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    Introduction of Varroa destructor has not altered honey bee queen mating success in the Hawaiian archipelago

    1.
    Roddy, K. M. & Arita-Tsutsumi, L. A history of honey bees in the Hawaiian islands. J. Hawaiian Pac. Agric. 8, 59–70 (1997).
    Google Scholar 
    2.
    Danka, R. G., Hellmich, R. L., Rinderer, T. E. & Collins, A. M. Diet-selection ecology of tropically and temperately adapted honey-bees. Anim. Behav. 35, 1858–1863 (1987).
    Article  Google Scholar 

    3.
    Roberts, J. M. K., Anderson, D. L. & Durr, P. A. Absence of deformed wing virus and Varroa destructor in Australia provides unique perspectives on honeybee viral landscapes and colony losses. Sci. Rep. https://doi.org/10.1038/s41598-017-07290-w (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    4.
    de Guzman, L. I., Rinderer, T. E. & Stelzer, J. A. DNA evidence of the origin of Varroa jacobsoni Oudemans in the Americas. Biochem. Genet. 35, 327–335. https://doi.org/10.1023/a:1021821821728 (1997).
    PubMed  Article  Google Scholar 

    5.
    Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. USA. 116, 1792–1801. https://doi.org/10.1073/pnas.1818371116 (2019).
    CAS  PubMed  Article  Google Scholar 

    6.
    Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).
    PubMed  Article  Google Scholar 

    7.
    Sammataro, D., Gerson, U. & Needham, G. Parasitic mites of honey bees: Life history, implications, and impact. Annu. Rev. Entomol. 45, 519–548 (2000).
    CAS  PubMed  Article  Google Scholar 

    8.
    Amdam, G. V., Hartfelder, K., Norberg, K., Hagen, A. & Omholt, S. W. Altered physiology in worker honey bees (Hymenoptera: Apidae) infested with the mite Varroa destructor (Acari: Varroidae): A factor in colony loss during overwintering?. J. Econ. Entomol. 97, 741–747 (2004).
    PubMed  Article  Google Scholar 

    9.
    Dejong, D., Dejong, P. H. & Goncalves, L. S. Weight-loss and other damage to developing worker honeybees from infestation with Varroa jacobsoni. J. Apic. Res. 21, 165–167. https://doi.org/10.1080/00218839.1982.11100535 (1982).
    Article  Google Scholar 

    10.
    Ramadan, M. M., Reimer, N. J., Oishi, D. E., Young, C. L. & Heu, R. A. Varroa Mite Varroa destructor Anderson and Trueman (Acari: Varroidae) (Springer, New York, 2019).
    Google Scholar 

    11.
    Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306. https://doi.org/10.1126/science.1220941 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    12.
    Seeley, T. D. Honey bees of the Arnot Forest: A population of feral colonies persisting with Varroa destructor in the northeastern United States. Apidologie 38, 19–29 (2007).
    Article  Google Scholar 

    13.
    Brettell, L. E. & Martin, S. J. Oldest Varroa tolerant honey bee population provides insight into the origins of the global decline of honey bees. Sci. Rep. https://doi.org/10.1038/srep45953 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Nielsen, D. I. Genetic structure of feral honey bee (Apis mellifera L.) populations in California Ph.D. thesis, University of California, Davis (2000).

    15.
    Doebler, S. A. The rise and fall of the honeybee: Mite infestations challenge the bee and the beekeeping industry. Bioscience 50, 738–742. https://doi.org/10.1641/0006-3568(2000)050[0738:Trafot]2.0.Co;2 (2000).
    Article  Google Scholar 

    16.
    Fuchs, S. Preference for drone brood cells by Varroa jacobsoni oud in colonies of Apis-Mellifera-Carnica. Apidologie 21, 193–199 (1990).
    Article  Google Scholar 

    17.
    Boot, W. J., Calis, J. N. M. & Beetsma, J. Differential periods of varroa mite invasion into worker and drone cells of honey-bees. Exp. Appl. Acarol. 16, 295–301 (1992).
    Article  Google Scholar 

    18.
    Estoup, A., Solignac, M. & Cornuet, J. Precise assessment of the number of patrilines and of genetic relatedness in honeybee colonies. Proc. R. Soc. Lond. B 258, 1–7 (1994).
    ADS  CAS  Article  Google Scholar 

    19.
    Tarpy, D. R., Nielsen, R. & Nielsen, D. I. A scientific note on the revised estimates of effective paternity frequency in Apis. Insectes Soc. 51, 203–204 (2004).
    Article  Google Scholar 

    20.
    Akyol, E., Yeninar, H. & Kaftanoglu, O. Live weight of queen honey bees (Apis mellifera L.) predicts reproductive characteristics. J. Kansas Entomol. Soc. 81, 92–100. https://doi.org/10.2317/jkes-705.13.1 (2008).
    Article  Google Scholar 

    21.
    Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects. https://doi.org/10.3390/insects8020048 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    22.
    Tarpy, D. R., Keller, J. J., Caren, J. R. & Delaney, D. A. Experimentally induced variation in the physical reproductive potential and mating success in honey bee queens. Insectes Soc. 58, 569–574. https://doi.org/10.1007/s00040-011-0180-z (2011).
    Article  Google Scholar 

    23.
    Hatjina, F. et al. A review of methods used in some European countries for assessing the quality of honey bee queens through their physical characters and the performance of their colonies. J. Apic. Res. 53, 337–363. https://doi.org/10.3896/ibra.1.53.3.02 (2014).
    Article  Google Scholar 

    24.
    De Souza, D. A. et al. Morphometric identification of queens, workers and intermediates in in vitro reared honey bees (Apis mellifera). PLoS ONE. https://doi.org/10.1371/journal.pone.0123663 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    25.
    Woyke, J. Correlations between the age at which honeybee brood was grafted, characteristics of the resultant queens, and results of insemination. J. Apic. Res. 10, 45–55 (1971).
    Article  Google Scholar 

    26.
    Dedej, S., Hartfelder, K., Aumeier, P., Rosenkranz, P. & Engels, W. Caste determination is a sequential process: Effect of larval age at grafting on ovariole number, hind leg size and cephalic volatiles in the honey bee (Apis mellifera carnica). J. Apic. Res. 37, 183–190 (1998).
    Article  Google Scholar 

    27.
    Al-Lawati, H., Kamp, G. & Bienefeld, K. Characteristics of the spermathecal contents of old and young honeybee queens. J. Insect Physiol. 55, 116–121 (2009).
    CAS  PubMed  Article  Google Scholar 

    28.
    Tarpy, D. R., Keller, J. J., Caren, J. R. & Delaney, D. A. Assessing the mating “health” of commercial honey bee queens. J. Econ. Entomol. 105, 20–25 (2012).
    PubMed  Article  Google Scholar 

    29.
    Pettis, J. S., Rice, N., Joselow, K., vanEngelsdorp, D. & Chaimanee, V. Colony failure linked to low sperm viability in honey bee (Apis mellifera) queens and an exploration of potential causative factors. PLoS ONE. https://doi.org/10.1371/journal.pone.0147220 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    30.
    Woyke, J. Natural and artificial insemination of queen honeybees. Bee World 43, 21–25 (1962).
    Article  Google Scholar 

    31.
    Delaney, D. A., Keller, J. J., Caren, J. R. & Tarpy, D. R. The physical, insemination, and reproductive quality of honey bee queens (Apis mellifera). Apidologie 42, 1–13. https://doi.org/10.1051/apido/2010027 (2011).
    Article  Google Scholar 

    32.
    McAfee, A. et al. Vulnerability of honey bee queens to heat-induced loss of fertility. Nat. Sustain. https://doi.org/10.1038/s41893-020-0493-x (2020).
    Article  Google Scholar 

    33.
    Chaimanee, V., Evans, J. D., Chen, Y., Jackson, C. & Pettis, J. S. Sperm viability and gene expression in honey bee queens (Apis mellifera) following exposure to the neonicotinoid insecticide imidacloprid and the organophosphate acaricide coumaphos. J. Insect Physiol. 89, 1–8 (2016).
    CAS  PubMed  Article  Google Scholar 

    34.
    Williams, G. R. et al. Neonicotinoid pesticides severely affect honey bee queens. Sci. Rep. https://doi.org/10.1038/srep14621 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    35.
    Rangel, J. & Tarpy, D. R. The combined effects of miticides on the mating health of honey bee (Apis mellifera L.) queens. J. Apicult. Res. 54, 275–283. https://doi.org/10.1080/00218839.2016.1147218 (2015).
    Article  Google Scholar 

    36.
    Buechler, R. et al. Standard methods for rearing and selection of Apis mellifera queens. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.1.07 (2013).
    Article  Google Scholar 

    37.
    Lee, K. V., Goblirsch, M., McDermott, E., Tarpy, D. R. & Spivak, M. Is the brood pattern within a honey bee colony a reliable indicator of queen quality?. Insects. https://doi.org/10.3390/insects10010012 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    38.
    de Miranda, J. R. et al. Standard methods for virus research in Apis mellifera. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.4.22 (2013).
    Article  Google Scholar 

    39.
    Kevill, J. L. et al. The pathogen profiles of queen honey bees does not reflect those of their colonies workers. Insects 11, 382. https://doi.org/10.3390/insects11060382 (2020).
    PubMed Central  Article  Google Scholar 

    40.
    Evans, J. D. et al. Standard methods for molecular research in Apis mellifera. J. Apicult. Res. https://doi.org/10.3896/ibra.1.52.4.11 (2013).
    Article  Google Scholar 

    41.
    Jones, O. R. & Wang, J. COLONY: A program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x (2010).
    PubMed  Article  Google Scholar 

    42.
    Nielsen, R., Tarpy, D. R. & Reeve, H. K. Estimating effective paternity number in social insects and the effective number of alleles in a population. Mol. Ecol. 12, 3157–3164 (2003).
    PubMed  Article  Google Scholar 

    43.
    Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49, 1–6 (2010).
    Article  Google Scholar 

    44.
    van Engelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invertebr. Pathol. 103, S80–S95 (2010).
    Article  Google Scholar 

    45.
    Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and Colony Collapse Disorder in the United States. J. Apic. Res. 49, 134–136. https://doi.org/10.3896/ibra.1.49.1.30 (2010).
    Article  Google Scholar 

    46.
    Lee, K. V. et al. A national survey of managed honey bee 2013–2014 annual colony losses in the USA: Results from the Bee Informed Partnership. Apidologie. https://doi.org/10.1007/s13592-015-0356-z (2015).
    Article  Google Scholar 

    47.
    Steinhauer, N. et al. Drivers of colony losses. Curr. Opin. Insect Sci. 26, 142–148. https://doi.org/10.1016/j.cois.2018.02.004 (2018).
    PubMed  Article  Google Scholar 

    48.
    Tarpy, D. R., Delaney, D. A. & Seeley, T. D. Mating frequencies of honey bee queens (Apis mellifera L.) in a population of feral colonies in the northeastern United States. PLoS ONE. https://doi.org/10.1371/journal.pone.0118734 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    49.
    Lensky, Y. & Demter, M. Mating flights of the queen honeybee (Apis mellifera) in a subtropical climate. Comp. Biochem. Physiol. 81, 229–241 (1985).
    Article  Google Scholar 

    50.
    USDA-NASS. (ed National Agricultural Statistics Service) (2018).

    51.
    DeGrandi-Hoffman, G. et al. Comparisons of pollen substitute diets for honey bees: Consumption rates by colonies and effects on brood and adult populations. J. Apic. Res. 47, 265–270. https://doi.org/10.3896/ibra.1.47.4.06 (2008).
    Article  Google Scholar 

    52.
    Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS ONE. https://doi.org/10.1371/journal.pone.0150362 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    53.
    Le Conte, Y. et al. Honey bee colonies that have survived Varroa destructor. Apidologie 38, 566–572 (2007).
    Article  Google Scholar 

    54.
    Chen, Y. P., Evans, J. & Feldlaufer, M. Horizontal and vertical transmission of viruses in the honeybee, Apis mellifera. J. Invertebr. Pathol. 92, 152–159 (2006).
    PubMed  Article  Google Scholar 

    55.
    de Miranda, J. R. & Fries, I. Venereal and vertical transmission of deformed wing virus in honeybees (Apis mellifera L.). J. Invertebr. Pathol. 98, 184–189 (2008).
    PubMed  Article  Google Scholar 

    56.
    Yue, C., Schroder, M., Bienefeld, K. & Genersch, E. Detection of viral sequences in semen of honeybees (Apis mellifera): Evidence for vertical transmission of viruses through drones. J. Invertebr. Pathol. 92, 105–108 (2006).
    CAS  PubMed  Article  Google Scholar 

    57.
    Amiri, E., Meixner, M. D. & Kryger, P. Deformed wing virus can be transmitted during natural mating in honey bees and infect the queens. Sci. Rep. https://doi.org/10.1038/srep33065 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    58.
    Szalanski, A. L., Tripodi, A. D., Trammel, C. E. & Downey, D. Mitochondrial DNA genetic diversity of honey bees, Apis mellifera in Hawaii. Apidologie 47, 679–687. https://doi.org/10.1007/s13592-015-0416-4 (2016).
    CAS  Article  Google Scholar 

    59.
    Danka, R. G., Harris, J. W., Villalobos, E. & Glenn, T. Varroa destructor resistance of honey bees in Hawaii, USA, with different genetic proportions of Varroa Sensitive Hygiene (VSH). J. Apic. Res. 51, 288–290. https://doi.org/10.3896/ibra.1.51.3.13 (2012).
    Article  Google Scholar 

    60.
    Metz, B. N. & Tarpy, D. R. Reproductive senescence in drones of the honey bee (Apis mellifera). Insects. https://doi.org/10.3390/insects10010011 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    61.
    Sturup, M., Baer-Imhoof, B., Nash, D. R., Boomsma, J. J. & Baer, B. When every sperm counts: Factors affecting male fertility in the honeybee Apis mellifera. Behav. Ecol. 24, 1192–1198. https://doi.org/10.1093/beheco/art049 (2013).
    Article  Google Scholar  More

  • in

    A system dynamics model for pests and natural enemies interactions

    1.
    FAO Food and agriculture data [Internet]. www.fao.org/faostat/en/#home. Accessed 17 July 2019 (2018).
    2.
    Badu-Apraku, B. & Fakorede, M. Maize in Sub-Saharan Africa: Importance and Production Constraints. Advances in Genetic Enhancement of Early and Extra-Early Maize for Sub-Saharan Africa 3–10 (Springer, Cham, 2017).
    Google Scholar 

    3.
    Jin, Z. et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sens. Environ. 228, 115–128 (2019).
    ADS  Article  Google Scholar 

    4.
    De Groote, H. et al. Spread and impact of fall armyworm (Spodoptera frugiperda J.E. Smith) in maize production areas of Kenya. Agric. Ecosyst. Environ. 292, 106804 (2020).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    5.
    Mumo, L., Yu, J. & Fang, K. Assessing impacts of seasonal climate variability on maize yield in Kenya. Int. J. Plant Prod. 12, 297–307 (2018).
    Article  Google Scholar 

    6.
    Mwalusepo, S. et al. Predicting the impact of temperature change on the future distribution of maize stem borers and their natural enemies along East African mountain gradients using phenology models. PLoS One 10, e0130427 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    7.
    GuoFa, Z., Overholt, W. A. & Mochiah, M. B. Changes in the distribution of lepidopteran maize stemborers in Kenya from the 1950s to 1990s. Int. J. Trop. Insect Sc. 21, 395–402 (2001).
    Article  Google Scholar 

    8.
    Tounou, A. K., Agboka, K., Agbodzavu, K. M. & Wegbe, K. Maize stemborers distribution, their natural enemies and farmers’ perception on climate change and stemborers in southern Togo. J. Appl. Biosci. 64, 4773–4786 (2013).
    Article  Google Scholar 

    9.
    Kfir, R., Overholt, W. A., Khan, Z. R. & Polaszek, A. Biology and management of economicaly important lepidopteran cereal stem borers in Africa. Annu. Rev. Entomol. 47, 701–731 (2002).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Nwilene, F. E., Nwanze, K. F. & Youdeowei, A. Impact of integrated pest management on food and horticultural crops in Africa. Entomol. Exp. Appl. 128, 355–363 (2008).
    Article  Google Scholar 

    11.
    Krüger, W., Van den Berg, J. & Van Hamburg, H. The relative abundance of maize stem borers and their parasitoids at the Tshiombo irrigation scheme in Venda, South Africa. S. Afr. J. Plant Soil 25, 144–151 (2008).
    Article  Google Scholar 

    12.
    Ongamo, G. et al. Distribution, pest status and agro-climatic preferences of lepidopteran stem borers of maize in Kenya. Ann. Soc. Entomol. Fr. 42, 171–177 (2006).
    Article  Google Scholar 

    13.
    Kfir, R. Competitive displacement of Busseola fusca (Lepidoptera: Noctuidae) by Chilo partellus (Lepidoptera: Pyralidae). Ann. Entomol. Soc. Am. 90, 619–624 (1997).
    Article  Google Scholar 

    14.
    Ofomata, V. C., Overholt, W. A., Lux, S. A., Van Huis, A. & Egwuatu, A. R. I. Comparative studies on the fecundity, egg survival, larval feeding, and development of Chilo partellus and Chilo orichalcociliellus (Lepidoptera: Crambidae) on five grasses. Ann. Entomol. Soc. Am. 93, 492–499 (2000).
    Article  Google Scholar 

    15.
    Ntiri, E. S., Calatayud, P.-A., Van den Berg, J., Schulthess, F. & Le Ru, B. P. Influence of temperature on intra- and interspecific resource utilization within a community of lepidopteran maize stemborers. PLoS One 11, e148735 (2016).
    Google Scholar 

    16.
    Fotso-Kuate, A. et al. Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) in Cameroon: Case study on its distribution, damage, pesticide use, genetic differentiation and host plants. PLoS One 14, e0215749 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS One 11, e0165632 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    18.
    Sokame, B. M. et al. Influence of temperature on the interaction for resource utilization between Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), and a community of lepidopteran maize stemborers larvae. Insects 11, 73 (2020).
    PubMed Central  Article  PubMed  Google Scholar 

    19.
    Sokame, B. M. et al. Impact of the exotic fall armyworm on larval parasitoids associated with the lepidopteran maize stemborers in Kenya. Biocontrol https://doi.org/10.1007/s10526-020-10059-2 (2020).
    Article  Google Scholar 

    20.
    Chabaane, Y., Laplanche, D., Turlings, T. C. & Desurmont, G. A. Impact of exotic insect herbivores on native tritrophic interactions: A case study of the African cotton leafworm, Spodoptera littoralis and insects associated with the field mustard Brassica rapa. J. Ecol. 103, 109–117 (2015).
    Article  Google Scholar 

    21.
    Forrester, J. W. Industrial Dynamics (The MIT Press, Cambridge, 1961).
    Google Scholar 

    22.
    Sapiri, H., Zulkepli, J., Abidin, N. Z., Ahmad, N. & Hawari, N. N. Introduction to System Dynamics Modelling and Vensim Software 173 (Universiti Utara Malaysia, Malaysia, 2016).
    Google Scholar 

    23.
    Maani, K. E. & Cavana, R. Y. System Thinking and Modelling: Understanding Change and Complexity (Prentice Hall, Auckland, 2000).
    Google Scholar 

    24.
    Mwalusepo, S., Tonnang, H. E. Z., Massawe, E. S., Johansson, T. & Le Ru, B. P. Stability analysis of competing insect species for a single resource. J. Appl. Math. 20, 2014 (2014).
    MathSciNet  MATH  Google Scholar 

    25.
    Neill, W. E. The community matrix and interdependence of the competition coefficients. Am. Nat. 108, 399–408 (1974).
    Article  Google Scholar 

    26.
    Calatayud, P.-A. et al. Can climate-driven change influence silicon assimilation by cereals and hence the distribution of lepidopteran stem borers in East Africa?. Agric. Ecosyst. Environ. 224, 95–103 (2016).
    CAS  Article  Google Scholar 

    27.
    Ntiri, E. S., Calatayud, P.-A., Van den Berg, J. & Le Ru, B. P. Spatio-temporal interactions between maize lepidopteran stemborer communities and possible implications from the recent invasion of Spodoptera frugiperda (Lepidoptera : Noctuidae) in sub-Saharan Africa. Environ. Entomol. 48, 573–582 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    28.
    Sisay, B. et al. First report of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), natural enemies from Africa. J. Appl. Entomol. 142, 800–804 (2018).
    Article  Google Scholar 

    29.
    Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Calatayud, P.-A. & Dupas, S. Species diversity of lepidopteran stem borer parasitoids in cultivated and natural habitats in Kenya. J. Appl. Entomol. 133, 416–429 (2009).
    Article  Google Scholar 

    30.
    Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Calatayud, P.-A. & Dupas, S. Geographic distribution, host range and perennation of Cotesia sesamiae and Cotesia flavipes Cameron in cultivated and natural habitats in Kenya. Biol. Control 54, 1–8 (2010).
    Article  Google Scholar 

    31.
    Mailafiya, D. M., Le Ru, B. P., Kairu, E. W., Dupas, S. & Calatayud, P.-A. Parasitism of lepidopterous stemborers in cultivated and natural habitats. J. Insect Sci. 11, 1–19 (2011).
    Article  Google Scholar 

    32.
    Sisay, B. et al. Fall armyworm, Spodoptera frugiperda infestations in East Africa: Assessment of damage and parasitism. Insects 10, 195 (2019).
    PubMed Central  Article  PubMed  Google Scholar 

    33.
    Pitre, H. N., Mulrooney, J. E. & Hogg, D. B. Fall armyworm (Lepidoptera: Noctuidae) oviposition: Crop preferences and egg distribution on plants. J. Econ. Entomol. 76, 463–466 (1983).
    Article  Google Scholar 

    34.
    Polaszek, A. African Cereal Stem Borers: Economic Importance, Taxonomy, Natural Enemies and Control 530 (CAB International, Wallingford, 1998).
    Google Scholar 

    35.
    Sokame, B. M., Subramanian, S., Kilalo, D. C., Juma, G. & Calatayud, P.-A. Larval dispersal of the invasive fall armyworm, Spodoptera frugiperda, the exotic stemborer, Chilo partellus, and the indigenous maize stemborers in Africa. Entomol. Exp. Appl. 168, 322–331 (2020).
    CAS  Article  Google Scholar 

    36.
    Morrill, W. L. & Greene, G. L. Distribution of fall Armyworm larvae. 1. Regions of field corn plants infested by larvae. Environ. Entomol. 2, 195–198 (1973).
    Article  Google Scholar 

    37.
    Van den Berg, J. Economy of Stem Borer Control in Sorghum. ARC-Crop Protection Series no 2 4 (South Africa, Potchefstroom, 1997).
    Google Scholar 

    38.
    CAB International. How to Identify Fall Armyworm. Poster. Plantwise, http://www.plantwise.org/FullTextPDF/2017/20177800461.pdf. Accessed 23 Nov 2018 (2017).

    39.
    Bischof, R. & Zedrosser, A. The educated prey: Consequences for exploitation and control. Behav. Ecol. 20, 1228–1235 (2009).
    Article  Google Scholar 

    40.
    Boukal, D. & Kivan, V. Lyapunov functions for Lotka–Volterra predator-prey models with optimal foraging behavior. J. Math. Biol. 39, 493–517 (1999).
    MathSciNet  MATH  Article  Google Scholar 

    41.
    Sterman, J. Business Dynamics: Systems Thinking and Modeling for a Complex World (Irwin/McGraw-Hill, Boston, 2000).
    Google Scholar 

    42.
    Din, Q. & Donchev, T. Global character of a host-parasite model. Chaos Soliton Fract. 54, 1–7 (2013).
    ADS  MathSciNet  MATH  Article  Google Scholar 

    43.
    Sarmento, R. D. A. et al. Biology review, occurrence and control of Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) in corn in Brazil. Biosci. J. 18, 41–48 (2002).
    Google Scholar 

    44.
    Chapman, J. W., Williams, T., Martínez, A. M. & Cisneros, J. Does cannibalism in Spodoptera frugiperda (Lepidoptera: Noctuidae) reduce the risk of predation?. Behav. Ecol. Sociobiol. 48, 321–327 (2000).
    Article  Google Scholar 

    45.
    Zhou, S. Z., Chen, Z.-P. & Xu, Z.-F. Niches and interspecific competitive relationships of the parasitoids, Microplitis prodeniae and Campoletis chlorldeae, of the oriental leafworm moth, Spodoptera litura, in tobacco. J. Insect Sci. 10, 10 (2010).
    PubMed  PubMed Central  Google Scholar 

    46.
    Bentivenha, J. P. F., Baldin, E. L. L., Hunt, T. E., Paula-Moraes, S. V. & Blankenship, E. E. Intraguild competition of three noctuid maize pests. Environ. Entomol. 45, 999–1008 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—the role of mutalists. Biol. Rev. 75, 65–93 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Sujay, Y. H., Sattagi, H. N. & Patil, R. K. Invasive alien insects and their impact on agroecosystem. Karnatka J. Agric. Sci. 23, 26–34 (2010).
    Google Scholar 

    49.
    Reitz, S. & Trumble, J. Competitive displacement among insects and arachnids. Annu. Rev. Entomol. 47, 435–465 (2002).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    50.
    McClure, M. S. Biology, population trends, and damage of Pineus boerneri and P. coloradiensis (Homoptera: Adelgidae) on red pine. Environ. Entomol. 18, 1066–1073 (1989).
    Article  Google Scholar 

    51.
    Ekesi, S., Billah, M. K., Nderitu, P. W., Lux, S. A. & Rwomushana, I. Evidence for competitive displacement of Ceratitis cosyra by the invasive fruit fly Bactrocera invadens (Diptera: Tephritidae) on mango and mechanisms contributing to the displacement. J. Econ. Entomol. 102, 981–991 (2009).
    PubMed  Article  Google Scholar 

    52.
    Rwomushana, I., Ekesi, S., Ogol, C. K. P. O. & Gordon, I. Mechanisms contributing to the competitive success of the invasive fruit fly Bactrocera invadens over the indigenous mango fruit fly, Ceratitis cosyra: The role of temperature and resource pre-emption. Entomol. Exp. Appl. 133, 27–37 (2009).
    Article  Google Scholar 

    53.
    Fabre, J. P., Auger-Rozenberg, M. A., Chalon, A., Boivin, S. & Roques, A. Competition between exotic and native insects for seed resources in trees of a Mediterranean forest ecosystem. Biol. Invas. 6, 11–22 (2004).
    Article  Google Scholar 

    54.
    Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
    Article  Google Scholar 

    55.
    Ventana. Ventana Systems Incl. Vensim software PLE 8.0.9. https://vensim.com/download/ (2019).

    56.
    Sokame, B. M. Functioning of a community of lepidopteran maize stemborers and associated parasitoids following the fall armyworm invasion in Kenya 276 (PhD thesis, University of Nairobi, Kenya, 2020).

    57.
    Tonnang, H. E. Z., Nedorezov, L. V., Ochanda, H., Owino, J. & Löhr, B. Assessing the impact of biological control of Plutella xylostella through the application of Lotka–Volterra model. Ecol. Model. 220, 60–70 (2009).
    Article  Google Scholar 

    58.
    Kroschel, J., Mujica, N., Carhuapoma, P. & Sporleder, M. Pest Distribution and Risk Atlas for Africa-Potential Global and Regional Distribution and Abundance of Agricultural and Horticultural Pests and Associated Biocontrol Agents Under Current and Future Climates (International Potato Center (CIP), Lima, 2016).
    Google Scholar 

    59.
    Prasanna, B. M., Huesing, J. E., Eddy, R. & Peschke, V. M. Fall Armyworm in Africa: A Guide for Integrated Pest Management. First Edition, Mexico (CDMX: IMMYT, Mexico, 2018).
    Google Scholar 

    60.
    Sokame, B. M. et al. Carry-over niches for lepidopteran maize stemborers and associated parasitoids during non-cropping season. Insect 10, 191 (2019).
    Article  Google Scholar  More

  • in

    Abundance, distribution, and growth characteristics of three keystone Vachellia trees in Gebel Elba National Park, south-eastern Egypt

    The keystone species concept is an important aspect of population ecology, community ecology, and conservation biology1,2, and its application is likely to be critical with ongoing climate change3. Keystone species can be identified because they have a larger effect on communities and ecosystems than would be predicted based on their abundance or dominance. Loss of keystone species within communities and ecosystems is likely to result in secondary extinction events, and in extreme cases these events can lead to community and ecosystem collapse4. The critical importance of keystone species is derived from the wide range of biotic interactions they engage in with other community members (predation, competition, herbivory, mutualism, facilitation, etc.) and their influence on abiotic environmental conditions2. Keystone species have been described in a range of ecosystems (e.g., marine, fresh water, terrestrial, etc.) and have included a variety of taxa (e.g., fungi, animals, and plants)1,3,5.
    Plant communities consisting of isolated or scattered trees occur across the globe, and such trees have been described as keystone species, or “keystone structures”6. This certainly applies to trees and shrubs that are members of plant communities in arid and semi-arid habitat7. Many members of Acacia s.l. (Fabaceae: Mimosoideae8), which are broadly distributed around the world, are considered keystone species within the communities they reside. For example, they are considered keystone species in parts of Australia9, Pakistan10, the Kalahari Desert, Botswana11, Tunisia12,13,14, the Sinai Desert, Egypt15,16, and south-eastern Egypt16,17. As pointed out by Abdallah et al.12, isolated trees in arid habitats, including Vachellia species., have several characteristics that contribute to their keystone status: (1) shade from their canopies prevents extreme temperature fluctuations, increases soil moisture levels, and provides shelter for wildlife, (2) they improve soil conditions through biological nitrogen fixation and litter fall by increasing soil nitrogen content, organic carbon, and water-holding capacity, (3) they increase plant and animal biodiversity as a consequence of characteristics one and two, (4) they provide a source of food for wildlife, and (5) they provide a source of fuel, fodder, and medicines for local people and their domesticated animals. Because of their critical importance, a full characterization of keystone species and the roles they play within communities and ecosystems is urgently needed; especially as they are adversely impacted by various human activities.
    The Gebel Elba mountain range is an extension of the Afromontane “biodiversity hotspot” and is at the northern limit of the Eritreo-Arabian province and the Sahel regional transition zone18. The relatively high abundance of moisture of this mountain range leads to higher plant biodiversity than reported elsewhere in Egypt, it consists of 458 species, which constitutes approximately 21% of the Egyptian flora19,20. According to the plant checklist provided by Boulos21, the flora of Egypt consists of 2100 taxa belonging to 755 genera and 129 families; including 45 genera and 228 taxa in the Fabaceae. Gebel Elba is one of the seven main phytogeographical regions in Egypt21. Additionally, the region’s tree and shrub species diversity is higher than in any other regions in Egypt19, with some Sahelian woody elements restricted to the Gebel Elba region and not reported elsewhere in Egypt. Of the 10 Vachellia (synonym: Acacia8) species reported in Egypt, seven are known to occur in the Gebel Elba region, with Vachellia asak (synonym: Acacia asak) and Vachellia oerfota subsp. oerfota (synonym: Acacia oerfota subsp. oerfota) restricted to this region.
    An analysis of the plant communities of wadi Yahmib and three of its tributaries, on the north-western slopes of Gebel Elba, revealed the presence of seven plant communities, with these communities being arrayed across an elevational (environmental) gradient17. The Vachellia tortilis subsp. tortilis (synonym: Acacia tortilis subsp. tortilis) community was the main vegetation type on Gebel Elba. This community type occurred commonly in the water channels of wadis and gravel terraces from low to mid elevations (130–383 m), and the species was a member of all of the other six communities in the study area17. In addition, Vachellia tortilis subsp. raddiana (synonym: Acacia tortilis subsp. raddiana) was an overstory co-dominant species in another community on Gebel Elba. Finally, a third acacia species, Vachellia etbaica (synonym: Acacia etbaica), was also detected in this study.
    Within arid and semi-arid ecosystems across north Africa and the Arabian Peninsula, plant ecologists have focused their attention on describing the vegetation of wadis that drain to the Red Sea, with these studies focusing on keystone Vachellia species12,13,14,15,16,17,22,23. The present study aimed to contribute to this body of knowledge by determining the distribution, abundance, and describing the growth characteristics of three Vachellia tree taxa in wadi Khoda and wadi Rahaba, in Gebel Elba National Park, south-eastern Egypt. These data will allow us to provide detailed descriptions of the characteristics of these three taxa. This study is essential at this moment because these tree taxa are keystone species within these ecosystems, and their presence and conservation are likely to be threatened by human activities and ongoing climate change. More

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    R–R–T (resistance–resilience–transformation) typology reveals differential conservation approaches across ecosystems and time

    1.
    IPCC, Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018).
    2.
    Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211 (2020).
    PubMed  Article  CAS  Google Scholar 

    3.
    Diversity, S.o.t.C.o.B., Global Biodiversity Outlook 5 (Montreal, Canada, 2020).

    4.
    Global Commission on Adaptation, Adapt Now: A Global Call for Leadership on Climate Resilience (2019).

    5.
    Gross, J. E., Woodley, S., Welling, L. A. & Watson, J. E. M. eds. Adapting to Climate Change: Guidance for protected area managers and planners. Best Practice Protected Area Guidelines Series No. 24 (IUCN: Gland, Switzerland, 2016).

    6.
    IPBES, Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Bonn, Germany: IPBES secretariat, 2019).

    7.
    Stein, B. A. & Shaw, M. R. Biodiversity conservation for a climate-altered future, In Successful Adaptation to Climate Change: Linking Science and Policy in a Rapidly Changing World (eds Moser, S. C. & Boykoff, M. T.) 50–66 (Routledge: London, UK, 2013).

    8.
    Colloff, M. J. et al. An integrative research framework for enabling transformative adaptation. Environ. Sci. Policy 68, 87–96 (2017).
    Article  Google Scholar 

    9.
    Dawson, T. P. et al. Beyond predictions: biodiversity conservation in a changing climate. Science 332, 53–58 (2011).
    CAS  PubMed  Article  Google Scholar 

    10.
    Hagerman, S., Dowlatabadi, H., Satterfield, T. & McDaniels, T. Expert views on biodiversity conservation in an era of climate change. Glob. Environ. Change 20, 192–207 (2010).
    Article  Google Scholar 

    11.
    Hagerman, S., Satterfield, T. & Dowlatabadi, H. Impacts, conservation and protected values: understanding promotion, ambivalence and resistance to policy change at the world conservation congress. Conserv. Soc. 8, 298–311 (2010).
    Article  Google Scholar 

    12.
    Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).
    PubMed  Article  Google Scholar 

    13.
    Colloff, M. J. et al. Transforming conservation science and practice for a postnormal world. Conserv. Biol. 31, 1008–1017 (2017).
    PubMed  Article  Google Scholar 

    14.
    Reside, A. E., Butt, N. & Adams, V. M. Adapting systematic conservation planning for climate change. Biodivers. Conserv. 27, 1–29 (2017).
    Article  Google Scholar 

    15.
    Dumroese, R. K., Williams, M. I., Stanturf, J. A. & Clair, J. B. S. Considerations for restoring temperate forests of tomorrow: forest restoration, assisted migration, and bioengineering. N. For. 46, 947–964 (2015).
    Google Scholar 

    16.
    Phelps, M. P., Seeb, L. W. & Seeb, J. E. Transforming ecology and conservation biology through genome editing. Conserv. Biol. 34, 54–65 (2019).
    PubMed  Article  Google Scholar 

    17.
    Hansen, L. J. & Hoffman, J. R. Climate savvy: adapting conservation and resource management to a changing world, (Washington, DC: Island Press, 2011).

    18.
    Stein, B. A. et al. Preparing for and managing change: climate adaptation for biodiversity and ecosystems. Front. Ecol. Environ. 11, 502–510 (2013).
    Article  Google Scholar 

    19.
    Hagerman, S. M. & Pelai, R. Responding to climate change in forest management: two decades of recommendations. Front. Ecol. Environ. 16, 579–587 (2018).
    Article  Google Scholar 

    20.
    Bertram, M. et al. Making Ecosystem-based Adaptation Effective: A Framework for Defining Qualification Criteria and Quality Standards (FEBA technical paper developed for UNFCCC-SBSTA 46). 2018, FEBA (Friends of Ecosystem-based Adaptation), GIZ, Bonn, Germany, IIED, London, UK, andIUCN, Gland, Switzerland. 1-14.

    21.
    Morecroft, M. D. et al. Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366, eaaw9256–eaaw9257 (2019).
    CAS  PubMed  Article  Google Scholar 

    22.
    Kareiva, P. & Fuller, E. Beyond resilience: how to better prepare for the profound disruption of the anthropocene. Glob. Policy 7, 107–118 (2016).
    Article  Google Scholar 

    23.
    Heller, N. E. & Hobbs, R. J. Development of a natural practice to adapt conservation goals to global change. Conserv. Biol. 28, 696–704 (2014).
    PubMed  Article  Google Scholar 

    24.
    Tompkins, E. L., Vincent, K., Nicholls, R. J. & Suckall, N. Documenting the state of adaptation for the global stocktake of the Paris Agreement. Wiley Interdiscip. Rev. Clim. Change 9, e545–e549 (2018).
    Article  Google Scholar 

    25.
    Heller, N. E. & Zavaleta, E. S. Biodiversity management in the face of climate change: a review of 22 years of recommendations. Biol. Conserv. 142, 14–32 (2009).
    Article  Google Scholar 

    26.
    Morgan, M. G. Use (and abuse) of expert elicitation in support of decision making for public policy. Proc. Natl Acad. Sci. USA 111, 7176–7184 (2014).
    CAS  PubMed  Article  Google Scholar 

    27.
    Mawdsley, J. R., O’Malley, R. & Ojima, D. S. A review of climate-change adaptation strategies for wildlife management and biodiversity conservation. Conserv. Biol. 23, 1080–1089 (2009).
    PubMed  Article  Google Scholar 

    28.
    Hagerman, S. M. & Satterfield, T. Agreed but not preferred: expert views on taboo options for biodiversity conservation, given climate change. Ecol. Appl. 24, 548–559 (2014).
    PubMed  Article  Google Scholar 

    29.
    Moser, S. C. and M. T. Boykoff, Climate change and adaptation success: the scope of the challenge. (Taylor & Francis, 2013). 1–34.

    30.
    Stein, B. A., Glick, P., Edelson, N. & Staudt, A. Climate-smart conservation: putting adaption principles into practice, (National Wildlife Federation: Washington D.C, 2014).

    31.
    Dudney, J. et al. Navigating novelty and risk in resilience management. Trends Ecol. Evolution 33, 863–873 (2018).
    Article  Google Scholar 

    32.
    Walker, B. H. Resilience: what it is and is not. Ecol. Soc. 25, art11–art13 (2020).
    Article  Google Scholar 

    33.
    Fisichelli, N. A., Schuurman, G. W. & Hoffman, C. H. Is ‘Resilience’ maladaptive? Towards an accurate Lexicon for climate change adaptation. Environ. Manag. 57, 753–758 (2016).
    Article  Google Scholar 

    34.
    Oliver, T. H. et al. A decision framework for considering climate change adaptation in biodiversity conservation planning. J. Appl. Ecol. 49, 1247–1255 (2012).
    Article  Google Scholar 

    35.
    Schmitz, O. J. et al. Conserving biodiversity: practical guidance about climate change adaptation approaches in support of land-use planning. Nat. Areas J. 35, 190–203 (2015).
    Article  Google Scholar 

    36.
    Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151 (2007).
    PubMed  Article  Google Scholar 

    37.
    Pelling, M., Adaptation to climate change: from resilience to transformation. In Adaptation to Climate Change. (2011).

    38.
    Clifford, K. R. et al. Navigating climate adaptation on public lands: how views on ecosystem change and scale interact with management approaches. Environ. Manag. 66, 1–15 (2020).
    Article  Google Scholar 

    39.
    Thompson, L. M. et al. Responding to ecosystem transformation: resist, accept, or direct? Fisheries p. 1–14 https://doi.org/10.1002/fsh.10506 (2020).

    40.
    Thurman, L. L. et al. Persist in place or shift in space? Evaluating the adaptive capacity of species to climate change. Front. Ecol. Environ. 18, 520–528 (2020).
    Article  Google Scholar 

    41.
    Watson, J. E. M., Rao, M., Ai-Li, K. & Yan, X. Climate change adaptation planning for biodiversity conservation: a review. Adv. Clim. Change Res. 3, 1–11 (2012).
    Article  Google Scholar 

    42.
    Cross, M. et al. Embracing Change: Adapting Conservation Approaches to Address a Changing Climate. (Wildlife Conservation Society: New York, NY, 2018).

    43.
    Prober, S. M. et al. Shifting the conservation paradigm: a synthesis of options for renovating nature under climate change. Ecol. Monogr. 89, e01333–23 (2019).
    Article  Google Scholar 

    44.
    Burgman, M. A. Trusting Judgements: How to Get the Best out of Experts. (Cambridge, UK: Cambridge University Press, 2016).

    45.
    Prober, S. M. et al. Facilitating adaptation of biodiversity to climate change: a conceptual framework applied to the world’s largest Mediterranean-climate woodland. Climatic Change 110, 227–248 (2012).
    Article  Google Scholar 

    46.
    Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers, In Ecosystem management (eds Samson, F. B. & Knopf, F. L.) 130–147 (Springer: Ney York, NY, 1996).

    47.
    Gibson, P. P. & Olden, J. D. Ecology, management, and conservation implications of North American beaver (Castor canadensis)in dryland streams. Aquat. Conserv. Mar. Freshw. Ecosyst. 24, 391–409 (2014).
    Article  Google Scholar 

    48.
    Hodgson, D., McDonald, J. L & Hosken, D. J. What do you mean, ‘resilient’? Trends Ecol. Evol. 30, 503–506 (2015).

    49.
    Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).
    Article  Google Scholar 

    50.
    Aitken, S. N. & Bemmels, J. B. Time to get moving: assisted gene flow of forest trees. Evolut. Appl. 9, 271–290 (2015).
    Article  Google Scholar 

    51.
    Hoegh-Guldberg, O. et al. Assisted colonization and rapid climate change. Science 321, 345–346 (2008).
    CAS  PubMed  Article  Google Scholar 

    52.
    Ste-Marie, C., Nelson, E. A., Dabros, A. & Bonneau, M.-E. Assisted migration: Introduction to a multifaceted concept. Forestry Chron. 87, 724–730 (2011).
    Article  Google Scholar 

    53.
    Mueller, J. M. & Hellmann, J. J. An assessment of invasion risk from assisted migration. Conserv. Biol. 22, 562–567 (2008).
    PubMed  Article  Google Scholar 

    54.
    Strayer, D. L. et al. Essay: Making the most of recent advances in freshwater mussel propagation and restoration. Conserv. Sci. Pract. 1, 27–29 (2019).
    Article  Google Scholar 

    55.
    Beechie, T. et al. Restoring salmon habitat for a changing climate. River Res. Appl. 29, 939–960 (2013).
    Article  Google Scholar 

    56.
    Keeley, A. T. H. et al. New concepts, models, and assessments of climate-wise connectivity. Environ. Res. Lett. 13, 073002–073019 (2018).
    Article  Google Scholar 

    57.
    Dittbrenner, B. J. et al. Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation. PLoS ONE 13, e0192538–15 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    58.
    Amaru, S. & Chhetri, N. B. Climate adaptation: institutional response to environmental constraints, and the need for increased flexibility, participation, and integration of approaches. Appl. Geogr. 39, 128–139 (2013).
    Article  Google Scholar 

    59.
    Dilling, L. et al. Is adaptation success a flawed concept? Nat. Clim. Change 9, 572–574 (2019).
    Article  Google Scholar 

    60.
    Múnera, C. & van Kerkhoff, L. Diversifying knowledge governance for climate adaptation in protected areas in Colombia. Environ. Sci. Policy 94, 39–48 (2019).
    Article  Google Scholar 

    61.
    Wyborn, C. et al. Future oriented conservation: knowledge governance, uncertainty and learning. Biodivers. Conserv. 25, 1401–1408 (2016).
    Article  Google Scholar 

    62.
    Peterson St-Laurent, G., Hagerman, S. M. & Kozak, R. A. What risks matter? Public views about assisted migration and other climate adaptive reforestation strategies. Climat. Change 151, 573–587 (2018).
    Article  Google Scholar 

    63.
    Peterson St-Laurent, G., Hagerman, S. M. & Kozak, R. A. Cross-jurisdictional insights from practitioners on novel climate-adaptive options for Canada’s forests. 2020, under review.

    64.
    Martin, T. G. & Watson, J. E. M. Intact ecosystems provide best defence against climate change. Nat. Clim. Change 6, 122–124 (2016).
    Article  Google Scholar 

    65.
    Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evolution 2, 599–610 (2018).
    Article  Google Scholar 

    66.
    Krippendorff, K. Content analysis: an introduction to its methodology. Fourth edition ed. 451 (Los Angeles, CA: SAGE, 2019).

    67.
    RStudio Team, RStudio: Integrated Development for R. 2020, RStudio, Inc. More

  • in

    Reproducing the Rift Valley fever virus mosquito-lamb-mosquito transmission cycle

    Virus and cells
    RVFV strain 35/74 was originally isolated from the liver of a sheep that died during a RVFV outbreak in the Free State province of South Africa in 197421. The strain was previously passaged four times in suckling mouse brain and three times in BHK cells. The virus used for IV inoculation of sheep was prepared by a further amplification in BHK-21 cells (ATCC CCL-10) cultured in CO2-independent medium (CIM, Invitrogen), supplemented with 5% FBS (Bodinco) and 1% Pen/Strep (Invitrogen).
    To prepare a virus-spiked blood meal for membrane feeding of mosquitoes, the virus was amplified in Aedes albopictus C6/36 cells (ATCC CRL-1660). To this end, C6/36 cells were inoculated with a multiplicity of infection of 0.005 and cultured at 28 °C in absence of CO2 in L-15 medium (Sigma) supplemented with 10% fetal bovine serum (FBS), 2% Tryptose Phosphate Broth (TPB) and 1% MEM nonessential amino acids solution (MEMneaa). At 4 days post infection, culture medium was harvested, cleared by slow-speed centrifugation and titrated using Vero-E6 cells (ATCC CRL-1586), grown in DMEM supplemented with GlutaMAX, 3% FBS, 1% Pen/Strep and 1% Fungizone (DMEM +) at 37 °C and 5% CO2. Titers were determined using the Spearman-Kärber algorithm22,23.
    Mosquito rearing and feeding on lambs
    Rockefeller strain Ae. aegypti mosquitoes (Bayer AG, Monheim, Germany) were maintained at Wageningen University, Wageningen, the Netherlands, as described24. Briefly, mosquitoes were kept in Bugdorm-1 rearing cages at a temperature of 27 °C with a 12:12 light:dark cycle and a relative humidity of 70% with a 6% glucose solution provided ad libitum. Mosquitoes were subsequently transported to biosafety level three (BSL-3) facilities of Wageningen Bioveterinary Research (Lelystad, the Netherlands), where the mosquitoes were maintained with sugar water (6% sucrose in H2O), provided via soaked cotton pads covered with a lid to prevent evaporation in an insect incubator (KBWF 240, Binder) at 28 °C at a humidity of 70% and a 16:8 light:dark cycle.
    Mosquito feeding on lambs was preceded by sedating the lambs with IV administration of medetomidine (Sedator). When fully sedated, cardboard boxes containing 40–50 female mosquitoes were placed on the shaved inner thigh of each hind leg (Fig. 1b,c). After 20 min of feeding, cardboard boxes were removed and atipamezol (Atipam) was administered via intramuscular (IM) route to wake up the animals. Fully engorged mosquitoes were collected using an automated insect aspirator and maintained with sugar water (6% sucrose in H2O), provided via soaked cotton pads covered with a lid to prevent evaporation, in an insect incubator (KBWF 240, Binder) at 28 °C at a humidity of 70% and a 16:8 light:dark cycle.
    Feeding of mosquitoes using a Hemotek system
    Blood meals to be used for Hemotek membrane feeding were prepared essentially as described before25. Briefly, erythrocytes were harvested from freshly collected bovine EDTA blood by slow-speed centrifugation (650 xg), followed by three wash steps with PBS. Washed erythrocytes were resuspended in L15 complete medium (L15 + 10% FBS, 2% TPB, 1% MEMneaa) to a concentration that is four times higher than found in blood. To prepare a blood meal, one part of the erythrocyte suspension was mixed with two parts of culture medium containing RVFV resulting in a final titer of 107.5 TCID50/ml as determined on Vero-E6 cells.
    Mosquitoes were allowed to take a RVFV-spiked blood meal through a Parafilm M membrane using the Hemotek PS5 feeding system (Discovery Workshops, Lancashire, United Kingdom). Feeding was performed in plastic buckets (1 l) covered with mosquito netting. After blood feeding for approximately 1.5–2 h, fully engorged mosquitoes were collected using an automated insect aspirator and maintained with sugar water (6% sucrose in H2O), provided via soaked cotton pads covered with a lid to prevent evaporation in an insect incubator (KBWF 240, Binder) at 28 °C at a humidity of 70% and a 16:8 light:dark cycle.
    Virus isolation
    Virus isolation from plasma samples was performed using BHK-21 cells, seeded at a density of 20,000 cells/well in 96-wells plates. Serial dilutions of samples were incubated with the cells for 1.5 h before medium replacement. Cytopathic effect was evaluated after 5–7 days post infection. Virus titers (TCID50/ml) were determined using the Spearman-Kärber algorithm22,23.
    To check for positive saliva, mosquitoes were sedated on a semi-permeable CO2-pad connected to 100% CO2 and wings and legs were removed. Saliva was collected by forced salivation using 20 µl filter tips containing 7 µl of a 1:1 mixture of FBS and 50% sucrose (capillary tube method). After 1–1.5 h, saliva samples were collected and used to inoculate Vero-E6 cell monolayers. Cytopathic effect (CPE) was scored 5–7 days later.
    Serology
    Weekly collected serum samples were used to detect RVFV-specific antibodies using the ID Screen Rift Valley Fever Competition Multi-species ELISA (ID-VET). This ELISA measures percentage competition between antibodies present in test sera and a monoclonal antibody. Neutralizing antibodies were detected using the RVFV-4 s-based virus neutralization test as described26.
    RT-qPCR
    Viral RNA was isolated with the NucliSENS easyMAG system according the manufacturer’s instructions (bioMerieux, France) from 0.5 ml plasma samples. Briefly, 5 µl RNA was used in a RVFV RT-qPCR using the LightCycler one-tube RNA Amplification Kit HybProbe (Roche, Almere, The Netherlands) in combination with a LightCycler 480 real-time PCR system (Roche) and the RVS forward primers (AAAGGAACAATGGACTCTGGTCA), the RVAs (CACTTCTTACTACCATGTCCTCCAAT) reverse primer and a FAM-labelled probe RVP (AAAGCTTTGATATCTCTCAGTGCCCCAA). Primers and probes were earlier described by Drosten et al.27. Virus isolations were performed on RT-qPCR positive samples with a threshold above 105 RNA copies/ml as this was previously shown to be a cut-off point below which no live virus can be isolated.
    Pathology and (immuno)histopathology
    Liver samples were placed on ice during the necropsies and subsequently stored at − 80 °C until virus isolations and RT-qPCR Tissue samples for histology and IHC were collected, placed in 10% neutral buffered formalin, embedded into paraffin and prepared for H&E staining or IHC staining for RVFV antigen using the RVFV Gn-specific 4-D4 mAb as described5.
    Statistics
    For statistical analysis, mosquito feeding and mosquito saliva positive rates per group were compared by fitting logistic regression mixed models where lamb or membrane were introduced as random effects. To compare viremia (based on virus isolation results) the area under the curve (AUC) representing the overall viremia during the infected period was calculated for each infected sheep. This AUC and peak of viremia was used for comparison between groups, which was done by fitting linear regression models.
    Additionally we also assessed the variability observed between groups on the above mentioned variables (feeding and saliva positive rates, AUC and peak viremia). For these comparisons, data were first assessed for normality using the Shapiro–Wilk test. If data from all groups were normally distributed, the Bartlett’s test of homogeneity of variance was used. If the data did not have a normal distribution, the Fligner-Killeen test was applied.
    Survival of infected lambs (time to death) was compared between experiment groups using Kaplan–Meier survival analysis and the mortality rates were compared fitting a logistic regression model.
    For all comparisons, the threshold for significance was p  More

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    Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

    Study area
    The study area was the Amazon basin (Fig. 6)42. The basin was sub-divided in four regions with markedly different forest dynamics, geography and substrate origin, adapted from the classification of Feldpausch et al.33: West (parts of Brazil, Colombia, Ecuador and Peru), Southeast (Bolivia and Brazil), Central-East (Brazil) and North (Brazil, Guyana, French Guiana and Venezuela). The natural vegetation mainly corresponds to broadleaf moist forests and tropical seasonal forests, with both terra firme and seasonally flooded forests. Across the Amazon, there is a wide range of average monthly rainfall (100–300 mm) and dry season length (DSL) (0–8 months)43.
    Figure 6

    The Amazon in South America with colored regions, defined in Feldpausch et al.33, indicating faster (West and Southeast) and slower forest dynamics (Central-East and North). Small black lines represent single-date airborne lidar data acquisitions from the EBA project (n = 610 flight lines). Red triangles illustrate multi-temporal lidar data acquisition over five sites (BON, DUC, FN1, TAL and TAP). Circles indicate the location of field inventory plots (n = 181). R v4.0.2 was used to plot this figure32.

    Full size image

    The five sites selected for the multi-temporal assessment of the static and dynamic gaps relationship (red triangles in Fig. 6) were: Adolpho Ducke forest (DUC), Tapajós National Forest (TAP), Feliz Natal (FN1), Bonal (BON) and Talismã (TAL). These areas were chosen to represent distinct forest types, vegetation structure and biomass stocks. The predominant vegetation types consisted of dense rain forests (DUC and TAP), seasonal forests (FN1), and open rain forests (TAL and BON). DUC and FN1 are mostly undisturbed forests, while TAP underwent fire and/or selective logging in the past. TAL and BON were affected by a known fire occurrence in 2010. The sites cover a gradient of aboveground biomass (AGB) that increase, in average, from TAL (185 Mg ha−1), FN1 (235 Mg ha−1), BON (251 Mg ha−1), and DUC (327 Mg ha−1) to TAP (364 Mg ha−1)44.
    Data acquisition and pre-processing
    Airborne lidar data
    Multi-temporal lidar data were obtained by an airplane at each of the five sites (red triangles in Fig. 6), as part of the Sustainable Landscapes Brazil project. The time-interval window was close to 5 years and was sufficient to measure the long-term aggregated dynamics of tree mortality. The area covered by lidar in the 2012–2018 period was ~ 43 km2, ranging from 480 ha at TAL site to 1200 ha at DUC site (Supplementary Table S5).
    In addition to the multi-temporal datasets, 610 single-date airborne discrete-return lidar data strips (approx. 300 m wide by 12.5 km long; ~ 3.75 km2 each) were acquired during 2016 (acquisition dates in Supplementary Figure S6A) using the Trimble HARRIER 68i system at an airplane. The average flight height was 600 m above ground and the scan angle was 45° (dataset from the EBA project31).
    For both lidar datasets, multiple lidar returns were recorded with a minimum point density of 4 points m−2. Horizontal and vertical accuracy ranging from 0.035 to 0.185 m and from 0.07 to 0.33 m, respectively.
    Following the procedures described by Dalagnol et al.19, the lidar point clouds were processed into canopy height models (CHM) of 1-m spatial resolution. The steps of CHM processing included the: (a) classification of the points between ground and vegetation using the lasground, lasheight, and lasclassify functions from the LAStools 3.1.145; (b) creation of a Digital Terrain Model (DTM) using the TINSurfaceCreate function from FUSION/LDV 3.646; (c) normalization of the point cloud height to height above ground using the DTM; and (d) CHM generation by extracting the highest height of vegetation using the CanopyModel function from FUSION.
    Environmental and climate data
    To analyze the environmental and climatic drivers of gap dynamics, we used a spatialized set of variables and products for the whole Amazon, including: (a) HAND product at 30 × 30 m47; (b) slope calculated from the Shuttle Radar Topography Mission (SRTM) at 30 × 30 m48; (c) soil fertility proxied by SCC at 11 × 11 km37; (d) floodplain cover map at 30 × 30 m49; (e) forest degradation proxied by a non-forest distance map derived from the 30-m global forest change dataset v1.4 (2000–2016)50; (f) monthly mean rainfall (mm), climate water deficit (mm) and wind speed (m s−1), obtained from the TerraClimate dataset at 5 × 5 km (1958–2015)43; and (g) DSL at 28 × 28 km51. All variables and products, except HAND and slope, were resampled to the predominant spatial resolution of most datasets (5 km × 5 km), especially the climate data. We used the SRTM instead of the lidar DTM because the very narrow lidar DTMs (300–500 m) would not permit to determining the lowest point in the terrain to accurately calculate the HAND for every pixel.
    Long-term field inventory data
    We used data from 181 long-term field inventory plots from the RAINFOR network (Fig. 6)5. The data were collected at closed canopy mixed forests with vegetation structure preserved from fire and logging. All trees with diameter at breast height (DBH) ≥ 10 cm were measured at least twice5. These plots had 852 censuses from 1975 to 2013 with median plot size of 1 ha. The mean re-census interval was 3 years. Tree stem mortality rates (m; % year−1) were calculated as the coefficient of exponential mortality for each census interval and each plot52 (Eq. 1). The m estimates were then averaged by plot and were weighted by the censuses interval length, in years1.

    $$m = left[ {lnleft( {N0} right) – lnleft( {Nt} right)} right]/t$$
    (1)

    where N0 and Nt are the initial and final number of trees, and t is the censuses interval.
    Data analysis
    Gap definition and static–dynamic gaps relationship
    Dynamic gaps were detected using multi-date lidar data at the five study sites: DUC, TAP, FN1, BON, and TAL. We define here dynamic gaps as those opened between two periods of observation associated with canopy turnover events, including tree mortality. For this purpose, we calculated a delta height difference of 10 m between the two acquisitions (~ 5 years apart) and filtered for detections with area greater than 10 m2. This height difference was strongly correlated with tree loss at the canopy level in previous studies19, 20. Because standing dead trees do not necessarily generate gaps, we assume that the dynamic gaps are mostly related to the felled canopy trees associated with broken and uprooted mode of death.
    Static gaps were delineated using the CHM from the second lidar acquisition at the five sites (Supplementary Material S1). We applied and compared two types of gap delineation: a traditional method based on a fixed height cutoff (H = 2, 5 or 10 m), and an alternative method based on the relative height (RH = 33, 50, and 66% maximum tree height) around a neighborhood (W = 5–45 m). Since the relative height method did not depend on absolute height values, it should better account for local canopy variability and lower stature vegetation, as opposed to the fixed height method. For both methods, we tested a variety of parameters in the search of an optimal calibration amongst the sites. We filtered gaps for a minimum area of 10 m2, which corresponded to an approximation of the mean canopy area of trees greater than 5-cm DBH in tropical forests21. We also filtered them for a maximum area of 1 ha to automatically exclude open areas that very likely did not correspond to small-scale disturbance from treefall gaps21.
    The spatial match between each static and dynamic gap event was assessed by intersecting the detections and calculating metrics of precision (p), recall (r) and F1-score (F) (Eqs. 2–4) (more information at Supplementary Material S1). p represents the percentage of total correct detections, r represents the percentage of reference data correctly mapped, and F represents the harmonic mean between p and r, that is, a balance between commission and omission errors. Methods and parameters were compared to determine the optimal method for static gap delineation, i.e. higher F means greater agreement between static and dynamic gaps.

    $$Precisionleft( p right) = true , positives/number , of , gap , polygons$$
    (2)

    $$Recallleft( r right) = true , positives/number , of , mortality , polygons$$
    (3)

    $$F1 – scoreleft( F right) = left( {2 times p times r} right)/left( {p + r} right)$$
    (4)

    Finally, considering the optimal gap delineation method, we modeled the relationship between static-dynamic gaps at the landscape scale using a linear regression. For this purpose, annualized dynamic gap fraction and static gap fraction (i.e., the area occupied by gaps in relation to the total area of the flight line) were calculated at the 5-ha scale. Following the strategy by Wagner et al.53, we defined this value after several simulation tests between variable estimates, change rates and plot area (Supplementary Figure S7). Data and residuals were inspected for normality, and variables were transformed to the logarithmic scale prior to the linear model fitting. To assess the model, we calculated the coefficient of determination (R2), absolute Root Mean Square Error (RMSE) and relative RMSE (%) (ratio of RMSE and the mean of observations). To obtain more reliable and unbiased estimates of the model predictive performance, we calculated the RMSE considering out-of-sample values with a leave-one-site-out cross-validation (CV) strategy. Thus, we fitted the model with four sites and calculated the RMSE with predicted and observed values for the site not used in the modeling. We repeated this process for all five sites. A 95% prediction interval described the variability of tree mortality estimates from the gap fraction.
    Spatial variability of static gaps across the Brazilian Amazon
    We delineated static gaps on the single-date airborne lidar datasets (n = 610 flight lines) using the optimal gap delineation method and parameters assessed in the previous section. To characterize the gaps variability across the region, we calculated the gap fraction and mean gap size for each site.
    Assessment of landscape- and regional-scale drivers of static canopy gaps
    To quantify the relationship between static gaps and landscape- and regional-scale predictors, we employed correlation matrices and generalized linear models (GLM). Binomial GLM and Gaussian GLM were applied for landscape and regional models, respectively (detailed information at Supplementary Material S2). Models were assessed using a tenfold CV approach with 30 repetitions. The gap data used in this analysis were those obtained from the 610 single-date lidar data. We defined landscape-scale drivers as those showing great heterogeneity intra-site such as the topography (HAND and slope variables). We defined regional-scale drivers as those having great variability across sites such as the rainfall (Mean_pr and SD_pr), wind speed (Mean_vs and SD_vs), climate water deficit (Mean_def and SD_def), DSL, SCC, floodplains, and non-forest distance.
    Through the modeling we evaluated if gap occurrence (presence or absence) and gap size increased at valleys and steep terrains of the Amazon, represented by low HAND and high slope, respectively. As previously demonstrated with tree mortality ground observations, we also tested if gap fraction would increase with: (1) higher water stress, represented by low Mean_pr, and high SD_pr, Mean_def, SD_def, and DSL; (2) higher soil fertility, expressed by high SCC; (3) higher wind speed, proxied by high Mean_vs and SD_vs; (4) higher forest degradation/fragmentation, represented by low non-forest distance; and (5) areas of seasonally flooded forests, expressed by high floodplains cover. Model residuals were inspected in comparison to fitted values using also variogram and Moran’s I analyses to assess for potential biases and spatial correlation (detailed information in Supplementary Material S2). Static gap fraction and Nonforest_dist were transformed to log-scale due to non-normality data.
    Amazon-wide dynamic gaps mapping and relationship with tree mortality
    To obtain a map of dynamic gap estimates over the Amazon, we first applied the GLM model based on environmental and climate drivers to estimate static gap fractions for the whole region. We then applied the static–dynamic gaps relationship to estimate annualized dynamic gap fraction (% year−1). To explore the opportunities for tree mortality estimates based on gap dynamics, we compared the spatialized dynamic gap estimates with time-averaged mortality rates from long-term field inventory data using a linear model. The model was assessed using a tenfold CV approach with 30 repetitions and the RMSE calculated out-of-sample. We acknowledge that the comparison between field tree mortality and lidar gap estimates is not trivial. However, it is the best source available of independent mortality data to compare the results. Field plot-estimates located within the same 5-km cell of the lidar gap estimates were averaged, resulting in 88 pairs of lidar- and field-estimates samples for validation. The mean annualized dynamic gap fraction per Amazonian region (Fig. 6) was extracted and compared using one-way ANOVA and post-hoc Tukey–Kramer tests. More

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    Reconstructing Late Pleistocene paleoclimate at the scale of human behavior: an example from the Neandertal occupation of La Ferrassie (France)

    1.
    Dansgaard, W. et al. A new Greenland deep ice core. Science 218, 1273–1277 (1982).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Stuiver, M. & Grootes, P. M. GISP2 oxygen isotope ratios. Quatern. Res. 53, 277–284 (2000).
    ADS  CAS  Article  Google Scholar 

    3.
    Genty, D. et al. Isotopic characterization of rapid climatic events during OIS3 and OIS4 in Villars Cave stalagmites (SW-France) and correlation with Atlantic and Mediterranean pollen records. Quatern. Sci. Rev. 29, 2799–2820 (2010).
    ADS  Article  Google Scholar 

    4.
    Pérez-Mejías, C. et al. Orbital-to-millennial scale climate variability during Marine Isotope Stages 5 to 3 in northeast Iberia. Quatern. Sci. Rev. 224, 105946 (2019).
    Article  Google Scholar 

    5.
    Sánchez Goñi, M. F., Bard, E., Landais, A., Rossignol, L. & D’Errico, F. Air–sea temperature decoupling in western Europe during the last interglacial–glacial transition. Nat. Geosci. 6, 837 (2013).
    ADS  Article  CAS  Google Scholar 

    6.
    Fletcher, W. J. et al. Millennial-scale variability during the last glacial in vegetation records from Europe. Quatern. Sci. Rev. 29, 2839–2864 (2010).
    ADS  Article  Google Scholar 

    7.
    Hofreiter, M. & Stewart, J. Ecological change, range fluctuations and population dynamics during the pleistocene. Curr. Biol. 19, R584–R594 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Hodgkins, J. et al. Climate-mediated shifts in Neandertal subsistence behaviors at Pech de l’Azé IV and Roc de Marsal (Dordogne Valley, France). J. Hum. Evol. 96, 1–18 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    9.
    Rendu, W. et al. Subsistence strategy changes during the Middle to Upper Paleolithic transition reveals specific adaptations of human populations to their environment. Sci. Rep. 9, 15817 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    10.
    Dibble, H. L. et al. How did hominins adapt to ice age Europe without fire?. Curr. Anthropol. 58, S278–S287 (2017).
    Article  Google Scholar 

    11.
    Sorensen, A. C. On the relationship between climate and Neandertal fire use during the Last Glacial in south-west France. Quatern. Int. 436, 114–128 (2017).
    Article  Google Scholar 

    12.
    Delagnes, A. & Rendu, W. Shifts in Neandertal mobility, technology and subsistence strategies in western France. J. Archaeol. Sci. 38, 1771–1783 (2011).
    Article  Google Scholar 

    13.
    Discamps, E., Jaubert, J. & Bachellerie, F. Human choices and environmental constraints: Deciphering the variability of large game procurement from Mousterian to Aurignacian times (MIS 5–3) in southwestern France. Quatern. Sci. Rev. 30, 2755–2775 (2011).
    ADS  Article  Google Scholar 

    14.
    Faivre, J.-P. et al. The contribution of lithic production systems to the interpretation of Mousterian industrial variability in south-western France: The example of Combe-Grenal (Dordogne, France). Quatern. Int. 350, 227–240 (2014).
    Article  Google Scholar 

    15.
    Hublin, J. J. The modern human colonization of western Eurasia: When and where?. Quatern. Sci. Rev. 118, 194–210 (2015).
    ADS  Article  Google Scholar 

    16.
    Higham, T. et al. The timing and spatiotemporal patterning of Neanderthal disappearance. Nature 512, 306–309 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Discamps, E. & Royer, A. Reconstructing palaeoenvironmental conditions faced by Mousterian hunters during MIS 5 to 3 in southwestern France: A multi-scale approach using data from large and small mammal communities. Quatern. Int. 433, 64–87 (2016).
    Article  Google Scholar 

    18.
    Bernard, A. et al. Pleistocene seasonal temperature variations recorded in the δ 18O of Bison priscus teeth. Earth Planet. Sci. Lett. 283, 133–143 (2009).
    ADS  CAS  Article  Google Scholar 

    19.
    Fabre, M. et al. Late Pleistocene climatic change in the French Jura (Gigny) recorded in the δ18O of phosphate from ungulate tooth enamel. Quatern. Res. 75, 605–613 (2011).
    ADS  CAS  Article  Google Scholar 

    20.
    Richards, M. P. et al. Temporal variations in Equus tooth isotope values (C, N, O) from the Middle Paleolithic site of Combe Grenal, France (ca. 150,000 to 50,000 BP). J. Archaeol. Sci. Rep. 14, 189–198 (2017).
    Google Scholar 

    21.
    Scherler, L., Tütken, T. & Becker, D. Carbon and oxygen stable isotope compositions of late Pleistocene mammal teeth from dolines of Ajoie (Northwestern Switzerland). Quatern. Res. (United States) 82, 378–387 (2014).
    ADS  CAS  Article  Google Scholar 

    22.
    Skrzypek, G., Winiewski, A. & Grierson, P. F. How cold was it for Neanderthals moving to Central Europe during warm phases of the last glaciation?. Quatern. Sci. Rev. 30, 481–487 (2011).
    ADS  Article  Google Scholar 

    23.
    Capitan, L. & Peyrony, D. Découverte d’un sixième squelette moustérien à La Ferrassie (Dordogne). Rev. Anthropol. 31, 382–388 (1921).
    Google Scholar 

    24.
    Peyrony, D. La Ferrassie. Moustérien, Périgordien, Aurignacien. Préhistoire III. Préhistoire (1934)

    25.
    Turq, A. et al. La Ferrassie: Rapport d’opération pour l’année 2012 (2012).

    26.
    Delporte, H. & Delibrias, G. Le grand abri de la Ferrassie: fouilles 1968–1973. (Ed. du Laboratoire de paléontologie humaine et de préhistoire, 1984).

    27.
    Guérin, G. et al. A multi-method luminescence dating of the Palaeolithic sequence of La Ferrassie based on new excavations adjacent to the La Ferrassie 1 and 2 skeletons. J. Archaeol. Sci. 58, 147–166 (2015).
    Article  Google Scholar 

    28.
    Talamo, S. et al. The new 14C chronology for the Palaeolithic site of La Ferrassie, France: The disappearance of Neanderthals and the arrival of Homo sapiens in France. J. Quatern. Sci. https://doi.org/10.1002/jqs.3236 (2020).
    Article  Google Scholar 

    29.
    Britton, K. et al. Sampling plants and malacofauna in 87Sr/86Sr bioavailability studies: Implications for isoscape mapping and reconstructing of past mobility patterns. Front. Ecol. Evol. 8, 579473 (2020). 

    30.
    Willmes, M. et al. Mapping of bioavailable strontium isotope ratios in France for archaeological provenance studies. Appl. Geochem. 90, 75–86 (2018).
    CAS  Article  Google Scholar 

    31.
    Deutscher Wetterdienst. Monthly mean air temperature of Gourdon, Dépt. Lot; Aquitaine/France (1996–2017) (2020).

    32.
    Hoppe, K. A. Correlation between the oxygen isotope ratio of North American bison teeth and local waters: Implication for paleoclimatic reconstructions. Earth Planet. Sci. Lett. 244, 408–417 (2006).
    ADS  CAS  Article  Google Scholar 

    33.
    D’Angela, D. & Longinelli, A. Oxygen isotopes in living mammal’s bone phosphate: Further results. Chem. Geol. Isot. Geosci. Sect. 86, 75–82 (1990).
    Article  Google Scholar 

    34.
    Rozanski, K., Araguás-Araguás, L. & Gonfiantini, R. Relation between long-term trends of oxygen-18 isotope composition of precipitation and source. Sci. New Ser. 258, 981–985 (1992).
    CAS  Google Scholar 

    35.
    Levin, N. E., Cerling, T. E., Passey, B. H., Harris, J. M. & Ehleringer, J. R. A stable isotope aridity index for terrestrial environments. Proc. Natl. Acad. Sci. U.S.A. 103, 11201–11205 (2006).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Kohn, M. J., Schoeninger, M. J. & Valley, J. W. Herbivore tooth oxygen isotope compositions: Effects of diet and physiology. Geochim. Cosmochim. Acta 60, 3889–3896 (1996).
    ADS  CAS  Article  Google Scholar 

    37.
    Bocherens, H., Koch, P. L., Mariotti, A., Geraads, D. & Jaeger, J.-J. Isotopic biogeochemistry (13C, 18O) of mammalian enamel from african pleistocene hominid sites. Palaios 11, 306–318 (1996).
    ADS  Article  Google Scholar 

    38.
    Shackleton, N. Oxygen isotope analyses and Pleistocene temperatures re-assessed. Nature 215, 15–17 (1967).
    ADS  CAS  Article  Google Scholar 

    39.
    Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).
    ADS  Article  Google Scholar 

    40.
    Gat, J. R. Isotope Hydrology: A Study of the Water Cycle Vol. 6 (Imperila College Press, London, 2010).
    Google Scholar 

    41.
    Pryor, A. J. E., Stevens, R. E., O’Connell, T. C. & Lister, J. R. Quantification and propagation of errors when converting vertebrate biomineral oxygen isotope data to temperature for palaeoclimate reconstruction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 412, 99–107 (2014).
    Article  Google Scholar 

    42.
    Rozanski, K., Araguás-Araguás, L. & Gonfiantini, R. Isotopic patterns in modern global precipitation. Clim. Change Cont. Isot. Rec. 78, 1–36 (1993).
    Google Scholar 

    43.
    Bocherens, H. et al. Direct isotopic evidence for subsistence variability in Middle Pleistocene Neanderthals (Payre, southeastern France). Quatern. Sci. Rev. 154, 226–236 (2016).
    ADS  Article  Google Scholar 

    44.
    Ingraham, N. L., Caldwell, E. A. & Verhagen, B. T. Arid Catchments. in Isotope tracers in catchment hydrology, 435–465 (Elsevier, 1998). https://doi.org/10.1016/B978-0-444-81546-0.50020-3.

    45.
    Tütken, T., Furrer, H. & Walter Vennemann, T. Stable isotope compositions of mammoth teeth from Niederweningen, Switzerland: Implications for the Late Pleistocene climate, environment, and diet. Quatern. Int. 164–165, 139–150 (2007).
    Article  Google Scholar 

    46.
    Ecker, M. et al. Middle pleistocene ecology and neanderthal subsistence: Insights from stable isotope analyses in Payre (Ardèche, southeastern France). J. Hum. Evol. 65, 363–373 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Stevens, R. E. et al. Nitrogen isotope analyses of reindeer (Rangifer tarandus), 45,000 BP to 9,000 BP: Palaeoenvironmental reconstructions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 262, 32–45 (2008).
    Article  Google Scholar 

    48.
    Stevens, R. E., Hermoso-Buxán, X. L., Marín-Arroyo, A. B., González-Morales, M. R. & Straus, L. G. Investigation of Late Pleistocene and Early Holocene palaeoenvironmental change at El Mirón cave (Cantabria, Spain): Insights from carbon and nitrogen isotope analyses of red deer. Palaeogeogr. Palaeoclimatol. Palaeoecol. 414, 46–60 (2014).
    Article  Google Scholar 

    49.
    Drucker, D. G., Bridault, A., Hobson, K. A., Szuma, E. & Bocherens, H. Can carbon-13 in large herbivores reflect the canopy effect in temperate and boreal ecosystems? Evidence from modern and ancient ungulates. Palaeogeogr. Palaeoclimatol. Palaeoecol. 266, 69–82 (2008).
    Article  Google Scholar 

    50.
    Diefendorf, A. F., Mueller, K. E., Wing, S. L., Koch, P. L. & Freeman, K. H. Global patterns in leaf 13C discrimination and implications for studies of past and future climate. Proc. Natl. Acad. Sci. 107, 5738–5743 (2010).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Feranec, R. S., García, N., Díez, J. C. & Arsuaga, J. L. Understanding the ecology of mammalian carnivorans and herbivores from Valdegoba cave (Burgos, northern Spain) through stable isotope analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 297, 263–272 (2010).
    Article  Google Scholar 

    52.
    Bocherens, H., Drucker, D. G. & Madelaine, S. Evidence for a 15N positive excursion in terrestrial foodwebs at the Middle to Upper Palaeolithic transition in south-western France: Implications for early modern human palaeodiet and palaeoenvironment. J. Hum. Evol. 69, 31–43 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    53.
    Sanchez Goni, M. F. et al. Contrasting impacts of Dansgaard-Oeschger events over a western European latitudinal transect modulated by orbital parameters. Quatern. Sci. Rev. 27, 1136–1151 (2008).
    ADS  Article  Google Scholar 

    54.
    Ruddiman, W. F. & McIntyre, A. Oceanic mechanisms for amplification of the 23,000-year ice-volume cycle. Science 212, 617–627 (1981).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Kindler, P. et al. Temperature reconstruction from 10 to 120 kyr b2k from the NGRIP ice core. Clim. Past 10, 887–902 (2014).
    Article  Google Scholar 

    56.
    Guiter, F. et al. The last climatic cycles in Western Europe: A comparison between long continuous lacustrine sequences from France and other terrestrial records. Quatern. Int. 111, 59–74 (2003).
    Article  Google Scholar 

    57.
    de Beaulieu, J.-L. & Reille, M. The last climatic cycle at La Grande Pile (Vosges, France) a new pollen profile. Quatern. Sci. Rev. 11, 431–438 (1992).
    ADS  Article  Google Scholar 

    58.
    Van Andel, T. H. & Tzedakis, P. C. Palaeolithic landscapes of Europe and environs, 150,000–25,000 years ago: An overview. Quatern. Sci. Rev. 15, 481–500 (1996).
    ADS  Article  Google Scholar 

    59.
    Ponel, P. Rissian, Eemian and Würmian Coleoptera assemblages from La Grande Pile (Vosges, France). Palaeogeogr. Palaeoclimatol. Palaeoecol. 114, 1–41 (1995).
    Article  Google Scholar 

    60.
    Royer, A. et al. Late Pleistocene (MIS 3–4) climate inferred from micromammal communities and δ 18O of rodents from Les Pradelles, France. Quatern. Res. (United States) 80, 113–124 (2013).
    ADS  CAS  Article  Google Scholar 

    61.
    Barron, E., Andel, T. H. van & Pollard, D. Glacial environments II: Reconstructing the climate of Europe in the last glaciation. Neanderthals and modern humans in the European landscape during the last glaciation 57–78 (2003).

    62.
    Guérin, G. et al. Multi-method (TL and OSL), multi-material (quartz and flint) dating of the Mousterian site of Roc de Marsal (Dordogne, France): Correlating Neanderthal occupations with the climatic variability of MIS 5-3. J. Archaeol. Sci. 39, 3071–3084 (2012).
    Article  CAS  Google Scholar 

    63.
    Copeland, S. R. et al. Strontium isotope ratios (87Sr/86Sr) of tooth enamel: A comparison of solution and laser ablation multicollector inductively coupled plasma mass spectrometry methods. Rapid Commun. Mass Spectrom. 22, 3187–3194 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Pseudomonas aeruginosa reverse diauxie is a multidimensional, optimized, resource utilization strategy

    1.
    Byrd, M. S. et al. Direct evaluation of Pseudomonas aeruginosa biofilm mediators in a chronic infection model. Infect. Immun. 79, 3087–3095. https://doi.org/10.1128/IAI.00057-11 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 
    2.
    Behrends, V. et al. Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections. Environ. Microbiol. 15, 398–408. https://doi.org/10.1111/j.1462-2920.2012.02840.x (2013).
    CAS  Article  PubMed  Google Scholar 

    3.
    Calhoun, J. H., Murray, C. K. & Manring, M. M. Multidrug-resistant organisms in military wounds from Iraq and Afghanistan. Clin. Orthop. Relat. Res. 466, 1356–1362. https://doi.org/10.1007/s11999-008-0212-9 (2008).
    Article  PubMed  PubMed Central  Google Scholar 

    4.
    Frykberg, R. G. & Banks, J. Challenges in the treatment of chronic wounds. Adv. Wound Care New Rochelle 4, 560–582. https://doi.org/10.1089/wound.2015.0635 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    5.
    Jarbrink, K. et al. The humanistic and economic burden of chronic wounds: A protocol for a systematic review. Syst. Rev. 6, 15. https://doi.org/10.1186/s13643-016-0400-8 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    6.
    Fife, C. E. & Carter, M. J. Wound care outcomes and associated cost among patients treated in US outpatient wound centers: Data from the US wound registry. Wounds 24, 10–17 (2012).
    PubMed  Google Scholar 

    7.
    Valot, B. et al. What it takes to be a Pseudomonas aeruginosa? The core genome of the opportunistic pathogen updated. PLoS ONE 10, e0126468. https://doi.org/10.1371/journal.pone.0126468 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    8.
    Rojo, F. Carbon catabolite repression in Pseudomonas: Optimizing metabolic versatility and interactions with the environment. FEMS Microbiol. Rev. 34, 658–684. https://doi.org/10.1111/j.1574-6976.2010.00218.x (2010).
    CAS  Article  PubMed  Google Scholar 

    9.
    Görke, B. & Stülke, J. Carbon catabolite repression in bacteria: Many ways to make the most out of nutrients. Nat. Rev. Microbiol. 6, 613. https://doi.org/10.1038/nrmicro1932 (2008).
    CAS  Article  PubMed  Google Scholar 

    10.
    Collier, D. N., Hager, P. W. & Phibbs, P. V. Catabolite repression control in the Pseudomonads. Res. Microbiol. 147, 551–561. https://doi.org/10.1016/0923-2508(96)84011-3 (1996).
    CAS  Article  PubMed  Google Scholar 

    11.
    Scitable by Nature EDUCATION 2005).

    12.
    Pellett, S., Bigley, D. V. & Grimes, D. J. Distribution of Pseudomonas aeruginosa in a riverine ecosystem. Appl. Environ. Microb. 45, 328–332 (1983).
    CAS  Article  Google Scholar 

    13.
    Döring, G. et al. Distribution and transmission of Pseudomonas aeruginosa andBurkholderia cepacia in a hospital ward. Pediatr. Pulmonol. 21, 90–100. https://doi.org/10.1002/(sici)1099-0496(199602)21:2%3c90::Aid-ppul5%3e3.0.Co;2-t (1996).
    Article  PubMed  Google Scholar 

    14.
    Romling, U., Kader, A., Sriramulu, D. D., Simm, R. & Kronvall, G. Worldwide distribution of Pseudomonas aeruginosa clone C strains in the aquatic environment and cystic fibrosis patients. Environ. Microbiol. 7, 1029–1038. https://doi.org/10.1111/j.1462-2920.2005.00780.x (2005).
    CAS  Article  PubMed  Google Scholar 

    15.
    Hamilton, W. A., Dawes, E. & A. ,. A diauxic effect with Pseudomonas aeruginosa. Biochem. J. 71, 25P-26P (1959).
    CAS  Google Scholar 

    16.
    Liu, Y., Gokhale, C. S., Rainey, P. B. & Zhang, X. X. Unravelling the complexity and redundancy of carbon catabolic repression in Pseudomonas fluorescens SBW25. Mol. Microbiol. 105, 589–605. https://doi.org/10.1111/mmi.13720 (2017).
    CAS  Article  PubMed  Google Scholar 

    17.
    Park, H., McGill, S. L., Arnold, A. D. & Carlson, R. P. Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor. Cell.Mol. Life Sci. https://doi.org/10.1007/s00018-019-03377-x (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    18.
    Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, Princeton, 2002).
    Google Scholar 

    19.
    Carlson, R. P. Metabolic systems cost-benefit analysis for interpreting network structure and regulation. Bioinformatics 23, 1258–1264. https://doi.org/10.1093/bioinformatics/btm082 (2007).
    CAS  Article  PubMed  Google Scholar 

    20.
    Carlson, R. P., Oshota, O. J. & Taffs, R. L. in Reprogramming Microbial Metabolic Pathways (eds Xiaoyuan Wang, Jian Chen, & Peter Quinn) 139–157 (Springer, Netherlands, 2012).

    21.
    Folsom, J. P. & Carlson, R. P. Physiological, biomass elemental composition and proteomic analyses of Escherichia coli ammonium-limited chemostat growth, and comparison with iron- and glucose-limited chemostat growth. Microbiology 161, 1659–1670. https://doi.org/10.1099/mic.0.000118 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    22.
    Carlson, R. P. Decomposition of complex microbial behaviors into resource-based stress responses. Bioinformatics 25, 90–97 (2009).
    CAS  Article  Google Scholar 

    23.
    Goelzer, A. & Fromion, V. Bacterial growth rate reflects a bottleneck in resource allocation. Biochim. Biophys. Acta 1810, 978–988. https://doi.org/10.1016/j.bbagen.2011.05.014 (2011).
    CAS  Article  PubMed  Google Scholar 

    24.
    Goelzer, A. & Fromion, V. Resource allocation in living organisms. Biochem. Soc. Trans. 45, 945–952. https://doi.org/10.1042/BST20160436 (2017).
    CAS  Article  Google Scholar 

    25.
    Yang, L. et al. solveME: Fast and reliable solution of nonlinear ME models. BMC Bioinform. 17, 391. https://doi.org/10.1186/s12859-016-1240-1 (2016).
    CAS  Article  Google Scholar 

    26.
    Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl. Acad. Sci. USA 104, 12663–12668. https://doi.org/10.1073/pnas.0609845104 (2007).
    ADS  CAS  Article  PubMed  Google Scholar 

    27.
    Vazquez, A. & Oltvai, Z. N. Macromolecular crowding explains overflow metabolism in cells. Sci. Rep. 6, 31007. https://doi.org/10.1038/srep31007 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    28.
    Zhuang, K., Vemuri, G. N. & Mahadevan, R. Economics of membrane occupancy and respiro-fermentation. Mol. Syst. Biol. 7, 500. https://doi.org/10.1038/msb.2011.34 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    29.
    Szenk, M., Dill, K. A. & de Graff, A. M. R. Why do fast-growing bacteria enter overflow metabolism? Testing the membrane real estate hypothesis. Cell Syst. 5, 95–104. https://doi.org/10.1016/j.cels.2017.06.005 (2017).
    CAS  Article  PubMed  Google Scholar 

    30.
    Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104. https://doi.org/10.1038/nature15765 (2015).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    31.
    Folsom, J. P., Parker, A. E. & Carlson, R. P. Physiological and proteomic analysis of Escherichia coli iron-limited chemostat growth. J. Bacteriol. 196, 2748–2761. https://doi.org/10.1128/JB.01606-14 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    32.
    Schuster, S., Boley, D., Moller, P., Stark, H. & Kaleta, C. Mathematical models for explaining the Warburg effect: A review focussed on ATP and biomass production. Biochem. Soc. Trans. 43, 1187–1194. https://doi.org/10.1042/BST20150153 (2015).
    CAS  Article  PubMed  Google Scholar 

    33.
    Woods, J. et al. Development and application of a polymicrobial in vitro wound biofilm model. J. Appl. Microbiol. 112, 998–1006. https://doi.org/10.1111/j.1365-2672.2012.05264.x (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    34.
    Yung, Y. P. et al. Reverse diauxie phenotype in Pseudomonas aeruginosa biofilm revealed by exometabolomics and label-free proteomics. NPJ Biofilms Microbiomes 5, 31. https://doi.org/10.1038/s41522-019-0104-7 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    35.
    Behrends, V., Ebbels, T. M., Williams, H. D. & Bundy, J. G. Time-resolved metabolic footprinting for nonlinear modeling of bacterial substrate utilization. Appl. Environ. Microbiol. 75, 2453–2463. https://doi.org/10.1128/AEM.01742-08 (2009).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    36.
    Berger, A. et al. Robustness and plasticity of metabolic pathway flux among uropathogenic isolates of Pseudomonas aeruginosa. PLoS ONE 9, e88368. https://doi.org/10.1371/journal.pone.0088368 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    37.
    Nouwens, A. S. et al. Complementing genomics with proteomics: The membrane subproteome ofPseudomonas aeruginosa PAO1. Electrophoresis 21, 3797–3809. https://doi.org/10.1002/1522-2683(200011)21:17%3c3797::Aid-elps3797%3e3.0.Co;2-p (2000).
    CAS  Article  PubMed  Google Scholar 

    38.
    Penesyan, A. et al. Genetically and phenotypically distinct Pseudomonas aeruginosa cystic fibrosis isolates share a core proteomic signature. PLoS ONE 10, e0138527. https://doi.org/10.1371/journal.pone.0138527 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    39.
    Nikel, P. I., Chavarria, M., Fuhrer, T., Sauer, U. & de Lorenzo, V. Pseudomonas putida KT2440 strain metabolizes glucose through a cycle formed by enzymes of the Entner-Doudoroff, Embden-Meyerhof-Parnas, and pentose phosphate pathways. J. Biol. Chem. 290, 25920–25932. https://doi.org/10.1074/jbc.M115.687749 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    40.
    Phalak, P., Chen, J., Carlson, R. P. & Henson, M. A. Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC Syst. Biol. 10, 90. https://doi.org/10.1186/s12918-016-0334-8 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    41.
    Oberhardt, M. A., Goldberg, J. B., Hogardt, M. & Papin, J. A. Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J. Bacteriol. 192, 5534–5548. https://doi.org/10.1128/JB.00900-10 (2010).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    42.
    Schuetz, R., Kuepfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3, 119. https://doi.org/10.1038/msb4100162 (2007).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Schuster, S., Pfeiffer, T. & Fell, D. A. Is maximization of molar yield in metabolic networks favoured by evolution?. J. Theor. Biol. 252, 497–504. https://doi.org/10.1016/j.jtbi.2007.12.008 (2008).
    MathSciNet  CAS  Article  PubMed  MATH  Google Scholar 

    44.
    Varma, A., Boesch, B. W. & Palsson, B. O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 59, 2465–2473 (1993).
    CAS  Article  Google Scholar 

    45.
    Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microb. 60, 3724–3731 (1994).
    CAS  Article  Google Scholar 

    46.
    Bar-Even, A. et al. The moderately efficient enzyme: Evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410. https://doi.org/10.1021/bi2002289 (2011).
    CAS  Article  PubMed  Google Scholar 

    47.
    Volkmer, B. & Heinemann, M. Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS ONE 6, e23126. https://doi.org/10.1371/journal.pone.0023126 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    48.
    Novak, M., Pfeiffer, T., Lenski, R. E., Sauer, U. & Bonhoeffer, S. Experimental tests for an evolutionary trade-off between growth rate and yield in E. coli. Am. Nat. 168, 242–251. https://doi.org/10.1086/506527 (2006).
    Article  PubMed  Google Scholar 

    49.
    Hoffmann, S., Hoppe, A. & Holzhütter, H.-G. Composition of metabolic flux distributions by functionally interpretable minimal flux modes (MinModes). Genome Inf. 17, 195–207 (2006).
    CAS  Google Scholar 

    50.
    Holzhutter, H. G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922. https://doi.org/10.1111/j.1432-1033.2004.04213.x (2004).
    CAS  Article  PubMed  Google Scholar 

    51.
    Carlson, R. P. & Taffs, R. L. Molecular-level tradeoffs and metabolic adaptation to simultaneous stressors. Curr. Opin. Biotechnol. 21, 670–676 (2010).
    CAS  Article  Google Scholar 

    52.
    Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M. & Sauer, U. Multidimensional optimality of microbial metabolism. Science New York NY 336, 601–604. https://doi.org/10.1126/science.1216882 (2012).
    CAS  Article  Google Scholar 

    53.
    Velayudhan, J., Jones, M. A., Barrow, P. A. & Kelly, D. J. l-Serine catabolism via an oxygen-labile l-serine dehydratase is essential for colonization of the avian gut by Campylobacter jejuni. Infect. Immun. 72, 260–268. https://doi.org/10.1128/iai.72.1.260-268.2004 (2004).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    54.
    Frimmersdorf, E., Horatzek, S., Pelnikevich, A., Wiehlmann, L. & Schomburg, D. How Pseudomonas aeruginosa adapts to various environments: a metabolomic approach. Environ. Microbiol. 12, 1734–1747. https://doi.org/10.1111/j.1462-2920.2010.02253.x (2010).
    CAS  Article  PubMed  Google Scholar 

    55.
    Tiwari, N. & Campbell, J. Enzymatic control of the metabolic activity of Pseudomonas aeruginosa grown in glucose or succinate media. Biochimica et Biophysica Acta BBA Gen. Subj. 192, 395–401. https://doi.org/10.1016/0304-4165(69)90388-2 (1969).
    CAS  Article  Google Scholar 

    56.
    Trautwein, K. et al. Benzoate mediates repression of C(4)-dicarboxylate utilization in “Aromatoleum aromaticum” EbN1. J. Bacteriol. 194, 518–528. https://doi.org/10.1128/JB.05072-11 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    57.
    Kremling, A., Geiselmann, J., Ropers, D. & de Jong, H. An ensemble of mathematical models showing diauxic growth behaviour. BMC Syst. Biol. 12, 1–16. https://doi.org/10.1186/s12918-018-0604-8 (2018).
    CAS  Article  Google Scholar 

    58.
    Kremling, A., Geiselmann, J., Ropers, D. & de Jong, H. Understanding carbon catabolite repression in Escherichia coli using quantitative models. Trends Microbiol. 23, 99–109. https://doi.org/10.1016/j.tim.2014.11.002 (2015).
    CAS  Article  PubMed  Google Scholar 

    59.
    Ibberson, C. B. & Whiteley, M. The social life of microbes in chronic infection. Curr. Opin. Microbiol. 53, 44–50. https://doi.org/10.1016/j.mib.2020.02.003 (2020).
    CAS  Article  PubMed  Google Scholar 

    60.
    King, A. N., de Mets, F. & Brinsmade, S. R. Who’s in control? Regulation of metabolism and pathogenesis in space and time. Curr. Opin. Microbiol. 55, 88–96. https://doi.org/10.1016/j.mib.2020.05.009 (2020).
    CAS  Article  PubMed  Google Scholar 

    61.
    Tuncil, Y. E. et al. Reciprocal prioritization to dietary glycans by gut bacteria in a competitive environment promotes stable coexistence. MBio 8, 66. https://doi.org/10.1128/mBio.01068-17 (2017).
    Article  Google Scholar 

    62.
    Goyal, A., Dubinkina, V. & Maslov, S. Multiple stable states in microbial communities explained by the stable marriage problem. ISME J. 12, 2823–2834. https://doi.org/10.1038/s41396-018-0222-x (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    63.
    Ren, D., Madsen, J. S., Sorensen, S. J. & Burmolle, M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 9, 81–89. https://doi.org/10.1038/ismej.2014.96 (2015).
    CAS  Article  PubMed  Google Scholar 

    64.
    Russel, J., Roder, H. L., Madsen, J. S., Burmolle, M. & Sorensen, S. J. Antagonism correlates with metabolic similarity in diverse bacteria. Proc. Natl. Acad. Sci. USA 114, 10684–10688. https://doi.org/10.1073/pnas.1706016114 (2017).
    CAS  Article  PubMed  Google Scholar 

    65.
    Brileya, K. A., Camilleri, L. B., Zane, G. M., Wall, J. D. & Fields, M. W. Biofilm growth mode promotes maximum carrying capacity and community stability during product inhibition syntrophy. Front. Microbiol. 5, 693. https://doi.org/10.3389/fmicb.2014.00693 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    66.
    Carlson, R. P. et al. Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia. Biochem. Soc. Trans. 46, 269–284. https://doi.org/10.1042/BST20170242 (2018).
    CAS  Article  PubMed  Google Scholar 

    67.
    Beck, A., Hunt, K., Bernstein, H. C. & Carlson, R. in Biotechnology for Biofuel Production and Optimization (eds Carrie A. Eckert & Cong T. Trinh) 407–432 (Elsevier, Amsterdam, 2016).

    68.
    Hillesland, K. L. & Stahl, D. A. Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc. Natl. Acad. Sci. USA 107, 2124–2129. https://doi.org/10.1073/pnas.0908456107 (2010).
    ADS  Article  PubMed  Google Scholar 

    69.
    DeLeon, S. et al. Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model. Infect. Immun. 82, 4718–4728. https://doi.org/10.1128/IAI.02198-14 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    70.
    Filkins, L. M. et al. Coculture of Staphylococcus aureus with Pseudomonas aeruginosa drives S. aureus towards fermentative metabolism and reduced viability in a cystic fibrosis model. J. Bacteriol. 197, 2252–2264. https://doi.org/10.1128/jb.00059-15 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    71.
    Bernstein, H. C., Paulson, S. D. & Carlson, R. P. Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity. J. Biotechnol. 157, 159–166. https://doi.org/10.1016/j.jbiotec.2011.10.001 (2012).
    CAS  Article  PubMed  Google Scholar 

    72.
    Bernier, S. P., Letoffe, S., Delepierre, M. & Ghigo, J. M. Biogenic ammonia modifies antibiotic resistance at a distance in physically separated bacteria. Mol. Microbiol. 81, 705–716. https://doi.org/10.1111/j.1365-2958.2011.07724.x (2011).
    CAS  Article  PubMed  Google Scholar 

    73.
    Palkova, Z. et al. Ammonia mediates communication between yeast colonies. Nature 390, 532–536. https://doi.org/10.1038/37398 (1997).
    ADS  CAS  Article  PubMed  Google Scholar 

    74.
    Wang, J., Yan, D., Dixon, R. & Wang, Y. P. Deciphering the principles of bacterial nitrogen dietary preferences: A strategy for nutrient containment. mBio https://doi.org/10.1128/mBio.00792-16 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    75.
    Schreiber, K. et al. The anaerobic regulatory network required for Pseudomonas aeruginosa nitrate respiration. J. Bacteriol. 189, 4310–4314. https://doi.org/10.1128/JB.00240-07 (2007).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    76.
    Stewart, P. S. Diffusion in biofilms. J. Bacteriol. 185, 1485–1491. https://doi.org/10.1128/JB.185.5.1485-1491.2003 (2003).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    77.
    Cornforth, D. M. & Foster, K. R. Competition sensing: The social side of bacterial stress responses. Nat. Rev. Microbiol. 11, 285. https://doi.org/10.1038/nrmicro2977 (2013).
    CAS  Article  PubMed  Google Scholar 

    78.
    Korgaonkar, A., Trivedi, U., Rumbaugh, K. P. & Whiteley, M. Community surveillance enhances Pseudomonas aeruginosa virulence during polymicrobial infection. Proc. Natl. Acad. Sci. USA 110, 1059–1064. https://doi.org/10.1073/pnas.1214550110 (2013).
    ADS  Article  PubMed  Google Scholar 

    79.
    Wang, M., Schaefer, A. L., Dandekar, A. A. & Greenberg, E. P. Quorum sensing and policing of Pseudomonas aeruginosa social cheaters. Proc. Natl. Acad. Sci. USA 112, 2187–2191. https://doi.org/10.1073/pnas.1500704112 (2015).
    ADS  CAS  Article  PubMed  Google Scholar 

    80.
    Allegretta, G. et al. In-depth profiling of MvfR-regulated small molecules in Pseudomonas aeruginosa after quorum sensing inhibitor treatment. Front. Microbiol. 8, 1–12. https://doi.org/10.3389/fmicb.2017.00924 (2017).
    Article  Google Scholar 

    81.
    Deziel, E. et al. Analysis of Pseudomonas aeruginosa 4-hydroxy-2-alkylquinolines (HAQs) reveals a role for 4-hydroxy-2-heptylquinoline in cell-to-cell communication. Proc. Natl. Acad. Sci. USA 101, 1339–1344. https://doi.org/10.1073/pnas.0307694100 (2004).
    ADS  CAS  Article  PubMed  Google Scholar 

    82.
    Meirelles, L. A. & Newman, D. K. Both toxic and beneficial effects of pyocyanin contribute to the lifecycle of Pseudomonas aeruginosa. Mol. Microbiol. 110, 995–1010. https://doi.org/10.1111/mmi.14132 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    83.
    Hall, S. et al. Cellular effects of pyocyanin, a secreted virulence factor of Pseudomonas aeruginosa. Toxins Basel https://doi.org/10.3390/toxins8080236 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    84.
    Price-Whelan, A., Dietrich, L. E. & Newman, D. K. Rethinking “secondary” metabolism: Physiological roles for phenazine antibiotics. Nat. Chem. Biol. 2, 71–78. https://doi.org/10.1038/nchembio764 (2006).
    CAS  Article  PubMed  Google Scholar 

    85.
    Noto, M. J., Burns, W. J., Beavers, W. N. & Skaar, E. P. Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus. J. Bacteriol. https://doi.org/10.1128/JB.00221-17 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    86.
    James, T. J., Hughes, M. A., Cherry, G. W. & Taylor, R. P. Simple biochemical markers to assess chronic wounds. Wound Repair. Regen. 8, 264–269. https://doi.org/10.1046/j.1524-475x.2000.00264.x (2000).
    CAS  Article  PubMed  Google Scholar 

    87.
    Trengove, N. J., Langton, S. R. & Stacey, M. C. Biochemical analysis of wound fluid from nonhealing and healing chronic leg ulcers. Wound Repair. Regen. 4, 234–239. https://doi.org/10.1046/j.1524-475X.1996.40211.x (1996).
    CAS  Article  PubMed  Google Scholar 

    88.
    Cox, K. et al. Prevalence and significance of lactic acidosis in diabetic ketoacidosis. J. Crit. Care 27, 132–137. https://doi.org/10.1016/j.jcrc.2011.07.071 (2012).
    CAS  Article  PubMed  Google Scholar 

    89.
    de Oliveira, F. P. et al. Prevalence, antimicrobial susceptibility, and clonal diversity of Pseudomonas aeruginosa in Chronic Wounds. J. Wound Ostomy Contin. Nurs. 44, 528–535. https://doi.org/10.1097/won.0000000000000373 (2017).
    Article  Google Scholar 

    90.
    Rhoads, D. D., Wolcott, R. D., Sun, Y. & Dowd, S. E. Comparison of culture and molecular identification of bacteria in chronic wounds. Int. J. Mol. Sci. 13, 2535–2550. https://doi.org/10.3390/ijms13032535 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    91.
    Dalton, T. et al. An in vivo polymicrobial biofilm wound infection model to study interspecies interactions. PLoS ONE 6, e27317. https://doi.org/10.1371/journal.pone.0027317 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    92.
    Kirketerp-Moller, K. et al. Distribution, organization, and ecology of bacteria in chronic wounds. J. Clin. Microbiol. 46, 2717–2722. https://doi.org/10.1128/JCM.00501-08 (2008).
    Article  PubMed  PubMed Central  Google Scholar 

    93.
    Murray, J. L., Connell, J. L., Stacy, A., Turner, K. H. & Whiteley, M. Mechanisms of synergy in polymicrobial infections. J. Microbiol. 52, 188–199. https://doi.org/10.1007/s12275-014-4067-3 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    94.
    Ferreira, M. T., Manso, A. S., Gaspar, P., Pinho, M. G. & Neves, A. R. Effect of oxygen on glucose metabolism: Utilization of lactate in Staphylococcus aureus as revealed by in vivo NMR studies. PLoS ONE 8, e58277. https://doi.org/10.1371/journal.pone.0058277 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    95.
    Tynecka, Z., Szcześniak, Z., Malm, A. & Los, R. Energy conservation in aerobically grown Staphylococcus aureus. Res. Microbiol. 150, 555–566. https://doi.org/10.1016/s0923-2508(99)00102-3 (1999).
    CAS  Article  PubMed  Google Scholar 

    96.
    Sanchez, C. J. Jr. et al. Biofilm formation by clinical isolates and the implications in chronic infections. BMC Infect. Dis. 13, 47. https://doi.org/10.1186/1471-2334-13-47 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    97.
    James, G. A. et al. Biofilms in chronic wounds. Wound Repair. Regen. 16, 37–44. https://doi.org/10.1111/j.1524-475X.2007.00321.x (2008).
    ADS  Article  PubMed  Google Scholar 

    98.
    Bacon, C. W. & White, J. Microbial Endophytes (CRC Press, Boca Raton, 2000).
    Google Scholar 

    99.
    Mann, M. Filter Aided Sample Preparation (FASP) Method. http://www.biochem.mpg.de/226356/FASP (2013).

    100.
    Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protocols 11, 2301–2319. https://doi.org/10.1038/nprot.2016.136 (2016).
    CAS  Article  PubMed  Google Scholar 

    101.
    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740. https://doi.org/10.1038/nmeth.3901 (2016).
    CAS  Article  PubMed  Google Scholar 

    102.
    Szklarczyk, D. et al. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452. https://doi.org/10.1093/nar/gku1003 (2015).
    CAS  Article  PubMed  Google Scholar  More