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    The fabrication and assessment of mosquito repellent cream for outdoor protection

    Chemicals and reagentsEOs of basil (Ocimum basilicum L.), bergamot (Citrus bergamia Risso & Poit), camphor [Cinnamomum camphora (L.) J. Presl.], cinnamon (Cinnamomum zeylanicum Blume), citronella [Cymbopogon nardus (L.) Rendle], clove (Eugenia caryophyllus Wight), eucalyptus (Eucalyptus globulus Labill.), jasmine (Jasminum officinale L.), lavender (Lavandula angustifolia Mill.), lemon grass [Cymbopogan citratus (DC.) Stapf], mentha (Mentha piperita L.), rosemary (Rosmarinus officinalis L.), patchouli (Pogostemon patchouli Benth), and wild turmeric (Curcuma aromatica Salisb.) were procured from Talent Technologies (Talent Technologies, Kanpur, India). Acetylcholinesterase (AChE) activity assay kit, Anti-OBP2A antibody, ELISA kits, 1,1-diphenyl-2-picrylhydrazyl (DPPH), radioimmunoprecipitation (RIPA) buffer and phosphate buffer saline (PBS) were purchased from Sigma Aldrich (Sigma Aldrich Chemical Co., St. Luis, USA). TRPV1 antibody was purchased from Santa Cruz (Santa Cruz, California, USA). 1-chloro-2,4-dinitrobenzene (CDNB) was purchased from Cayman (Cayman Chemical Company, Michigan, USA). Human normal lung cell line (L-132) was obtained from the National Centre for Cell Sciences (NCCS), Pune, India. High performance liquid chromatography (HPLC) grade acetone was purchased from Merck (Merck Pvt. Ltd., Mumbai, India). All other chemicals used were of the highest analytical grade available.Test insects5–7 days old adult female Ae. albopictus mosquitoes were housed at the laboratory insectary, Division of Pharmaceutical Technology, Defence Research Laboratory, Tezpur, Assam, India. Mosquitoes were reared by maintaining temperature at 27 ± 2 °C, relative humidity: 75 ± 5% RH and 14L:10D h of light–dark alternative cycles in standard-sized wooden cages (75 cm × 60 cm × 60 cm) with a sleeve opening on one side as described previously63. 10% sucrose solution ad libitum were provided for nourishment. Before testing, the mosquitoes were starved for 24 h.Screening of EOsDose response study was performed to evaluate the best oils among the fourteen EOs. This study was approved (approval number: 032/2021TMCH, 28/08/2018) by the Institutional Human Ethical Committee (IHEC), of the Tezpur Medical College & Hospital (TMCH), Tezpur, Assam, India, and all experiments were performed in accordance with relevant guidelines and regulations. Five volunteers are chosen, not allergic to mosquito bite and all volunteers provided written informed consent. A volunteer’s thigh was marked according to the door opening hole of the K&D module as described by Klun and Debboun64. It is made of Plexiglas and the base of the rectangular cage (26 cm × 5 cm × 5 cm) has six holes, each with rectangular 3 × 4 cm holes that are opened and closed by a sliding door (Supplementary Fig. S8: Provide the photograph of K&D module). The flexor region of the forearms of a human volunteer was outlined with four rectangular (3 cm × 4 cm) test areas. A volume of 25 µL of each concentration of the EOs in soybean oil (40, 4 and 0.4 µg/cm2) and 25 µL of the soybean oil (diluent) as control was applied to the marked areas. After air drying for 5 min, a K&D module with matching cut outs in its floor was placed over the treated areas, containing five nulliparous 5–7 days old female mosquitoes in each hole. The doors of the cells were opened and the number of mosquitoes biting in each cell was recorded within a 2 min exposure, after which the doors were closed. After completion of each observation, mosquitoes were freed by opening cells of the K&D module in a sleeved screened cage. For each test, fresh sets of mosquitoes are used. Five replications for each test were carried out. The efficacy of EOs were determined by the percentage repellency against mosquitoes, using the formula or Eq. (2) described by WHO46.$$% ;{text{repellency}} = frac{C – T}{C} times 100$$
    (2)
    where, C is the number of mosquitoes landing, or biting at the control area; T is the number of mosquitoes landing or biting at the treated area.Fourier transform-infra red spectroscopy (FT-IR)Study of chemical compatibility for each formulation ingredients are necessary. All formulation ingredients possess specific value of vibrational frequency and have varied functional groups in their chemical structures. For compatibility study, each EOs, excipients to be used in cream formulation, and their physical mixture was placed one by one over the sample plate of the FT-IR instrument (Bruker, ALPHA, Billerica, MA, USA). The covering probe was placed over the sample and IR spectra was obtained over a wavelength of 2.5–25 μm at room temperature. Functional groups possessed by each individual ingredient should be identical in their physical mixture which confirms their compatibility37.Thermogravimetric analysis (TGA)The thermal behaviour of citronella oil, clove oil, lemon grass oil, their mixture and EO-MRC were evaluated using a thermal analyser (TG 209 F1 Libra®, NETZSCH-Gerätebau GmbH, 95100 Selb, Germany). Approximately about 10 mg sample weight was placed in the crucible each time. Nitrogen was used as a shielding gas. Heating program was fixed as 30–600 °C at a rate of 10 °C/min.Formulation development and optimizationFor optimization, a 17-run, 3-factor, 3-level Box-Behnken design (BBD) was utilized. A second order polynomial model was constructed by quadratic response surface methodology (RSM) using Design-Expert software (Version 6.0.8, Stat-Ease Inc., USA). Total seventeen formulations were obtained using EO concentrations as dependent variables against complete protection time (CPT) as independent variable or response variable. Analysis of variance (ANOVA) was performed using the same software to obtain the most effective formulation.Preparation of creamPhase inversion temperature method was applied for the preparation of EO-based mosquito repellent cream (EO-MRC). About 50 g cream sample was prepared in order to get enough for performing the various qualitative and quantitative assay. The oil phase (phase B) was prepared by dissolving the oil soluble excipients, except phase A (mosquito repellent active ingredients) under mild heating at 200 rpm in a hot magnetic plate stirrer (Magnetic Stirrer IKA RCT basic) and heated to 65 °C. The aqueous phase was prepared by mixing various aqueous soluble ingredients (phase C) under gentle heating and stirring. Temperature of the aqueous phase was raised to 65 °C. Phase A was gently added to the oil phase at a stirring speed of 200 rpm and 55 ± 2 °C. The mixture was then emulsified by adding phase C slowly and kept for 1 h at a stirring rate of 800 rpm and 60 ± 2 °C. The formulated EO-MRC was then kept for natural cooling.Efficacy assessmentCPT of the developed cream (EO-MRC) formulation was carried out by arm in cage bioassay. 1 mL EO-MRC was applied to ≈ 600 cm2 area of the forearm skin between the wrist and elbow and 1 mL of the 12% N, N-di ethyl benzamide (DEBA) based marketed cream (DBMC) was compared on the other arm. Two mosquito cages (size: 40 × 40 × 40 cm) each containing 200–250 non-blood-fed female Ae. Albopictus were used. One cage is designated for testing the EO-MRC and the other for the positive control (DBMC). During testing, hands were protected by surgical gloves for which the mosquitoes cannot bite while the volunteer avoids movement of the arm. EO-MRC and DBMC treated arms were exposed for 3 min at 30 min intervals to determine landing and/or probing activity. A single landing or probing of mosquito within a 3 min test interval concludes the test. CPT was calculated as the time (min) required for the first mosquito landing or probing after repellent application to the treated area. The median CPT and confidence intervals were estimated from the Kaplan–Meier Survival Function46.Efficacy was correlated with DEBA based marketed cream (DBMC). The inclusion of the specific commercial product DBMC is for comparison and does not constitute any recommendations.CharacterizationGas chromatography-mass spectroscopy (GC–MS)Qualitative studyDifferent chemical components in fourteen EOs and the selected blend were identified by a GC–MS system of Agilent Technologies (5301 Stevens Creek Blvd. Santa Clara, CA 95051, United States). Test sample concentration of 500 μg/mL was prepared in GC grade acetone. A sample volume of 1 μL was introduced into the injector held at 250 °C. Oven temperature of 40–300 °C was programmed at 20 °C/min. Helium was used as carrier gas at flow rate 1 mL/min. The injector and detector temperature were set at 250 °C and 230 °C (quad) and 150 °C (core) respectively37. Standard C7–C30 saturated alkanes were purchased from Sigma Aldrich Chemicals Co., St. Louis, USA. Retention indices (RI) of the identified components were determined for identification of the detected components.% Assay by GC–MS studyCalibration samples of eugenol and citronellol were prepared by dissolving an appropriate amount in GC grade acetone to get concentrations of 62.5 μg/mL, 125 μg/mL, 250 μg/mL and 500 μg/mL. Test samples of EO-MRC, clove oil and citronella oil were prepared by dissolving a required amount in acetone to quantify the EO components in the final formulation. A sample volume of 1 μL was introduced into the injector as described in ‘Qualitative study’ section.Physicochemical parametersPhysical parameters of the EO-MRC and placebo formulations were determined in order to establish aesthetic compliance and consumer acceptability. To determine the viscosity, a programmable viscometer was used (Model: DV2T, Ametek Brookfield, Middleboro, MA, USA); combined with software Rheo3000, version 1.2.2019.1 [R]. Sample volume was fixed at 30 g and viscosities were determined at 10 rpm for 40 s at room temperature using a T-Bar spindle (B-92) (Helipath spindle set, Brookfield Engineering Labs. Inc). Density was determined by using a pycnometer. pH of EO-MRC was checked by using digital pH meter (Labman Scientific instruments, Tamil Nadu, India).Spread ability of EO-MRC was determined as per the method reported earlier by Sabale65. In brief, 1 g of EO-MRC was placed on 1 cm2 pre-marked circular area on the glass slide (7.5 cm × 2.5 cm). EO-MRC was compressed using another glass slide placed from edge to centre of primary slide. 200 g of commercial weight was placed on the set up and allowed the gel to spread for the period of 1 min. The spread diameter was calculated with the aid of graph paper and spread ability was evaluated using formula expressed as Eq. (3):$$mathrm{Spread, ability}=mathrm{m}times frac{mathrm{l}}{mathrm{t}}$$
    (3)
    where, m is the commercial weight placed on the setup; l is the length of cream spread; and t is the time.Safety assessmentCytotoxicity by MTT assayThe reduction of tetrazolium salts is now widely accepted as a reliable way to examine cell proliferation. The yellow tetrazolium MTT (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) is reduced by metabolically active cells, in part by the action of dehydrogenase enzymes, to generate reducing equivalents such as NADH and NADPH. With the help of spectrophotometric means, the resulting intracellular purple formazan can be quantified. The assay measures the cell proliferation rate and conversely, when metabolic events cause apoptosis or necrosis, the reduction in cell viability66.Cells cultured in T-25 flasks were trypsinized and aspirated into a 5 mL centrifuge tube. Cell pellet was obtained by centrifugation at 3000 rpm. The cell count was adjusted, using DMEM HG medium, such that 200 μL of suspension contained approximately 10,000 cells. To each well of the 96 well microtiter plate, 200 μL of the cell suspension was added and the plate was incubated at 37 ℃ and 5% CO2 atmosphere for 24 h. After 24 h, the spent medium was aspirated. 200 μL of different test concentrations viz. 62 µg/mL, 125 µg/mL, 250 µg/mL, 500 µg/mL, and 1000 µg/mL, of EO-MRC were added to the respective wells. The plate was then incubated at 37 °C and 5% CO2 atmosphere for 24 h. The plate was removed from the incubator and the drug containing media was aspirated. 200 μL of medium containing10% MTT reagent was then added to each well to get a final concentration of 0.5 mg/mL and the plate was incubated at 37 ℃ and 5% CO2 atmosphere for 3 h. Without disturbing the crystals formed in the wells, culture medium was completely removed. 100 μL of solubilisation solution (DMSO) was added to each well and the plate was then gently shake in a rocking shaker (ROCKYMAX™, Tarsons, Kolkata, India) to solubilize the formed formazan. The absorbance was measured at a wavelength of 570 nm and also at 630 nm using a microplate reader. The percentage growth inhibition was calculated and concentration of EO-MRC needed to inhibit cell growth by 50% (IC50) was generated from the dose–response curve for the cell line.Animals and ethics statementAll experimenting protocols using animal were performed according to the “Principles of Laboratory Animal care” (NIH publication 85–23, revised 1985) and approved by the Institutional Animal Ethical Committee (IAEC) of Defence Research Laboratory (DRL), Tezpur, Assam, India (approval no. CPCSEA/DRL/Protocol no. 3, 20/06/2018). All studies involving animals are reported in accordance with the ARRIVE guidelines for reporting experiments involving animals67. All efforts were made during the study period to minimize the suffering of animals and to reduce the number of animals used.5–8 weeks old, about 210–250 g of male healthy adult Wistar rats (Rattus norvegicus) and young and healthy New Zealand albino rabbits (Oryctolagus cuniculus) were obtained from the institutional animal housing facility and allowed to acclimatize for 7 days prior to the study. Standard food and purified water ad libitum were provided in clean and hygienic condition at 22–25 ℃, 40–70% RH with 12 h light–dark cycles.Acute dermal irritation studyAcute dermal irritation study was conducted on healthy New Zealand albino rabbits following the OECD test guidelines 40468. Approximately 24 h before the test, fur was removed from the dorsal area of the trunk. 0.5 g EO-MRC, was directly applied to the skin and after 4 h exposure period, residual EO-MRC was removed by using water without disturbing the integrity of the epidermis and examined for signs of erythema and oedema, at 60 min, and then at 24 h, 48 h and 72 h after EO-MRC removal. Dermal reactions are graded and recorded according to the grades in the Table 8. As per the method described by Banerjee et al.69; primary irritation index (PII) was calculated. Further, we have followed the Draize method of classification for PII scoring as non-irritant (if PII  More

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    Characterization and phylogeny of fungi isolated from industrial wastewater using multiple genes

    1.Ramganesh, S., Timothy, S., Sudharshan, S. & Willem, A. J. N. Industrial effluents harbor a unique diversity of fungal community structures as revealed by high-throughput sequencing analysis. Pol. J. Environ. Stud. 28(4), 2353–2362. https://doi.org/10.15244/pjoes/90791 (2019).Article 

    Google Scholar 
    2.Hailemariam, A. A. et al. Diversity, co-occurrence and implications of fungal communities in wastewater treatment plants. Sci. Rep. 9, 14056. https://doi.org/10.1038/s41598-019-50624-z (2019).CAS 
    Article 

    Google Scholar 
    3.Maza-Márquez, P., Lee, M. D. & Bebout, B. M. The abundance and diversity of fungi in a hypersaline microbial mat from Guerrero Negro, Baja California, México. J. Fungi 7, 210. https://doi.org/10.3390/jof7030210 (2021).CAS 
    Article 

    Google Scholar 
    4.Ma, X., Baron, J. L., Vikram, A., Stout, J. E. & Bibby, K. Fungal diversity and presence of potentially pathogenic fungi in a hospital hot water system treated with on-site monochloramine. Water Res. 71, 197–206 (2015).CAS 
    Article 

    Google Scholar 
    5.Wei, Z. et al. The divergence between fungal and bacterial communities in seasonal and spatial variations of wastewater treatment plants. Sci. Total Environ. 628, 969–978 (2018).ADS 
    Article 

    Google Scholar 
    6.Ekowati, Y. et al. Clinically relevant fungi in water and on surfaces in an indoor swimming pool facility. Int. J. Hyg. Environ. Health. 220, 1152–1160 (2017).Article 

    Google Scholar 
    7.Manoharachary, C., Kunwar, I. K. & Reddy, S. V. Biodiversity, phylogeny and evolution of fungi. In Nature at Work: Ongoing Saga of Evolution (ed. Sharma, V. P.) (Springer, New Delhi, 2010). https://doi.org/10.1007/978-81-8489-992-4_10.Chapter 

    Google Scholar 
    8.Raja, H. A., Miller, A. N., Pearce, C. J. & Oberlies, N. H. Fungal identification using molecular tools: A primer for the natural products research community. J. Nat. Prod. 80, 756–770. https://doi.org/10.1021/acs.jnatprod.6b01085 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Liu, J., Li, J., Tao, Y., Sellamuthu, B. & Walsh, R. Analysis of bacterial, fungal and archaeal populations from a municipal wastewater treatment plant developing an innovative aerobic granular sludge process. World J. Microbiol. Biotechnol. 33, 14 (2017).Article 

    Google Scholar 
    10.Simeos, M. F. et al. Soil and rhizosphere associated fungi in gray Mangroves (Avicennia marina) from the Red Sea—A metagenomic approach. Genom. Proteom. Bioinform. 13, 310–320. https://doi.org/10.1016/j.gpb.2015.07.002 (2015).Article 

    Google Scholar 
    11.Helal, G. A., Mostafa, M. H. & El-Said, M. A. Fungi in the sewage-treatment Zeinein plant, Cairo, Egypt. J. Basic Appl. Mycol. 2(2011), 69–82 (2011).
    Google Scholar 
    12.Mishra, S. & Mishra, A. To study the diversity of fungal species in sewage water of Durg district. IOSR J. Environ. Sci. Toxicol. Food Technol. 1(6), 45–49 (2015).
    Google Scholar 
    13.Das, S., Dash, H. R., Mangwani, N., Chakraborty, J. & Kumari, S. Understanding molecular identification and polyphasic taxonomic approaches for genetic relatedness and phylogenetic relationships of microorganisms. J. Microbiol. Methods 103, 80–100. https://doi.org/10.1016/j.mimet.2014.05.013 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Yin, G., Zhang, Y., Pennerman, K. K., Wu, G. & Hua, S. S. T. Characterization of Blue Mold Penicillium Species isolated from stored fruits using multiple highly conserved loci. J. Fungi. 3, 1–10. https://doi.org/10.3390/jof3010012 (2017).CAS 
    Article 

    Google Scholar 
    15.Rajeshkumar, K. C., Yilmaz, N. & Marathe, S. D. Morphology and multigene phylogeny of Talaromyces amyrossmaniae, a new synnematous species belonging to the section Trachyspermi from India. Mycokeys 45, 41–56. https://doi.org/10.3897/mycokeys.45.32549 (2019).Article 

    Google Scholar 
    16.Adeniyi, M. et al. Molecular identification of some wild Nigerian mushrooms using internal transcribed spacer: Polymerase chain reaction. AMB Express 8, 1–9. https://doi.org/10.1186/s13568-018-0661-9 (2018).CAS 
    Article 

    Google Scholar 
    17.Houbraken, J. & Samson, R. A. Phylogeny of Penicillium and the segregation of Trichocomaceae into three families. Stud. Mycol. 70, 1–51. https://doi.org/10.3114/sim.2011.70.01 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Visagie, C. M. et al. Studies in mycology. Stud. Mycol. 78, 343–371. https://doi.org/10.1016/j.simyco.2014.09.001 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Asan, A., Kolanlarli, T. K., Sen, B. & Okten, S. Biodiversity of Penicillium species isolated from Edirne Söğütlük Forest soil (Turkey ). Nisan 10, 26–39 (2019).
    Google Scholar 
    20.De Carvalho, M. J. A. et al. Functional and genetic characterization of calmodulin from the dimorphic and pathogenic fungus Paracoccidioides brasiliensis. Fungal Genet. Biol. 39, 204–210. https://doi.org/10.1016/S1087-1845(03)00044-6 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.De Cassia Garcia Simao, R. & Gomes, S. L. Structure, expression, and functional analysis of the gene coding for calmodulin in the chytridiomycete Blastocladiella emersonii. J. Bacteriol. 183, 2280–2288. https://doi.org/10.1128/JB.183.7.2280-2288.2001 (2001).Article 

    Google Scholar 
    22.Gerber, A., Ito, K., Chu, C. N. & Roeder, R. G. Induced RPB1 depletion reveals a direct gene-specific control of RNA Polymerase III function by RNA Polymerase II. Mol. Cell 78, 765–778. https://doi.org/10.1016/j.molcel.2020.03.023 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Malkus, A. et al. RNA polymerase II gene (RPB2) encoding the second largest protein subunit in Phaeosphaeria nodorum and P. avenaria. Mycol. Res. 110, 1152–1164 (2006).CAS 
    Article 

    Google Scholar 
    24.Vetrovsky, T., Kolarik, M., Zifcakova, L., Zelenka, T. & Baldrian, P. The rpb2 gene represents a viable alternative molecular marker for the analysis of environmental fungal communities. Mol. Ecol. Resour. 16, 388–401. https://doi.org/10.1111/1755-0998.12456 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Machido, D. A., Ezeonuegbu, B. A. & Yakubu, S. E. Resistance to some heavy metals among fungal flora of raw refinery effluent. J. Appl. Sci. Environ. Manag. 18, 623–627. https://doi.org/10.4314/jasem.v18i4.10 (2014).CAS 
    Article 

    Google Scholar 
    26.Ezeonuegbu, B. A., Machido, D. A. & Yakubu, S. E. Resistance of some heavy metals among fungal flora of raw refinery effluent. J. Appl. Sci. Environ. Manag. 18, 623–627 (2014).CAS 

    Google Scholar 
    27.Barnett, H. L. & Hunter, B. B. Illustrated Genera of Imperfect Fungi 4th edn. (Prentice Hall, 1999).
    Google Scholar 
    28.Hakeem, A. S. & Bhatnagar, B. Heavy metal reduction of pulp and paper mill effluent by indigenous microbes. Asian J. Exp. Biol. Sci. 1, 203–210 (2010).
    Google Scholar 
    29.Viegas, C., Sabino, R., Botelho, D., Santos, M. & Gomes, A. Q. Assessment of exposure to Penicillium glabrum complex in cork industry using complementing methods. Arch. Ind. Hyg. Toxicol. 66, 203–207. https://doi.org/10.1515/aiht-2015-66-2614 (2015).Article 

    Google Scholar 
    30.Khandavilli, R., Meena, R. & Bd, S. Fungal phylogenetic diversity in estuarine sediments of Gautami. Curr. Res. Environ. Appl. Mycol. 6, 268–276. https://doi.org/10.5943/cream/6/4/4 (2016).Article 

    Google Scholar 
    31.Houbraken, J., Frisvad, J. C. & Samson, R. A. Sex in penicillium series roqueforti. IMA Fungus 1, 171–180 (2010).Article 

    Google Scholar 
    32.Goujon, M. et al. A new bioinformatics analysis tools framework at EMBL_EBI. Nucleic Acids Res. 38, W695–W699 (2010).CAS 
    Article 

    Google Scholar 
    33.Kumar, S., Stecher, G. & Tamura, K. MEGA 7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870 (2015).Article 

    Google Scholar 
    34.Sidiq, F., Hoostal, M. & Rogers, S. O. Rapid identification of fungi in culture – negative clinical blood and respiratory samples by DNA sequence analyses. BMC. Res. Notes 9, 1–8. https://doi.org/10.1186/s13104-016-2097-0 (2016).CAS 
    Article 

    Google Scholar 
    35.Oyebanji, E. O., Adekunle, A. A., Coker, H. A. B. & Adebami, G. E. Mycotic loads’ determination of non-sterile pharmaceuticals in lagos state and 16s rdna identification of the fungal isolates. J. Appl. Pharm. Res. 6, 16–28. https://doi.org/10.18231/2348-0335.2018.0007 (2018).CAS 
    Article 

    Google Scholar 
    36.Tiwari, P., Kumar, B., Kaur, G. & Kaur, H. Phytochemical screening and extraction: A review. Int. Pharm. Sci. 1, 98–106 (2011).
    Google Scholar 
    37.Ozdil, S., Asan, A., Sen, B. & Okten, S. Biodiversity of Airborne Fungi in the Indoor Environment of Refrigerators Used in Houses. J. Fungus. 8, 109–124. https://doi.org/10.15318/fungus.2017.41 (2017).Article 

    Google Scholar 
    38.Ashtiani, N. M., Kachuei, R., Yalfani, R. & Harchegani, A. B. Identification of Aspergillus sections Flavi, Nigri, and Fumigati and their differentiation using specific primers. Infez. Med. 2, 127–132 (2017).
    Google Scholar 
    39.Eulalia, M. M., Agnieszka, F. & Zalewska, E. D. Aspergillus penicillioides Speg. Implicated in Keratomycosis. Pol. J. Microbiol. 67, 407–416 (2018).Article 

    Google Scholar 
    40.Kamarudin, N. A. & Zakaria, L. Characterization of two xerophilic Aspergillus spp. from peanuts (Arachis hypogaea) Nur. Malays. J. Microbiol. 14, 41–48 (2018).CAS 

    Google Scholar 
    41.Samson, R. A. et al. Phylogeny, identification and nomenclature of the genus Aspergillus. Stud. Mycol. 78, 141–173. https://doi.org/10.1016/j.simyco.2014.07.004 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Wolski, E. A., Barrera, V., Castellari, C. & Gonzalez, J. F. Biodegradation of phenol in static cultures by Penicillium chrysogenum EK1: catalytic abilities and residual photo toxicity. Rev. Argent. Microbiol. 44, 113–121 (2012).CAS 
    PubMed 

    Google Scholar  More

  • in

    Beyond Demonstrators—tackling fundamental problems in amplifying nature-based solutions for the post-COVID-19 world

    1.Rosenbloom, D. & Markard, J. A COVID-19 recovery for climate. Science 368, 447 (2020).CAS 

    Google Scholar 
    2.European Commission. Towards an EU Research and Innovation policy agenda for nature-based solutions and renaturing cities. Final Report of the Horizon 2020 expert group on nature-based solutions and re-naturing cities, (European Commission, Brussels, 2015).3.Cohen-Shacham, E. et al. Core principles for successfully implementing and upscaling nature-based solutions. Environ. Sci. Policy 98, 20–29 (2019).
    Google Scholar 
    4.Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019).
    Google Scholar 
    5.Keeler, B. L. et al. Social-ecological and technological factors moderate the value of urban nature. Nat. Sustain 2, 29–38 (2019).
    Google Scholar 
    6.Escobedo, F. J., Giannico, V., Jim, C. Y., Sanesi, G. & Lafortezza, R. Urban forests, ecosystem services, green infrastructure and nature-based solutions: Nexus or evolving metaphors? Urban For. Urban Greening 37, 3–12 (2019).
    Google Scholar 
    7.Pan, H., Page, J., Cong, C., Barthel, S. & Kalantari, Z. How ecosystems services drive urban growth: Integrating nature-based solutions. Anthropocene 35, 100297 (2021).
    Google Scholar 
    8.Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).CAS 

    Google Scholar 
    9.Hack, J. & Schröter, B. Nature-based solutions for river restoration in metropolitan areas. Brears, R. The Palgrave Encyclopedia of Urban and Regional Futures. 1–10 (Springer International Publishing, Cham, 2021).10.Lam, D. P. M. et al. Scaling the impact of sustainability initiatives: a typology of amplification processes. Urban Transform 2, 3 (2020).
    Google Scholar 
    11.Seddon, N. et al. Global recognition of the importance of nature-based solutions to the impacts of climate change. Glob. Sustain 3, e15 (2020).
    Google Scholar 
    12.Faivre, N., Fritz, M., Freitas, T., de Boissezon, B. & Vandewoestijne, S. Nature-based solutions in the EU: innovating with nature to address social, economic and environmental challenges. Environ. Res. 159, 509–518 (2017).CAS 

    Google Scholar 
    13.Sabel, C. F. & Zeitlin, J. Experimentalist Governance. Levi-Faur, D. The Oxford Handbook of Governance. 169–183 (Oxford Univ. Press, Oxford, 2012).14.Kern, K. Cities as leaders in EU multilevel climate governance: embedded upscaling of local experiments in Europe. Environ. Polit. 28, 125–145 (2019).
    Google Scholar 
    15.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
    Google Scholar 
    16.Chini, C. M., Canning, J. F., Schreiber, K. L., Peschel, J. M. & Stillwell, A. S. The green experiment: cities, green stormwater infrastructure, and sustainability. Sustainability 9 (2017).17.McPhearson, T. et al. Radical changes are needed for transformations to a good Anthropocene. npj Urban Sustain. 1, 5 (2021).
    Google Scholar 
    18.Scoones, I. et al. Transformations to sustainability: combining structural, systemic and enabling approaches. Curr. Opin. Environ. Sustain. 42, 65–75 (2020).
    Google Scholar 
    19.Han, S. & Kuhlicke, C. Reducing hydro-meteorological risk by nature-based solutions: what do we know about people’s perceptions? Water 11, 2599 (2019).
    Google Scholar 
    20.Albert, C. et al. Planning nature-based solutions: principles, steps, and insights. Ambio, 1446–1461 (2020).21.Matthews, T., Lo, A. Y. & Byrne, J. A. Reconceptualizing green infrastructure for climate change adaptation: barriers to adoption and drivers for uptake by spatial planners. Landsc. Urban Planning 138, 155–163 (2015).
    Google Scholar 
    22.Myllyvirta, L. China’s CO2 emissions have surged back from the coronavirus lockdown, rising by 4-5% year-on-year in May, analysis of new government data shows. https://www.carbonbrief.org/analysis-chinas-co2-emissions-surged-past-pre-coronavirus-levels-in-may (2020).23.Samuelsson, K., Barthel, S., Colding, J., Macassa, G. & Giusti, M. Urban nature as a source of resilience during social distancing amidst the coronavirus pandemic. Preprint at https://doi.org/10.31219/osf.io/3wx5a (2020).24.Mahoney, J. Path dependence in historical sociology. Theory Soc. 29, 507–548 (2000).
    Google Scholar 
    25.Davies, C. & Lafortezza, R. Transitional path to the adoption of nature-based solutions. Land Use Policy 80, 406–409 (2019).
    Google Scholar 
    26.Kuzemko, C. et al. Covid-19 and the politics of sustainable energy transitions. Energy Res. Soc. Sci. 68, 101685 (2020).
    Google Scholar 
    27.Kanda, W. & Kivimaa, P. What opportunities could the COVID-19 outbreak offer for sustainability transitions research on electricity and mobility? Energy Res. Soc. Sci. 68, 101666 (2020).
    Google Scholar 
    28.Cohen, M. J. Does the COVID-19 outbreak mark the onset of a sustainable consumption transition? Sustain.: Sci. Pract. Policy 16, 1–3 (2020).
    Google Scholar 
    29.Pearson, R. M., Sievers, M., McClure, E. C., Turschwell, M. P. & Connolly, R. M. COVID-19 recovery can benefit biodiversity. Science 368, 838 (2020).
    Google Scholar 
    30.Everard, M., Johnston, P., Santillo, D. & Staddon, C. The role of ecosystems in mitigation and management of Covid-19 and other zoonoses. Environ. Sci. Policy 111, 7–17 (2020).CAS 

    Google Scholar 
    31.Kavousi, J., Goudarzi, F., Izadi, M. & Gardner, C. J. Conservation needs to evolve to survive in the post-pandemic world. Glob. Change Biol. 26, 4651–4653 (2020).
    Google Scholar 
    32.Lal, R. Home gardening and urban agriculture for advancing food and nutritional security in response to the COVID-19 pandemic. Food Sec., 1–6 (2020).33.Khetan, A. K. COVID-19: why declining biodiversity puts us at greater risk for emerging infectious diseases, and what we can do. J. Gen. Intern. Med. 35, 2746–2747 (2020).
    Google Scholar 
    34.Sugiyama, T. et al. Four Recommendations for Greener, Healthier Cities in the Post-Pandemic. https://www.thenatureofcities.com/2020/06/30/four-recommendations-for-greener-healthier-cities-in-the-post-pandemic/ (2020).35.Thorslund, J. et al. Wetlands as large-scale nature-based solutions: status and challenges for research, engineering and management. Ecol. Eng. 108, 489–497 (2017).
    Google Scholar 
    36.Albert, C. et al. Addressing societal challenges through nature-based solutions: how can landscape planning and governance research contribute? Landsc.Urban Plan. 182, 12–21 (2019).
    Google Scholar 
    37.Albert, C., Von Haaren, C., Othengrafen, F., Krätzig, S. & Saathoff, W. Scaling policy conflicts in ecosystem services governance: a framework for spatial. Analysis. J. Environ. Policy Plan. 19, 574–592 (2017).
    Google Scholar 
    38.Hutchins, M. G. et al. Why scale is vital to plan optimal nature-based solutions for resilient cities. Environ. Res. Lett. 16, 044008 (2021).
    Google Scholar 
    39.Raška, P., Slavíková, L. & Sheehan, J. in Nature-Based Flood Risk Management on Private Land: Disciplinary Perspectives on a Multidisciplinary Challenge 9–20 (Springer International Publishing, 2019).40.Frantzeskaki, N. et al. Nature-based solutions for urban climate change adaptation: linking science, policy, and practice communities for evidence-based decision-making. BioScience 69, 455–466 (2019).
    Google Scholar 
    41.Watkin, L. J., Ruangpan, L., Vojinovic, Z., Weesakul, S. & Torres, A. S. A framework for assessing benefits of implemented nature-based solutions. Sustainability 11, 6788 (2019).
    Google Scholar 
    42.Wurzel, R. K. W., Liefferink, D. & Torney, D. Pioneers, leaders and followers in multilevel and polycentric climate governance. Environ. Polit. 28, 1–21 (2019).
    Google Scholar 
    43.Frantzeskaki, N. et al. Examining the policy needs for implementing nature-based solutions in cities: findings from city-wide transdisciplinary experiences in Glasgow (UK), Genk (Belgium) and Poznań (Poland). Land Use Policy 96, 104688 (2020).
    Google Scholar 
    44.Zingraff-Hamed, A. et al. Governance models for nature-based solutions: cases from Germany. Ambio 50, 1610–1627 (2020).
    Google Scholar 
    45.Toxopeus, H. et al. How ‘just’ is hybrid governance of urban nature-based solutions? Cities 105, 102839 (2020).
    Google Scholar 
    46.Wamsler, C. et al. Environmental and climate policy integration: targeted strategies for overcoming barriers to nature-based solutions and climate change adaptation. J. Clean. Prod. 247, 119154 (2020).
    Google Scholar 
    47.Pérez Rubi, M. & Hack, J. Co-design of experimental nature-based solutions for decentralized dry-weather runoff treatment retrofitted in a densely urbanized area in Central America. Ambio 50, 1498–1513 (2021).
    Google Scholar 
    48.Chapa, F., Pérez, M. & Hack, J. Experimenting transition to sustainable urban drainage systems—identifying constraints and unintended processes in a tropical highly urbanized. Watershed. Water 12, 3554 (2020).
    Google Scholar 
    49.Chen, V., Bonilla Brenes, J. R., Chapa, F. & Hack, J. Development and modelling of realistic retrofitted Nature-based Solution scenarios to reduce flood occurrence at the catchment scale. Ambio 50, 1462–1476 (2021).
    Google Scholar 
    50.Hüesker, F. & Moss, T. The politics of multi-scalar action in river basin management: Implementing the EU Water Framework Directive (WFD). Land Use Policy 42, 38–47 (2015).
    Google Scholar 
    51.WBCSD. Incentives for Natural Infrastructure: review of existing policies, incentives and barriers related to permitting, finance and insurance of natural infrastructure. (World Business Council for Sustainable Development, Geneva, 2017).52.Nesshöver, C. et al. The science, policy and practice of nature-based solutions: an interdisciplinary perspective. Sci. Total Environ. 579, 1215–1227 (2017).
    Google Scholar 
    53.Toxopeus, H. S. Taking Action for Urban Nature: Business Model Catalogue, NATURVATION Guide (2019).54.Duraiappah, A. K. et al. Managing the mismatches to provide ecosystem services for human well-being: a conceptual framework for understanding the New Commons. Curr. Opin.Environ. Sustain 7, 94–100 (2014).
    Google Scholar 
    55.Young, O. R. Vertical interplay among scale-dependent environmental and resource regimes. Ecol. Soc. 11, 27 (2006).
    Google Scholar 
    56.Cumming, G. S., Cumming, D. H. M. & Redman, C. L. Scale mismatches in social-ecological systems: causes, consequences, and solutions. Ecol. Soc. 11, 14 (2006).
    Google Scholar 
    57.Naidoo, R. & Fisher, B. Sustainable development goals: pandemic reset. Nature 583, 198–201 (2020).CAS 

    Google Scholar 
    58.Fyfe, J. C. et al. Quantifying the influence of short-term emission reductions on climate. Sci. Adv. 7, eabf7133 (2021).CAS 

    Google Scholar 
    59.Linnér, B.-O. & Wibeck, V. Conceptualising variations in societal transformations towards sustainability. Environ. Sci.Pol. 106, 221–227 (2020).
    Google Scholar 
    60.Harrabin, R. Coronavirus: Lockdown ‘could boost wild flowers’. https://www.bbc.com/news/science-environment-52215273 (2020).61.Bratman, G. N. et al. Nature and mental health: an ecosystem service perspective. Sci. Adv. 5, eaax0903 (2019).
    Google Scholar 
    62.Honey-Rosés, J. et al. The impact of COVID-19 on public space: an early review of the emerging questions—design, perceptions and inequities. Cities & Health, 1-17(2020).63.Sanyé-Mengual, E., Anguelovski, I., Oliver-Solà, J., Montero, J. I. & Rieradevall, J. Resolving differing stakeholder perceptions of urban rooftop farming in Mediterranean cities: promoting food production as a driver for innovative forms of urban agriculture. Agric. Human Values 33, 101–120 (2016).
    Google Scholar 
    64.PIANC. Guide for applying Working with Nature to navigation infrastructure projects. (Brussels, Belgium, 2018).65.Rijke, J., van Herk, S., Zevenbergen, C. & Ashley, R. Room for the River: delivering integrated river basin management in the Netherlands. Int. J. River Basin Manage. 10, 369–382 (2012). https://doi.org/10.1080/15715124.2012.739173.66.Li, H., Ding, L., Ren, M., Li, C. & Wang, H. Sponge City Construction in China: A Survey of the Challenges and Opportunities. Water (Australia) 9, 594 (2017).67.Kurth, A.-M. & Schirmer, M. Thirty years of river restoration in Switzerland: implemented measures and lessons learned. Environ. Earth Sci. 72, 2065–2079 (2014). https://doi.org/10.1007/s12665-014-3115-y.68.Petty, K. Wildflowers on road verges: an uplifting sight during the coronavirus lockdown. (2020). https://www.plantlife.org.uk/uk/blog/wildflowers-on-road-verges-an-uplifting-sight-during-the-coronavirus-lockdown. More

  • in

    Climate-related drivers of nutrient inputs and food web structure in shallow Arctic lake ecosystems

    1.Lefébure, R. et al. Impacts of elevated terrestrial nutrient loads and temperature on pelagic food-web efficiency and fish production. Glob. Change Biol. 19, 1358–1372 (2013).ADS 

    Google Scholar 
    2.Roussel, J.-M. et al. Stable isotope analyses on archived fish scales reveal the long-term effect of nitrogen loads on carbon cycling in rivers. Glob. Change Biol. 20, 523–530 (2014).ADS 

    Google Scholar 
    3.Creed, I. F. et al. Global change-driven effects on dissolved organic matter composition: Implications for food webs of northern lakes. Glob. Change Biol. 24, 3692–3714 (2018).ADS 

    Google Scholar 
    4.Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Kumar, A., Yadav, J. & Mohan, R. Spatio-temporal change and variability of Barents-Kara sea ice, in the Arctic: Ocean and atmospheric implications. Sci. Total Environ. 753, 142046 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    6.Vincent, W. F., Laurion, I., Pienitz, R. & Walter Anthony, K. M. Climate Impacts on Arctic Lake Ecosystems. In Climatic Change and Global Warming of Inland Waters (eds Goldman, C. R. et al.) 27–42 (Wiley, 2012). https://doi.org/10.1002/9781118470596.ch2.Chapter 

    Google Scholar 
    7.Kim, K.-Y. et al. Vertical feedback mechanism of winter Arctic amplification and sea ice loss. Sci. Rep. 9, 1184 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Shaver, G. R. & Chapin, F. S. Response to fertilization by various plant growth forms in an Alaskan tundra: Nutrient accumulation and growth. Ecology 61, 662–675 (1980).CAS 

    Google Scholar 
    9.Meunier, C. L., Gundale, M. J., Sánchez, I. S. & Liess, A. Impact of nitrogen deposition on forest and lake food webs in nitrogen-limited environments. Glob. Change Biol. 22, 164–179 (2016).ADS 

    Google Scholar 
    10.Arctic Climate Impact Assessment. Arctic climate impact assessment (Cambridge University Press, Cambridge, 2005).
    Google Scholar 
    11.Hay, W. W. The accelerating rate of global change. Rendiconti Lincei 25, 29–48 (2014).
    Google Scholar 
    12.Prowse, T. D. et al. Climate change effects on hydroecology of Arctic freshwater ecosystems. AMBIO J. Hum. Environ. 35, 347–358 (2006).CAS 

    Google Scholar 
    13.Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    14.Ward, R. D. Carbon sequestration and storage in Norwegian Arctic coastal wetlands: Impacts of climate change. Sci. Total Environ. 748, 141343 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    15.Lin, J., Huang, J., Prell, C. & Bryan, B. A. Changes in supply and demand mediate the effects of land-use change on freshwater ecosystem services flows. Sci. Total Environ. 763, 143012 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    16.Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).ADS 

    Google Scholar 
    17.Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).ADS 
    CAS 

    Google Scholar 
    18.St. Pierre, K. A. et al. Contemporary limnology of the rapidly changing glacierized watershed of the world’s largest High Arctic lake. Sci. Rep. 9, 4447 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Woelders, L. et al. Recent climate warming drives ecological change in a remote high-Arctic lake. Sci. Rep. 8, 6858 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Blaen, P. J., Milner, A. M., Hannah, D. M., Brittain, J. E. & Brown, L. E. Impact of changing hydrology on nutrient uptake in high Arctic rivers: Nutrient uptake in Arctic rivers. River Res. Appl. 30, 1073–1083 (2014).
    Google Scholar 
    21.Szkokan-Emilson, E. J. et al. Dry conditions disrupt terrestrial-aquatic linkages in northern catchments. Glob. Change Biol. 23, 117–126 (2017).ADS 

    Google Scholar 
    22.Thackeray, S. J. et al. Food web de-synchronization in England’s largest lake: An assessment based on multiple phenological metrics. Glob. Change Biol. 19, 3568–3580 (2013).ADS 

    Google Scholar 
    23.Pacheco, J. P. et al. Small-sized omnivorous fish induce stronger effects on food webs than warming and eutrophication in experimental shallow lakes. Sci. Total Environ. 797, 148998 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    24.Kuijper, D. P. J., Ubels, R. & Loonen, M. J. J. E. Density-dependent switches in diet: A likely mechanism for negative feedbacks on goose population increase?. Polar Biol. 32, 1789–1803 (2009).
    Google Scholar 
    25.Sjögersten, S., van der Wal, R., Loonen, M. J. J. E. & Woodin, S. J. Recovery of ecosystem carbon fluxes and storage from herbivory. Biogeochemistry 106, 357–370 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    26.Buij, R., Melman, T. C. P., Loonen, M. J. J. E. & Fox, A. D. Balancing ecosystem function, services and disservices resulting from expanding goose populations. Ambio 46, 301–318 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    27.Nishizawa, K. et al. Long-term consequences of goose exclusion on nutrient cycles and plant communities in the high-Arctic. Polar Sci. 27, 100631 (2021).
    Google Scholar 
    28.Bjerke, J. W., Tombre, I. M., Hanssen, M. & Olsen, A. K. B. Springtime grazing by Arctic-breeding geese reduces first- and second-harvest yields on sub-Arctic agricultural grasslands. Sci. Total Environ. 793, 148619 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    29.Van Geest, G. J. et al. Goose-mediated nutrient enrichment and planktonic grazer control in Arctic freshwater ponds. Oecologia 153, 653–662 (2007).ADS 
    PubMed 

    Google Scholar 
    30.Calizza, E., Rossi, L. & Costantini, M. L. Predators and resources influence phosphorus transfer along an invertebrate food web through changes in prey behaviour. PLoS ONE 8, e65186 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Rossi, L., di Lascio, A., Carlino, P., Calizza, E. & Costantini, M. L. Predator and detritivore niche width helps to explain biocomplexity of experimental detritus-based food webs in four aquatic and terrestrial ecosystems. Ecol. Complex. 23, 14–24 (2015).
    Google Scholar 
    32.Caputi, S. S. et al. Seasonal food web dynamics in the Antarctic benthos of Tethys Bay (Ross Sea): Implications for biodiversity persistence under different seasonal sea-ice coverage. Front. Mar. Sci. 7, 594454 (2020).
    Google Scholar 
    33.Careddu, G., Calizza, E., Costantini, M. L. & Rossi, L. Isotopic determination of the trophic ecology of a ubiquitous key species—The crab Liocarcinus depurator (Brachyura: Portunidae). Estuar. Coast. Shelf Sci. 191, 106–114 (2017).ADS 
    CAS 

    Google Scholar 
    34.Careddu, G. et al. Diet composition of the Italian crested newt (Triturus carnifex) in structurally different artificial ponds based on stomach contents and stable isotope analyses. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 1505–1520 (2020).
    Google Scholar 
    35.Zhao, Q., De Laender, F. & Van den Brink, P. J. Community composition modifies direct and indirect effects of pesticides in freshwater food webs. Sci. Total Environ. 739, 139531 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    36.Rossi, L., Costantini, M. L., Carlino, P., di Lascio, A. & Rossi, D. Autochthonous and allochthonous plant contributions to coastal benthic detritus deposits: A dual-stable isotope study in a volcanic lake. Aquat. Sci. 72, 227–236 (2010).CAS 

    Google Scholar 
    37.Rossi, L. et al. Antarctic food web architecture under varying dynamics of sea ice cover. Sci. Rep. 9, 12454 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Careddu, G. et al. Effects of terrestrial input on macrobenthic food webs of coastal sea are detected by stable isotope analysis in Gaeta Gulf. Estuar. Coast. Shelf Sci. 154, 158–168 (2015).ADS 
    CAS 

    Google Scholar 
    39.Careddu, G. et al. Gaining insight into the assimilated diet of small bear populations by stable isotope analysis. Sci. Rep. 11, 14118 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Blais, J. M. Arctic seabirds transport marine-derived contaminants. Science 309, 445–445 (2005).CAS 
    PubMed 

    Google Scholar 
    41.Bentivoglio, F. et al. Site-scale isotopic variations along a river course help localize drainage basin influence on river food webs. Hydrobiologia 770, 257–272 (2016).CAS 

    Google Scholar 
    42.Rossi, L. et al. Space-time monitoring of coastal pollution in the Gulf of Gaeta, Italy, using δ15N values of Ulva lactuca, landscape hydromorphology, and Bayesian Kriging modelling. Mar. Pollut. Bull. 126, 479–487 (2018).CAS 
    PubMed 

    Google Scholar 
    43.Calizza, E. et al. Isotopic biomonitoring of N pollution in rivers embedded in complex human landscapes. Sci. Total Environ. 706, 136081 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    44.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).
    Google Scholar 
    45.Mansouri, F. et al. Evidence of multi-decadal behavior and ecosystem-level changes revealed by reconstructed lifetime stable isotope profiles of baleen whale earplugs. Sci. Total Environ. 757, 143985 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    46.Hawley, K. L., Rosten, C. M., Christensen, G. & Lucas, M. C. Fine-scale behavioural differences distinguish resource use by ecomorphs in a closed ecosystem. Sci. Rep. 6, 24369 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Michener, R. H. & Lajtha, K. Stable Isotopes in Ecology and Environmental Science (Blackwell Publication, 2007).
    Google Scholar 
    48.Cicala, D. et al. Spatial variation in the feeding strategies of Mediterranean fish: Flatfish and mullet in the Gulf of Gaeta (Italy). Aquat. Ecol. 53, 529–541 (2019).CAS 

    Google Scholar 
    49.Calizza, E. et al. Stable isotopes and digital elevation models to study nutrient inputs in high-Arctic lakes. Rendiconti Lincei 27, 191–199 (2016).
    Google Scholar 
    50.Calizza, E., Careddu, G., Sporta Caputi, S., Rossi, L. & Costantini, M. L. Time- and depth-wise trophic niche shifts in Antarctic benthos. PLoS ONE 13, e0194796 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    51.Mehlum, F. Svalbards fugler og pattedyr (Norsk polarinstitutt, 1989).
    Google Scholar 
    52.Christoffersen, K. Predation on Daphnia pulex by Lepidurus arcticus. Hydrobiologia 442, 223–229 (2001).
    Google Scholar 
    53.Lakka, H.-K. The ecology of a freshwater crustacean: Lepidurus arcticus (Brachiopoda; Notostraca) in a High Arctic region. Dissertation, University of Helsinky (2013).54.Westergaard-Nielsen, A. et al. Transitions in high-Arctic vegetation growth patterns and ecosystem productivity tracked with automated cameras from 2000 to 2013. Ambio 46, 39–52 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    55.Pyke, G. H., Pulliam, H. R. & Charnov, E. L. Optimal foraging: A selective review of theory and tests. Q. Rev. Biol. 52, 137–154 (1977).
    Google Scholar 
    56.Kondoh, M. & Ninomiya, K. Food-chain length and adaptive foraging. Proc. R. Soc. B Biol. Sci. 276, 3113–3121 (2009).
    Google Scholar 
    57.Calizza, E., Costantini, M. L., Rossi, D., Carlino, P. & Rossi, L. Effects of disturbance on an urban river food web: Disturbance of a river food web. Freshw. Biol. 57, 2613–2628 (2012).
    Google Scholar 
    58.McMeans, B. C., McCann, K. S., Humphries, M., Rooney, N. & Fisk, A. T. Food web structure in temporally-forced ecosystems. Trends Ecol. Evol. 30, 662–672 (2015).PubMed 

    Google Scholar 
    59.Pimm, S. L. & Lawton, J. H. Number of trophic levels in ecological communities. Nature 268, 329–331 (1977).ADS 

    Google Scholar 
    60.Elser, J. J. et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 408, 578–580 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    61.Hall, S. R. Stoichiometrically explicit food webs: Feedbacks between resource supply, elemental constraints, and species diversity. Annu. Rev. Ecol. Evol. Syst. 40, 503–528 (2009).
    Google Scholar 
    62.Hessen, D. O., Ågren, G. I., Anderson, T. R., Elser, J. J. & de Ruiter, P. C. Carbon sequestration in ecosystems: The role of stoichiometry. Ecology 85, 1179–1192 (2004).
    Google Scholar 
    63.Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sens. Environ. 89, 281–308 (2004).ADS 

    Google Scholar 
    64.Maher, A. I., Treitz, P. M. & Ferguson, M. A. D. Can Landsat data detect variations in snow cover within habitats of Arctic ungulates?. Wildl. Biol. 18, 75–87 (2012).
    Google Scholar 
    65.Raynolds, M. K., Walker, D. A., Verbyla, D. & Munger, C. A. Patterns of change within a tundra landscape: 22-year Landsat NDVI trends in an area of the Northern Foothills of the Brooks Range, Alaska. Arct. Antarct. Alp. Res. 45, 249–260 (2013).
    Google Scholar 
    66.Bokhorst, S. et al. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio 45, 516–537 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    67.Härer, S., Bernhardt, M., Siebers, M. & Schulz, K. On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales. Cryosphere 12, 1629–1642 (2018).ADS 

    Google Scholar 
    68.Karlsen, S. R., Anderson, H. B., van der Wal, R. & Hansen, B. B. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).ADS 

    Google Scholar 
    69.Karlsen, S. R., et al. Sentinel satellite-based mapping of plant productivity in relation to snow duration and time of green-up. https://zenodo.org/record/4704361. https://doi.org/10.5281/ZENODO.4704361 (2020).70.Beamish, A. et al. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sens. Environ. 246, 111872 (2020).ADS 

    Google Scholar 
    71.Layton-Matthews, K., Hansen, B. B., Grøtan, V., Fuglei, E. & Loonen, M. J. J. E. Contrasting consequences of climate change for migratory geese: Predation, density dependence and carryover effects offset benefits of high-Arctic warming. Glob. Change Biol. 26, 642–657 (2020).ADS 

    Google Scholar 
    72.Owen, M. The selection of feeding site by White-fronted geese in winter. J. Appl. Ecol. 8, 905 (1971).
    Google Scholar 
    73.Ydenberg, R. C. & Prins, H. HTh. Spring grazing and the manipulation of food quality by Barnacle geese. J. Appl. Ecol. 18, 443 (1981).
    Google Scholar 
    74.Bos, D. et al. Utilisation of Wadden Sea salt marshes by geese in relation to livestock grazing. J. Nat. Conserv. 13, 1–15 (2005).
    Google Scholar 
    75.Barrio, I. C. et al. Developing common protocols to measure tundra herbivory across spatial scales. Arct. Sci. https://doi.org/10.1139/as-2020-0020 (2021).Article 

    Google Scholar 
    76.Jensen, T. C. et al. Changes in trophic state and aquatic communities in high Arctic ponds in response to increasing goose populations. Freshw. Biol. 64, 1241–1254 (2019).CAS 

    Google Scholar 
    77.Bartoli, M. et al. Denitrification, nitrogen uptake, and organic matter quality undergo different seasonality in sandy and muddy sediments of a turbid estuary. Front. Microbiol. 11, 612700 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    78.van der Wal, R., van Lieshout, S. M. J. & Loonen, M. J. J. E. Herbivore impact on moss depth, soil temperature and Arctic plant growth. Polar Biol. 24, 29–32 (2001).
    Google Scholar 
    79.Wookey, P. A. et al. Differential growth, allocation and photosynthetic responses of Polygonum viviparum to simulated environmental change at a high Arctic polar semi-desert. Oikos 70, 131 (1994).
    Google Scholar 
    80.Wookey, P. A. et al. Environmental constraints on the growth, photosynthesis and reproductive development of Dryas octopetala at a high Arctic polar semi-desert, Svalbard. Oecologia 102, 478–489 (1995).ADS 
    CAS 
    PubMed 

    Google Scholar 
    81.Jefferies, R. L. Agricultural food subsidies, migratory connectivity and large-scale disturbance in arctic coastal systems: A case study. Integr. Comp. Biol. 44, 130–139 (2004).CAS 
    PubMed 

    Google Scholar 
    82.Hik, D. S. & Jefferies, R. L. Increases in the net above-ground primary production of a salt-marsh forage grass: A test of the predictions of the herbivore-optimization model. J. Ecol. 78, 180 (1990).
    Google Scholar 
    83.Rautio, M., Mariash, H. & Forsström, L. Seasonal shifts between autochthonous and allochthonous carbon contributions to zooplankton diets in a subarctic lake. Limnol. Oceanogr. 56, 1513–1524 (2011).ADS 
    CAS 

    Google Scholar 
    84.Crump, B. C., Kling, G. W., Bahr, M. & Hobbie, J. E. Bacterioplankton community shifts in an Arctic lake correlate with seasonal changes in organic matter source. Appl. Environ. Microbiol. 69, 2253–2268 (2003).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Berggren, M., Ziegler, S. E., St-Gelais, N. F., Beisner, B. E. & del Giorgio, P. A. Contrasting patterns of allochthony among three major groups of crustacean zooplankton in boreal and temperate lakes. Ecology 95, 1947–1959 (2014).PubMed 

    Google Scholar 
    86.Stasko, A. D., Gunn, J. M. & Johnston, T. A. Role of ambient light in structuring north-temperate fish communities: Potential effects of increasing dissolved organic carbon concentration with a changing climate. Environ. Rev. 20, 173–190 (2012).CAS 

    Google Scholar 
    87.Milardi, M., Käkelä, R., Weckström, J. & Kahilainen, K. K. Terrestrial prey fuels the fish population of a small, high-latitude lake. Aquat. Sci. 78, 695–706 (2016).CAS 

    Google Scholar 
    88.Vincent, W. F. & Laybourn-Parry, J. Polar Lakes and Rivers (Oxford University Press, 2008). https://doi.org/10.1093/acprof:oso/9780199213887.001.0001.Book 

    Google Scholar 
    89.Calizza, E., Costantini, M. L., Careddu, G. & Rossi, L. Effect of habitat degradation on competition, carrying capacity, and species assemblage stability. Ecol. Evol. 7, 5784–5796 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    90.Van der Velden, S., Dempson, J. B., Evans, M. S., Muir, D. C. G. & Power, M. Basal mercury concentrations and biomagnification rates in freshwater and marine food webs: Effects on Arctic charr (Salvelinus alpinus) from eastern Canada. Sci. Total Environ. 444, 531–542 (2013).ADS 
    PubMed 

    Google Scholar 
    91.Kozak, N. et al. Environmental and biological factors are joint drivers of mercury biomagnification in subarctic lake food webs along a climate and productivity gradient. Sci. Total Environ. 779, 146261 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    92.Longhurst, A. R. A review of the Notostraca. Bull. Br. Mus. Nat. Hist. 3, 1–57 (1955).
    Google Scholar 
    93.King, J. L. & Hanner, R. Cryptic species in a “living fossil” lineage: Taxonomic and phylogenetic relationships within the genus Lepidurus (Crustacea: Notostraca) in North America. Mol. Phylogenet. Evol. 10, 23–36 (1998).CAS 
    PubMed 

    Google Scholar 
    94.Hessen, D. O., Rueness, E. K. & Stabell, M. Circumpolar analysis of morphological and genetic diversity in the Notostracan Lepidurus arcticus. Hydrobiologia 519, 73–84 (2004).
    Google Scholar 
    95.Pasquali, V., Calizza, E., Setini, A., Hazlerigg, D. & Christoffersen, K. S. Preliminary observations on the effect of light and temperature on the hatching success and rate of Lepidurus arcticus eggs. Ethol. Ecol. Evol. 31, 348–357 (2019).
    Google Scholar 
    96.Tanentzap, A. J. et al. Climate warming restructures an aquatic food web over 28 years. Glob. Change Biol. 26, 6852–6866 (2020).ADS 

    Google Scholar 
    97.Polvani, L. M., Previdi, M., England, M. R., Chiodo, G. & Smith, K. L. Substantial twentieth-century Arctic warming caused by ozone-depleting substances. Nat. Clim. Change 10, 130–133 (2020).ADS 
    CAS 

    Google Scholar 
    98.di Lascio, A. et al. Stable isotope variation in macroinvertebrates indicates anthropogenic disturbance along an urban stretch of the river Tiber (Rome, Italy). Ecol. Indic. 28, 107–114 (2013).
    Google Scholar 
    99.Moore, I. D., Grayson, R. B. & Ladson, A. R. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process. 5, 3–30 (1991).ADS 

    Google Scholar 
    100.Vaze, J., Teng, J. & Spencer, G. Impact of DEM accuracy and resolution on topographic indices. Environ. Model. Softw. 25, 1086–1098 (2010).
    Google Scholar 
    101.Johansen, B. E., Karlsen, S. R. & Tømmervik, H. Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data. Polar Rec. 48, 47–63 (2012).
    Google Scholar 
    102.Hall, D. K., Riggs, G. A. & Salomonson, V. V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 54, 127–140 (1995).ADS 

    Google Scholar 
    103.Vogel, S. W. Usage of high-resolution Landsat 7 band 8 for single-band snow-cover classification. Ann. Glaciol. 34, 53–57 (2002).ADS 

    Google Scholar 
    104.Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).ADS 

    Google Scholar 
    105.Dozier, J. Spectral signature of alpine snow cover from the landsat thematic mapper. Remote Sens. Environ. 28, 9–22 (1989).ADS 

    Google Scholar 
    106.Jensen, J. R. Remote Sensing of the Environment: An Earth Resource Perspective (Pearson Prentice Hall, 2007).
    Google Scholar 
    107.Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G. & Hagolle, O. Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data. Earth Syst. Sci. Data 11, 493–514 (2019).ADS 

    Google Scholar 
    108.Simon, G., Manuel, G., Tristan, K. & Germain, S. Algorithm Theoretical basis documentation for an operational snow cover product from Sentinel-2 and Landsat-8 data (let-it-snow) (2018). https://doi.org/10.5281/ZENODO.1414452.109.Stahl, J. & Loonen, M. J. Effects of predation risk on site selection of barnacle geese during brood-rearing. In Research on Arctic Geese, 91 (1998).110.McCutchan, J. H., Lewis, W. M., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).CAS 

    Google Scholar 
    111.Calizza, E., Rossi, L., Careddu, G., Sporta Caputi, S. & Costantini, M. L. Species richness and vulnerability to disturbance propagation in real food webs. Sci. Rep. 9, 19331 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    112.Mantel, N. & Valand, R. S. A technique of nonparametric multivariate analysis. Biometrics 26, 547 (1970).CAS 
    PubMed 

    Google Scholar 
    113.Signa, G. et al. Horizontal and vertical food web structure drives trace element trophic transfer in Terra Nova Bay, Antarctica. Environ. Pollut. 246, 772–781 (2019).CAS 
    PubMed 

    Google Scholar  More

  • in

    Alternative splicing in seasonal plasticity and the potential for adaptation to environmental change

    1.West-Eberhard, M. J. Developmental plasticity and evolution. (Oxford University Press, 2003).2.de Jong, G. Evolution of phenotypic plasticity: patterns of plasticity and the emergence of ecotypes. N. Phytologist 166, 101–118 (2005).
    Google Scholar 
    3.Ezard, T. H. G., Prizak, R. & Hoyle, R. B. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Funct. Ecol. 28, 693–701 (2014).
    Google Scholar 
    4.Williams, C. M. et al. Understanding evolutionary impacts of seasonality: an introduction to the symposium. Integr. Comp. Biol. 57, 921–933 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    5.Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    7.Beldade, P., Mateus, A. R. A. & Keller, R. A. Evolution and molecular mechanisms of adaptive developmental plasticity. Mol. Ecol. 20, 1347–1363 (2011).PubMed 

    Google Scholar 
    8.Lafuente, E. & Beldade, P. Genomics of developmental plasticity in animals. Front. Genet. 10, (2019).9.Marden, J. H. Quantitative and evolutionary biology of alternative splicing: how changing the mix of alternative transcripts affects phenotypic plasticity and reaction norms. Heredity 100, 111–120 (2008).CAS 
    PubMed 

    Google Scholar 
    10.Baralle, F. E. & Giudice, J. Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell Biol. 18, 437–451 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Bush, S. J., Chen, L., Tovar-Corona, J. M. & Urrutia, A. O. Alternative splicing and the evolution of phenotypic novelty. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20150474 (2017).
    Google Scholar 
    12.Marden, J. H. & Cobb, J. R. Territorial and mating success of dragonflies that vary in muscle power output and presence of gregarine gut parasites. Anim. Behav. 68, 857–865 (2004).
    Google Scholar 
    13.Kijimoto, T., Moczek, A. P. & Andrews, J. Diversification of doublesex function underlies morph-, sex-, and species-specific development of beetle horns. Proc. Natl Acad. Sci. USA 109, 20526–20531 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Bear, A., Prudic, K. L. & Monteiro, A. Steroid hormone signaling during development has a latent effect on adult male sexual behavior in the butterfly Bicyclus anynana. PLoS ONE 12, e0174403 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    15.Martin Anduaga, A. et al. Thermosensitive alternative splicing senses and mediates temperature adaptation in Drosophila. eLife 8, e44642 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    16.Deshmukh, R., Lakhe, D. & Kunte, K. Tissue-specific developmental regulation and isoform usage underlie the role of doublesex in sex differentiation and mimicry in Papilio swallowtails. R. Soc. Open Sci. 7, 200792 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Grantham, M. E. & Brisson, J. A. Extensive differential splicing underlies phenotypically plastic aphid morphs. Mol. Biol. Evol. 35, 1934–1946 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Price, J. et al. Alternative splicing associated with phenotypic plasticity in the bumble bee Bombus terrestris. Mol. Ecol. 27, 1036–1043 (2018).CAS 
    PubMed 

    Google Scholar 
    19.Lees, J. G., Ranea, J. A. & Orengo, C. A. Identifying and characterising key alternative splicing events in Drosophila development. BMC Genomics 16, 608 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    20.Jakšić, A. M. & Schlötterer, C. The interplay of temperature and genotype on patterns of alternative splicing in Drosophila melanogaster. Genetics 204, 315–325 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    21.Healy, T. M. & Schulte, P. M. Patterns of alternative splicing in response to cold acclimation in fish. J. Exp. Biol. 222, jeb193516 (2019).22.Signor, S. & Nuzhdin, S. Dynamic changes in gene expression and alternative splicing mediate the response to acute alcohol exposure in Drosophila melanogaster. Heredity 121, 342–360 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Lang, A. S., Austin, S. H., Harris, R. M., Calisi, R. M. & MacManes, M. D. Stress-mediated convergence of splicing landscapes in male and female rock doves. BMC Genomics 21, 251 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    24.Suresh, S., Crease, T. J., Cristescu, M. E. & Chain, F. J. J. Alternative splicing is highly variable among Daphnia pulex lineages in response to acute copper exposure. BMC Genomics 21, 433 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Thorstensen, M. J., Baerwald, M. R. & Jeffries, K. M. RNA sequencing describes both population structure and plasticity-selection dynamics in a non-model fish. BMC Genomics 22, 273 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Singh, A. & Agrawal, A. F. Sexual dimorphism in gene expression: coincidence and population genomics of two forms of differential expression in Drosophila melanogaster. bioRxiv (2021) https://doi.org/10.1101/2021.02.08.429268.27.Rogers, T. F., Palmer, D. H. & Wright, A. E. Sex-specific selection drives the evolution of alternative splicing in birds. Mol. Biol. Evolution 38, 519–530 (2021).CAS 

    Google Scholar 
    28.Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B: Biol. Sci. 374, 20180174 (2019).
    Google Scholar 
    29.Kelly, M. Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes. Philos. Trans. R. Soc. B: Biol. Sci. 374, 20180176 (2019).
    Google Scholar 
    30.Oostra, V., Saastamoinen, M., Zwaan, B. J. & Wheat, C. W. Strong phenotypic plasticity limits potential for evolutionary responses to climate change. Nat. Commun. 9, 1–11 (2018).CAS 

    Google Scholar 
    31.Wang, Y. et al. Mechanism of alternative splicing and its regulation (Review). Biomed. Rep. 3, 152–158 (2015).CAS 
    PubMed 

    Google Scholar 
    32.Ule, J. & Blencowe, B. J. Alternative splicing regulatory networks: functions, mechanisms, and evolution. Mol. Cell 76, 329–345 (2019).CAS 
    PubMed 

    Google Scholar 
    33.McManus, C. J., Coolon, J. D., Eipper-Mains, J., Wittkopp, P. J. & Graveley, B. R. Evolution of splicing regulatory networks in Drosophila. Genome Res. 24, 786–796 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Gao, Q., Sun, W., Ballegeer, M., Libert, C. & Chen, W. Predominant contribution of cis-regulatory divergence in the evolution of mouse alternative splicing. Mol. Syst. Biol. 11, 816 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    35.Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    36.Wang, X. et al. Cis-regulated alternative splicing divergence and its potential contribution to environmental responses in Arabidopsis. Plant J. 97, 555–570 (2019).CAS 
    PubMed 

    Google Scholar 
    37.Huang, Y., Lack, J. B., Hoppel, G. T. & Pool, J. E. Parallel and population-specific gene regulatory evolution in cold-adapted fly populations. bioRxiv (2021) https://doi.org/10.1101/795716.38.Lewis, J. J., Van Belleghem, S. M., Papa, R., Danko, C. G. & Reed, R. D. Many functionally connected loci foster adaptive diversification along a neotropical hybrid zone. Sci. Adv. 6, eabb8617 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Lewis, J. J. & Reed, R. D. Genome-wide regulatory adaptation shapes population-level genomic landscapes in Heliconius. Mol. Biol. Evol. 36, 159–173 (2019).CAS 
    PubMed 

    Google Scholar 
    40.Martin, S. H. et al. Natural selection and genetic diversity in the butterfly Heliconius melpomene. Genetics 203, 525–541 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Brakefield, P. M., Beldade, P. & Zwaan, B. J. The African Butterfly Bicyclus anynana: a model for evolutionary genetics and evolutionary developmental biology. Cold Spring Harb. Protoc. 2009, pdb.emo122 (2009).PubMed 

    Google Scholar 
    42.Mateus, A. R. A. et al. Adaptive developmental plasticity: compartmentalized responses to environmental cues and to corresponding internal signals provide phenotypic flexibility. BMC Biol. 12, 97 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    43.Oostra, V. et al. Ecdysteroid hormones link the juvenile environment to alternative adult life histories in a seasonal insect. Am. Naturalist 184, E79–E92 (2014).
    Google Scholar 
    44.van Bergen, E. et al. Conserved patterns of integrated developmental plasticity in a group of polyphenic tropical butterflies. BMC Evolut. Biol. 17, 59 (2017).
    Google Scholar 
    45.Singh, P. et al. Complex multi-trait responses to multivariate environmental cues in a seasonal butterfly. Evol. Ecol. (2020) https://doi.org/10.1007/s10682-020-10062-0.46.Prudic, K. L., Jeon, C., Cao, H. & Monteiro, A. Developmental plasticity in sexual roles of butterfly species drives mutual sexual ornamentation. Science 331, 73–75 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    47.Chen, L., Bush, S. J., Tovar-Corona, J. M., Castillo-Morales, A. & Urrutia, A. O. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol. Biol. Evol. 31, 1402–1413 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    48.Hamid, F. M. & Makeyev, E. V. Emerging functions of alternative splicing coupled with nonsense-mediated decay. Biochem. Soc. Trans. 42, 1168–1173 (2014).CAS 
    PubMed 

    Google Scholar 
    49.Tabrez, S. S., Sharma, R. D., Jain, V., Siddiqui, A. A. & Mukhopadhyay, A. Differential alternative splicing coupled to nonsense-mediated decay of mRNA ensures dietary restriction-induced longevity. Nat. Commun. 8, 306 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Uller, T., Moczek, A. P., Watson, R. A., Brakefield, P. M. & Laland, K. N. Developmental bias and evolution: a regulatory network perspective. Genetics 209, 949–966 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    51.Nijhout, H. F. To plasticity and back again. eLife 4, e06995 (2015).PubMed Central 

    Google Scholar 
    52.Helanterä, H. & Uller, T. Neutral and adaptive explanations for an association between caste-biased gene expression and rate of sequence evolution. Front. Genet. 5, 297 (2014).53.Pespeni, M. H., Ladner, J. T. & Moczek, A. P. Signals of selection in conditionally expressed genes in the diversification of three horned beetle species. J. Evolut. Biol. 30, 1644–1657 (2017).CAS 

    Google Scholar 
    54.Plass, M. & Eyras, E. Differentiated evolutionary rates in alternative exons and the implications for splicing regulation. BMC Evol. Biol. 6, 50 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    55.Chen, F.-C., Pan, C.-L. & Lin, H.-Y. Independent effects of alternative splicing and structural constraint on the evolution of mammalian coding exons. Mol. Biol. Evolution 29, 187–193 (2012).CAS 

    Google Scholar 
    56.Peña, C., Nylin, S. & Wahlberg, N. The radiation of Satyrini butterflies (Nymphalidae: Satyrinae): a challenge for phylogenetic methods. Zool. J. Linn. Soc. 161, 64–87 (2011).
    Google Scholar 
    57.Bhardwaj, S. et al. Origin of the mechanism of phenotypic plasticity in satyrid butterfly eyespots. eLife 9, e49544 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lewis, B. P., Green, R. E. & Brenner, S. E. Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans. PNAS 100, 189–192 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    59.Akerman, M. & Mandel-Gutfreund, Y. Alternative splicing regulation at tandem 3′ splice sites. Nucleic Acids Res. 34, 23–31 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Moran, N. A. The evolutionary maintenance of alternative phenotypes. Am. Naturalist 139, 971–989 (1992).
    Google Scholar 
    61.Nijhout, H. F. Development and evolution of adaptive polyphenisms. Evolution Dev. 5, 9–18 (2003).
    Google Scholar 
    62.Mank, J. E. The transcriptional architecture of phenotypic dimorphism. Nat. Ecol. Evolution 1, 1–7 (2017).
    Google Scholar 
    63.Scheiner, S. M., Barfield, M. & Holt, R. D. The genetics of phenotypic plasticity. XVII. Response to climate change. Evolut. Appl. 13, 388–399 (2020).
    Google Scholar 
    64.Osada, N., Miyagi, R. & Takahashi, A. Cis- and trans-regulatory effects on gene expression in a natural population of Drosophila melanogaster. Genetics 206, 2139–2148 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Cooper, R. D. & Shaffer, H. B. Allele-specific expression and gene regulation help explain transgressive thermal tolerance in non-native hybrids of the endangered California tiger salamander (Ambystoma californiense). Mol. Ecol. 30, 987–1004 (2021).CAS 
    PubMed 

    Google Scholar 
    66.Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Baruzzo, G. et al. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat. Methods 14, 135–139 (2017).CAS 
    PubMed 

    Google Scholar 
    68.Schuierer, S. et al. A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples. BMC Genomics 18, 442 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    69.Broad Institute. Picard toolkit. (2019).70.Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res 47, e47–e47 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Chen, Y., Lun, A. T. L. & Smyth, G. K. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 5, 1438 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    73.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).74.Shen, L. GeneOverlap: Test and visualize gene overlaps. (2020).75.Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 

    Google Scholar 
    76.Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47, D309–D314 (2019).CAS 
    PubMed 

    Google Scholar 
    77.Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment analysis for Gene Ontology. (2016).78.Larsson, J. et al. eulerr: Area-Proportional Euler and Venn Diagrams with Ellipses. (2021).79.Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Gu, Z. & Hübschmann, D. simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results. 2020.10.27.312116 (2020) https://doi.org/10.1101/2020.10.27.312116.81.Gu, Z. simplifyEnrichment: Simplify Functional Enrichment Results. (Bioconductor version: Release (3.13), 2021). https://doi.org/10.18129/B9.bioc.simplifyEnrichment.82.de Jong, M. A., Wahlberg, N., Eijk, M., van, Brakefield, P. M. & Zwaan, B. J. Mitochondrial DNA signature for range-wide populations of Bicyclus anynana suggests a rapid expansion from recent Refugia. PLoS ONE 6, e21385 (2011).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.de Jong, M. A., Collins, S., Beldade, P., Brakefield, P. M. & Zwaan, B. J. Footprints of selection in wild populations of Bicyclus anynana along a latitudinal cline. Mol. Ecol. 22, 341–353 (2013).PubMed 

    Google Scholar 
    84.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
    Google Scholar 
    85.Joshi, N. & Fass, J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files.86.Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997 [q-bio] (2013).87.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 356 (2014).
    Google Scholar 
    88.Nowell, R. W. et al. A high-coverage draft genome of the mycalesine butterfly Bicyclus anynana. GigaScience 6, (2017).89.Xu, L. et al. OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species. Nucleic Acids Res. 47, W52–W58 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Ranwez, V., Harispe, S., Delsuc, F. & Douzery, E. J. P. MACSE: multiple alignment of coding SEquences accounting for frameshifts and stop codons. PLoS ONE 6, e22594 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Lucaci, A. G., Wisotsky, S. R., Shank, S. D., Weaver, S. & Kosakovsky Pond, S. L. Extra base hits: widespread empirical support for instantaneous multiple-nucleotide changes. PLoS One 16, e0248337 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Buerkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    93.Buerkner, P.-C. Advanced Bayesian multilevel modeling with the R Package brms. R. J. 10, 395–411 (2018).
    Google Scholar 
    94.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. (2021).95.Shen, S. et al. rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Alamancos, G. P., Pagès, A., Trincado, J. L., Bellora, N. & Eyras, E. Leveraging transcript quantification for fast computation of alternative splicing profiles. RNA 21, 1521–1531 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Wang, Q. & Rio, D. C. JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns. Proc. Natl Acad. Sci. USA115, E8181–E8190 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 

    Google Scholar 
    99.Kassambara, A. ggpubr” ‘ggplot2’ based publication-ready plots. (2020).100.Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). (2021).101.South, A. afrilearndata: Small Africa Map Datasets for Learning. (2021).102.Inkscape Project. Inkscape. (2021).103.Steward, R. A., Oostra, V. & Wheat, C. W. B_anynana_differentialSplicing Github. zenodo.org https://zenodo.org/badge/latestdoi/255903232 (2021). More

  • in

    Dispersal of Aphanoascus keratinophilus by the rook Corvus frugilegus during breeding in East Poland

    1.Dynowska, M., Meissner, W. & Pacyńska, J. Mallard duck (Anas platyrhynchos) as a potential link in the epidemiological chain mycoses originating from water reservoirs. Bull. Vet. Inst. Pulawy 57, 323–328 (2013).
    Google Scholar 
    2.Georgopoulou, I. & Tsiouris, V. The potential role of migratory birds in the transmission of zoonoses. Vet. Ital. 44, 671–677 (2008).PubMed 

    Google Scholar 
    3.Hubálek, Z. An annotated checklist of pathogenic microorganisms associated with migratory birds. J. Wildl. Dis. 40, 639–659 (2004).PubMed 

    Google Scholar 
    4.Korniłłowicz, T. K. I. Diversity of fungi in nests and pellets of Montagu’s harrier (Circus pygargus) from eastern Poland—Importance of chemical and ecological factors. Ecol. Chem. Eng. 16, 453–471 (2009).
    Google Scholar 
    5.Korniłłowicz-Kowalska, T. & Kitowski, I. Aspergillus fumigatus and other thermophilic fungi in nests of wetland birds. Mycopathologia 175, 43–56 (2013).PubMed 

    Google Scholar 
    6.Kiziewicz B. The occurrence fungi and zoosporic fungi-like organisms on feathers of birds Corvidae. in Corvids of Poland (ed. Jerzak, L.). 147–154. (Bogucki Wydawnictwo Naukowe Poznan, 2005).7.Kasprzykowski, Z. Habitat preferences of foraging Rooks Corvus frugilegus during the breeding period in the agricultural landscape of eastern Poland. Acta Ornithol. 38, 27–31 (2003).
    Google Scholar 
    8.Czarnecka, J. & Kitowski, I. Seed dispersal by the rook Corvus frugilegus l. In agricultural landscape—Mechanisms and ecological importance. Polish J. Ecol. 58, 511–523 (2010).
    Google Scholar 
    9.Czarnecka, J. et al. Seed dispersal in urban green space—Does the rook Corvus frugilegus L. contribute to urban flora homogenization?. Urban For. Urban Green. 12, 359–366 (2013).
    Google Scholar 
    10.Gromadzka, J. Food composition and food consumption of the Rook Corvus frugilegus in agrocoenoses in Poland. Acta Ornithol. 17, 11 (1980).
    Google Scholar 
    11.Green, A. J., Elmberg, J. & Lovas-Kiss, Á. Beyond scatter-hoarding and frugivory: European corvids as overlooked vectors for a broad range of plants. Front. Ecol. Evolut. 7, 133 (2019).
    Google Scholar 
    12.Jędrzejewski, S., Majewska, A., Zduniak, P. & Graczyk, T. Parasites of Polish corvids—Knowledge and potential risk for human. in Corvids of Poland (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 137–145. (Bogucki Wydawnictwo Naukowe, 2005).13.Kiziewicz, B. The occurrenceof fungy and zoosporic fungus like organisms on feathers of birds Corvids. in Corvids in Poland. (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 147–154. (Bogucki Wydawnictwo Naukowe, 2005).14.Camin, A. M., Chabasse, D. & Guiguen, C. Keratinophilic fungi associated with starlings (Sturnus vulgaris) in Brittany, France. Mycopathologia 143, 9–12 (1998).
    Google Scholar 
    15.Hubálek, Z. Keratinophilic fungi associated with free-living mammals and birds. Biol. Dermatophytes Keratinophilic Fungi 93, 1036 (2000).
    Google Scholar 
    16.Mandeel, Q., Nardoni, S. & Mancianti, F. Keratinophilic fungi on feathers of common clinically healthy birds in Bahrain. Mycoses 54, 71–77 (2011).PubMed 

    Google Scholar 
    17.Ciesielska, A., Kawa, A., Kanarek, K., Soboń, A. & Szewczyk, R. Metabolomic analysis of Trichophyton rubrum and Microsporum canis during keratin degradation. Sci. Rep. 11, 1–10 (2021).
    Google Scholar 
    18.Leibner-Ciszak, J., Dobrowolska, A., Krawczyk, B., Kaszuba, A. & Sta̧czek, P. Evaluation of a PCR melting profile method for intraspecies differentiation of Trichophyton rubrum and Trichophyton interdigitale. J. Med. Microbiol. 59, 185–192 (2010).19.Ciesielska, A., Oleksak, B. & Stączek, P. Reference genes for accurate evaluation of expression levels in Trichophyton interdigitale grown under different carbon sources, pH levels and phosphate levels. Sci. Rep. 9, 1–9 (2019).CAS 

    Google Scholar 
    20.Calvo, A., Vidal, M. & Guarro, J. Keratinophilic fungi from urban soils of Barcelona, Spain. Mycopathologia 85, 145–147 (1984).
    Google Scholar 
    21.R.S/, C. Taxonomy of the Onygenales: Arthrodermataceae, Gymnoasceae, Myxotrichaceae and Onygenaceae. Mycotaxon 24, 1–216 (1985).22.Korniłłowicz-Kowalska, T. Studies on the decomposition of keratin wastes by saprotrophic microfungi. P. I. Criteria for evaluating keratinolytic activity. Acta Mycol. 175, 43–56 (1997).
    Google Scholar 
    23.van Oorschot, C. A. N. A revision of Chrysosporium and allied genera. Stud. Mycol. 20, 1–89 (1980).
    Google Scholar 
    24.Domsch, K. H. & Gams, W. A. T. H. Compedium of Soil Fungi (Academic, 1980).
    Google Scholar 
    25.Gan, G. G. et al. Non-sporulating Chrysosporium: An opportunistic fungal infection in a neutropenic patient. Med. J. Malaysia 57, 118–122 (2002).CAS 
    PubMed 

    Google Scholar 
    26.de Hoog, G. S., Guarro, J. & Gene, J. Atlas of clinical fungi. Int. Microbiol 2, 51–52 (2001).
    Google Scholar 
    27.Manzano-Gayosso, P. et al. Onychomycosis incidence in type 2 diabetes mellitus patients. Mycopathologia 166, 41–45 (2008).PubMed 

    Google Scholar 
    28.Palma, M. A. G., Espín, L. A. & Pérez, A. F. Invasine sinusal mycosis due to Chrysosporium tropicum. Acta Otorrinolaringol. Esp. 58, 164–166 (2007).
    Google Scholar 
    29.Stillwell, W. T. & Rubin, B. O. Chrysosporium, a new causative agent in osteomycelitis. Clin. Orthopaed. Relat. Res. 184, 190–192 (1984).
    Google Scholar 
    30.Gueho, E. V. J. G. R. A new human case of Anixiopsis stercomia mycosis: Discussion of its taxonomy and pathogenicity. Mycoses 28, 430–436 (1985).CAS 

    Google Scholar 
    31.Nieuwenhuis, B. P. S. & James, T. Y. The frequency of sex in fungi. Philos. Trans. R. Soc. B Biol. Sci. 371, 0540 (2016).
    Google Scholar 
    32.Neubauer, G. & Sikora, A. C. T. Monitoring populacji ptaków Polski w latach 2008–2009. Biuletyn Monitoringu Przyrody 8, 1–40 (2011).
    Google Scholar 
    33.Jackson, C. J., Barton, R. C. & Evans, E. G. V. Species identification and strain differentiation of dermatophyte fungi by analysis of ribosomal-DNA intergenic spacer regions. J. Clin. Microbiol. 37, 931–936 (1999).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Mochizuki, T. et al. Restriction fragment length polymorphism analysis of ribosomal DNA intergenic regions is useful for differentiating strains of Trichophyton mentagrophytes. J. Clin. Microbiol. 41, 4583–4588 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Garg, A. P., Gandotra, S., Mukerji, K. G. & Pugh, G. J. F. Ecology of keratinophilic fungi. Proc. Plant Sci. 94, 149–163 (1985).
    Google Scholar 
    36.Abulreesh, H. H., Goulder, R. & Scott, G. W. Wild birds and human pathogens in the context of ringing and migration. Ringing Migr. 23, 193–200 (2007).
    Google Scholar 
    37.Prinzinger, R., Preßmar, A. & Schleucher, E. Body temperature in birds. Comp. Biochem. Physiol. Part A Physiol. 99, 499–506 (1991).
    Google Scholar 
    38.Summerbell, R. C. Form and function in the evolution of dermatophytes. Rev. Iberoam. Micol. 44, 30–43 (2000).
    Google Scholar 
    39.Warwick, A., Ferrieri, P., Burke, B. & Blazar, B. R. Presumptive invasive Chrysosporium infection in a bone marrow transplant recipient. Bone Marrow Transplant 8, 319–322 (1991).CAS 
    PubMed 

    Google Scholar 
    40.Kitowski, I., Ciesielska, A., Korniłłowicz-Kowalska, T., Bohacz, J., & Świetlicki, M. Estimation of Chrysosporium keratinophilum Dispersal by the Rook Corvus frugilegus in Chełm (East Poland) in Urban Fauna-Animal, Man, and the City—Interactions and Relationships. (Indykiewicz, P. & Böhner, J. eds). 263–269. (Art Studio, 2014)41.Gopal, K. A., Kalaivani, V. & Anandan, H. Pulmonary infection by Chrysosporium species in a preexisting tuberculous cavity. Int. J. Appl. Basic Med. Res. 10, 62 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    42.Krawczyk, B., Samet, A., Leibner, J., Śledzińska, A. & Kur, J. Evaluation of a PCR melting profile technique for bacterial strain differentiation. J. Clin. Microbiol. 44, 2327–2332 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Ciesielska, A. et al. Application of microsatellite-primed PCR (MSP-PCR) and PCR melting profile (PCR-MP) method for intraspecies differentiation of dermatophytes. Pol. J. Microbiol. 63, 283–290 (2014).PubMed 

    Google Scholar 
    44.Orłowski, G. & Czapulak, A. Different extinction risks of the breeding colonies of rooks Corvus frugilegus in rural and urban areas of SW Poland. Acta Ornithologica 42, 145–155 (2007).
    Google Scholar 
    45.Bohacz, J. & Korniłłowicz-Kowalska, T. Species diversity of keratinophilic fungi in various soil types. Cent. Eur. J. Biol. 7, 259–266 (2012).
    Google Scholar 
    46.Papini, R., Mancianti, F., Grassotti, G. & Cardini, G. Survey of keratinophilic fungi isolated from city park soils of Pisa, Italy. Mycopathologia 143, 17–23 (1998).CAS 
    PubMed 

    Google Scholar 
    47.Singh, I. K. R. Dermatophytes and related keratinophilic fungi in soil of parks and agricultural fields of Uttar Pradesh, India. Indian J. Dermatol. 55, 306–308 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    48.Gungnani, H. C., Sharma, S. & Gupta, B. Keratinophilic fungi recovered from feathers of different species of birds in St Kitts and Nevis. West Indian Med. J. 61, 912–915 (2012).CAS 
    PubMed 

    Google Scholar 
    49.Jadczyk P, J. Z. Wintering of rooks Corvus frugilegus in Poland. in Corvids of Poland (ed. Jerzak, L.). 541–556. (Bogucki Wydawnictwo Naukowe Poznan, 2005).50.Wilk, T., Chodkiewicz, T., Sikora, A., Chylarecki, P. & Kuczyński, L. Red List of Polish Birds. (OTOP, 2020).51.Oke, T.R. The heat island of the urban boundary layer: Characteristics, causes and effects. in eWind Climate in Cities. NATO ASI Series E (ed. JE, C.). 81–107. (Kluwer Academy, 1995).52.Vidal, P., de Vinuesa, M., Los, A., Sánchez-Puelles, J. M. & Guarro, J. Phylogeny of the anamorphic genus Chrysosporium and related taxa based on rDNA internal transcribed spacer sequences. Rev. Iberoam. Micol. 17, 22–29 (2000).
    Google Scholar 
    53.Korniłłowicz, T. Studies on mycoflora colonizing raw keratin wastes in arable soil. Mycologica 27, 231–245 (1991).
    Google Scholar 
    54.Orłowski, G., Kasprzykowski, Z., Zawada, Z. & Kopij, G. Stomach content and grit ingestion by rook Corvus frugilegus nestlings. Ornis Fennica 86, 117–122 (2009).
    Google Scholar 
    55.Luniak, M. Consumption and digestion of food in the rook, Corvus frugilegus, in the condition of an aviary. Acta Ornithol. 16, 213–234 (1977).
    Google Scholar 
    56.Liu, D., Coloe, S., Baird, R. & Pedersen, J. Rapid mini-preparation of fungal DNA for PCR [5]. J. Clin. Microbiol. 38, 471 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Hunter, P. R. & Gaston, M. A. Numerical index of the discriminatory ability of typing systems: An application of Simpson’s index of diversity. J. Clin. Microbiol. 26, 2465–2466 (1988).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Greenwell, J. R. Introduction to biostatistics, 2nd edn. By R. R. Sokal and F. J. Rohlf. pp. 363. F. H. Freeman and Co., 1987. £44.99 hardback. ISBN 0 7167 18057. Exp. Physiol. 80, 681 (1995) More

  • in

    Surveillance and genetic data support the introduction and establishment of Aedes albopictus in Iowa, USA

    1.Reiter, P. & Sprenger, D. The used tire trade: a mechanism for the worldwide dispersal of container breeding mosquitoes. J. Am. Mosq. Control Assoc. 3, 494–501 (1987).CAS 
    PubMed 

    Google Scholar 
    2.Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    3.Kraemer, M. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Bonizzoni, M., Gasperi, G., Chen, X. & James, A. A. The invasive mosquito species Aedes albopictus: Current knowledge and future perspectives. Trends Parasitol. 29, 460–468 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    5.Sprenger, D. & Wuithiranyagool, T. The discovery and distribution of Aedes albopictus in Harris County, Texas. J. Am. Mosq. Control Assoc. 2, 217–219 (1986).CAS 
    PubMed 

    Google Scholar 
    6.Yee, D. A. Thirty years of Aedes albopictus (Diptera: Culicidae) in America: An introduction to current perspectives and future challenges. J. Med. Entomol. 53, 989–991 (2016).PubMed 

    Google Scholar 
    7.Hahn, M. B. et al. Reported Distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995–2016 (Diptera: Culicidae). J. Med. Entomol. 53, 1169–1175 (2016).PubMed 

    Google Scholar 
    8.Hahn, M. B. et al. Reported distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995–2016. J. Med. Entomol. 54, 1420–1424 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    9.Egizi, A., Healy, S. P. & Fonseca, D. M. Rapid blood meal scoring in anthropophilic Aedes albopictus and application of PCR blocking to avoid pseudogenes. Infect. Genet. Evol. 16, 122–128 (2013).CAS 
    PubMed 

    Google Scholar 
    10.Paupy, C., Delatte, H., Bagny, L., Corbel, V. & Fontenille, D. Aedes albopictus, an arbovirus vector: From the darkness to the light. Microbes Infect. 11, 1177–1185 (2009).CAS 
    PubMed 

    Google Scholar 
    11.Grard, G. et al. Zika virus in Gabon (Central Africa)—2007: A new threat from Aedes albopictus?. PLoS Negl. Trop. Dis. 8, e2681 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    12.McKenzie, B. A., Wilson, A. E. & Zohdy, S. Aedes albopictus is a competent vector of Zika virus: A meta-analysis. PLoS ONE 14, e0216794 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Claborn, D. M., Poiry, M., Famutimi, O. D., Duitsman, D. & Thompson, K. R. A survey of mosquitoes in southern and western missouri. J. Am. Mosq. Control Assoc. 34, 131–133 (2018).CAS 
    PubMed 

    Google Scholar 
    14.Janousek, T. E., Plagge, J. & Kramer, W. L. Record of Aedes albopictus in Nebraska with notes on its biology. J. Am. Mosq. Cont. Control Assoc. 17, 265–267 (2001).CAS 

    Google Scholar 
    15.Richards, T. et al. First detection of Aedes albopictus (Diptera: Culicidae) and expansion of Aedes japonicus japonicus in Wisconsin, United States. J. Med. Entomol. 56, 291–296 (2019).PubMed 

    Google Scholar 
    16.Stone, C. M. et al. Spatial, temporal, and genetic invasion dynamics of Aedes albopictus (Diptera: Culicidae) in Illinois. J. Med. Entomol. 57, 1488–1500 (2020).CAS 
    PubMed 

    Google Scholar 
    17.Dunphy, B. M., Rowley, W. A. & Bartholomay, L. C. A taxonomic checklist of the mosquitoes of Iowa. J. Am. Mosq. Control Assoc. 30, 119–121 (2014).PubMed 

    Google Scholar 
    18.Johnson, T. L. et al. Modeling the environmental suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the contiguous United States. J. Med. Entomol. 54, 1605–1614 (2017).PubMed 

    Google Scholar 
    19.Kovach, K. B. & Smith, R. C. Surveillance of mosquitoes (Diptera: Culicidae) in southern Iowa, 2016. J. Med. Entomol. 55, 1341–1345 (2018).PubMed 

    Google Scholar 
    20.Braks, M. A. H., Honório, N. A., Lourenço-De-Oliveira, R., Juliano, S. A. & Lounibos, L. P. Convergent habitat segregation of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in southeastern Brazil and Florida. J. Med. Entomol. 40, 785–794 (2003).PubMed 

    Google Scholar 
    21.Delatte, H. et al. Evidence of habitat structuring Aedes albopictus populations in Réunion Island. PLoS Negl. Trop. Dis. 7, e2111 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    22.Zhong, D. et al. Genetic analysis of invasive Aedes albopictus populations in Los Angeles County, California and its potential public health impact. PLoS ONE 8, e68586 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Lee, E. J. et al. Geographical genetic variation and sources of Korean Aedes albopictus (Diptera: Culicidae) populations. J. Med. Entomol. 57, 1057–1068 (2020).CAS 
    PubMed 

    Google Scholar 
    24.Nawrocki, S. J. & Hawley, W. A. Estimation of the northern limits of distribution of Aedes albopictus in North America. J. Am. Mosq. Control Assoc. 3, 314–317 (1987).CAS 
    PubMed 

    Google Scholar 
    25.Moore, C. G. Aedes albopictus in the United States: Current status and prospects for further spread. J. Am. Mosq. Control Assoc. 15, 221–227 (1999).CAS 
    PubMed 

    Google Scholar 
    26.Armstrong, P. M., Andreadis, T. G., Shepard, J. J. & Thomas, M. C. Northern range expansion of the Asian tiger mosquito (Aedes albopictus): Analysis of mosquito data from Connecticut, USA. PLoS Negl. Trop. Dis. 11, e0005623 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    27.Rochlin, I., Ninivaggi, D. V., Hutchinson, M. L. & Farajollahi, A. Climate change and range expansion of the Asian tiger mosquito (Aedes albopictus) in Northeastern USA: Implications for public health practitioners. PLoS ONE 8, e60874 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Hanson, S. M. & Craig, G. B. Aedes albopictus (Diptera: Culicidae) eggs: Field survivorship during northern Indiana winters. J. Med. Entomol. 32, 599–604 (1995).CAS 
    PubMed 

    Google Scholar 
    29.Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    30.Yang, J. & Bou-Zeid, E. Should cities embrace their heat islands as shields from extreme cold?. J. Appl. Meteorol. Climatol. 57, 1309–1320 (2018).ADS 

    Google Scholar 
    31.Macintyre, H. L., Heaviside, C., Cai, X. & Phalkey, R. Comparing temperature-related mortality impacts of cool roofs in winter and summer in a highly urbanized European region for present and future climate. Environ. Int. 154, 106606 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    32.Ward, T. B. Influence of an Urban Heat Island on Mosquito Development and Survey of Biting Midge Species Associated with White-Tailed Deer Farms (Oklahoma State University, 2011).
    Google Scholar 
    33.Dunphy, B. M. et al. Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus. Sci. Rep. 9, 6637 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Paupy, C., Girod, R., Salvan, M., Rodhain, F. & Failloux, A. B. Population structure of Aedes albopictus from La Réunion Island (Indian Ocean) with respect to susceptibility to a dengue virus. Heredity 87, 273–283 (2001).CAS 
    PubMed 

    Google Scholar 
    35.Vazeille, M. et al. Population genetic structure and competence as a vector for dengue type 2 virus of Aedes aegypti and Aedes albopictus from Madagascar. Am. J. Trop. Med. Hyg. 65, 491–497 (2001).CAS 
    PubMed 

    Google Scholar 
    36.Chouin-Carneiro, T. et al. Differential susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika virus. PLoS Negl. Trop. Dis. 10, e0004543 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    37.Faraji, A. & Unlu, I. The eye of the tiger, the thrill of the fight: Effective larval and adult control measures against the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae), North America. J. Med. Entomol. 53, 1029–1047 (2016).PubMed 

    Google Scholar 
    38.Lambrechts, L., Scott, T. W. & Gubler, D. J. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Negl. Trop. Dis. 4, e646 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    39.Vega-Rua, A., Zouache, K., Girod, R., Failloux, A.-B. & Lourenco-de-Oliveira, R. High level of vector competence of Aedes aegypti and Aedes albopictus from ten American countries as a crucial factor in the spread of chikungunya virus. J. Virol. 88, 6294–6306 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Vega-Rúa, A. et al. Chikungunya virus transmission potential by local Aedes mosquitoes in the Americas and Europe. PLoS Negl. Trop. Dis. 9, e0003780 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    41.Fikrig, K. & Harrington, L. C. Understanding and interpreting mosquito blood feeding studies: The case of Aedes albopictus. Trends Parasitol. 37, 959–975 (2021).CAS 
    PubMed 

    Google Scholar 
    42.Gerhardt, R. R. et al. First isolation of La Crosse virus from naturally infected Aedes albopictus. Emerg. Infect. Dis. 7, 807–811 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Sardelis, M. R., Turell, M. J., O’Guinn, M. L., Andre, R. G. & Roberts, D. R. Vector competence of three North American strains of Aedes albopictus for West Nile virus. J. Am. Mosq. Control Assoc. 18, 284–289 (2002).PubMed 

    Google Scholar 
    44.Eiras, A. E., Buhagiar, T. S. & Ritchie, S. A. Development of the gravid Aedes trap for the capture of adult female container-exploiting mosquitoes (Diptera: Culicidae). J. Med. Entomol. 51, 200–209 (2014).PubMed 

    Google Scholar 
    45.Maciel-de-Freitas, R., Eiras, Á. E. & Lourenço-de-Oliveira, R. Field evaluation of effectiveness of the BG-Sentinel, a new trap for capturing adult Aedes aegypti (Diptera: Culicidae). Mem. Inst. Oswaldo Cruz 101, 321–325 (2006).PubMed 

    Google Scholar 
    46.Farajollahi, A. et al. Field efficacy of BG-Sentinel and industry-standard traps for Aedes albopictus (Diptera: Culicidae) and West Nile virus surveillance. J. Med. Entomol. 46, 919–925 (2009).PubMed 

    Google Scholar 
    47.Meeraus, W. H., Armistead, J. S. & Arias, J. R. Field comparison of novel and gold standard traps for collecting Aedes albopictus in Northern Virginia. J. Am. Mosq. Control Assoc. 24, 244–248 (2008).PubMed 

    Google Scholar 
    48.Johnson, B. J. et al. Field comparisons of the Gravid Aedes Trap (GAT) and BG-Sentinel Trap for Monitoring Aedes albopictus (Diptera: Culicidae) populations and notes on indoor GAT collections in Vietnam. J. Med. Entomol. 54, 340–348 (2018).
    Google Scholar 
    49.Darsie, R. & Ward, R. Identification and Geographical Distribution of the Mosquitoes of North America (North of Mexico. University Press of Florida, 2005).
    Google Scholar 
    50.Multi-Resolution Land Characteristics Consortium. NLCD 2016 Land Cover (CONUS).51.Bonnet, D. D. & Worcester, D. J. The dispersal of Aedes albopictus in the territory of Hawaii. Am. J. Trop. Med. Hyg. 26, 465–476 (1946).CAS 
    PubMed 

    Google Scholar 
    52.Niebylski, M. L. & Craig, G. B. Dispersal and survival of Aedes albopictus at a scrap tire yard in Missouri. J. Am. Mosq. Control Assoc. 10, 339–343 (1994).CAS 
    PubMed 

    Google Scholar 
    53.Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): A metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).
    Google Scholar 
    54.Post, R. J., Flook, P. K. & Millest, A. L. Methods for the preservation of insects for DNA studies. Biochem. Syst. Ecol. 21, 85–92 (1993).CAS 

    Google Scholar 
    55.Field, E. N., Gehrke, E. J., Ruden, R. M., Adelman, J. S. & Smith, R. C. An improved multiplex Polymerase Chain Reaction (PCR) assay for the identification of mosquito (Diptera: Culicidae) blood meals. J. Med. Entomol. 57, 557–562 (2020).CAS 
    PubMed 

    Google Scholar 
    56.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
    PubMed 

    Google Scholar 
    57.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
    Google Scholar 
    58.Ogden, R., Shuttleworth, C., McEwing, R. & Cesarini, S. Median-joining networks for inferring intraspecific phylogenies. Conserv. Genet. 6, 37–48 (2005).
    Google Scholar  More

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    Substantial loss of isoprene in the surface ocean due to chemical and biological consumption

    Evidence for biological and chemical isoprene consumption in coastal seawaterThe time course of isoprene concentration in coastal seawater samples incubated in closed glass bottles at the in situ temperature and in the dark demonstrated sustained loss for at least 45 h (Fig. 1a). Enclosure without headspace prevented isoprene loss by ventilation, and darkness was assumed to arrest all or most of the biological production25 and any photochemical production15 or degradation. Thus, the measured loss was considered the result of microbial degradation and chemical oxidation. In most cases an exponential function fitted better the decay than a linear function (Supplementary Table 1), indicating first-order (concentration-dependent) kinetics for isoprene loss.Fig. 1: Isoprene loss in dark incubations of coastal seawater.a Time course of isoprene concentration in 2 L dark incubations of non-filtered seawater samples from the back-reef lagoon of Mo’orea in April (blue) and the coastal Mediterranean in March (red) and May (green). Filled and open symbols correspond to duplicate incubations. Exponential fits to the data are shown by lines. See Supplementary Table 1 for fit equations and metrics, water temperatures and chlorophyll a concentrations. b Time course of isoprene concentration in series of 30 mL dark incubations of coastal Mediterranean seawater. Dark blue: non-filtered; red: filtered through 0.2 µm; green: filtered + 10 µmol L−1 H2O2; purple: filtered + 0.0025 units mL−1 bromoperoxidase (BrPO); light blue: filtered + H2O2 + BrPO. Exponential fit results in Supplementary Table 2.Full size imageIncubation of microorganism-devoid (filtered through 0.2 µm) coastal seawater sampled next to seaweeds showed an isoprene loss (0.12 d−1) that was half the loss in non-filtered water (0.20 d−1; Fig. 1b and Supplementary Table 2), implying that chemical oxidation accounted for half the total loss. Oxidation by OH·, the fastest amongst isoprene reactions with oxidative transients for which reaction rate data exist19, could account for the observed chemical loss. However, the possibility of oxidation by hitherto overlooked, pervasive oxidants like H2O2 deserved consideration. The addition of unrealistically high concentrations of either H2O2 or the enzyme bromoperoxidase (BrPO), substantially speeded up the chemical loss (0.91 d−1 with 10 µmol H2O2 L−1, 0.31 d−1 with 0.0025 units BrPO mL−1; Fig. 1b and Supplementary Table 2). Isoprene could have reacted with H2O2 in seawater as it does in acidic aerosols26. Besides, should dissolved27 BrPOs from seaweeds or outer-membrane-bound28 BrPOs from phytoplankton occur, they would have reacted with added H2O2 to produce hypobromous acid (HOBr), a strong oxidant29 that would further remove isoprene. Indeed, the addition of BrPO consumed isoprene because it produced HOBr by reaction with the naturally occurring H2O2. Confirming this interpretation, large HOBr production by simultaneous addition of BrPO and H2O2 caused complete isoprene removal in less than 4 h (Fig. 1b). Therefore, the results shown in Fig. 1b indicate that isoprene is reactive to pervasive H2O2 either directly or through the formation of enzymatically derived HOBr. All in all, first-order total isoprene loss (Fig. 1a) is expected to depend on photochemically-produced oxidants30 like H2O2, OH· and 1O2 as well as on microbiota through (a) microbial uptake and catabolism11 and (b) reaction with biologically produced oxidants26,31,32 like HOBr, H2O2 or superoxide.Variability of isoprene loss rate constants in the open oceanTen of the eleven offshore experimental sites were located in the open ocean, and one was located on the Southwestern Atlantic Shelf. Altogether they covered wide ranges of latitude (40°N–61°S), sea surface temperature (−0.8–28.6 °C), daily-averaged wind speed (3–12 m s−1), fluorometric chlorophyll-a (chla) concentration (0.1–5.8 mg m−3), and isoprene concentration (4–104 nmol m−3) (Fig. 2, Table 1 and Supplementary Table 3). Unfiltered seawater samples from the surface ocean were incubated in glass bottles for 24 h, at the in situ temperature and in the dark, and first-order loss rate constants were determined from initial and final isoprene concentrations (see Methods). Note that loss was determined under the assumption that isoprene production was arrested in the dark25. There is published evidence that residual isoprene production may occur in the dark33, but in our incubations, it was insufficient to counteract loss. Thus, isoprene losses caused by processes other than ventilation may have been underestimated.Fig. 2: Geographical distribution of the offshore experiments.Location of the sampling and incubation sites are shown by circles, coloured for isoprene concentration.Full size imageTable 1 Measured biological variables and isoprene process rate constants.Full size tableLoss rate constants (kloss = kbio + kchem) varied over an order of magnitude, ranging 0.03–0.64 d−1 with a median of 0.08 d−1 (Table 1). They did not show any significant relationship to sea surface temperature (SST) (Supplementary Fig. 1) but showed proportionality to the chla concentration (Fig. 3a) that was best described by the following linear regression equation:$${k}_{{{{{{rm{loss}}}}}}}=0.10; (pm 0.01),{{{{{rm{x}}}}}}, [{{{{{rm{chl}}}}}}a]+0.05; (pm 0.01)$$
    (1)
    Fig. 3: Isoprene processes and their main drivers.a Rate constant of isoprene loss in dark incubations (kloss, considered to be microbial and chemical consumption) vs. chlorophyll-a concentration. The linear regression equation is kloss = 0.10 × [chla] + 0.05 (R2 = 0.96, p = 10−7, n = 11). The standard error of the slope is 0.01 L mg−1 d−1, and the standard error of the intercept is 0.01 d−1. Error bars represent the experimentally determined standard error of kloss. The colour scale of the circles indicates bacterial abundances. b Specific (chla-normalised) rate of isoprene production vs seawater temperature (SST) across the sample series. The dashed line is the general smoothed trend. The blue line is the exponential adjustment at SST , 1000)$$
    (2)
    Substitution in Eq. (1) results in:$${k}_{{{{{{rm{loss}}}}}}}=0.14,{{{{{rm{x}}}}}}, {[{{{{{rm{chl}}}}}}{a}_{{{{{{rm{sat}}}}}}}]}^{1.28}+0.05$$
    (3)
    which is our recommended equation for kloss prediction from satellite chla. Note that only the variable term (kbio) changes from Eq. (1), while the intercept (kchem) is maintained at 0.05 d−1.Comparison of isoprene sinks and total turnover timeThe change of isoprene concentration ([iso]) in the surface mixed layer over time can be described as the budget of sources and sinks:$$varDelta [{{{{{rm{iso}}}}}}]/varDelta {{{{{rm{t}}}}}}=[{{{{{rm{iso}}}}}}]cdot ({k}_{{{{{{rm{prod}}}}}}} – {k}_{{{{{{rm{loss}}}}}}} – {k}_{{{{{{rm{vent}}}}}}} – {k}_{{{{{{rm{mix}}}}}}})$$
    (4)
    where kprod, kvent and kmix are the rate constants of isoprene production, ventilation to the atmosphere and vertical downward mixing by turbulent diffusion, respectively.We calculated kvent from our sampling sites over a period of 24 h (Table 1). Ventilation has been considered the main isoprene sink from the upper mixed layer of the ocean18. In our sampling sites, kloss was 0.4 to 10 times the kvent (median factor: 1.2). That is, loss through microbial + chemical consumption was of the same order as ventilation, sometimes considerably faster. Vertical mixing, kmix, was estimated to be one order of magnitude lower than the other process rates (Table 1), and in all cases but one it was calculated or assumed not to be a loss term but an import term into the mixed layer, because vertical profiles generally show maximum isoprene concentrations below the mixed layer and turbulent diffusion causes upward transport14,17. Altogether, the microbial, chemical, ventilation, and, where relevant, mixing losses resulted in total turnover times (1/(kloss + kvent + kmix)) of isoprene between 1.4 and 16 days, median 5 days (Table 1).Isoprene productionAssuming steady-state for isoprene concentrations over 24 h (Supplementary Fig. 2), i.e. Δ[iso]/Δt = 0 in Eq. (4), the sum of the daily rate constants of all sinks (kloss + kvent) equals the rate constant of isoprene production (kprod), with kmix adding to either side depending on whether it is an import to or an export from the mixed layer (Table 1). Note that kprod was the highest coinciding with higher [chla]. This is consistent with a recent study44 where measurement of the net biological isoprene production (i.e. production — consumption rates) across seasons in the open ocean was attempted; net production rates increased in May, coinciding with a large increase in [chla] and phytoplankton cell abundance.The product of kprod by the isoprene concentration gives the daily isoprene production rate, which can be normalised by dividing it by the chla concentration. In our study, this specific isoprene production rate varied between 1 and 38 nmol (mg chla)−1 d−1 (Table 1), median 8 nmol (mg chla)−1 d−1. These values are within the broad range reported across phytoplankton taxa from laboratory studies with monocultures41,45 (0.3–32, median 3 nmol (mg chla)−1 d−1, n = 124). Five of the eleven sites gave values >13 nmol (mg chla)−1 d−1, i.e. in the higher end of the laboratory data range. This is not unexpected, since measurements in monoculture experiments are typically conducted before reaching nutrient limitation, below light saturation and in the absence of UV radiation, to mention three stressors commonly occurring in the surface open ocean. If isoprene biosynthesis and release is enhanced by any of these stressors, as is the case in vascular plants7,10, then monoculture-derived results will easily render underestimates of isoprene production in the open ocean. Production by heterotrophic bacteria46 could have also contributed to increase apparent specific isoprene production rates, but the occurrence and importance of this process in the marine environment is unknown.When plotted against the SST, which was also the temperature of the incubations, specific isoprene production rates increased exponentially between −0.8 and 23 °C and dropped drastically at higher SST (Fig. 3b). Several studies with phytoplankton monocultures have reported positive dependence of specific isoprene production rates on temperature45,47,48,49,50. One of these studies45 described that the increase with temperature reaches an optimum for production that varies among phytoplankton strains and with light intensity, but falls around 23–26 °C. The most detailed study47 was conducted with a Prochlorococcus strain; remarkably, the shape of the specific production rate vs. temperature curve for this cyanobacterium strain was almost identical to that of Fig. 3b, with an exponential increase until 23 °C and a drop thereafter. This is the canonical curve type of enzymatic activities, but the thermal behaviour of the enzymes for isoprene synthesis in marine unicellular algae has not yet been characterised12.Revising the magnitude and players of the marine isoprene cycleOur results allow redrawing the isoprene cycle in the surface mixed layer of the ocean. Figure 4 sketches the magnitude of the rate constants for production and sinks presented in Table 1, averaged according to a chla concentration threshold: the blue and green arrows correspond to the experiments in waters with [chla] lower and higher than 0.4 mg m−3, respectively. Isoprene production in productive (chla-richer) waters is faster than in oligotrophic (chla-poorer) waters. Vertical mixing is assumed to majorly constitute an input into the mixed layer, yet very small. Photochemical production and emission from surfactants15 in the surface microlayer of productive waters is depicted as uncertain. Among sinks, the microbiota-dependent consumption is much faster in productive waters; actually, the statistical uncertainty of Eq. (1) and the uneven distribution of incubation results along the [chla] axis hamper resolving kbio in phytoplankton-poor waters ( More