Ecology

Moyle, P. B. Inland fishes of California (University of California Press, 2002).
Google Scholar 
Moyle, P. B., Brown, L. R., Durand, J. R. & Hobbs, J. A. Delta smelt: Life history and decline of a once-abundant species in the San Francisco Estuary. San Franc. Estuary Watershed Sci. 14, 1–28 (2016).
Google Scholar 
U. S. Fish and Wildlife Service. Endangered and threatened wildlife and plants: Determination of threatened status of the Delta Smelt. Federal Regist. 58, 12854–12864 (1993).
Google Scholar 
California Department of Fish and Wildlife. State and federally listed endangered and threatened animals of California. California Department of Fish and Wildlife, (The Natural Resources Agency, North Highlands, 2017).Moyle, P. B. & Bennett, W. A. The future of the Delta ecosystem and its fish, Technical Appendix D. Comparing Futures for the Sacramento-San Joaquin Delta. San Francisco (CA): Public Policy Institute of California (2008).Lund, J. R. et al. Comparing futures for the Sacramento-San Joaquin Delta (Public Policy Institute of California, 2010).Book 

Google Scholar 
Moyle, P. B., Bennett, W. A., Fleenor, W. E. & Lund, J. R. Habitat variability and complexity in the upper San Francisco Estuary. San Franc. Estuary Watershed Sci. 8, 1–24 (2010).
Google Scholar 
Feyrer, F., Newman, K., Nobriga, M. & Sommer, T. Modeling the effects of future outflow on the abiotic habitat of an imperiled estuarine fish. Estuaries Coast. 34, 120–128 (2011).Article 

Google Scholar 
Cloern, J. E. & Jassby, A. D. Drivers of change in estuarine-coastal ecosystems: Discoveries from four decades of study in San Francisco bay. Rev. Geophys. 50, RG4001 (2012).ADS 
Article 

Google Scholar 
Moyle, P. B., Hobbs, J. A. & Durand, J. R. Delta Smelt and water politics in California. Fisheries 43, 42–60 (2018).Article 

Google Scholar 
Mahardja, B. et al. Resistance and resilience of pelagic and littoral fishes to drought in the San Francisco estuary. Ecol. Appl. 31, e02243 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Börk, K., Moyle, P., Durand, J., Hung, T.-C. & Rypel, A. L. Small populations in jeopardy: Delta smelt case study. Environ. Law Reporter 50, 10714–10722 (2020).
Google Scholar 
Moyle, P. B. 2021. Experimental habitats for hatchery Delta Smelt. California WaterBlog https://californiawaterblog.com/2021/07/25/experimental-habitats-for-hatchery-delta-smelt/ (2021).Jeffries, K. M. et al. Effects of high temperatures on threatened estuarine fishes during periods of extreme drought. J. Exp. Biol. 219, 1705–1716 (2016).PubMed 
Article 

Google Scholar 
Bashevkin, S. M. & Mahardja, B. Seasonally variable relationships between surface water temperature and inflow in the upper San Francisco Estuary. Limnol. Oceanogr. 67, 684–702 (2022).ADS 
Article 

Google Scholar 
Brown, L. R. et al. Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish. PLoS ONE 11, e0146724 (2015).Article 

Google Scholar 
Kurobe, T. et al. Reproductive strategy of Delta Smelt Hypomesus transpacificus and impacts of drought on reproductive performance. PLoS ONE 17, e0264731 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lewis, L. S. et al. Otolith-based approaches indicate strong effects of environmental variation on growth of a critically endangered estuarine fish. Mar. Ecol. Prog. 676, 37–56 (2021).Article 

Google Scholar 
Hammock, B. G. et al. Patterns and predictors of condition indices in a critically endangered fish. Hydrobiologia 849, 675–695 (2021).Article 

Google Scholar 
Bennett, W. A. Critical assessment of the delta smelt population in the San Francisco Estuary, California. San Franc. Estuary Watershed Sci. 3(1), (2005).Komoroske, L. M. et al. Ontogeny influences sensitivity to climate change stressors in an endangered fish. Conserv. Physiol. 2, cou008 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moyle, P. B., Herbold, B., Stevens, D. E. & Miller, L. W. Life history of delta smelt in the Sacramento-San Joaquin Estuary California. Trans. Am. Fish. Soc. 121, 67–77 (1992).Article 

Google Scholar 
Kimmerer, W. J., MacWilliams, M. L. & Gross, E. S. Variation of fish habitat and extent of the low-salinity zone with freshwater flow in the San Francisco Estuary. San Franc. Estuary Watershed Sci. 11 (2013).Sommer, T. & Meija, F. A place to call home: A synthesis of delta smelt habitats in the upper San Francisco Estuary. San Franc. Estuary Watershed Sci. 9 (2013).Hammock, B. G. et al. Foraging and metabolic consequences of semi-anadromy for an endangered estuarine fish. PLoS ONE 12, e0173497 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Cox, D. Effects of three heating rates on the critical thermal maximum of Bluegill. In W Gibbons, R Sharitz, eds, Thermal Ecology. National Technical Information Service, 158–163 (Springfield, IL, 1974).Beitinger, T. L., Bennett, W. A. & McCauley, R. W. Temperature tolerances of North American freshwater fishes exposed to dynamic changes in temperature. Environ. Biol. Fishes 58, 237–275 (2000).Article 

Google Scholar 
Davis, B. E. et al. Sensitivities of an endemic, endangered California smelt and two non-native fishes to serial increases in temperature and salinity: Implications for shifting community structure with climate change. Conserv. Physiol. 7, coy076 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Swanson, C., Reid, T., Young, P. S. & Cech, J. J. Jr. Comparative environmental tolerances of threatened delta smelt (Hypomesus transpacificus) and introduced wakasagi (H. nipponensis) in an altered California estuary. Oecologia 123, 384–390 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hammock, B. G., Hobbs, J. A., Slater, S. B., Acuña, S. & Teh, S. J. Contaminant and food limitation stress in an endangered estuarine fish. Sci. Total Environ. 532, 316–326 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamilton, S. A. & Murphy, D. D. Analysis of limiting factors across the life cycle of delta smelt (Hypomesus transpacificus). Environ. Manage. 62, 365–382 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feyrer, F., Nobriga, M. L. & Sommer, T. R. Multidecadal trends for three declining fish species: Habitat patterns and mechanisms in the San Francisco Estuary, California USA. Can. J. Fish. Aquat. Sci. 64, 723–734 (2007).Article 

Google Scholar 
Nobriga, M. L., Sommer, T. R., Feyrer, F. & Fleming, K. Long-term trends in summertime habitat suitability for delta smelt (Hypomesus transpacificus). San Franc. Estuary Watershed Sci. 6(1), (2008).Brown, L. R. et al. Implications for future survival of delta smelt from four climate change scenarios for the Sacramento-San Joaquin Delta California. Estuaries Coast. 36, 754–774 (2013).CAS 
Article 

Google Scholar 
Moyle, P., Kiernan, J. D., Crain, P. K. & Quiñones, R. M. Climate change vulnerability of native and alien freshwater fishes of California: A systematic assessment approach. PLoS ONE 8, e63883 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hobbs, J. A., Lewis, L. S., Willmes, M., Denney, C. & Bush, E. Complex life histories discovered in a critically endangered fish. Sci. Rep. 9, 16772 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bennett, W. A. & Burau, J. R. Riders on the storm: Selective tidal movements facilitate the spawning migration of threatened Delta Smelt in the San Francisco Estuary. Estuaries Coast. 38, 826–835 (2015).Article 

Google Scholar 
Hirvonen, H., Ranta, E., Piironen, J., Laurila, A. & Peuhkuri, N. Behavioral responses of naive Arctic charr to chemical cues from salmonid and non-salmonid fish. Oikos 88, 191–199 (2000).Article 

Google Scholar 
Correia, A. M., Bandeira, N. & Anastacio, P. M. Influence of chemical and visual stimuli in food-search behaviour of Procambarus clarkii under clear conditions. Mar. Freshw. Behav. Physiol. 40, 189–194 (2007).CAS 
Article 

Google Scholar 
Nay, T. J. et al. Habitat complexity influences selection of thermal environment in a common coral reef fish. Conserv. Physiol. 8, coaa070 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Horning, W. B. & Weber, C. I. Short-term methods for estimating the chronic toxicity of effluents and receiving waters to freshwater organisms. EPA/600/4-85/014, 58–75 (1985).Lindberg, J. et al. Aquaculture methods for a genetically managed population of endangered delta smelt. N. Am. J. Aquac. 75, 186–196 (2013).Article 

Google Scholar 
Ferrari, M. C. O. et al. Effects of turbidity and an invasive waterweed on predation by introduced largemouth bass. Environ. Biol. Fishes 97, 79–90 (2014).Article 

Google Scholar 
Petersen, M. F. & Steffensen, T. F. Preferred temperature of juvenile Atlantic cod Gadus morhua with different haemoglobin genotypes at normoxia and moderate hypoxia. J. Exp. Biol. 206, 359–364 (2003).CAS 
PubMed 
Article 

Google Scholar 
Meager, J. J. & Utne-Palm, A. C. Effect of turbidity on habitat preference of juvenile Atlantic cod Gadus morhua. Environ. Biol. Fishes 81, 149–155 (2008).Article 

Google Scholar 
Serrano, X., Grosell, M. & Serafy, J. E. Salinity selection and preference of the grey snapper Lutjanus griseus: Field and laboratory observations. J. Fish Biol. 76, 1592–1608 (2010).CAS 
PubMed 
Article 

Google Scholar 
Stol, J. A., Svendsen, J. C. & Enders, E. C. Determining the thermal preferences of Carmine Shiner (Notropis percobromus) and Lake Sturgeon (Acipenser fulvescens) using an automated shuttlebox. Can. Tech. Rep. Fish. Aquat. Sci. 3038 (2013).Hammock, B. G. et al. The health and condition responses of delta smelt to fasting: A time series experiment. PLoS ONE 15, e0239358 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McElreath, R. Statistical rethinking: A Bayesian course with examples in R and Stan. (CRC Press, 2016.R Core Team. R: A language and environment for statistical computing (2021).Bates, D., Maechler, M. & Bolker, B. lme4: Linear mixed-effects models using S4 classes (2012).Korner-Nievergelt, F. et al. Bayesian data analysis in ecology using linear models with R (Elsevier, 2015).
Google Scholar 
Gilliam, J. F. & Fraser, D. F. Habitat selection under predation hazard: Test of a model with foraging minnows. Ecology 68, 1856–1862 (1987).PubMed 
Article 

Google Scholar 
Metcalfe, N. B., Fraser, N. H. & Burns, M. D. Food availability and the nocturnal vs. diurnal foraging trade-off in juvenile salmon. J. Anim. Ecol. 68, 371–381 (1999).Article 

Google Scholar 
Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).Article 

Google Scholar 
Bull, H. O. Studies on conditioned responses in fishes. Part VII. Temperature perception in teleosts. J. Mar. Biol. Assoc. U. K. 21, 1–27 (1936).Article 

Google Scholar 
Steffel, S., Magnuson, J. J., Dizon, A. E. & Neill, W. H. Temperature discrimination by captive free-swimming tuna Euthynnus affinis. Trans. Am. Fish. Soc. 105, 588–591 (1976).Article 

Google Scholar 
Dülger, N. et al. Thermal tolerance of European Sea bass (Dicentrarchus labrax) juveniles acclimated to three temperature levels. J. Therm. Biol. 37, 79–82 (2012).Article 

Google Scholar 
Hung, T.-C. et al. A pilot study of the performance of captive-reared delta smelt Hypomesus transpacificus in a semi-natural environment. J. Fish Biol. 95, 1517–1522 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Navarro, I. & Gutiérrez, J. Fasting and starvation. Biochemistry and molecular biology of fishes. 4: Elsevier. p. 393–434 (1995).Finger, A. J. et al. A conservation hatchery population of Delta Smelt shows evidence of genetic adaptation to captivity after 9 generations. J. Hered. 109, 689–699 (2018).PubMed 
Article 

Google Scholar 
Middaugh, D. P., Davis, W. R. & Yokum, R. L. The response of larval fish, Leiostomus xanthurus, to environmental stress following sublethal cadmium exposure. Contrib. Mar. Sci. 19, 13–19 (1975).CAS 

Google Scholar 
Stevens, E. D. & Sutterlin, A. M. Heat transfer between fish and ambient water. J. Exp. Biol. 65, 131–145 (1976).CAS 
PubMed 
Article 

Google Scholar 
Beitinger, T. L., Thommes, M. M. & Spigarelli, S. A. Relative roles of conduction and convection in the body temperature change of gizzard shad Dorosoma cepedianum. Comp. Biochem. Physiol. 57A, 275–279 (1977).Article 

Google Scholar 
Neill, W. H. & Magnuson, J. J. Distributional ecology and behavioral thermoregulation of fishes in relation to heated effluents from a power plant at Lake Monona Wisconsin. Trans. Am. Fish. Soc. 103, 663–710 (1974).Article 

Google Scholar 
Coutant, C. C. Temperature selection by fish–a factor in power plant impact assessments. pp. 575–597. In: Environmental Effects of Cooling Systems at Nuclear Power Plants, Internat. Atomic Energy Agency, Vienna (1975).Richards, F. P., Reynolds, W. W. & McCauley, R. W. Temperature preference studies in environmental impact assessment: An overview with procedural recommendations. J. Fish. Res. Board Can. 34, 728–761 (1977).Article 

Google Scholar 
Swanson, C., Mager, R. C., Doroshov, S. I. & Cech, J. J. Jr. Use of salts, anesthetics, and polymers to minimize handling and transport mortality in delta smelt. Trans. Am. Fish. Soc. 125, 326–329 (1996).CAS 
Article 

Google Scholar 
Komoroske, L. M. et al. Sublethal salinity stress contributes to habitat limitation in an endangered estuarine fish. Evol. Appl. 9, 963–981 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Feyrer, F., Sommer, T. & Harrell, W. Importance of flood dynamics versus intrinsic physical habitat in structuring fish communities: Evidence from two adjacent engineered floodplains on the Sacramento river California. N. Am. J. Aquac. 26, 408–417 (2006).
Google Scholar  More

Benton MJ, Donoghue PCJ. Paleontological evidence to date the tree of life. Mol Biol Evol. 2006;24:26–53.PubMed 

Google Scholar 
Roll U, Feldman A, Novosolov M, Allison A, Bauer AM, Bernard R, et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat Ecol Evol. 2017;1:1677–82.PubMed 

Google Scholar 
IUCN, The IUCN Red List of Threatened Species. Version 2021-3. 2021.Medicine, N.L.o., NCBI Genome. 2022, National Center for Biotechnology Information.Hotaling S, Kelley JL, Frandsen PB. Toward a genome sequence for every animal: Where are we now? Proc Natl Acad Sci. 2021;118:e2109019118.PubMed 
PubMed Central 

Google Scholar 
Shi M, Lin XD, Chen X, Tian JH, Chen LJ, Li K, et al. The evolutionary history of vertebrate RNA viruses. Nature. 2018;556:197–202.PubMed 

Google Scholar 
Parry R, Wille M, Turnbull OMH, Geoghegan JL, Holmes EC. Divergent influenzalike viruses of amphibians and fish support an ancient evolutionary association. Viruses. 2020;12:1042.PubMed Central 

Google Scholar 
Peck KM, Lauring AS, Christopher S, Complexities of viral mutation rates. J Virol. 92: e01031-17.Latney LV, Klaphake E. Selected emerging infectious diseases of amphibians. Vet Clin N Am—Exotic Animal Pract. 2020;23:397–412.
Google Scholar 
Zhang J, Finlaison DS, Frost MJ, Gestier S, Gu X, Hall J, et al. Identification of a novel nidovirus as a potential cause of large scale mortalities in the endangered Bellinger River snapping turtle (Myuchelys georgesi). PLOS ONE. 2018;13:e0205209.PubMed 
PubMed Central 

Google Scholar 
Parrish K, Kirkland PD, Skerratt LF, Ariel E. Nidoviruses in reptiles: a review. Front Vet Sci. 2021;8:733404.PubMed 
PubMed Central 

Google Scholar 
Chang WS, Li CX, Hall J, Eden JS, Hyndman TH, Holmes EC, et al. Metatranscriptomic discovery of a divergent circovirus and a chaphamaparvovirus in captive reptiles with proliferative respiratory syndrome. Viruses. 2020;12:1073.PubMed Central 

Google Scholar 
Mendoza-Roldan JA, Mendoza-Roldan MA, Otranto D. Reptile vector-borne diseases of zoonotic concern. Int J Parasitol: Parasites Wildl. 2021;15:132–42.
Google Scholar 
Essbauer S, Ahne W. Viruses of lower vertebrates. J Vet Med Ser B. 2001;48:403–75.
Google Scholar 
Mercer LK, Harding EF, Yan GJH, White PA. Novel viruses discovered in the transcriptomes of agnathan fish. J Fish Dis. 2022;45:931–8.PubMed 
PubMed Central 

Google Scholar 
Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat biotechnol. 2011;29:644–52.PubMed 
PubMed Central 

Google Scholar 
Harding EF, Russo AG, Yan GJH, Waters PD, White PA. Ancient viral integrations in marsupials: a potential antiviral defence. Virus Evol. 2021;7:veab076.PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.PubMed 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
PubMed Central 

Google Scholar 
Kelly AG, Netzler NE, White PA. Ancient recombination events and the origins of hepatitis E virus. BMC Evol Biol. 2016;16:210.PubMed 
PubMed Central 

Google Scholar 
Rector A, Van, Ranst M. Animal papillomaviruses. Virology. 2013;445:213–23.PubMed 

Google Scholar 
Blahak S, Jenckel M, Höper D, Beer M, Hoffmann B, Schlottau K. Investigations into the presence of nidoviruses in pythons. Virol J. 2020;17:6.PubMed 
PubMed Central 

Google Scholar 
Marschang RE. Viruses infecting reptiles. Viruses. 2011;3:2087–126.PubMed 
PubMed Central 

Google Scholar 
Horie M, Akashi H, Kawata M, Tomonaga K. Identification of a reptile lyssavirus in Anolis allogus provided novel insights into lyssavirus evolution. Virus Genes. 2021;57:40–49.PubMed 

Google Scholar 
Stenglein MD, Sanders C, Kistler AL, Ruby JG, Franco JY, Reavill DR, et al. Identification, characterization, and in vitro culture of highly divergent arenaviruses from boa constrictors and annulated tree boas: candidate etiological agents for snake inclusion body disease. mBio. 2012;3:e00180–12.PubMed 
PubMed Central 

Google Scholar 
Garver KA, Leskisenoja K, Macrae R, Hawley LM, Subramaniam K, Waltzek TB, et al. An alloherpesvirus infection of european perch perca fluviatilis in Finland. Dis Aquat Org. 2018;128:175–85.
Google Scholar 
Hellebuyck T, Couck L, Ducatelle R, Broeck WV, Marschang RE. Cheilitis associated with a novel herpesvirus in two panther chameleons (Furcifer pardalis). J Comp Pathol. 2021;182:58–66.PubMed 

Google Scholar 
Altan E, Kubiski SV, Burchell J, Bicknese E, Deng X, Delwart E. The first reptilian circovirus identified infects gut and liver tissues of black-headed pythons. Vet Res. 2019;50:35.PubMed 
PubMed Central 

Google Scholar 
Russo AG, Harding EF, Yan GJH, Selechnik D, Ducatez S, DeVore JL, et al. Discovery of novel viruses associated with the invasive cane toad (Rhinella marina) in its native and introduced ranges. Front Microbiol. 2021;12:733631.PubMed 
PubMed Central 

Google Scholar 
Chen X-X, Wu W-C, Shi M. Discovery and characterization of actively replicating DNA and retro-transcribing viruses in lower vertebrate hosts based on RNA sequencing. Viruses. 2021;13:1042.PubMed 
PubMed Central 

Google Scholar 
Russo AG, Eden JS, Tuipulotu DE, Shi M, Selechnik D, Shine R, et al. Viral discovery in the invasive Australian cane toad (Rhinella marina) using metatranscriptomic and genomic approaches. J Virol. 2018;92:e00768–18.PubMed 
PubMed Central 

Google Scholar 
López-Bueno A, Mavian C, Labella AM, Castro D, Borrego JJ, Alcami A, et al. Concurrence of Iridovirus, Polyomavirus, and a unique member of a new group of fish Papillomaviruses in Lymphocystis disease-affected gilthead sea bream. Journal of virology. 2016;90:8768–79.PubMed 
PubMed Central 

Google Scholar 
Bentley K, Evans DJ. Mechanisms and consequences of positive-strand RNA virus recombination. J Gen Virol. 2018;99:1345–56.PubMed 

Google Scholar 
Diemer GS, Stedman KM. A novel virus genome discovered in an extreme environment suggests recombination between unrelated groups of RNA and DNA viruses. Biol Direct. 2012;7:13.PubMed 
PubMed Central 

Google Scholar 
Welch, NL, MJ Tisza, GJ Starrett, AK Belford, DV Pastrana, Y-YS Pang, et al. Identification of Adomavirus Virion proteins. bioRxiv. 2020:341131. https://doi.org/10.1101/341131Dill JA, Camus AC, Leary JH, Ng TFF, Zheng Z-M, Meng X-J. Microscopic and Molecular Evidence of the First Elasmobranch Adomavirus, the Cause of Skin Disease in a Giant Guitarfish, Rhynchobatus djiddensis. mBio. 2018;9:e00185–18.PubMed 
PubMed Central 

Google Scholar 
Yang J-X, Chen X, Li Y-Y, Song T-Y, Ge J-Q. Isolation of a novel adomavirus from cultured American eels, Anguilla rostrata, with haemorrhagic gill necrosis disease. J Fish Dis. 2021;44:1811–8.PubMed 

Google Scholar 
King AMQ, Adams MJ, Carstens EB & Lefkowitz EJ, Order – Nidovirales, in virus taxonomy: classification and nomenclature of viruses. 2012, Elsevier/Academic Press: San Diego.Lyu S, Yuan X, Zhang H, Shi W, Hang X, Liu L, et al. Complete genome sequence and analysis of a new lethal arterivirus, Trionyx sinensis hemorrhagic syndrome virus (TSHSV), amplified from an infected Chinese softshell turtle. Arch Virol. 2019;164:2593–7.PubMed 
PubMed Central 

Google Scholar 
Sinzelle L, Carradec Q, Paillard E, Bronchain OJ, Pollet N. Characterization of a Xenopus tropicalis endogenous retrovirus with developmental and stress-dependent expression. J Virol. 2011;85:2167–79.PubMed 

Google Scholar 
Wei X, Chen Y, Duan G, Holmes EC, Cui J. A reptilian endogenous foamy virus sheds light on the early evolution of retroviruses. Virus Evol. 2019;5:vez001.PubMed 
PubMed Central 

Google Scholar 
Debat HJ, Ng TFF. Complete genome sequence of a divergent strain of Tibetan frog hepatitis B virus associated with a concave-eared torrent frog (Odorrana tormota). Arch Virol. 2019;164:1727–32.PubMed 

Google Scholar 
Reuter G, Boros Á, Tóth Z, Gia Phan T, Delwart E, Pankovics P. A highly divergent picornavirus in an amphibian, the smooth newt (Lissotriton vulgaris). J Gen Virol. 2015;96:2607–13.PubMed 
PubMed Central 

Google Scholar 
ICTV. Subfamily: Secondpapillomavirinae. 2021 [cited 2022 15/06/2022]; Virus Taxonomy: 2021 Release:[Available from: https://talk.ictvonline.org/ictv-reports/ictv_online_report/dsdna-viruses/w/papillomaviridae/894/subfamilysecondpapillomavirinae.Willemsen A, Bravo IG. Origin and evolution of papillomavirus (onco)genes and genomes. Philos Trans R Soc B: Biol Sci. 2019;374:20180303.
Google Scholar 
Agius JE, Phalen DN, Rose K, Eden J-S. New insights into Sauropsid Papillomaviridae evolution and epizootiology: discovery of two novel papillomaviruses in native and invasive Island geckos. Virus Evol. 2019;5:vez051.PubMed 
PubMed Central 

Google Scholar 
Bienentreu J-F, Lesbarrères D. Amphibian disease ecology: are we just scratching the surface? Herpetologica. 2020;76:153–66.
Google Scholar 
Mashkour N, Jones K, Wirth W, Burgess G, Ariel E. The concurrent detection of Chelonid Alphaherpesvirus 5 and Chelonia mydas Papillomavirus 1 in tumoured and non-tumoured green turtles. Animals. 2021;11:697.PubMed 
PubMed Central 

Google Scholar 
Hoon-Hanks LL, Layton ML, Ossiboff RJ, Parker JSL, Dubovi EJ, Stenglein MD. Respiratory disease in ball pythons (Python regius) experimentally infected with ball python nidovirus. Virology. 2018;517:77–87.PubMed 

Google Scholar 
Dervas E, Hepojoki J, Smura T, Prähauser B, Windbichler K, Blümich S, et al. Serpentoviruses: More than respiratory pathogens. J Virol. 2020;94:e00649–20.PubMed 
PubMed Central 

Google Scholar 
O’Dea MA, Jackson B, Jackson C, Xavier P, Warren K. Discovery and Partial Genomic Characterisation of a Novel Nidovirus Associated with Respiratory Disease in Wild Shingleback Lizards (Tiliqua rugosa). PloS One. 2016;11:e0165209.PubMed 
PubMed Central 

Google Scholar 
Dervas E, Hepojoki J, Laimbacher A, Romero-Palomo F, Jelinek C, Keller S, et al. Nidovirus-associated proliferative pneumonia in the green tree python (Morelia viridis). J Virol. 2017;91:e00718–17.PubMed 
PubMed Central 

Google Scholar 
Oberhuber M, Schopf A, Hennrich AA, Santos-Mandujano R, Huhn AG, Seitz S, et al. Glycoproteins of predicted amphibian and reptile lyssaviruses can mediate infection of mammalian and reptile cells. Viruses. 2021;13:1726.PubMed 
PubMed Central 

Google Scholar 
Ritchie BW, Niagro FD, Lukert PD, Steffens WL, Latimer KS. Characterization of a new virus from cockatoos with psittacine beak and feather disease. Virology. 1989;171:83–88.PubMed 

Google Scholar 
Eleni C, Corteggio A, Altamura G, Meoli R, Cocumelli C, Rossi G, et al. Detection of Papillomavirus DNA in cutaneous squamous cell carcinoma and multiple papillomas in captive reptiles. J Comp Pathol. 2017;157:23–26.PubMed 

Google Scholar 
Tessier TM, Dodge MJ, MacNeil KM, Evans AM, Prusinkiewicz MA, Mymryk JS. Almost famous: Human adenoviruses (and what they have taught us about cancer). Tumour. Virus Res. 2021;12:200225.
Google Scholar 
Chen XX, Wu WC, Shi M. Discovery and characterization of actively replicating dna and retro-transcribing viruses in lower vertebrate hosts based on rna sequencing. Viruses. 2021;13:1042.PubMed 
PubMed Central 

Google Scholar 
Liu W, Zhang Y, Ma J, Jiang N, Fan Y, Zhou Y, et al. Determination of a novel parvovirus pathogen associated with massive mortality in adult tilapia. PLOS Pathogens. 2020;16:e1008765.PubMed 
PubMed Central 

Google Scholar  More

Sampling location and sediment coringSamples were collected during IODP Exp. 382 ‘Iceberg Alley and Subantarctic Ice and Ocean Dynamics’ on-board RV Joides Resolution between 20 March and 20 May 2019. Specifically, we collected samples at Site U1534 (Falkland Plateau, 606 m water depth), U1536 (Dove Basin, Scotia Sea, 3220 m water depth), and Site U1538 (Pirie Basin, Scotia Sea, 3130 m water depth) (Fig. 1). Site U1534 is located at the Subantarctic Front on a contourite drift at the northern limit of the Scotia Sea. This setting is ideal to study the poorly understood role of Antarctic Intermediate Water (AAIC) and its impact on the Atlantic Meridional Overturning Circulation (AMOC) along the so-called ‘cold water route’ that connects to the Pacific Ocean through the Drake Passage, as opposed to the ‘warm water route’ that connects to the Indian Ocean via the Agulhas Current42. Sites U1536 and U1538 are located in the southern and central Scotia Sea, respectively, and were drilled to study the Neogene flux of icebergs through ‘Iceberg Alley’, the main pathway along which icebergs calved from the margin of the AIS travel as they move equatorward into the warmer waters of the Antarctic Circumpolar Current (ACC)23. sedaDNA samples collected at Site U1534 were from Hole C, at Site U1536 from Hole B, and at Site U1538 from Holes C and D (Table 1), and in the following we refer to site names only. IODP Expedition proposals undergo a rigorous environmental protection and safety review, which is approved by the IODP’s Environmental Protection and Safety Panel (EPSP) and/or the Safety Panel. The same procedure was applied to IODP Exp. 382 and approval was provided by the EPSP. Sediment samples for sedaDNA analyses were imported to Australia under Import Permit number 0002658554 provided by the Australian Government Department for Agriculture and Water Resources (date of issue: 19 September 2018), and were stored and extracted at a quarantine approved facility (AA Site No. S1253, Australian Centre for Ancient DNA). No ethical approval was required for this study.Table 1 Sampling location and sample detailsFull size tableSample age determinationAge control for Site U1534 is based on tuning of benthic foraminifera δ18O to the LR04 stack43. Wherever present specimens of Uvigerina bifurcata were picked from samples at 10 cm intervals. During warmer periods when U. bifurcata was not present, Melonis affinis and/or Hoeglundina elegans were analysed. Sedimentation rates over the intervals sampled for sedaDNA typically range between 6 and 30 cm/kyr, with rates exceeding 100 cm/kyr during the Last Glacial Maximum ~20,000 years ago (20 ka). For our deepest sample, U1534C-10H-6_115cm (90.95 mbsf), we only have biostratigraphically assigned ages available (shipboard data), which date this sample as early Pleistocene (~2.5–0.7 million years ago, Ma44).Low-resolution age control for both Sites U1536 and U1538 was established using shipboard magneto- and biostratigraphy21,23. Average sedimentation rates are ~10 cm/kyr for Site U1536, with elevated values (up to 20 cm/kyr) in the upper ~80 mbsf (the last ~400 ka). Site U1538 average sedimentation rates are twice as high, averaging ~20 cm/kyr. Especially in the upper ~430 mbsf (the last 1.8 Ma), rates are up to 40 cm/kyr. Higher resolution age models are based on dust climate couplings, correlating sedimentary dust proxy records such as magnetic susceptibility and sedimentary Ca and Fe records to ice-core dust proxy records over the last 800 ka45 and to a benthic isotopic stack26 before that. These age models were established for Site U1537 (adjacent to Site U1536) and provide orbital to millennial scale resolution. For this study we correlated sedimentary cycles of Sites U1536 and U1538 to U1537 to achieve similar resolution and to be able to determine if a sample originates from a glacial or interglacial period (Table 1).Sampling of sedaDNAA detailed description of sedaDNA sampling methods can be found in ref. 24. In brief, we used advanced piston coring (APC) to acquire sediment cores, which recovers the least disturbed sediments46,47,48 and is thus the preferred technique for sedaDNA sampling. All samples were taken on the ship’s ‘catwalk’, where, once the core was on deck, the core liners were wiped clean twice (3% sodium hypochlorite, ‘bleach’) at each cutting point. Core cutting tools were sterilised before each cut (3% bleach and 80% ethanol) of the core in 1 m sections. The outer ~3 mm of surface material were removed from the bottom of each core section to be sampled, using sterilised scrapers (~4 cm wide; bleach and ethanol treated). A cylindrical sample was taken from the core centre using a sterile (autoclaved) 10 mL cut-tip syringe, providing ~5 cm3 of sediment material. The syringe was placed in a sterile plastic bag (Whirl-Pak) and immediately frozen at −80 °C. The mudline (sediment/seawater interface) was transferred from the core liner into a sterile bucket (3% bleach treated), and 10 mL sample was retained in a sterile 15 mL centrifuge tube (Falcon) and frozen at −80 °C. Samples were collected at various depth intervals depending on the site to span the Holocene up to ~1 million years (Table 1). This lower depth/age limit was determined by switching coring system from APC to the extended core barrel (XCB) system.To test for potential airborne contamination, at least one air control was taken during the sedaDNA sampling process per site. For this, an empty syringe was held for a few seconds in the sampling area and then transferred into a sterile plastic bag and frozen at −80 °C. The air controls were processed, sequenced and analysed alongside the sediment samples.Contamination control using perfluoromethyldecalin tracersAs part of the APC process, drill fluid (basically, seawater) is pumped into the borehole to trigger the hydraulic coring system, therefore, the potential for contamination exists due to drill fluid making contact with the core liner. To assess the latter, we added the non-toxic chemical tracer perfluoromethyldecalin (PFMD) to the drill fluid at a rate of ~0.55 mL min−1 for cores collected at Sites U1534 and U153649. As we found that PFMD concentrations were very low at these sites (Results section), the infusion rate was doubled prior to sedaDNA sampling at Site U1538 to ensure low PFMD concentrations represent low contamination and not delivery failure of PFMD to the core. At each sedaDNA sampling depth, one PFMD sample was taken from the periphery of the core (prior to scraping, to test whether drill fluid reached the core pipe), and one next to the sedaDNA sample in the centre of the core (after scraping, to minimise differences to the sedaDNA sample, and testing if drill fluid had reached the core centre). We transferred ~3 cm3 of sediment using a disposable, autoclaved 5 mL cut-tip syringe into a 20 mL headspace vial with metal caps and Teflon seals. We also collected a sample of the tracer-infused drill fluid at each site, by transferring ~10 mL of the fluid collected at the injection pipe on the rig floor via a sterile plastic bottle into a 15 mL centrifuge tube (inside a sterile plastic bag) and freezing it at −80 °C. These drill fluid controls were processed and analysed in the same way as the sedaDNA samples including sequencing. Samples were analysed using gas chromatography (GC-µECD; Hewlett-Packard 6890).A detailed description of the PFMD GC measurements is provided in ref. 24. Briefly, PFMD measurements were undertaken in batches per site for U1534, U1536 and U1538. This included the analyses of PFMD samples collected at two additional holes at these sites, U1534D and U1536C, from which we also collected sedaDNA samples but that are not part of this study. PFMD is categorised as the stereoisomers of PFMD (C11F20), which add up to 87-88% (and with the remaining 12% being additional perfluoro compounds unable to be separated by the manufacturer). We exclusively refer to the first and measurable PFMD category, calibrating for the 88% in bottle concentrations during concentration calculations. Each GC analysis run included the measurement of duplicate blanks and duplicate PFMD standards. Due to a large sample number, PFMD at Site U1538 was measured in three separate runs, with the first and last run including triplicate blank and triplicate PFMD standards (duplicates in the second run), and the last run also containing a drill-fluid sample. To blank-correct PFMD concentrations, we subtracted the average PFMD concentration of all blanks per run from PFMD measurements in that run. To determine the detection limit of PFMD, we used three times the standard deviation of the average blank PFMD values per run; due to all blank values for the U1538 runs being 0, we used three times the standard deviation of the lowest PFMD standard for this site in this calculation. This provided us with a PFMD detection limit of 0.2338 ng mL−1. Any PFMD measurements of samples below this limit were rejected.
sedaDNA extractions and metagenomic library preparationsA total of 80 sedaDNA extracts and metagenomic shotgun libraries (Table 1) were prepared following8,10. For the sedaDNA extractions, we randomised our samples and controls and extracted sedaDNA in batches of 16 extracts/libraries at a time, with each batch including at least one air control and one extraction blank control (EBC), and the last batch including mudline and PFMD samples to avoid contamination of the sedaDNA samples. In brief, we used 20 µL sedaDNA extracts in a repair reaction (using T4 DNA polymerase, New England Biolabs, USA; 15 min, 25 °C), then purified the sedaDNA (MinElute Reaction Cleanup Kit, Qiagen, Germany), ligated adaptors (T4 DNA ligase, Fermentas, USA, where truncated Illumina-adaptor sequences containing two unique 7 base-pair (bp) barcodes were attached to the double-stranded DNA; 60 min, 22 °C), purified the sedaDNA again (MinElute Reaction Cleanup Kit, Qiagen), and then added a fill-in reaction with adaptor sequences (Bst DNA polymerase, New England Biolabs, USA; 30 min, 37 °C, with polymerase deactivation for 10 min, 80 °C). We amplified the barcoded libraries using IS7/IS8 primers50 (8 replicates per sample, where each replicate was a 25 µL reaction containing 3 µL DNA template; using 22 cycles), purified (AxyPrep magnetic beads, Axygen Biosciences, USA; 1:1.8 library:beads) and quantified them (Qubit dsDNA HS Assay, Invitrogen, Molecular Probes, USA). We amplified the libraries (8 replicates per sample, 13 amplification cycles) using IS4 and GAII Indexing Primers50, purified (AxyPrep magnetic beads, at a ratio of 1:1.1 library:beads), quantified and quality-checked using Qubit (dsDNA HS Assay, Invitrogen, USA) and TapeStation (Agilent Technologies, USA). We combined the libraries into an equimolar pool (volume of 68 µL in total), diluted this pool with nuclease-free H2O to 100 µL, and performed a ‘reverse’ AxyPrep clean-up to retain only the small DNA fragments typical for ancient DNA (≤ 500 bp; initial library:beads ratio of 1:0.6, followed by 1:1.1, and double-eluted in 30 µL nuclease-free H2O8,51). We added one more AxyPrep clean-up to remove primer-dimer (library:beads ratio of 1:1.05) and checked sedaDNA quantity and quality via TapeStation and qPCR (QuantStudio, Applied Biosystems, USA). The libraries sequenced at the Garvan Institute for Medical Research, Sydney, Australia (Illumina NovaSeq 2 × 100 bp).
sedaDNA data processingThe sequencing data was processed and filtered as described in detail in refs. 8, 10. Briefly, data filtering involved the removal of sequences More

For several years, systematic research has been carried out on the pollution of the night sky by artificial light in the city of Toruń11,36,38. The main objective is to monitor this phenomenon, including its spatial and temporal variability and the most important factors affecting it. Based on previous experience, an in-house measurement device was constructed to automate the process of data acquisition.Genesis of the projectThe first measurements pertaining to the phenomenon of night sky pollution in Toruń were made in autumn 2017, followed by regular observations using a handheld SQM photometer (Unihedron, Canada) as part of a project implemented in 2017–2018. To this end, a permanent measurement network distributed throughout the city was established, consisting of 24 locations. During a one night measurement session taking place during the astronomical night (there is no such period at the latitude of Toruń in the summer), sky brightness was measured at all sites. The results of spot monitoring were plotted using interpolation methods and visualisation tools available in GIS systems11,39, which helped to determine the spatial distribution and extent of night sky light pollution. The intensity of this phenomenon at each of the surveyed points was also explored in relation to the distinguished landcover categories and types of urban development.Repeatable measurements performed regularly over such a long period of time were characterised by significant limitations. One measurement session was very time-consuming, as it lasted about two hours, during which time all measurement locations were visited, covering a distance of almost 50 km each night by car. Despite the observance of all time frames and sticking to the plan of fieldwork, measurements were not carried out simultaneously at all the locations, which affected the results, especially during the night with changing cloud cover. Although the measurements were carried out with great consistency and care, they were performed in a spatial buffer of about 5 m, which could unintentionally slightly affect the obtained results. An additional limitation was also a one-time night measurement at one point, instead of a whole series of measurements at specific time intervals. Inaccuracies in the readings within a single session could have been caused by sudden changes in meteorological parameters. In the adopted procedure, it was not possible to carry out simultaneous measurements in identical time and weather conditions at all the locations, not to mention the involvement of the personnel in each tour of the measurement network points.Using the experience gained and after an analysis of the identified constraints and the technical capabilities at hand, work began in 2019 on developing a network for automatic remote monitoring of light pollution of the night sky in Toruń, based on designed in-house recording devices.Design, functional and utility features of the deviceTo enhance the research on light pollution in urban space, work has begun on the construction of a device that would perform automatic measurements, would be mobile, battery-powered and use long-range wireless communication. All the aforementioned features are in line with the strategy of Industry 4.0 and modern solutions proposed as part of the Smart City concept.The concept of Industry 4.0 assumes the more and more common use of process automation as well as the processing and exchange of data with the use of new transmission technologies26. LoRaWAN is one of the solutions used for communication of Internet of Things (IoT) devices, which supports the development of Smart Cities in the Smart Environment area. As a result, the interactivity, frequency, and scope of measurements carried out in urbanized areas are increased40,41.According to the developed project, the device was to serve as a meter of very low intensity light observed in the night sky. In this respect, it was necessary to use a sensor with technical parameters suitable for very accurate measurements of light intensity. To locally verify the weather conditions occurring during the operation of the device, it was decided to carry out additional simultaneous measurements of other environmental parameters—temperature and moisture content. The analysis of the spatial coverage of the study area indicated that 36 measurement devices should be deployed to provide full coverage of Toruń. The concept of creating an urban measurement network assumes the selection of points covering the whole city relatively evenly and representing different types of housing development and elements of land cover. It was assumed that measurements will be made only at night, between 21 p.m. and 6 a.m. on the following day, at 15 min intervals, and in addition, weather conditions will be recorded twice a day.Construction and technical parameters of the device, and selected characteristics of its componentsA prototype device meeting all the predefined functionalities was constructed based on available electronic modules. The B-L072Z-LRWAN Discovery developmental board from STMicroelectronics42 was selected as the main electronic component providing wireless communication. This board has an integrated LoRa communication module, enabling low-power wireless messaging, and also allows the board to enter a low-power state during hibernation, and thus target long-term battery-powered operation. This module is fully programmable, which enables future expansion of the set with other functionalities. The TSL2591 light sensor from AMS, which has high sensitivity and registration accuracy, was selected as a component implementing the light intensity measurement. Its great advantage is a wide measurement range of 188 μlx to 88 000 lx, sensitivity reaching 0.000377 lx, and a wide dynamic range (WDR) of 600 M:143. The sensor used has two diodes with different spectral properties. One of them registers visible light together with infrared (in the range from 400 to 1 100 nm), while the other is responsible for the registration of infrared light (between 500 and 1 100 nm). Thanks to this solution, we can use the results in various ways. The use of the formula provided by the manufacturer allows us to obtain spectral characteristics similar to the human eye. The presence of a compensating diode makes a difference compared to the sensor used in the SQM device, so the results obtained in the measurements may be slightly different.To measure additional environmental parameters, the X-NUCLEO-IKS01A2 development board from STMicroelectronics was used, which is connected to the STM32 microcontroller via the I2C interface44. This board enables the recording of a number of parameters, however, in the constructed device it is only responsible for reading the temperature and humidity of the environment. This results from the necessity to limit the size of the message packets sent, while at the same time improving the operating range and reducing the power consumption of the device.Once all the components had been selected, tested and integrated, the process of final connection and programming was carried out. The base of the device, i.e. the B-L072Z-LRWAN development board was connected to the X-NUCLEO-IKS01A2 environmental sensor board, using Arduino connectors. Using standard wires, a TSL2591 light sensor was added by connecting the corresponding I2C (SCL and SDA), power supply (VIN), sensor ground (GND) pins and the X-NUCLEO-IKS01A2 board.All components used were placed in a standard external casing with dimensions of 8.0 × 5.4 × 15.8 cm. In its lower part an opening was made for an external antenna, while in the upper part a specifically selected opening was cut out, protected with a glass pane, through which measurements are performed by the light sensor (Fig. 2).Figure 2(photo by Dominika Karpińska).Constructed device viewFull size imageFollowing the above steps, an automatic device was constructed to record light intensity in the lower troposphere, i.e. to measure the pollution of the night sky by artificial light coming from the Earth’s surface. Selected technical parameters of the device are presented in Table 1.Table 1 Selected technical parameters of the device for measuring light pollution of the night sky.Full size tableFlowchart of the system operationAfter constructing the device and writing the control software, the construction of the entire measurement system was started. Each of the measuring instruments is ultimately connected to the communication gateway using LoRa technology. A MultiTech communication gateway with a LoRaWAN module was used as an access device. To successfully connect the gateway to the measuring device, it was necessary to configure the communication gateway software. To this end, the information about the unique device number (Dev EUI) and the application key and its number (App EUI and App Key) was used. Once the unit is configured, it is possible to send data to the communication gateway and read them using NodeRED, a programming tool where data are redirected to a selected server, which stores all measurement results. Figure 3 shows a schematic representation of the constructed measurement system.Figure 3Schematic diagram of the measurement system.Full size image More

Elmhagen, B. & Rushton, S. P. Trophic control of mesopredators in terrestrial ecosystems: Top-down or bottom-up?. Ecol. Lett. 10, 197–206 (2007).PubMed 
Article 

Google Scholar 
Newsome, T. M. et al. Top predators constrain mesopredator distributions. Nat. Commun. 8, 15469 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Dill, L. M., Roberts, D. & Zanette, L. Y. Fear of large carnivores causes a trophic cascade. Nat. Commun. 7, 10698 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).PubMed 
Article 

Google Scholar 
Selva, N., Jȩdrzejewska, B., Jȩdrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).Article 

Google Scholar 
McArthur, C., Banks, P. B., Boonstra, R. & Forbey, J. S. The dilemma of foraging herbivores: Dealing with food and fear. Oecologia 176, 667–689 (2014).ADS 
Article 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 

Google Scholar 
Kuijper, D. P. J. et al. Paws without claws? Ecological effects of large carnivores in anthropogenic landscapes. Proc. R. Soc. B Biol. Sci. 283, 20161625 (2016).Article 

Google Scholar 
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolfes, elk, and bison: Reestablishing the ‘landscape of fear’ in Yellowstone National Park, U.S.A. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 
Article 

Google Scholar 
Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12, 982–998 (2009).PubMed 
Article 

Google Scholar 
Leo, V., Reading, R. P. & Letnic, M. Interference competition: Odours of an apex predator and conspecifics influence resource acquisition by red foxes. Oecologia 179, 1033–1040 (2015).ADS 
PubMed 
Article 

Google Scholar 
Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27, 1826–1832 (2016).
Google Scholar 
Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: how mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Switalski, T. A. Coyote foraging ecology and vigilance in response to gray wolf reintroduction in Yellowstone National Park. Can. J. Zool. 81, 985–993 (2003).Article 

Google Scholar 
Wikenros, C., Jarnemo, A., Frisén, M., Kuijper, D. P. J. & Schmidt, K. Mesopredator behavioral response to olfactory signals of an apex predator. J. Ethol. 35, 161–168 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Palomares, F., Ferreras, P., Fedriani, J. M. & Delibes, M. Spatial relationships between Iberian Lynx and other carnivores in an area of south-western Spain. J. Appl. Ecol. 33, 5–13 (1996).Article 

Google Scholar 
Salo, P., Nordström, M., Thomson, R. L. & Korpimäki, E. Risk induced by a native top predator reduces alien mink movements. J. Anim. Ecol. 77, 1092–1098 (2008).PubMed 
Article 

Google Scholar 
Haswell, P. M., Kusak, J. & Hayward, M. W. Large carnivore impacts are context-dependent. Food Webs 12, 3–13 (2017).Article 

Google Scholar 
Parsons, M. H. et al. Biologically meaningful scents: A framework for understanding predator–prey research across disciplines. Biol. Rev. 93, 98–114 (2018).PubMed 
Article 

Google Scholar 
Sivy, K. J., Pozzanghera, C. B., Grace, J. B. & Prugh, L. R. Fatal attraction? Intraguild facilitation and suppression among predators. Am. Nat. 190, 663–679 (2017).PubMed 
Article 

Google Scholar 
Ruprecht, J. et al. Variable strategies to solve risk-reward tradeoffs in carnivore communities. Proc. Natl. Acad. Sci. USA. 118, e2101614118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jędrzejewska, B. & Jędrzejewski, W. Predation in Vertebrate Communities Vol. 135 (Springer, 1998).Book 

Google Scholar 
Jȩdrzejewski, W. et al. Kill rates and predation by wolves on ungulate populations in Białowieża primeval forest (Poland). Ecology 83, 1341–1356 (2002).
Google Scholar 
Selva, N. The role of scavenging in the predator community of Białowieża Primeval Forest (Poland). PhD Thesis. (University of Sevilla, 2004).Kowalczyk, R., Zalewski, A., Jędrzejewska, B., Ansorge, H. & Bunevich, A. N. Reproduction and mortality of invasive raccoon dogs (Nyctereutes procyonoides) in the Biatowieža Primeval Forest (eastern Poland). Ann. Zool. Fennici 46, 291–303 (2009).Article 

Google Scholar 
Ballard, W. B., Carbyn, L. N. & Smith, D. W. Wolf interactions with non-prey. In Wolves: Behavior, Ecology, and Conservation (eds Mech, D. & Boitani, L.) 259–271 (University of Chicago Press, 2003).
Google Scholar 
Brown, J. S. Patch use as an indicator of habitat preference, predation risk, and competition. Behav. Ecol. Sociobiol. 22, 37–47 (1988).Article 

Google Scholar 
Bedoya-Perez, M. A., Carthey, A. J. R., Mella, V. S. A., McArthur, C. & Banks, P. B. A practical guide to avoid giving up on giving-up densities. Behav. Ecol. Sociobiol. 67, 1541–1553 (2013).Article 

Google Scholar 
Kwiatkowski, W. Vegetation landscapes of Białowieża Forest. Phytocoen. Suppl. Cart. Geobot 6, 35–87 (1994).
Google Scholar 
European Court of Justice Judgment of the Court (Grand Chamber) of 17 April 2018. European Commission vs. Republic of Poland. Case C-441/17. https://curia.europa.eu/jcms/upload/docs/application/pdf/2018-04/cp180048en.pdf.Bubnicki, J. W., Churski, M., Schmidt, K., Diserens, T. A. & Kuijper, D. P. J. Linking spatial patterns of terrestrial herbivore community structure to trophic interactions. Elife 8, e44937 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kowalczyk, R., Bunevich, A. N. & Jędrzejewska, B. Badger density and distribution of setts in Bialowieza Primeval Forest (Poland and Belarus) compared to other Eurasian populations. Acta Theriol. 45, 395–408 (2000).Article 

Google Scholar 
Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Kowalczyk, R. Territory size of wolves Canis lupus: Linking local (Białowieża Primeval Forest, Poland) and holarctic-scale patterns. Ecography 30, 66–67 (2007).
Google Scholar 
Schmidt, K., Jędrzejewski, W., Okarma, H. & Kowalczyk, R. Spatial interactions between grey wolves and Eurasian lynx in Białowieża Primeval Forest, Poland. Ecol. Res. 24, 207–214 (2009).Article 

Google Scholar 
Bytheway, J. P., Carthey, A. J. R. & Banks, P. B. Risk vs reward: How predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725 (2013).Article 

Google Scholar 
Carthey, A. J. R. & Banks, P. B. Naiveté is not forever: responses of a vulnerable native rodent to its long term alien predators. Oikos 125, 918–926 (2016).Article 

Google Scholar 
Blanchard, C. D. & Blanchard, R. J. Antipredator DEFENSE. In The Behavior of the Laboratory Rat: A Handbook with Tests (eds Whishaw, I. Q. & Kolb, B.) 335–343 (Oxford University Press, 2004).Chapter 

Google Scholar 
Masini, C. V., Sauer, S. & Campeau, S. Ferret odor as a processive stress model in rats: Neurochemical, behavioral, and endocrine evidence. Behav. Neurosci. 119, 280–292 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bubnicki, J. W., Churski, M. & Kuijper, D. P. J. Trapper: An open source web-based application to manage camera trapping projects. Methods Ecol. Evol. 7, 1209–1216 (2016).Article 

Google Scholar 
Jędrzejewski, W., Schmidt, K., Theuerkauf, J., Jędrzejewska, B. & Okarma, H. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza Primeval Forest in Poland. Can. J. Zool. 79, 1993–2004 (2001).Article 

Google Scholar 
Theuerkauf, J., Jędrzejewski, W., Schmidt, K. & Gula, R. Spatiotemporal segregation of wolves from humans in the Bialowieza Forest (Poland). J. Wildl. Manage. 67, 706–716 (2003).Article 

Google Scholar 
Theuerkauf, J., Rouys, S. & Jędrzejewski, W. Selection of den, rendezvous, and resting sites by wolves in the Bialowieza Forest, Poland. Can. J. Zool. 81, 163–167 (2003).Article 

Google Scholar 
Miller, B. J., Harlow, H. J., Harlow, T. S., Biggins, D. & Ripple, W. J. Trophic cascades linking wolves (Canis lupus), coyotes (Canis latrans), and small mammals. Can. J. Zool. 90, 70–78 (2012).Article 

Google Scholar 
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: An R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
Zoller, H. & Drygala, F. Activity patterns of the invasive raccoon dog (Nyctereutes procyonoides) in North East Germany. Folia Zool. 62, 290–296 (2013).Article 

Google Scholar 
Díaz-Ruiz, F., Caro, J., Delibes-Mateos, M., Arroyo, B. & Ferreras, P. Drivers of red fox (Vulpes vulpes) daily activity: Prey availability, human disturbance or habitat structure?. J. Zool. 298, 128–138 (2016).Article 

Google Scholar 
Mukherjee, S., Zelcer, M. & Kotler, B. P. Patch use in time and space for a meso-predator in a risky world. Oecologia 159, 661–668 (2009).ADS 
PubMed 
Article 

Google Scholar 
Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 102, e03336 (2021).PubMed 
Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).Magnusson, A. et al. R Package ‘glmmTMB’. (2020).Hartig, F. R Package ‘DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models’ (2021).Fox, J. et al. R Package ‘effects’. (2020).Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
Article 

Google Scholar 
Diserens, T. A. et al. Fossoriality in a risky landscape: Badger sett use varies with perceived wolf risk. J. Zool. 313, 76–85 (2021).Article 

Google Scholar 
Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
Article 

Google Scholar 
Scheinin, S., Yom-Tov, Y., Motro, U. & Geffen, E. Behavioural responses of red foxes to an increase in the presence of golden jackals: A field experiment. Anim. Behav. 71, 577 (2006).Article 

Google Scholar 
Vanak, A. T., Thaker, M. & Gompper, M. E. Experimental examination of behavioural interactions between free-ranging wild and domestic canids. Behav. Ecol. Sociobiol. 64, 279–287 (2009).Article 

Google Scholar 
Creel, S., Winnie, J. A., Christianson, D. & Liley, S. Time and space in general models of antipredator response: Tests with wolves and elk. Anim. Behav. 76, 1139–1146 (2008).Article 

Google Scholar 
Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128 (2017).PubMed 
Article 

Google Scholar 
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Holker, F., Wolter, C., Perkin, E. K. & Tockner, K. Light pollution as a biodiversity threat. Trends Ecol. Evol. 25, 681–682. https://doi.org/10.1016/j.tree.2010.09.007 (2010).Article 
PubMed 

Google Scholar 
Kyba, C., Mohar, A. & Posch, T. How bright is moonlight?. Astron. Geophys. 58, 1.31-1.32 (2017).
Google Scholar 
Hölker, F. et al. The dark side of light: A transdisciplinary research agenda for light pollution policy. Ecol. Soc. 15, 150413 (2010).
Google Scholar 
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).PubMed 

Google Scholar 
Gaston, K. J., Bennie, J., Davies, T. W. & Hopkins, J. The ecological impacts of nighttime light pollution: A mechanistic appraisal. Biol. Rev. 88, 912–927. https://doi.org/10.1111/brv.12036 (2013).Article 
PubMed 

Google Scholar 
Gaston, K. J. & Bennie, J. Demographic effects of artificial nighttime lighting on animal populations. Environ. Rev. 22, 323–330. https://doi.org/10.1139/er-2014-0005 (2014).Article 

Google Scholar 
Gaston, K. J., Visser, M. E. & Hoelker, F. The biological impacts of artificial light at night: The research challenge. R. Soc. Philos. Trans. Biol. Sci. 370, 20140133–20140133 (2015).
Google Scholar 
Ouyang, J. Q. et al. Stressful colours: Corticosterone concentrations in a free-living songbird vary with the spectral composition of experimental illumination. Biol. Lett. https://doi.org/10.1098/rsbl.2015.0517 (2016).Article 

Google Scholar 
Ouyang, J. Q., Davies, S. & Dominoni, D. Hormonally mediated effects of artificial light at night on behavior and fitness: Linking endocrine mechanisms with function. J. Exp. Biol. https://doi.org/10.1242/jeb.156893 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. Biol. Sci. 280(1756), 20123017. https://doi.org/10.1098/rspb.2012.3017 (2012).CAS 
Article 

Google Scholar 
Ayalon, I. et al. Coral gametogenesis collapse under artificial light pollution. Curr. Biol. 31, 413–419 (2021).CAS 
PubMed 

Google Scholar 
Ayalon, I., de Barros Marangoni, L. F., Benichou, J. I., Avisar, D. & Levy, O. Red Sea corals under Artificial Light Pollution at Night (ALAN) undergo oxidative stress and photosynthetic impairment. Glob. Change Biol. 25, 4194–4207 (2019).ADS 

Google Scholar 
Amichai, E. & Kronfeld-Schor, N. Artificial light at night promotes activity throughout the night in nesting common swifts (Apus apus). Sci. Rep. 9, 11052 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kronfeld-Schor, N. et al. Drivers of infectious disease seasonality: Potential implications for COVID-19. J. Biol. Rhythms 36, 35–54 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. Lond. B https://doi.org/10.1098/rstb.2016.0248 (2017).Article 

Google Scholar 
Kronfeld-Schor, N. et al. Chronobiology by moonlight. Proc. R. Soc. B 280, 20123088 (2013).PubMed 
PubMed Central 

Google Scholar 
Stevenson, T. J. et al. Disrupted seasonal biology impacts health, food security and ecosystems. Proc. R. Soc. Lond. B. https://doi.org/10.1098/rspb.2015.1453 (2015).Article 

Google Scholar 
Kaniewska, P., Alon, S., Karako-Lampert, S., Hoegh-Guldberg, O. & Levy, O. Signaling cascades and the importance of moonlight in coral broadcast mass spawning. eLife 4, e09991 (2015).PubMed 
PubMed Central 

Google Scholar 
Liu, J. A., Meléndez-Fernández, O. H., Bumgarner, J. R. & Nelson, R. J. Effects of light pollution on photoperiod-driven seasonality. Horm. Behav. 141, 105150. https://doi.org/10.1016/j.yhbeh.2022.105150 (2022).Article 
PubMed 

Google Scholar 
Grubisic, M. et al. Light pollution, circadian photoreception, and melatonin in vertebrates. Sustainability 11, 6400 (2019).CAS 

Google Scholar 
Stevenson, T. J. & Prendergast, B. J. Photoperiodic time measurement and seasonal immunological plasticity. Front. Neuroendocrinol. 37, 76–88. https://doi.org/10.1016/j.yfrne.2014.10.002 (2015).Article 
PubMed 

Google Scholar 
Bumgarner, J. R. & Nelson, R. J. Light at night and disrupted circadian rhythms alter physiology and behavior. Integr. Comp. Biol. 61, 1160–1169 (2021).PubMed 
PubMed Central 

Google Scholar 
Mishra, I. et al. Light at night disrupts diel patterns of cytokine gene expression and endocrine profiles in zebra finch (Taeniopygia guttata). Sci. Rep. 9, 1–12 (2019).
Google Scholar 
Grunst, M. L. et al. Early-life exposure to artificial light at night elevates physiological stress in free-living songbirds. Environ. Pollut. 259, 113895 (2020).CAS 
PubMed 

Google Scholar 
Bedrosian, T., Galan, A., Vaughn, C., Weil, Z. M. & Nelson, R. J. Light at night alters daily patterns of cortisol and clock proteins in female Siberian hamsters. J. Neuroendocrinol. 25, 590–596 (2013).CAS 
PubMed 

Google Scholar 
Touzot, M. et al. Artificial light at night alters the sexual behaviour and fertilisation success of the common toad. Environ. Pollut. 259, 113883 (2020).CAS 
PubMed 

Google Scholar 
de Jong, M. et al. Effects of nocturnal illumination on life-history decisions and fitness in two wild songbird species. Philos. Trans. R. Soc. B 370, 20140128 (2015).
Google Scholar 
Spoelstra, K. et al. Experimental illumination of natural habitat: An experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition. Philos. Trans. R. Soc. Lond. B 370, 20140129 (2015).
Google Scholar 
Hattar, S., Liao, H. W., Takao, M., Berson, D. M. & Yau, K. W. Melanopsin-containing retinal ganglion cells: Architecture, projections, and intrinsic photosensitivity. Science 295, 1065–1070. https://doi.org/10.1126/science.1069609 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gutman, R., Dayan, T., Levy, O., Schubert, I. & Kronfeld-Schor, N. The effect of the lunar cycle on fecal cortisol metabolite levels and foraging ecology of nocturnally and diurnally active spiny mice. PLoS ONE 6, e23446 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dhairykar, M., Singh, K. P., Kumar Jadav, K. & Rajput, N. Comparison of cortisol level in Asian elephants of different tiger reserves of Madhya Pradesh. Int. J. Vet. Sci. Anim. Husb. 5, 152–155 (2020).
Google Scholar 
Sosnowski, M. J., Benítez, M. E. & Brosnan, S. F. Endogenous cortisol correlates with performance under pressure on a working memory task in capuchin monkeys. Sci. Rep. 12, 1–10. https://doi.org/10.1038/s41598-022-04986-6 (2022).CAS 
Article 

Google Scholar 
Bewick, V., Cheek, L. & Ball, J. Statistics review 12: survival analysis. Crit. care 8, 1–6 (2004).
Google Scholar 
Shkolnik, A. Studies in the Comparative Biology of Israel’s Two Species of Spiny Mice (genus Acomys). Hebrew (1966).Shkolnik, A. Diurnal activity in a small desert rodent. Int. J. Biometeorol. 15, 115–120 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. The relationship between the golden spiny mouse circadian system and its diurnal activity: An experimental field enclosures and laboratory study. Chronobiol. Int. 24, 599–613. https://doi.org/10.1080/07420520701534640 (2007).Article 
PubMed 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. Interspecific competition and torpor in golden spiny mice: Two sides of the energy-acquisition coin. Integr. Comp. Biol. 51, 441–448. https://doi.org/10.1093/icb/icr071 (2011).Article 
PubMed 

Google Scholar 
Jones, M. & Dayan, T. Foraging behavior and microhabitat use by spiny mice, Acomys cahirinus and A. russatus, in the presence of Blanford’s fox (Vulpes cana) odor. J. Chem. Ecol. 26, 455–469 (2000).CAS 

Google Scholar 
Jones, M., Mandelik, Y. & Dayan, T. Coexistence of temporally partitioned spiny mice: Roles of habitat structure and foraging behavior. Ecology 82, 2164–2176 (2001).
Google Scholar 
Kronfeld, N., Dayan, T., Zisapel, N. & Haim, A. Coexisting populations of Acomys cahirinus and A. russatus: A preliminary report. Isr. J. Zool. 40, 177–183 (1994).
Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181. https://doi.org/10.1146/annurev.ecolsys.34.011802.132435 (2003).Article 

Google Scholar 
Kronfeld-Schor, N. & Dayan, T. The dietary basis for temporal partitioning: Food habits of coexisting Acomys species. Oecologia 121, 123–128 (1999).ADS 
PubMed 

Google Scholar 
Pinter-Wollman, N., Dayan, T., Eilam, D. & Kronfeld-Schor, N. Can aggression be the force driving temporal separation between competing common and golden spiny mice?. J. Mammal. 87, 48–53 (2006).
Google Scholar 
Shargal, E., Kronfeld-Schor, N. & Dayan, T. Population biology and spatial relationships of coexisting spiny mice (Acomys) in Israel. J. Mammal. 81, 1046–1052 (2000).
Google Scholar 
Pasco, R., Gardner, D. K., Walker, D. W. & Dickinson, H. A superovulation protocol for the spiny mouse (Acomys cahirinus). Reprod. Fertil. Dev. 24, 1117–1122 (2012).CAS 
PubMed 

Google Scholar 
Lee, T. E., Watkins, J. F. & Cash, C. G. Acomys russatus. Mammal. Species 550, 1–4 (1998).
Google Scholar 
Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. R. Soc. B 280, 20123017 (2013).PubMed 
PubMed Central 

Google Scholar 
Kempenaers, B., Borgström, P., Loës, P., Schlicht, E. & Valcu, M. Artificial night lighting affects dawn song, extra-pair siring success, and lay date in songbirds. Curr. Biol. 20, 1735–1739. https://doi.org/10.1016/j.cub.2010.08.028 (2010).CAS 
Article 
PubMed 

Google Scholar 
Le Tallec, T., Théry, M. & Perret, M. Melatonin concentrations and timing of seasonal reproduction in male mouse lemurs (Microcebus murinus) exposed to light pollution. J. Mammal. 97, 753–760 (2016).
Google Scholar 
Vonshak, M., Dayan, T. & Kronfeld-Schor, N. Arthropods as a prey resource: Patterns of diel, seasonal, and spatial availability. J. Arid Environ. 73, 458–462. https://doi.org/10.1016/j.jaridenv.2008.11.013 (2009).ADS 
Article 

Google Scholar 
Levy, O., Dayan, T. & Kronfeld-Schor, N. Adaptive thermoregulation in golden spiny mice: The influence of season and food availability on body temperature. Physiol. Biochem. Zool. 84, 175–184 (2011).PubMed 

Google Scholar 
Levy, O., Dayan, T., Rotics, S. & Kronfeld-Schor, N. Foraging sequence, energy intake and torpor: An individual-based field study of energy balancing in desert golden spiny mice. Ecol. Lett. 15, 1240–1248. https://doi.org/10.1111/j.1461-0248.2012.01845.x (2012).Article 
PubMed 

Google Scholar 
Katz, N., Dayan, T. & Kronfeld-Schor, N. Fitness effects of interspecific competition between two species of desert rodents. Zoology 128, 62–68 (2018).PubMed 

Google Scholar 
Brzezinski, A. Melatonin in humans. N. Engl. J. Med. 336, 186–195 (1997).CAS 
PubMed 

Google Scholar 
Hastings, M., Vance, G. & Maywood, E. Some reflections on the phylogeny and function of the pineal. Experientia 45, 903–909 (1989).CAS 
PubMed 

Google Scholar 
Oster, H. et al. The circadian rhythm of glucocorticoids is regulated by a gating mechanism residing in the adrenal cortical clock. Cell Metab. 4, 163–173 (2006).CAS 
PubMed 

Google Scholar 
Mora, F., Segovia, G., Del Arco, A., de Blas, M. & Garrido, P. Stress, neurotransmitters, corticosterone and body–brain integration. Brain Res. 1476, 71–85 (2012).CAS 
PubMed 

Google Scholar 
Farrell, M. R. Sex Differences and Stress Effects in Corticolimbic Structure and Function (Indiana University, 2013).
Google Scholar 
Son, G. H., Chung, S. & Kim, K. The adrenal peripheral clock: Glucocorticoid and the circadian timing system. Front. Neuroendocrinol. 32, 451–465 (2011).CAS 
PubMed 

Google Scholar 
Schradin, C. Seasonal changes in testosterone and corticosterone levels in four social classes of a desert dwelling sociable rodent. Horm. Behav. 53, 573–579 (2008).CAS 
PubMed 

Google Scholar 
Zatra, Y. et al. Seasonal changes in plasma testosterone and cortisol suggest an androgen mediated regulation of the pituitary adrenal axis in the Tarabul’s gerbil Gerbillus tarabuli (Thomas, 1902). Gen. Comp. Endocrinol. 258, 173–183 (2018).CAS 
PubMed 

Google Scholar 
Richardson, C. S., Heeren, T. & Kunz, T. H. Seasonal and sexual variation in metabolism, thermoregulation, and hormones in the big brown bat (Eptesicus fuscus). Physiol. Biochem. Zool. 91, 705–715 (2018).PubMed 

Google Scholar 
Touitou, S., Heistermann, M., Schülke, O. & Ostner, J. Triiodothyronine and cortisol levels in the face of energetic challenges from reproduction, thermoregulation and food intake in female macaques. Horm. Behav. 131, 104968 (2021).CAS 
PubMed 

Google Scholar 
Rotics, S., Dayan, T. & Kronfeld-Schor, N. Effect of artificial night lighting on temporally partitioned spiny mice. J. Mammal. 92, 159–168. https://doi.org/10.1644/10-mamm-a-112.1 (2011).Article 

Google Scholar 
Rotics, S., Dayan, T., Levy, O. & Kronfeld-Schor, N. Light masking in the field: An experiment with nocturnal and diurnal spiny mice under semi-natural field conditions. Chronobiol. Int. 28, 70–75. https://doi.org/10.3109/07420528.2010.525674 (2011).Article 
PubMed 

Google Scholar 
Padgett, D. A. & Glaser, R. How stress influences the immune response. Trends Immunol. 24, 444–448 (2003).CAS 
PubMed 

Google Scholar 
Khansari, D. N., Murgo, A. J. & Faith, R. E. Effects of stress on the immune system. Immunol. Today 11, 170–175 (1990).CAS 
PubMed 

Google Scholar 
Zozaya, S. M., Alford, R. A. & Schwarzkopf, L. Invasive house geckos are more willing to use artificial lights than are native geckos. Austral. Ecol. 40, 982–987 (2015).
Google Scholar 
Komine, H., Koike, S. & Schwarzkopf, L. Impacts of artificial light on food intake in invasive toads. Sci. Rep. 10, 1–8 (2020).
Google Scholar 
Murphy, S., Vyas, D., Sher, A. & Grenis, K. Light pollution affects invasive and native plant traits important to plant competition and herbivorous insects. Biol. Invasions 24, 599–602. https://doi.org/10.1007/s10530-021-02670-w (2022).Article 

Google Scholar 
Murphy, S. M. et al. Streetlights positively affect the presence of an invasive grass species. Ecol. Evol. 11, 10320–10326 (2021).PubMed 
PubMed Central 

Google Scholar  More

Pax, F. Grundzüge der Pflanzenverbreitung in den Karpathen. 1–342 (W. Engelmann, 1898). https://doi.org/10.5962/bhl.title.20419.Popov [Попов], M. G. [М. Г.]. Ocherk rastitel’nosti i flory Karpat [Очерк растительности и флоры Карпат]. vol. 5 (XIII) (Izdatel’stvo Moskovskogo Obshchestva Ispytateley Prirody [Издательство Московского Общества Испытателей Природы], 1949).Mráz, P. & Ronikier, M. Biogeography of the Carpathians: Evolutionary and spatial facets of biodiversity. Biol. J. Linn. Soc. 119, 528–559 (2016).Article 

Google Scholar 
Breman, E. et al. Conserving the endemic flora of the Carpathian Region: An international project to increase and share knowledge of the distribution, evolution and taxonomy of Carpathian endemics and to conserve endangered species. Plant Syst. Evol. 306, 59 (2020).Article 

Google Scholar 
Bálint, M. et al. The Carpathians as a Major Diversity Hotspot in Europe. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds. Zachos, F. E. & Habel, J. C.) 189–205 (Springer, 2011). https://doi.org/10.1007/978-3-642-20992-5_11.Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hurdu, B. et al. Patterns of plant endemism in the Romanian Carpathians (South-Eastern Carpathians). Contrib. Bot. 47, 25–38 (2012).
Google Scholar 
Pawłowski, B. Remarques sur l’endemisme dans la flore des Alpes et des Carpates. Plant Ecol. 21, 181–243 (1970).Article 

Google Scholar 
Ronikier, M. Biogeography of high-mountain plants in the Carpathians: An emerging phylogeographical perspective. Taxon 373–389 (2011).Hendrych, R. Primula vulgaris in der Slowakei und in den umliegenden Gebieten. Preslia Praha 68, 135–156 (1996).
Google Scholar 
Hendrych, R. & Hendrychová, H. Preliminary report on the Dacian migroelement in the flora of Slovakia. Preslia Praha 51, 313–332 (1979).
Google Scholar 
Sramkó, G. „Dunántúli” közép-dunai flóraválasztós fajok a Matricum flórájában. KITAIBELIA 9, 31–56 (2004).
Google Scholar 
Juřičková, L. et al. Early postglacial recolonisation, refugial dynamics and the origin of a major biodiversity hotspot. A case study from the Malá Fatra mountains, Western Carpathians, Slovakia. The Holocene 28, 583–594 (2018).Kliment, J., Turis, P. & Janišová, M. Taxa of vascular plants endemic to the Carpathian Mts. Preslia -Praha- 88, 19–76 (2016).
Google Scholar 
Konowalik, K. Reconstructing reticulate relationships in the polyploid complex of Leucanthemum Mill. (Compositae, Anthemideae). (Fakultät für Biologie und Vorklinische Medizin, Universität Regensburg, 2014).Konowalik, K., Wagner, F., Tomasello, S., Vogt, R. & Oberprieler, C. Detecting reticulate relationships among diploid Leucanthemum Mill. (Compositae, Anthemideae) taxa using multilocus species tree reconstruction methods and AFLP fingerprinting. Mol. Phylogenet. Evol. 92, 308–328 (2015).Wagner, F. et al. ‘At the crossroads towards polyploidy’: Genomic divergence and extent of homoploid hybridization are drivers for the formation of the ox-eye daisy polyploid complex (Leucanthemum, Compositae-Anthemideae). New Phytol. 223, 2039–2053 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wagner, F., Härtl, S., Vogt, R. & Oberprieler, C. “Fix Me Another Marguerite!”: Species delimitation in a group of intensively hybridizing lineages of ox-eye daisies (Leucanthemum Mill., Compositae-Anthemideae). Mol. Ecol. 26, 4260–4283 (2017).Piękoś-Mirkowa, H., Mirek, Z. & Miechowka, A. Endemic vascular plants in the Polish Tatra Mts. – distribution and ecology. Pol. Bot. Stud. 12, (1996).Zelený, V. Taxonomisch-chorologische Studie über die Art Leucanthemum rotundifolium (W. K.) DC. Folia Geobot. 5, 369–400 (1970).Piękoś, H. Nowy mieszaniec między Leucanthemum rotundifolium (W. et K.) DC. a L. vulgare Lam. var. alpicolum Gremli – Hybrida nova inter Leucanthemum rotundifolium (W. et K.) DC. et L. vulgare Lam. var. alpicolum Gremli. Fragm. Florist. Geobot. 16, 319–326 (1970).Rogalski, M., do Nascimento Vieira, L., Fraga, H. P. & Guerra, M. P. Plastid genomics in horticultural species: importance and applications for plant population genetics, evolution, and biotechnology. Front. Plant Sci. 6, (2015).Greiner, R., Vogt, R. & Oberprieler, C. Evolution of the polyploid north-west Iberian Leucanthemum pluriflorum clan (Compositae, Anthemideae) based on plastid DNA sequence variation and AFLP fingerprinting. Ann. Bot. 111, 1109–1123 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oberprieler, C., Konowalik, K., Fackelmann, A. & Vogt, R. Polyploid speciation across a suture zone: phylogeography and species delimitation in S French Leucanthemum Mill. representatives (Compositae–Anthemideae). Plant Syst. Evol. 304, 1141–1155 (2018).Oberprieler, C., Greiner, R., Konowalik, K. & Vogt, R. The reticulate evolutionary history of the polyploid NW Iberian Leucanthemum pluriflorum clan (Compositae, Anthemideae) as inferred from nrDNA ETS sequence diversity and eco-climatological niche-modelling. Mol. Phylogenet. Evol. 70, 478–491 (2014).CAS 
PubMed 
Article 

Google Scholar 
Alexander, P. J., Rajanikanth, G., Bacon, C. D. & Bailey, C. D. Recovery of plant DNA using a reciprocating saw and silica-based columns. Mol. Ecol. Notes 7, 5–9 (2007).CAS 
Article 

Google Scholar 
Sang, T., Crawford, D. & Stuessy, T. Chloroplast DNA phylogeny, reticulate evolution, and biogeography of Paeonia (Paeoniaceae). Am. J. Bot. 84, 1120 (1997).CAS 
PubMed 
Article 

Google Scholar 
Scheunert, A., Dorfner, M., Lingl, T. & Oberprieler, C. Can we use it? On the utility of de novo and reference-based assembly of Nanopore data for plant plastome sequencing. PLoS ONE 15, e0226234 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Timme, R. E., Kuehl, J. V., Boore, J. L. & Jansen, R. K. A comparative analysis of the Lactuca and Helianthus (Asteraceae) plastid genomes: Identification of divergent regions and categorization of shared repeats. Am. J. Bot. 94, 302–312 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hall, T. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser 41, 95–98 (1999).CAS 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381 (2000).CAS 
PubMed 
Article 

Google Scholar 
Müller, K. SeqState: Primer design and sequence statistics for phylogenetic DNA datasets. Appl. Bioinformatics 4, 65–69 (2005).PubMed 
Article 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Meth. 9, 772 (2012).CAS 
Article 

Google Scholar 
Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 
Article 

Google Scholar 
Jukes, T. H. & Cantor, C. R. Evolution of Protein Molecules. in Mammalian Protein Metabolism 21–132 (Elsevier, 1969). https://doi.org/10.1016/B978-1-4832-3211-9.50009-7.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamura, K., Tao, Q. & Kumar, S. Theoretical foundation of the reltime method for estimating divergence times from variable evolutionary rates. Mol. Biol. Evol. 35, 1770–1782 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamura, K. et al. Estimating divergence times in large molecular phylogenies. Proc. Natl. Acad. Sci. 109, 19333–19338 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tao, Q., Tamura, K., Mello, B. & Kumar, S. Reliable confidence intervals for reltime estimates of evolutionary divergence times. Mol. Biol. Evol. 37, 280–290 (2020).CAS 
PubMed 
Article 

Google Scholar 
Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 15, e1006650 (2019).Mello, B., Tao, Q., Barba-Montoya, J. & Kumar, S. Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches. Mol. Ecol. Resour. 21, 122–136 (2021).CAS 
PubMed 
Article 

Google Scholar 
Wang, wei-M. On the origin and development of Artemisia (Asteraceae) in the geological past. Bot. J. Linn. Soc. 145, 331–336 (2004).Clement, M., Snell, Q., Walker, P., Posada, D. & Crandall, K. TCS: Estimating Gene Genealogies. in Proceedings of the 16th International Parallel and Distributed Processing Symposium 311 (IEEE Computer Society, 2002).Leigh, J. W. & Bryant, D. popart: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. & Corander, J. RhierBAPS: An R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 3, 93 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, Y., Blair, C. & He, X. RASP 4: Ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).CAS 
PubMed 
Article 

Google Scholar 
Ali, S. S., Yu, Y., Pfosser, M. & Wetschnig, W. Inferences of biogeographical histories within subfamily Hyacinthoideae using S-DIVA and Bayesian binary MCMC analysis implemented in RASP (Reconstruct Ancestral State in Phylogenies). Ann. Bot. 109, 95–107 (2012).PubMed 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).Konowalik, K. & Nosol, A. Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci. Rep. 11, 1482 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamner, B., Frasco, M. & LeDell, E. Metrics: Evaluation metrics for machine learning (2018).Ripley, B. & Venables, W. nnet: Feed-forward neural networks and multinomial log-linear models. (2020).Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. (2020).Therneau, T., Atkinson, B., port, B. R. (producer of the initial R. & maintainer 1999–2017). rpart: Recursive Partitioning and Regression Trees. (2019).Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
MATH 
Article 

Google Scholar 
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. (2017).Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).Article 

Google Scholar 
Jasiewicz, A. Rośliny naczyniowe Bieszczadów Zachodnich [The Vascular Plants of the Western Bieszczady Mts. (East Carpathians)]. Monogr. Bot. 20, 1–340 (1965).Kornaś, J. Charakterystyka geobotaniczna Gorców [Caractéristique géobotanique des Gorces (Karpathes Occidentales Polonaises)]. Monogr. Bot. 3, 3–230 (1955).Article 

Google Scholar 
de Oliveira, G., Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C. & Diniz-Filho, J. A. F. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography 37, 637–647 (2014).Article 

Google Scholar 
Sobral-Souza, T., Lima-Ribeiro, M. S. & Solferini, V. N. Biogeography of Neotropical Rainforests: past connections between Amazon and Atlantic Forest detected by ecological niche modeling. Evol. Ecol. 29, 643–655 (2015).Article 

Google Scholar 
Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37, 1084–1091 (2014).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. 7266827510 bytes (2018) 10.5061/DRYAD.KD1D4.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Wing, M. K. C. from J. et al. caret: Classification and regression training. (2019).Smith, A. B. & Santos, M. J. Testing the ability of species distribution models to infer variable importance. Ecography 43, 1801–1813 (2020).Article 

Google Scholar 
Evans, J. S., Murphy, M. A. & Ram, K. spatialEco: Spatial analysis and modelling utilities. (2021).Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Araújo, M. B., Whittaker, R. J., Ladle, R. J. & Erhard, M. Reducing uncertainty in projections of extinction risk from climate change. Glob. Ecol. Biogeogr. 14, 529–538 (2005).Article 

Google Scholar 
Zhu, G., Fan, J. & Peterson, A. T. Cautions in weighting individual ecological niche models in ensemble forecasting. Ecol. Model. 448, 109502 (2021).Article 

Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. (2021).R Core Team. R: A language and environment for statistical computing. (2019).QGIS Development Team. QGIS geographic information system. (2019).Frajman, B. & Oxelman, B. Reticulate phylogenetics and phytogeographical structure of Heliosperma (Sileneae, Caryophyllaceae) inferred from chloroplast and nuclear DNA sequences. Mol. Phylogenet. Evol. 43, 140–155 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ronikier, M., Cieślak, E. & Korbecka, G. High genetic differentiation in the alpine plant Campanula alpina Jacq. (Campanulaceae): evidence for glacial survival in several Carpathian regions and long-term isolation between the Carpathians and the Alps. Mol. Ecol. 17, 1763–1775 (2008).Ehrich, D. et al. Genetic consequences of Pleistocene range shifts: contrast between the Arctic, the Alps and the East African mountains. Mol. Ecol. 16, 2542–2559 (2007).CAS 
PubMed 
Article 

Google Scholar 
Šrámková, G. et al. Phylogeography and taxonomic reassessment of Arabidopsis halleri—a montane species from Central Europe. Plant Syst. Evol. 305, 885–898 (2019).Article 

Google Scholar 
Birks & Willis, K. J. Alpines, trees, and refugia in Europe. Plant Ecol. Divers. 1, 147–160 (2008).Jarčuška, B., Kaňuch, P., Naďo, L. & Krištín, A. Quantitative biogeography of Orthoptera does not support classical qualitative regionalization of the Carpathian Mountains. Biol. J. Linn. Soc. 128, 887–900 (2019).Article 

Google Scholar 
Tadono, T. et al. Precise global DEM generation by ALOS PRISM. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, 71–76 (2014).Article 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, 1 (2005).
Google Scholar  More

Filgueira, R. R., Fournier, L. L., Cerisola, C. I., Gelati, P. & Garcia, M. G. Particle-size distribution in soils: A critical study of the fractal model validation. Geoderma 134, 327–334 (2006).ADS 
Article 

Google Scholar 
Deng, J. F., Li, J. H., Deng, G., Zhu, H. Y. & Zhang, R. H. Fractal scaling of particle-size distribution and associations with soil properties of Mongolian pine plantations in the Mu Us Desert, China. Sci. Rep. 7, 6742 (2018).ADS 
Article 

Google Scholar 
Gao, Y. J. et al. “Fertile islands” beneath three desert vegetation on soil phosphorus fractions, enzymatic activities, and microbial biomass in the desert-oasis transition zone. CATENA 212, 106090 (2022).CAS 
Article 

Google Scholar 
Zeraatpisheh, M., Ayoubi, S., Mirbagheri, Z., Mosaddeghi, M. R. & Xu, M. Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables. Geoderma Regioanl. 27, e00440 (2021).Article 

Google Scholar 
Zha, C., Shao, M., Jia, X. & Zhang, C. Particle size distribution of soils (0–500 cm) in the Loess Plateau, China. Geoderma 7, 251–258 (2016).Article 

Google Scholar 
Callesen, I., Keck, H. & Andersen, T. J. Particle size distribution in soils and marine sediments by laser diffraction using Malvern Mastersizer 2000-method uncertainty including the effect of hydrogen peroxide pretreatment. J. Soils Sediments 18, 2500–2510 (2018).CAS 
Article 

Google Scholar 
He, Y. J. & Lv, D. Y. Fractal expression of soil particle-size distribution at the basin scale. Open Geosci. 14, 70–78 (2022).Article 

Google Scholar 
Besalatpour, A. A., Ayoubi, S., Hajabbasi, M. A., Mosaddeghi, M. R. & Schulin, R. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. CATENA 111, 72–79 (2013).Article 

Google Scholar 
Besalatpour, A. A., Ayoubi, S., Hajabbasi, M. A., Yousefian, J. A. & Gharipour, A. Feature selection using parallel genetic algorithm for the prediction of geometric mean diameter of soil aggregates by machine learning methods. Arid Land Res. Manag. 28, 383–394 (2014).Article 

Google Scholar 
Xu, G. C., Li, Z. B. & Li, P. Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. CATENA 101, 17–23 (2013).CAS 
Article 

Google Scholar 
Jia, W. R. et al. Grain size distribution at four developmental stages of crescent dunes in the hinterland of the Taklimakan Desert, China. J. Arid Land 8, 722–733 (2016).Article 

Google Scholar 
Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: A review. Geoderma 314, 122–137 (2018).ADS 
Article 

Google Scholar 
Ghanbarian, B. & Daigle, H. Fractal dimension of soil fragment mass-size distribution: A critical analysis. Geoderma 245–246, 98–103 (2015).ADS 
Article 

Google Scholar 
Deng, Y., Cai, C., Xia, D., Ding, S. & Chen, J. Fractal features of soil particle size distribution under different land-use patterns in the alluvial fans of collapsing gullies in the hilly granitic region of southern China. PLoS ONE 12, e0173555 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhai, J. Y. et al. Change in soil particle size distribution and erodibility with latitude and vegetation restoration chronosequence on the Loess Plateau, China. Int. J Environ. Res. Public Health 17, 822 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Gao, Z. Y., Niu, F. J., Lin, Z. J. & Luo, J. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China. CATENA 203, 105373 (2021).Article 

Google Scholar 
Chen, T. L. et al. Multifractal characteristics and spatial variability of soil particle-size distribution in different land use patterns in a small catchment of the Three Gorges Reservoir Region, China. J. Mt. Sci. 18, 111–125 (2021).Article 

Google Scholar 
Gui, D. W. et al. Characterizing variations in soil particle size distribution in oasis farmlands-a case study of the Cele Oasis. Math. Comput. Model. 51, 1306–1311 (2010).Article 

Google Scholar 
Millán, H., Gonzalez-Posada, M., Aguilar, M., Domınguez, J. & Céspedes, L. On the fractal scaling of soil data. Particle-size distributions. Geoderma 117, 117–128 (2003).ADS 
Article 

Google Scholar 
Qi, F. et al. Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil Till. Res. 184, 45–51 (2018).Article 

Google Scholar 
Muhtar, Q., Hiroki, T. & Mijit, H. Formation and internal structure of Tamarix cones in the Taklimakan Desert. J. Arid Environ. 50, 81–97 (2002).Article 

Google Scholar 
Zhao, Y. J. & Xia, X. C. Research on the Relationship Between Tamarix Cone and Environmental Change in Lop Nur Region of Xinjiang 38–142 (Sci. Press, 2011) (in Chinese).
Google Scholar 
Yin, C. H., Shi, Q. M., Liang, F. & Tian, C. Y. Distribution pattern of soil salinity in Tamarix Nebkhas in Tarim Basin. Bull Soil Water Conserv. 33, 287–293 (2013) (in Chinese).
Google Scholar 
Zheng, T., Li, J. G., Li, W. H. & Wan, J. H. Soil heterogeneity and its effects on plant community in oasis desert transition zone in the lower peaches of Tarim River. J. Desert Res. 30, 128–134 (2010) (in Chinese).
Google Scholar 
Liu, J. H., Wang, X. Q., Ma, Y. & Tan, F. Z. Spatial variation of soil salinity on Tamarix ramosissima nebkhas and interdune in oasis-desert ecotone. J. Desert Res. 36, 181–189 (2016) (in Chinese).CAS 

Google Scholar 
Dong, Z. W. et al. Stoichiometric features of C, N, and P in soil and litter of Tamarix cones and their relationship with environmental factors in the Taklimakan Desert, China. J. Soils Sediments 20, 690–704 (2020).CAS 
Article 

Google Scholar 
Dong, Z. W., Li, S. Y., Mao, D. L. & Lei, J. Q. Distribution pattern of soil grain size in Tamarix sand dune in the southwest of Gurbantunggut Desert. J. Soil Water Conserv. 35, 64-72/79 (2021) (in Chinese).
Google Scholar 
Dong, Z. W., Zhao, Y., Lei, J. Q. & Xi, Y. Q. Distribution pattern and influencing factors of soil salinity at Tamarix cones in the Taklimakan Desert. Chin. J. Plant Eco. 42, 873–884 (2018) (in Chinese).Article 

Google Scholar 
Xu, L. S. et al. Oasis microclimate effect on the dust deposition in Cele Oasis at southern Tarim Basin, China. Arab J. Geosci. 9, 294 (2016).Article 

Google Scholar 
Liu, J. H., Wang, X. Q., Ma, Y. & Tan, F. Z. Spatial heterogeneity of soil grain size on Tamarix ramosissima nebkhas and interdune in desert-oasis ecotone. J. Beijing For. Univ. 37, 89–99 (2015) (in Chinese).CAS 

Google Scholar 
Mao, D. L. et al. Fractal characteristics of grain size of sand and dust in aeolian sand movement in Cele oasis-desert ecotone in Xinjiang, China. Acta Pedol. Sinica 55, 88–99 (2018) (in Chinese).
Google Scholar 
Li, J. R. & Ravib, S. Interactions among hydrological-aeolian processes and vegetation determine grain-size distribution of sediments in a semi-arid coppice dune (nebkha) system. J. Arid Environ. 154, 24–33 (2018).ADS 
Article 

Google Scholar 
Ayoubi, S., Karchegani, P. M., Mosaddeghi, M. R. & Honarjoo, N. Soil aggregation and organic carbon as affected by topography and land use change in western Iran. Soil Till. Res. 121, 18–26 (2012).Article 

Google Scholar 
Wang, X. M., Dong, Z. B., Zhang, J. W. & Chen, G. T. Geomorphology of sand dunes in the Northeast Taklimakan Desert. Geomorphology 42, 183–195 (2002).ADS 
Article 

Google Scholar 
Liu, W. G. et al. Onset of permanent Taklimakan Desert linked to the mid-Pleistocene transition. Geology 48, 782–786 (2020).ADS 
CAS 
Article 

Google Scholar 
Yang, X. H. et al. Characteristics of soil particle size distribution and its effect on dust emission in Taklimakan Desert. Trans. CSAE 36, 167–174 (2020) (in Chinese).
Google Scholar 
Bao, S. D. Soil agricultural chemistry analysis 152–200 (China Agr. Press, 2000) (in Chinese).
Google Scholar 
Folk, R. L. & Ward, W. C. Brazos Riverbar: A study in the significance of grain size parameters. J. Sediment Petrol. 27, 3–26 (1957).ADS 
Article 

Google Scholar 
Weil, R. R. & Brady, N. C. The Nature and Properties of Soils 15th edn. (PrenticeHall Press, 2017).
Google Scholar 
Churchman, G. J. Game changer in soil science. Functional role of clay minerals in soil. J. Plant Nutr. Soil Sci. 181, 99–103 (2018).CAS 
Article 

Google Scholar 
Tyler, S. W. & Wheatcraft, S. W. Fractal scaling of soil particle size distributions: Analysis and limitations. Soil Sci. Soc. Am. J. 56, 362–369 (1992).ADS 
Article 

Google Scholar 
Lin, Y. C., Mu, G. J., Xu, L. S. & Zhao, X. The origin of bimodal grain-size distribution for aeolian deposits. Aeolian Res. 20, 80–88 (2016).ADS 
Article 

Google Scholar 
Sha, G. L., Wei, T. X., Chen, Y. X., Fu, Y. C. & Ren, K. Characteristics of soil particle size distribution of typical plantcommunities on the hilly areas of Loess Plateau. Arid Land Geogr. https://doi.org/10.12118/j.issn.1000-6060.2021.487 (2022) (in Chinese).Article 

Google Scholar 
Yang, J. D., Li, G. J., Dai, Y., Rao, W. B. & Ji, J. F. Isotopic evidences for provenances of loess of the Chinese Loess Plateau. Earth Sci. Front. 16, 195–206 (2009) (in Chinese).
Google Scholar 
Wu, L. & Zhang, Y. M. Precipitation and soil particle size co-determine spatial distribution of biological soil crusts in the Gurbantunggut Desert, China. J. Arid Land 10, 701–711 (2018).Article 

Google Scholar 
Li, X. B. et al. Relationship between soil particle size distribution and soil nutrient distribution characteristics in typical communities of desert grassland. Actabot. Boreal-Occident Sin. 37, 1635–1644 (2017) (in Chinese).
Google Scholar 
Gao, G. L. et al. Fractal scaling of particle size distribution and relationships with topsoil properties affected by biological soil crusts. PLoS ONE 9, e88559 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, Y., Feng, Q. & Yang, H. Soil salinity distribution and its relationship with soil particle size in the lower reaches of Heihe River, Northwestern China. Environ. Earth Sci. 75, 1–18 (2016).ADS 
Article 

Google Scholar 
Zhang, X. Y. et al. Sources of Asian dust and role of climate change versus desertification in Asian dust emission. Geophys. Res. Lett. 30, 2272 (2003).ADS 
Article 

Google Scholar  More