Ecology

Occurrence of pesticides in small mammals: general patternsA total of 112 different compounds were detected over the 140 parent pesticides and metabolites screened in hair samples (80% of the compounds screened). The full lists of compounds with their acronyms, the details of their full names and chemical families are provided in Tables 1 and 2.Table 1 Concentrations of banned and restricted pesticides (BRPs) in small mammal hair samples, classified by decreasing number of detection.Full size tableTable 2 Concentrations of currently used pesticides (CUPs) in small mammal hair samples, ordered by decreasing number of detection.Full size tableAs a whole, 51 BRPs over 67 analyzed (76%) were detected in small mammal hair, with 27 parent chemicals detected out of 39 screened (67%) and 25 metabolites detected out of 28 (89%) (Table 1). Thirteen compounds were present in more than 75% of individuals: DMP, PNP, 1-(3,4-dichlorophenyl)urea, DEP, PCP, 3Me4NP, 1-(3,4-dichlorophenyl)-3-methylurea, DETP, fipronil, fipronil sulfone, trifluralin, DMTP and HCB. Most of them are transformation products of organochlorine, organophosphorous, urea and phenylpyrazole pesticides. Then, the proportion of detection rapidly dropped under 25% of the samples. Only three compounds were detected in 50–75% of the individuals (Table 1: lindane γ-HCH (organochlorine insecticide), terbutryn (triazine/triazinone herbicide) and fenuron (urea herbicide). Five substances were found in 25–50% of the animals: DMST (metabolite of tolylfluanide, an amide fungicide), flusilazole (azole fungicide), α-endosulfan (organochlorine insecticide), DMDTP (organophosphorous insecticide metabolite) and diuron (urea herbicide). The 10 highest measured concentrations ranged between 30 and 118 ng/g, and were mostly represented by DMP (seven of the 10 values) together with PNP and 1-(3,4-dichlorophenyl)urea. Seven compounds exhibited concentrations higher than 10 ng/g, which were the same as the most frequent: DMP, PNP, 1-(3,4-dichlorophenyl)urea, DEP, PCP, 3Me4NP, plus DEDTP (organophosphorous metabolite, 6% of individuals). Considering the 16 BRPs that have never been detected, 13 were parent pesticides and three were metabolites, distributed in one fungicide, three herbicides, and 12 insecticides/biocides. The non-detected compounds belong to several chemical families including organochlorines, organophosphorous, carbamate, and urea pesticides.A total of 61 CUPs out of 73 analyzed were detected in small mammal hair, with 54 parent pesticides out of 66 tested (82%) and seven metabolites detected out of seven screened (100%) (Table 2). Many of the detected CUPs were found in a large proportion of individuals: 25 compounds were detected in more than 75% of the individuals, which means that 41% of the 61 detected CUPs were present in 75–100% of individuals. These 25 most frequently detected compounds belonged to various chemical families and all uses of CUPs (Table 2). The herbicides belonged to the families of organochlorines (metolachlor and metazachlor), acid herbicides (MCPA, 2,4-d,dichlorprop and mecoprop), thiocarbamates (prosulfocarb), amide pesticides (dimethachlor), uracils (lenacil), and dinitroaniline (pendimethalin). The fungicides were of the main families strobilurines (azoxystrobin and pyraclostrobin), azoles (tebuconazole, epoxiconazole, thiabendazole, prochloraz, and propiconazole; cyproconazole in 73% of individuals), carbamates (carbendazim) and carboxamides (boscalid). The most frequently detected insecticides were mainly metabolites of pyrethroids (3-PBA, Cl2CA, and ClCF3CA), as well as neonicotinoids (thiacloprid and imidacloprid) and the specific metabolite of chlorpyrifos TCPy (3,5,6-trichloro-2-pyridinol; organophosphorous pesticide). Noticeably, the five herbicides isoproturon (urea), propyzamide (benzamide), chlortoluron (urea), oxadiazon (oxadiazin) and diflufenican (carboxamide), as well as the fungicide trifloxystrobin (strobilurin) and the insecticide cypermethrine (pyrethroid), were detected in at least 50% of the samples (Table 2). Five more compounds were detected in 25–50% of animals: zoxamide (benzamide), difenoconazole (azole), cyhalothrin and Br2CA (pyrethroids), and 2,4-DB (acid herbicide). The 10 highest measured concentrations ranged from 200 to 500 ng/g, which were far higher than for BRPs. These high concentrations were found for the fungicides boscalid, carbendazim, and prochloraz and the herbicides dichlorprop, MCPA, and propyzamide. A greater number of compounds exhibited higher concentrations than observed for BRPs, since 29 compounds presented concentrations higher than 10 ng/g. Moreover, 16 compounds were quantified at higher levels than 50 ng/g, and 10 compounds at higher levels than 100 ng/g (Table 2). The 10 compounds that had the highest concentrations were the herbicides propyzamide, MCPA, dichlorprop, diflufenican, mecoprop, and metolachlor, and the fungicides boscalid, epoxiconazole, carbendazim, and prochloraz. They were not all among the most detected compounds (Table 2). Six compounds exhibited concentrations ranging from 50 to 100 ng/g: the insecticide imidacloprid, the herbicides aclonifen and isoproturon, and the fungicides cyproconazole, propiconazole and tebuconazole. Various chemical families are represented among the CUPs exhibiting high concentrations in small mammals, including carbamates, carboxamids and benzamids, acid and urea herbicids, azoles and neonicotinoids (Table 2). The insecticides showed concentrations overall lower than herbicides and fungicides, since no value above 50 ng/g was measured within insecticides except for imidacloprid. Besides the neonicotinoid imidacloprid, the insecticides showing the highest values ( > 10 ppb) were all pyrethroids, either parents or their metabolites (cyfluthrine, cyhalothrine, permethrine, 3-PBA, Br2CA, Cl2CA). Among the 12 CUPs that have never been detected, only parent compounds were present, with six fungicides, two herbicides and four insecticides belonging to various chemical families such as azole, carbamate, organophosphorous, triazine, neonicotinoid, strobilurine, oxadiazine and urea pesticides.A significant positive relationship was found between detections of CUPs in small mammal hair samples and the quantities of pesticides sold in 2016 in the Region were the ZAPVS is located (i.e. Deux-Sèvre, where most of small mammals in this study were captured and analyzed) (Spearman’s rho = 0.66, p-value More

Yuan, F. & Miyamoto, S. Dominant processes controlling water chemistry of the Pecos River in American Southwest. Geophys. Res. Lett. 32(17), L17406. https://doi.org/10.1029/2005GL023359 (2005).ADS 
CAS 
Article 

Google Scholar 
Yuan, F., Miyamoto, S. & Anand, S. Changes in major element hydrochemistry of the Pecos River in the American Southwest since 1935. Appl. Geochem. 22(8), 1798–1813. https://doi.org/10.1016/j.apgeochem.2007.03.036 (2007).ADS 
CAS 
Article 

Google Scholar 
Harley, G. L. & Maxwell, J. T. Current declines of Pecos River (New Mexico, USA) streamflow in a 700-year context. Holocene 28(5), 766–777. https://doi.org/10.1177/0959683617744263 (2018).ADS 
Article 

Google Scholar 
Jensen, R., Hatler, W., Mecke, M. & Hart, C. The Influences of Human Activities on the Water of the Pecos River Basin of Texas: A Brief Overview. Technical Report. SR-2006-03. Texas Water Resources Institute (2006).Hoagstrom, C. W. Causes and impacts of salinization in the lower Pecos River. Gt. Plains Res. 19(1), 27–44 (2009).
Google Scholar 
Williams, A. P., Cook, B. I. & Smerdon, J. E. Rapid intensification of the emerging North American megadrought in 2020–2021. Nat. Clim. Change 12(3), 232–234. https://doi.org/10.1038/s41558-022-01290-z (2022).ADS 
Article 

Google Scholar 
Cheek, C. A. & Taylor, C. M. Salinity and geomorphology drive long-term changes to local and regional fish assemblage attributes in the lower Pecos River, Texas. Ecol. Freshw. Fish 25(3), 340–351. https://doi.org/10.1111/eff.12214 (2015).Article 

Google Scholar 
Pease, A. A. & Delaune, K. D. Dried and salted: cumulative impacts of diminished flows and salinization on the lower Pecos River food webs. In Proceedings of the Desert Fishes Council Special Publication. Vol. 2021, 2–19. https://doi.org/10.26153/tsw/12364 (2021)Linam, G. W. & Kleinsasser, L. J. Relationships Between Fishes and Water Quality in the Pecos River, Texas. River Studies Report. No. 9. Texas Parks and Wildlife Department (1996).Hoagstrom, C. W., Zymonas, N. D., Davenport, S. R., Propst, D. L. & Brooks, J. E. Rapid species replacements between fishes of the North American plains: A case history from the Pecos River. Aquat. Invasions 5(2), 141–153. https://doi.org/10.3391/ai.2010.5.2.03 (2010).Article 

Google Scholar 
Randklev, C. R. et al. A semi-arid river in distress: Contributing factors and recovery solutions for three imperiled freshwater mussels (Family Unionidae) endemic to the Rio Grande Basin in North America. Sci. Total Environ. 631–632, 733–744. https://doi.org/10.1016/j.scitotenv.2018.03.032 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kimmons, J. B. & Moll, D. Seed dispersal by Red-eared sliders (Trachemys scripta elegans) and Common snapping turtles (Chelydra serpentina). Chelonian Conserv. Biol. 9(2), 289–294. https://doi.org/10.2744/CCB-0797.1 (2010).Article 

Google Scholar 
Lazar, B. et al. Loggerhead sea turtles (Caretta caretta) as bioturbators in neritic habitats: An insight through the analysis of benthic molluscs in the diet. Mar. Ecol. 32(1), 65–74. https://doi.org/10.1111/j.1439-0485.2010.00402.x (2011).ADS 
Article 

Google Scholar 
Lovich, J. E., Ennen, J. R., Agha, M. & Gibbons, J. W. Where have all the turtles gone, and why does it matter?. Bioscience 68(10), 771–781. https://doi.org/10.1093/biosci/biy095 (2018).Article 

Google Scholar 
de Solla, S. R., Fernie, K. J. & Ashpole, S. Snapping turtles (Chelydra serpentina) as bioindicators in Canadian areas of concern in the Great Lakes Basin. II. Changes in hatching success and hatchling deformities in relation to persistent organic pollutants. Environ. Pollut. 153(3), 529–536. https://doi.org/10.1016/j.envpol.2007.09.017 (2008).CAS 
Article 
PubMed 

Google Scholar 
Adams, C. I. M., Baker, J. E. & Kjellerup, B. V. Toxicological effects of polychlorinated biphenyls (PCBs) on freshwater turtles in the United States. Chemosphere 154, 148–154. https://doi.org/10.1016/j.chemosphere.2016.03.102 (2016).ADS 
CAS 
Article 

Google Scholar 
Beau, F., Bustamante, P., Michaud, B. & Brischoux, F. Environmental causes and reproductive correlates of mercury contamination in European pond turtles (Emys orbicularis). Environ. Res. 172(4), 338–344. https://doi.org/10.1016/j.envres.2019.01.043 (2019).CAS 
Article 
PubMed 

Google Scholar 
van Dijk, P. P. Pseudemys gorzugi (errata version published in 2016). The IUCN Red List of Threatened Species Vol. 2011, e.T18459A97. (2011).NMDGF [New Mexico Department of Game and Fish]. Threatened and Endangered Species of New Mexico, 2020 Biennial Review. Management and Fisheries Management Divisions (2020).SEMARNAT [Secretaríade Medio Ambiente y Recursos Naturales]. NORMA Oficial Mexicana NOM-059-SEMARNAT-2010, Protección ambiental–Especies nativas de México de flora y fauna silvestres–Categorías de riesgo y especificaciones para su inclusión, exclusión o cambio–Lista de especies en riesgo. Diario Oficial de la Federación Vol. 2 (2010).TPWD [Texas Parks & Wildlife Department]. Species Account: the Rio Grande River Cooter (Pseudemys gorzugi). In Texas Comprehensive Wildlife Conservation Strategy 2005–2010 (eds Bender, S., Shelton, S., Bender, K. & Kalmbach, A.). Nongame Division, 1075–7076 (2012).Pierce, L. J. S., Stuart, J. N., Ward, J. P. & Painter, C. W. Pseudemys gorzugi Ward 1984–Rio Grande Cooter, Western River Cooter, Tortuga de Oreja Amarilla, Jicotéa del Rio Bravo In Conservation Biology of Freshwater Turtles and Tortoises: A Compilation Project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group (eds. Rhodin, A. G. J. et al.). Chelonian Res. Monog. Vol. 5, No. 9, 100.1–100.12. https://doi.org/10.3854/crm.5.100.gorzugi.v1.2016 (2016).Endangered and Threatened Wildlife and Plants. Endangered and Threatened Wildlife and Plants; three species not warranted for listing as endangered or threatened species. Fed. Reg. 87(49), 14227–14228 (2022).
Google Scholar 
Bailey, L. A., Forstner, M. R. J., Dixon, J. R. & Hudson, R. Contemporary status of the Rio Grande Cooter (Testudines: Emydidae: Pseudemys gorzugi) in Texas: phylogenetic, ecological and conservation consideration. In Proceedings of the Sixth Symposium on the Natural Resources of the Chihuahuan Desert Region (eds. Hoyt, C. A. & Karges, J.) 320–324. (Chihuahuan Desert Research Institute, 2014).Suriyamongkol, T., Waldon, K. J. & Mali, I. Trachemys scripta (Red-eared Slider) and Pseudemys gorzugi (Rio Grande Cooter). Fish hook ingestion and shooting. Herpetol. Rev. 50(4), 776–777 (2019).
Google Scholar 
Degenhardt, W. G., Painter, C. W. & Price, A. H. Amphibians and Reptiles of New Mexico (University of New Mexico Press, 1996).
Google Scholar 
Ernst, C. H. Turtles of the United States and Canada 2nd edn. (Johns Hopkins University Press, 2009).
Google Scholar 
Dixon, J. R. Amphibians and Reptiles of Texas: With Keys, Taxonomic Synopses, Bibliography, and Distribution Maps 3rd edn. (Texas A&M University Press, 2013).
Google Scholar 
Suriyamongkol, T. et al. Geographic distribution. Pseudemys gorzugi (Rio Grande Cooter). Herpetol. Rev. 51(3), 536–537 (2020).
Google Scholar 
Christman, B. L. & Kamees, L. K. Current Distribution of the Blotched Watersnake (Nerodia erythrogaster) and the Rio Grande Cooter (Pseudemys gorzugi) in the Lower Pecos River System Eddy County, New Mexico 2006–2007. Final Report. New Mexico Department of Game and Fish (2007).Bogolin, A. P., Davis, D. R., Ruppert, K. M., Kline, R. J. & Rahmann, A. F. Geographic distribution. Pseudemys gorzugi (Rio Grande Cooter). Herpetol. Rev. 50(4), 745 (2019).
Google Scholar 
Congdon, J. D., Dunham, A. E. & Van Loben Sels, R. C. Demographics of common snapping turtles (Chelydra serpentina): Implications for conservation and management of long-lived organisms. Am. Zool. 34, 397–408. https://doi.org/10.1093/icb/34.3.397 (1994).Article 

Google Scholar 
Brooks, R. J., Brown, G. P. & Galbraith, D. A. Effects of a sudden increase in natural mortality of adults on a population of the common snapping turtle (Chelydra serpentina). Can. J. Zool. 69, 1314–1320. https://doi.org/10.1139/z91-185 (1991).Article 

Google Scholar 
Congdon, J. D., Dunham, A. E. & Van Loben Sels, R. C. Delayed sexual maturity and demographics of Blanding’s turtles (Emydoidea blandingii): Implications for conservation and management of long-lived organisms. Conserv. Biol. 7(4), 826–833. https://doi.org/10.1046/j.1523-1739.1993.740826.x (1993).Article 

Google Scholar 
Suriyamongkol, M. & Mali, I. Aspects of the reproductive biology of the Rio Grande Cooter (Pseudemys gorzugi) on the Black River, New Mexico. Chelonian Conserv. Biol. https://doi.org/10.2744/CCB-1385.1 (2019).Article 

Google Scholar 
Bailey, L. A., Dixon, J. R., Hudson, R. & Forstner, M. R. J. Minimal genetic structure of the Rio Grande Cooter (Pseudemys gorzugi). Southwest. Nat. 53(3), 406–411. https://doi.org/10.1894/GC-179.1 (2008).Article 

Google Scholar 
Mali, I., Duarte, A. & Forstner, M. R. J. Comparison of hoop-net trapping and visual surveys to monitor abundance of the Rio Grande Cooter (Pseudemys gorzugi). PeerJ 6, e4677:1-16. https://doi.org/10.7717/peerj.4677 (2018).Article 

Google Scholar 
Hart, C. R., McDonald, A. & Hatler, W. Pecos River Ecosystem Monitoring Project. Technical Report. Texas Cooperative Extension: The Texas A&M University System. (2005).Hong, M., Zhang, K., Shu, C., Xie, D. & Shi, H. Effect of salinity on the survival, ions, and urea modulation in Red-eared Slider (Trachemys scripta elegans). Asian Herpetol. Res. 5(2), 128–136. https://doi.org/10.3724/SP.J.1245.2014.00128 (2014).Article 

Google Scholar 
Hintz, W. D. et al. Salinization triggers a trophic cascade in experimental freshwater communities with varying food-chain length. Ecol. Appl. 27(3), 833–844. https://doi.org/10.1002/eap.1487 (2017).Article 
PubMed 

Google Scholar 
Letter, A. W., Waldon, K. J., Pollock, D. A. & Mali, I. Dietary habits of Rio Grande Cooters (Pseudemys gorzugi) from two sites within the Black River, Eddy County, New Mexico, USA. J. Herpetol. 53(3), 204–208. https://doi.org/10.1670/18-057 (2019).Article 

Google Scholar 
Suriyamongkol, T., Ortega-Berno, V., Mahan, L. B. & Mali, I. Using stable isotopes to study resource partitioning between Red-eared Slider and Rio Grande Cooter in the Pecos River watershed. Ichthyol. Herpetol. 110(1), 96–105. https://doi.org/10.1643/h2021023 (2022).Article 

Google Scholar 
Bassett, L. G., Mali, I., Nowlin, W. H., Foley, D. H. & Forstner, M. R. J. Diet and isotopic niche of the Rio Grande Cooter (Pseudemys gorzugi) and syntopic Red-eared Slider (Trachemys scripta elegans) in San Felipe Creek, Texas, USA. Chelonian Conserv. Biol. (in Press).Bárcenas-García, A. et al. Impacts of dams on freshwater turtles: A global review to identify conservation solutions. Trop. Conserv. Sci. 15(4), 1–21. https://doi.org/10.1177/194008292211037098 (2021).Article 

Google Scholar 
Smith, M. J. et al. Association between anuran tadpoles and salinity in a landscape mosaic of wetlands impacted by secondary salinisation. Freshw. Biol. 52(1), 75–84. https://doi.org/10.1111/j.1365-2427.2006.01672.x (2007).Article 

Google Scholar 
Wohner, P. J. et al. Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts. Ecol. Model. 471(2022), 110058. https://doi.org/10.1016/j.ecolmodel.2022.110058 (2022).Article 

Google Scholar 
Suriyamongkol, T., Tian, W. & Mali, I. Monitoring the basking behavior of Rio Grande Cooter (Pseudemys gorzugi) through game camerias in southeastern New Mexico, USA. West. N. Am. Nat. 81(3), 361–371. https://doi.org/10.3398/064.081.0305 (2021).Article 

Google Scholar 
Painter, C. W. Preliminary Investigations of the Distribution and Natural History of the Rio Grande River Cooter (Pseudemys gorzugi) in New Mexico. Preliminary Report. (United States Department of the Interior–Bureau of Land Management, 1993).Hak, J. C. & Comer, P. J. Modeling landscape condition for biodiversity assessment—Application in temperate North America. Ecol. Indic. 82, 206–216. https://doi.org/10.1016/j.ecolind.2017.06.049 (2017).Article 

Google Scholar 
ESRI. ArcGIS Desktop. Ver. 10.8 (Environmental System Research Institute, 2020).MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8), 2248–2255. https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 (2002).Article 

Google Scholar 
Tyre, A. J. et al. Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecol. Appl. 13(6), 1790–1801. https://doi.org/10.1890/02-5078 (2003).Article 

Google Scholar 
Mackenzie, D. I. et al. Occupancy Estimation and Modeling: Inferring Dynamics of Species Occurrence 2nd edn. (Elsevier, 2017).
Google Scholar 
Duarte, A., Whitlock, S. L. & Peterson, J. T. Species distribution modeling. In Encyclopedia of Ecology 2nd edn (ed. Fath, B. D.) (Elsevier, 2019).
Google Scholar 
MacLaren, A. R., Foley, D. H., Sirsi, S. & Forstner, M. R. J. Updating methods of satellite transmitter attachment for long-term monitoring of the Rio Grande Cooter (Pseudemys gorzugi). Herpetol. Rev. 48(1), 48–52 (2017).
Google Scholar 
MacLaren, A. R., Sirsi, S., Foley, D. H. & Forstner, M. R. J. Pseudemys gorzugi (Rio Grande Cooter). Long distance dispersal. Herpetol. Rev. 48(1), 180–181 (2017).
Google Scholar 
Fiske, I. & Chandler, R. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43(10), 1–23. https://doi.org/10.18637/jss.v043.i10 (2011).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (Foundation For Statistical Computing, 2021).
Google Scholar 
Morin, D. J. et al. Is your ad hoc model selection strategy affecting your multimodel inference?. Ecosphere 11(1), e02997. https://doi.org/10.1002/ecs2.2997 (2020).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretic Approach 1st edn. (Springer, XXX, 1998).Book 

Google Scholar 
Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression 3rd edn. (Wiley, 2013).Book 

Google Scholar 
Gasparrini, A., Armstrong, B. & Kenward, M. G. Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat. Med. 31(29), 3821–3839. https://doi.org/10.1002/sim.5471 (2012).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, D., White, I. R. & Riley, R. D. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biom. J. 55(2), 231–245. https://doi.org/10.1002/bimj.201200152 (2013).MathSciNet 
Article 
PubMed 
PubMed Central 
MATH 

Google Scholar  More

Predator abundanceThe number of predators on the aphid colonies varied spatiotemporally (Fig. 2). In particular, the number of predators in population A was significantly larger than that in population B in August but not in September (August, t20 = 3.93, P  0.05). In population A, we found predators on the aphid colonies in August and September, but not in June and July. In August, the only predators found were A. ignipicta larvae (0.76 ± 0.19 individuals per aphid colony), whereas in September the predators comprised both A. ignipicta larvae (0.033 ± 0.033 individuals per aphid colony) and T. hamada larvae (0.033 ± 0.033 individuals per aphid colony). In population B, we found no predators in any of the months.Figure 2Temporal and between-population variation in the number of predators per aphid colony. The number of predators represents the sum of the numbers of A. ignipicta and T. hamada larvae. Error bars denote s.e. Asterisks indicate a significant difference between populations (***P  More

Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nussey, D. H., Froy, H., Lemaitre, J.-F., Gaillard, J.-M. & Austad, S. N. Senescence in natural populations of animals: Widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).PubMed 
Article 

Google Scholar 
Sergio, F. et al. Variation in age-structured vital rates of a long-lived raptor: Implications for population growth. Basic Appl. Ecol. 12, 107–115 (2011).Article 

Google Scholar 
Sydeman, W. J., Huber, H. R., Emslie, S. D., Ribic, C. A. & Nur, N. Age-specific weaning success of northern elephant seals in relation to previous breeding experience. Ecology 76, 2204–2217 (1991).Article 

Google Scholar 
Gaillard, J.-M., Festa-Bianchet, M., Yoccoz, N. G., Loison, A. & Toïgo, C. Temporal variation in fitness components and population dynamics of large herbivores. Annu. Rev. Ecol. Syst. 31, 367–393 (2000).Article 

Google Scholar 
Caswell, H. Matrix Population Models. Online Library (Wiley, 2001).
Google Scholar 
Colchero, F. et al. The diversity of population responses to environmental change. Ecol. Lett. 22, 342–353 (2019).PubMed 
Article 

Google Scholar 
Morris, W. F. & Doak, D. F. Quantitative Conservation Biology (Sinauer, 2002).
Google Scholar 
Stott, I., Townley, S. & Hodgson, D. J. A framework for studying transient dynamics of population projection matrix models: A synthesis of transient demography. Ecol. Lett. 14, 959–970 (2011).PubMed 
Article 

Google Scholar 
Vitousek, P. M. Beyond global warming: Ecology and global change. Ecology 75, 1861–1876 (1994).Article 

Google Scholar 
Gamelon, M. et al. Influence of life-history tactics on transient dynamics: A comparative analysis across mammalian populations. Am. Nat. 184, 673–683 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Jackson, J., Mar, K. U., Htut, W., Childs, D. Z. & Lummaa, V. Changes in age?structure over four decades were a key determinant of population growth rate in a long-lived mammal. J. Anim. Ecol. 89, 2268–2278 (2020).PubMed 
Article 

Google Scholar 
Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
Oro, D. & Pradel, R. Recruitment of audouin’s gull to the ebro delta colony at metapopulation level in the western mediterranean. Mar. Ecol. Prog. Ser. 180, 267–273 (1999).ADS 
Article 

Google Scholar 
Payo-Payo, A. et al. Predator arrival elicits differential dispersal, change in age structure and reproductive performance in a prey population. Sci. Rep. 8, 1–7 (2018).CAS 
Article 

Google Scholar 
Dunshea, G. et al. Telomeres as age markers in vertebrate molecular ecology. Mol. Ecol. Resour. 11, 225–235 (2011).CAS 
PubMed 
Article 

Google Scholar 
De Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird (Ardenna tenuirostris) using dna methylation-based biomarkers. Mol. Ecol. Resour. 19, 411–425 (2019).PubMed 
Article 

Google Scholar 
Horvath, S. Dna methylation age of human tissues and cell types. Genome Biol. 14, 1–20 (2013).Article 

Google Scholar 
Xia, X., Chen, W., McDermott, J. & Han, J.-D.J. Molecular and phenotypic biomarkers of aging. F1000Research 6, 860 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Parrott, B. B. & Bertucci, E. M. Epigenetic aging clocks in ecology and evolution. Trends Ecol. Evol. 34, 767–770 (2019).PubMed 
Article 

Google Scholar 
Monaghan, P., Maklakov, A. A. & Metcalfe, N. B. Intergenerational transfer of ageing: Parental age and offspring lifespan. Trends Ecol. Evol. 35, 927–937 (2020).PubMed 
Article 

Google Scholar 
Moorad, J. A. & Nussey, D. H. Evolution of maternal effect senescence. Proc. Natl. Acad. Sci. 113, 362–367 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ivimey-Cook, E. & Moorad, J. The diversity of maternal-age effects upon pre-adult survival across animal species. Proc. R. Soc. B 287, 20200972 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Krist, M. Egg size and offspring quality: A meta-analysis in birds. Biol. Rev. 86, 692–716 (2011).PubMed 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
Article 

Google Scholar 
Oro, D., Hernández, N., Jover, L. & Genovart, M. From recruitment to senescence: Food shapes the age-dependent pattern of breeding performance in a long-lived bird. Ecology 95, 446–457 (2014).PubMed 
Article 

Google Scholar 
Lemaitre, J.-F. & Gaillard, J.-M. Reproductive senescence: New perspectives in the wild: Reproductive senescence in the wild. Biol. Rev. 92, 2182–2199 (2017).PubMed 
Article 

Google Scholar 
Forslund, P. & Part, T. Age and reproduction in birds-hypotheses and tests. Trends Ecol. Evol. 10, 374–378 (1995).CAS 
PubMed 
Article 

Google Scholar 
Reid, J. M., Bignal, E. M., Bignal, S., McCracken, D. I. & Monaghan, P. Age-specific reproductive performance in red-billed choughs Pyrrhocorax pyrrhocorax: Patterns and processes in a natural population. J. Anim. Ecol. 72, 765–776 (2003).Article 

Google Scholar 
Cam, E., Aubry, L. M. & Authier, M. The conundrum of heterogeneities in life history studies. Trends Ecol. Evol. 31, 872–886 (2016).PubMed 
Article 

Google Scholar 
Hamel, S., Côté, S. D., Gaillard, J.-M. & Festa-Bianchet, M. Individual variation in reproductive costs of reproduction: High-quality females always do better. J. Anim. Ecol. 78, 143–151 (2009).PubMed 
Article 

Google Scholar 
Hamel, S., Gaillard, J.-M. & Yoccoz, N. G. Introduction to: Individual heterogeneity—the causes and consequences of a fundamental biological process (2018).Gimenez, O., Cam, E. & Gaillard, J.-M. Individual heterogeneity and capture-recapture models: What, why and how?. Oikos 127, 664–686 (2018).Article 

Google Scholar 
Räsänen, K. & Kruuk, L. E. B. Maternal effects and evolution at ecological time-scales. Funct. Ecol. 21, 408–421 (2007).Article 

Google Scholar 
Clutton-Brock, T. & Sheldon, B. C. Individuals and populations: The role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562–573 (2010).PubMed 
Article 

Google Scholar 
Mills, J. A. et al. Archiving primary data: Solutions for long-term studies. Trends Ecol. Evol. 30, 581–589 (2015).PubMed 
Article 

Google Scholar 
Conde, D. A. et al. Data gaps and opportunities for comparative and conservation biology. Proc. Natl. Acad. Sci. 116, 9658–9664 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beamonte-Barrientos, R., Velando, A., Drummond, H. & Torres, R. Senescence of maternal effects: Aging influences egg quality and rearing capacities of a long-lived bird. Am. Nat. 175, 469–480 (2010).PubMed 
Article 

Google Scholar 
Descamps, S. et al. Detecting population heterogeneity in effects of north Atlantic oscillations on seabird body condition: Get into the rhythm. Oikos 119, 1526–1536 (2010).Article 

Google Scholar 
Froy, H., Phillips, R. A., Wood, A. G., Nussey, D. H. & Lewis, S. Age-related variation in reproductive traits in the wandering albatross: Evidence for terminal improvement following senescence. Ecol. Lett. 16, 642–649 (2013).PubMed 
Article 

Google Scholar 
Giron, D. & Casas, J. Mothers reduce egg provisioning with age. Ecol. Lett. 6, 273–277 (2003).Article 

Google Scholar 
Lecomte, V. J. et al. Patterns of aging in the long-lived wandering albatross. Proc. Natl. Acad. Sci. 107, 6370–6375 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Massot, M. et al. An integrative study of ageing in a wild population of common lizards: Integrative study of ageing. Funct. Ecol. 25, 848–858 (2011).Article 

Google Scholar 
Nussey, D. H. et al. Inter and intrasexual variation in aging patterns across reproductive traits in a wild red deer population. Am. Nat. 174, 342–357 (2009).PubMed 
Article 

Google Scholar 
Vega-Trejo, R., Kruuk, L. E. B., Jennions, M. D. & Head, M. L. What happens to offspring when parents are inbred, old or had a poor start in life? Evidence for sex-specific parental effects. J. Evol. Biol. 31, 1138–1151 (2018).PubMed 
Article 

Google Scholar 
Attard, M. R. G. et al. A new, three-dimensional geometric morphometric approach to assess egg shape. PeerJ 6, e5052 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holveck, M.-J., Guerreiro, R., Perret, P., Doutrelant, C. & Gregoire, A. Eggshell coloration indicates female condition during egg-laying: A field experiment in blue tits. Biol. J. Lin. Soc. 128, 181–200 (2019).Article 

Google Scholar 
Siefferman, L., Navara, K. J. & Hill, G. E. Egg coloration is correlated with female condition in eastern bluebirds (Sialia sialis). Behav. Ecol. Sociobiol. 59, 651–656 (2006).Article 

Google Scholar 
Valcu, C.-M. et al. Life history shapes variation in egg composition in the blue tit Cyanistes caeruleus. Commun. Biol. 2, 1–14 (2019).Article 

Google Scholar 
Genovart, M., Oro, D. & Tenan, S. Immature survival, fertility, and density dependence drive global population dynamics in a long-lived species. Ecology 99, 2823–2832 (2018).CAS 
PubMed 
Article 

Google Scholar 
Fernandez-Chacon, A. et al. When to stay, when to disperse and where to go: Survival and dispersal patterns in a spatially structured seabird population. Ecography 36, 1117–1126 (2013).Article 

Google Scholar 
Tavecchia, G., Pradel, R., Genovart, M. & Oro, D. Density-dependent parameters and demographic equilibrium in open populations. Oikos 116, 1481–1492 (2007).Article 

Google Scholar 
Oro, D. Perturbation, Behavioural Feedbacks, and Population Dynamics in Social Animals: When to Leave and Where to Go (Oxford Univ Press, 2020).Book 

Google Scholar 
Ferrer, M. & Penteriani, V. A process of pair formation leading to assortative mating: Passive age-assortative mating by habitat heterogeneity. Anim. Behav. 66, 137–143 (2003).Article 

Google Scholar 
Ludwig, S. C. & Becker, P. H. Supply and demand: Causes and consequences of assortative mating in common terns sterna hirundo. Behav. Ecol. Sociobiol. 62, 1601–1611 (2008).Article 

Google Scholar 
Oro, D., Jover, L. & Ruiz, X. Influence of trawling activity on the breeding ecology of a threatened seabird, audouin’s gull Larus audouinii. Mar. Ecol. Prog. Ser. 139, 19–29 (1996).ADS 
Article 

Google Scholar 
Hoyt, D. F. Practical methods of estimating volume and fresh weight of bird eggs. Auk 96, 73–77 (1979).
Google Scholar 
Oro, D., Ruiz, X., Jover, L., Pedrocchi, V. & González-Solís, J. Diet and adult time budgets of audouin’s gull Larus audouinii in response to changes in commercial fisheries. Ibis 139, 631–637 (1997).Article 

Google Scholar 
Hurrell, J. W., Kushnir, Y., Ottersen, G. & Visbeck, M. An overview of the north atlantic oscillation. In Geophysical Monograph Series Vol. 134 (eds Hurrell, J. W. et al.) (American Geophysical Union, 2003).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).RStudio Team. RStudio: Integrated Development Environment for R (RStudio, PBC, 2022).Breiman, L. (ed.) Classification and Regression Trees repr. (Chapman and Hall, 1998).
Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomforest. R News 2, 18–22 (2002).
Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation (2018). R package version 0.7.6.Kuhn, M. The caret package. J. Stat. Softw. 28, 25 (2012).
Google Scholar  More

Bi, X. et al. Response of grassland productivity to climate change and anthropogenic activities in arid regions of Central Asia. Peer J. 8, e9797–e9797 (2020).PubMed 
PubMed Central 

Google Scholar 
Zhou, W. et al. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010. Ecol. Indic. 83, 303–313 (2017).
Google Scholar 
Liu, Y. Y. et al. Assessing the effects of climate variation and human activities on grassland degradation and restoration across the globe. Ecol. Indic. 106, 105504–105504 (2019).
Google Scholar 
Zhang, Y. et al. Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region, China from 1982 to 2012. Sci. Total Environ. 563–564, 210–220 (2016).ADS 
PubMed 

Google Scholar 
Wang, Z. et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai-Tibet Plateau, China. Ecol. Inf. 33, 32–44 (2016).CAS 

Google Scholar 
He, C. Y., Tian, J., Gao, B. & Zhao, Y. Y. Differentiating climate- and human-induced drivers of grassland degradation in the Liao River Basin, China. Environ. Monit. Assess. 187(1), 4199 (2015).PubMed 

Google Scholar 
Liu, Y. Y. et al. Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013. Sci. Total Environ. 690, 27–39 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Jiang, L. L., Jiapaer, G., Bao, A. M., Guo, H. & Ndayisaba, F. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total Environ. 599–600, 967–980 (2017).ADS 
PubMed 

Google Scholar 
Chen, T. et al. Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982–2015. Sci. Total Environ. 653, 1311–1325 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gang, C. et al. The impacts of land conversion and management measures on the grassland net primary productivity over the Loess Plateau, Northern China. Sci. Total Environ. 645, 827–836 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Guo, D. & Wang, H. Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981–2010. J. Geophys. Res. Atmos. 118, 5216–5230 (2013).ADS 

Google Scholar 
Yang, Y. et al. Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013. J. Arid Environ. 135, 164–172 (2016).ADS 

Google Scholar 
Li, C. X., Jong, R., Schmid, B., Wulf, H. & Michael, E. S. Changes in grassland cover and in its spatial heterogeneity indicate degradation on the Qinghai-Tibetan Plateau. Ecol. Indic. 119, 106641 (2020).
Google Scholar 
Li, F., Chen, W., Zeng, Y., Zhao, Q. J. & Wu, B. F. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in Inner Mongolia, China. Remote Sens. 6, 4705–4722 (2014).ADS 

Google Scholar 
Wang, J., Brown, D. G. & Chen, J. Q. Drivers of the dynamics in net primary productivity across ecological zones on the Mongolian plateau. Landsc. Ecol. 28(4), 725–739 (2014).
Google Scholar 
Han, D. M. et al. Evaluation of semiarid grassland degradation in north China from multiple perspectives. Ecol. Eng. 112, 41–50 (2018).
Google Scholar 
Liu, H. X. et al. Response of vegetation productivity to climate change and human activities in the Shaanxi–Gansu–Ningxia region, China. J. Indian Soc. Remote Sens. 46(7), 1081–1092 (2018).
Google Scholar 
Zheng, K. et al. Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Sci. Total Environ. 660, 236–244 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Yan, Y. C., Liu, X. P., Wen, Y. Y. & Ou, J. P. Quantitative analysis of the contributions of climatic and human factors to grassland productivity in northern China. Ecol. Indic. 103, 542–553 (2019).
Google Scholar 
Wang, H. et al. Impacts of climate change on net primary productivity in arid and semiarid regions of China. Chin. Geogra. Sci. 26, 35–47 (2016).CAS 

Google Scholar 
Thomas, M. et al. Human land-use practices lead to global long-term increases in photosynthetic capacity. Remote Sens. 6(6), 5717–5731 (2014).
Google Scholar 
Becerril-Pina, R., Mastachi-Loza, C. A., Gonzalez-Sosa, E., Diaz-Delgado, C. & Ba, K. M. Assessing desertification risk in the semi-arid highlands of central Mexico. J. Arid Environ. 120, 4–13 (2015).ADS 

Google Scholar 
Evans, J. & Geerken, R. Discrimination between climate and human-induced dryland degradation. J. Arid Environ. 57(4), 535–554 (2004).ADS 

Google Scholar 
Meng, M. et al. Vegetation change in response to climate factors and human activities on the Mongolian Plateau. Peer J. 7, e7735 (2019).PubMed 
PubMed Central 

Google Scholar 
Burrell, A. L., Evans, J. P. & Liu, Y. Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens Environ. 197, 43–57 (2017).ADS 

Google Scholar 
Gedefaw, M. G., Geli, H. M. E. & Abera, T. A. Assessment of rangeland degradation in New Mexico using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens. 13(9), 1618–1618 (2021).ADS 

Google Scholar 
Zhang, F. Changes of Grassland Net Primary Productivity in the Qinghai Tibet Plateau During the Past 34 Years and Analysis of Its Local Degradation Characteristics (Lanzhou University, 2021).
Google Scholar 
Li, L. H. et al. Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau. Ecol. Evol. 8(11), 5949–5963 (2018).PubMed 
PubMed Central 

Google Scholar 
Zhu, Z. C. et al. Greening of the earth and its drivers. Nat. Clim. Change. 6, 791–795 (2016).ADS 
CAS 

Google Scholar 
Song, L. C., Ma, W. W., Li, G., Liu, S. N. & Lu, G. Effect of temperature changes on nitrogen mineralization in soils with different degradation gradients in Gahai Wetland. Acta Pratacul. Sin. 30(09), 27–37 (2021).
Google Scholar 
Dai, L. C. et al. Effect of grazing management strategies on alpine grassland on the northeastern Qinghai-Tibet Plateau. Ecol. Eng. 173, 106418 (2021).
Google Scholar 
Liu, Y. Y. et al. Evaluating the dynamics of grassland net primary productivity in response to climate change in China. Glob. Ecol. Conserv. 28, e01574 (2021).
Google Scholar 
Bestelmeyer, B. T., Duniway, M. C., James, D. K., Burkett, L. M. & Havstad, K. M. A test of critical thresholds and their indicators in a desertification-prone ecosystem: More resilience than we thought. Ecol. Lett. 16, 339–345 (2013).PubMed 

Google Scholar 
Kéfi, S. et al. Early warning signals of ecological transitions: Methods for spatial patterns. PLoS ONE 9(3), e92097 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Li, J. Z. et al. IKONOS image-based extraction of the distribution area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China. Remote Sens. 8(2), 148 (2016).ADS 

Google Scholar 
Liu, Y. Q. & Lu, C. H. Quantifying grass coverage trends to identify the hot plots of grassland degradation in the Tibetan Plateau during 2000–2019. Int. J. Environ. Res. Public Health. 18(2), 416 (2021).MathSciNet 
PubMed Central 

Google Scholar 
Kendall, M. G. Rank Correlation Methods (Griffin, 1948).MATH 

Google Scholar 
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).MathSciNet 
MATH 

Google Scholar 
Zhang, Z. M. & Lu, C. H. Clustering analysis of soybean production to understand its spatiotemporal dynamics in the North China Plain. Sustainability. 12(15), 6178 (2020).
Google Scholar 
Pei, T. T. et al. The sensitivity of vegetation phenology to extreme climate indices in the Loess Plateau, China. Sustainability. 13(14), 7623–7623 (2021).
Google Scholar 
Lu, B. B., Charlton, M., Harris, P. & Fotheringham, A. S. Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. Int. J. Geogr. Inf. Sci. 28(4), 660–681 (2014).
Google Scholar 
Sun, L. Q., Zhang, F. H., Yang, S. W., Qiu, A. G. & Zhang, X. L. The method of selecting geographically and temporally weight regression variable based on stepwise regression. Sci. Surv. Mapp. 44(01), 73–78+97 (2019).
Google Scholar 
Jiang, W. G. et al. Spatio-temporal analysis of vegetation variation in the Yellow River basin. Ecol. Indic. 51, 117–126 (2015).
Google Scholar 
Ndayisaba, F. et al. Understanding the spatial temporal vegetation dynamics in Rwanda. Remote Sens. 8(2), 129 (2016).ADS 

Google Scholar 
Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449(7159), 213–217 (2007).ADS 
PubMed 

Google Scholar 
Chen, J. J., Yi, S. H. & Qin, Y. The contribution of plateau pika disturbance and erosion on patchy alpine grassland soil on the Qinghai-Tibetan Plateau: Implications for grassland restoration. Geoderma 297, 1–9 (2017).ADS 
CAS 

Google Scholar 
Cai, H. Y., Yang, X. H. & Xu, X. L. Human-induced grassland degradation/restoration in the central Tibetan Plateau: The effects of ecological protection and restoration projects. Ecol. Eng. 83, 112–119 (2015).
Google Scholar 
Wang, P., Lassoie, J. P., Morreale, S. J. & Dong, S. K. A critical review of socioeconomic and natural factors in ecological degradation on the Qinghai-Tibetan Plateau. China. Rangel. J. 37(1), 1–9 (2015).
Google Scholar 
Lu, C. B. & Hou, L. F. Cause analysis and Control Countermeasures of grassland degradation in Qilian County, Qinghai Province. Today Anim. Husb. Vet. Med. 34(02), 62 (2018).
Google Scholar 
Guo, X. W. et al. Light grazing significantly reduces soil water storage in Alpine Grasslands on the Qinghai-Tibet Plateau. Sustainability. 12(6), 2523–2523 (2020).
Google Scholar 
Bai, Y. F. et al. Climate warming benefits alpine vegetation growth in Three-River Headwater Region, China. Sci. Total Environ. 742, 140574–140574 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Chen, T. et al. Unraveling the relative impacts of climate change and human activities on grassland productivity in Central Asia over last three decades. Sci. Total Environ. 743, 140649 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Li, A., Wu, J. G. & Huang, J. H. Distinguishing between human-induced and climate-driven vegetation changes: A critical application of RESTREND in inner Mongolia. Landsc. Ecol. 27(7), 969–982 (2012).CAS 

Google Scholar 
Wu, J. S. et al. Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau. J. Environ. Manag. 281, 111875–111875 (2020).
Google Scholar 
Gang, C. et al. Comparative assessment of grassland NPP dynamics in response to climate change in China, North America, Europe and Australia from 1981 to 2010. J. Agron. Crop Sci. 201(1), 57–68 (2015).
Google Scholar 
Gang, C. C. et al. Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environ. Earth Sci. 72(11), 4273–4282 (2014).
Google Scholar 
Chen, Y. Z. et al. Grassland carbon sequestration ability in China: A new perspective from terrestrial aridity zones. Rangeland Ecol. Manag. 69(1), 84–94 (2016).
Google Scholar 
Mowll, W. et al. Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming. Oecologia 177(4), 959–969 (2015).ADS 
PubMed 

Google Scholar 
Zhou, Y. et al. Climate contributions to vegetation variations in central Asian Drylands: Pre- and post-USSR collapse. Remote Sens. 7(3), 2449–2470 (2015).ADS 

Google Scholar 
Ji, Y. et al. Variation of net primary productivity and its drivers in China’s forests during 2000–2018. For. Ecosyst. 7(1), 1–11 (2020).CAS 

Google Scholar 
Zeng, B. & Yang, T. B. Impacts of climate warming on vegetation in Qaidam Area from 1990 to 2003. Environ. Monit. Assess. 144(1–3), 403–417 (2008).PubMed 

Google Scholar 
Duan, A. M. & Xiao, Z. X. Does the climate warming hiatus exist over the Tibetan Plateau?. Sci. Rep. 5(1), 13711 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fang, J. Y. et al. Precipitation patterns alter growth of temperate vegetation. Geophys. Res. Lett. 32(21), L21411 (2005).ADS 

Google Scholar 
Zhao, X., Tan, K., Zhao, S. & Fang, J. Changing climate affects vegetation growth in the arid region of the northwestern China. J. Arid Environ. 75(10), 946–952 (2011).ADS 

Google Scholar 
Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change. 6(1), 75–78 (2016).ADS 

Google Scholar 
Dong, S. K., Shang, Z. H., Gao, J. X. & Boone, R. B. Enhancing sustainability of grassland ecosystems through ecological restoration and grazing management in an era of climate change on Qinghai-Tibetan Plateau. Agric. Ecosyst. Environ. 287(C), 106684 (2019).
Google Scholar 
Xu, H. P. et al. Responses of plant productivity and soil nutrient concentrations to different alpine grassland degradation levels. Environ Monit Assess. 191(11), 678 (2019).CAS 
PubMed 

Google Scholar 
Wen, W. Y. et al. Research on soil net nitrogen mineralization in Stipa grandis grassland with different stages of degradation. Geosci J. 20(4), 485–494 (2016).ADS 
CAS 

Google Scholar 
She, Y. et al. Vegetation attributes and soil properties of alpine grassland in different degradation stages on the Qinghai-Tibet Plateau, China: A meta-analysis. Arab J Geosci. 15, 193 (2022).
Google Scholar 
Xu, G. P. Study on the Change of Vegetation and Soil Nutrients of Alpine Meadow Under Different Degradation Degrees in Eastern Qilian Mountains (Gansu Agricultural University, 2006).
Google Scholar 
Anderson, K. et al. Vegetation expansion in the subnival Hindu Kush Himalaya. Glob. Chang. Biol. 26(3), 1608–1625 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chen, B. X. et al. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agric. For. Meteorol. 189–190, 11–18 (2014).ADS 

Google Scholar 
Zhang, X. W., Li, G., Dong, K. H. & Zhao, X. Effects of grazing and enclosure on community characteristics and biodiversity in Leymus chinensis grassland. J. Grassl. Forage Sci. 4, 22–27 (2019).
Google Scholar 
Huang, K. et al. The influences of climate change and human activities on vegetation dynamics in the Qinghai-Tibet Plateau. Remote Sens. 8(10), 876 (2016).ADS 

Google Scholar 
Duan, Q. T., Luo, L. H., Zhao, W. Z., Zhuang, Y. L. & Liu, F. Mapping and evaluating human pressure changes in the Qilian mountains. Remote Sens. 13(12), 2400–2400 (2021).ADS 

Google Scholar 
Wang, Y. et al. Performance and obstacle tracking to natural forest resource protection project: A rangers’ case of Qilian mountain, China. Int. J. Environ. Res. Public Health. 17(16), 5672 (2020).PubMed Central 

Google Scholar 
Li, Z. Y. et al. Changes in nutrient balance, environmental effects, and green development after returning farmland to forests: A case study in Ningxia, China. Sci. Total Environ. 735, 139370 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Li, C. X., de Jong, R., Schmid, B., Wulf, H. & Schaepman, M. E. Spatial variation of human influences on grassland biomass on the Qinghai-Tibetan plateau. Sci. Total Environ. 665, 678–689 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Li, X. L. et al. Rangeland degradation on the Qinghai-Tibet Plateau: Implications for rehabilitation. Land Degrad. Dev. 24, 72–80 (2011).
Google Scholar 
Li, C. B. et al. Regional vegetation dynamics and its response to climate change—a case study in the Tao River Basin in Northwestern China. Environ. Res. Lett. 9(12), 125003–125003 (2014).ADS 

Google Scholar 
Liu, Y. Y. et al. Untangling the effects of management measures, climate and land use cover change on grassland dynamics in the Qinghai-Tibet Plateau, China. Land Degrad. Dev. 32(17), 4974–4987 (2021).
Google Scholar 
Hou, X. Chinese Grassland Science (Science Press, 2013) (In Chinese).
Google Scholar  More

Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449. https://doi.org/10.1126/science.1155121 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Le Quere, C. et al. Global carbon budget 2016. Earth Syst. Sci. Data 8, 605–649. https://doi.org/10.5194/essd-8-605-2016 (2016).ADS 
Article 

Google Scholar 
DavidMorales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecol. Manag. 352, 68–77. https://doi.org/10.1016/j.foreco.2015.06.011 (2015).Article 

Google Scholar 
Kauppi, P. E., Sandstrom, V. & Lipponen, A. Forest resources of nations in relation to human well-being. PLoS One 13, e0196248. https://doi.org/10.1371/journal.pone.0196248 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, 1327. https://doi.org/10.1126/science.aaz7005 (2020).CAS 
Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landsc. Ecol. 31, 219–227. https://doi.org/10.1007/s10980-015-0312-3 (2016).Article 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on earth’s ecosystems. Sci. Adv. 1, e1500052. https://doi.org/10.1126/sciadv.1500052 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Holl, K. D. Restoring tropical forests from the bottom up. Science 355, 455–456. https://doi.org/10.1126/science.aam5432 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Audino, L. D., Murphy, S. J., Zambaldi, L., Louzada, J. & Comita, L. S. Drivers of community assembly in tropical forest restoration sites: Role of local environment, landscape, and space. Ecol. Appl. 27, 1731–1745. https://doi.org/10.1002/eap.1562 (2017).Article 
PubMed 

Google Scholar 
Temperton, V. M., Hobbs, R. J., Nuttle, T. & Halle, S. in Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice [Science and Practice of Ecological Restoration]. i–xv, 1–439 (2004).Young, T. P., Chase, J. M. & Huddleston, R. T. Community succession and assembly: Comparing, contrasting and combining paradigms in the context of ecological restoration. Ecol. Restor. 19, 5–18 (2001).Article 

Google Scholar 
Vellend, M. The Theory of Ecological Communities (Princeton University Press, 2016).
Google Scholar 
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43(43), 227–248. https://doi.org/10.1146/annurev-ecolsys-110411-160411 (2012).Article 

Google Scholar 
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. In Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G. R.) (Centre for Agricultural Publishing and Documentation, 1971).
Google Scholar 
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501. https://doi.org/10.1086/282687 (1970).Article 

Google Scholar 
Schmid, J. S., Taubert, F., Wiegand, T., Sun, I. F. & Huth, A. Network science applied to forest megaplots: Tropical tree species coexist in small-world networks. Sci. Rep. https://doi.org/10.1038/s41598-020-70052-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, H. X. et al. Prevalence of inter-tree competition and its role in shaping the community structure of a natural Mongolian scots pine (Pinus sylvestris var. mongolica) forest. Forests https://doi.org/10.3390/f8030084 (2017).Article 

Google Scholar 
Hubbell, S. P. et al. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Janik, D. et al. Breaking through beech: A three-decade rise of sycamore in old-growth European forest. Forest Ecol. Manag. 366, 106–117. https://doi.org/10.1016/j.foreco.2016.02.003 (2016).Article 

Google Scholar 
Svatek, M., Rejzek, M., Kvasnica, J., Repka, R. & Matula, R. Frequent fires control tree spatial pattern, mortality and regeneration in argentine open woodlands. Forest Ecol. Manag. 408, 129–136. https://doi.org/10.1016/j.foreco.2017.10.048 (2018).Article 

Google Scholar 
Giammarchi, F. et al. Effects of the lack of forest management on spatiotemporal dynamics of a subalpine Pinus cembra forest. Scand. J. Forest Res. 32, 142–153. https://doi.org/10.1080/02827581.2016.1207802 (2017).Article 

Google Scholar 
Janik, D. et al. Patterns of Fraxinus angustifolia in an alluvial old-growth forest after declines in flooding events. Eur. J. Forest Res. 135, 215–228. https://doi.org/10.1007/s10342-015-0925-8 (2016).Article 

Google Scholar 
Bagchi, R. et al. Defaunation increases the spatial clustering of lowland western amazonian tree communities. J. Ecol. 106, 1470–1482. https://doi.org/10.1111/1365-2745.12929 (2018).Article 

Google Scholar 
Zhang, L. Y., Dong, L. B., Liu, Q. & Liu, Z. G. Spatial patterns and interspecific associations during natural regeneration in three types of secondary forest in the central part of the greater Khingan mountains, Heilongjiang province, China. Forests https://doi.org/10.3390/f11020152 (2020).Article 

Google Scholar 
Obiang, N. L. E. et al. Determinants of spatial patterns of canopy tree species in a tropical evergreen forest in Gabon. J. Veg. Sci. 30, 929–939. https://doi.org/10.1111/jvs.12778 (2019).Article 

Google Scholar 
Wiegand, T. et al. Spatially explicit metrics of species diversity, functional diversity, and phylogenetic diversity: Insights into plant community assembly processes. Annu. Rev. Ecol. Evol. Syst. 48(48), 329–351. https://doi.org/10.1146/annurev-ecolsys-110316-022936 (2017).Article 

Google Scholar 
Gabriel, E. Spatial point patterns: Methodology and applications with R. Math. Geosci. 49, 815–817. https://doi.org/10.1007/s11004-016-9670-x (2017).CAS 
Article 
MATH 

Google Scholar 
Baddeley, A., Rubak, R. & Turner, R. Spatial Point Patterns, Methodology and Applications with R (CRC Press, 2016).MATH 

Google Scholar 
Wiegand, T. & Moloney, K. A. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

Google Scholar 
Plotkin, J. B., Chave, J. M. & Ashton, P. S. Cluster analysis of spatial patterns in Malaysian tree species. Am. Nat. 160, 629–644. https://doi.org/10.1086/342823 (2002).Article 
PubMed 

Google Scholar 
Ripley, B. D. Modeling spatial patterns. J. R. Stat. Soc. B 39, 172–212 (1977).
Google Scholar 
He, F. L. & Gaston, K. J. Estimating species abundance from occurrence. Am. Nat. 156, 553–559. https://doi.org/10.1086/303403 (2000).Article 
PubMed 

Google Scholar 
Diggle, P. Statistical Analysis of Spatial Point Patterns (Academic Press, 1983).MATH 

Google Scholar 
Pielou, E. C. The use of point-to-plant distances in the study of the pattern of plant-populations. J. Ecol. 47, 607–613. https://doi.org/10.2307/2257293 (1959).Article 

Google Scholar 
Losapio, G., Montesinos-Navarro, A. & Saiz, H. Perspectives for ecological networks in plant ecology. Plant Ecol. Divers. 12, 87–102. https://doi.org/10.1080/17550874.2019.1626509 (2019).Article 

Google Scholar 
Fuller, M. M., Wagner, A. & Enquist, B. J. Using network analysis to characterize forest structure. Nat. Resour. Model. 21, 225–247. https://doi.org/10.1111/j.1939-7445.2008.00004.x (2008).MathSciNet 
Article 

Google Scholar 
Montoya, J. M., Pimm, S. L. & Sole, R. V. Ecological networks and their fragility. Nature 442, 259–264. https://doi.org/10.1038/nature04927 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 345–353. https://doi.org/10.1016/j.tree.2005.04.004 (2005).Article 
PubMed 

Google Scholar 
Nakagawa, Y., Yokozaw, M. & Hara, T. Complex network analysis reveals novel essential properties of competition among individuals in an even-aged plant population. Ecol. Complex 26, 95–116. https://doi.org/10.1016/j.ecocom.2016.03.005 (2016).Article 

Google Scholar 
Wiegand, T. & Moloney, K. A. Handbook of Spatial Point Pattern Analysis in Ecology (CRC Press, 2013).Book 

Google Scholar 
Barthelemy, M. Spatial networks. Phys. Rep. Rev. Sect. Phys. Lett. 499, 1–101. https://doi.org/10.1016/j.physrep.2010.11.002 (2011).MathSciNet 
CAS 
Article 

Google Scholar 
Keren, S. Modeling tree species count data in the understory and canopy layer of two mixed old-growth forests in the Dinaric region. Forests https://doi.org/10.3390/f11050531 (2020).Article 

Google Scholar 
Podlaski, R. Models of the fine-scale spatial distributions of trees in managed and unmanaged forest patches with Abies alba Mill. and Fagus sylvatica L. Forest Ecol. Manag. 439, 1–8 (2019).Article 

Google Scholar 
Levin, S. A. Theoretical ecology—Principles and applications, 3rd edition. Science 316, 1699–1700. https://doi.org/10.1126/science.1141870 (2007).CAS 
Article 

Google Scholar 
Martinez-Lopez, V., Garcia, C., Zapata, V., Robledano, F. & De la Rua, P. Intercontinental long-distance seed dispersal across the Mediterranean basin explains population genetic structure of a bird-dispersed shrub. Mol. Ecol. 29, 1408–1420. https://doi.org/10.1111/mec.15413 (2020).Article 
PubMed 

Google Scholar 
Dale, M. R. T. & Fortin, M. J. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41, 21–38. https://doi.org/10.1146/annurev-ecolsys-102209-144718 (2010).Article 

Google Scholar 
Silva, C. A. et al. Treetop: A shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists. Methods Ecol. Evol. 13, 1164–1176. https://doi.org/10.1111/2041-210x.13830 (2022).Article 

Google Scholar 
Tatsumi, S., Yamaguchi, K. & Furuya, N. Forestscanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13900 (2022).Article 

Google Scholar 
Ferraz, A., Saatchi, S. S., Longo, M. & Clark, D. B. Tropical tree size-frequency distributions from airborne LiDAR. Ecol. Appl. 30, e02154. https://doi.org/10.1002/eap.2154 (2020).Article 
PubMed 

Google Scholar 
Bianchi, E., Bugmann, H., Hobi, M. L. & Bigler, C. Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland. Forest Ecol. Manag. 494, 119315. https://doi.org/10.1016/j.foreco.2021.119315 (2021).Article 

Google Scholar 
Tatsumi, S., Owari, T., Yin, M. F. & Ning, L. Z. Neighborhood analysis of underplanted Korean pine demography in larch plantations: Implications for uneven-aged management in northeast china. Forest Ecol. Manag. 322, 10–18. https://doi.org/10.1016/j.foreco.2014.03.022 (2014).Article 

Google Scholar 
Cornett, M. W., Reich, P. B. & Puettmann, K. J. Canopy feedbacks and microtopography regulate conifer seedling distribution in two Minnesota conifer-deciduous forests. Ecoscience 4, 353–364. https://doi.org/10.1080/11956860.1997.11682414 (1997).Article 

Google Scholar 
Wang, X. F., Zheng, G., Yun, Z. X. & Moskal, L. M. Characterizing tree spatial distribution patterns using discrete aerial LiDAR data. Remote Sens. Basel 12, 712. https://doi.org/10.3390/rs12040712 (2020).ADS 
Article 

Google Scholar 
Matérn, B. Spatial variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations. Meddelanden från Statens Skogsforskningsinstitut 49, 1–144 (1960).MathSciNet 

Google Scholar 
Matérn, B. Spatial Variation. Lecture Notes in Statistics Vol. 36 (Springer, 1986).Book 

Google Scholar 
Thomas, M. A generalisation of Poisson’s binomial limit for use in ecology. Biometrika 36, 18–25 (1949).MathSciNet 
CAS 
Article 

Google Scholar 
Lotwick, H. W. Simulation of some spatial hard core models, and the complete packing problem. J. Stat. Comput. Simul. 15, 295–314 (1982).MathSciNet 
Article 

Google Scholar 
Strauss, D. J. A model for clustering. Biometrika 62, 467–475 (1975).MathSciNet 
Article 

Google Scholar 
Cressie Noel, A. C. Statistics for Spatial Data (Wiley-Interscience, 1993).Book 

Google Scholar 
Besag, J. E. Contribution to the discussion of the paper by Ripley. J. R. Stat. Soc. 39, 193–195 (1977).MathSciNet 

Google Scholar  More

Royse DJ. A global perspective on the high five: Agaricus, Pleurotus, Lentinula, Auricularia and Flammulina. In: Singh M, editor. Proceedings of the 8th International Conference on Mushroom Biology and Mushroom Products. New Delhi; 2014. p. 1–6.Vos AM, Heijboer A, Boschker HTS, Bonnet B, Lugones LG, Wosten HAB. Microbial biomass in compost during colonization of Agaricus bisporus. AMB Express. 2017; 7:12.Jurak E, Punt AM, Arts W, Kabel MA, Gruppen H. Fate of carbohydrates and lignin during composting and mycelium growth of Agaricus bisporus on wheat straw based compost. PLoS ONE. 2015;10:e0138909.PubMed 
PubMed Central 
Article 

Google Scholar 
Beyer DM. Basic procedures for Agaricus mushroom growing PennState Extension: the Pennsylvania State University. 2003. https://extension.psu.edu/basic-procedures-for-agaricus-mushroom-growing.Wang L, Mao J, Zhao H, Li M, Wei Q, Zhou Y, et al. Comparison of characterization and microbial communities in rice straw- and wheat straw-based compost for Agaricus bisporus production. J Ind Microbiol Biotechnol. 2016;43:1249–60.CAS 
PubMed 
Article 

Google Scholar 
Adams JDW, Frostick LE. Investigating microbial activities in compost using mushroom (Agaricus bisporus) cultivation as an experimental system. Bioresour Technol. 2008;99:1097–102.CAS 
PubMed 
Article 

Google Scholar 
Liu L, Wang S, Guo X, Zhao T, Zhang B. Succession and diversity of microorganisms and their association with physicochemical properties during green waste thermophilic composting. Waste Manage. 2018;73:101–12.CAS 
Article 

Google Scholar 
Reyes-Torres M, Oviedo-Ocana ER, Dominguez I, Komilis D, Sanchez A. A systematic review on the composting of green waste: feedstock quality and optimization strategies. Waste Manage. 2018;77:486–99.CAS 
Article 

Google Scholar 
Pardo‐Giménez A, González JEP, Zied DC. Casing materials and techniques in Agaricus bisporus cultivation. In: Zied DC, Pardo‐Giménez A, editors. Edible and medicinal mushrooms technology and applications. Chichester, UK: Wiley; 2017. p. 149–74.Baars JJP, Scholtmeijer K, Sonnenberg ASM, van Peer A. Critical factors involved in primordia building in Agaricus bisporus: a review. Molecules. 2020;25:2984.Vieira FR, Pecchia JA. Bacterial community patterns in the Agaricus bisporus cultivation system, from compost raw materials to mushroom caps. Microb Ecol. 2021;84:20–32.PubMed 
Article 

Google Scholar 
Kristensen JB, Thygesen LG, Felby C, Jorgensen H, Elder T. Cell-wall structural changes in wheat straw pretreated for bioethanol production. Biotechnol Biofuels. 2008;1:1–9.Article 

Google Scholar 
Jurak E, Patyshakuliyeva A, de Vries RP, Gruppen H, Kabel MA. Compost grown Agaricus bisporus lacks the ability to degrade and consume highly substituted xylan fragments. PLoS ONE. 2015;10:e0134169.PubMed 
PubMed Central 
Article 

Google Scholar 
Ryckeboer J, Mergaert J, Vaes K, Klammer S, De Clercq D, Coosemans J, et al. A survey of bacteria and fungi occurring during composting and self-heating processes. Ann Microbiol. 2003;53:349–410.
Google Scholar 
Kutzner HJ. Microbiology of composting. In: Rehm H-J, Reed G, editors. Biotechnology. 11c. 2nd ed. Verlag: Wiley-VCH; 2000. p. 35–100.Carrasco J, Garcia-Delgado C, Lavega R, Tello ML, De Toro M, Barba-Vicente V, et al. Holistic assessment of the microbiome dynamics in the substrates used for commercial champignon (Agaricus bisporus) cultivation. Microb Biotechnol. 2020;13:1933–47.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vieira FR, Pecchia JA. Bacterial community patterns in the Agaricus bisporus cultivation system, from compost raw materials to mushroom caps. Microb Ecol. 2021;82. https://doi.org/10.1007/s00248-021-1833-5.Vieira FR, Pecchia JA. An exploration into the bacterial community under different pasteurization conditions during substrate preparation (composting–Phase II) for Agaricus bisporus cultivation. Microb Ecol. 2018;75:318–30.CAS 
PubMed 
Article 

Google Scholar 
Cao GT, Song TT, Shen YY, Jin QL, Feng WL, Fan LJ, et al. Diversity of bacterial and fungal communities in wheat straw compost for Agaricus bisporus cultivation. Hortscience. 2019;54:100–9.CAS 
Article 

Google Scholar 
Wiegant WM. Growth characteristics of the thermophilic fungus Scytalidium thermophilum in relation to production of mushroom compost. Appl Environ Microbiol. 1992;58:1301–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fermor T, Randle P, Smith J. Compost as a substrate and its preparation. In: Flegg PB, Spencer DM, Wood D, editors. The biology and technology of the cultivated mushroom. Chichester, UK: John Wiley & Sons, Ltd; 1985. p. 81–109.Straatsma G, Samson RA, Olijnsma TW, Op den Camp HJM, Gerrits JPG, Griensven LJLDV. Ecology of thermophilic fungi in mushroom compost, with emphasis on Scytalidium thermophilum and growth stimulation of Agaricus bisporus mycelium. Appl Environ Microbiol. 1994;60:454–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ross RC, Harris PJ. An investigation into the selective nature of mushroom compost. Sci Hortic. 1983;19:55–64.Article 

Google Scholar 
Coello-Castillo MM, Sanchez JE, Royse DJ. Production of Agaricus bisporus on substrates pre-colonized by Scytalidium thermophilum and supplemented at casing with protein-rich supplements. Bioresour Technol. 2009;100:4488–92.CAS 
PubMed 
Article 

Google Scholar 
Szekely A, Sipos R, Berta B, Vajna B, Hajdu C, Marialigeti K. DGGE and T-RFLP analysis of bacterial succession during mushroom compost production and sequence-aided T-RFLP profile of mature compost. Microb Ecol. 2009;57:522–33.PubMed 
Article 

Google Scholar 
Kertesz M, Safianowicz K, Bell TL. New insights into the microbial communities and biological activities that define mushroom compost. Sci Cultiv Edible Fungi. 2016;19:161–5.
Google Scholar 
McGee CF, Byrne H, Irvine A, Wilson J. Diversity and dynamics of the DNA and cDNA-derived bacterial compost communities throughout the Agaricus bisporus mushroom cropping process. Ann Microbiol. 2017;67:751–61.CAS 
Article 

Google Scholar 
McGee CF, Byrne H, Irvine A, Wilson J. Diversity and dynamics of the DNA- and cDNA-derived compost fungal communities throughout the commercial cultivation process for Agaricus bisporus. Mycologia. 2017;109:475–84.CAS 
PubMed 
Article 

Google Scholar 
Yeates C, Gillings MR. Rapid purification of DNA from soil for molecular biodiversity analysis. Lett Appl Microbiol. 1998;27:49–53.CAS 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
White TJ, Bruns T, Lee S, Taylor JW. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR protocols: a guide to methods and applications. New York: Academic Press; 1990. p. 315–22.
Google Scholar 
Lever MA, Torti A, Eickenbusch P, Michaud AB, Santl-Temkiv T, Jorgensen BB. A modular method for the extraction of DNA and RNA, and the separation of DNA pools from diverse environmental sample types. Front Microbiol. 2015;6:476.Muyzer G, Waal ECD, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
PubMed 
Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation For Statistical Computing; 2019.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina aplicon data. Nat Meth. 2016;13:581–3.CAS 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 

Google Scholar 
Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2010;27:592–3.PubMed 
PubMed Central 
Article 

Google Scholar 
Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 2016;8:352–9.Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dixon P. VEGAN, a package of R functions for community ecology. J Veget Sci. 2003;14:927–30.Article 

Google Scholar 
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.Sharma HS, Kilpatrick M. Mushroom (Agaricus bisporus) compost quality factors for predicting potential yield of fruiting bodies. Can J Microbiol. 2000;46:515–9.CAS 
PubMed 
Article 

Google Scholar 
Seaby DA. Mushroom (Agaricus bisporus) yield modelling for the bag method of mushroom production using commercial yields and from micro plots. Sci Cultiv Edible Fungi. 1995;14:409–16.
Google Scholar 
O’Donoghue DC. Relationship between some compost factors and their effects on yield of Agaricus. Mushroom Sci. 1965;6:245–54.
Google Scholar 
Andersen B, Sorensen JL, Nielsen KF, van den Ende BG, de Hoog S. A polyphasic approach to the taxonomy of the Alternaria infectoria species-group. Fungal Genet Biol. 2009;46:642–56.CAS 
PubMed 
Article 

Google Scholar 
van den Brink J, Samson RA, Hagen F, Boekhout T, de Vries RP. Phylogeny of the industrial relevant, thermophilic genera Myceliophthora and Corynascus. Fungal Divers. 2012;52:197–207.Article 

Google Scholar 
Souza TP, Marques SC, Santos D, Dias ES. Analysis of thermophilic fungal populations during phase II of composting for the cultivation of Agaricus subrufescens. World J Microbiol Biotechnol. 2014;30:2419–25.PubMed 
Article 

Google Scholar 
Vajna B, Szili D, Nagy A, Márialigeti K. An improved sequence-aided T-RFLP analysis of bacterial succession during oyster mushroom substrate preparation. Microb Ecol. 2012;64:702–13.CAS 
PubMed 
Article 

Google Scholar 
Du R, Yan J, Li S, Zhang L, Zhang S, Li J, et al. Cellulosic ethanol production by natural bacterial consortia is enhanced by Pseudoxanthomonas taiwanensis. Biotechnol Biofuels. 2015;8:10.Kato S, Haruta S, Cui ZJ, Ishii M, Igarashi Y. Stable coexistence of five bacterial strains as a cellulose-degrading community. Appl Environ Microbiol. 2005;71:7099–106.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haruta S, Cui Z, Huang Z, Li M, Ishii M, Igarashi Y. Construction of a stable microbial community with high cellulose-degradation ability. Appl Microbiol Biotechnol. 2002;59:529–34.CAS 
PubMed 
Article 

Google Scholar 
Vajna B, Adrienn N, Sajben-Nagy E, Manczinger L, Szijártó N, Kádár Z, et al. Microbial community structure changes during oyster mushroom substrate preparation. Appl Microbiol Biotechnol. 2010;86:367–75.CAS 
PubMed 
Article 

Google Scholar 
Karadag D, Özkaya B, Ölmez E, Nissilä ME, Çakmakçı M, Yıldız Ş, et al. Profiling of bacterial community in a full-scale aerobic composting plant. Int Biodeter Biodeg. 2013;77:85–90.CAS 
Article 

Google Scholar 
Rathinam NK, Gorky, Bibra M, Salem DR, Sani RK. Bioelectrochemical approach for enhancing lignocellulose degradation and biofilm formation in Geobacillus strain WSUCF1. Bioresour Technol. 2020;295:122271.Song TT, Shen YY, Jin QL, Feng WL, Fan LJ, Cao GT, et al. Bacterial community diversity, lignocellulose components, and histological changes in composting using agricultural straws for Agaricus bisporus production. PeerJ. 2021;9:e10452.PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang X, Zhong Y, Yang S, Zhang W, Xu M, Ma A, et al. Diversity and dynamics of the microbial community on decomposing wheat straw during mushroom compost production. Bioresour Technol. 2014;170:183–95.CAS 
PubMed 
Article 

Google Scholar 
Goodfellow M, Maldonado LA, Quintana ET. Reclassification of Nonomuraea flexuosa (Meyer 1989) Zhang et al. 1998 as Thermopolyspora flexuosa gen. nov., comb. nov., nom. rev. Int J Syst Evol Microbiol. 2005;55:1979–83.CAS 
PubMed 
Article 

Google Scholar 
Lin SB, Stutzenberger FJ. Purification and characterization of the major beta-1,4-endoglucanase from Thermomonospora curvata. J Appl Bacteriol. 1995;79:447–53.CAS 
PubMed 
Article 

Google Scholar 
Kukolya J, Nagy I, Láday M, Tóth E, Oravecz O, Márialigeti K, et al. Thermobifida cellulolytica sp. nov., a novel lignocellulose-decomposing actinomycete. Int J Syst Evol Microbiol. 2002;52:1193–9.CAS 
PubMed 

Google Scholar 
Weon H-Y, Lee S-Y, Kim B-Y, Noh H-J, Schumann P, Kim J-S, et al. Ureibacillus composti sp. nov. and Ureibacillus thermophilus sp. nov., isolated from livestock-manure composts. Int J Syst Evol Microbiol. 2007;57:2908–11.CAS 
PubMed 
Article 

Google Scholar 
Poli A, Laezza G, Gul-Guven R, Orlando P, Nicolaus B. Geobacillus galactosidasius sp. nov., a new thermophilic galactosidase-producing bacterium isolated from compost. Syst Appl Microbiol. 2011;34:419–23.CAS 
PubMed 
Article 

Google Scholar 
Gavande PV, Basak A, Sen S, Lepcha K, Murmu N, Rai V, et al. Functional characterization of thermotolerant microbial consortium for lignocellulolytic enzymes with central role of Firmicutes in rice straw depolymerization. Sci Rep. 2021;11:3032.Xu JQ, Lu YY, Shan GC, He XS, Huang JH, Li QL. Inoculation with compost-born thermophilic complex microbial consortium induced organic matters degradation while reduced nitrogen loss during co-composting of dairy manure and sugarcane leaves. Waste Biomass Valor. 2019;10:2467–77.CAS 
Article 

Google Scholar 
Yoon JH, Kang SJ, Im WT, Lee ST, Oh TK. Chelatococcus daeguensis sp nov., isolated from wastewater of a textile dye works, and emended description of the genus Chelatococcus. Int J Syst Evol Microbiol. 2008;58:2224–8.CAS 
PubMed 
Article 

Google Scholar 
Zhou C, Liu Z, Huang Z-L, Dong M, Yu X-L, Ning P. A new strategy for co-composting dairy manure with rice straw: addition of different inocula at three stages of composting. Waste Manage. 2015;40:38–43.CAS 
Article 

Google Scholar 
Gómez A. New technology in Agaricus bisporus cultivation. In: Zied DC, Pardo-Giménez A, editors. Edible and medicinal mushrooms. Chichester, UK: John Wiley & Sons; 2017. p. 211–20.von Minnigerode HF, editor. Method for controlling and regulating the composting process. Proceedings of the Eleventh International Scientific Congress on the Cultivation of Edible Fungi. Sydney, Australia: The International Society for Mushroom Science; 1981.Jurak E, Gruppen H, Kabel MA, Eggink G, Meyer AS, van der Maarel MJEC, et al. How mushrooms feed on compost: conversion of carbohydrates and lignin in industrial wheat straw based compost enabling the growth of Agaricus bisporus. Wageningen University—Graduate School VLAG; 2015.Miller FC, Macauley BJ, Harper ER. Investigation of various gases, pH and redox potential in mushroom composting Phase-I stacks. Aust J Exper Agric. 1991;31:415–25.Article 

Google Scholar 
Miller FC, Harper ER, Macauley BJ, Gulliver A. Composting based on moderately thermophilic and aerobic conditions for the production of commercial growing compost. Aust J Exper Agric. 1990;30:287–96.Article 

Google Scholar 
Carrasco J, Preston GM. Growing edible mushrooms: a conversation between bacteria and fungi. Environ Microbiol. 2020;22:858–72.PubMed 
Article 

Google Scholar  More

Stigall, A. L., Edwards, C. T., Freeman, R. L. & Rasmussen, C. M. Ø. Coordinated biotic and abiotic change during the Great Ordovician Biodiversification Event: Darriwilian assembly of early Paleozoic building blocks. Palaeogeogr. Palaeoclimatol. Palaeoecol. 530, 249–270 (2019).
Google Scholar 
Alroy, J. Colloquium paper: dynamics of origination and extinction in the marine fossil record. Proc. Natl. Acad. Sci. USA 105, 11536–11542 (2008). Suppl 1.CAS 

Google Scholar 
Servais, T., Cascales-Miñana, B. & Harper, D. A. T. The Great Ordovician Biodiversification Event (GOBE) is not a single event. Paleontological Res. 25, 315–328 (2021).Miller, A. I. & Foote, M. Calibrating the Ordovician Radiation of marine life: implications for Phanerozoic diversity trends. Paleobiology 22, 304–309 (1996).CAS 

Google Scholar 
Sepkoski, J. J. A compendium of marine fossil genera. vol. 2002 (Paleontological Research Institution, 2002).Zhan, R. & Harper, D. A. T. Biotic diachroneity during the Ordovician Radiation: evidence from South China. Lethaia 39, 211–226 (2006).
Google Scholar 
Fan, J. et al. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity. Science 367, 272–277 (2020).CAS 

Google Scholar 
Deng, Y. et al. Timing and patterns of the Great Ordovician Biodiversification Event and Late Ordovician mass extinction: Perspectives from South China. Earth-Sci. Rev. 220, 103743 (2021).
Google Scholar 
Kröger, B., Franeck, F. & Rasmussen, C. M. Ø. The evolutionary dynamics of the early Palaeozoic marine biodiversity accumulation. Proc. R. Soc. B: Biol. Sci. 286, 3–8 (2019).
Google Scholar 
Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl. Acad. Sci. USA 116, 7207–7213 (2019).CAS 

Google Scholar 
Sepkoski, J. J. A factor analytic description of the phanerozoic marine fossil record. Paleobiology 7, 36–53 (1981).
Google Scholar 
Sepkoski, J. J. & Sheehan, P. M. Diversification, Faunal Change, and Community Replacement during the Ordovician Radiations. in Biotic interactions in recent and fossil benthic communities (eds. Tevesz, M. J. S. & McCall, P. L.) 673–717 (Plenum Press, 1983).Harper, D. A. T. The Ordovician biodiversification: Setting an agenda for marine life. Palaeogeogr. Palaeoclimatol. Palaeoecol. 232, 148–166 (2006).
Google Scholar 
Stigall, A. L., Bauer, J. E., Lam, A. R. & Wright, D. F. Biotic immigration events, speciation, and the accumulation of biodiversity in the fossil record. Global Planet. Change 148, 242–257 (2017).
Google Scholar 
Copper, P. Coral Reefs Reports Ancient reef ecosystem expansion and collapse. Coral Reefs 13, 3–11 (1994).
Google Scholar 
Trotter, J. A., Williams, I. S., Barnes, C. R., Lécuyer, C. & Nicoll, R. S. Did Cooling Oceans Trigger Ordovician Biodiversification? Evidence from Conodont Thermometry. Science 321, 550–554 (2008).CAS 

Google Scholar 
Lindström, M. The Ordovician climate based on the study of carbonate rocks. in Aspects of the Ordovician System, Paleontological Contribution of the University of Oslo (ed. Bruton, D. L.) vol. 295 81–88 (Universitetsforlaget, 1984).Rasmussen, C. M. Ø., Nielsen, A. T. & Harper, D. A. T. Ecostratigraphical interpretation of lower Middle Ordovician East Baltic sections based on brachiopods. Geological Mag. 146, 717–731 (2009).
Google Scholar 
Dabard, M. P. et al. Sea-level curve for the Middle to early Late Ordovician in the Armorican Massif (western France): Icehouse third-order glacio-eustatic cycles. Palaeogeogr. Palaeoclimatol. Palaeoecol. 436, 96–111 (2015).
Google Scholar 
Rasmussen, C. M. Ø. et al. Onset of main Phanerozoic marine radiation sparked by emerging Mid Ordovician icehouse. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).CAS 

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 
Saltzman, M. R., Edwards, C. T., Adrain, J. M. & Westrop, S. R. Persistent oceanic anoxia and elevated extinction rates separate the Cambrian and Ordovician radiations. Geology 43, 807–811 (2015).CAS 

Google Scholar 
Edwards, C. T., Saltzman, M. R., Royer, D. L. & Fike, D. A. Oxygenation as a driver of the Great Ordovician Biodiversification Event. Nature Geoscience 10, 925–929 (2017).CAS 

Google Scholar 
Sperling, E. A., Knoll, A. H. & Girguis, P. R. The ecological physiology of earth’s second oxygen revolution. Ann. Rev. Ecol. Evolut. Syst. 46, 215–235 (2015).
Google Scholar 
Dahl, T. W. et al. Reorganisation of Earth’s biogeochemical cycles briefly oxygenated the oceans 520 Myr ago. Geochem. Perspect. Lett. 210–220 (2017).Dahl, T. W. et al. Atmosphere-ocean oxygen and productivity dynamics during early animal radiations. Proc. Natl. Acad. Sci. 116, 19352–19361 (2019).CAS 

Google Scholar 
Nursall, J. R. Oxygen as a prerequisite to the origin of the metazoa. Nature 183, 1170–1172 (1959).Knoll, A. H. Biological and Biogeochemical Preludes to the Ediacaran Radiation. In Origin and Early Evolution of the Metazoa (eds. Lipps, J. H. & Signor, P. W.) 53–84 (Springer US, 1992).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. 108, 17631–17634 (2011).CAS 

Google Scholar 
Lau, K. V. et al. Marine anoxia and delayed Earth system recovery after the end-Permian extinction. Proc. Natl. Acad. Sci. USA 113, 2360–2365 (2016).CAS 

Google Scholar 
Zhang, F. et al. Congruent Permian-Triassic δ238U records at Panthalassic and Tethyan sites: Confirmation of global-oceanic anoxia and validation of the U-isotope paleoredox proxy. Geology 46, 327–330 (2018).CAS 

Google Scholar 
Andersen, M. B., Stirling, C. H. & Weyer, S. Uranium isotope fractionation. Rev. Mineral. Geochem. 82, 799–850 (2017).CAS 

Google Scholar 
Chen, X. et al. Diagenetic effects on uranium isotope fractionation in carbonate sediments from the Bahamas. Geochimica et Cosmochimica Acta 237, 294–311 (2018).CAS 

Google Scholar 
Zhang, F. et al. Uranium isotopes in marine carbonates as a global ocean paleoredox proxy: A critical review. Geochimica et Cosmochimica Acta 287, 27–49 (2020).CAS 

Google Scholar 
Stylo, M. et al. Uranium isotopes fingerprint biotic reduction. Proc. Natl. Acad. Sci. 112, 5619–5624 (2015).CAS 

Google Scholar 
Basu, A. et al. Microbial U isotope fractionation depends on the U(VI) reduction rate. Environ. Sci. Technol. 54, 2295–2303 (2020).CAS 

Google Scholar 
Dunk, R. M., Mills, R. A. & Jenkins, W. J. A reevaluation of the oceanic uranium budget for the Holocene. Chem. Geol. 190, 45–67 (2002).CAS 

Google Scholar 
Dahl, T. W. et al. Uranium isotopes distinguish two geochemically distinct stages during the later Cambrian SPICE event. Earth Planet. Sci. Lett. 401, 313–326 (2014).CAS 

Google Scholar 
Tissot, F. L. H. & Dauphas, N. Uranium isotopic compositions of the crust and ocean: Age corrections, U budget and global extent of modern anoxia. Geochimica et Cosmochimica Acta 167, 113–143 (2015).CAS 

Google Scholar 
Romaniello, S. J., Herrmann, A. D. & Anbar, A. D. Uranium concentrations and 238U/235U isotope ratios in modern carbonates from the Bahamas: Assessing a novel paleoredox proxy. Chem. Geology 362, 305–316 (2013).CAS 

Google Scholar 
Chen, X., Romaniello, S. J., Herrmann, A. D., Samankassou, E. & Anbar, A. D. Biological effects on uranium isotope fractionation (238U/235U) in primary biogenic carbonates. Geochimica et Cosmochimica Acta 240, 1–10 (2018).CAS 

Google Scholar 
Tissot, F. L. H. et al. Controls of eustasy and diagenesis on the 238U/235U of carbonates and evolution of the seawater (234U/238U) during the last 1.4 Myr. Geochimica et Cosmochimica Acta 242, 233–265 (2018).CAS 

Google Scholar 
Lindskog, A. & Eriksson, M. E. Megascopic processes reflected in the microscopic realm: sedimentary and biotic dynamics of the Middle Ordovician “orthoceratite limestone” at Kinnekulle, Sweden. Gff 139, 163–183 (2017).
Google Scholar 
Jaanusson, V. Aspects of carbonate sedimentation in the Ordovician of Baltoscandia. Lethaia 6, 11–34 (1973).
Google Scholar 
Bergström, S. M., Chen, X., Gutiérrez-marco, J. C. & Dronov, A. The new chronostratigraphic classification of the Ordovician System and its relations to major regional series and stages and to δ13C chemostratigraphy. Lethaia 42, 97–107 (2008).
Google Scholar 
Lindskog, A., Lindskog, A. M., Johansson, J. V., Ahlberg, P. & Eriksson, M. E. The Cambrian–Ordovician succession at Lanna, Sweden: stratigraphy and depositional environments. Estonian J. Earth Sci 67, 133 (2018).
Google Scholar 
Bábek, O. et al. Redox geochemistry of the red ‘orthoceratite limestone’ of Baltoscandia: Possible linkage to mid-Ordovician palaeoceanographic changes. Sedimentary Geology 420, 105934 (2021).
Google Scholar 
Azmy, K. et al. Carbon-isotope stratigraphy of the Lower Ordovician succession in Northeast Greenland: Implications for correlations with St. George Group in western Newfoundland (Canada) and beyond. Sedimentary Geology 225, 67–81 (2010).CAS 

Google Scholar 
Bartlett, R. et al. Abrupt global-ocean anoxia during the Late Ordovician–early Silurian detected using uranium isotopes of marine carbonates. Proc Natl Acad Sci USA 115, 5896–5901 (2018).CAS 

Google Scholar 
Dahl, T. W., Hammarlund, E. U., Rasmussen, C. M. Ø., Bond, D. P. G. & Canfield, D. E. Sulfidic anoxia in the oceans during the Late Ordovician mass extinctions – insights from molybdenum and uranium isotopic global redox proxies. Earth-Sci. Rev. 220, 103748 (2021).CAS 

Google Scholar 
Del Rey, Á., Havsteen, J., Bizzarro, M., Connelly, J. & Dahl, T. W. Untangling the diagenetic history of Uranium isotopes in marine carbonates: a case study tracing d238U of late Silurian oceans using calcitic brachiopod shells. Geochimica et Cosmochimica Acta 2020, 93–110.Rasmussen, J. A., Thibault, N. & Mac Ørum Rasmussen, C. Middle Ordovician astrochronology decouples asteroid breakup from glacially-induced biotic radiations.Nat Commun12, 6430 (2021).CAS 

Google Scholar 
Ainsaar, L. et al. Middle and Upper Ordovician carbon isotope chemostratigraphy in Baltoscandia: A correlation standard and clues to environmental history. Palaeogeogr. Palaeoclimatol. Palaeoecol. 294, 189–201 (2010).
Google Scholar 
Wu, R., Calner, M. & Lehnert, O. Integrated conodont biostratigraphy and carbon isotope chemostratigraphy in the Lower-Middle Ordovician of southern Sweden reveals a complete record of the MDICE. Geological Mag. 154, 334–353 (2017).CAS 

Google Scholar 
Lindskog, A., Eriksson, M. E., Bergström, S. M. & Young, S. A. Lower–Middle Ordovician carbon and oxygen isotope chemostratigraphy at Hällekis, Sweden: implications for regional to global correlation and palaeoenvironmental development. Lethaia 52, 204–219 (2019).
Google Scholar 
Rasmussen, C. M. Ø., Hansen, J. & Harper, D. A. T. Baltica: A mid Ordovivian diversity hotspot. Historical Biology 19, 255–261 (2007).
Google Scholar 
Zhang, J. Lithofacies and stratigraphy of the Ordovician Guniutan Formation in its type area, China. Geol. J. 31, 201–215 (1996).
Google Scholar 
Eriksson, M. E. et al. Biotic dynamics and carbonate microfacies of the conspicuous Darriwilian (Middle Ordovician) ‘Täljsten’ interval, south-central Sweden. Palaeogeogr. Palaeoclimatol. Palaeoecol. 367–368, 89–103 (2012).
Google Scholar 
Lindström, M., Jun-Yuan, C. & Jun-Ming, Z. Section at Daping reveals Sino-Baltoscandian parallelism of facies in the Ordovician. Geologiska Föreningen i Stockholm Förhandlingar 113, 189–205 (1991).
Google Scholar 
Edward, O. et al. A Baltic perspective on the early to early late ordovician δ13 C and δ18 O Records and its paleoenvironmental significance. Paleoceanog and Paleoclimatol 37, e2021PA004309 (2022).Pörtner, H. Climate change and temperature-dependent biogeography: oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
Google Scholar 
Gueguen, B. et al. The chromium isotope composition of reducing and oxic marine sediments. Geochimica et Cosmochimica Acta 184, 1–19 (2016).CAS 

Google Scholar 
Weyer, S. et al. Natural fractionation of 238U/235U. Geochimica et Cosmochimica Acta 72, 345–359 (2008).CAS 

Google Scholar 
Condon, D. J., McLean, N., Noble, S. R. & Bowring, S. A. Isotopic composition (238U/235U) of some commonly used uranium reference materials. Geochimica et Cosmochimica Acta 74, 7127–7143 (2010).CAS 

Google Scholar 
Wang, X., Planavsky, N. J., Reinhard, C. T., Hein, J. R. & Johnson, T. M. A cenozoic seawater redox record derived from 238U/235U in ferromanganese crusts. Am. J. Sci. 315, 64–83 (2016).
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 
Scotese, C. R. Atlas of Silurian and Middle-Late Ordovician Paleogeographic Maps (Mollweide Projection), Maps 73-80, Volumes 5, The Early Paleozoic, PALEOMAP Atlas for ArcGIS, PALEOMAP Project, Evanston, IL. (2014). More