Philippa Kaur

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

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

Wehner, R., Michel, B. & Antonsen, P. Visual navigation in insects: Coupling of egocentric and geocentric information. J. Exp. Biol. 199(1), 129–140 (1996).CAS 
PubMed 

Google Scholar 
Collett, M., Chittka, L. & Collett, T. S. Spatial memory in insect navigation. Curr. Biol. 23(17), R789–R800 (2013).CAS 
PubMed 

Google Scholar 
Cheng, K., Schultheiss, P., Schwarz, S., Wystrach, A. & Wehner, R. Beginnings of a synthetic approach to desert ant navigation. Behav. Proc. 102, 51–61 (2014).
Google Scholar 
Freas, C. A. & Schultheiss, P. How to navigate in different environments and situations: Lessons from ants. Front. Psych. 9, 841 (2018).
Google Scholar 
Wehner, R. Desert ant navigation: how miniature brains solve complex tasks. J. Comp. Physiol. A 189(8), 579–588 (2003).ADS 
CAS 

Google Scholar 
Wehner, R. The desert ant’s navigational toolkit: Procedural rather than positional knowledge. Navigation 55(2), 101–114 (2008).
Google Scholar 
Wehner, R. Desert Navigator (The Belknap Press of Harvard University Press, 2020).
Google Scholar 
Kohler, M. & Wehner, R. Idiosyncratic route-based memories in desert ants, Melophorus bagoti: How do they interact with path-integration vectors?. Neurobiol. Learn. Mem. 83(1), 1–12 (2005).PubMed 

Google Scholar 
Müller, M. & Wehner, R. Path integration provides a scaffold for landmark learning in desert ants. Curr. Biol. 20(15), 1368–1371 (2010).PubMed 

Google Scholar 
Mangan, M. & Webb, B. Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox). Behav. Ecol. 23(5), 944–954 (2012).
Google Scholar 
Schwarz, S., Wystrach, A. & Cheng, K. Ants’ navigation in an unfamiliar environment is influenced by their experience of a familiar route. Sci. Rep. 7(1), 1–10 (2017).
Google Scholar 
Graham, P. & Cheng, K. Ants use the panoramic skyline as a visual cue during navigation. Curr. Biol. 19, R935–R937 (2009).CAS 
PubMed 

Google Scholar 
Wystrach, A., Beugnon, G. & Cheng, K. Landmarks or panoramas: What do navigating ants attend to for guidance?. Front. Zool. 8(1), 21 (2011).PubMed 
PubMed Central 

Google Scholar 
Wehner, R., Meier, C. & Zollikofer, C. The ontogeny of foraging behaviour in desertants, Cataglyphis bicolor. Ecol. Entom. 29, 240–250 (2004).
Google Scholar 
Zeil, J. & Fleischmann, P. N. The learning walks of ants (Hymenoptera: Formicidae). Myrmecol. News. 29, 93–110 (2019).
Google Scholar 
Schultheiss, P. et al. Crucial role of ultraviolet light for desert ants in determining direction from the terrestrial panorama. Anim. Behav. 115, 19–28 (2016).
Google Scholar 
Freas, C. A., Wystrach, A., Narendra, A. & Cheng, K. The view from the trees: Nocturnal bull ants, Myrmecia midas, use the surrounding panorama while descending from trees. Front. Psych. 9, 1–10 (2018).
Google Scholar 
Freas, C. A. & Cheng, K. Landmark learning, cue conflict, and outbound view sequence in navigating desert ants. J. Exp. Psych. Anim. Learn. Cogn. 44(4), 409–421 (2018).
Google Scholar 
Freas, C. A. & Spetch, M. L. Terrestrial cue learning and retention during the outbound and inbound foraging trip in the desert ant, Cataglyphis bicolor. J. Comp. Physiol. A. 205(2), 177–189 (2019).
Google Scholar 
Narendra, A., Si, A., Sulikowski, D. & Cheng, K. Learning, retention and coding of nest-associated visual cues by the Australian desert ant, Myrmecia midas. Behav. Ecol. Sociobiol. 61(10), 1543–1553 (2007).
Google Scholar 
Zeil, J. Visual homing: an insect perspective. Curr. Opin. Neurobiol. 22(2), 285–293 (2012).CAS 
PubMed 

Google Scholar 
Zeil, J., Hofmann, M. I. & Chahl, J. S. Catchment areas of panoramic snapshots in outdoor scenes. J. Optic. Soc. Am. A. 20(3), 450 (2003).ADS 

Google Scholar 
Wystrach, A., Cheng, K., Sosa, S. & Beugnon, G. Geometry, features, and panoramic views: Ants in rectangular arenas. J. Exp. Psychol. 37(4), 420–435 (2011).
Google Scholar 
Baddeley, B., Graham, P., Husbands, P. & Philippides, A. A model of ant route navigation driven by scene familiarity. PLoS Comp. Biol. 8(1), e1002336 (2012).ADS 
CAS 

Google Scholar 
Kodzhabashev, A. & Mangan, M. Route Following Without Scanning In Biomimetic and Biohybrid Systems 199–210 (Springer, 2015).
Google Scholar 
Möller, R. A model of ant navigation based on visual prediction. J. Theo. Biol. 305, 118–130 (2012).ADS 
MathSciNet 
MATH 

Google Scholar 
Le Möel, F. & Wystrach, A. Opponent processes in visual memories: A model of attraction and repulsion in navigating insects’ mushroom bodies. PLoS Comp. Biol. 16, e1007631 (2020).
Google Scholar 
Murray, T. et al. The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi). J. Exp. Biol. 223, 21002 (2020).
Google Scholar 
Jayatilaka, P., Murray, T., Narendra, A. & Zeil, J. The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi. J. Exp. Biol. 221(20), 185306 (2018).
Google Scholar 
Schwarz, S., Mangan, M., Webb, B. & Wystrach, A. Route-following ants respond to alterations of the view sequence. J. Exp. Biol. 223, 218701 (2020).
Google Scholar 
Wystrach, A., Buehlmann, C., Schwarz, S., Cheng, K. & Graham, P. Rapid aversive and memory trace learning during route navigation in desert ants. Curr. Biol. 30(100), 1927–1933 (2020).CAS 
PubMed 

Google Scholar 
Wystrach, A., Philippides, A., Aurejac, A., Cheng, K. & Graham, P. Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti. J. Comp. Physiol. A 200(7), 615–626 (2014).
Google Scholar 
Wystrach, A., Schwarz, S., Graham, P. & Cheng, K. Running paths to nowhere: Repetition of routes shows how navigating ants modulate online the weights accorded to cues. Anim. Cogn. 2, 213–222 (2019).
Google Scholar 
MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100(916), 603–609 (1966).
Google Scholar 
Krebs, J. R. Foraging Theory (Princeton University Press, 1986).
Google Scholar 
Kacelnik, A. & Bateson, M. Risky theories: The effects of variance on foraging decisions. Am. Zool. 36(4), 402–434 (1996).
Google Scholar 
Kacelnik, A. & Abreu, F. B. Risky choice and Weber’s law. J. Theor. Biol. 194(2), 289–298 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Fechner, G. T. Elemente der Psychophysik Vol. 2 (Breitkopf u Härtel, 1860).
Google Scholar 
Bruce, A. C. & Johnson, J. E. V. Decision-making under risk: Effect of complexity on performance. Psychol. Rep. 79(1), 67–76 (1996).
Google Scholar 
Stevens, S. S. & Marks, L. E. Psychophysics: Introduction to its Perceptual, Neural, and Social Prospects (Routledge, 2017).
Google Scholar 
Kacelnik, A. & El Mouden, C. Triumphs and trials of the risk paradigm. Anim. Behav. 86(6), 1117–1129 (2013).
Google Scholar 
Hübner, C. & Czaczkes, T. J. Risk preference during collective decision making: Ant colonies make risk-indifferent collective choices. Anim. Behav. 132, 21–28 (2017).
Google Scholar 
De Agrò, M., Grimwade, D., Bach, R. & Czaczkes, T. J. Irrational risk aversion in an ant. Anim. Cogn. 1, 1–9 (2021).
Google Scholar 
Waddington, K. D., Allen, T. & Heinrich, B. Floral preferences of bumblebees (Bombus edwardsii) in relation to intermittent versus continuous rewards. Anim. Behav. 29(3), 779–784 (1981).
Google Scholar 
Cartar, R. V. A test of risk-sensitive foraging in wild bumble bees. Ecology 72(3), 888–895 (1991).
Google Scholar 
Perez, S. M. & Waddington, K. D. Carpenter bee (Xylocopa micans) risk indifference and a review of nectarivore risk-sensitivity studies. Am. Zool. 36(4), 435–446 (1996).
Google Scholar 
Fülöp, A. & Menzel, R. Risk-indifferent foraging behaviour in honeybees. Anim. Behav. 60(5), 657–666 (2000).PubMed 

Google Scholar 
Burns, D. D., Sendova-Franks, A. B. & Franks, N. R. The effect of social information on the collective choices of ant colonies. Behav. Ecol. 27(4), 1033–1040 (2016).
Google Scholar 
Sasaki, T., Pratt, S. C. & Kacelnik, A. Parallel vs. comparative evaluation of alternative options by colonies and individuals of the ant Temnothorax rugatulus. Sci. Rep. 8(1), 1–8 (2018).
Google Scholar 
Sasaki, T., Stott, B. & Pratt, S. C. Rational time investment during collective decision making in Temnothorax ants. Biol. Lett. 15(10), 20190542 (2019).PubMed 
PubMed Central 

Google Scholar 
Freas, C. A., Fleischmann, P. N. & Cheng, K. Experimental ethology of learning in desert ants: Becoming expert navigators. Behav. Proc. 158, 181–191 (2019).
Google Scholar 
Le Moël, F. & Wystrach, A. Towards a multi-level understanding in insect navigation. Curr. Opin. Inst. Sci. 42, 110–117 (2020).
Google Scholar 
Heinze, S. Visual navigation: Ants lose track without mushroom bodies. Curr. Biol. 30(17), R984–R986 (2020).CAS 
PubMed 

Google Scholar 
Ardin, P., Peng, F., Mangan, M., Lagogiannis, K. & Webb, B. Using an insect mushroom body circuit to encode route memory in complex natural environments. PLOS Comp. Biol. 12(2), e1004683 (2016).ADS 

Google Scholar 
Buehlmann, C. et al. Mushroom bodies are required for learned visual navigation, but not for innate visual behavior, in ants. Curr. Biol. 30(17), 3438–3443 (2020).CAS 
PubMed 

Google Scholar 
Kamhi, J. F., Barron, A. B. & Narendra, A. Vertical lobes of the mushroom bodies are essential for view-based navigation in Australian Myrmecia ants. Curr. Biol. 30(17), 3432–3437 (2020).CAS 
PubMed 

Google Scholar 
Heisenberg, M. Mushroom body memoir: From maps to models. Nat. Rev. Neurosci. 4(4), 266–275 (2003).CAS 
PubMed 

Google Scholar 
Webb, B. & Wystrach, A. Neural mechanisms of insect navigation. Curr. Opin. Inst. Sci. 15, 27–39 (2016).
Google Scholar 
Habenstein, J., Amini, E., Grübel, K., El Jundi, B. & Rössler, W. The brain of Cataglyphis ants: Neuronal organization and visual projections. J. Comp. Neurol. 528(18), 3479–3506 (2020).PubMed 

Google Scholar 
Cohn, R., Morantte, I. & Ruta, V. Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila. Cell 163(7), 1742–1755 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aso, Y. & Rubin, G. M. Dopaminergic neurons write and update memories with cell-type-specific rules. Elife 5, e16135 (2015).
Google Scholar 
Beck, C. D. O., Schroeder, B. & Davis, R. L. Learning performance of normal and mutant Drosophila after repeated conditioning trials with discrete stimuli. J. Neurosci. 20(8), 2944–2953 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boto, T. & Ramaswami, M. Learning and memory: Clashing engrams in the fly brain. Curr. Biol. 31(16), R1009–R1011 (2021).CAS 
PubMed 

Google Scholar 
Bennett, J. E. M., Philippides, A. & Nowotny, T. Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nat. Commun. 12, 22595 (2021).
Google Scholar 
Rescorla, R. A. & Wagner, A. R. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Classical Conditioning Ii: Current Theory and Research (eds Black, A. & Prokasy, W.) (Appleton-Century-Crofts, 1972).
Google Scholar  More

Study islands and bird dataSystematic ringing in spring on Mediterranean islands has been promoted by the Piccole Isole project since 198826. Standard methods of the project involve ringing between 16th April and 15th May attempting to include the peak of the spring passage of long-distance migrants. Ringing is performed from dawn to nightfall using a constant number of nets within ringing stations placed at stable sites located at representative habitats in each island (Supplementary Table S1). The use of tape-lures is not allowed. We have compiled ringing data for all the Spanish Mediterranean islands that have been applying this methodology, with the exception of Mallorca and Menorca where the ringing stations were located in wetlands and captured a large percentage of local birds (Fig. 2, Table 1). The nine study islands are spread along a south-west to north-east gradient and, with the exception of Columbrets, they are distributed in pairs of similar longitude but different latitudes (Fig. 2). Ringing stations have been operating over a variable number of years (5–27 years), with the maximum number of ringing stations operating at the same time occurring between 2003 and 2010. To include between-year variation on islands that started ringing campaigns more recently we used data from the years 2003–2018.Figure 2Image source: Google Earth. Data SIO, NOAA, US Navy, NGA, GEBCO. Image Landsat/Copernicus.Geographical location of studied islands in the western Mediterranean.Full size imageTable 1 Period of activity of the ringing stations located on each island between the years 1992 and 2018.Full size tableThe ringing period within each spring also varied in most islands, owing to funding or logistic limitations; thus, to reduce the possible effects on migrant composition we only used data from the standard period of the Piccole Isole project and from years that included at least one week of ringing in the fortnight of each month within this interval. This procedure excluded the use of some years for several islands, and the final number of data years for islands ranged between 5 and 16 (Table 1).We used only data for trans-Saharan nocturnal migrant passerines, which form the bulk of species ringed on Mediterranean islands during the standard period. The standard ringing period only covers the tail end of the short-distance migrants’ passage; thus, these species were excluded as their contribution to composition of migrants could vary mainly due to between-year variation in migration phenology. Diurnal migrants, like hirundinids and fringillids, also represent a small fraction of birds ringed and may use different cues to select stopover islands. In addition, some of these species nest in some of the islands studied and birds ringed could include breeding birds. To avoid the distorting effect of species that are captured accidentally in very small numbers, we considered only the species that were ringed in at least five separate years, or on five different islands, which limited the species considered to 35 (Supplementary Table S2). This led to the exclusion of just two species (Ficedula semitorquata with three individuals ringed in two islands and Locustella luscinioides with one individual ringed in Aire island). In addition, we only considered the number of ringed birds, since the proportion of recaptures varies among islands, likely reflecting variation in the duration of stopovers21, which could bias the comparison of the patterns of migrant species composition.Island descriptorsWe obtained two groups of variables describing the characteristics of the study islands (Tables 2, 3): (1) Variables related to geographical location: latitude, longitude, straight distance and minimum distance to the North African coast, minimum distance to the closest large body of land (continent or large island) in any direction and to the closest large body of land situated in a southerly angle between SW and SE. (2) Variables related to the habitat characteristics of the islands: area, maximum altitude and Normalized Difference Vegetation Index (NDVI). We estimated NDVI from Landsat 8 Images taken during the standard ringing period in the years 2015 and 2016. Pixels containing shoreline were excluded and the average NDVI was calculated for the rest of the pixels.Table 2 Variables describing the characteristics of the islands that included the ringing stations studied.Full size tableTable 3 Values of the island descriptors (see Table 2) and two measures of temporal variability of migrant composition in each island: average local contribution of each island to beta diversity (LCBD) and beta diversity for each island (BDTi).Full size tableContinental abundance dataAbundance estimates for western Europe were obtained from the European Red List of Birds27. We used the mean of the minimum and maximum number of pairs estimated for the 27 EU Member States as a measure of continental abundance (Supplementary Table S2).Data analysisAll analyses were done using R 3.6.128. We built a matrix of island-year x species containing the number of individuals of each selected species ringed in the study period in each island and year. Average number of individuals of each species ringed at each island was calculated and was used (log-transformed) as a dependent variable in a linear model with continental abundance (log-transformed), island and their interaction as predictors. This model was simplified using AICc as criteria to identify the best model.To analyze variation of species composition, the matrix of island-year x species was transformed using the chord transformation29 with the function decostand in the vegan package30.Using the function beta.div of the adespatial package31 we calculated beta diversity, including temporal and between-island variability (BDI,T), as the total variance of the aforementioned transformed matrix (BDTotal in29), which varies between 0 and 1 when chord distance is used. Considering that yijk is the chord transformed abundance of the species j in the island i and year k and (overline{{y }_{j}}) is the mean for species j in all islands and years altogether, then:$${SS}_{Total}=sum_{i=1}^{n}sum_{j=1}^{p}{sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{j})}^{2}}$$$$BD_{I,T} = , SS_{Total} /left( {N – 1} right)$$where N is the total number of samples. The function beta.div also provides an estimation of contribution of localities (LCBD) and species (SCBD) to beta diversity (Table 3). Yearly LCBD (log transformed because of skewed distribution) of each island were averaged and compared between islands using ANOVA and a post-hoc Tukey test.We partitioned the above sum of squares in several ways. First, we calculated a beta diversity that considered only between-island variability, excluding temporal variability (BDI), by averaging the chord transformed abundances of each species j in each island along study years (({overline{y} }_{ij})) and applying the same procedure, but using the number of studied islands (n):$${SS}_{I}=sum_{i=1}^{n}sum_{j=1}^{p}{{({overline{y} }_{ij}-{overline{y} }_{j})}^{2}}$$$$BD_{I} = SS_{I} /left( {n – 1} right)$$Second, we calculated a beta diversity due to inter-annual variation of migrant composition within islands (BDT) as:$${SS}_{Temp}=sum_{i=1}^{n}sum_{j=1}^{p}{sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{ij})}^{2}}$$$$BD_{T} = SS_{Temp} /left( {Y – n} right)$$where Y is the total number of study years and n is the number of studied islands (9). We also calculated a temporal beta diversity for each island i (BDTi) as the sum of squares due to variation within the island divided by the number of the island study years (Yi) minus 1:$${SS}_{Temp,i}=sum_{j=1}^{p}sum_{k=1}^{q}{({y}_{ijk}-{overline{y} }_{ij})}^{2}$$$$BD_{Ti} = SS_{Temp,i} /left( {Y_{i} – 1} right)$$Differences in temporal variability between islands could be due to different predominance of species that are more or less variable between years. To check this, we calculated Spearman’s rank correlation between the percentage of captures of each species in the total ringed on each island and BDTi and LCDB indices, for species present on all islands.We tested for the existence of differences between islands in migrant species composition using Permutational Multivariate Analysis of Variance (PERMANOVA) using the function adonis2 in the vegan package. We performed a multivariate test of homogeneity of variances using the betadisper function (vegan package) with the adjustment for small sample bias, to test if temporal variability in species composition differed between islands. We made post-hoc comparisons between islands with False Discovery Rate (FDR) correction using the function pairwise.perm.manova of the package RVAideMemoire32.To identify gradients in migrant species composition and the island characteristics that were associated with them, we employed Redundancy Analysis using the rda function (vegan package). We used the chord transformed matrix of species x island-year as a response matrix. We used two explanatory matrices, one including variables of geographical location and the other the variables related to habitat characteristics of the islands. We evaluated the relative importance of each group of variables to explain migrant species composition by performing a variation partitioning analysis, using the varpart function (vegan package). For that analysis, we followed the steps and R scripts recommended in33.Variables describing island characteristics were transformed using natural logarithms and collinearity within each group was evaluated with variance inflation factor (VIF)34. All the habitat variables presented VIF  More

Eddy-covariance observationsWe used half-hourly or hourly GPP, air temperature, VPD, SWC and incoming shortwave radiation from the recently released ICOS (Integrated Carbon Observation System)44 and the FLUXNET2015 dataset of energy, water, and carbon fluxes and meteorological data, both of which have undergone a standardized set of quality control and gap filling19. Data were already processed following a consistent and uniform processing pipeline19. This data processing pipeline mainly included: (1) thorough data quality control checks; (2) calculation of a range of friction velocity thresholds; (3) gap-filling of meteorological and flux measurements; (4) partitioning of CO2 fluxes into respiration and photosynthesis components; and (5) calculation of a correction factor for energy fluxes19. All the corrections listed were already applied to the available product19. We used incoming shortwave radiation, temperature, VPD, and SWC that were gap-filled using the marginal distribution method21. The GPP estimates from the night-time partitioning method were used for the analysis (GPP_NT_VUT_REF). SWC was measured as volumetric SWC (percentage) at different depths, varying across sites. We mainly used the surface SWC observations but deeper SWC measurements were also used when available. Data were quality controlled so that only measured and good-quality gap filled data (QC = 0 or 1) were used.Analysis of the extreme summer drought in 2018 in Europe to prove nonlinearityTo analyze the effect of summer drought in 2018 on GPP in Europe, we selected 15 sites with measurements during 2014–2018 from the ICOS dataset, representing the major ecosystems across Europe (Supplementary Table 1). Croplands were excluded due to the effect of management on the seasonal timing of ecosystem fluxes, both from crop rotation that change from year to year and from the variable timing of planting and harvesting. In croplands, the changes of GPP anomalies across different growing season could be mainly depend on crop varieties and management activities. Information of crop varieties, growing times yearly and other management data for each cropland site should be collected in future in order to fully consider and disentangle the impacts of SWC and VPD on its photosynthesis. Wetland sites were also removed because they are influenced by upstream organic matter and nutrient input, as well as fluctuating water tables. Daytime half-hourly data (7 am to 19 pm) were aggregated to daily values. At each site, the relative changes ((triangle {{{{{rm{X}}}}}})) of summer (June–July–August) GPP, SWC and VPD during 2014–2018 refer to the summer average of 2014–2018 were calculated for each year. For example, the calculation of the relative change in 2018 is shown in Eq. (1):$$triangle {{{{{rm{X}}}}}}=frac{{X}_{2018}-,{X}_{{average};{of};2014-2018}}{{X}_{{average};{of};2014-2018}}times 100 %$$
(1)
where X2018 is the mean of the daily values of (X) (GPP, SWC, or VPD) during the summer of 2018, and Xaverage of 2014–2018 is the mean of the daily values of (X) over all the summers of the 2014–2018 period. The average (triangle {{{{{rm{X}}}}}}) across a certain number of sites at each bin were used for the results in Fig. 1a.Daily time series of GPP, SWC and VPD during summer for each site were normalized (z-scores) to derive the standardized sensitivity of GPP to SWC and VPD. For each variable, the mean value across the summer of 2014–2018 was subtracted for each day at each site and then normalized by its standard deviation. At each site, we used a multiple linear regression (Eq. 2) to estimate daily GPP anomalies sensitivities to SWC and VPD anomalies across 2014–2018 and 2014–2017, respectively:$${GPP}={beta }_{1},{SWC}+{beta }_{2},{VPD}+{beta }_{3},{SWC},times {VPD}+{beta }_{4},{T}_{a}+{beta }_{5}{RAD}+b+varepsilon$$
(2)
where ({beta }_{i}) is the standardized sensitivity of GPP to each variable; ({T}_{a}) represents the air temperature; ({RAD}) represents the incoming shortwave radiation;(,b) represents the intercept; and (varepsilon) is the random error term. We compared estimated sensitivities with and without 2018 data to quantify the impacts of extreme drought in 2018 on GPP sensitivity to SWC (Fig. 1d) and VPD (Fig. 1e). The slope was calculated at each site and then the distribution of slopes across sites were plotted in Fig. 1d, e.Global analysis of the sensitivities of GPP to SWC and VPDFor the global analysis, instead of summer, we focused on the growing season and days when the SWC and VPD effects were most likely to control ecosystem fluxes and screen out days when other meteorological drivers were likely to have a larger influence on fluxes. Following previous studies5,8,45, for each site, we restrict our analyses to the days in which: (i) the daily average temperature >15 °C; (ii) sufficient evaporative demand existed to drive water fluxes, constrained as daily average VPD  > 0.5 kPa; (iii) high solar radiation, constrained as daily average incoming shortwave radiation >250 Wm−2.By combining ICOS and FLUXNET2015 data, at the global scale, we evaluated 67 sites with at least 300 days observations over the growing seasons for the years available (Supplementary Table 2). We excluded cropland and wetland sites for the above-mentioned reasons. These 67 sites were used to calculate the relative effects of low SWC and high VPD on GPP following the approach of ref. 5 (see below sections). For 8 sites, the ANN results failed performance criteria (the correlation between predicted GPP and observed GPP is {{VPD}}_{0}\ {beta }_{0},,{VPD}le {{VPD}}_{0}end{array}right.$$
(7)
where β0 and k are fitted parameters and VPD0 is 1 kPa48. Following Luo and Keenan48, we applied this method to a short time window (2–14 days) of Fc depending on the availability of flux measurements and assumed that every day in the same time window has the same daily Amax. We retrieved the daily Amax by implementing Eqs. (6) and (7) using the REddyProc R package (https://github.com/bgctw/REddyProc)20.Vcmax represents the activity of the primary carboxylating enzyme ribulose 1,5-bisphosphate carboxylase–oxygenase (Rubisco) as measured under light-saturated conditions. To evaluate the responses of Vcmax to SWC and VPD, we first calculated the daily internal leaf CO2 partial pressure (ci) in the middle of the day (11:00–14:00) via Fick’s Law (Eq. 8), excluding periods with low incoming shortwave radiation (0.7 at most sites. During the training process, weight and bias values were optimized using the Levenberg–Marquardt optimization58,59. The maximum number of epochs to train is 1000. An example to demonstrate the ANN training at one site was shown in Supplementary Fig. 3.At each site, ANN was run and sensitivities were calculated for all data within each SWC and VPD bin and the median value was used. For each of the five trained ANNs, one of the predictor variables was perturbed by one standard deviation (a value of 1 due to the initial input data normalization), and GPP was predicted again using the existing ANN with the predictors including the perturbed variable; this process was repeated for each predictor variable. The predicted values of GPP obtained with and without perturbation were then compared to determine the sensitivity values. The sample equation showing the calculation of the GPP sensitivity to VPD is shown in Eq. (10).$${{{{{{rm{Sensitivity}}}}}}}_{{VPD}}={median}left(,frac{{{GPP}}_{left({ANN};{VPD}+{stdev}left({VPD}right)right)}-{{GPP}}_{left({ANN};{all};{VAR}right)}}{{stdev}left({VPD}right)}right)$$
(10)
We repeated the ANN and sensitivity analyses five times and the median of these were used at each site. Across all sites, significances of the sensitivities for each bin were tested using t-tests (p  More

Mack, R. N. et al. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710. https://doi.org/10.1890/1051-0761 (2000).Article 

Google Scholar 
Dukes, J. S. & Mooney, H. A. Disruption of ecosystem processes in western North America by invasive species. Rev. Chil. Hist. Nat. 77, 411–437 (2004).Article 

Google Scholar 
Vitousek, P. M. Biological invasions and ecosystem processes: towards an integration of population biology and ecosystem studies. Oikos 57, 7–13. https://doi.org/10.2307/3565731 (1990).Article 

Google Scholar 
Richardson, D. M. et al. Naturalization and invasion of alien plants: concepts and definitions. Diver. Distrib. 6, 93–107 (2000).Article 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. New Phytol. 176, 256–273 (2007).Article 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: species traits, habitats, propagule pressure, residence time. Ecology 96, 762–774. https://doi.org/10.1890/14-1005.1 (2015).Article 
PubMed 

Google Scholar 
Estrada, J. A., Wilson, C. H. & Flory, S. L. Clonal integration enhances performance of an invasive grass. Oikos https://doi.org/10.1111/oik.07016 (2020).Article 

Google Scholar 
Otfinowski, R. & Kenkel, N. C. Clonal integration facilitates the proliferation of smooth brome clones invading northern fescue prairies. Plant Ecol. 199, 235–242. https://doi.org/10.1007/s11258-008-9428-8 (2008).Article 

Google Scholar 
Pyšek, P. & Richardson, D. M. in Biological Invasions (ed N. Nentwig) pp. 97–125 (Springer, New York, 2007).Klimešová, J. & Klimeš, L. Clonal growth diversity and bud banks of plants in the Czech flora: an evaluation using the CLO-PLA3 database. Preslia 80, 255–275 (2008).
Google Scholar 
Klimešová, J. et al. Handbook of standardized protocols for collecting plant modularity traits. Persp. Plant Ecol. https://doi.org/10.1016/j.ppees.2019.125485 (2019).Article 

Google Scholar 
Wang, Y. J. et al. Invasive alien plants benefit more from clonal integration in heterogeneous environments than natives. New Phytol. 216, 1072–1078 (2017).Article 

Google Scholar 
Klimešová, J. in Encyclopedia of Invasive Introduced Species (eds D. Simberloff & M. Reimanek) pp. 678–679 (University of California Press, California, 2011).Ott, J. P., Klimešová, J. & Hartnett, D. C. The ecology and significance of below-ground bud banks in plants. Ann. Bot. Lond. 123, 1099–1118. https://doi.org/10.1093/aob/mcz051 (2019).Article 

Google Scholar 
Sanchez, J. M., Sanchez, C. & Navarro, L. Can asexual reproduction by plant fragments help to understand the invasion of the NW Iberian coast by Spartina patens? Flora 257, 151410. https://doi.org/10.1016/j.flora.2019.05.009 (2019).Speek, T. A. A. et al. Factors relating to regional and local success of exotic plant species in their new range. Diver. Distrib. 17, 542–551 (2011).Article 

Google Scholar 
Wang, J. Y. et al. A meta-analysis of effects of physiological integration in clonal plants under homogeneous vs heterogeneous environments. Funct. Ecol. https://doi.org/10.1111/1365-2435.13732 (2020).Article 

Google Scholar 
Maurer, D. A. & Zedler, J. B. Differential invasion of a wetland grass explained by tests of nutrients and light availability on establishment and clonal growth. Oecologia 131, 279–288. https://doi.org/10.1007/s00442-002-0886-8 (2002).ADS 
Article 
PubMed 

Google Scholar 
Mueller, I. M. & Weaver, J. E. Relative drought resistance of seedlings of dominant prairie grasses. Ecology 23, 387–398 (1942).Article 

Google Scholar 
Vetter, V. M. S. et al. Invasion windows for a global legume invader are revealed after joint examination of abiotic and biotic filters. Plant Biol. 21, 832–843. https://doi.org/10.1111/plb.12987 (2019).CAS 
Article 
PubMed 

Google Scholar 
Ibanez, I. et al. Integrated assessment of biological invasions. Ecol. Appl. 24, 25–37. https://doi.org/10.1890/13-0776.1 (2014).Article 
PubMed 

Google Scholar 
Diez, J. M. et al. Will extreme climatic events facilitate biological invasions?. Front. Ecol. Environ. 10, 249–257. https://doi.org/10.1890/110137 (2012).Article 

Google Scholar 
Davis, M. A., Grime, J. P. & Thompson, K. Fluctuating resources in plant communities: a general theory of invasibility. J. Ecol. 88, 528–534. https://doi.org/10.1046/j.1365-2745.2000.00473.x (2000).Article 

Google Scholar 
Li, W. & Stevens, M. H. H. Fluctuating resource availability increases invasibility in microbial microcosms. Oikos 121, 435–441. https://doi.org/10.1111/j.1600-0706.2011.19762.x (2012).Article 

Google Scholar 
Koerner, S. E. et al. Invasibility of a mesic grassland depends on the time-scale of fluctuating resources. J. Ecol. 103, 1538–1546. https://doi.org/10.1111/1365-2745.12479 (2015).Article 

Google Scholar 
Hendrickson, J. R. & Lund, C. Plant community and target species affect responses to restoration strategies. Rangel. Ecol. Manag. 63, 435–442 (2010).Article 

Google Scholar 
Bennett, J., Smart, A. & Perkins, L. Using phenological niche separation to improve management in a Northern Glaciated Plains grassland. Restor. Ecol. 27, 745–749. https://doi.org/10.1111/rec.12932 (2019).Article 

Google Scholar 
Jordan, N. R., Larson, D. L. & Huerd, S. C. Soil modification by invasive plants: effects on native and invasive species of mixed-grass prairies. Biol. Invas. 10, 177–190. https://doi.org/10.1007/s10530-007-9121-1 (2008).Article 

Google Scholar 
Piper, C. L., Lamb, E. G. & Siciliano, S. D. Smooth brome changes gross soil nitrogen cycling processes during invasion of a rough fescue grassland. Plant Ecol. 216, 235–246. https://doi.org/10.1007/s11258-014-0431-y (2015).Article 

Google Scholar 
Stotz, G. C., Gianoli, E. & Cahill, J. F. Biotic homogenization within and across eight widely distributed grasslands following invasion by Bromus inermis. Ecology https://doi.org/10.1002/ecy.2717 (2019).Article 
PubMed 

Google Scholar 
Dillemuth, F. P., Rietschier, E. A. & Cronin, J. T. Patch dynamics of a native grass in relation to the spread of invasive smooth brome (Bromus inermis). Biol. Invas. 11, 1381–1391. https://doi.org/10.1007/s10530-008-9346-7 (2009).Article 

Google Scholar 
Trammell, M. A. & Butler, J. L. Effects of exotic plants on native ungulate use of habitat. J. Wildlife Manag. 59, 808–816. https://doi.org/10.2307/3801961 (1995).Article 

Google Scholar 
Gibson, D. J. Grasses and Grassland Ecology (Oxford Univ. Press, 2009).
Google Scholar 
Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484. https://doi.org/10.1126/science.291.5503.481 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Easterling, D. R. et al. Precipitation change in the United States. pp. 207–230 (Washington, D.C. USA, 2017).Gutschick, V. P. & BassiriRad, H. Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytol. 160, 21–42. https://doi.org/10.1046/j.1469-8137.2003.00866.x (2003).Article 
PubMed 

Google Scholar 
Briske, D. D. in Grazing management: An ecological perspective (eds R.K. Heitschmidt & J.W. Stuth) pp. 85–108 (Timber Press, Inc., 1991).Liu, F., Liu, J. & Dong, M. Ecological consequences of clonal integration in plants. Front. Plant Sci. 217, 277–287 (2016).
Google Scholar 
Hoover, D. L., Knapp, A. K. & Smith, M. D. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology 95, 2646–2656. https://doi.org/10.1890/13-2186.1 (2014).Article 

Google Scholar 
VanderWeide, B. L., Hartnett, D. C. & Carter, D. L. Belowground bud banks of tallgrass prairie are insensitive to multi-year, growing-season drought. Ecosphere. https://doi.org/10.1890/Es14-00058.1 (2014).Article 

Google Scholar 
VanderWeide, B. L. & Hartnett, D. C. Belowground bud bank response to grazing under severe, short-term drought. Oecologia 178, 795–806. https://doi.org/10.1007/s00442-015-3249-y (2015).ADS 
Article 
PubMed 

Google Scholar 
Ott, J. P., Butler, J. L., Rong, Y. P. & Xu, L. Greater bud outgrowth of Bromus inermis than Pascopyrum smithii under multiple environmental conditions. J. Plant Ecol. 10, 518–527. https://doi.org/10.1093/jpe/rtw045 (2017).Article 

Google Scholar 
Oesterheld, M., Loreti, J., Semmartin, M. & Sala, O. E. Inter-annual variation in primary production of a semi-arid grassland related to previous-year production. J. Veg. Sci. 12, 137–142. https://doi.org/10.1111/j.1654-1103.2001.tb02624.x (2001).Article 

Google Scholar 
Ott, J. P. & Hartnett, D. C. Bud bank dynamics and clonal growth strategy in the rhizomatous grass, Pascopyrum smithii. Plant Ecol. 216, 395–405. https://doi.org/10.1007/s11258-014-0444-6 (2015).Article 

Google Scholar 
Carlsson, B. A. & Callaghan, T. V. Programmed tiller differentiation, intraclonal density regulation and nutrient dynamics in Carex bigelowii. Oikos 58, 219–230. https://doi.org/10.2307/3545429 (1990).Article 

Google Scholar 
Ye, X. H., Yu, F. H. & Dong, M. A trade-off between guerrilla and phalanx growth forms in Leymus secalinus under different nutrient supplies. Ann. Bot. Lond. 98, 187–191. https://doi.org/10.1093/aob/mcl086 (2006).Article 

Google Scholar 
Dibbern, J. C. Vegetative responses of Bromus inermis to certain variations in environment. Bot. Gazette 109, 44–58 (1947).Article 

Google Scholar 
Dong, X., Patton, J., Wang, G., Nyren, P. & Peterson, P. Effect of drought on biomass allocation in two invasive and two native grass species dominating the mixed-grass prairie. Grass Forage Sci. 69, 160–166. https://doi.org/10.1111/gfs.12020 (2014).Article 

Google Scholar 
Saeidnia, F., Majidi, M. M., Mirlohi, A. & Soltan, S. Physiological and tolerance indices useful for drought tolerance selection in smooth bromegrass. Crop Sci. 57, 282–289. https://doi.org/10.2135/cropsci2016.07.0636 (2017).CAS 
Article 

Google Scholar 
Vinton, M. A. & Hartnett, D. C. Effects of bison grazing on Andropogon gerardii and Panicum virgatum in burned and unbruned tallgrass prairie. Oecologia 90, 374–382. https://doi.org/10.1007/bf00317694 (1992).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Eneboe, E. J., Sowell, B. F., Heitschmidt, R. K., Karl, M. G. & Haferkamp, M. R. Drought and grazing: IV. Blue grama and western wheatgrass. J. Range Manag. 55, 197–203. https://doi.org/10.2307/4003357 (2002).Article 

Google Scholar 
Broadbent, T. S., Bork, E. W. & Willms, W. D. Divergent effects of defoliation intensity and frequency on tiller growth and production dynamics of Pascopyrum smithii and Hesperostipa comata. Grass Forage Sci. 73, 532–543. https://doi.org/10.1111/gfs.12318 (2018).Article 

Google Scholar 
Donkor, N. T., Bork, E. W. & Hudson, R. J. Bromus-Poa response to defoliation intensity and frequency under three soil moisture levels. Can. J. Plant Sci. 82, 365–370. https://doi.org/10.4141/p01-040 (2002).Article 

Google Scholar 
Reynolds, J. H. & Smith, D. Trend of carbohydrate reserves in alfalfa, smooth bromegrass, and timothy grown under various cutting schedules. Crop Sci. 2, 333–336 (1962).CAS 
Article 

Google Scholar 
Lamp, H. F. Reproductive activity in Bromus inermis in relation to phases of tiller development. Bot. Gazette 113, 413–438 (1952).Article 

Google Scholar 
Paulsen, G. M. & Smith, D. Organic reserves, axillary bud activity, and herbage yields of smooth bromegrass as influenced by time of cutting, nitrogen fertilization, and shading. Crop Sci. 9, 529–534 (1969).Article 

Google Scholar 
Ott, J. P. & Hartnett, D. C. Contrasting bud bank dynamics of two co-occurring grasses in tallgrass prairie: implications for grassland dynamics. Plant Ecol. 213, 1437–1448. https://doi.org/10.1007/s11258-012-0102-9 (2012).Article 

Google Scholar 
Busso, C. A., Mueller, R. J. & Richards, J. H. Effects of drought and defoliation on bud viability in 2 caespitose grasses. Ann. Bot. Lond. 63, 477–485. https://doi.org/10.1093/oxfordjournals.aob.a087768 (1989).Article 

Google Scholar 
Tuomi, J., Nilsson, P. & Astrom, M. Plant compensatory responses-bud dormancy as an adaptation to herbivory. Ecology 75, 1429–1436. https://doi.org/10.2307/1937466 (1994).Article 

Google Scholar 
US Department of Agriculture. The PLANTS Database, (2006).Gong, K. et al. Analysis on the distribution, breeding and utilization of Bromus inermis germplasm resource in China. Heilongjiang Anim. Sci. Vet. Med. 21, 33–36 (2019).
Google Scholar 
Coupland, R. T. & Johnson, R. E. Rooting characteristics of native grassland species in Saskatchewan. J. Ecol. 53, 475–507 (1965).Article 

Google Scholar 
Gist, G. R. & Smith, R. M. Root development of several common forage grasses to a depth of eighteen inches. Agron. J. 1036–1042 (1948).Okamoto, H., Ishii, K. & An, P. Effects of soil moisture deficit and subsequent watering on the growth of four temperate grasses. Grassl. Sci. 57, 192–197. https://doi.org/10.1111/j.1744-697X.2011.00232.x (2011).Article 

Google Scholar 
Morrow, L. A. & Power, J. F. Effect of soil temperature on development of perennial forage grasses. Agron. J. 71, 7–10 (1979).Article 

Google Scholar 
Duell, E. B., Wilson, G. W. T. & Hickman, K. R. Above- and below-ground responses of native and invasive prairie grasses to future climate scenarios. Botany 94, 471–479. https://doi.org/10.1139/cjb-2015-0238 (2016).Article 

Google Scholar 
Duell, E. B., Londe, D. W., Hickman, K. R., Greer, M. J. & Wilson, G. W. T. Superior performance of invasive grasses over native counterparts will remain problematic under warmer and drier conditions. Plant Ecol. 222, 993–1006 (2021).Article 

Google Scholar 
Cully, A. C., Cully, J. F. & Hiebert, R. D. Invasion of exotic plant species in tallgrass prairie fragments. Conser. Biol. 17, 990–998. https://doi.org/10.1046/j.1523-1739.2003.02107.x (2003).Article 

Google Scholar 
DeKeyser, E. S., Meehan, M., Clambey, G. & Krabbenhoft, K. Cool season invasive grasses in northern great plains natural areas. Nat. Areas J. 33, 81–90. https://doi.org/10.3375/043.033.0110 (2013).Article 

Google Scholar 
Grant, T. A., Shaffer, T. L. & Flanders, B. Resiliency of native prairies to invasion by kentucky bluegrass, smooth brome, and woody vegetation. Rangeland Ecol. Manag. 73, 321–328. https://doi.org/10.1016/j.rama.2019.10.013 (2020).Article 

Google Scholar 
Otfinowski, R., Kenkel, N. C. & Catling, P. M. The biology of Canadian weeds. 134. Bromus inermis Leyss. Can. J. Plant Sci. 87, 183–198. https://doi.org/10.4141/p06-071 (2007).Article 

Google Scholar 
Moore, K. J. et al. Describing and quantifying growth stages of perennial forage grasses. Agron. J. 83, 1073–1077 (1991).Article 

Google Scholar 
SAS Institute. SAS 9.4. (SAS Institute Inc, 2017). More

Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Weng, L. et al. Mineral industries, growth corridors and agricultural development in Africa. Glob. Food Sec. 2, 195–202 (2013).
Google Scholar 
Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669 (2009).PubMed 

Google Scholar 
Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 14, 18–30 (2000).
Google Scholar 
van der Ree, R., Smith, D. J. & Grilo, C. The ecological effects of linear infrastructure and traffic. in Handbook of road ecology 1–9 (John Wiley and Sons, Ltd., 2015). https://doi.org/10.1002/9781118568170.ch1.Grilo, C., Smith, D. J. & Klar, N. Carnivores: Struggling for survival in roaded landscapes. in Handbook of road ecology 300–312 (John Wiley and Sons, Ltd., 2015). doi:https://doi.org/10.1002/9781118568170.ch35.Wallach, A. D., Izhaki, I., Toms, J. D., Ripple, W. J. & Shanas, U. What is an apex predator?. Oikos 124, 1453–1461 (2015).
Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, (2014).Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Stolton, S. & Dudley, N. The New Lion Economy. Unlocking the value of lions and their landscapes. http://lionrecoveryfund.org/newlioneconomy (2019).Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018). Data is available at http://www.globio.infoAscensão, F. et al. Environmental challenges for the belt and road initiative. Nat. Sustain. 1, 206–209 (2018).
Google Scholar 
Dulac, J. Global land transport infrastructure requirements – Estimating road and railway infrastructure capacity and costs to 2050. (International Energy Agency, 2013).Laurance, W. F. et al. Reducing the global environmental impacts of rapid infrastructure expansion. Curr. Biol. 25, R259–R262 (2015).CAS 
PubMed 

Google Scholar 
Vilela, T. et al. A better Amazon road network for people and the environment. Proc. Natl. Acad. Sci. 117, 7095–7102 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Laurance, W. F., Sloan, S., Weng, L. & Sayer, J. A. Estimating the environmental costs of Africa’s massive “development corridors”. Curr. Biol. 25, 3202–3208 (2015).CAS 
PubMed 

Google Scholar 
Sharma, R., Rimal, B., Stork, N., Baral, H. & Dhakal, M. Spatial assessment of the potential impact of infrastructure development on biodiversity conservation in lowland Nepal. ISPRS Int. J. Geo Inf. 7, 365 (2018).
Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2018-1. http://www.iucnredlist.org (2018).Garrote, G. et al. Prediction of Iberian lynx road-mortality in southern Spain: A new approach using the MaxEnt algorithm. Anim. Biodivers. Conserv. 41, 217–225 (2018).
Google Scholar 
Parchizadeh, J. et al. Roads threaten Asiatic cheetahs in Iran. Curr. Biol. 28, R1141–R1142 (2018).CAS 
PubMed 

Google Scholar 
Crooks, K. R., Burdett, C. L., Theobald, D. M., Rondinini, C. & Boitani, L. Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. B Biol. Sci. 366, 2642–2651 (2011).
Google Scholar 
Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38, 155–163 (2004).
Google Scholar 
Valeix, M., Loveridge, A. J. & Macdonald, D. W. Influence of prey dispersion on territory and group size of African lions: A test of the resource dispersion hypothesis. Ecology 93, 2490–2496 (2012).PubMed 

Google Scholar 
Holderegger, R. & Giulio, M. D. The genetic effects of roads: a review of empirical evidence. Basic Appl. Ecol. 11, 522–531 (2010).
Google Scholar 
Riley, S. P. D. et al. A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol. Ecol. 15, 1733–1741 (2006).CAS 
PubMed 

Google Scholar 
Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. R. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
Google Scholar 
Riley, S. P. D. et al. Individual behaviors dominate the dynamics of an urban mountain lion population isolated by roads. Curr. Biol. 24, 1989–1994 (2014).CAS 
PubMed 

Google Scholar 
Janečka, J. E. et al. Reduced genetic diversity and isolation of remnant ocelot populations occupying a severely fragmented landscape in southern Texas. Anim. Conserv. 14, 608–619 (2011).
Google Scholar 
Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E. & Ramakrishnan, U. Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol. Cons. 218, 181–191 (2018).
Google Scholar 
Vaeokhaw, S. et al. Effects of a highway on the genetic diversity of Asiatic black bears. Ursus 2020, 1–15 (2020).
Google Scholar 
Benı́tez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).ADS 
PubMed 

Google Scholar 
Clements, G. R. et al. Where and how are roads endangering mammals in Southeast Asia’s forests?. PLoS ONE 9, e115376 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sharma, K., Wright, B., Joseph, T. & Desai, N. Tiger poaching and trafficking in India: Estimating rates of occurrence and detection over four decades. Biol. Cons. 179, 33–39 (2014).
Google Scholar 
Espinosa, S., Branch, L. C. & Cueva, R. Road development and the geography of hunting by an Amazonian indigenous group: Consequences for wildlife conservation. PLoS ONE 9, e114916 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wato, Y. A., Wahungu, G. M. & Okello, M. M. Correlates of wildlife snaring patterns in Tsavo west national park Kenya. Biol. Conserv. 132, 500–509 (2006).
Google Scholar 
Watson, F., Becker, M. S., McRobb, R. & Kanyembo, B. Spatial patterns of wire-snare poaching: Implications for community conservation in buffer zones around national parks. Biol. Cons. 168, 1–9 (2013).
Google Scholar 
Henschel, P., Hunter, L. T. B., Coad, L., Abernethy, K. A. & Mühlenberg, M. Leopard prey choice in the Congo Basin rainforest suggests exploitative competition with human bushmeat hunters. J. Zool. 285, 11–20 (2011).
Google Scholar 
Espinosa, S., Celis, G. & Branch, L. C. When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation. PLoS ONE 13, e0189740 (2018).PubMed 
PubMed Central 

Google Scholar 
Parsons, M. A., Newsome, T. M. & Young, J. K. The consequences of predators without prey. Front. Ecol. Environ. https://doi.org/10.1002/fee.2419 (2021).Article 

Google Scholar 
Caro, T., Dobson, A., Marshall, A. J. & Peres, C. A. Compromise solutions between conservation and road building in the tropics. Curr. Biol. 24, R722–R725 (2014).CAS 
PubMed 

Google Scholar 
Grilo, C. et al. Conservation threats from roadkill in the global road network. Glob. Ecol. Biogeogr. 30, 2200–2210 (2021).
Google Scholar 
Carter, N., Killion, A., Easter, T., Brandt, J. & Ford, A. Road development in Asia: assessing the range-wide risks to tigers. Sci. Adv. 6(18), eaaz9619 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ceia-Hasse, A., Borda-de-Água, L., Grilo, C. & Pereira, H. M. Global exposure of carnivores to roads. Glob. Ecol. Biogeogr. 26, 592–600 (2017).
Google Scholar 
Gaveau, D. L. A. et al. Four decades of forest persistence, clearance and logging on Borneo. PLoS ONE 9, e101654 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gerber, B. D., Karpanty, S. M. & Randrianantenaina, J. The impact of forest logging and fragmentation on carnivore species composition, density and occupancy in Madagascar’s rainforests. Oryx 46, 414–422 (2012).
Google Scholar 
Cullen, L. et al. Implications of fine-grained habitat fragmentation and road mortality for jaguar conservation in the Atlantic forest, Brazil. PLoS ONE 11, e0167372 (2016).PubMed 
PubMed Central 

Google Scholar 
Kirby, K. R. et al. The future of deforestation in the Brazilian Amazon. Futures 38, 432–453 (2006).
Google Scholar 
UNEP-WCMC & IUCN. Protected planet: The world database on protected areas. http://www.protectedplanet.net (2019).de la Torre, J. A., Gonzalez-Maya, J. F., Zarza, H., Ceballos, G. & Medellin, R. A. The jaguars spots are darker than they appear: assessing the global conservation status of the jaguar Panthera onca. Oryx 52, 300–315 (2017).
Google Scholar 
Coelho, L., Romero, D., Queirolo, D. & Guerrero, J. C. Understanding factors affecting the distribution of the maned wolf (Chrysocyon brachyurus) in South America: spatial dynamics and environmental drivers. Mamm. Biol. 92, 54–61 (2018).
Google Scholar 
Fearnside, P. M. Brazil’s Cuiabá- Santarém (BR-163) highway: The environmental cost of paving a soybean corridor through the Amazon. Environ. Manage. 39, 601–614 (2007).ADS 
PubMed 

Google Scholar 
Vetter, D., Hansbauer, M. M., Végvári, Z. & Storch, I. Predictors of forest fragmentation sensitivity in neotropical vertebrates: A quantitative review. Ecography 34, 1–8 (2011).
Google Scholar 
Morcatty, T. Q. et al. Illegal trade in wild cats and its link to Chinese-led development in central and South America. Conserv. Biol. 34, 1525–1535 (2020).PubMed 

Google Scholar 
Ramsar. Ngiri-Tumba-Maindombe. Ramsar Sites Information Service https://rsis.ramsar.org/ris/1784 (2017).Dobson, A. P. et al. Road will ruin Serengeti. Nature 467, 272–273 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2012).
Google Scholar 
Government of Nepal. Economic survey 2019/20. (Ministry of Finance, 2020).Jnawali, S. R. et al. The status of Nepal mammals: The national red list series. (Department of National Parks and Wildlife Conservation, 2011).Joshi, A. R. Nepal court blocks road construction in rhino stronghold of Chitwan Park. https://news.mongabay.com/2019/02/nepal-court-blocks-road-construction-in-rhino-stronghold-of-chitwan-park/ (2019).Government of Nepal. Conservation Landscapes of Nepal. (Ministry of Forest and Soil Conservation, 2016).Poudel, A. et al. Biological and socio-economic study in corridors of Terai Arc Landscape, Nepal. (Center for Policy Analysis; Development, 2013).Arlidge, W. N. S. et al. A Global Mitigation Hierarchy for Nature Conservation. Bioscience 68, 336–347 (2018).PubMed 
PubMed Central 

Google Scholar 
Ekstrom, J., Bennun, L. & Mitchell, R. A Cross-Sector Guide for Implementing the Mitigation Hierarchy. (The Biodiversity Consultancy, 2015).Malo, J. E., Suárez, F. & Díez, A. Can we mitigate animal-vehicle accidents using predictive models?. J. Appl. Ecol. 41, 701–710 (2004).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (Version 4.0.3). https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).
Google Scholar 
Santini, L., Boitani, L., Maiorano, L. & Rondinini, C. Effectiveness of protected areas in conserving large carnivores in Europe. in Protected areas 122–133 (John Wiley and Sons, Ltd., 2016). https://doi.org/10.1002/9781118338117.ch7.Rodrigues, A. S. L., Pilgrim, J. D., Hoffmann, M. & Lamoreux, J. F. The value of the IUCN red list for conservation. Trends Ecol. Evol. 21, 71–76 (2006).PubMed 

Google Scholar 
Government of Brazil. Mapas multimodais. Ministério da Infraestrutura http://www.infraestrutura.gov.br/ (2018).Assis, L. F. F. G. et al. TerraBrasilis: A spatial data analytics infrastructure for large-scale thematic mapping. ISPRS Int. J. Geo Inf. 8, 513 (2019).
Google Scholar  More