burgos, P., Madejón, E., Pérez-de-Mora, A. & Cabrera, F. Horizontal and vertical variability of soil properties in a trace element contaminated area. Int. J. Appl. Earth Obs. Geoinf. 10(1), 11–25. https://doi.org/10.1016/j.jag.2007.04.001 (2008).ADS 
Article 

Google Scholar 
Olinic, T. & Olinic, E. The effect of quicklime stabilization on soil properties. Agric. Agric. Sci. Procedia 10, 444–451. https://doi.org/10.1016/j.aaspro.2016.09.013 (2016).Article 

Google Scholar 
Madaras, M.; Lipavský, J. Interannual Dynamics of Available Potassium in a Long-Term Fertilization Experiment; 2009; Vol. 55. https://doi.org/10.17221/34/2009-pse.Madaras, M., Koubova, M. & Lipavský, J. Stabilization of available potassium across soil and climatic conditions of the Czech Republic. Arch. Agron. Soil Sci. 56(4), 433–449. https://doi.org/10.1080/03650341003605750 (2010).CAS 
Article 

Google Scholar 
Pulkrabová, J. et al. Is the long-term application of sewage sludge turning soil into a sink for organic pollutants?: Evidence from field studies in the Czech Republic. J. Soils Sedim. 19(5), 2445–2458. https://doi.org/10.1007/s11368-019-02265-y (2019).CAS 
Article 

Google Scholar 
Asare, M. O., Horák, J., Šmejda, L., Janovský, M. & Hejcman, M. A medieval hillfort as an island of extraordinary fertile archaeological dark earth soil in the Czech Republic. Eur. J. Soil Sci. 72(1), 98–113. https://doi.org/10.1111/ejss.12965 (2021).CAS 
Article 

Google Scholar 
Zádorová, T. et al. Identification of Neolithic to Modern Erosion-Sedimentation Phases Using Geochemical Approach in a Loess Covered Sub-Catchment of South Moravia Czech Republic. Geoderma 195–196, 56–69. https://doi.org/10.1016/j.geoderma.2012.11.012 (2013).ADS 
CAS 
Article 

Google Scholar 
Tlustoš, P. et al. Nutrient status of soil and winter wheat (Triticum Aestivum L.) in response to long-term farmyard manure application under different climatic and soil physicochemical conditions in the Czech Republic. Arch. Agron. Soil Sci. 64(1), 70–83. https://doi.org/10.1080/03650340.2017.1331297 (2018).Article 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. 260, 2 (2020).
Google Scholar 
Yan, P., Peng, H., Yan, L. & Lin, K. Spatial variability of soil physical properties based on GIS and geo-statistical methods in the red beds of the Nanxiong Basin, China. Polish J. Environ. Stud. 28, 2961–2972 (2019).Article 

Google Scholar 
Beguin, J., Fuglstad, G. A., Mansuy, N. & Paré, D. Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma 306, 195–205 (2017).ADS 
CAS 
Article 

Google Scholar 
Adhikary, P. P., Dash, C. J., Bej, R. & Chandrasekharan, H. Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ. Monit. Assess. 176, 663–676 (2011).CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 18, 3327–3342 (2021).CAS 
Article 

Google Scholar 
Eldeiry, A. A. & Garcia, L. A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci. Soc. Am. J. 72, 201–211 (2008).ADS 
CAS 
Article 

Google Scholar  More

Plant materials, growth conditions and salt treatmentsSoil samples were performed as in previous experiments with S. europaea25, seeds were collected at two maternal sites, the first of which represents natural salinity related to inland salt springs at the health resort of Ciechocinek (Cie) (52°53′N, 18°47′E) characterised by a high soil salinity of ca 100 dS m−1 (~ 1000 mM NaCl), and the second of which is associated with soda factory waste that affects the local environment in Inowrocław-Mątwy (Inw) (52°48′N, 18°15′E) and with a lower salinity of ca 55 dS m−1 (~ 550 mM NaCl). The complete soil description is reported in Piernik et al.51 and Szymanska et al.52,53. Populations are isolated by a distance of ca 40 km without any saline environment between them, however, they were somehow connected due to the presence of salt springs in the nineteenth century. The seeds came from one generation and were collected in early November 2018. The seeds were germinated and grown according to the same steps reported in Cárdenas-Pérez et al.25 with a slight modification in the number of salt treatments at 0, 200, 400, 600, 800 and 1000 mM NaCl. In total, 144 plants were cultivated, and, therefore, a complete randomised factorial design 26 was used, which included (12 plants × 6 treatments × 2 populations) with 14 response variables. After 2 months of development, anatomical analysis such as cell area (A), roundness (R) and maximum cell diameter (Cdiam) were estimated in 12 samples, whereas high and low methyl esterified pectins (HM-HGs and LM-HGs), proline (P), hydrogen peroxide (HP), total soluble protein (Prot), catalase activity (CAT), peroxidase activity (POD), chlorophyll a, b and total (Cha, Chb and TC), carotenoid (Carot) contents, as well as SeNHX1 and SeSOS1 gene expression, were all determined per triplicate (plants were randomly selected). The collection of plant material, comply with relevant institutional, national, and international guidelines and legislation, IUCN Policy Statement on Research Involving Species at Risk of Extinction and Convention on the Trade in Endangered Species of Wild Fauna and Flora. The voucher specimen of the plant material has been deposited in a publicly available herbarium of the Nicolaus Copernicus University in Toruń (Index Herbarium code TRN), deposition number not available (dr. hab. Agnieszka Piernik, prof. NCU undertook the formal identification of plant species, and permission to work with the seeds was provided by the Regional Director of Environmental Protection in Bydgoszcz, WOP.6400.12.2020.JC).Anatomical image analysisFrom the middle primary branch (fleshy segment shoot) of S. europaea plant treatments (0, 200, 400, 600, 800 and 1000 mM NaCl), slices of fresh tissue were obtained by cutting them with a sharp bi-shave blade. The thinner slices of approximately 0.5 mm were selected and used in the microstructure analysis. The size and shape of the stem-cortex cells from the fresh water-storing tissue were characterised by a light microscope (Olympus BX51, USA) connected to a digital camera (DP72 digital microscope camera) and digital acquisition software (DP2-BSW). The microscope images were captured at a magnification of 10 ×/0.30 in RGB scale and stored in TIFF format at 1280 × 1024 pixels. A total of 300 ± 50 cells from five individuals per treatment were analysed. Finally, the shape and size of the cells were obtained from the captured images. Cell image analysis (IA) was performed in ImageJ v. 1.47 (National Institutes of Health, Bethesda, MD, USA). The following anatomical parameters were obtained. Firstly, the cell area (A) was estimated as the number of pixels within the boundary. Secondly, the maximum cell’s diameter (Cdiam) was determined by the distance between the two points separated by the largest coordinates in different orientations, and the cell roundness (R) was obtained through the equation R = (4 A)/(π (Cdiam)2)—where a perfectly round cell has R = 1.0, while elongated cells will show an R → 0. Finally, the degree of succulence (S) in stem was calculated according to24 with slight change S = (Fresh Weight-Dry Weight)/stem Area, where the Area of the stem (As) was calculated as: As = π × r2, the diameter of the stems was obtained according to Cárdenas-Pérez et al.25.Immunolocalisation experimentsThe samples dissected from the middle segment of the shoot (3 individuals per treatment) were prepared for embedding in BMM resin (butyl methacrylate, methyl methacrylate, 0.5% benzoyl ethyl ether (Sigma) with 10 mM DDT (Thermo Fisher Scientific) according to Niedojadło et al.54. Next, specimens were cut on a Leica UCT ultramicrotome into serial semi-thin cross sections (1.5 µm) that were collected on Thermo Scientific Polysine adhesion microscope slides. Before immunocytochemical reaction, the resin was removed with two changes of acetone and washed in distilled water and PBS pH 7.2. After incubation with blocking solution containing 2% BSA (bovine serum albumin, Sigma) in PBS pH 7.2 for 30 min at room temperature, the sections were incubated with anti-pectin rat monoclonal primary antibody JIM7 (recognises partially methylesterified epitopes of homogalacturonan [HG] but does not bind to fully de-esterified HGs) or antibody LM19 (recognises partially methylesterified epitopes of HG and binds strongly to de-esterified HGs) (Plant Probes) diluted 1:50 in 0.2% BSA in PBS pH 7.2 overnight at 4 °C. After washing with PBS pH 7.2, the material was incubated with AlexaFluor 488-conjugated goat anti-rat secondary antibody (Thermo Fisher Scientific) diluted 1:1000 in 0.2% BSA in PBS pH 7.2 for 1 h at 37 °C. Finally, the sections were washed in PBS pH 7.2, dried at room temperature and covered with ProLongTMGold antifade reagent (Thermo Fisher Scientific). The control reactions were performed with the omission of incubation with primary antibodies. Semithin sections were analysed with an Olympus BX50 fluorescence microscope, with an UPlanFI 1009 (N.A. 1.3) oil immersion lens and narrow band filters (U-MNU, U-MNG). The results were recorded with an Olympus XC50 digital colour camera and CellB software (Olympus Soft Imaging Solutions GmbH, Germany).Fluorescence quantitative evaluationFor the quantitative measurement, each experiment was performed using consistent temperatures, incubation times and concentrations of antibodies. The aforementioned ImageJ (1.47v) software was used for image processing and analysis. The fluorescence intensity was measured for five semi-thin sections for each experimental population (Inowrocław and Ciechocinek) at the same magnification (100 ×) and the constant exposure time to ensure comparable results. The threshold fluorescence in the sample was established based on the autofluorescence of the control reaction. The level of signal intensity was expressed in arbitrary units (a.u.) as the mean intensity per μm2 according to Niedojadło et al.54.Biochemical analysisProline content (P) was measured according to Ábrahám et al.55. Five hundred milligrams of fresh stem material was minced on ice and homogenised with 3% aqueous sulfosalicylic acid solution (5 μl mg−1 fresh plant material), centrifuged at 18,000×g, 10 min at 4 °C, and the supernatant was collected. The reaction mixture: 100 μl of 3% sulphosalicylic acid, 200 μl of glacial acetic acid, 200 μl of acidic ninhydrin reagent and 100 μl of supernatant. Acidic ninhydrin reagent was prepared according to Bates et al.56. The standard curve for proline in the concentration range of 0 to 40 μg ml−1. The standard curve equation was y = 0.0467x − 0.0734, R2 = 0.963. P was expressed in mg of proline per gram of fresh weight. Hydrogen peroxide (HP) levels were determined according to the methods described by Velikova et al.57, and 500 mg of stem tissues were homogenised with 5 ml trichloroacetic acid 0.1% (w:v) in an ice bath. The homogenate was centrifuged (12,000×g, 4 °C, 15 min) and 0.5 ml of the supernatant was added to potassium phosphate buffer (0.5 ml) (10 mM, pH 7.0) and 2 ml of 1 M KI. The absorbance was read at 390 nm, and the HP content was given on a standard curve from 0 to 40 mM. The standard curve equation was y = 0.0188x + 0.046, R2 = 0.987. HP concentrations were expressed in nM per gram of fresh weight. Chlorophylls (Cha and Chb) and carotenoids were extracted from fresh plant stems (100 mg) using 80% acetone for 6 h in darkness, and then centrifuged at 10,000 rpm, 10 min. Supernatants were quantified spectrophotometrically. Absorbance was determined at 646, 663 and 470 nm and calculations were performed according to Lichtenthaler and Wellburn58, when 80% of acetone is used as dissolvent. Total chlorophyll content was calculated as the sum of chlorophyll a and b contents.Total CAT activity was determined spectrophotometrically by following the decline in A240 as H2O2 (ε = 39.9 M−1 cm−1) was catabolised, according to the method of Beers and Sizer59. Decrease in absorbance of the reaction at 240 nm was recorded after every 20 s. One unit CAT was defined as an absorbance change of 0.01 units min−1. Total POD activity was determined spectrophotometrically by monitoring the formation of tetraguaiacol (ε = 26.6 mM−1 cm−1) from guaiacol at A470 in the presence of H2O2 by the method of Chance and Maehly60. Increase in absorbance of the reaction solution at 470 nm was recorded after every 20 s. One unit of POD activity was defined as an absorbance change of 0.01 units min−1. Total soluble protein (Prot) content was measured according to Bradford61 using bovine serum albumin (BSA) as a protein standard. Fresh leaf samples (1 g) were homogenised with 4 ml Na-phosphate buffer (pH 7.2) and then centrifuged at 4 °C. Supernatant and dye were pipetted in spectrophotometer cuvettes and absorbances were measured using a UV–vis spectrophotometer (PG instruments T80) at 595 nm62. Prot was determined based on the standard curve y = 1.6565x + 0.0837, R2 = 0.982, for total soluble protein in the concentration range of 0 to 1.2 mg ml−1 BSA. Triplicates per treatment were used for each analysis.Total RNA isolationAfter 2 months of salt treatment, shoots of S. europaea plants (3 individuals per treatment) were washed several times with tap water and then three times with miliQ water. After drying, plant material was frozen in liquid nitrogen, and stored at − 80 °C. Total RNA isolation was performed using RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The quality and quantity of RNA was checked on 1.5% agarose gels in TAE (Tris–HCl, acetic acid, EDTA, pH 8.3) buffer stained with ethidium bromide, and by spectrophotometric measurement (NanoDrop Lite, Thermo Fisher Scientific, Waltham, MA, USA).Cloning of SOS1 gene from S. europaea (SeSOS1)One microgram (1 µg) of total RNA isolated from shoots of S. europaea was primed with 0.5 µg of oligo (dT)20 primer for 5 min at 70 °C. Then 4 µl of ImProm-II 5 × reaction buffer, 2 mM MgCl2, 0.5 mM each dNTP, 20 U of recombinant RNasin ribonuclease inhibitor, and 1 µl of ImProm-II reverse transcriptase (Promega, Madison, WI, USA) were added to a final volume of 20 µl. The reaction was performed at 42 °C for 60 min. To design degenerate primers for SOS1, cDNA sequences from Arabidopsis thaliana (NM_126259.4), Lycopersicon esculentum (AJ717346.1), Mesembryanthemum crystallinum (EF207776.1), Oryza sativa (AY785147.1), Triticum aestivum (AY326952.3), Salicornia brachiata (EU879059.1) were obtained from NCBI GeneBank. The sequences were aligned using the Clustal Omega tool (https://www.ebi.ac.uk/Tools/msa/clustalo/) and three pairs of degenerate primes were designed (listed in Table 4). The PCR reaction mixture includes cDNA, 0.2 µM each primer, 0.2 mM each dNTP, 4 µl of 5 × HF buffer, and 0.5 U of Phusion High-Fidelity DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA) in a total volume of 20 µl. The thermal conditions were as follows: 98 °C for 30 s, 98 °C for 10 s, gradient between 48 °C and 56 °C for 20 s, 72 °C for 60 s, 32 cycle, final extension for 10 min at 72 °C. A pair of primers deg2_F and deg2_R yielded a PCR product with expected size. The PCR product was purified from agarose gel, cloned into pJET1.2 vector (Thermo Fisher Scientific, Waltham, MA, USA) according to manufacturer’s protocol and sequenced (Genomed, Warsaw, Poland). The obtained partial cDNA sequence was named SeSOS1 and deposited in NCBI GeneBank (acc. no. MZ707082).Table 4 Sequences of the primers used for cloning of SeSOS1 and quantitative real-time PCR.Full size tableReverse transcription reaction and quantitative real-time PCR (qPCR) SeNHX1 and SeSOS1 gene expression analysisPrior to reverse transcription reaction, RNA was treated with DNaseI (Thermo Fisher Scientific, Waltham, MA, USA). The cDNA was synthesised from 1.5 µg of total RNA using a mixture of 2.5 µM oligo(dT)20 primer and 0.2 µg of random hexamers with NG dART RT Kit (Eurx, Gdańsk, Poland) according to the manufacturer’s protocol. The reaction was performed at 25 °C for 10 min, followed by 50 min at 50 °C. The cDNA was stored at − 20 °C.The PCR reaction mixture includes 4 µl of 1/20 diluted cDNA, 0.5 µM gene-specific primers (Table 4) and 5 µl of LightCycler 480 SYBR Green I Master (Roche, Penzberg, Germany) in a total volume of 10 µl. Clathrin adaptor complexes (CAC) was used as a reference gene63. The reaction was performed in triplicate (technical replicates) in LightCycler 480 Instrument II (Roche, Penzberg, Germany). The thermal cycling conditions were as follows: 95 °C for 5 min, 95 °C for 10 s, 60 °C for 20 s, 72 °C for 20 s, 40 cycles. The SYBR Green I fluorescence signal was recorded at the end of the extension step in each cycle. The specificity of the assay was confirmed by the melt curve analysis i.e., increasing the temperature from 55 to 95 °C at a ramp rate 0.11 °C/s. The fold-change in gene expression was calculated using LightCycler 480 Software release 1.5.1.62 (Roche, Penzberg, Germany).Statistical and multivariate analysisIn order to determine the projection of the effect of salt treatment in plants we followed Cárdenas-Pérez et al.25 methodology. A principal component analysis (PCA) was developed using XLSTAT software version 2019.4.165. For this analysis, 14 variables were used, (A, Cdiam, R, Prot, CAT, POD, HM-HGs, LM-HGs, P, HP, Cha, Chb, TC, Carot), arranged in a matrix with the average values obtained from replicates of each treatment and population. A two-way ANOVA comparing treatments within populations and populations within treatments was conducted for all the results with the Holm–Sidak method. The data was fit with a modified three parameter exponential decay using SigmaPlot version 11.066. The relationships between variables were performed using a Pearson analysis, while a significance test (Kaisere Meyere Olkin) was performed in order to determine which variables had a significant correlation with each other (α = 0.05). Then, a 3D plot was developed using the three principal component factors according to the Kaiser criterion which stated that the factors below the unit are irrelevant. The three main factorial scores of the PCA from each sample were used to calculate the distance (D) between the two points (populations) under the same treatment P1 = (x1, y1, z1) and P2 = (x2, y2, z2) in 3D space of the PCA (Eq. 1).$$D ( {P_{1} ,, P_{2} } ) = sqrt {( {x_{2} – x_{1} } )^{2} + ( {y_{2} – y_{1} } )^{2} + ( {z_{2} – z_{1} } )^{2} }$$
(1)
where x, y, and z are the three main factorial scores in the PCA corresponding to the evaluated treatment in Inw and in Cie. Distances were used to evaluate and determine in which salt treatment the greatest differences between the populations were recorded. More

Wignall, P. B. The Worst of Times (Princeton Univ. Press, 2015).Black, B. A., Karlstrom, L. & Mather, T. A. The life cycle of large igneous provinces. Nat. Rev. Earth Environ. 2, 840–857 (2021).
Google Scholar 
Jin, Y. G. et al. Pattern of marine mass extinction near the Permian–Triassic boundary in south China. Science 289, 432–436 (2000).
Google Scholar 
Song, H., Wignall, P. B., Tong, J. & Yin, H. Two pulses of extinction during the Permian–Triassic crisis. Nat. Geosci. 6, 52–56 (2013).
Google Scholar 
Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in Earth history. Proc. Natl Acad. Sci. USA 113, E6325–E6334 (2016).
Google Scholar 
Benton, M. J. & Newell, A. J. Impacts of global warming on Permo–Triassic terrestrial ecosystems. Gondwana Res. 25, 1308–1337 (2014).
Google Scholar 
Brayard, A. et al. Transient metazoan reefs in the aftermath of the end-Permian mass extinction. Nat. Geosci. 4, 693–697 (2011).
Google Scholar 
Brayard, A. et al. Good genes and good luck: ammonoid diversity and the end-Permian mass extinction. Science 325, 1118–1121 (2009).
Google Scholar 
Scheyer, T. M., Romano, C., Jenks, J. & Bucher, H. Early triassic marine biotic recovery: the predators’ perspective. PLoS ONE 9, e88987 (2014).
Google Scholar 
Retallack, G. J., Veevers, J. J. & Morante, R. Global coal gap between Permian–Triassic extinction and Middle Triassic recovery of peat-forming plants. Bull. Geolog. Soc. Am. 108, 195–207 (1996).
Google Scholar 
Payne, J. L. et al. Large perturbations of the carbon cycle during recovery from the end-Permian extinction. Science 305, 506–509 (2004).
Google Scholar 
Song, H., Wignall, P. B. & Dunhill, A. M. Decoupled taxonomic and ecological recoveries from the Permo–Triassic extinction. Sci. Adv. 4, eaat5091 (2018).
Google Scholar 
Retallack, G. J. Postapocalyptic greenhouse paleoclimate revealed by earliest Triassic paleosols in the Sydney basin, Australia. Bull. Geol. Soc. Am. 111, 52–70 (1999).
Google Scholar 
Ward, P. D., Montgomery, D. R. & Smith, R. Altered river morphology in South Africa related to the Permian–Triassic extinction. Science 289, 1740–1743 (2000).
Google Scholar 
Wignall, P. B. & Twitchett, R. J. Extent, duration, and nature of the Permian–Triassic superanoxic event. Spec. Pap. Geol. Soc. Am. 356, 395–413 (2002).
Google Scholar 
Rampino, M. R. & Stothers, R. B. Flood basalt volcanism during the past 250 million years. Science 241, 663–668 (1988).
Google Scholar 
Renne, P. R. & Basu, A. R. Rapid eruption of the Siberian traps flood basalts at the Permo–Triassic boundary. Science 253, 176–179 (1991).
Google Scholar 
Burgess, S. D. & Bowring, S. A. High-precision geochronology confirms voluminous magmatism before, during, and after Earth’s most severe extinction. Sci. Adv. 1, e1500470 (2015).
Google Scholar 
Vasiljev, Y. R., Zolotukhin, V. V., Feoktistov, G. D. & Prusskaya, S. N. Volume estimation and genesis of Permian–Triassic trap magmatism from Siberian platform. Russ. Geol. Geophys. 41, 1696–1705 (2000).
Google Scholar 
Dobretsov, N. L. Large igneous provinces of Asia (250 Ma): Siberian and Emeishan traps (plateau basalts) and associated granitoids. Geol. Geof. 46, 870–890 (2005).
Google Scholar 
Augland, L. E. et al. The main pulse of the Siberian Traps expanded in size and composition. Sci. Rep. 9, 18723 (2019).
Google Scholar 
Kasbohm, J., Schoene, B. & Burgess, S. in Large Igneous Provinces: A Driver of Global Environmental and Biotic Changes (eds Ernst, R. E., Dickson, A. & Bekker, A.) 27–82 (Wiley, 2021).Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).
Google Scholar 
Posenato, R. Marine biotic events in the lopingian succession and latest Permian extinction in the Southern Alps (Italy). Geol. J. 45, 195–215 (2010).
Google Scholar 
Posenato, R. The end-Permian mass extinction (EPME) and the early Triassic biotic recovery in the western Dolomites (Italy): state of the art. Bull. Soc. Paleontol. Ital. 58, 11–34 (2019).
Google Scholar 
Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).
Google Scholar 
Chu, D. et al. Ecological disturbance in tropical peatlands prior to marine Permian–Triassic mass extinction. Geology 48, 288–292 (2020).
Google Scholar 
Gastaldo, R. A. et al. The base of the Lystrosaurus Assemblage Zone, Karoo basin, predates the end-Permian marine extinction. Nat. Commun. 11, 1428 (2020).
Google Scholar 
Foote, M. Morphological and taxonomic diversity in clade’s history: the blastoid record and stochastic simulations. Contrib. Mus. Paleontol. 28, 101–140 (1991).
Google Scholar 
Stanley, S. M. & Yang, X. A double mass extinction at the end of the Paleozoic era. Science 266, 1340–1344 (1994).
Google Scholar 
Wang, X. D. & Sugiyama, T. Diversity and extinction patterns of Permian coral faunas of China. Lethaia 33, 285–294 (2000).
Google Scholar 
Hallam, A. & Wignall, P. B. Mass Extinctions and their Aftermath (Oxford Univ. Press, 1997).Orchard, M. J. Conodont diversity and evolution through the latest Permian and Early Triassic upheavals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 252, 93–117 (2007).
Google Scholar 
Romano, C. et al. Permian–Triassic Osteichthyes (bony fishes): diversity dynamics and body size evolution. Biol. Rev. 91, 106–147 (2016).
Google Scholar 
Tu, C., Chen, Z. Q. & Harper, D. A. T. Permian–Triassic evolution of the Bivalvia: extinction-recovery patterns linked to ecologic and taxonomic selectivity. Palaeogeogr. Palaeoclimatol. Palaeoecol. 459, 53–62 (2016).
Google Scholar 
Schaal, E. K., Clapham, M. E., Rego, B. L., Wang, S. C. & Payne, J. L. Comparative size evolution of marine clades from the Late Permian through Middle Triassic. Paleobiology 42, 127–142 (2016).
Google Scholar 
Chen, J. et al. Size variation of brachiopods from the late Permian through the middle Triassic in south China: evidence for the Lilliput effect following the Permian–Triassic extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 519, 248–257 (2019).
Google Scholar 
Feng, Y., Song, H. & Bond, D. P. G. Size variations in foraminifers from the early Permian to the Late Triassic: implications for the Guadalupian–Lopingian and the Permian–Triassic mass extinctions. Paleobiology 46, 511–532 (2020).
Google Scholar 
Luo, G., Lai, X., Jiang, H. & Zhang, K. Size variation of the end-Permian conodont Neogondolella at Meishan section, Changxing, Zhejiang and its significance. Sci. China Ser. D 49, 337–347 (2006).
Google Scholar 
Brayard, A. et al. Early Triassic Gulliver gastropods: spatio-temporal distribution and significance for biotic recovery after the end-Permian mass extinction. Earth Sci. Rev. 146, 31–64 (2015).
Google Scholar 
Knoll, A. H., Bambach, R. K., Canfield, D. E. & Grotzinger, J. P. Comparative Earth history and late Permian mass extinction. Science 273, 452–457 (1996).
Google Scholar 
Knoll, A. H., Bambach, R. K., Payne, J. L., Pruss, S. & Fischer, W. W. Paleophysiology and end-Permian mass extinction. Earth Planet. Sci. Lett. 256, 295–313 (2007).
Google Scholar 
Clapham, M. E. & Payne, J. L. Acidification, anoxia, and extinction: a multiple logistic regression analysis of extinction selectivity during the Middle and Late Permian. Geology 39, 1059–1062 (2011).
Google Scholar 
Vázquez, P. & Clapham, M. E. Extinction selectivity among marine fishes during multistressor global change in the end-Permian and end-Triassic crises. Geology 45, 395–398 (2017).
Google Scholar 
Payne, J. L. & Finnegan, S. The effect of geographic range on extinction risk during background and mass extinction. Proc. Natl Acad. Sci. USA 104, 10506–10511 (2007).
Google Scholar 
Dai, X. & Song, H. Toward an understanding of cosmopolitanism in deep time: a case study of ammonoids from the middle Permian to the Middle Triassic. Paleobiology 46, 533–549 (2020).
Google Scholar 
Kiessling, W. et al. Pre-mass extinction decline of latest Permian ammonoids. Geology 46, 283–286 (2018).
Google Scholar 
Rampino, M. R. & Adler, A. C. Evidence for abrupt latest Permian mass extinction of foraminifera: results of tests for the Signor–Lipps effect. Geology 26, 415–418 (1998).
Google Scholar 
Song, H., Tong, J., Chen, Z. Q., Yang, H. & Wang, Y. End-Permian mass extinction of foraminifers in the Nanpanjiang basin, south China. J. Paleontol. 83, 718–738 (2009).
Google Scholar 
Wignall, P. B. & Hallam, A. Anoxia as a cause of the Permian/Triassic mass extinction: facies evidence from northern Italy and the western United States. Palaeogeogr. Palaeoclimatol. Palaeoecol. 93, 21–46 (1992).
Google Scholar 
Shen, S. Z. et al. A sudden end-Permian mass extinction in south China. Bull. Geol. Soc. Am. 131, 205–223 (2019).
Google Scholar 
Angiolini, L., Checconi, A., Gaetani, M. & Rettori, R. The latest Permian mass extinction in the Alborz Mountains (North Iran). Geol. J. 45, 216–229 (2010).
Google Scholar 
Yin, H., Feng, Q., Lai, X., Baud, A. & Tong, J. The protracted Permo-Triassic crisis and multi-episode extinction around the Permian–Triassic boundary. Glob. Planet. Change 55, 1–20 (2007).
Google Scholar 
Wignall, P. B. & Newton, R. Contrasting deep-water records from the Upper Permian and Lower Triassic of South Tibet and British Columbia: evidence for a diachronous mass extinction. Palaios 18, 153–167 (2003).
Google Scholar 
Wang, Y. et al. Quantifying the process and abruptness of the end-Permian mass extinction. Paleobiology 40, 113–129 (2014).
Google Scholar 
Liu, X., Song, H., Bond, D. P. G., Tong, J. & Benton, M. J. Migration controls extinction and survival patterns of foraminifers during the Permian–Triassic crisis in south China. Earth Sci. Rev. 209, 103329 (2020).
Google Scholar 
Chen, Z. Q. et al. Environmental and biotic turnover across the Permian–Triassic boundary on a shallow carbonate platform in western Zhejiang, south China. Aust. J. Earth Sci. 56, 775–797 (2009).
Google Scholar 
He, W. H. et al. Late Permian marine ecosystem collapse began in deeper waters: evidence from brachiopod diversity and body size changes. Geobiology 13, 123–138 (2015).
Google Scholar 
Burgess, S. D., Bowring, S. & Shen, S. Z. High-precision timeline for Earth’s most severe extinction. Proc. Natl Acad. Sci. USA 111, 3316–3321 (2014).
Google Scholar 
Yang, H. et al. Composition and structure of microbialite ecosystems following the end-Permian mass extinction in south China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 308, 111–128 (2011).
Google Scholar 
Tian, L. et al. Distribution and size variation of ooids in the aftermath of the Permian–Triassic mass extinction. Palaios 30, 714–727 (2015).
Google Scholar 
Retallack, G. J. Permian–Triassic life crisis on land. Science 267, 77–80 (1995).
Google Scholar 
Looy, C. V., Brugman, W. A., Dilcher, D. L. & Visscher, H. The delayed resurgence of equatorial forests after the Permian–Triassic ecologic crisis. Proc. Natl Acad. Sci. USA 96, 13857–13862 (1999).
Google Scholar 
Hermann, E. et al. Terrestrial ecosystems on North Gondwana following the end-Permian mass extinction. Gondwana Res. 20, 630–637 (2011).
Google Scholar 
Cascales-Miñana, B., Diez, J. B., Gerrienne, P. & Cleal, C. J. A palaeobotanical perspective on the great end-Permian biotic crisis. Hist. Biol. 28, 1066–1074 (2016).
Google Scholar 
Yu, J. et al. Vegetation changeover across the Permian–Triassic boundary in southwest China. Extinction, survival, recovery and palaeoclimate: a critical review. Earth Sci.Rev. 149, 203–224 (2015).
Google Scholar 
Vajda, V. et al. End-Permian (252 Mya) deforestation, wildfires and flooding—an ancient biotic crisis with lessons for the present. Earth Planet. Sci. Lett. 529, 115875 (2020).
Google Scholar 
Schneebeli-Hermann, E., Hochuli, P. A. & Bucher, H. Palynofloral associations before and after the Permian–Triassic mass extinction, Kap Stosch, East Greenland. Glob. Planet. Change 155, 178–195 (2017).
Google Scholar 
Nowak, H., Schneebeli-Hermann, E. & Kustatscher, E. No mass extinction for land plants at the Permian–Triassic transition. Nat. Commun. 10, 384 (2019).
Google Scholar 
Chu, D. et al. Biostratigraphic correlation and mass extinction during the Permian–Triassic transition in terrestrial-marine siliciclastic settings of south China. Glob. Planet. Change 146, 67–88 (2016).
Google Scholar 
Zhang, H. et al. The terrestrial end-Permian mass extinction in south China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 448, 108–124 (2016).
Google Scholar 
Krassilov, V. & Karasev, E. Paleofloristic evidence of climate change near and beyond the Permian–Triassic boundary. Palaeogeogr. Palaeoclimatol. Palaeoecol. 284, 326–336 (2009).
Google Scholar 
Mcloughlin, S., Lindström, S. & Drinnan, A. N. Gondwanan floristic and sedimentological trends during the Permian–Triassic transition: new evidence from the Amery Group, northern Prince Charles Mountains, east Antarctica. Antarctic Sci. 9, 281–298 (1997).
Google Scholar 
Kerp, H., Hamad, A. A., Vörding, B. & Bandel, K. Typical Triassic Gondwanan floral elements in the Upper Permian of the paleotropics. Geology 34, 265–268 (2006).
Google Scholar 
Eshet, Y., Rampino, M. R. & Visscher, H. Fungal event and palynological record of ecological crisis and recovery across the Permian–Triassic boundary. Geology 23, 967–970 (1995).
Google Scholar 
Visscher, H. et al. Environmental mutagenesis during the end-Permian ecological crisis. Proc. Natl Acad. Sci. USA 101, 12952–12956 (2004).
Google Scholar 
Looy, C. V., Collinson, M. E., Van Konijnenburg-Van Cittert, J. H. A., Visscher, H. & Brain, A. P. R. The ultrastructure and botanical affinity of end-Permian spore tetrads. Int. J. Plant Sci. 166, 875–887 (2005).
Google Scholar 
Foster, C. B. & Afonin, S. A. Abnormal pollen grains: an outcome of deteriorating atmospheric conditions around the Permian–Triassic boundary. J. Geol. Soc. 162, 653–659 (2005).
Google Scholar 
Hochuli, P. A., Schneebeli-Hermann, E., Mangerud, G. & Bucher, H. Evidence for atmospheric pollution across the Permian–Triassic transition. Geology 45, 1123–1126 (2017).
Google Scholar 
Rampino, M. R. & Eshet, Y. The fungal and acritarch events as time markers for the latest Permian mass extinction: an update. Geosci. Front. 9, 147–154 (2018).
Google Scholar 
Benca, J. P., Duijnstee, I. A. P. & Looy, C. V. UV-B–induced forest sterility: implications of ozone shield failure in Earth’s largest extinction. Sci. Adv. 4, e1700618 (2018).
Google Scholar 
Chu, D. et al. Metal-induced stress in survivor plants following the end-Permian collapse of land ecosystems. Geology 49, 657–661 (2021).
Google Scholar 
Schneebeli-Hermann, E. et al. Vegetation history across the Permian–Triassic boundary in Pakistan (Amb section, Salt Range). Gondwana Res. 27, 911–924 (2015).
Google Scholar 
Visscher, H. et al. The terminal paleozoic fungal event: evidence of terrestrial ecosystem destabilization and collapse. Proc. Natl Acad. Sci. USA 93, 2155–2158 (1996).
Google Scholar 
Visscher, H., Sephton, M. A. & Looy, C. V. Fungal virulence at the time of the end-Permian biosphere crisis? Geology 39, 883–886 (2011).
Google Scholar 
Looy, C. V., Twitchett, R. J., Dilcher, D. L., Van Konijnenburg-Van Cittert, J. H. A. & Visscher, H. Life in the end-Permian dead zone. Proc. Natl Acad. Sci. USA 98, 7879–7883 (2001).
Google Scholar 
Bercovici, A. & Vajda, V. Terrestrial Permian–Triassic boundary sections in south China. Glob. Planet. Change 143, 31–33 (2016).
Google Scholar 
Hochuli, P. A. Interpretation of “fungal spikes” in Permian–Triassic boundary sections. Glob. Planet. Change 144, 48–50 (2016).
Google Scholar 
Angielczyk, K. D., Roopnarine, P. D. & Wang, S. C. Modeling the role of primary productivity disruption in end-Permian extinctions, Karoo basin, South Africa. New Mex. Mus. Nat. Hist. Sci. Bull. 30, 16–23 (2005).
Google Scholar 
Labandeira, C. C. & Sepkoski, J. J. Insect diversity in the fossil record. Science 261, 310–315 (1993).
Google Scholar 
Shcherbakov, D. E. On Permian and Triassic insect faunas in relation to biogeography and the Permian-Triassic crisis. Paleontol. J. 42, 15–31 (2008).
Google Scholar 
Condamine, F. L., Clapham, M. E. & Kergoat, G. J. Global patterns of insect diversification: towards a reconciliation of fossil and molecular evidence? Sci. Rep. 6, 19208 (2016).
Google Scholar 
Smith, R. M. H. & Ward, P. D. Pattern of vertebrate extinctions across an event bed at the Permian–Triassic boundary in the Karoo basin of South Africa. Geology 29, 1147 (2001).
Google Scholar 
Benton, M. J., Tverdokhlebov, V. P. & Surkov, M. V. Ecosystem remodelling among vertebrates at the Permian–Triassic boundary in Russia. Nature 432, 97–100 (2004).
Google Scholar 
Viglietti, P. A. et al. Evidence from South Africa for a protracted end-Permian extinction on land. Proc. Natl Acad. Sci. USA 118, e2017045118 (2021).
Google Scholar 
Sennikov, A. G. & Golubev, V. K. Vyazniki biotic assemblage of the terminal Permian. Paleontol. J. 40, S475–S481 (2006).
Google Scholar 
Sennikov, A. G. & Golubev, V. K. On the faunal verification of the Permo–Triassic boundary in continental deposits of eastern Europe: 1. Gorokhovets–Zhukov ravine. Paleontol. J. 46, 313–323 (2012).
Google Scholar 
Zhu, Z. et al. Altered fluvial patterns in north China indicate rapid climate change linked to the Permian–Triassic mass extinction. Sci. Rep. 9, 16818 (2019).
Google Scholar 
Shen, S. Z. et al. Calibrating the end-Permian mass extinction. Science 334, 1367–1372 (2011).
Google Scholar 
Twitchett, R. J., Looy, C. V., Morante, R., Visscher, H. & Wignall, P. B. Rapid and synchronous collapse of marine and terrestrial ecosystems during the end-Permian biotic crisis. Geology 29, 351–354 (2001).
Google Scholar 
Biswas, R. K., Kaiho, K., Saito, R., Tian, L. & Shi, Z. Terrestrial ecosystem collapse and soil erosion before the end-Permian marine extinction: organic geochemical evidence from marine and non-marine records. Glob. Planet. Change 195, 103327 (2020).
Google Scholar 
Aftabuzzaman, M. D. et al. End-Permian terrestrial disturbance followed by the complete plant devastation, and the vegetation proto-recovery in the earliest-Triassic recorded in coastal sea sediments. Glob. Planet. Change 205, 103621 (2021).
Google Scholar 
Gastaldo, R. A., Neveling, J., Geissman, J. W., Kamo, S. L. & Looy, C. V. A tale of two Tweefonteins: what physical correlation, geochronology, magnetic polarity stratigraphy, and palynology reveal about the end-Permian terrestrial extinction paradigm in South Africa. GSA Bull. https://doi.org/10.1130/b35830.1 (2021).Yan, Z. et al. Frequent and intense fires in the final coals of the Paleozoic indicate elevated atmospheric oxygen levels at the onset of the end-Permian mass extinction event. Int. J.Coal Geol. 207, 75–83 (2019).
Google Scholar 
DiMichele, W. A., Bashforth, A. R., Falcon-Lang, H. J. & Lucas, S. G. Uplands, lowlands, and climate: taphonomic megabiases and the apparent rise of a xeromorphic, drought-tolerant flora during the Pennsylvanian–Permian transition. Palaeogeogr. Palaeoclimatol. Palaeoecol. 559, 109965 (2020).
Google Scholar 
Smith, R. M. H. & Botha-Brink, J. Anatomy of a mass extinction: sedimentological and taphonomic evidence for drought-induced die-offs at the Permo-Triassic boundary in the main Karoo basin, South Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol. 396, 99–118 (2014).
Google Scholar 
Xiong, C. & Wang, Q. Permian–Triassic land-plant diversity in south China: was there a mass extinction at the Permian/Triassic boundary? Paleobiology 37, 157–167 (2011).
Google Scholar 
Yu, J. et al. Terrestrial events across the Permian–Triassic boundary along the Yunnan–Guizhou border, SW China. Glob. Planet. Change 55, 193–208 (2007).
Google Scholar 
Becker, L., Poreda, R. J., Hunt, A. G., Bunch, T. E. & Rampino, M. Impact event at the Permian–Triassic boundary: evidence from extraterrestrial noble gases in fullerenes. Science 291, 1530–1533 (2001).
Google Scholar 
Basu, A. R., Petaev, M. I., Poreda, R. J., Jacobsen, S. B. & Becker, L. Chondritic meteorite fragments associated with the Permian–Triassic boundary in Antarctica. Science 302, 1388–1392 (2003).
Google Scholar 
Isozaki, Y. Permo–Triassic boundary superanoxia and stratified superocean: records from lost deep sea. Science 276, 235–238 (1997).
Google Scholar 
French, B. M. & Koeberl, C. The convincing identification of terrestrial meteorite impact structures: what works, what doesn’t, and why. Earth Sci. Rev. 98, 123–170 (2010).
Google Scholar 
Saunders, A. D., England, R. W., Reichow, M. K. & White, R. V. A mantle plume origin for the Siberian traps: uplift and extension in the west Siberian basin, Russia. Lithos 79, 407–424 (2005).
Google Scholar 
Reichow, M. K. et al. Petrogenesis and timing of mafic magmatism, south Taimyr, Arctic Siberia: a northerly continuation of the Siberian Traps? Lithos 248–251, 382–401 (2016).
Google Scholar 
Naldrett, A. J., Lightfoot, P. C., Fedorenko, V., Doherty, W. & Gorbachev, N. S. Geology and geochemistry of intrusions and flood basalts of the Noril’sk region, USSR, with implications for the origin of the Ni-Cu ores. Econ. Geol. 87, 975–1004 (1992).
Google Scholar 
Hawkesworth, C. J. et al. Magma differentiation and mineralisation in the Siberian continental flood basalts. Lithos 34, 61–88 (1995).
Google Scholar 
Fedorenko, V. A. et al. Petrogenesis of the flood-basalt sequence at Noril’sk, north central Siberia. Int. Geol. Rev. 38, 99–135 (1996).
Google Scholar 
Arndt, N., Chauvel, C., Czamanske, G. & Fedorenko, V. Two mantle sources, two plumbing systems: tholeiitic and alkaline magmatism of the Maymecha River basin, Siberian flood volcanic province. Contribut. Mineral. Petrol. 133, 297–313 (1998).
Google Scholar 
Sobolev, S. V. et al. Linking mantle plumes, large igneous provinces and environmental catastrophes. Nature 477, 312–316 (2011).
Google Scholar 
Sobolev, A. V., Arndt, N. T., Krivolutskaya, N. A., Kuzmin, D. V. & Sobolev, S. V. in Volcanism and Global Environmental Change (eds Schmidt, A. Fristad, K. & Elkins-Tanton, L.) 147–163 (Cambridge Univ. Press, 2015).Black, B. A., Elkins-Tanton, L. T., Rowe, M. C. & Peate, I. U. Magnitude and consequences of volatile release from the Siberian Traps. Earth Planet. Sci. Lett. 317–318, 363–373 (2012).
Google Scholar 
Broadley, M. W., Barry, P. H., Ballentine, C. J., Taylor, L. A. & Burgess, R. End-Permian extinction amplified by plume-induced release of recycled lithospheric volatiles. Nat. Geosci. 11, 682–687 (2018).
Google Scholar 
Elkins-Tanton, L. T. et al. Field evidence for coal combustion links the 252 Ma Siberian Traps with global carbon disruption. Geology 48, 986–991 (2020).
Google Scholar 
Grasby, S. E. & Beauchamp, B. Latest Permian to Early Triassic basin-to-shelf anoxia in the Sverdrup basin, Arctic Canada. Chem. Geol. 264, 232–246 (2009).
Google Scholar 
Grasby, S. E., Sanei, H. & Beauchamp, B. Catastrophic dispersion of coal fly ash into oceans during the latest Permian extinction. Nat. Geosci. 4, 104–107 (2011).
Google Scholar 
Sanei, H., Grasby, S. E. & Beauchamp, B. Latest Permian mercury anomalies. Geology 40, 63–66 (2012).
Google Scholar 
Reichow, M. K., Saunders, A. D., White, R. V., Al’Mukhamedov, A. I. & Medvedev, A. Y. Geochemistry and petrogenesis of basalts from the west Siberian basin: an extension of the Permo–Triassic Siberian Traps, Russia. Lithos 79, 425–452 (2005).
Google Scholar 
Jerram, D. A., Svensen, H. H., Planke, S., Polozov, A. G. & Torsvik, T. H. The onset of flood volcanism in the north-western part of the Siberian Traps: explosive volcanism versus effusive lava flows. Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 38–50 (2016).
Google Scholar 
Svensen, H. et al. Siberian gas venting and the end-Permian environmental crisis. Earth Planet. Sci.Lett. 277, 490–500 (2009).
Google Scholar 
Svensen, H. H. et al. Sills and gas generation in the Siberian Traps. Phil. Trans. R. Soc. A 376, 20170080 (2018).
Google Scholar 
Davydov, V. I. Tunguska сoals, Siberian sills and the Permian–Triassic extinction. Earth Sci. Rev. 212, 103438 (2021).
Google Scholar 
Callegaro, S. et al. Geochemistry of deep Tunguska basin sills, Siberian Traps: correlations and potential implications for the end-Permian environmental crisis. Contribut. Mineral. Petrol. 176, 49 (2021).
Google Scholar 
Wooden, J. L. et al. Isotopic and trace-element constraints on mantle and crustal contributions to Siberian continental flood basalts, Noril’sk area, Siberia. Geochim. Cosmochim. Acta 57, 3677–3704 (1993).
Google Scholar 
Arndt, N. T., Czmanske, G. K., Walker, R. J., Chauvel, C. & Fedorenko, V. A. Geochemistry and origin of the intrusive hosts of the Noril’sk-Talnakh Cu-Ni-PGE sulfide deposits. Eco. Geol. 98, 495–515 (2003).
Google Scholar 
Pang, K. N. et al. A petrologic, geochemical and Sr-Nd isotopic study on contact metamorphism and degassing of Devonian evaporites in the Norilsk aureoles, Siberia. Contrib. Mineral. Petrol. 165, 683–704 (2013).
Google Scholar 
Yao, Z. S. & Mungall, J. E. Linking the Siberian flood basalts and giant Ni-Cu-PGE sulfide deposits at Norilsk. J. Geophys. Res. Solid Earth 126, e2020JB020823 (2021).
Google Scholar 
Sibik, S., Edmonds, M., Maclennan, J. & Svensen, H. Magmas erupted during the main pulse of Siberian Traps volcanism were volatile-poor. J. Petrol. 56, 2089–2116 (2015).
Google Scholar 
Retallack, G. J. & Jahren, A. H. Methane release from igneous intrusion of coal during late Permian extinction events. J. Geol. 116, 1–20 (2008).
Google Scholar 
Iacono-Marziano, G. et al. Gas emissions due to magma-sediment interactions during flood magmatism at the Siberian Traps: gas dispersion and environmental consequences. Earth Planet. Sci. Lett. 357–358, 308–318 (2012).
Google Scholar 
Fristad, K. E., Svensen, H. H., Polozov, A. & Planke, S. Formation and evolution of the end-Permian Oktyabrsk volcanic crater in the Tunguska basin, eastern Siberia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 76–87 (2017).
Google Scholar 
Polozov, A. G. et al. The basalt pipes of the Tunguska basin (Siberia, Russia): high temperature processes and volatile degassing into the end-Permian atmosphere. Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 51–64 (2016).
Google Scholar 
Elkins-Tanton, L. T. et al. The last lavas erupted during the main phase of the Siberian flood volcanic province: results from experimental petrology. Contribut. Mineral. Petrol. 153, 191–209 (2007).
Google Scholar 
Schmidt, A. et al. Selective environmental stress from sulphur emitted by continental flood basalt eruptions. Nat. Geosci. 9, 77–82 (2016).
Google Scholar 
Black, B. A. et al. Systemic swings in end-Permian climate from Siberian Traps carbon and sulfur outgassing. Nat. Geosci. 11, 949–954 (2018).
Google Scholar 
Schobben, M., Joachimski, M. M., Korn, D., Leda, L. & Korte, C. Palaeotethys seawater temperature rise and an intensified hydrological cycle following the end-Permian mass extinction. Gondwana Res. 26, 675–683 (2014).
Google Scholar 
Chen, J. et al. Abrupt warming in the latest Permian detected using high-resolution in situ oxygen isotopes of conodont apatite from Abadeh, central Iran. Palaeogeogr. Palaeoclimatol. Palaeoecol. 560, 109973 (2020).
Google Scholar 
Joachimski, M. M., Alekseev, A. S., Grigoryan, A. & Gatovsky, Y. A. Siberian trap volcanism, global warming and the Permian–Triassic mass extinction: new insights from Armenian Permian–Triassic sections. Bull. Geol. Soc. Am. 132, 427–443 (2020).
Google Scholar 
Sun, Y. et al. Lethally hot temperatures during the early Triassic greenhouse. Science 338, 366–370 (2012).
Google Scholar 
Joachimski, M. M. et al. Climate warming in the latest Permian and the Permian–Triassic mass extinction. Geology 40, 195–198 (2012).
Google Scholar 
Jiang, H., Joachimski, M. M., Wignall, P. B., Zhang, M. & Lai, X. A delayed end-Permian extinction in deep-water locations and its relationship to temperature trends (Bianyang, Guizhou province, south China). Palaeogeogr. Palaeoclimatol. Palaeoecol. 440, 690–695 (2015).
Google Scholar 
Chen, J. et al. High-resolution SIMS oxygen isotope analysis on conodont apatite from south China and implications for the end-Permian mass extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 448, 26–38 (2016).
Google Scholar 
Shen, S. et al. Permian integrative stratigraphy and timescale of China. Sci. China Earth Sci. 62, 154–188 (2019).
Google Scholar 
Pörtner, H. O. Oxygen- And capacity-limitation of thermal tolerance: a matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).
Google Scholar 
Pörtner, H. O. Integrating climate-related stressor effects on marine organisms: unifying principles linking molecule to ecosystem-level changes. Mar. Ecol. Progr. Ser. 470, 273–290 (2012).
Google Scholar 
Bijma, J., Pörtner, H. O., Yesson, C. & Rogers, A. D. Climate change and the oceans — what does the future hold? Mar. Pollut. Bull. 74, 495–505 (2013).
Google Scholar 
Song, H. et al. Flat latitudinal diversity gradient caused by the Permian–Triassic mass extinction. Proc. Natl Acad. Sci. USA 117, 17578–17583 (2020).
Google Scholar 
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).
Google Scholar 
Benton, M. J. Hyperthermal-driven mass extinctions: killing models during the Permian–Triassic mass extinction. Phil. Trans. R. Soc. A 376, 20170076 (2018).
Google Scholar 
Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant Cell Envir. 38, 1699–1712 (2015).
Google Scholar 
Cai, Y. F., Zhang, H., Feng, Z. & Shen, S. Z. Intensive wildfire associated with volcanism promoted the vegetation changeover in southwest china during the Permian−Triassic transition. Front. Earth Sci. 9, 615841 (2021).
Google Scholar 
Grasby, S. E. et al. Progressive environmental deterioration in northwestern Pangea leading to the latest Permian extinction. Bull. Geol. Soc. Am. 127, 1331–1347 (2015).
Google Scholar 
Beauchamp, B. & Grasby, S. E. Permian lysocline shoaling and ocean acidification along NW Pangea led to carbonate eradication and chert expansion. Palaeogeogr. Palaeoclimatol. Palaeoecol. 350–352, 73–90 (2012).
Google Scholar 
Wignall, P. B. & Twitchett, R. J. Oceanic anoxia and the end Permian mass extinction. Science 272, 1155–1158 (1996).
Google Scholar 
Wignall, P. B. et al. Ultra-shallow-marine anoxia in an Early Triassic shallow-marine clastic ramp (Spitsbergen) and the suppression of benthic radiation. Geol. Mag. 153, 316–331 (2016).
Google Scholar 
Proemse, B. C., Grasby, S. E., Wieser, M. E., Mayer, B. & Beauchamp, B. Molybdenum isotopic evidence for oxic marine conditions during the latest Permian extinction. Geology 41, 967–970 (2013).
Google Scholar 
Grasby, S. E. et al. Transient Permian–Triassic euxinia in the southern Panthalassa deep ocean. Geology 49, 889–893 (2021).
Google Scholar 
Wignall, P. B. et al. An 80 million year oceanic redox history from Permian to Jurassic pelagic sediments of the Mino-Tamba terrane, SW Japan, and the origin of four mass extinctions. Glob. Planet. Change 71, 109–123 (2010).
Google Scholar 
Song, H. et al. Geochemical evidence from bio-apatite for multiple oceanic anoxic events during Permian–Triassic transition and the link with end-Permian extinction and recovery. Earth Planet. Sci. Lett. 353–354, 12–21 (2012).
Google Scholar 
Grasby, S. E., Beauchamp, B., Embry, A. & Sanei, H. Recurrent Early Triassic ocean anoxia. Geology 41, 175–178 (2013).
Google Scholar 
Takahashi, S., Yamasaki, S. I., Ogawa, K., Kaiho, K. & Tsuchiya, N. Redox conditions in the end-Early Triassic Panthalassa. Palaeogeogr. Palaeoclimato. Palaeoecol. 432, 15–28 (2015).
Google Scholar 
Brennecka, G. A., Herrmann, A. D., Algeo, T. J. & Anbar, A. D. Rapid expansion of oceanic anoxia immediately before the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 108, 17631–17634 (2011).
Google Scholar 
Takahashi, S. et al. Bioessential element-depleted ocean following the euxinic maximum of the end-Permian mass extinction. Earth Planet. Sci. Lett 393, 94–104 (2014).
Google Scholar 
Newton, R. J., Pevitt, E. L., Wignall, P. B. & Bottrell, S. H. Large shifts in the isotopic composition of seawater sulphate across the Permo–Triassic boundary in northern Italy. Earth Planet. Sci. Lett. 218, 331–345 (2004).
Google Scholar 
Grice, K. et al. Photic zone euxinia during the Permian–Triassic superanoxic event. Science 307, 706–709 (2005).
Google Scholar 
Ingall, E. & Jahnke, R. Evidence for enhanced phosphorus regeneration from marine sediments overlain by oxygen depleted waters. Geochim. Cosmochim. Acta 58, 2571–2575 (1994).
Google Scholar 
Sun, Y. D. et al. Ammonium ocean following the end-Permian mass extinction. Earth Planet. Sci. Lett. 518, 211–222 (2019).
Google Scholar 
Grasby, S. E., Beauchamp, B. & Knies, J. Early Triassic productivity crises delayed recovery from world’s worst mass extinction. Geology 44, 779–782 (2016).
Google Scholar 
Schoepfer, S. D., Henderson, C. M., Garrison, G. H. & Ward, P. D. Cessation of a productive coastal upwelling system in the Panthalassic Ocean at the Permian–Triassic boundary. Palaeogeogr. Palaeoclimatol. Palaeoecol. 313–314, 181–188 (2012).
Google Scholar 
Schobben, M. et al. Flourishing ocean drives the end-Permian marine mass extinction. Proc. Natl Acad. Sci. USA 112, 10298–10303 (2015).
Google Scholar 
Grasby, S. E. et al. Global warming leads to Early Triassic nutrient stress across northern Pangea. Bull. Geol. Soc. Am. 132, 943–954 (2020).
Google Scholar 
Song, H. et al. Conodont calcium isotopic evidence for multiple shelf acidification events during the Early Triassic. Chem. Geol. 562, 120038 (2021).
Google Scholar 
Jurikova, H. et al. Permian–Triassic mass extinction pulses driven by major marine carbon cycle perturbations. Nat. Geosci. 13, 745–750 (2020).
Google Scholar 
Garbelli, C., Angiolini, L. & Shen, S. Z. Biomineralization and global change: a new perspective for understanding the end-Permian extinction. Geology 45, 19–22 (2017).
Google Scholar 
Clarkson, M. O. et al. Ocean acidification and the Permo–Triassic mass extinction. Science 348, 229–232 (2015).
Google Scholar 
Zhang, S. et al. Investigating controls on boron isotope ratios in shallow marine carbonates. Earth Planet. Sci. Lett. 458, 380–393 (2017).
Google Scholar 
Hinojosa, J. L. et al. Evidence for end-Permian ocean acidification from calcium isotopes in biogenic apatite. Geology 40, 743–746 (2012).
Google Scholar 
Komar, N. & Zeebe, R. E. Calcium and calcium isotope changes during carbon cycle perturbations at the end-Permian. Paleoceanography 31, 115–130 (2016).
Google Scholar 
Silva-Tamayo, J. C. et al. Global perturbation of the marine calcium cycle during the Permian–Triassic transition. Bull. Geol. Soc. Am. 130, 1323–1338 (2018).
Google Scholar 
Payne, J. L. et al. Calcium isotope constraints on the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 107, 8543–8548 (2010).
Google Scholar 
Lau, K. V. et al. The influence of seawater carbonate chemistry, mineralogy, and diagenesis on calcium isotope variations in Lower–Middle Triassic carbonate rocks. Chem. Geol. 471, 13–37 (2017).
Google Scholar 
Wang, J. et al. Coupled δ44/40Ca, δ88/86Sr, and 87Sr/86Sr geochemistry across the end-Permian mass extinction event. Geochim. Cosmochim. Acta 262, 143–165 (2019).
Google Scholar 
Kiessling, W. & Simpson, C. On the potential for ocean acidification to be a general cause of ancient reef crises. Glob. Change Biol. 17, 56–67 (2011).
Google Scholar 
Chen, Z. Q., Kaiho, K. & George, A. D. Early Triassic recovery of the brachiopod faunas from the end-Permian mass extinction: a global review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 224, 270–290 (2005).
Google Scholar 
Dai, X., Korn, D. & Song, H. Morphological selectivity of the Permian–Triassic ammonoid mass extinction. Geology 49, 1112–1116 (2021).
Google Scholar 
Fijałkowska-Mader, A. in Morphogenesis, Environmental Stress and Reverse Evolution (eds Guex, J., Torday, J. S. & Miller, W. B. Jr) 23–35 (Springer, 2020).Beerling, D. J., Harfoot, M., Lomax, B. & Pyle, J. A. The stability of the stratospheric ozone layer during the end-Permian eruption of the Siberian Traps. Phil. Trans. R. Soc. A 365, 1843–1866 (2007).
Google Scholar 
Svensen, H., Schmidbauer, N., Roscher, M., Stordal, F. & Planke, S. Contact metamorphism, halocarbons, and environmental crises of the past. Environ. Chem. 6, 466–471 (2009).
Google Scholar 
Black, B. A., Lamarque, J. F., Shields, C. A., Elkins-Tanton, L. T. & Kiehl, J. T. Acid rain and ozone depletion from pulsed siberian traps magmatism. Geology 42, 67–70 (2014).
Google Scholar 
Likens, G. E. & Butler, T. J. in Encyclopedia of the Anthropocene (eds Dellasala, D. A. & Goldstein, M. I.) 23–31 (Elsevier, 2018).Sephton, M. A., Jiao, D., Engel, M. H., Looy, C. V. & Visscher, H. Terrestrial acidification during the end-Permian biosphere crisis? Geology 43, 159–162 (2015).
Google Scholar 
Sheldon, N. D. Abrupt chemical weathering increase across the Permian–Triassic boundary. Palaeogeogr. Palaeoclimatol. Palaeoecol. 231, 315–321 (2006).
Google Scholar 
Maruoka, T., Koeberl, C., Hancox, P. J. & Reimold, W. U. Sulfur geochemistry across a terrestrial Permian–Triassic boundary section in the Karoo basin, South Africa. Earth Planet. Sci. Lett. 206, 101–117 (2003).
Google Scholar 
Grasby, S. E., Them, T. R., Chen, Z., Yin, R. & Ardakani, O. H. Mercury as a proxy for volcanic emissions in the geologic record. Earth Sci. Rev. 196, 102880 (2019).
Google Scholar 
Dal Corso, J. et al. Permo–Triassic boundary carbon and mercury cycling linked to terrestrial ecosystem collapse. Nat. Commun. 11, 2962 (2020).
Google Scholar 
Rugenstein, M. A. A., Sedláček, J. & Knutti, R. Nonlinearities in patterns of long-term ocean warming. Geophys. Res. Lett. 43, 3380–3388 (2016).
Google Scholar 
Yang, H. & Zhu, J. Equilibrium thermal response timescale of global oceans. Geophys. Res. Lett. 38, L14711 (2011).
Google Scholar 
Song, H. et al. Anoxia/high temperature double whammy during the Permian–Triassic marine crisis and its aftermath. Sci. Rep. 4, 4132 (2014).
Google Scholar 
Alroy, J. Accurate and precise estimates of origination and extinction rates. Paleobiology 40, 374–397 (2014).
Google Scholar 
Scotese, C. R. Atlas of Permo-Triassic paleogeographic maps (Mollweide projection), maps 43–52, vol. 3/4 of the PALEOMAP Atlas. ResearchGate https://doi.org/10.13140/2.1.2609.9209 (2014).Zhang, F. et al. Two distinct episodes of marine anoxia during the Permian–Triassic crisis evidenced by uranium isotopes in marine dolostones. Geochim. Cosmochim. Acta 287, 165–179 (2020).
Google Scholar 
Wu, Y. et al. Six-fold increase of atmospheric pCO2 during the Permian–Triassic mass extinction. Nat. Commun. 12, 2137 (2021).
Google Scholar 
Grossman, E. L. & Joachimski, M. M. Oxygen isotope stratigraphy. Geol. Time Scale 1, 279–307 (2020).
Google Scholar 
Trotter, J. A., Williams, I. S., Barnes, C. R., Männik, P. & Simpson, A. New conodont δ18O records of Silurian climate change: implications for environmental and biological events. Palaeogeogr. Palaeoclimatol. Palaeoecol. 443, 34–48 (2016).
Google Scholar 
Kaiho, K. et al. End-Permian catastrophe by a bolide impact: evidence of a gigantic release of sulfur from the mantle. Geology 29, 815–818 (2001).
Google Scholar 
Chu, D. et al. Lilliput effect in freshwater ostracods during the Permian–Triassic extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 435, 38–52 (2015).
Google Scholar 
Shen, J. et al. Mercury evidence of intense volcanic effects on land during the Permian–Triassic transition. Geology 47, 1117–1121 (2019).
Google Scholar 
Cao, C., Wang, W., Liu, L., Shen, S. & Summons, R. E. Two episodes of 13C-depletion in organic carbon in the latest Permian: evidence from the terrestrial sequences in northern Xinjiang, China. Earth Planet. Sci. Lett. 270, 251–257 (2008).
Google Scholar 
Shen, J. et al. Evidence for a prolonged Permian–Triassic extinction interval from global marine mercury records. Nat. Commun. 10, 1563 (2019).
Google Scholar 
Wang, X. et al. Mercury anomalies across the end Permian mass extinction in south China from shallow and deep water depositional environments. Earth Planet Sci.Lett. 496, 159–167 (2018).
Google Scholar 
Holser, W. T. et al. A unique geochemical record at the Permian/Triassic boundary. Nature 337, 39–44 (1989).
Google Scholar 
Korte, C. & Kozur, H. W. Carbon-isotope stratigraphy across the Permian–Triassic boundary: a review. J. Asian Earth Sci. 39, 215–235 (2010).
Google Scholar 
Luo, G. et al. Stepwise and large-magnitude negative shift in δ13Ccarb preceded the main marine mass extinction of the Permian–Triassic crisis interval. Palaeogeogr. Palaeoclimatol. Palaeoecol. 299, 70–82 (2011).
Google Scholar 
Shen, S. Z. et al. High-resolution δ13Ccarb chemostratigraphy from latest Guadalupian through earliest Triassic in south China and Iran. Earth Planet. Sci. Lett. 375, 156–165 (2013).
Google Scholar 
Hermann, E. et al. A close-up view of the Permian-Triassic boundary based on expanded organic carbon isotope records from Norway (Trøndelag and Finnmark platform). Glob. Planet. Change 74, 156–167 (2010).
Google Scholar 
Luo, G. et al. Vertical δ13Corg gradients record changes in planktonic microbial community composition during the end-Permian mass extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 396, 119–131 (2014).
Google Scholar 
Schneebeli-Hermann, E. et al. Evidence for atmospheric carbon injection during the end-Permian extinction. Geology 41, 579–582 (2013).
Google Scholar 
Williams, M. L., Jones, B. G. & Carr, P. F. The interplay between massive volcanism and the local environment: geochemistry of the Late Permian mass extinction across the Sydney basin, Australia. Gondwana Res. 51, 149–169 (2017).
Google Scholar 
Wu, Y. et al. Organic carbon isotopes in terrestrial Permian–Triassic boundary sections of North China: implications for global carbon cycle perturbations. Bull. Geol. Soc. Am. 132, 1106–1118 (2020).
Google Scholar 
Grasby, S. E., Liu, X., Yin, R., Ernst, R. E. & Chen, Z. Toxic mercury pulses into late Permian terrestrial and marine environments. Geology 48, 830–833 (2020).
Google Scholar  More

Mercader, J., Panger, M. & Boesch, C. Excavation of a Chimpanzee stone tool site in the African rainforest. Science 296, 1452–1455 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Mercader, J. et al. 4,300-year-old chimpanzee sites and the origins of percussive stone technology. PNAS 104, 3043–3048 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haslam, M. et al. Primate archaeology. Nature 460, 339–344 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Plummer, T. W. & Finestone, E. Rethinking Human Evolution (ed. Schwartz, J.). 267–296. (MIT Press, 2018).Toth, N. & Schick, K. An overview of the cognitive implications of the Oldowan industrial complex. Azania Archaeol. Res. Afr. 53, 3–39 (2018).Plummer, T. Flaked stones and old bones: Biological and cultural evolution at the dawn of technology. Yearb. Phys. Anthropol. 47, 118–164 (2004).
Google Scholar 
Ferraro, J. V. et al. Earliest archaeological evidence of persistent hominin carnivory. PLoS ONE 8, e62174 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Braun, D. R. et al. Early hominin diet included diverse terrestrial and aquatic animals 1.95 Ma in East Turkana, Kenya. Proc. Natl. Acad. Sci. 107, 10002–10007 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sahnouni, M. et al. 1.9-million- and 2.4-million-year-old artefacts and stone tool-cutmarked bones from Ain Boucherit, Algeria. Science 362, 1297–1301 (2018).Stahl, A. B. Hominid dietary selection before fire. Curr. Anthropol. 25, 151–168 (1984).
Google Scholar 
Laden, G. & Wrangham, R. The rise of hominids as an adaptive shift in fallback foods: Plant underground storage organs (USOs) and Australopith origins. J. Hum. Evol. 49, 482–498 (2005).PubMed 

Google Scholar 
Peters, C. & Vogel, J. Africa’s wild C4 plant foods and possible early hominid diets. J. Hum. Evol. 48, 219–236 (2005).PubMed 

Google Scholar 
Copeland, S. R. Vegetation and plant food reconstruction of lowermost bed II, Olduvai Gorge, using modern analogs. J. Hum. Evol. 53, 146–175 (2007).PubMed 

Google Scholar 
Domínguez Rodrigo, M. Interdisciplinary Approaches to the Oldowan (eds. Hovers, E. & Braun, D.R.). 129–147. (Springer, 2009).Hovers, E. Origins of Human Innovation and Creativity (ed Elias, S.). 51–68. (Elsevier, 2012).Domínguez Rodrigo, M. Meat eating by early hominids at the FLK 22 Zinjanthropus site, Olduvai Gorge, Tanzania: An experimental approach using cut mark data. J. Hum. Evol. 33, 669–690 (1997).PubMed 

Google Scholar 
Pobiner, B. L., Rogers, M. J., Monahan, C. M. & Harris, J. W. New evidence for hominin carcass processing strategies at 1.5 Ma, Koobi Fora, Kenya. J. Hum. Evolut. 55, 103–130 (2018).
Google Scholar 
Marreiros, J. et al. Rethinking use-wear analysis and experimentation as applied to the study of past hominin tool use. J. Paleolithic Archaeol. 3, 475–502 (2020).
Google Scholar 
de la Torre, I., Benito-Calvo, A., Arroyo, A., Zupancich, A. & Proffitt, T. Experimental protocols for the study of battered stone anvils from Olduvai Gorge (Tanzania). J. Archaeol. Sci. 40, 313–332. https://doi.org/10.1016/j.jas.2012.08.007 (2013).Article 

Google Scholar 
Caruana, M. V., Carvalho, S., Braun, D. R., Presnyakova, D. & Haslam, M. Quantifying traces of tool use: A novel morphometric analysis of damage patterns on percussive tools. PLoS ONE 9, e113856 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Benito-Calvo, A., Carvalho, S., Arroyo, A., Matsuzawa, T. & de la Torre, I. First GIS analysis of modern stone tools used by wild chimpanzees (Pan troglodytes verus) in Bossou, Guinea, West Africa (PLOS One, 2015). https://doi.org/10.1371/journal.pone.0121613.Book 

Google Scholar 
Sánchez-Yustos, P. et al. Production and use of percussive stone tools in the Early Stone Age: Experimental approach to the lithic record of Olduvai Gorge, Tanzania. J. Archaeol. Sci. Rep. 2, 367–383 (2015).
Google Scholar 
Arroyo, A., Hirata, S., Matsuzawa, T. & De La Torre, I. Nut cracking tools used by captive chimpanzees (Pan troglodytes) and their comparison with Early Stone Age percussive artefacts from Olduvai Gorge. PLoS ONE 11, e0166788 (2016).PubMed 
PubMed Central 

Google Scholar 
Arroyo, A. & de la Torre, I. Assessing the function of pounding tools in the early stone age: A microscopic approach to the analysis of percussive artefacts from beds I and II, Olduvai Gorge (Tanzania). J. Archaeol. Sci. 74, 23–34 (2016).
Google Scholar 
Proffitt, T. et al. Analysis of wild macaque stone tools used to crack oil palm nuts 5, 1–16 (2018).
Google Scholar 
Titton, S. et al. Active percussion tools from the Oldowan site of Barranco León (Orce, Andalusia, Spain): The fundamental role of pounding activities in hominin lifeways. J. Archaeol. Sci. 96, 131–147 (2018).
Google Scholar 
Lemorini, C. et al. Old stones’ song: Use-wear experiments and analysis of the Oldowanquartz and quartzite assemblage from Kanjera South (Kenya). J. Hum. Evol. 72, 10–25 (2014).PubMed 

Google Scholar 
Keeley, L. H. & Toth, N. Microwear polishes on early stone tools from Koobi Fora, Kenya. Nature 293, 464–465 (1981).ADS 

Google Scholar 
Longo, L. et al. A multi-dimensional approach to investigate use-related biogenic residues on palaeolithic ground stone tools. Environ. Archaeol. 21, 1–29 (2021).
Google Scholar 
Langejans, G. H. J. Remains of the day-preservation of organic micro-residues on stone tools. J. Archaeol. Sci. 37, 971–985 (2010).
Google Scholar 
Langejans, G. H. J. Micro-residue analysis on early stone age tools from Sterkfontein, South Africa: A methodological enquiry. S. Afr. Archaeol. Bull. 67, 200–213 (2012).
Google Scholar 
Pedergnana, A. & Ollé, A. Building an experimental comparative reference collection for lithic micro-residue analysis based on a multi-analytical approach. J. Archaeol. Method Theory 25, 117–154 (2018).
Google Scholar 
Crowther, A., Haslam, M., Oakden, N., Walde, D. & Mercader, J. Documenting contamination in ancient starch laboratories. J. Archaeol. Sci. 49, 90–104 (2014).CAS 

Google Scholar 
Pedergnana, A., Asryan, L., Fernández-Marchena, J. L. & Ollé, A. Modern contaminants affecting microscopic residue analysis on stone tools: A word of caution. Micron 86, 1–21. https://doi.org/10.1016/j.micron.2016.04.003 (2016).CAS 
Article 
PubMed 

Google Scholar 
Mercader, J. et al. Starch contamination landscapes in field archaeology: Olduvai Gorge, Tanzania. Boreas 46, 918–934. https://doi.org/10.1111/bor.12241.ISSN0300-9483 (2017).Article 

Google Scholar 
Barton, H., Torrence, R. & Fullagar, R. Clues to stone tool function re-examined: Comparing starch grain frequencies on used and unused obsidian artefacts. J. Archaeol. Sci. 25, 1231–1238 (1998).
Google Scholar 
Atchison, J. & Fullagar, R. A Closer Look: Recent Australian Studies of Stone Tools Sydney University Archaeological Methods Series (ed Fullagar, R.). Chap. 8. 110–125. (1998).Hardy, B. L. & Garufi, G. T. Identification of woodworking on stone tools through residue and use-wear analyses: Experimental results. J. Archaeol. Sci. 25, 177–184 (1998).
Google Scholar 
Kealhofer, L., Torrence, R. & Fullagar, R. Integrating phytoliths within use-wear/residue studies of stone tools. J. Archaeol. Sci. 26, 527–546 (1999).
Google Scholar 
Fullagar, R. et al. Evidence for Pleistocene seed grinding at Lake Mungo, south-eastern Australia. Archaeol. Ocean. 50, 3–19 (2015).
Google Scholar 
Ma, Z., Perry, L., Li, Q. & Yang, X. Morphological changes in starch grains after dehusking and grinding with stone tools. Sci. Rep. 9, 2355 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Briuer, F. L. New clues to stone tool function: Plant and animal residues. Am. Antiq. 41, 478–484 (1976).
Google Scholar 
Mora, R. & de la Torre, I. Percussion tools in Olduvai Beds I and II (Tanzania): Implication for early human activities. J. Anthropol. Archaeol. 24, 179–192 (2005).
Google Scholar 
Diez-Martín, F., Sánchez, P., Domínguez-Rodrigo, M., Mabulla, A. & Barba, R. Were Olduvai Hominins making butchering tools or battering tools? Analysis of a recently excavated lithic assemblage from BK (Bed II, Olduvai Gorge, Tanzania). J. Anthropol. Archaeol. 28, 274–289 (2009).
Google Scholar 
McHenry, L. J. & de la Torre, I. Hominin raw material procurement in the Oldowan-Acheulean transition at Olduvai Gorge. J. Hum. Evol. https://doi.org/10.1016/j.jhevol.2017.11.010 (2018).Article 
PubMed 

Google Scholar 
Soto, M. et al. Systematic sampling of quartzite in sourcing analysis: intra-outcrop variability at Naibor Soit, Tanzania (part I). Archaeol. Anthropol. Sci. 12, 1–14 (2020).
Google Scholar 
Zupancich, A. & Cristiani, E. Functional analysis of sandstone ground stone tools: Arguments for a qualitative and quantitative synergetic approach. Sci. Rep. 10, 1–13 (2020).
Google Scholar 
Mercader, J. et al. Soil and plant phytoliths from the Acacia-Commiphora mosaics at Oldupai Gorge (Tanzania). PeerJ 7, e8211 (2019).PubMed 
PubMed Central 

Google Scholar 
Krumbein, W. C. Measurement and geological significance of shape and roundness of sedimentary particles. Journal of Sedimentary Research 11, 64–72 (1941).CAS 

Google Scholar 
Favreau, J. et al. Petrographic Characterization of Raw Material Sources at Oldupai Gorge, Tanzania. Frontiers in Earth Science 8, 1–26, https://doi.org/10.31219/osf.io/s2vgr (2020).Article 

Google Scholar 
Soto, M. et al. Fingerprinting of quartzitic outcrops at Oldupai Gorge, Tanzania. Journal of Archaeological Science: Reports 29, 102010 (2020).
Google Scholar 
Anderson, G. D. & Talbot, L. M. Soil Factors Affecting the Distribution of the Grassland Types and their Utilization by Wild Animals on the Serengeti Plains, Tanganyika. Journal of Ecology 53, 33–56 (1965).
Google Scholar 
Leakey, M. D. Olduvai Gorge Vol. 3: Excavations in Beds I and II, 1960–1963. (Cambridge University Press, 1971).Dorn, R. I. Rock Coatings. Vol. 6 (Elsevier, 1998).Madella, M., Alexandre, A. & Ball, T. International code for phytolith nomenclature 10. Ann. Bot. 96, 253–260 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mercader, J. et al. Morphometrics of Starch Granules From Sub-Saharan Plants and the Taxonomic Identification of Ancient Starch. Frontiers in Earth Science 6, https://doi.org/10.3389/feart.2018.00146 (2018).ADS 
Article 

Google Scholar 
Rots, V., Hayes, E., Cnuts, D., Lepers, C. & Fullagar, R. Making sense of residues on flaked stone artefacts: learning from blind tests. PLOS One 11, e0150437. https://doi.org/10.1371/journal.pone.0150437 (2016).Hayes, E. & Rots, V. Documenting scarce and fragmented residues on stone tools: an experimental approach using optical microscopy and SEM-EDS. Archaeological and Anthropological Sciences 11, 3065–3099 (2019).
Google Scholar 
Stoodley, P., Sauer, K., Davies, D. G. & Costerton, J. W. Biofilms as Complex Differentiated Communities. Annual Review of Microbiology 56, 187–209 (2002).CAS 
PubMed 

Google Scholar 
Krumbein, W. E., Paterson, D. M. & Zavarzin, G. A. Fossil and Recent Biofilms: A Natural History of Life on Earth. (Springer Science & Business Media, 2003).Wanger, G., Southam, G. & Onstott, T. C. Structural and Chemical Characterization of a Natural Fracture Surface from 2.8 Kilometers Below Land Surface: Biofilms in the Deep Subsurface. Geomicrobiology Journal 23, 443-452 (2006).CAS 

Google Scholar 
Anders, M. H., Laubach, S. E. & Scholz, C. H. Microfractures: A Review. Journal of Structural Geology 69, 377–394 (2014).Fletcher, M. Attachment of Pseudomonas fluorescens to glass and influence of electrolytes on bacterium substratum separation distance. Journal of Bacteriology 170, 2027–2030 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fong, J. N. & Tildiz, F. H. Biofilm Matrix Proteins. Microbiology Spectrum 3, 1–16 (2015).CAS 

Google Scholar 
Cnuts, D. & Rots, V. Extracting residues from stone tools for optical analysis: towards an experiment-based protocol. Archaeological and Anthropological Sciences 10, 1717–1736 (2018).
Google Scholar 
Xhauflair, H. et al. Use-related or contamination? Residue and use-wear mapping on stone tools used for experimental processing of plants from Southeast Asia. Quaternary International 427, 80–93 (2017).Pedergnana, A. “All that glitters is not gold”: Evaluating the Nature of the Relationship Between Archeological Residues and Stone Tool Function. Journal of Paleolithic Archaeology 3, 225–254 (2019).
Google Scholar  More

Arora, P. K. & Bae, H. Bacterial degradation of chlorophenols and their derivatives. Microb. Cell Fact. 13, 31–36 (2014).Article 

Google Scholar 
Solyanikova, I. P. & Golovleva, L. A. Bacterial degradation of chlorophenols: Pathways, biochemica, and genetic aspects. J. Environ. Sci. Health B 39, 333–351 (2004).Article 

Google Scholar 
Olaniran, A. O. & Igbinosa, E. O. Chlorophenols and other related derivatives of environmental concern: Properties, distribution and microbial degradation processes. Chemosphere 83, 1297–1306 (2011).ADS 
CAS 
Article 

Google Scholar 
Kusmierek, K. The removal of chlorophenols from aqueous solutions using activated carbon adsorption integrated with H2O2 oxidation. Reac. Kinet. Mech. Cat. 119, 19–34 (2016).CAS 
Article 

Google Scholar 
Igbinosa, E., Odjadjare, E., Vicent, N. & Ideemndia, O. Toxicological profile of chlorophenols and their derivatives in the environment: The public health perspective. Sci. World J. 2013, 11 (2013).
Google Scholar 
Hossain, G. & McLaughlan, R. Kinetic investigations of oxidation of chlorophenols by permanganate. J. Environ. Chem. Ecotoxicol 5, 81–89 (2013).
Google Scholar 
Ryan, D., Leukes, W. & Burton, S. Improving the bioremediation of phenolic wastewaters by Trametes versicolor. Bioresour. Technol 98, 579–587 (2016).Article 

Google Scholar 
Zhao, L., Wu, Q. & Ma, A. Biodegradation of phenolic contaminants: Current status and perspectives. In International Conference on Advanced Environmental Engineering IOP Publishing. Series: Earth and Environmental Science. Vol 111, 012024 (2018).Walter, M., Boul, L., Chong, R. & Ford, C. Growth substrate selection and biodegradation of PCP by New Zealand white-rot fungi. J. Environ. Qual. 24(36), 1749–1759 (2004).
Google Scholar 
Cameron, M. D., Timofeevski, S. & Aust, S. D. Enzymology of Phanerochaete chrysosporium with respect to the degradation of recalcitrant compounds and xenobiotics. Appl. Microbiol. Biotechnol. 54, 751–758 (2000).CAS 
Article 

Google Scholar 
Tuomela, M., Lyytikainen, M., Oivanena, P. & Hatakka, A. Mineralization and conversion of pentachlorophenol (PCP) in soil inoculated with the white-rot fungus Trametes versicolor. Soil Biol. Biochem. 31, 65–74 (1999).CAS 
Article 

Google Scholar 
Field, J. & Sierra-Alvarez, R. Microbial degradation of chlorinated phenols. Rev. Environ. Sci. Biotechnol 7, 211–241 (2008).CAS 
Article 

Google Scholar 
Bosso, L. & Cristinzio, G. A. A comprehensive overview of bacteria and fungi used for pentachlorophenol biodegradation. Rev. Environ. Sci. Biotechnol 13, 387–427 (2014).CAS 
Article 

Google Scholar 
Field, J. A. & Sierra-Alvarez, R. Microbial transformation and degradation of polychlorinated biphenyls. Environ. Pollut 155, 1–12 (2008).CAS 
Article 

Google Scholar 
Nikolaivits, E. et al. Degradation mechanism of 2,4-dichlorophenol by fungi isolated from marine invertebrates. Int. J. Mol. Sci 21, 3317. https://doi.org/10.3390/ijms21093317 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Cser-jesi, A. J. & Johnson, E. Methylation of entachlorophenol by Trichoderma virgatum. Can. J. Microbiol. 18, 45–49 (1972).CAS 
Article 

Google Scholar 
van Leeuwen, J., Nicholson, B., Hayes, K. & Mulcahy, D. Degradation of chlorophenolic compounds by Trichoderma harzianum isolated from Lake Bonney, South-Eastern South Australia. Environ Toxicol. Water Qual. 12, 335–342 (1997).ADS 
Article 

Google Scholar 
Carvalho, M. B. et al. Screening pentachlorophenol degradation ability by environmental fungal strains belonging to the phyla Ascomycota and Zygomycota. J. Ind. Microbiol. Biotechnol. 36, 1249–1256 (2009).CAS 
Article 

Google Scholar 
Chakroun, H., Mechichi, T., Martinez, M. J., Dhouib, A. & Sayadi, S. Purification and characterization of a novel laccase from the ascomycete Trichoderma atroviride: Application on bioremediation of phenolic compounds ‬. Process Biochem. 45, 507–513 (2010).CAS 
Article 

Google Scholar 
Abdel-Fatah, O. M. et al. Physiological studies on carboxymethyl cellulase formation by Aspergillus terreus DSM 826. Braz. J. Microbiol. 43(1), 01–11 (2012).CAS 
Article 

Google Scholar 
Sonika, P. et al. Trichoderma species cellulases produced by solid state fermentation. J. Data Min. Genom. Proteom. 6, 2 (2015).
Google Scholar 
Al-Hawash, B. A. et al. Isolation and characterization of two crude oil-degrading fungi strains from Rumaila oil field. Iraq. Biotechnol. Rep 17, 104–109. https://doi.org/10.1016/j.btre.2017.12.006 (2018).Article 

Google Scholar 
Zafra, G., Absalón, A. E. & Cortes-Espinosa, D. V. Morphological changes and growth of filamentous fungi in the presence of high concentrations of PAHs. Braz. J. Microbiol 46, 937–941. https://doi.org/10.1590/S1517-838246320140575 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smit, E., Leeflang, P., Glandorf, B., van Elsas, J. D. & Wernars, K. Analysis of fungal diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis. Appl. Environ. Microbiol. 65(6), 2614–2621 (1999).ADS 
CAS 
Article 

Google Scholar 
White, T. J. Analysis of phylogenetic relationships by amplification and direct sequencing of ribosomal genes. In PCR Protocols: A Guide to Methods and Applications 315–22 (1990).Ryu, W. R. et al. Biodegradation of white rot fungi under ligninolytic and nonligninolytic conditions. Biotechnol Bioproc. E 5, 211–214 (2000).CAS 
Article 

Google Scholar 
Dubois, K., Gilles, J., Hamilton, P., Rebers, A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).CAS 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49(W1), W293–W296. https://doi.org/10.1093/nar/gkab301 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33(7), 1870–1874 (2016).CAS 
Article 

Google Scholar 
Statistical Packages for Software Sciences. Version 21.0 Armonk (New York: IBM Corporation, 2013).Lin, S.-H. & Juang, R.-S. Adsorption of phenol and its derivatives from water using synthetic resins and low-cost natural adsorbents: A review. J. Environ. Manage 90, 1336–1349. https://doi.org/10.1016/j.jenvman.2008.09.003 (2009).CAS 
Article 
PubMed 

Google Scholar 
Kumar, S. N., Subbaiah, V. M., Reddy, S. A. & Krishnaiah, A. Biosorption of phenolic compounds from aqueous solutions onto chitosan-abrus precatorius blended beads. J. Chem. Technol. Biotechnol 84, 972–981. https://doi.org/10.1002/jctb.2120 (2009).CAS 
Article 

Google Scholar 
Wang, C. C., Lee, C. M., Lu, C. J., Chuang, M. S. & Huang, C. Z. Biodegradation of 2,4,6-trichlorophenol in the presence of primary substrate by immobilized pure culture bacteria. Chemosphere 41, 1873–1879. https://doi.org/10.1016/S00456535(00)00090-4 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kavamura, V. N. & Esposito, E. Biotechnological strategies applied to the decontamination of soils polluted with heavy metals. Biotechnol. Adv 28, 61–69 (2010).CAS 
Article 

Google Scholar 
Mohsenzade, F., Chehregani, A. & Akbari, M. Evaluation of oil removal efficiency and enzymatic activity in some fungal strains for bioremediation of petroleum-polluted soils. Iran J. Environ. Health. Eng 9, 26–34 (2012).Article 

Google Scholar 
Nikolaivits, E. et al. Unraveling the detoxification mechanism of 2,4-dichlorophenol by marine-derived mesophotic symbiotic fungi isolated from marine invertebrates. Mar. Drugs. 17, 564. https://doi.org/10.3390/md17100564 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Scientific opinion on risk assessment for a selected group of pesticides from the triazole group to test possible methodologies to assess cumulative effects from exposure through food from these pesticides on human health. EFSA J. 7, 1167. https://www.efsa.europa.eu/en/efsajournal/pub/1167 (2009).Brotman, Y., Kapuganti, J. G. & Viterbo, A. Trichoderma. Curr. Biol. 20, R390–R439 (2010).CAS 
Article 

Google Scholar 
Boroujeni, N. A., Hassanshahian, M., Mohammad, S. & Khoshrou, R. Isolation and characterization of phenol degrading bacteria from Persian Gulf. IJABBR 2, 408–416 (2014).CAS 

Google Scholar 
Roostaei, N. & Tezel, F. H. Removal of phenol from aqueous solutions by adsorption. J. Environ. Manage 70, 157–164. https://doi.org/10.1016/j.jenvman.2003.11.004 (2004).Article 
PubMed 

Google Scholar 
Demnerova, K. et al. Two approaches to biological decontamination of groundwater and soil polluted by aromatics-characterization of microbial populations. Int. Microbiol 8, 205–211 (2005).CAS 
PubMed 

Google Scholar 
Reddy, G. V. B. & Gold, M. H. Degradation of pentachlorophenol by Phanerochaete chrysosporium: Intermediates and reactions involved. Microbiology 146, 405–413 (2000).CAS 
Article 

Google Scholar 
Cortés, D. V., Bernal, R. & Tomasini, A. Efecto de las condiciones de cultivo sumergido en la degradación de pentaclorofenol. Información Tecnológica 12, 75–80 (2001).
Google Scholar 
Crawford, R. L., Jung, C. M. & Strap, J. L. The recent evolution of pentachlorophenol (PCP)-4-monooxygenase (PcpB) and associated pathways for bacterial degradation of PCP. Biodegradation 18, 525–539 (2007).CAS 
Article 

Google Scholar 
Bergauer, P., Fonteyne, P. A., Nolard, N., Schinner, F. & Margesin, R. Biodegradation of phenol and phenol-related compounds by psychrophilic and cold-tolerant alpine yeasts. Hemosphere 59, 909–918 (2005).ADS 
CAS 
Article 

Google Scholar 
Bovio, E. et al. The culturable mycobiota of a Mediterranean marine site after an oil spill: Isolation, identification and potential application in bioremediation. Sci. Total Environ. 576, 310–318 (2017).ADS 
CAS 
Article 

Google Scholar  More