Ecology

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

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

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

Jiang, Z. H. Bamboo and Rattan in the World (China Forest Publishing House, 2007).
Google Scholar 
Zhao, J., Wang, B., Li, Q., Yang, H. & Xu, K. Analysis of soil degradation causes in Phyllostachys edulis forests with different mulching years. Forests 9(3), 149 (2018).Article 

Google Scholar 
Su, W., Fan, S., Zhao, J. & Cai, C. Effects of various fertilization placements on the fate of urea-15N in moso bamboo forests. For. Ecol. Manag. 453, 117632 (2019).Article 

Google Scholar 
Zhao, J. et al. Ammonia volatilization and nitrogen runoff losses from moso bamboo forests under different fertilization practices. Can. J. For. Res. 49(3), 213–220 (2019).CAS 
Article 

Google Scholar 
Yin, J. et al. Abandonment lead to structural degradation and changes in carbon allocation patterns in Moso bamboo forests. For. Ecol. Manag. 449, 117449 (2019).Article 

Google Scholar 
Xu, Q. F. et al. Rapid bamboo invasion (expansion) and its effects on biodiversity and soil processes. Glob. Ecol. Conserv. 21, e00787 (2020).Article 

Google Scholar 
Prayogo, C., Sholehuddin, N., Putra, E. Z. H. S. & Rachmawati, R. Soil macrofauna diversity and structure under different management of pine-coffee agroforestry system. J. Degrade. Min. Land Manage. 6(3), 1727–1736 (2019).Article 

Google Scholar 
Coleman, B. R., Martin, A. R., Thevathasan, N. V., Gordon, A. M. & Isaac, M. E. Leaf trait variation and decomposition in short-rotation woody biomass crops under agroforestry management. Agric. Ecosyst. Environ. 298, 106971 (2020).CAS 
Article 

Google Scholar 
Cai, C. J., Fan, S. H., Liu, G. L., Wang, S. M. & Feng, Y. Research and development advance of compound management of bamboo forests. World Bamboo Rattan 16(5), 47–52 (2018) (in Chinese).
Google Scholar 
Song, Z. et al. Characteristics of Se-enriched mycelia by Stropharia rugoso-annulata and its antioxidant activities in vivo. Biol. Trace Elem. Res. 113(1), 81–89 (2009).Article 

Google Scholar 
Wang, Q. et al. Effects of drying on the structural characteristics and antioxidant activities of polysaccharides from Stropharia rugosoannulata. J. Food Sci. Technol. 58, 3622–3631 (2021).CAS 
Article 

Google Scholar 
Yan, P., Jiang, J. & Cui, W. Characterization of protoplasts prepared from the edible fungus, Stropharia rugoso-annulata. World J. Microbiol. Biotechnol. 20(2), 173–177 (2004).CAS 
Article 

Google Scholar 
Frouz, J. Effects of soil macro- and mesofauna on litter decomposition and soil organic matter stabilition. Geoderma 332, 161–172 (2018).ADS 
CAS 
Article 

Google Scholar 
Lin, D. et al. Soil fauna promote litter decomposition but do not alter the relationship between leaf economics spectrum and litter decomposability. Soil Biol. Biochem. 136, 107519 (2019).CAS 
Article 

Google Scholar 
Meehan, M. L. et al. Response of soil fauna to simulated global change factors depends on ambient climate conditions. Pedobiologia 83, 150672 (2020).Article 

Google Scholar 
Tan, B. et al. Soil fauna show different degradation patterns of lignin and cellulose along an elevational gradient. Appl. Soil Ecol. 155, 103673 (2020).Article 

Google Scholar 
John, K., Zaitsev, A. S. & Wolters, V. Soil fauna groups respond differentially to changes in crop rotation cycles in rice production systems. Pedobiologia 84, 150703 (2021).Article 

Google Scholar 
Qin, Z. et al. Changes in the soil meso- and micro-fauna community under the impacts of exotic Ambrosia artemisiifolia. Ecol. Res. 34(2), 265–276 (2019).Article 

Google Scholar 
Chauvat, M., Titsch, D., Zaytesev, A. S. & Wolters, V. Changes in soil faunal assemblages during conversion from pure to mixed forest stands. For. Ecol. Manag. 262(3), 317–324 (2011).Article 

Google Scholar 
Yan, S. et al. A soil fauna index for assessing soil quality. Soil Biol. Biochem. 47(2), 158–165 (2012).CAS 
Article 

Google Scholar 
Reeve, J. R. et al. Effects of soil type and farm management on soil ecological functional genes and microbial activities. ISME J. 4, 1099–1107 (2010).Article 

Google Scholar 
Lavelle, P., Bignell, D. & Lepage, M. Soil function in a changing world: The role of invertebrate engineers. Eur. J. Soil Biol. 33, 159–193 (1997).CAS 

Google Scholar 
Zhu, X. & Zhu, B. Diversity and abundance of soil fauna as influenced by long-term fertilization in cropland of purple soil, China. Soil Till. Res. 146, 39–46 (2015).Article 

Google Scholar 
Zhang, L., Wang, G. & Cao, F. The effect of ginkgo agroforestry patterns on soil fauna diversity. J. Nanjing For. Univ. 39(2), 27–32 (2015) (in Chinese).
Google Scholar 
Liu, P. et al. Impact of straw returning on cropland soil mesofauna community in the western part of black soil area. Chin. J. Ecol. 37(1), 139–146 (2018) (in Chinese).
Google Scholar 
Liu, M. Study on the model of interplanting edible fungi under bamboo (Phyllostachys edulis) forest and comprehensive benefit comparative. Master’s Thesis, Chinese Academy of Forestry (2021) (in Chinese).Wang, B., Shen, Q., Zhu, W., Shen, X. & Li, Q. Effects of interplanting Dictyophora echinovolvata on physicochemical properties, phospholipid fatty acids characters and enzyme activities in soil of Phyllostachy heterocycla cv. pubescens. For. Environ. Sci. 32(4), 28–32 (2016) (in Chinese).Article 

Google Scholar 
Ying, G. H. et al. Effect of cultivation of Dictyophora echinovolvata on shoot yield and soil under Phyllostachy heterocycla cv. pubescens stand. J. Zhejiang For. Sci. Technol. 34(6), 65–67 (2014) (in Chinese).
Google Scholar 
Sokol, N. W. et al. Life and death in the soil microbiome: How ecological processes influence biogeochemistry. Nat. Rev. Microbiol. 20, 415–430 (2022).CAS 
Article 

Google Scholar 
Fujii, K., Hayakawa, C., Inagaki, Y. & Kosaki, T. Effects of land use change on turnover and storage of soil organic matter in a tropical forest. Plant Soil 446(1), 425–439 (2020).CAS 
Article 

Google Scholar 
Fujii, K. & Toma, T. Comparison of soil acidification rates under different land uses in Indonesia. Plant Soil 465(1–2), 1–17 (2021).CAS 
Article 

Google Scholar 
Poss, R., Smith, C. J., Dunin, F. X. & Angus, J. F. Rate of soil acidification under wheat in a semi-arid environment. Plant Soil 177, 85–100 (1995).CAS 
Article 

Google Scholar 
Yin, X. et al. Distribution and diversity partterns of soil fauna in different salinization habitats of Songnen Grasslands, China. Appl. Soil Ecol. 123, 375–383 (2018).Article 

Google Scholar 
Luo, M. L. et al. Effects of different rice straw returning quantities on soil fauna community structure. J. Zhejiang A&F Univ. 37(1), 85–92 (2020) (in Chinese).
Google Scholar 
Peng, C. Y. et al. Community structure characteristics of medium- and small-sized soil faunas in typical artificial plantation in the upper reaches of Yangtze River. J. Zhejiang Univ. 45(5), 585–595 (2019) (in Chinese).
Google Scholar 
Carmen, M. U., Edmond, R. Z. & Michelle, M. W. Nematode indicators as integrative measures of soil condition in organic cropping systems. Soil Biol. Biochem. 64, 103–113 (2013).Article 

Google Scholar 
Kamau, S., Karanja, N. K., Ayuke, F. O. & Lehmann, J. Short-term influence of biochar and fertilizer-biochar blends on soil nutrients, fauna and maize growth. Biol. Fertil. Soils 55(7), 661–673 (2019).CAS 
Article 

Google Scholar 
Fu, X., Shao, M., Wei, X. & Horton, R. Soil organic carbon and total nitrogen as affected by vegetation types in Northern Loess Plateau of China. Geoderma 155(1–2), 31–35 (2010).ADS 
CAS 
Article 

Google Scholar 
Guan, F., Tang, X., Fan, S., Zhao, J. & Peng, C. Changes in soil carbon and nitrogen stocks followed the conversion from secondary forest to Chinese fir and Moso bamboo plantations. Catena 133, 455–460 (2015).CAS 
Article 

Google Scholar 
Liu, Y. et al. Higher soil fauna abundance accelerates litter carbon release across an alpine forest-tundra ecotone. Sci. Rep. 9, 10561 (2019).ADS 
CAS 
Article 

Google Scholar  More

Díez B, Ininbergs K. Ecological importance of cyanobacteria. In Cyanobacteria (pp. 41–63). John Wiley & Sons, Ltd. (2013) https://doi.org/10.1002/9781118402238.ch3Fristachi A, Sinclair JL, Hall S, Berkman JAH, Boyer G, Burkholder J, et al. Occurrence of cyanobacterial harmful algal blooms workgroup report. Adv Experimental Med Biol. 2008;619:45–103. https://doi.org/10.1007/978-0-387-75865-7_3CAS 
Article 

Google Scholar 
Huisman J, Codd GA, Paerl HW, Ibelings BW, Verspagen JMH, Visser PM. Cyanobacterial blooms. Nat Rev Microbiol. 2018;16:471–83. https://doi.org/10.1038/s41579-018-0040-1CAS 
Article 
PubMed 

Google Scholar 
Plaas HE, Paerl HW. Toxic Cyanobacteria: A Growing Threat to Water and Air Quality. In Environmental Science and Technology (Vol. 55, Issue 1, pp. 44–64). American Chem Soc. 2021. https://doi.org/10.1021/acs.est.0c06653Kurmayer R, Deng L, Entfellner E. Role of toxic and bioactive secondary metabolites in colonization and bloom formation by filamentous cyanobacteria Planktothrix. Harmful Algae. 2016;54:69–86. https://doi.org/10.1016/j.hal.2016.01.004CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rohrlack T, Christiansen G, Kurmayer R. Putative antiparasite defensive system involving ribosomal and nonribosomal oligopeptides in cyanobacteria of the genus planktothrix. Appl Environ Microbiol. 2013;79:2642–7. https://doi.org/10.1128/AEM.03499-12CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Legnani E, Copetti D, Oggioni A, Tartari G, Palumbo MT, Morabito G. Planktothrix rubescens’ seasonal dynamics and vertical distribution. J Limnol. 2005;64:61–73.Article 

Google Scholar 
Walsby A, Ng G, Dunn C, Davis PA. Comparison of the depth where Planktothrix rubescens stratifies and the depth where the daily insolation supports its neutral buoyancy. New Phytologist. 2004;162:133–45. https://doi.org/10.1111/j.1469-8137.2004.01020.xArticle 

Google Scholar 
Bruning K. Effects of temperature and light on the population dynamics of the Asterionella-Rhizophydium association. J Plankton Res. 1991a;13:707–19. https://doi.org/10.1093/plankt/13.4.707Article 

Google Scholar 
Rohrlack T, Haande S, Molversmyr Å, Kyle M. Environmental Conditions Determine the Course and Outcome of Phytoplankton Chytridiomycosis. 2015;1–17. https://doi.org/10.1371/journal.pone.0145559Tao Y, Wolinska J, Hölker F, Agha R. Light intensity and spectral distribution affect chytrid infection of cyanobacteria via modulation of host fitness. Parasitology. 2020;147:1206–15. https://doi.org/10.1017/S0031182020000931CAS 
Article 
PubMed 

Google Scholar 
Davis PA, Walsby A. Comparison of measured growth rates with those calculated from rates of photosynthesis in Planktothrix spp. isolated from Blelham Tarn, English Lake District. New Phytologist. 2002;156:225–39. https://doi.org/10.1046/j.1469-8137.2002.00495.xCAS 
Article 
PubMed 

Google Scholar 
Oberhaus L, Briand JF, Leboulanger C, Jacquet S, Humbert JF. Comparative effects of the quality and quantity of light and temperature on the growth of Planktothrix agardhii and P. rubescens 1. J Phycol. 2007;43:1191–9. https://doi.org/10.1111/j.1529-8817.2007.00414.xCAS 
Article 

Google Scholar 
Reynolds CS Growth and replication of phytoplankton. In The Ecology of Phytoplankton (pp. 145–77). Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511542145.005Litchman E, Klausmeier CA . Trait-based community ecology of phytoplankton. Ann Rev Ecol, Evol, Syst. 2008;39:615–39.Edwards KF, Thomas MK, Klausmeier CA, Litchman E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol Oceanography. 2016;61:1232–44.Article 

Google Scholar 
Thomas MK, Kremer CT, Litchman E. Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Global Ecol Biogeog. 2016;25:75–86. https://doi.org/10.1111/geb.12387Article 

Google Scholar 
Bright DI, Walsby A. The daily integral of growth by Planktothrix rubescens calculated from growth rate in culture and irradiance in Lake Zürich. New Phytologist. 2000;146:301–16. https://doi.org/10.1046/j.1469-8137.2000.00640.xArticle 
PubMed 

Google Scholar 
Jann-Para G, Schwob I, Feuillade M. Occurrence of toxic Planktothrix rubescens blooms in lake Nantua, France. Toxicon. 2004;43:279–85.CAS 
Article 

Google Scholar 
Jacquet S, Briand JF, Leboulanger C, Avois-Jacquet C, Oberhaus L, Tassin B, et al. The proliferation of the toxic cyanobacterium Planktothrix rubescens following restoration of the largest natural French lake (Lac du Bourget). Harmful Algae. 2005;4:651–72.Article 

Google Scholar 
Lenard T. Metalimnetic bloom of Planktothrix rubescens in relation to environmental conditions. Oceanological Hydrobiological Studies. 2009;38:45–53.
Google Scholar 
Van den Wyngaert S, Salcher MM, Pernthaler J, Zeder M, Posch T. Quantitative dominance of seasonally persistent filamentous cyanobacteria (Planktothrix rubescens) in the microbial assemblages of a temperate lake. Limnol Oceanogr. 2011;56:97–109.Article 

Google Scholar 
Walsby A. Stratification by cyanobacteria in lakes: A dynamic buoyancy model indicates size limitations met by Planktothrix rubescens filaments. New Phytologist. 2005;168:365–76. https://doi.org/10.1111/j.1469-8137.2005.01508.xArticle 
PubMed 

Google Scholar 
Conroy JD, Kane DD, Quinlan EL, Edwards WJ, Culver DA. Abiotic and biotic controls of phytoplankton biomass dynamics in a freshwater tributary, estuary, and large lake ecosystem: Sandusky bay (lake erie) chemostat. Inland Waters. 2017;7:473–92. https://doi.org/10.1080/20442041.2017.1395142CAS 
Article 

Google Scholar 
Sommer U, Maciej Gliwics Z, Lampert W, Duncan A. The PEG-model of seasonal succession of planktonic events in fresh waters. Archiv Für Hydrobiologie. 1986;106:433–71.
Google Scholar 
Sommer U, Adrian R, De Senerpont Domis L, Elser JJ, Gaedke U, Ibelings B, et al. Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession. Ann Rev Ecol, Evol, Syst. 2012;43:429–48. https://doi.org/10.1146/annurev-ecolsys-110411-160251Article 

Google Scholar 
Hatcher MJ, Dunn AM Parasites in ecological communities: from interactions to ecosystems. Cambridge University Press (2011).Marcogliese DJ. Parasites: Small Players with Crucial Roles in the Ecological Theater. EcoHealth. 2004;1:151–64. https://doi.org/10.1007/s10393-004-0028-3Article 

Google Scholar 
Sime-Ngando T, Lafferty KD, Biron DG. Roles and Mechanisms of Parasitism in Aquatic Microbial Communities. 2007. https://doi.org/10.3389/978-2-88919-588-6Frenken T, Alacid E, Berger SA, Bourne EC, Gerphagnon M, Grossart HP, et al. Integrating chytrid fungal parasites into plankton ecology: research gaps and needs. Environmental Microbiology. 2017;19:3802–22. https://doi.org/10.1111/1462-2920.13827Article 
PubMed 

Google Scholar 
Brussaard CPD, Kuipers B, Veldhuis MJW. A mesocosm study of Phaeocystis globosa population dynamics: I. Regulatory role of viruses in bloom control. Harmful Algae. 2005;4:859–74. https://doi.org/10.1016/j.hal.2004.12.015Article 

Google Scholar 
Gerphagnon M, Macarthur DJ, Gachon C, Van Ogtrop F, Latour D, et al. The biological factors affecting the dynamics of cyanobacterial blooms. 2009.Gleason FH, Jephcott TG, Küpper FC, Gerphagnon M, Sime-Ngando T, Karpov SA, et al. Potential roles for recently discovered chytrid parasites in the dynamics of harmful algal blooms. Fungal Biol Rev. 2015;29:20–33. https://doi.org/10.1016/j.fbr.2015.03.002Article 

Google Scholar 
Ibelings BW, Gsell AS, Mooij WM, Van Donk E, Van Den Wyngaert S, De Senerpont Domis LN. Chytrid infections and diatom spring blooms: Paradoxical effects of climate warming on fungal epidemics in lakes. Freshwater Biol. 2011;56:754–66. https://doi.org/10.1111/j.1365-2427.2010.02565.xArticle 

Google Scholar 
Kagami M, De Bruin A, Ibelings BW, Van Donk E. Parasitic chytrids: Their effects on phytoplankton communities and food-web dynamics. Hydrobiologia. 2007;578:113–29. https://doi.org/10.1007/s10750-006-0438-zArticle 

Google Scholar 
Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. PNAS. 2005;103:3165–70.Article 

Google Scholar 
McKenzie VJ, Peterson AC. Pathogen pollution and the emergence of a deadly amphibian pathogen. Molecular Ecol. 2012;21:5151–4. https://doi.org/10.1111/mec.12013Article 

Google Scholar 
Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth. 2007;4:125–34. https://doi.org/10.1007/s10393-007-0093-5Article 

Google Scholar 
Ibelings BW, De Bruin A, Kagami M, Rijkeboer M, Brehm M, Van Donk E. Host parasite interactions between freshwater phytoplankton and chytrid fungi (Chytridiomycota). J Phycol. 2004;40:437–53.Article 

Google Scholar 
Bosch J, Martínez-Solano I, García-París. Evidence of a chytrid fungus infection involved in the decline of the common midwife toad (Alytes obstetricans) in protected areas of central Spain. Biological Conserv. 2001;97:331–7. https://doi.org/10.1016/S0006-3207(00)00132-4Article 

Google Scholar 
Bruning K, Lingeman R, Ringelberg J. Estimating the impact of fungal parasites on phytoplankton populations. Limnol Oceanogr. 1992;37:252–60. https://doi.org/10.4319/lo.1992.37.2.0252Article 

Google Scholar 
Paterson RA. Infestation of Chytridiaceous Fungi on Phytoplankton in Relation to Certain Environmental Factors. Ecology. 1960;41:416–24. https://doi.org/10.2307/1933316Article 

Google Scholar 
Ṣen B. Fungal parasitism of planktonic algae in Shearwater. IV: Parasitic occurrence of a new chytrid species on the blue-green alga Microcystis aeruginosa Kuetz. emend. Elenkin. 1998.van Donk E, Ringelberg J. The effect of fungal parasitism on the succession of diatoms in Lake Maarsseveen I. Netherlands Freshwater Biol. 1983;13:241–51. https://doi.org/10.1111/j.1365-2427.1983.tb00674.xArticle 

Google Scholar 
Agha R, Saebelfeld M, Manthey C, Rohrlack T, Wolinska J. Chytrid parasitism facilitates trophic transfer between bloom-forming cyanobacteria and zooplankton (Daphnia). Scientific Rep. 2016;6. https://doi.org/10.1038/srep35039Frenken T, Wierenga J, van Donk E, Declerck SAJ, de Senerpont Domis LN, Rohrlack T, et al. Fungal parasites of a toxic inedible cyanobacterium provide food to zooplankton. Limnol Oceanogr. 2018;63:2384–93. https://doi.org/10.1002/lno.10945Article 

Google Scholar 
Kagami M, von Elert E, Ibelings BW, de Bruin A, van Donk E. The parasitic chytrid, Zygorhizidium, facilitates the growth of the cladoceran zooplankter, Daphnia, in cultures of the inedible alga, Asterionella. Proc Biological Sci/ Royal Soc. 2007;274:1561–6. https://doi.org/10.1098/rspb.2007.0425Article 

Google Scholar 
Gsell AS, de Senerpont Domis LN, van Donk E, Ibelings BW. Temperature alters host genotype-specific susceptibility to chytrid infection. PLoS One. 2013;8:e71737. https://doi.org/10.1371/journal.pone.0071737CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKindles KM, Manes MA, McKay RM, Davis TW, Bullerjahn GS. Environmental factors affecting chytrid (Chytridiomycota) infection rates on Planktothrix agardhii. J Plankton Res. 2021a;43:658–72.Article 

Google Scholar 
Fallowfield HJ, Daft MJ. The extracellular release of dissolved organic carbon by freshwater cyanobacteria and algae and the interaction with Lysobacter CP-1. Br Phycol J. 1988;1617:317–26. https://doi.org/10.1080/00071618800650351Article 

Google Scholar 
Mueller B, den Haan J, Visser PM, Vermeij MJA, van Duyl FC. Effect of light and nutrient availability on the release of dissolved organic carbon (DOC) by Caribbean turf algae. Scientific Rep. 2016;6:1–9. https://doi.org/10.1038/srep23248CAS 
Article 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. II. Effects of light on survival and epidemic development of the parasite. J Plankton Res. 1991c;13:119–29. https://doi.org/10.1093/plankt/13.1.119Article 

Google Scholar 
Van den Wyngaert S, Gsell AS, Spaak P, Ibelings BW. Herbicides in the environment alter infection dynamics in a microbial host-parasite system. Environ Microbiol. 2013;15:837–47. https://doi.org/10.1111/j.1462-2920.2012.02874.xCAS 
Article 
PubMed 

Google Scholar 
Almocera AES, Hsu SB, Sy PW. Extinction and uniform persistence in a microbial food web with mycoloop: Limiting behavior of a population model with parasitic fungi. Mathematical Biosci Eng. 2019;16:516–37.Article 

Google Scholar 
Frenken T, Miki T, Kagami M, Van de Waal DB, Van Donk E, Rohrlack T, et al. The potential of zooplankton in constraining chytrid epidemics in phytoplankton hosts. Ecology. 2020;101. https://doi.org/10.1002/ecy.2900Gerla DJ, Gsell AS, Kooi BW, Ibelings BW, Van Donk E, Mooij WM. Alternative states and population crashes in a resource-susceptible-infected model for planktonic parasites and hosts. FMeier, M. H. et al. (2015) Neuropsychological Decline in Schizophrenia from the Premorbid to Post-Onset Period: Evidence from a Population-Representative Longitudinal Study. American J Psychiatry. 2013;58:538–51. https://doi.org/10.1111/fwb.12010Article 

Google Scholar 
Miki T, Takimoto G, Kagami M. Roles of parasitic fungi in aquatic food webs: A theoretical approach. Freshwater Biol. 2011;56:1173–83. https://doi.org/10.1111/j.1365-2427.2010.02562.xArticle 

Google Scholar 
Guillard RRL, Lorenzen CJ. Yellow-green algae with chlorophyllid C. In Phycology. 1972;8:10–14.CAS 

Google Scholar 
McKindles KM, Jorge AN, McKay RM, Davis TW, Bullerjahn GS. Isolation and characterization of Rhizophydiales (Chytridiomycota), obligate parasites of Planktothrix agardhii in a Laurentian Great Lakes embayment. Appl Environ Microbiol. 2021b;87:e02308–20.CAS 
Article 

Google Scholar 
R Core Team. (2021). R: A Language and Environment for Statistical Computing.RStudio Team. (2021). RStudio: Integrated Development Environment for R (1.4.1106).Wickham, H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the {tidyverse}. J Open Source Software. 2019;4:1686. https://doi.org/10.21105/joss.01686Article 

Google Scholar 
Champely, S (2018). PairedData (1.1.1).Soetaert K, Petzoldt T, Setzer RW. Solving Differential Equations in {R}: Package deSolve. J Statistical Software. 2010;33:1–25. https://doi.org/10.18637/jss.v033.i09Article 

Google Scholar 
Frenken T, Velthuis M, de Senerpont Domis LN, Stephan S, Aben R, Kosten S, et al. Warming accelerates termination of a phytoplankton spring bloom by fungal parasites. Global Change Biol. 2016;22:299–309. https://doi.org/10.1111/gcb.13095Article 

Google Scholar 
Scholz B, Vyverman W, Küpper FC, Ólafsson HG, Karsten U. Effects of environmental parameters on chytrid infection prevalence of four marine diatoms: A laboratory case study. Botanica Marina. 2017;60:419–31. https://doi.org/10.1515/bot-2016-0105CAS 
Article 

Google Scholar 
Sønstebø JH, Rohrlack T. Possible implications of Chytrid parasitism for population subdivision in freshwater cyanobacteria of the genus Planktothrix. Appl Environ Microbiol. 2011;77:1344–51. https://doi.org/10.1128/AEM.02153-10CAS 
Article 
PubMed 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. I. Effects of light on reproduction and infectivity of the parasite. J Plankton Res. 1991b;13:103–17. https://doi.org/10.1093/plankt/13.1.103Article 

Google Scholar 
Muehlstein LK, Amon JP, Leffler DL. Chemotaxis in the Marine Fungus Rhizophydium littoreum. Appl Environ Microbiol. 1988;54:1668–72. https://doi.org/10.1128/aem.54.7.1668-1672.1988CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Esch GW, Fernández JC. Introduction. In A Functional Biology of Parasitism (pp. 1–25). Springer Netherlands (1993). https://doi.org/10.1007/978-94-011-2352-5_1Gerphagnon M, Colombet J, Latour D, Sime-Ngando T. Spatial and temporal changes of parasitic chytrids of cyanobacteria. Scientific Rep. 2017;7:6056. https://doi.org/10.1038/s41598-017-06273-1CAS 
Article 

Google Scholar 
Maier MA, Peterson TD. Prevalence of chytrid parasitism among diatom populations in the lower Columbia River (2009–2013). Freshwater Biol. 2017;62:414–28. https://doi.org/10.1111/fwb.12876CAS 
Article 

Google Scholar 
Sime-Ngando T. Phytoplankton chytridiomycosis: Fungal parasites of phytoplankton and their imprints on the food web dynamics. Front Microbiol. 2012;3:361. https://doi.org/10.3389/fmicb.2012.00361Article 
PubMed 
PubMed Central 

Google Scholar 
Kagami M, Urabe J. Mortality of the planktonic desmid, Staurastrum dorsidentiferum, due to interplay of fungal parasitism and low light conditions. SIL Proceed. 2002;28:1001–5. https://doi.org/10.1080/03680770.2001.11901868Article 

Google Scholar  More

Berghauser Pont, M. Y., Perg, P. G., Haupt, P. A. & Heyman, A. A systematic review of the scientifically demonstrated effects of densification. IOP Conf. Ser. Earth Environ. Sci. 588, 052031 (2020).
Google Scholar 
Cimburova, Z. & Berghauser Pont, M. Location matters: A systematic review of spatial contextual factors mediating ecosystem services of urban trees. Ecosyst. Serv. 50, 101296 (2021).
Google Scholar 
De Valck, J. et al. Valuing urban ecosystem services in sustainable brownfield redevelopment. Ecosyst. Serv. 35, 139–149 (2019).
Google Scholar 
Zuzolo, D. et al. Divide et disperda: Thirty years of fragmentation and impacts on the eco-mosaic in the case study of the metropolitan city of Naples. Land 10, 485 (2021).
Google Scholar 
Nelson, E. The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations , edited by Pushpam Kumar, London, Earthscan Publications, United Nations Environment Programme, 2010, xxxix + 410 pp., US$76.95 (hardback), ISBN 978-1-84971-212-5. J. Nat. Resour. Policy Res. 5, 68–70 (2013).
Google Scholar 
Duraiappah, A. K. et al. Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: Synthesis. World Resources Institute vol. 5 http://www.who.int/entity/globalchange/ecosystems/ecosys.pdf (2005).Cariñanos, P., Casares-Porcel, M. & Quesada-Rubio, J. M. Estimating the allergenic potential of urban green spaces: A case-study in Granada, Spain. Landsc. Urban Plan. 123, 134–144 (2014).
Google Scholar 
Haase, D. et al. A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio 43, 413–433 (2014).PubMed 
PubMed Central 

Google Scholar 
Mexia, T. et al. Ecosystem services: Urban parks under a magnifying glass. Environ. Res. 160, 469–478 (2018).CAS 
PubMed 

Google Scholar 
Brzoska, P., Grunewald, K. & Bastian, O. A multi-criteria analytical method to assess ecosystem services at urban site level, exemplified by two German city districts. Ecosyst. Serv. 49, 101268 (2021).
Google Scholar 
Zulian, G. et al. Practical application of spatial ecosystem service models to aid decision support. Ecosyst. Serv. 29, 465–480 (2018).PubMed 
PubMed Central 

Google Scholar 
Balmford, A. et al. Ecology: Economic reasons for conserving wild nature. Science (80-). 297, 950–953 (2002).ADS 
CAS 

Google Scholar 
Koulov, B., Ivanova, E., Borisova, B., Assenov, A. & Ravnachka, A. GIS-based valuation of ecosystem services in mountain regions: A case study of the Karlovo municipality in Bulgaria. One Ecosyst. 2, e14062 (2017).
Google Scholar 
Robertson, G. P. & Swinton, S. M. Reconciling agricultural productivity and environmental integrity: A grand challenge for agriculture. Front. Ecol. Environ. 3, 38–46 (2005).
Google Scholar 
Sandhu, H. S., Wratten, S. D., Cullen, R. & Case, B. The future of farming: The value of ecosystem services in conventional and organic arable land. An experimental approach. Ecol. Econ. 64, 835–848 (2008).
Google Scholar 
Berglihn, E. C. & Gómez-Baggethun, E. Ecosystem services from urban forests: The case of Oslomarka, Norway. Ecosyst. Serv. 51, 101358 (2021).
Google Scholar 
Nowak, D. J. Understanding i-Tree. (2020). https://doi.org/10.2737/NRS-GTR-200.Selvakumaran, S., Plank, S., Geiß, C., Rossi, C. & Middleton, C. Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques. Int. J. Appl. Earth Obs. Geoinf. 73, 463–470 (2018).ADS 

Google Scholar 
Gómez-Baggethun, E. & Barton, D. N. Classifying and valuing ecosystem services for urban planning. Ecol. Econ. 86, 235–245 (2013).
Google Scholar 
Gren, Å. & Andersson, E. Being efficient and green by rethinking the urban-rural divide—Combining urban expansion and food production by integrating an ecosystem service perspective into urban planning. Sustain. Cities Soc. 40, 75–82 (2018).
Google Scholar 
Grêt-Regamey, A., Celio, E., Klein, T. M. & Wissen Hayek, U. Understanding ecosystem services trade-offs with interactive procedural modeling for sustainable urban planning. Landsc. Urban Plan. 109, 107–116 (2013).
Google Scholar 
Bennett, E. M., Peterson, G. D. & Gordon, L. J. Understanding relationships among multiple ecosystem services. Ecol. Lett. 12, 1394–1404 (2009).PubMed 

Google Scholar 
Bradford, J. B. & D’Amato, A. W. Recognizing trade-offs in multi-objective land management. Front. Ecol. Environ. 10, 210–216 (2012).
Google Scholar 
Cueva, J. et al. Synergies and trade-offs in ecosystem services from urban and peri-urban forests and their implication to sustainable city design and planning. Sustain. Cities Soc. 82, 103903 (2022).
Google Scholar 
Allocca, V., Coda, S., Calcaterra, D. & De Vita, P. Groundwater rebound and flooding in the Naples’ periurban area (Italy). J. Flood Risk Manag. 15, e12775 (2022).
Google Scholar 
Padulano, R. et al. Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis. Hydrol. Process. 35, e14436 (2021).
Google Scholar 
D’Amato, G. et al. Allergenic pollen and pollen allergy in Europe. Allergy 62, 976–990 (2007).PubMed 

Google Scholar 
Prigioniero, A., Zuzolo, D., Sciarrillo, R. & Guarino, C. Assessing pollinosis risk in the Vesuvius National Park: A novel approach for Index of Urban Green Zones Allergenicity. Environ. Res. 197, 111063 (2021).CAS 
PubMed 

Google Scholar 
AgCult 2020 Classifica visitatori 2019: Capodimonte rientra nella classifica dei primi 30 musei d’Italia.La Valva, V., Guarino, C., De Natale, A., Cuozzo, V., Menale, B. La flora del Parco di Capodimonte di Napoli. in 33–34: 143–177. (Delpinoa, 1992).Stevens, P. F. Angiosperm Phylogeny Website. 2001. http://www.mobot.org/MOBOT/research/APweb/. (2017).James Barth, B., Ian FitzGibbon, S. & Stuart Wilson, R. New urban developments that retain more remnant trees have greater bird diversity. Landsc. Urban Plan. 136, 122–129 (2015).
Google Scholar 
Heckmann, K. E., Manley, P. N. & Schlesinger, M. D. Ecological integrity of remnant montane forests along an urban gradient in the Sierra Nevada. For. Ecol. Manage. 255, 2453–2466 (2008).
Google Scholar 
Prigioniero, A. et al. Role of historic gardens in biodiversity-conservation strategy: the example of the Giardino Inglese of Reggia di Caserta (UNESCO) (Italy). Plant Biosyst. 155, 983–993 (2021).
Google Scholar 
Song, Q., Wang, B., Wang, J. & Niu, X. Endangered and endemic species increase forest conservation values of species diversity based on the Shannon-Wiener index. IForest 9, 469–474 (2016).
Google Scholar 
Hess, M. C. M., Mesléard, F. & Buisson, E. Priority effects: Emerging principles for invasive plant species management. Ecol. Eng. 127, 48–57 (2019).
Google Scholar 
Carli, E. et al. Using vegetation dynamics to face the challenge of the conservation status assessment in semi-natural habitats. Rend. Lincei. Sci. Fis. e Nat. 29, 363–374 (2018).
Google Scholar 
Canedoli, C. et al. Evaluation of ecosystem services in a protected mountain area: Soil organic carbon stock and biodiversity in alpine forests and grasslands. Ecosyst. Serv. 44, 101135 (2020).
Google Scholar 
FAO. Global Forest Resources Assessment 2010. Main report. (2010).Lindén, L., Riikonen, A., Setälä, H. & Yli-Pelkonen, V. Quantifying carbon stocks in urban parks under cold climate conditions. Urban For. Urban Green. 49, 126633 (2020).
Google Scholar 
Nowak, D. J., Hirabayashi, S., Bodine, A. & Greenfield, E. Tree and forest effects on air quality and human health in the United States. Environ. Pollut. 193, 119–129 (2014).CAS 
PubMed 

Google Scholar 
Nowak, D. J., Crane, D. E. & Stevens, J. C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4, 115–123 (2006).
Google Scholar 
Nowak, D. J. & Crane, D. E. Carbon storage and sequestration by urban trees in the USA. Environ. Pollut. 116, 381–389 (2002).CAS 
PubMed 

Google Scholar 
Kocić, K., Spasić, T., Urošević, M. A. & Tomašević, M. Trees as natural barriers against heavy metal pollution and their role in the protection of cultural heritage. J. Cult. Herit. 15, 227–233 (2014).
Google Scholar 
Yang, J., McBride, J., Zhou, J. & Sun, Z. The urban forest in Beijing and its role in air pollution reduction. Urban For. Urban Green. 3, 65–78 (2005).
Google Scholar 
Zupancic, T., Westmacott, C., Bulthuis, M. The impact of green space on heat and air pollution in urban communities: A meta-narrative systematic review (2015).Cariñanos, P., Adinolfi, C., Díaz de la Guardia, C., De Linares, C. & Casares-Porcel, M. Characterization of Allergen Emission Sources in Urban Areas. J. Environ. Qual. 45, 244–252 (2016).PubMed 

Google Scholar 
D’Auria, A., De Toro, P., Fierro, N. & Montone, E. Integration between GIS and multi-criteria analysis for ecosystem services assessment: A methodological proposal for the National Park of Cilento, Vallo di Diano and Alburni (Italy). Sustain 10, 3329 (2018).
Google Scholar 
Prigioniero, A., Zuzolo, D., Niinemets, Ü. & Guarino, C. Nature-based solutions as tools for air phytoremediation: A review of the current knowledge and gaps. Environ. Pollut. 277, 116817 (2021).CAS 
PubMed 

Google Scholar 
Szkop, Z. Evaluating the sensitivity of the i-Tree Eco pollution model to different pollution data inputs: A case study from Warsaw, Poland. Urban For. Urban Green. 55, 126859 (2020).
Google Scholar 
Tao, J. et al. Elevation-dependent effects of growing season length on carbon sequestration in Xizang Plateau grassland. Ecol. Indic. 110, 105880 (2020).CAS 

Google Scholar 
Chen, Y. et al. Grassland carbon sequestration ability in China: A new perspective from Terrestrial Aridity Zones. Rangel. Ecol. Manag. 69, 84–94 (2016).
Google Scholar 
Gopalakrishnan, V., Hirabayashi, S., Ziv, G. & Bakshi, B. R. Air quality and human health impacts of grasslands and shrublands in the United States. Atmos. Environ. 182, 193–199 (2018).ADS 
CAS 

Google Scholar 
Pace, R. et al. Comparing i-Tree eco estimates of particulate matter deposition with leaf and canopy measurements in an urban mediterranean Holm Oak Forest. Environ. Sci. Technol. 55, 6613–6622 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Losos, J. B., Walton, B. M. & Bennett, A. F. Trade-offs between sprinting and clinging ability in Kenyan Chameleons. Funct. Ecol. 7, 281 (1993).
Google Scholar 
Pretzsch, H., Moser-Reischl, A., Rahman, M. A., Pauleit, S. & Rötzer, T. Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application. Trees – Struct. Funct. (2021). https://doi.org/10.1007/s00468-021-02100-3.Grunewald, K. et al. Lessons learned from implementing the ecosystem services concept in urban planning. Ecosyst. Serv. 49, 101273 (2021).
Google Scholar 
Baldacchini, C., Sgrigna, G., Clarke, W., Tallis, M. & Calfapietra, C. An ultra-spatially resolved method to quali-quantitative monitor particulate matter in urban environment. Environ. Sci. Pollut. Res. 26, 18719–18729 (2019).CAS 

Google Scholar 
De Luca, P., Guarino, C., Gullo, G., La Valva V., 1992. Il Parco di Capodimonte di Napoli: storia ed attualità. in 33–34: 143–177. (Delpinoa, 1992).Pignatti, S. Flora d’Italia vol.2. (2017).Braun-Blanquet, J. Plant Sociology (McGraw-Hill Book Company, 1932).
Google Scholar 
Catorci, A. et al. Reproductive traits variation in the herb layer of a submediterranean deciduous forest landscape. Plant Ecol. 214, 737–749 (2013).
Google Scholar 
Šumrada, T. et al. Are result-based schemes a superior approach to the conservation of High Nature Value grasslands? Evidence from Slovenia. Land Use Policy 111, 105749 (2021).
Google Scholar 
POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Board of Trustees of the Royal Botanic Gardens, Kew http://www.plantsoftheworldonline.org/ (2022).Bímová, K., Mandák, B. & Kašparová, I. How does Reynoutria invasion fit the various theories of invasibility?. J. Veg. Sci. 15, 495–504 (2004).
Google Scholar 
Wild, J., Neuhäuslová, Z. & Sofron, J. Changes of plant species composition in the Šumava spruce forests, SW Bohemia, since the 1970s. For. Ecol. Manag. 187, 117–132 (2004).
Google Scholar 
Damato, G. & Lobefalo, G. Allergenic pollens in the southern Mediterranean area. J. Allergy Clin. Immunol. 83, 116–122 (1989).CAS 

Google Scholar 
Cariñanos, P. et al. Assessing allergenicity in urban parks: A nature-based solution to reduce the impact on public health. Environ. Res. 155, 219–227 (2017).PubMed 

Google Scholar 
Cariñanos, P. et al. Estimation of the allergenic potential of urban trees and urban parks: Towards the healthy design of urban green spaces of the future. Int. J. Environ. Res. Public Health 16, 1357 (2019).PubMed Central 

Google Scholar  More