More stories

  • in

    Evolutionary assembly of flowering plants into sky islands

    1.Lavergne, S., Mouquet, N., Thuiller, W. & Ronce, O. Biodiversity and climate change: integrating evolutionary and ecological responses of species and communities. Annu. Rev. Ecol. Evol. Syst. 41, 321–350 (2010).Article 

    Google Scholar 
    2.Ricklefs, R. E. Community diversity: relative roles of local and regional processes. Science 235, 167–171 (1987).CAS 
    Article 

    Google Scholar 
    3.Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

    Google Scholar 
    4.Münkemüller, T., Boucher, F., Thuiller, W. & Lavergne, S. Common conceptual and methodological pitfalls in the analysis of phylogenetic niche conservatism. Funct. Ecol. 29, 627–639 (2015).Article 

    Google Scholar 
    5.Behrensmeyer, A. K. et al. (eds) Terrestrial Ecosystems Through Time: Evolutionary Paleoecology of Terrestrial Plants and Animals (Univ. of Chicago Press, 1992).6.Graham, A. Late Cretaceous and Cenozoic History of North American Vegetation North of Mexico (Oxford Univ. Press, 1999).7.Latham, R. E. & Ricklefs, R. E. in Species Diversity in Ecological Communities (eds Ricklefs, R. E. & Schluter, D.) 294–314 (Univ. of Chicago Press, 1993).8.Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).CAS 
    Article 

    Google Scholar 
    9.Wiens, J. J. & Donoghue, M. J. Historical biogeography, ecology, and species richness. Trends Ecol. Evol. 19, 639–644 (2004).Article 

    Google Scholar 
    10.Ricklefs, R. E. Evolutionary diversification and the origin of the diversity–environment relationship. Ecology 87, S3–S13 (2006).Article 

    Google Scholar 
    11.Qian, H. & Sandel, B. Phylogenetic structure of regional angiosperm assemblages across latitudinal and climatic gradients in North America. Glob. Ecol. Biogeogr. 26, 1258–1269 (2017).Article 

    Google Scholar 
    12.Körner, C. Why are there global gradients in species richness? Mountains might hold the answer. Trends Ecol. Evol. 15, 513–514 (2000).Article 

    Google Scholar 
    13.Pulsipher, L. M. & Pulsipher, A. World Regional Geography: Global Patterns, Local Lives 6th edn (W.H. Freeman, 2014).14.Culmsee, H. & Leuschner, C. Consistent patterns of elevational change in tree taxonomic and phylogenetic diversity across Malesian mountain forests. J. Biogeogr. 40, 1997–2010 (2013).Article 

    Google Scholar 
    15.González-Caro, S., Umaña, M. N., Álvarez, E., Stevenson, P. R. & Swenson, N. G. Phylogenetic alpha and beta diversity in tropical tree assemblages along regional scale environmental gradients in northwest South America. J. Plant Ecol. 7, 145–153 (2014).Article 

    Google Scholar 
    16.Qian, H., Zhang, Y., Zhang, J. & Wang, X. Latitudinal gradients in phylogenetic relatedness of angiosperm trees in North America. Glob. Ecol. Biogeogr. 22, 1183–1191 (2013).Article 

    Google Scholar 
    17.Qian, H., Field, R., Zhang, J., Zhang, J. & Chen, S. Phylogenetic structure and ecological and evolutionary determinants of species richness for angiosperm trees in forest communities in China. J. Biogeogr. 43, 603–615 (2016).Article 

    Google Scholar 
    18.Qian, H. & Ricklefs, R. E. Out of the tropical lowlands: latitude versus elevation. Trends Ecol. Evol. 31, 738–741 (2016).Article 

    Google Scholar 
    19.Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).Article 

    Google Scholar 
    20.Jin, Y. & Qian, H. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography https://doi.org/10.1111/ecog.04434 (2019).21.Mazel, F. et al. Influence of tree shape and evolutionary time-scale on phylogenetic diversity metrics. Ecography 39, 913–920 (2016).CAS 
    Article 

    Google Scholar 
    22.Thuiller, W. et al. Resolving Darwin’s naturalization conundrum: a quest for evidence. Divers. Distrib. 16, 461–475 (2010).Article 

    Google Scholar 
    23.Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems 2nd edn (Springer, 2003).24.Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).Article 

    Google Scholar 
    25.Gallien, L., Zurell, D. & Zimmermann, N. E. Frequency and intensity of facilitation reveal opposing patterns along a stress gradient. Ecol. Evol. 8, 2171–2181 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    26.Choler, P., Michalet, R. & Callaway, R. M. Facilitation and competition on gradients in alpine plant communities. Ecology 82, 3295–3308 (2001).Article 

    Google Scholar 
    27.Butterfield, B. J. et al. Alpine cushion plants inhibit the loss of phylogenetic diversity in severe environments. Ecol. Lett. 16, 478–486 (2013).CAS 
    Article 

    Google Scholar 
    28.Steinbauer et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

    Google Scholar 
    29.Takhtajan, A. L. Flowering Plants: Origin and Dispersal (Oliver & Boyd, 1969).30.Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 

    Google Scholar 
    31.Heald, W. Sky Island (D. Van Nostrand Co., Inc., 1967).32.Marx, H. E. et al. Riders in the sky (islands): using a mega-phylogenetic approach to understand plant species distribution and coexistence at the altitudinal limits of angiosperm plant life. J. Biogeogr. 44, 2618–2630 (2017).Article 

    Google Scholar 
    33.Humboldt, A. V. & Bonpland, A. Essai sur la Géographie des Plantes: Accompagné d’un Tableau Physique des Régions Équinoxiales (Arno Press, 1977).34.Qian, H., White, P. S., Klinka, K. & Chourmouzis, C. Phytogeographical and community similarities of alpine tundras of Changbaishan Summit, China, and Indian Peaks, USA. J. Veg. Sci. 10, 869–882 (1999).Article 

    Google Scholar 
    35.Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73–78 (2011).Article 

    Google Scholar 
    36.Chapin, F. S. III & Körner, C. in Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences (eds Chapin, F. S. III & Körner, C.) 313–320 (Springer, 1995).37.Angiosperm Phylogeny Group. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).Article 

    Google Scholar 
    38.Webb, C., Ackerly, D. & Kembel, S. Phylocom: Software for the analysis of phylogenetic community structure and character evolution, with Phylomatic. R package version 4.2 (2011).39.Qian, H. & Jin, Y. Are phylogenies resolved at the genus level appropriate for studies on phylogenetic structure of species assemblages? Plant Divers. https://doi.org/10.1016/j.pld.2020.11.005 (2021).40.Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

    Google Scholar 
    41.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

    Google Scholar 
    42.Tsirogiannis, C., Sandel, B. & Cheliotis, D. Efficient computation of popular phylogenetic tree measures. Lect. Notes Comput. Sci. 7534, 30–43 (2012).Article 

    Google Scholar 
    43.Tsirogiannis, C., Sandel, B. & Kalvisa, A. New algorithms for computing phylogenetic biodiversity. Lect. Notes Comput. Sci. 8701, 187–203 (2014).Article 

    Google Scholar 
    44.Tsirogiannis, C. & Sandel, B. PhyloMeasures: a package for computing phylogenetic biodiversity measures and their statistical moments. Ecography 39, 709–714 (2016).Article 

    Google Scholar  More

  • in

    Further behavioural parameters support reciprocity and milk theft as explanations for giraffe allonursing

    1.Rippeyoung, P. L. F. & Noonan, M. C. The economic costs of breastfeeding for women. Breastfeed Med. 6(5), 325–327 (2011).PubMed 
    Article 

    Google Scholar 
    2.Gloneková, M., Vymyslická, P. J., Žáčková, M. & Brandlová, K. Giraffe nursing behaviour reflects environmental conditions. Behaviour 154, 115–129 (2017).Article 

    Google Scholar 
    3.Hejcmanová, P. et al. Suckling behaviour of eland antelopes (Taurotragus spp.) under semi-captive and farm conditions. J. Ethol. 29, 161–168 (2011).Article 

    Google Scholar 
    4.Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).
    Google Scholar 
    5.Gittleman, J. L. & Thompson, S. D. Energy allocation in mammalian reproduction. Am. Zool. 28, 863–875 (1988).Article 

    Google Scholar 
    6.Arnold, L. C., Habe, M., Troxler, J., Nowack, J. & Vetter, S. G. Rapid establishment of teat order and allonursing in wild boar (Sus scrofa). Ethology 125, 940–948 (2019).Article 

    Google Scholar 
    7.Pluháček, J., Olléová, M., Bartošová, J. & Bartoš, L. Effect of ecological adaptation on suckling behaviour in three zebra species. Behaviour 149(13–14), 1395–1411 (2012).
    Google Scholar 
    8.Pluháček, J., Olléová, M., Bartoš, L. & Bartošová, J. Time spent suckling is affected by different social organization in three zebra species. J. Zool. 292, 10–17 (2014).Article 

    Google Scholar 
    9.Packer, C., Lewis, S. & Pusey, A. A comparative analysis of non-offspring nursing. Anim. Behav. 43, 265–281 (1992).Article 

    Google Scholar 
    10.Gloneková, M., Brandlová, K. & Pluháček, J. Higher maternal care and tolerance in more experienced giraffe mothers. Acta Ethol. 23, 1–7 (2020).Article 

    Google Scholar 
    11.MacLeod, K., Nielsen, J. F. & Clutton-Brock, T. H. Factors predicting the frequency, likelihood and duration of allonursing in the cooperatively breeding meerkat. Anim. Behav. 86, 1059–1067 (2013).Article 

    Google Scholar 
    12König, B. Cooperative care of young in mammals. Naturwissenschaften 84, 489–497 (1997).
    Google Scholar 
    13.Roulin, A. Why do lactating females nurse alien offspring? A review of hypotheses and empirical evidence. Anim. Behav. 63, 201–208 (2002).Article 

    Google Scholar 
    14.Bartoš, L., Vaňková, D., Hyánek, J. & Šiler, J. Impact of allosucking on growth of farmed red deer calves (Cervus elaphus). Anim. Sci. 72, 493–500 (2001).Article 

    Google Scholar 
    15.Bartoš, L., Vaňková, D., Šiler, J. & Illmann, G. Adoption, allonursing and allosucking in farmed red deer (Cervus elaphus). Anim. Sci. 72, 483–492 (2001).Article 

    Google Scholar 
    16.Engelhardt, S. C., Weladji, R. B., Holand, Ø. & Nieminen, M. Allosuckling in reindeer (Rangifer tarandus): A test of the improved nutrition and compensation hypotheses. Mammal. Biol. Z Säugetierkd 81(2), 146–152 (2016).Article 

    Google Scholar 
    17.Víchová, J. & Bartoš, L. Allosuckling in cattle: Gain or compensation?. Appl. Anim. Behav. Sci. 94, 223–235 (2005).Article 

    Google Scholar 
    18.Engelhardt, S. C. et al. Allosuckling in reindeer (Rangifer tarandus): Milk-theft, mismothering or kin selection?. Behav. Process. 107, 133–141 (2014).Article 

    Google Scholar 
    19.Gloneková, M., Brandlová, K. & Pluháček, J. Stealing milk by young and reciprocal mothers: High incidence of allonursing in giraffes, Giraffa camelopardalis. Anim. Behav. 113, 113–123 (2016).Article 

    Google Scholar 
    20.Pluháček, J., Bartošová, J. & Bartoš, L. Suckling behavior in captive plains zebra (Equus burchellii): Sex differences in foal behavior. J. Anim. Sci. 88(1), 131–136 (2010).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    21.Gloneková, M., Brandlová, K. & Pluháček, J. Giraffe males have longer suckling bouts than females. J. Mammal. 101(2), 558–653 (2020).Article 

    Google Scholar 
    22.Pluháček, J., Bartošová, J. & Bartoš, L. Further evidence for sex differences in suckling behaviour of captive plains zebra foals. Acta Ethol. 14, 91–95 (2011).Article 

    Google Scholar 
    23.Drábková, J. et al. Sucking and allosucking duration in farmed red deer (Cervus elaphus). Appl. Anim. Behav. Sci. 113(1), 215–223 (2008).Article 

    Google Scholar 
    24.Mendl, M. & Paul, E. S. Observation of nursing and sucking behaviour as an indicator of milk transfer and parental investment. Anim. Behav. 37, 513–515 (1989).Article 

    Google Scholar 
    25.Therrien, J. F., Cote, S. D., Festa-Bianchet, M. & Ouellet, J. P. Maternal care in white-tailed deer: Trade-off between maintenance and reproduction under food restriction. Anim. Behav. 75, 235–243 (2007).Article 

    Google Scholar 
    26.Plesner Jensen, S., Siefert, L., Okori, J. & Clutton-Brock, T. Age-related participation in allosuckling by nursing warthogs (Phacochoerus africanus). J. Zool. 248, 443–449 (1999).Article 

    Google Scholar 
    27Trivers, R. L. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35–57 (1971).Article 

    Google Scholar 
    28.Engelhardt, S. C., Weladji, R. B., Holand, Ø., Røed, K. H. & Nieminen, M. Evidence of reciprocal allonursing in reindeer, Rangifer tarandus. Ethology 121(3), 245–259 (2015).Article 

    Google Scholar 
    29.Jones, J. D. & Treanor, J. J. Allonursing and cooperative birthing behavior in Yellowstone bison, Bison bison. Can. Field-Nat. 122(2), 171–172 (2008).Article 

    Google Scholar 
    30.Pusey, A. E. & Packer, C. Non-offspring nursing in social carnivores—Minimizing the costs. Behav. Ecol. 5, 362–374 (1994).Article 

    Google Scholar 
    31Murphey, R. M., Paranhos da Costa, M. J. R., Gomes da Silva, R. & de Souza, R. Allonursing in river buffalo, Bubalis bubalis: Nepotism, incompetence, or thivery?. Anim. Behav. 49, 1611–1616 (1995).Article 

    Google Scholar 
    32.Olléová, M., Pluháček, J. & King, S. R. B. Effect of social system on allosuckling and adoption in zebras. J. Zool. 288(2), 127–134 (2012).Article 

    Google Scholar 
    33.Hamilton, W. D. The genetical evolution of social behaviour. II. J. Theor. Biol. 7, 17–52 (1964).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Clutton-Brock, T. H. Reproductive effort and terminal investment in iteroparous animals. Am. Nat. 123, 212–229 (1984).Article 

    Google Scholar 
    35.Baldovino, M. C. & Di Bitetti, M. S. Allonursing in tufted capuchin monkeys (Cebus nigritus): Milk or pacifier?. Folia Primatol. 79, 79–92 (2007).Article 

    Google Scholar 
    36.Boness, D. J., Craig, M. P., Honigman, L. & Austin, S. Fostering behavior and the effect of female density in Hawaiian monk seals, Monachus schauinslandi. J. Mammal. 79, 1060–1069 (1998).Article 

    Google Scholar 
    37.Cassinello, J. Allosuckling behaviour in Ammotragus. Z. Saugetierkd 64(6), 363–370 (1999).
    Google Scholar 
    38.Nuñez, C. M., Adelman, J. S. & Rubenstein, D. I. A free-ranging, feral mare Equus caballus affords similar maternal care to her genetic and adopted offspring. Am. Nat. 182, 674–681 (2013).PubMed 
    Article 

    Google Scholar 
    39.Brandlová, K., Bartoš, L. & Haberová, T. Camel calves as opportunistic milk thefts? The first description of allosuckling in domestic bactrian camel (Camelus bactrianus). PLoS ONE 8(1), e53052 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    40.Zapata, B., Gaete, G., Correa, L., González, B. & Ebensperger, L. A case of allosuckling in wild guanacos (Lama guanicoe). J. Ethol. 27, 295–297 (2009).Article 

    Google Scholar 
    41.Bond, M. L. & Lee, D. E. Simultaneous multiple-calf allonursing by a wild Masai giraffe. Afr. J. Ecol. 58(1), 126–128 (2020).Article 

    Google Scholar 
    42.Pratt, D. M. & Anderson, V. H. Giraffe cowecalf relationships and social development of the calf in the Serengeti. Z. Tierpsychol. 51(3), 233–251 (1979).Article 

    Google Scholar 
    43.Saito, M. & Idani, G. Suckling and allosuckling behavior in wild giraffe (Giraffa camelopardalis tippelskirchi). Mammal. Biol. 93, 1–4 (2018).Article 

    Google Scholar 
    44.Zoelzer, F., Engel, C., Paul, W. D. & Anna Lena, B. A comparative study of nightly allonursing behaviour in four zoo-housed groups of giraffes (Giraffa camelopardalis). J. Zoo Aquar. Res. 8(3), 175–180 (2020).
    Google Scholar 
    45.Schino, G. & Aureli, F. The relative roles of kinship and reciprocity in explaining primate altruism. Ecol. Lett. 13, 45–50 (2010).PubMed 
    Article 

    Google Scholar 
    46.Bercovitch, F. B., Bashaw, M. J. & del Castillo, S. M. Sociosexual behavior, male mating tactics, and the reproductive cycle of giraffe Giraffa camelopardalis. Horm. Behav. 50(2), 314–321 (2006).PubMed 
    Article 

    Google Scholar 
    47.Bercovitch, F. B. & Berry, P. S. M. Herd composition, kinship and fission—fusion social dynamics among wild giraffe. Afr. J. Ecol. 51(2), 206–216 (2013).Article 

    Google Scholar 
    48.Carter, K. D., Seddon, J. M., Frere, C. H., Carter, J. K. & Goldizen, A. W. Fission-fusion dynamics in wild giraffes may be driven by kinship, spatial overlap and individual social preferences. Anim. Behav. 85, 385–394 (2013).Article 

    Google Scholar 
    49.D’haen, M., Fennessy, J., Stabach, J. & Brandlová, K. Population structure and spatial ecology of Kordofan giraffe in Garamba National Park, Democratic Republic of Congo. Ecol. Evol. 9(19), 11395–11405 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Horová, E., Brandlová, K. & Gloneková, M. The first description of dominance hierarchy in captive giraffe: Not loose and egalitarian, but clear and linear. PLoS ONE 10(5), e0124570 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Jůnková Vymyslická, P., Brandlová, K., Hozdecká, K., Žáčková, M. & Hejcmanová, P. Feeding rank in the Derby eland: Lessons for management. Afr. Zool. 50(4), 313–320 (2015).Article 

    Google Scholar 
    52.Broussard, D. R., Risch, T. S., Dobson, F. S. & Murie, J. O. Senescence and age-related reproduction of female Columbian ground squirrels. J. Anim. Ecol. 72, 212–219 (2003).Article 

    Google Scholar 
    53.Cameron, E. Z., Linklater, W. L., Stafford, K. J. & Minot, E. O. Aging and improving reproductive success in horses: declining residual reproductive value or just older and wiser?. Behav. Ecol. Sociobiol. 47(4), 243–249 (2000).Article 

    Google Scholar 
    54.Cameron, E. Z., Linklater, W. L., Stafford, K. J. & Minot, E. O. A case of cooperative nursing and offspring care by mother and daughter feral horses. J. Zool. 249, 486–489 (1999).Article 

    Google Scholar 
    55.Ekvall, K. Effects of social organization, age and aggressive behaviour on allosuckling in wild fallow deer. Anim. Behav. 56, 695–703 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Birgersson, B. & Ekvall, K. Suckling time and fawn growth in fallow deer (Dama dama). Zoology 232, 641–650 (1994).
    Google Scholar 
    57.Fennessy, J. et al. Multi-locus analyses reveal four giraffe species instead of one. Curr. Biol. 26(18), 2543–2549 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Estes, R. The Behavior Guide to African Mammals (University of California Press, 1991).
    Google Scholar 
    59.Altmann, J. Observational study of behaviour: Sampling methods. Behaviour 49, 227–267 (1974).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Špinka, M. & Illmann, G. Suckling behaviour of young dairy calves with their own and alien mothers. Appl. Anim. Behav. Sci. 33(2), 165–173 (1992).Article 

    Google Scholar 
    61.Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).Article 

    Google Scholar  More

  • in

    Emerging strains of watermelon mosaic virus in Southeastern France: model-based estimation of the dates and places of introduction

    DataPathosystemWMV is widespread in cucurbit crops, mostly in temperate and Mediterranean climatic regions of the world16. WMV has a wide host range including some legumes, orchids and many weeds that can be alternative hosts16. Like other potyviruses, it is non-persistently transmitted by at least 30 aphid species16. In temperate regions, WMV causes summer epidemics on cucurbit crops, and it can overwinter in several common non-cucurbit weeds when no crops are present16,34. WMV has been common in France for more than 40 years, causing mosaics on leaves and fruits in melon, but mostly mild symptoms on zucchini squash. Since 2000, new symptoms were observed in southeastern France on zucchini squash: leaf deformations and mosaics, as well as fruit discoloration and deformations that made them unmarketable. This new agronomic problem was correlated to the introduction of new molecular groups of WMV strains. At least four new groups have emerged since 2000 and they have rapidly replaced the native “classical” strains, causing important problems for the producers35. These new groups, hereafter “emerging strains” (ES) are significantly more related molecularly to worldwide strains than to any other isolates from the French populations36. As emphasised in35, this supports that the new group of emerging strains has arisen through introductions, mostly from Southeastern Asia, rather than through local differentiation.In this study, we focus on the pathosystem corresponding to a classical strain (CS) and four emerging strains (ESk, (k = 1, ldots ,4)) of WMV and their cucurbit hosts.Study area and samplingThe study area, located in Southeastern France, is included in a rectangle of about 25,000 km2 and is bounded on the South by the Mediterranean Sea. Between 2004 and 2008, the presence of WMV had been monitored in collaboration with farmers, farm advisers and seed companies. Each year, cultivated host plants were collected in different fields and at different dates between May 1st and September 30th. In total, more than two thousand plant samples were collected over the entire study area. All plant samples were analyzed in the INRAE Plant Pathology Unit to confirm the presence of WMV and determine the molecular type of the virus strain causing the infection (see35 for detail on field and laboratory protocols). All infected host plants were cucurbits, mostly melon and different squashes (e.g., zucchini, pumpkins).Observations In the absence of individual geographic coordinates, all infected host plants were attributed to the centroid of the municipality (French administrative unit, median size about 10 km2) where they have been collected. Then for one date, one observation corresponded to a municipality in which at least one infected host plant was sampled. Table 1 summarizes for each year, the number of observations (i.e. number of municipalities), the number of infected plants sampled and the proportion of each WMV strain (CS, and ES1 to ES4) found in the infected host plants. Errors in assignment of virus samples to the CS or ES strains was negligible because of the large genetic distance separating them: 5 to 10% nucleotide divergence both in the fragment used in the study and in complete genomes35, also precluding the possibility of local jumps between groups by accumulation of mutations.Table 1 Number of observations and corresponding proportions of classical and emerging strains.Full size tableLandscapeTo approximate the density of WMV host plants over the study area, we used 2006 land use data (i.e. BD Ocsol 2006 PACA and LR) produced by the CRIGE PACA (http://www.crige-paca.org/) and the Association SIG-LR (http://www.siglr.org/lassociation/la-structure.html). Based on satellite images, land use is determined at a spatial resolution of 1/50,000 using an improved three-level hierarchical typology derived from the European Corine Land Cover nomenclature. Here we used the third hierarchical level of the BD Ocsol typology (i.e. 42 land use classes) to classify the entire study area in three habitats: (1) WMV-susceptible crops, (2) habitats unfavorable to WMV host plants (e.g. forests, industrial and commercial units…) and, (3) non-terrestrial habitat (i.e. water). The proportion of WMV-susceptible crops was then computed within all cells of a raster covering the entire study area, with a spatial resolution of (1.4 times 1.4) km2. These proportions were used to approximate host plant density (zleft( {varvec{x}} right)), which was normalized, so that (zleft( {varvec{x}} right) = 0) corresponds to an absence of host plants and (zleft( {varvec{x}} right) = 1) to the maximum density of host plants (Fig. 1).Figure 1Approximated density (zleft( x right)) of the host plants in the study area. The density was normalized, so that (zleft( x right) = zleft( {x_{1} ,x_{2} } right) = 0) corresponds to an absence of cucurbit plants and (zleft( x right) = 1) to the maximum density. The axes (x_{1}) and (x_{2}) correspond to Lambert93 coordinates (in km). The white regions are non-terrestrial habitats (water). Land use data were not available in the gray regions; the host plant density was then computed by interpolation.Full size imageMechanistic-statistical modelThe general modeling strategy is based on a mechanistic-statistical approach12,22,33. This type of approach combines a mechanistic model describing the dynamics under investigation with a probabilistic model conditional on the dynamics, describing how the measurements have been collected. This method that has already proved its theoretical effectiveness in determining dispersal parameters using simulated genetic data12 aims at bridging the gap between the data and the model for the determination of virus dynamics.Here, the mechanistic part of the model describes the spatio-temporal dynamics of the virus strains, given the model parameters (demographic parameters, introduction dates/sites). This allows us to compute the expected proportions of the five types of virus strains (CS and ES1 to ES4) at each date and site of observation. The probabilistic part of the mechanistic-statistical model describes the conditional distribution of the observed proportions of the virus strains, given the expected proportions. Using this approach, it is then possible to derive a numerically tractable formula for the likelihood function associated with the model parameters.Population dynamicsThe model is segmented into two stages: (1) the intra-annual stage describes the dispersal and growth of the five virus strains during the summer epidemics on cucurbit crops, and the competition between them, during a period ranging from May 1st (noted (t = 0)) to September 30 (noted (t = t_{f}), (t_{f} = 153) days); (2) the inter-annual stage describes the winter decay of the different strains when no crops are present and the virus overwinters in weeds. We denote by (c^{n} left( {t,{varvec{x}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}} right)) the densities of classical strain (CS) and emerging strains (ESk, (k = 1, ldots ,4)), at position ({varvec{x}}) and at time (t) of year (n.)Dynamics of the classical strain before the first introduction eventsBefore the introduction of the first emerging strain, the intra-annual dynamics of the population CS is described by a standard diffusion model with logistic growth: (partial_{t} c^{n} = D{Delta }c^{n} + rc^{n} left( {zleft( {varvec{x}} right) – c^{n} } right)). Here, ({Delta }) is the Laplace 2D diffusion operator (sum of the second derivatives with respect to coordinate). This operator describes uncorrelated random walk movements of the viruses, with the coefficient (D) measuring the mobility of the viruses (e.g.,26,37). The term (r zleft( {varvec{x}} right)) is the intrinsic growth rate (i.e., growth rate in the absence of competition) of the population, which depends on the density of host plants (zleft( {varvec{x}} right)) and on a coefficient (r) (intrinsic growth rate at maximum host density). Under these assumptions, the carrying capacity at a position ({varvec{x}}) is equal to (zleft( {varvec{x}} right)), which means that the population densities are expressed in units of the maximum host population density. The model is initialized by setting (c^{1980} left( {0,{varvec{x}}} right) = (1 – m_{c} ) zleft( {varvec{x}} right)), where (m_{c}) is the winter decay rate of the CS (see the description of the inter-annual stage below). In other terms, we assume that the CS density is at the carrying capacity in 1979, i.e., 5 years after its first detection and 20 years before the first detections of ESs38.Introduction eventsThe ESs are introduced during years noted (n_{k} ge 1981), at the beginning of the intra-annual stage (other dates of introduction within the intra-annual stage would lead—at most—to a one-year lag in the dynamics). Their densities are (0) before introduction: (e_{k}^{n} = 0) for (n < n_{k}). Once introduced, the initial density of any ES is assumed to be 1/10th of the carrying capacity at the introduction point (other values have been tested without much effect, see Supplementary Fig. S1), with a decreasing density as the distance from this point increases:$$e_{k}^{{n_{k} }} left( {0,x} right) = frac{{zleft( {varvec{x}} right)}}{10}exp left( { - frac{|{{varvec{x}} - {varvec{X}}_{{varvec{k}}}|^{2} }}{{2sigma^{2} }}} right),$$where ({varvec{X}}_{{varvec{k}}}) is the location of introduction of the strain (k.) In our computations, we took (sigma = 5) km for the standard deviation.Intra-annual dynamics after the first introduction eventIntra-annual dynamics were described by a neutral competition model with diffusion (properties of this model have been analyzed in [54]):$$left{ {begin{array}{*{20}c} {partial_{t} c^{n} left( {t,x} right) = DDelta c^{n} + rc^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ {partial_{t} e_{k}^{n} left( {t,x} right) = DDelta e_{k}^{n} + re_{k}^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ end{array} } right.,$$for (t = 0 ldots t_{f}) and for all introduced emerging strains, i.e. all (k) such that (n ge n_{k} .) We assume reflecting boundary conditions, meaning that the population flows vanish at the boundary of the study area, due to truly reflecting boundaries (e.g., sea coast in the southern part of the site) or symmetric inward and outward fluxes26. In addition, in order to limit the number of unknown parameters, and in the absence of precise knowledge on the differences between the strains, we assume here that the diffusion, competition and growth coefficients are common to all the strains during the intra-annual stage (see the discussion for more details on this assumption).Inter-annual dynamicsThe population densities at time (t = 0) of year (n) are connected with those of year (n - 1,) at time (t = t_{f} ,) through the following formulas:$$left{ {begin{array}{*{20}c} {c^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{c} } right)c^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for } n ge 1981} \ {e_{k}^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{e} } right)e_{k}^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for }n ge n_{k} + 1} \ end{array} } right.$$with (m_{c}) and (m_{e}) the winter decay rates of the CS and ESs strains (note that (m_{e}) is common to all of the ESs). Estimation of CS and ES decay rates provides an assessment of the competitive advantage of one type of strain vs the other.Numerical computationsThe intra-annual dynamics were solved using Comsol Multiphysics time-dependent solver, which is based on a finite element method (FEM). The triangular mesh which was used for our computations is available as Supplementary Fig. S2.Probabilistic model for the observation processDuring the years (n = 2004, ldots ,2008), (I_{n}) observations were made (see Section Observations above and Table 1). They consist in counting data, that we denote by (C_{i}) and (E_{k,i}) for (k = 1, ldots ,4) and (i = 1, ldots ,I_{n}), corresponding to the number of samples infected by the CS and ESs strains, respectively, at each date of observation and location (left( {t_{i} ,{varvec{x}}_{i} } right)). Note that these dates and locations depend on the year of observation (n). More generally, the above quantities should be noted (C_{i}^{n} , E_{k,i}^{n} , t_{i}^{n} , {varvec{x}}_{i}^{n} ;) for simplicity, the index (n) is omitted in the sequel, unless necessary.We denote by (V_{i} = C_{i} + mathop sum nolimits_{k = 1}^{4} E_{k,i}) the total number of infected samples observed at (left( {t_{i} ,{varvec{x}}_{i} } right)). The conditional distribution of the vector (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)), given (V_{i}) can be described by a multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) with ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)) the vector of the respective proportions of each strain in the population at (left( {t_{i} ,{varvec{x}}_{i} } right)). We chose to work conditionally to (V_{i}) because the sample sizes are not related to the density of WMV.Statistical inferenceUnknown parametersWe denote by ({{varvec{Theta}}}) the vector of unknown parameters: the diffusion coefficient (D,) the intrinsic growth rate at maximum host density (r), the winter decay rates ((m_{c} , m_{e} )) and the locations ((x_{k} in {mathbb{R}}^{2})) and years ((n_{k})) of introduction, for (k = 1, ldots ,4.) Thus ({{varvec{Theta}}} in {mathbb{R}}^{16} .)Computation of a likelihoodGiven the set of parameters ({{varvec{Theta}}}), the densities (c^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) can be computed explicitly with the mechanistic model for population dynamics. Thus, at a given year (n), at (left( {t_{i} ,x_{i} } right)), the parameter ({varvec{p}}_{i}) of the multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) writes:$$p_{i}^{c} left( {{varvec{Theta}}} right) = frac{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} left( {t_{i} ,{varvec{x}}_{i} {|}{{varvec{Theta}}}} right)}}, p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right) = frac{{e_{k}^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} (t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}})}}.$$The probability (P(C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} |{{varvec{Theta}}},{text{V}}_{{text{i}}} )) of the observed outcome (C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i}) is then$$Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right) = frac{{left( {V_{i} } right)!}}{{C_{i} ! mathop prod nolimits_{k = 1}^{4} E_{k,i} !}}left( {p_{i}^{c} left( {{varvec{Theta}}} right)} right)^{{C_{i} }} mathop prod limits_{k = 1}^{4} (p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right))^{{E_{k,i} }} .$$Assuming that the observations during each year and at each date/location are independent from each other conditionally on the virus strain proportions, we get the following formula for the likelihood:$${mathcal{L}}left( {{varvec{Theta}}} right) = mathop prod limits_{n = 2004}^{2008} mathop prod limits_{{i = 1, ldots , I_{n} }} Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right).$$A priori constraints on the parameters By definition and for biological reasons, the parameter vector ({{varvec{Theta}}}) satisfies some constraints. First, (D in left( {10^{ - 4} ,10} right){text{ km}}^{2} /{text{day}}), (r in left( {0.1,1} right) {text{day}}^{ - 1} ,) and (m_{c} , m_{e} in left{ {0,0.1,0.2, ldots ,0.9} right},) (see Supplementary Note S7 for a biological interpretation of these values). Second, we assumed that the locations of introductions ({varvec{X}}_{{varvec{k}}}) belong to the study area. To facilitate the estimation procedure, the points ({varvec{X}}_{{varvec{k}}}) were searched in a regular grid with 20 points (see Supplementary Fig. S3), and the dates of introduction (n_{k}) were searched in (left{ {1985,1990,1995,2000} right}.)Inference procedureDue to the important computation time (4 min in average for one simulation of the model on an Intel(R) Core(R) CPU i7-4790 @ 3.60 GHz), we were not able to compute an a posteriori distribution of the parameters in a Bayesian framework. Rather, we used a simulated annealing algorithm to compute the maximum likelihood estimate (MLE), i.e., the parameter ({{varvec{Theta}}}^{*}) which leads to the highest log-likelihood. This is an iterative algorithm, which constructs a sequence (({{varvec{Theta}}}_{j} )_{j ge 1}) converging in probability towards a MLE. It is based on an acceptance-rejection procedure, where the acceptance rate depends on the current iteration (j) through a "cooling rate" ((alpha )). Empirically, a good trade-off between quality of optimization and time required for computation (number of iterations) is obtained with exponential cooling rates of the type (T_{0} alpha^{j}) with (0 < alpha < 1) and some constant (T_{0} gg 1) (this cooling schedule was first proposed in= 39 = 39). Too rapid cooling ((alpha ll 1)) results in a system frozen into a state far from the optimal one, whereas too slow cooling ((alpha approx 1)) leads to important computation times due to very slow convergence. Here, we ran (6) parallel sequences with cooling rates (alpha in left{ {0.995,0.999,0.9995} right}). For this type of algorithm, there are no general rules for the choice of the stopping criterion [HenJac03], which should be heuristically adapted to the considered optimization problem. Here, our stopping criterion was that ({{varvec{Theta}}}_{j}) remained unchanged during 500 iterations. The computations took about 100 days (CPU time).Confidence intervals and goodness-of-fitTo assess the model’s goodness-of-fit, 95% confidence regions were computed for the observations (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)) at each date/location (left( {t_{i} ,{varvec{x}}_{i} } right),) and for each year of observation. The confidence regions were computed by assessing the probability of each possible outcome of the observation process, at each date/location, based on the computed proportions ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)), corresponding to the output of the mechanistic model using the MLE ({{varvec{Theta}}}^{user2{*}}) and given the total number of infected samples (V_{i}). Then, we checked if the observations belonged to the 95% most probable outcomes. More

  • in

    Functional groups in microbial ecology: updated definitions of piezophiles as suggested by hydrostatic pressure dependence on temperature

    1.Capece MC, Clark E, Saleh JK, Halford D, Heinl N, Hoskins S, et al. Polyextremophiles and the constraints for terrestrial habitability. In: Seckbach J, Oren A, Stan-Lotter H, editors. Polyextremophiles. Life under muliple forms of stress. Dordrecht, Neaderlands: Springer; 2013. p. 3–60.2.Harrison JP, Gheeraert N, Tsigelnitskiy D, Cockell CS. The limits for life under multiple extremes. Trends Microbiol. 2013;21:204–12.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Oger PM, Jebbar M. The many ways of coping with pressure. Res Microbiol. 2010;161:799–809.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Bartlett DH. Pressure effects on in vivo microbial processes. Biochim Biophys Acta. 2002;1595:367–81.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Aertsen A, Meersman F, Hendrickx ME, Vogel RF, Michiels CW. Biotechnology under high pressure: applications and implications. Trends Biotechnol. 2009;27:434–41.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Jannasch HW, Taylor CD. Deep-sea microbiology. Ann Rev Microbiol. 1984;38:487–514.CAS 
    Article 

    Google Scholar 
    8.Fang J, Zhang L, Bazylinski DA. Deep-sea piezosphere and piezophiles: geomicrobiology and biogeochemistry. Trends Microbiol. 2010;18:413–22.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Yayanos AA. Microbiology to 10,500 meters in the deep sea. Ann Rev Microbiol. 1995;49:777–805.CAS 
    Article 

    Google Scholar 
    10.Eloe EA, Lauro FM, Vogel RF, Bartlett DH. The deep-sea bacterium Photobacterium profundum SS9 utilizes separate flagellar systems for swimming and swarming under high-pressure conditions. Appl Environ Microbiol. 2008;74:6298–305.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Horikoshi K, Bull AT Prologue: Definition, categories, distribution, origin and evolution, pioneering studies, and emerging fields of extremophiles. In: Horikoshi K, editor. Extremophiles handbook. Tokyo, Japan: Springer; 2011. p. 3–18.12.Holt RD. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc Natl Acad Sci USA. 2009;106:19659–65.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Talley LD, Pickard GL, Emery WJ, Swift JH. Typical distributions of water characteristics. In: Descriptive physical oceanography, 6th ed. London, UK: Elsevier; 2011. p. 67–110.14.Jebbar M, Franzetti B, Girard E, Oger P. Microbial diversity and adaptation to high hydrostatic pressure in deep-sea hydrothermal vents prokaryotes. Extremophiles. 2015;19:721–40.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Berhardt G, Jaenicke R, Ludemann H-D, Konig H, Stetter KO. High pressure enhances the growth rate of the thermophilic archaebacterium Methanococcus thermolithotrophicus without extending its temperature range. Appl Environ Microbiol. 1998;54:1258–61.Article 

    Google Scholar 
    16.Scoma A, Garrido-Amador P, Nielsen SD, Roy H, Kjeldsen KU. The polyextremophilic bacterium Clostridium paradoxum attains piezophilic traits by modulating its energy metabolism and cell membrane composition. Appl Environ Microbiol. 2019;85:e00802–19.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Wiegel J. Temperature spans for growth: hypothesis and discussion. FEMS Microbiol Rev. 1990;75:155–70.Article 

    Google Scholar 
    18.Morita RY. Psychrophilic bacteria. Bacteriol Rev. 1975;39:144–67.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Zeikus JG. Thermophilic Bacteria—Ecology. Physiol Technol Enz Microb Technol. 1979;1:243–52.CAS 
    Article 

    Google Scholar 
    20.Yayanos AA. Evolutional and ecological implications of the properties of deep-sea barophilic bacteria. Proc Natl Acad Sci USA. 1986;83:9542–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Jannasch HW, Wirsen CO. Variability of pressure adaptation in deep sea bacteria. Arch Microbiol. 1984;139:281–8.Article 

    Google Scholar 
    22.Yayanos AA, Chastain R. The influence of nutrition on the physiology of piezophilic bacteria. In: Bell CR, Brylinsky M, Johnson-Green P, Eds. Proceedings of the 8th International Symposium on Microbial Ecology. Halifax, NS, Canada: Atlantic Canada Society for Microbial Ecology; 6; 1999.23.Matsumura P, Keller DM, Marquis RE. Restricted pH ranges and reduced yields for bacterial growth under pressure. Microb Ecol. 1974;1:176–89.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Oren A. Bioenergetic aspects of halophilism. Microbiol Mol Biol Rev. 1999;63:334–48.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Yayanos AA, Dietz AS, Van, Boxtel R. Dependence of reproduction rate on pressure as a hallmark of deep-sea bacteria. Appl Environ Microbiol. 1982;44:1356–61.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Deming JW, Hada H, Colwell RR, Luehrsen KR, Fox GE. The ribonucleotide sequence of 5S rRNA from two strains of deep-sea barophilic bacteria. J Gen Microbiol. 1984;130:1911–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Lauro FM, Chastain RA, Blankenship LE, Yayanos AA, Bartlett DH. The unique 16S rRNA genes of piezophiles reflect both phylogeny and adaptation. Appl Environ Microbiol. 2007;73:838–45.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Marteinsson VT, Birrien J-L-, Reysenbach A-L, Vernet M, Marie D, Gambacorta A, et al. Thermococcus barophilus sp. nov., a new barophilic and hyperthermophilic archaeon isolated under high hydrostatic pressure from a deep-sea hydrothermal vent. Int J Syst Bacteriol. 1999;49:351–9.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Alain K. Marinitoga piezophila sp. nov., a rod-shaped, thermo-piezophilic bacterium isolated under high hydrostatic pressure from a deep-sea hydrothermal vent. Int J Sys Evol Microbiol. 2002;52:1331–9.CAS 

    Google Scholar 
    30.Canganella F, Gonzalez JM, Yanagibayashi M, Kato C, Horikoshi K. Pressure and temperature effects on growth and viability of the hyperthermophilic archaeon Thermococcus peptonophilus. Arch Microbiol. 1997;168:1–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Canganella F, Gambacorta A, Kato C, Horikoshi K. Effects of hydrostatic pressure and temperature on physiological traits of Thermococcus guaymasensis and Thermococcus aggregans growing on starch. Microbiol Res. 2000;154:297–306.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Tamburini C, Boutrif M, Garel M, Colwell RR, Deming JW. Prokaryotic responses to hydrostatic pressure in the ocean-a review. Environ Microbiol. 2013;15:1262–74.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Nogi Y, Masui N, Kato C. Photobacterium profundum sp. nov., a new, moderately barophilic bacterial species isolated from a deep-sea sediment. Extremophiles. 1998;2:1–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Arakawa S, Nogi Y, Sato T, Yoshida Y, Usami R, Kato C. Diversity of piezophilic microorganisms in the closed ocean Japan Sea. Biosci Biotechnol Biochem. 2006;70:749–52.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Xu Y, Nogi Y, Kato C, Liang Z, Ruger H-J, De Kegel D, et al. Moritella profunda sp. nov. and Moritella abyssi sp. nov., two psychropiezophilic organisms isolated from deep Atlantic sediments. Int J Syst Evol Microbiol. 2003;53:533–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Sekiguchi T, Sato T, Enoki M, Kanehiro H, Kato C. Procedure for isolation of the plastic degrading piezophilic bacteria from deep-sea environments. J Jap Soc Extremophil. 2010a;9:25–30.Article 

    Google Scholar 
    37.Sekiguchi T, Sato T, Enoki M, Kanehiro H, Uematsu K, Kato C. Isolation and characterization of biodegradable plastic degrading bacteria from deep-sea environments. JAMSTEC Rep. Res Dev. 2010b;11:33–41.
    Google Scholar 
    38.Nogi Y, Kato C, Horikoshi K. Psychromonas kaikoae sp. nov., a novel piezophilic bacterium from the deepest cold-seep sediments in the Japan Trench. Int J Syst Evol Microbiol. 2002;52:1527–32.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Yayanos AA, Dietz AS, van Boxtel R. Isolation of a deep-sea barophilic bacterium and some of its growth characteristics. Science. 1979;205:808–10.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Nogi Y, Hosoya S, Kato C, Horikoshi K. Colwellia piezophila sp. nov., a novel piezophilic species from deep-sea sediments of the Japan Trench. Int J Syst Evol Microbiol. 2004;54:1627–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Kato C, Sato T, Horikoshi K. Isolation and properties of barophilic and barotolerant bacteria from deep-sea mud samples. Biodiv Cons. 1995;4:1–9.Article 

    Google Scholar 
    42.Kato C, Inoue A, Horikoshi K. Isolating and characterizing deep-sea marinemicroorganisms. Tibtech. 1996;14:6–12.CAS 
    Article 

    Google Scholar 
    43.Nogi Y, Kato C. Taxonomic studies of extremely barophilic bacteria isolated from the Mariana Trench and description of Moritella yayanosii sp. nov., a new barophilic bacterial isolate. Extremophiles. 1999;3:71–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Deming JW, Somers LK, Straube WL, Swartz DG, Macdonell MT. Isolation of an Obligately Barophilic Bacterium and Description of a New Genus, Colwellia gen. nov. Systematic and Applied Microbiology. 1988;10:152–60.Article 

    Google Scholar 
    45.Kusube M, Kyaw TS, Tanikawa K, Chastain RA, Hardy KM, Cameron J, et al. Colwellia marinimaniae sp. nov., a hyperpiezophilic species isolated from an amphipod within the Challenger Deep, Mariana Trench. Int J Syst Evol Microbiol. 2017;67:824–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Cao J, Lai Q, Liu P, Wei Y, Wang L, Liu R, et al. Salinimonas sediminis sp. nov., a piezophilic bacterium isolated from a deep-sea sediment sample from the New Britain Trench. Int J Syst Evol Microbiol. 2018;68:3766–71.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Liu P, Ding W, Lai Q, Liu R, Wei Y, Wang L, et al. Physiological and genomic features of Paraoceanicella profunda gen. nov., sp. nov., a novel piezophile isolated from deep seawater of the Mariana Trench. MicrobiologyOpen. 2019;00:e966.
    Google Scholar 
    48.Quéméneur M, Erauso G, Frouin E, Zeghal E, Vandecasteele C, Ollivier B, et al. Hydrostatic Pressure Helps to Cultivate an Original Anaerobic Bacterium From the Atlantis Massif Subseafloor (IODP Expedition 357): Petrocella atlantisensis gen. nov. sp. nov. Front Microbiol. 2019;10:1497.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Xiao X, Wang P, Zeng X, Bartlett DH, Wang F. Shewanella psychrophila sp. nov. and Shewanella piezotolerans sp. nov., isolated from west Pacific deep-sea sediment. Int J Syst Evol Microbiol. 2007;57:60–5.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Alazard D, Dukan S, Urios A, Verhe F, Bouabida N, Morel F, et al. Desulfovibrio hydrothermalis sp. nov., a novel sulfate-reducing bacterium isolated from hydrothermal vents. Int J Syst Evol Microbiol. 2003;53:173–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Pathom-Aree W, Nogi Y, Sutcliffe IC, Ward AC, Horikoshi K, Bull AT, et al. Dermacoccus abyssi sp. nov., a piezotolerant actinomycete isolated from the Mariana Trench. Int J Syst Evol Microbiol. 2006;56:1233–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Takai K, Miyazaki M, Hirayama H, Nakagawa S, Querellou J, Godfroy A. Isolation and physiological characterization of two novel, piezophilic, thermophilic chemolithoautotrophs from a deep-sea hydrothermal vent chimney. Environ Microbiol. 2009;11(8):1983–97.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Erauso G, Reysenbach A-L, Godfroy A, Meunier J-R, Crump B, Partensky F, et al. Pyrococcus abyssi sp. nov., a new hyperthermophilic archaeon isolated from a deep-sea hydrothermal vent. Arch Microbiol. 1993;160:338–49.CAS 
    Article 

    Google Scholar 
    54.Li Y, Mandelco L, Wiegel J. Isolation and Characterization of a Moderately Thermophilic Anaerobic Alkaliphile. Clostridium paradoxum sp. nov. Int J Sys Bacteriol. 1993;43:450–60.Article 

    Google Scholar 
    55.Zhao W, Zeng X, Xiao X. Thermococcus eurythermalis sp. nov., a conditional piezophilic, hyperthermophilic archaeon with a wide temperature range for growth, isolated from an oil-immersed chimney in the Guaymas Basin. Int J Sys Evol Microbiol. 2015;65:30–5.CAS 
    Article 

    Google Scholar 
    56.Takai K, Nakamura K, Toki T, Tsunogai U, Miyazaki M, Miyazaki J, et al. Cell proliferation at 122 degrees C and isotopically heavy CH4 production by a hyperthermophilic methanogen under high-pressure cultivation. Proc Natl Acad Sci U S A. 2008;105:10949–54.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.González JM, Kato C, Horikoshi K. Thermococcus peptonophilus sp. nov., a fast-growing, extremely thermophilic archaebacterium isolated from deep-sea hydrothermal vents. Arch Microbiol. 1995;164:159–64.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Jones WJ, Leigh JA, Mayer F, Woese CR, Wolfe RS. Methanococcusjannaschii sp. nov., an extremely thermophilic methanogen from a submarine hydrothermal vent. Arch Microbiol. 1983;136:254–61.CAS 
    Article 

    Google Scholar  More

  • in

    Biogeography of ammonia oxidizers in New England and Gulf of Mexico salt marshes and the potential importance of comammox

    1.Prosser, J. I. & Nicol, G. W. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ. Microbiol. 10, 2931–2941 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Bernhard, A. E. & Bollmann, A. Estuarine nitrifiers: new players, patterns and processes. Estuar. Coast. Shelf Sci. 88, 1–11 (2010).CAS 
    Article 

    Google Scholar 
    3.Martiny, J. B. H., Eisen, J., Penn, K., Allison, S. D. & Horner-Devine, M. C. Drivers of bacterial beta-diversity depend on spatial scale. Proc. Natl Acad. Sci. USA 108, 7850–7854 (2011).4.Nelson, M. B., Martiny, A. C. & Martiny, J. B. H. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Marton, J. M., Roberts, B. J., Bernhard, A. E. & Giblin, A. E. Spatial and temporal variability of nitrification potential and ammonia-oxidizer abundances in Louisiana salt marshes. Estuaries Coast. 38, 1824–1837 (2015).CAS 
    Article 

    Google Scholar 
    7.Martens-Habbena, W., Berube, P. M., Urakawa, H., de la Torre, J. R. & Stahl, D. A. Ammonia oxidation kinetics determine niche separation of nitrifying archaea and bacteria. Nature 461, 976–981 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Dimitri Kits, K. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    9.Hink, L., Nicol, G. W. & Prosser, J. I. Archaea produce lower yields of N2O than bacteria during aerobic ammonia oxidation in soil. Environ. Microbiol. 19, 4829–4837 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Bernhard, A. E., Donn, T., Giblin, A. E. & Stahl, D. A. Loss of diversity of ammonia-oxidizing bacteria correlates with increasing salinity in an estuary system. Environ. Microbiol. 7, 1289–1297 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Moin, N. S., Nelson, K. A., Bush, A. & Bernhard, A. E. Distribution and diversity of archaeal and bacterial ammonia oxidizers in salt marsh sediments. Appl. Environ. Microbiol. 75, 7461–7468 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Bernhard, A. E. et al. Abundance of ammonia-oxidizing archaea and bacteria along an estuarine salinity gradient in relation to potential nitrification rates. Appl. Environ. Microbiol. 76, 1285–1289 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Francis, C. A., O’Mullan, G. D. & Ward, B. B. Diversity of ammonia monooxygenase (amoA) genes across environmental gradients in Chesapeake Bay sediments. Geobiology 1, 129–140 (2003).CAS 
    Article 

    Google Scholar 
    14.Ward, B. B. et al. Ammonia-oxidizing bacterial community composition in estuarine and oceanic environments assessed using a functional gene microarray. Environ. Microbiol. 9, 2522–2538 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Mills, H. J. et al. Characterization of nitrifying, denitrifying, and overall bacterial communities in permeable marine sediments of the northeastern Gulf of Mexico. Appl. Environ. Microbiol. 74, 4440–4453 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Newell, S. E. et al. A shift in the archaeal nitrifier community in response to natural and anthropogenic disturbances in the northern Gulf of Mexico. Environ. Microbiol. Rep. 6, 106–112 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Bernhard, A. E., Sheffer, R., Giblin, A. E., Marton, J. M. & Roberts, B. J. Population dynamics and community composition of ammonia oxidizers in salt marshes after the Deepwater Horizon oil spill. Front. Microbiol. 7, 854 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    18.Bernhard, A. E., Chelsky, A., Giblin, A. E. & Roberts, B. J. Influence of local and regional drivers on spatial and temporal variation of ammonia-oxidizing communities in Gulf of Mexico salt marshes. Environ. Microbiol. Rep. 11, 825–834 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Nelson, K. A., Moin, N. S. & Bernhard, A. E. Archaeal diversity and the prevalence of Crenarchaeota in salt marsh sediments. Appl. Environ. Microbiol. 75, 4211–4215 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Peng, X. et al. Differential responses of ammonia-oxidizing archaea and bacteria to long-term fertilization in a New England salt marsh. Front. Microbiol. 3, 445 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    21.Bernhard, A. E., Marshall, D. & Yiannos, L. Increased variability of microbial communities in restored salt marshes nearly 30 years after tidal flow restoration. Estuaries Coast. 35, 1049–1059 (2012).CAS 
    Article 

    Google Scholar 
    22.Marton, J. M. & Roberts, B. J. Spatial variability of phosphorus sorption dynamics in Louisiana salt marshes. J. Geophys. Res. Biogeosci. 119, 451–465 (2014).CAS 
    Article 

    Google Scholar 
    23.Hill, T. D. & Roberts, B. J. Effects of seasonality and environmental gradients on Spartina alterniflora allometry and primary production. Ecol. Evol. 7, 9676–9688 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Bernhard, A. E., Tucker, J., Giblin, A. E. & Stahl, D. A. Functionally distinct communities of ammonia-oxidizing bacteria along an estuarine salinity gradient. Environ. Microbiol. 9, 1439–1447 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Schutte, C. A., Marton, J. M., Bernhard, A. E., Giblin, A. E. & Roberts, B. J. No evidence for long-term impacts of oil spill contamination on salt marsh soil nitrogen cycling processes. Estuaries Coast. 43, 865–879 (2020).Article 

    Google Scholar 
    26.Bernhard, A. E., Dwyer, C., Idrizi, A., Bender, G. & Zwick, R. Long-term impacts of disturbance on nitrogen-cycling bacteria in a New England salt marsh. Front. Microbiol. 6 https://doi.org/10.3389/fmicb.2015.00046 (2015).27.Pjevac, P. et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front. Microbiol. 8 https://doi.org/10.3389/fmicb.2017.01508 (2017).28.Francis, C. A., Roberts, K. J., Beman, J. M., Santoro, A. E. & Oakley, B. B. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc. Natl Acad. Sci. USA 102, 14683–14688 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Park, S.-J., Park, B.-J. & Rhee, S.-K. Comparative analysis of archaeal 16S rRNA and amoA genes to estimate the abundance and diversity of ammonia-oxidizing archaea in marine sediments. Extremophiles 12, 605–615 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).34.Turner, R. E., Rabalais, N. N. & Justic, D. Predicting summer hypoxia in the northern Gulf of Mexico: riverine N, P, and Si loading. Mar. Pollut. Bull. 52, 139–148 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Tian, H. et al. Long-term trajectory of nitrogen loading and delivery from Mississippi river basin to the Gulf of Mexico. Glob. Biogeochem. Cycles 34, 6475 (2020).Article 
    CAS 

    Google Scholar 
    36.Dang, H. et al. Diversity, abundance, and spatial distribution of sedimet ammonia-oxidizing Betaproteobacteria in response to environmental gradients and coastal eutrophication in Jiaozhou Bay, China. Appl. Environ. Microbiol. 76, 4691–4702 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Sims, A., Zhang, Y., Gajaraj, S., Brown, P. B. & Hu, Z. Toward the development of microbial indicators for wetland assessment. Water Res. 47, 1711–1725 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Zhang, Q. -F. et al. Impacts of Spartina alterniflora invasion on abundance and composition of ammonia oxidizers in estuarine sediment. J. Soils Sediment. 11, 1020–1031 (2011).Article 

    Google Scholar 
    39.Jin, T. et al. Diversity and quantity of ammonia-oxidizing archaea and bacteria in sediment of the Pearl River Estuary, China. Appl. Microbiol. Biotechnol. 90, 1137–1145 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Meinhardt, K. A. et al. Evaluation of revised polymerase chain reaction primers for more inclusive quantification of ammonia-oxidizing archaea and bacteria. Environ. Microbiol. Rep. 7, 354–363 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Marshall, A. et al. Primer selection influences abundance estimates of ammonia oxidizing archaea in coastal marine sediments. Mar. Environ. Res. 140, 90–95 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Koops, H. P. & Pommerening-Roser, A. Distribution and ecophysiology of the nitrifying bacteria emphasizing cultured species. FEMS Microbiol. Ecol. 37, 1–9 (2001).CAS 
    Article 

    Google Scholar 
    43.Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Pommier, T. et al. Global patterns of diversity and community structure in marine bacterioplankton. Mol. Ecol. 16, 867–880 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. 103, 626–631 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Hendershot, J. N., Read, Q. D., Henning, J. A., Sanders, N. J. & Classen, A. T. Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales. Ecology 98, 1757–1763 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Hitchcock, J. N., Mitrovic, S. M., Kobayashi, T. & Westhorpe, D. P. Responses of estuarine bacterioplankton, phytoplankton and zooplankton to dissolved organic carbon (DOC) and inorganic nutrient additions. Estuaries Coast. 33, 78–91 (2010).CAS 
    Article 

    Google Scholar 
    48.Guo, X. -P. et al. Bacterial community structure in response to environmental impacts in the intertidal sediments along the Yangtze Estuary, China. Mar. Pollut. Bull. 126, 141–149 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Howarth, R. W. Nutrient limitation of net primary production in marine ecosystems. Annu. Rev. Ecol. 19, 89–110 (1988).Article 

    Google Scholar 
    50.Murrell, M. C. et al. Evidence that phosphorus limits phytoplankton growth in a Gulf of Mexico estuary: Pensacola Bay, Florida, USA. Bull. Mar. Sci. 70, 155–167 (2002).
    Google Scholar 
    51.Johnson, M. W., Heck, K. L. Jr & Fourqurean, J. W. Nutrient content of seagrasses and epiphytes in the northern Gulf of Mexico: evidence of phosphorus and nitrogen limitation. Aquat. Bot. 85, 103–111 (2006).CAS 
    Article 

    Google Scholar 
    52.Rysgaard, S., Thastum, P., Dalsgaard, T., Christensen, P. B. & Sloth, N. P. Effects of salinity on NH4+ adsorption capacity, nitrification, and denitrification in Danish estuarine sediments. Estuaries 22, 21–30 (1999).CAS 
    Article 

    Google Scholar 
    53.Peng, X. et al. Long-term fertilization alters the relative importance of nitrate reduction pathways in salt marsh sediments. J. Geophys. Res. Biogeosci. 121, 2082–2095 (2016).CAS 
    Article 

    Google Scholar 
    54.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    55.Taylor, A. E., Giguere, A. T., Zoebelein, C. M., Myrold, D. D. & Bottomley, P. J. Modeling of soil nitrification responses to temperature reveals thermodynamic differences between ammonia-oxidizing activity of archaea and bacteria. ISME J. 11, 896–908 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Ouyang, Y., Norton, J. M. & Stark, J. M. Ammonium availability and temperature control contributions of ammonia oxidizing bacteria and archaea to nitrification in an agricultural soil. Soil Biol. Biochem. 113, 161–172 (2017).CAS 
    Article 

    Google Scholar 
    57.Mukhtar, H., Lin, Y. -P., Lin, C. -M. & Lin, Y. -R. Relative abundance of ammonia oxidizing archaea and bacteria influences soil nitrification responses to temperature. Microorganisms 7, 526 (2019).
    Google Scholar 
    58.Fierer, N., Carney, K. M., Horner-Devine, M. C. & Megonigal, J. P. The biogeography of ammonia-oxidizing bacterial communities in soil. Microb. Ecol. 58, 435–445 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Park, H.-D., Lee, S.-Y. & Hwang, S. Redundancy analysis demonstration of the relevance of temperature to ammonia-oxidizing bacterial community compositions in a full-scale nitrifying bioreactor treating saline wastewater. J. Microbiol. Biotechnol. 19, 346–350 (2009).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    60.Avrahami, S., Liesack, W. & Conrad, R. Effects of temperature and fertilizer on activity and community structure of soil ammonia oxidizers. Environ. Microbiol. 5, 691–705 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Avrahami, S. & Conrad, R. Patterns of community change among ammonia oxidizers in meadow soils upon long-term incubation at different temperatures. Appl. Environ. Microbiol. 69, 6152–6164 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Seitzinger, S. P., Gardner, W. S. & Spratt, A. K. The effect of salinity on ammonium sorption in aquatic sediments—implications for benthic nutrient recycling. Estuaries 14, 167–174 (1991).CAS 
    Article 

    Google Scholar 
    63.Dollhopf, S. L. et al. Quantification of ammonia-oxidizing bacteria and factors controlling nitrification in salt marsh sediments. Appl. Environ. Microbiol. 71, 240–246 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Beman, J. M., Bertics, V. J., Braunschweiler, T. & Wilson, J. M. Quantification of ammonia oxidation rates and the distribution of ammonia-oxidizing archaea and bacteria in marine sediment depth profiles from Catalina Island, California. Front. Microbiol. 3, 263 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Nicol, G. W., Leininger, S., Schleper, C. & Prosser, J. I. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ. Microbiol. 10, 2966–2978 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Lehtovirta, L. E., Prosser, J. I. & Nicol, G. W. Soil pH regulates the abundance and diversity of group 1.1c Crenarchaeota. FEMS Microbiol. Ecol. 70, 367–376 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Bello, M. O., Thion, C., Gubry-Rangin, C. & Prosser, J. I. Differential sensitivity of ammonia oxidising archaea and bacteria to matric and osmotic potential. Soil Biol. Biochem. 129, 184–190 (2019).CAS 
    Article 

    Google Scholar 
    68.Fuchslueger, L. et al. Effects of drought on nitrogen turnover and abundances of ammonia-oxidizers in mountain grassland. Biogeosciences. 11, 6003–6015 (2014).Article 

    Google Scholar 
    69.Thion, C. & Prosser, J. I. Differential response of nonadapted ammonia-oxidising archaea and bacteria to drying-rewetting stress. FEMS Microbiol. Ecol. 90, 380–389 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Fowler, S. J., Palomo, A., Dechesne, A., Mines, P. D. & Smets, B. F. Comammox Nitrospira are abundant ammonia oxidizers in diverse groundwater-fed rapid sand filter communities. Environ. Microbiol. 20, 1002–1015 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.How, S. W., Chua, A. S. M., Ngoh, G. C., Nittami, T. & Curtis, T. P. Enhanced nitrogen removal in an anoxic-oxic-anoxic process treating low COD/N tropical wastewater: low-dissolved oxygen nitrification and utilization of slowly-biodegradable COD for denitrification. Sci. Total Environ. 693, 133526 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Gonzalez-Martinez, A., Rodriguez-Sanchez, A., van Loosdrecht, M. C. M., Gonzalez-Lopez, J. & Vahala, R. Detection of comammox bacteria in full-scale wastewater treatment bioreactors using tag-454-pyrosequencing. Environ. Sci. Pollut. Res. 23, 25501–25511 (2016).CAS 
    Article 

    Google Scholar 
    73.Wang, D. -Q., Zhou, C. -H., Nie, M., Gu, J. -D. & Quan, Z. -X. Abundance and niche specificity of different types of complete ammonia oxidizers (comammox) in salt marshes covered by different plants. Sci. Total Environ. 768, 144933 (2021).
    Google Scholar 
    74.Xia, F. et al. Ubiquity and diversity of complete ammonia oxidizers (comammox). Appl. Environ. Microbiol. 84, e01390 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Yu, C. et al. Evidence for complete nitrification in enrichment culture of tidal sediments and diversity analysis of clade a comammox Nitrospira in natural environments. Appl. Microbiol. Biotechnol. 102, 9363–9377 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Zhao, Z. et al. Abundance and community composition of comammox bacteria in different ecosystems by a universal primer set. Sci. Total Environ. 691, 146–155 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Identify potential allelochemicals from Humulus scandens (Lour.) Merr. root extracts that induce allelopathy on Alternanthera philoxeroides (Mart.) Griseb.

    1.Pomella, A. W. V., Barreto, R. W. & Charudattan, R. Nimbya alternantherae a potential biocontrol agent for alligatorweed, Alternanthera philoxeroides. Biocontrol 52, 271–288 (2007).CAS 
    Article 

    Google Scholar 
    2.Barreto, R. W. & Torres, A. N. L. Nimbya alternantherae and Cercospora alternantherae: two new records of fungal pathogens on Alternanthera philoxeroides (alligatorweed) in Brazil, Australas. Plant Pathol 28, 103–107 (1999).
    Google Scholar 
    3.Ridenour, W. M. & Callaway, R. M. The relative importance of allelopathy in interference: the effects of an invasive weed on a native bunchgrass. Oecologia 126, 444–450 (2001).ADS 
    Article 

    Google Scholar 
    4.Tanveer, A., Ali, H. H. & Manalil, S. Eco-biology and management of alligator weed [Alternanthera philoxeroides (Mart.) Griseb.] a review. Wetlands 38, 1067–1079 (2018).Article 

    Google Scholar 
    5.Garbari, F. & Pedullà, M. L. Alternanthera philoxeroides (Mart) Griseb (Amaranthaceae), a new species for the exotic flora of Italy. J. Plant Taxon Geogr 56, 139–143 (2001).
    Google Scholar 
    6.Chen, X., Wang, R. & Cao, Q. The relationship between the distribution of invasive plant Alternanthera philoxeroides and soil properties is scale-dependent. Pol. J. Environ. Stud. 24, 1931–1938 (2015).Article 

    Google Scholar 
    7.Wang, T., Hu, J. & Miao, L. The invasive stoloniferous clonal plant Alternanthera philoxeroides outperforms its co-occurring non-invasive functional counterparts in heterogeneous soil environments-invasion implications. Sci. Rep. 6, 38036 (2016).ADS 
    CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    8.Wang, B., Li, W. & Wang, J. Genetic diversity of Alternanthera philoxeroides in China. Aquat. Bot. 81, 277–283 (2005).Article 

    Google Scholar 
    9.Shen, J., Shen, M. & Wang, X. Effect of environmental factors on shoot emergence and vegetative growth of alligatorweed (Alternanthera philoxcroides). Weed Sci. 53, 471–478 (2005).CAS 
    Article 

    Google Scholar 
    10.Bassett, I., Paynter, Q. & Hankin, R. Characterising alligator weed (Alternanthera philoxeroides; Amaranthaceae) invasion at a northern New Zealand lake. N. Z. J. Ecol. 36, 216–222 (2012).
    Google Scholar 
    11.Pan, X. Y. Invasive Alternanthera philoxeroides: biology, ecology and management. Acta Phytotaxonomica Sinica 45, 884–900 (2007).Article 

    Google Scholar 
    12.Phung, T., Xuan, T. & Tu, A. T. Weed suppressing potential and isolation of potent plant growth inhibitors from Castanea crenata Sieb. et Zucc. Molecules 23, 345 (2018).Article 
    CAS 

    Google Scholar 
    13.Yu, Z. & Bi, H. Status Quo of research on ecosystem services value in China and suggestions to future research. Energy Procedia 5, 1044–1048 (2011).Article 

    Google Scholar 
    14.Yang, S., Wang, Q. & Hu, T. Physiological responses to allelopathy of decomposing Cinnamomum septentrionale leaf litter of three crops (corn, cucumber, and cowpea). Chin. J. App. Environ. Biol. 29, 292–298 (2018).
    Google Scholar 
    15.Dong, B. C., Fu, T. & Luo, F. L. Herbivory-induced maternal effects on growth and defense traits in the clonal species Alternanthera philoxeroides. Sci. Total Environ. 605–606, 114–123 (2017).ADS 
    Article 
    CAS 

    Google Scholar 
    16.Dugdale, T. M., Clements, D. & Hunt, T. D. Alligatorweed produces viable stem fragments in response to herbicide treatment. J. Aquat. Plant Manag. 48, 84–91 (2010).
    Google Scholar 
    17.Clements, D., Dugdale, T. M. & Butler, K. L. Management of aquatic alligator weed in an early stage of invasion. Manag. Biol. Invas. 5, 327–339 (2014).Article 

    Google Scholar 
    18.Clements, D., Dugdale, T. M. & Butler, K. L. Herbicide efficacy for aquatic Alternanthera philoxeroides management in an early stage of invasion: integrating above-ground biomass, below-ground biomass and viable stem fragmentation. Weed Res. 57, 257–266 (2017).CAS 
    Article 

    Google Scholar 
    19.Bond, W. & Grundy, A. Non-chemical weed management in organic farming systems. Weed Res. 41, 383–405 (2001).Article 

    Google Scholar 
    20.Schooler, S., Cook, T. & Bourne, A. Selective herbicides reduce alligator weed (Alternanthera Philoxeroides) biomass by enhancing competition. Weed Sci. 56, 259–264 (2008).CAS 
    Article 

    Google Scholar 
    21.Sainty, G., Mccorkelle, G., & Julien, M. Control and spread of alligator weed Alternanthera philoxeroides (Mart.) Griseb., in Australia: Lessons for other regions. Wetlands Ecol. Manage. 5, 195–201 (1997).Article 

    Google Scholar 
    22.Annett, R., Habibi, H. R. & Hontela, A. Impact of glyphosate and glyphosate-based herbicides on the freshwater environment. J. Appl. Toxicol. 34, 458–479 (2014).CAS 
    Article 

    Google Scholar 
    23.Bais, H. P., Vepachedu, R. & Gilroy, S. Allelopathy and exotic plant invasion: from molecules and genes to species interactions. Science 301, 1377–1380 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Zhang, Z., Deng, L. L. & Wang, L. C. Allelopathic potential of Phragmites australis extracts on the growth of invasive plant Alternanthera philoxeroides. Allelopath. J. 45, 54–63 (2018).Article 

    Google Scholar 
    25.Jabran, K., Mahajan, G. & Sardana, V. Allelopathy for weed control in agricultural systems. Crop Prot. 72, 57–65 (2015).Article 

    Google Scholar 
    26.Kumbhar, B. A. & Patel, D. D. Allelopathic effects of different weed species on crop. J. Pharm. Sci. Biosci. Res. 6, 801–805 (2016).
    Google Scholar 
    27.Weston, L. A. & Duke, S. O. Weed and crop allelopathy. Crit. Rev. Plant Sci. 22, 367–389 (2003).CAS 
    Article 

    Google Scholar 
    28.Rice, E.L., Allelopathy, 2nd Ed, A. Press, Editor. 1-50 (1984).29.Da Silva, I. F. & Vieira, E. A. Phytotoxic potential of Senna occidentalis (L.) Link extracts on seed germination and oxidative stress of Ipe seedlings. Plant Biol. 21, 770–779 (2019).Article 
    CAS 

    Google Scholar 
    30.Zeng, R. S. Allelopathy—the solution is indirect. J. Chem. Ecol. 40, 515–516 (2014).CAS 
    Article 

    Google Scholar 
    31.Otusanya, O. O., Ilori, O. J. & Adelusi, A. A. Allelopathic effects of tithonia diversifolia (Hemsl) A. gray on germination and growth of Amaranthus cruentus. Res. J. Environ. Sci. 1, 285–293 (2007).CAS 
    Article 

    Google Scholar 
    32.Chen, Z. & Meng, S. Research progress of Humulus scandens. Chin. Pharm. Affairs 24, 73–77 (2011).CAS 

    Google Scholar 
    33.Cao, Y., Wang, T. & Xiao, Y. A. The interspecific competition between Humulus scandens and Alternanthera philoxeroides. J. Plant Interactions 9, 194–199 (2013).Article 
    CAS 

    Google Scholar 
    34.Huang, Y. M., Zhang, Y. & Liu, Q. Research on allelopathy of aqueous extract from tagetes patula to four garden plants. Acta pratacultural sinica (Chinese) 24, 150–158 (2015).
    Google Scholar 
    35.Li, W., Luo, J. & Tian, X. A new strategy for controlling invasive weeds: selecting valuable native plants to defeat them. Sci. Rep. 5, 11004 (2015).ADS 
    CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    36.Cheng, T. S. The toxic effects of diethyl phthalate on the activity of glutamine synthetase in greater duckweed (Spirodela polyrhiza L.). Aquat. Toxicol. 124, 171–178 (2012).Article 
    CAS 

    Google Scholar 
    37.Makoi, J. H. & Ndakidemi, P. A. Biological, ecological and agronomic significance of plant phenolic compounds in rhizosphere of the symbiotic legumes. Afr. J. Biotech. 6, 739–748 (2007).
    Google Scholar 
    38.Zhang, K. M., Shen, Y. & Yang, J. The defense system for Bidens pilosa root exudate treatments in Pteris multifida gametophyte. Ecotoxicol. Environ. Saf. 173, 203–213 (2019).CAS 
    Article 

    Google Scholar 
    39.Noctor, G., Reichheld, J.-P. & Foyer, C. H. ROS-related redox regulation and signaling in plants. Semin. Cell Dev. Biol. 80, 3–12 (2017).Article 
    CAS 

    Google Scholar 
    40.Kaur, N., Chugh, V. & Gupta, A. K. Essential fatty acids as functional components of foods-a review. J. Food Sci. Technol. 51, 2289–2303 (2014).CAS 
    Article 

    Google Scholar 
    41.Sun, C. H., Li, Y. & He, H. Y. Physiological and biochemical responses of Chenopodium album to drought stresses. Atca Ecologica Sinica 25, 2556–2561 (2005).CAS 

    Google Scholar 
    42.Li, P., Wang, X. & Li, Y. The contents of phenolic acids in continuous cropping peanut and their allelopathy. Acta Ecol. Sin. 30, 2128–2134 (2010).MathSciNet 
    Article 

    Google Scholar 
    43.Wang, Y. X., Sun, G. R. & Wang, J. B. Relationships among MDA content, plasma membrane permeability and the chlorophyll fluorescence parameters of Puccinellia tenuiflora seedlings under NaCl stress. Acta Ecol. Sin. 26, 122–129 (2006).CAS 

    Google Scholar 
    44.Li, Z. Q., Li, J. T. & Bing, J. The role analysis of APX gene family in the growth and developmental processes and in response to abiotic stresses in Arabidopsis thaliana. Hereditas (Beijing) 41, 534–547 (2019).
    Google Scholar 
    45.Tang, K., Ming, L. & Shan, D. Allelopathy autotoxicity effects of aquatic extracts from rhizospheric soil on rooting and growth of stem cuttings in Pogostemon cablin. J. Chin. Med. Mater. 37, 935–939 (2014).CAS 

    Google Scholar 
    46.Coelho, E. M. P., Barbosa, M. C. & Mito, M. S. The activity of the antioxidant defense system of the weed species Senna obtusifolia L and its resistance to allelochemical stress. J. Chem. Ecol. 43, 725–738 (2017).CAS 
    Article 

    Google Scholar 
    47.Li, J. & Wang, X. Advance of research on Humulus scandens. Qilu Pharm. Affairs 26, 353–355 (2007).
    Google Scholar 
    48.Xu, B., Jin, Y. & Yihan, W. Chemical constituents from stems and leaves of Humulus scandens. Chin. Tradit. Herb. Drugs 45, 1228–1231 (2014).CAS 

    Google Scholar 
    49.Zhang, J., Liu, J. & Dai, L.-F. Unlocking the potential antioxidant and anti-inflammatory activities of Rhododendron molle G. Don. Pak. J. Pharm. Sci. 32, 2375–2383 (2019).CAS 

    Google Scholar 
    50.Chen, Z., Guo, Q. & Huang, K. Analysis of volatile components of solidago canadensis by SPME/GC-MS. Acta Agriculturae Jiangxi (Chinese) 26, 1 (2008).
    Google Scholar 
    51.Wang, Y., Yu, J. & Zhang, Y. Effects of two allelochemicals on growth and physiological characteristics of eggplant seedlings. J. Gansu Agric. Univ. (Chinese) 3, 47–50 (2007).
    Google Scholar 
    52.Yu, J., Zhang, Y. & Niu, C. Effects of two kinds of allelochemicals on photosynthesis and chlorophyll fluorescence parameters of Solanum melongena L. seedlings. J. Appl. Ecol. 17, 1629–1632 (2006).ADS 
    CAS 

    Google Scholar 
    53.Lande, M. L., Kanedi, M. & Zulkifli, Z. Supperssive effexts of lantana camara leaf extracts on the growth of red chillli (Carsicum annuum). World J. Pharm. Life Sci. 3, 543–551 (2017).
    Google Scholar 
    54.Erida, G. & Saidi, N. Allelopathic screening of several weed species as potential bioherbicides. IOP Conf. Ser. Earth Environ. Sci. 334, 12–34 (2019).Article 

    Google Scholar 
    55.Cimmino, A., Masi, M. & Rubiales, D. Allelopathy for parasitic plant management. Nat. Prod. Commun. 13, 289–294 (2018).
    Google Scholar 
    56.Deng, J., Zhang, Y. & Hu, J. Autotoxicity of phthalate esters in tobacco root exudates: Effects on seed germination and seedling growth. Pedosphere 27, 1073–1082 (2017).CAS 
    Article 

    Google Scholar 
    57.Gu, S., Zheng, H. & Xu, Q. Comparative toxicity of the plasticizer dibutyl phthalate to two freshwater algae. Aquat. Toxicol. 191, 122–130 (2017).CAS 
    Article 

    Google Scholar 
    58.Perveen, S., Yousaf, M. & Zahoor, A. F. Extraction, isolation, and identification of various environment friendly components from cock’s comb (Celosia argentea) leaves for allelopathic potential. Toxicol. Environ. Chem. Rev. 96, 1523–1534 (2014).CAS 
    Article 

    Google Scholar 
    59.Alara, O. R., Abdurahman, N. H. & Ukaegbu, C. I. Extraction and characterization of bioactive compounds in Vernonia amygdalina leaf ethanolic extract comparing soxhlet and microwave-assisted extraction techniques. J. Taibah Univ. Sci. 13, 414–422 (2019).Article 

    Google Scholar 
    60.Wei, W., Hou, Y. & Peng, S. Effects of light intensity on growth and biomass allocation of invasive plants Mikania micrantha and Chromolaena odorata. Acta Ecol. Sin. 37, 6021–6028 (2017).Article 

    Google Scholar 
    61.Williamson, G. B. & Richardson, D. Bioassays for allelopathy: measuring treatment responses with independent controls. J. Chem. Ecol. 14, 181–187 (1988).Article 

    Google Scholar 
    62.Gao, Y. B., Li, G. P. & Shi, H. Allelopathic effect of endophyte-infected achnatherum sibiricum on stipa grandis. Acta Ecol. Sin. 37, 1063–1073 (2017).Article 

    Google Scholar 
    63.Zhang, L., Wang, X. & Guo, J. Metabolic profiling of Chinese tobacco leaf of different geographical origins by GC-MS. Agric. Food Chem. 61, 2597–2605 (2013).CAS 
    Article 

    Google Scholar  More

  • in

    Inferring ecosystem networks as information flows

    Two species unidirectional coupling ecosystemThis bio-inspired ecosystem S((beta _{xy}=0), (beta _{yx})) describing the unidirectional coupling is run for 1000 time steps for reaching stationarity, generating a set of 1000 points long time-series dependent on (beta _{yx}). This means that species X has an increasing effect on Y with the increase of (beta _{yx}), but Y has no effect on X. Both CCM and the proposed OIF model are separately used to quantify the potential causality between species X and Y. The inferred causality dependent on (beta _{yx}) only (as a physical interaction) is shown in Fig. 2A. (beta), (rho) and TE have different units; specifically (beta) and (rho) are dimensionless while TE is measured in bits or nats (a logarithmic unit of information or entropy). Therefore, any comparison is done considering gradients of change when these variables vary together rather than making comparisons between absolute values which are meaningless. Figure 2A shows that under the condition of (beta _{xy})=0, results of “Y to X” (i.e. the estimated effect on Y on X) is close to 0 for the OIF model ((TE_{Y rightarrow X}(beta _{yx}))) that precisely describe the no-effect of Y on X. “X to Y” ((TE_{X rightarrow Y}(beta _{yx}))) well tracks the increasing strength of the effect of X on Y for increasing values of the physical interaction (beta _{yx}) embedded into the mathematical model. However, considering results of the CCM model, “Y to X” ((rho _{Y rightarrow X}(beta _{yx}))) presents non obvious (and likely wrong) non-zero values with higher fluctuations compared to (TE_{Y rightarrow X}(beta _{yx})) especially for lower values of (beta _{yx}). This erroneous estimates of CCM is likely related to the need of CCM for convergence. For CCM, “X to Y” (((rho _{X rightarrow Y}(beta _{yx})))) shows an increasing trend for increasing values of (beta _{yx}) and decreasing when (beta _{yx}) is greater than (sim)0.5 non-trivially. In consideration of these results for the unidirectional coupling ecosystem, the OIF model performs better over CCM in terms of unidirectional causality inference.Two species bidirectional coupling ecosystemIn this case, the effect between two species is bidirectional. Species X has an effect on species Y and vice versa. The univariate dynamical systems S(0.2/0.5/0.8, (beta _{yx})) are run for 1000 time steps under the same conditions determined by (beta _{xy}). Certainly this situation is fictional since in real ecosystems the interaction strength is changing when other interacting species change their interactions.Thus, keeping one interaction fixed around one value is a strong unrealistic simplification (analogous of one-factor at-a-time sensitivity analyses) but it is a toy model that allows to verify the power of network inference models. These models generate three sets of 1000 points long time-series dependent of (beta _{yx}) for each fixed (beta _{xy}). OIF and CCM are used to infer “causality” between X and Y—in the form of (rho) and TE—and compare that against the real embedded interaction (beta _{yx}) and (beta _{xy}) shown in Fig. 2B,C,D. Considering all results of Fig. 2 corresponding to fixed (beta _{xy})s, the correlation coefficient (rho) yielded from CCM and TE from OIF are both able to track the strength of causal trajectories. However, TE seems to perform better in term of ability to infer fine-scale changes in interactions. In particular, considering Fig. 2D (right plot), higher (TE_{yx}) higher for low (beta _{yx}) makes sense because (beta _{xy} > beta _{yx}) that means Y has a larger influence on X than vice versa and then Y is able to predict X. Additionally, TE does not suffer of convergence problems; specifically, considering Fig. 2A (left plot), higher (rho) for small (beta _{yx}) is not sensical and that is likely related to convergence problems of CCM.Considering all results of Fig. 2 corresponding to fixed (beta _{xy})s, the correlation coefficient (rho) yielded from CCM and TE from OIF are both able to track the strength of causal trajectories. Ideally, the causality from Y to X is a constant since (beta _{xy}) is a fixed value for each case. In this figure, the red curve in the right panel representing the OIF-inferred (TE-based) causality from Y to X is higher for greater (beta _{xy})s, while red curves representing CCM-inferred ((rho)) causality in the left panel present higher fluctuations especially for lower (beta _{yx}). For the causality from X to Y determined by (beta _{yx}) in the mathematical model, theoretically speaking, the causality from X to Y should monotonously grow when (beta _{yx}) increases from 0 to 1. In Fig. 2, blue curves in the right panel representing the OIF-inferred (TE-based) causality from X to Y present monotonously increasing features as a whole with the increasing (beta _{yx}), while those from CCM model ((rho)) do not and show considerable fluctuations.Therefore, OIF outperforms CCM in terms of the ability to infer the fine-scale changes in causality. In particular, considering Fig. 2D (right plot), higher (TE_{yx}) higher for low (beta _{yx}) makes sense because (beta _{xy} > beta _{yx}) that means Y has a larger influence on X than vice versa and then Y is able to predict X. Additionally, TE does not suffer of convergence problems; specifically, considering Fig. 2A (left plot), higher (rho) for small (beta _{yx}) is not sensical and that is likely related to convergence problems of CCM.Additionally, (rho _{Y rightarrow X}(beta _{yx})) shows higher fluctuations on average especially for the condition of lower (beta _{yx})s compared to (TE_{Y rightarrow X}(beta _{yx})). When considering the effect of X on Y that is a function of (beta _{yx}) for CCM, (rho _{X rightarrow Y}) reaches an extreme value at around (beta _{yx}) = 0.5 and then declines for larger values of (beta _{yx}). This is not consistent with the expected effect of X on Y that should be proportional to (beta _{yx}) embedded into the mathematical model. The ability of (rho) to reflect the proportional relationship between the effect of X on Y (manifested by (beta _{yx})) vanishes for high (beta _{xy})s due to unexpected and somewhat inconspicuous changes in (rho _{X rightarrow Y}) for larger (beta _{yx}). In simple words, the expected increasing trend of (rho) is lost for larger (beta _{xy}) that is counterintuitive. On the other side, (TE_{X rightarrow Y}(beta _{yx})) invariably maintains an increasing trend for increasing values of (beta _{xy}). OIF is also performing better than CCM when predicting higher average values of (TE_{Y rightarrow X}) for increasing values of (beta _{xy}) (red curves in Fig. 2A–D, right plots) as expected by the fixed effect in the mathematical model of Y on X. These results suggest that when compared to (rho) of CCM, TE can track well the causal interactions over (beta _{yx}) with higher performance and without considering the convergence requirement of CCM. CCM needs to consider the length of time series that makes (rho _{X rightarrow Y}(beta _{yx})) convergent to a stable value, but uncertain for large differences in time-series length of (X,Y) and sensitive to short time series.In more realistic settings for real ecosystems (and in analogy to global sensitivity analyses) when (beta _{xy}) and (beta _{yx}) are both considered as arguments of the two-variable (X,Y) bio-inspired model, the simulated ecosystem becomes a truly bivariate system, yet yielding complexity but more interest into the causality inference (Fig. 3). The dynamical system S((beta _{xy}), (beta _{yx})) was generated for 800 time steps under the same conditions mentioned above. We generated the datasets that allowed us to study linear and non-linear predictability indicators for inferring the embedded physical interactions. Specifically, we measure undirected linear correlation coefficient (corr_{X;Y}(beta _{xy},beta _{yx})), non-linear undirected mutual information (MI_{X;Y}(beta _{xy},beta _{yx})), directed non-linear correlation coefficient (rho _{X rightarrow Y}(beta _{xy},beta _{yx})) and (rho _{Y rightarrow X}(beta _{xy},beta _{yx})), and non-linear directed transfer entropy (TE_{X rightarrow Y}(beta _{xy},beta _{yx})) and (TE_{Y rightarrow X}(beta _{xy},beta _{yx})) as shown in Fig. 3. These 2D phase-space maps in Fig. 3 show strikingly similar patterns for classical linear correlation coefficients, MI, (rho) of CCM and TE of OIF which underline the fact that all methods are able to infer the interdependence patterns of interacting variables explicitly defined by (beta _{xy}) and (beta _{yx}). The color of phase-space maps is proportional to the inferred interaction between X and Y when the mutual physical interactions are varying according to the mathematical model in Eq. (1). In Fig. 3, even though phase-space maps of undirected (corr_{X;Y}(beta _{xy},beta _{yx})) and (MI_{X;Y}(beta _{xy},beta _{yx})) present similar patterns (in value organization and not value range) to those of directed (rho) and TE, neither (corr_{X;Y}((beta _{xy},beta _{yx})) and (MI_{X;Y}((beta _{xy},beta _{yx})) provide information about the direction of causality. As expected MI shows the opposite pattern of the average TE due to the fact that MI is the amount of shared information (or similarity) versus the amount of divergent information (divergence and asynchronicity) between X and Y.Figure 3Phase-space maps of normalized coupling predictive causation via correlation, mutual information, CCM and OIF for varying true causal interactions. Both true causal interactions (beta _{xy}) and (beta _{yx}) are free varying within the range [0, 1], indicating a bivariate model S((beta _{xy}),(beta _{yx})) where both species (or variables more generally) are interacting with each other with different strength. (A) normalized correlation coefficient, (B) normalized mutual information, (C) and (E) normalized CCM correlation coefficient ((rho)) for interaction directions of (X rightarrow Y) and (Y rightarrow X), (D) and (F) normalized transfer entropy (TE) from OIF model for interaction directions of (X rightarrow Y) and (Y rightarrow X).Full size imageFigure 4Dynamics of abundance and predictability for the bidirectional two species ecosystem model. (A) plots refer to the species abundance in time for the mathematical model in Eq. 1 for different predictability regimes associated to different interaction dynamics from low to high complexity ecosystem associated to low and high predictability. Blue, green and red refer to a range of predictable interactions as in Fig. 3: specifically, Blue is for ((beta _yx), (beta _xy))=(0.18, 0.39) (small mutual interaction, and predominant effect of Y on X), Green is for (0.64, 0.57) (high mutual interactions, and slightly predominant effect of X on Y), and Red for (0.94, 0.34) (high mutual interactions, and predominant effect of X on Y). (B) phase-space plots showing the non-time delayed associations between X and Y corresponding to synchronous and homogeneous, mildly asynchronous and divergent, and asynchronous and divergent dynamics. The transition from synchronous/small interactions to asynchronous/high interaction leads to a transition from modular to nested ecosystem interactions when more than one species exist (Fig. 6).Full size imageIn a biological sense TE should be interpreted as the probability of likely uncooperative dynamics (leading to or driven by environmental or biological heterogeneity) while MI as the probability of cooperative dynamics (leading to or driven by homogeneity). Here we refer to cooperative and uncooperative interactions based on the similarity or dissimilarity in pair dynamics manifested by species abundance fluctuations. For instance divergence and asynchronicity (that define TE) in pair species dynamics manifest uncooperative interactions. The balance of cooperative and uncooperative interactions can result into net interactions at the ecosystem scale manifesting neutral patterns, or net interactions may lead to niche patterns biased toward strong environmental or biological factors52. Certainly, cooperation in a biological sense should be interpreted on a case by case basis. In a broader uncertainty propagation perspective49, “cooperation” between variables means that variables contribute similarly to the uncertainty propagation, while “competition” means that one variable is predominant over the other in terms of magnitude of effects since TE is proportional to the magnitude rather than the frequency of effects. For the former case the total entropy of the system is higher than the latter case. Interestingly, correlation (text{ corr }(beta )), (rho) and TE show similar patterns in both organization and value range (but not in singular values of course), which sheds some important conclusions about the similarity and divergence of these methods as well as their capacity and limitations in characterizing non-linear systems.When comparing the phase-space patterns from CCM and OIF (displaying (rho) and TE) a more colorful and informative pattern is revealed by OIF. This means that TE gives a better gradient when tracking the increasing strength of causality for increasing values of (beta _{xy}) and (beta _{yx}). When comparing the phase-space patterns for the two causal directions of (“X rightarrow Y”) and (“Y rightarrow X”), phase-space maps from CCM are very similar, while those from TE present apparent differences in the strength of effects for the two opposite direction of interaction. Therefore, OIF is more sensitive to the direction of interaction compared to CCM when detecting directional causality.These results imply that TE performs better to distinguish directional embedded physical interactions (that are dependent on direct interactions (beta)-s, species growth rate (r_x) and (r_y), and contingent values X(t) and Y(t) determining the total interaction as seen in the model of Eq. (1)) in the species causal relationships. It should be emphasized how all linear and non-linear interaction indicators are inferring the total interaction and not only those exerted by (beta)-s. In a broad uncertainty purview49 the importance of these three factors ((beta)-s, r-s and X(t)/Y(t)) depends on their values and probability distributions that define the dynamics of the system; dynamics such as defined by the regions identified by patterns in Fig. 3 for the predator-prey system in Eq. (1). In principle, the higher the difference between these three interaction factors in the species considered, the higher the predictability and sensitivity of OIF. Figure 4 highlights three different dynamics corresponding to the TE blue, green and red regions in Fig. 3.In all dynamical states represented by Fig. 3, species are interacting with different magnitudes and this defines distinct network topologies. Three prototypical dynamics are shown in Fig. 4 with colors representative of (rho) and TE in Fig. 3. The “blue” deterministic dynamics has very high synchronicity and no divergence considering variable fluctuation range (the gap is deterministic and related to the numerically imposed (u=1)), as well as no linear correlation between non-lagged variables. In perfect synchrony one would have one point in the phase-space. Thus, absence of correlation does not imply complete decoupling of species but it can be a sign of small interactions. The “green” dynamics shows a relatively high synchronicity and medium divergence. In the phase-space of synchronous values of X and Y a correlation is observed with relatively small fluctuations because the divergence is small. Lastly, the “red” dynamics shows a relatively high asynchronicity and divergence. The stochasticity is higher than previous dynamics and the “mirage correlation” in the phase space has higher variance. Time-dependent mirage correlations in sign and magnitude mean that correlation (that may suggest common dynamics in a linear framework) does not imply similarity in dynamics for the two species. Non-linearity is higher from blue to red dynamics as well as predictability but lower absolute information entropy. Then, it is safe to say that linear dynamics (or small stochasticity) does not imply higher predictability.Real-world sardine–anchovy-temperature ecosystemCCM and proposed OIF model are also used for a real-world fishery ecosystem to infer potential causal interactions between Pacific sardines (Sardinops sagax) landings, Northern anchovies (Engraulis mordax) and sea-surface temperature (SST) recorded at Scripps Pier and Newport Pier, California. Sardines and anchovies do not interact physically (or the interaction is low in number), while both of them are influenced by the external environmental SST that is the external forcing. To quantify the likely causal interactions between species and SST based on real data, we use CCM considering the length of time series for convergence of (rho), as well as OIF considering a set of time delays for acquiring stable values of inferred interactions TEs.Figure 5Inferred predictive causality for the sardine-anchovy-Sea Surface Temperature ecosystem. CCM correlation coefficient ((rho)) and OIF predictor (TE) are shown in the left and middle plots for different pairs considered (sardine–anchovy, sardine and SST, anchovy and SST from top to bottom).Full size imageResults from CCM in Fig. 5A (plots from top to bottom) show that no significant interaction can be claimed between sardines and anchovies, as well as from sardines or anchovies in the SST manifold which expectedly indicates that neither sardines nor anchovies affect SST. This latter results, considering its biological plausibility should be taken as one validation criteria of predictive models, or complimentary as a test for anomaly detection of spurious interactions. The reverse effect of SST on sardines and anchovies can be quantitatively detected with the correlation coefficient (rho) as well as TE. Although the calculated causations between SST and sardines or anchovies are moderate, CCM is able to provide a good performance in causality inference when the length of time series used is long enough due to convergence requirement.Figure 5B shows OIF’s results of inferred causal interactions between sardines, anchovies and SST dependent on the time delay u. For sardines and anchovies, OIF exposes bidirectional interactions that are actually biologically plausible, especially when both populations coexist in the same habitat, versus the results of CCM that infer (rho =0). Ecologically speaking, even though fish populations do not directly influence sea temperature, we can find some clues about SST in fish populations influenced by SST. These clues can be interpreted as information of SST encoded in fish populations over abundance time records. So, observations of fish populations can be used to inversely predict the change of SST; this can be interpreted as “reverse predictability” (or “biological hindcasting”) in a similar way of when predicting historical climate change from ice cores. This information is captured by OIF, leading to nonzero values of TE from fish populations to SST. In this regard, we emphasize the distinction between direct and indirect (reverse) information flow, where direct information flow is most of the time larger and signifies causality (e.g. of SST for sardine and anchovies), and indirect (reverse) information flow that is typically smaller and signifies predictability (e.g. sardine and anchovies for ocean fluctuations). It is possible—especially for linear systems where an effect is observed immediately after a change—that information of SST encoded in fish populations is high if the interdependence, represented by the functional time delay u, of the environment-biota is small. However, for highly non-linear systems such as fishes and the ocean, changes in temperature may take a while before being encoded into fish population abundance53. Thus, it is correct that the highest values of TE are for high u. Values of TE for small u-s are numerical artifacts related to systematic errors leading to overestimation of interactions that are time-delayed eventually. One way to circumvent this problem, largely present for short time series, would be to extend time series by conserving their dynamics (see Li and Convertino39) or to bound the calculation of TE only for the u that maximizes the Mutual Information; this would provide an average u within a range where TE is approximately invariant. Thus, for the effect of external SST on sardines and anchovies, OIF model gives unstable causal interactions with bias for lower time delays due to known dependencies of TE on u (such as cross-correlation for instance) that establishes the temporal lag on which the dependency between X and Y is evaluated. In a sense, plots in Fig. 5B are like cross-variograms for the pairs of variables considered. TE becomes stable when the time delay is located in an appropriate range. It means that OIF requires an optimal time delay that makes results of the causality inference robust and that is related to optimal TEs (as highlighted in Li and Convertino39 and Servadio and Convertino49) that defines the most likely interdependency between variables for the u with the highest predictability. The fact that TE of sardine and anchovies to SST is high for same small ranges of u may be also a byproduct of data sampling, i.e., fish and SST sampling locations are different (fish abundance is actually about fish landings) and that can introduce spurious correlations/causation. Overall, these findings suggest that the OIF model provides more plausible results, but it requires careful selection of optimal time delays.Figure S1 shows the relationships between normalized (rho) and TE estimated for all selected values of L and u of pairs in Fig. 5 (sardine-anchovy, sardine and SST, anchovy and SST). These plots show opposite results than the proportionality between (rho) and TE in Fig. 3 because non-optimal values are used, that is non-convergent (rho)-s and suboptimal TE during the interaction inference procedure (Fig. S1). TE for too small u-s determines overestimation of interactions due to the implicit assumptions that variables have an immediate effect on each other and that is not always the case as highlighted by the vast time-lagged determined non-linear regions in Fig. 3. If “transitory” values of (rho) for small L are disregarded, as well as TEs for small u-s, the relationship between (rho) and TE shows a correct linear proportionality.Real-world multispecies ecosystemInteractions between fish species living in the Maizuru bay are intimately related to external environmental factors of the ecosystem where they live, the number of species living in this region considering also the unreported ones and biological species interactions, which lead to a complex dynamical nonlinear system. In Fig. 6 the network of observed fish species (Table S1) is reported where only the interactions considered in Ushio et al.37 for the CCM are reported. This is because the goal is to compare the CCM inferred network to the TE-based one based on abundance. Figure 7 shows the temporal fluctuations of abundance and the functional interaction matrices of (rho) and TE. In this paper we study and compare average ecosystem networks for the whole time period considered but dynamical networks can also be extracted via time-fluctuating (rho) and TE as shown in Fig. S3. These dynamical networks can be useful for studying how diversity is changing over time and ecosystem stability (Figs. S4, S6–S7) as well as understanding the relationship between (rho) and TE (Fig. S5). In the network of Fig. 6 the color and width of links are proportional to the magnitude of TE (Table S2); for the former a red-blue scale is adopted where the red/blue is for the highest/lowest TEs. The diameter is proportional to the Shannon entropy of the species abundance pdf (Table S3). The color of nodes is proportional to the structural node degree, i.e. how many species are interconnected to others. Therefore, the network in Fig. 6 is focusing on uncooperative species whose divergence and/or asynchronicity (that is a predominant factor in determining TE over divergence) is large. Yet, the connected species are rarely but strongly interacting in magnitude rather than frequently and weakly (i.e., cooperative or similar dynamics). Additionally, the species with the smallest variance in abundance are characterized by the smallest Shannon entropy (smallest nodes) and more power-law distribution although the latter is not a stringent requirement since both pdf shape and abundance range (in particular maximum abundance) play a role in the magnitude of entropy. Average entropy such as average abundance are quantities with limited utility in understanding the dynamics of an ecosystem as well as ecological function. Nonetheless, species with high average abundance (e.g. species 5) have a very regular seasonal oscillations and the largest number of interactions with divergent species. This dynamics is expected considering the population size of these dominant species and their synchrony with regular environmental fluctuations.Figure 6Part of the estimated species interaction network for the Maizuru Bay ecosystem. Species properties are reported in Table S1. The color and width of links are proportional to the magnitude of TE (Table S2); for the former a red-blue scale is adopted where the red/blue is for the highest/lowest TEs. The diameter is proportional to the Shannon entropy of the species abundance (Table S3) that is directly proportional to the degree of uniformity of the abundance pdf and the diversity of abundance values (e.g., the higher the zero abundance instances the lower the entropy). The color of nodes is proportional to the structural node degree, i.e. how many species are interconnected to others after considering only the CCM derived largest interactions (see Ushio et al.37 and Fig. 7). Other interactions exist between species as reported in Fig. 7. TE is on average proportional to (rho) (Figs. S4 and S5). Freely available fish images are from FishBase https://www.fishbase.in/search.php; the network was created in Matlab and the composition of network and images was made in Adobe Illustrator version 21 (2017) https://www.adobe.com/products/illustrator.html.Full size imageFigure 7Normalized species interactions matrices inferred by CCM and OIF models for Maizuru Bay ecosystem. In the census of the aquatic community, 15 fish species were counted in total. Interaction inferential models use time lagged abundance magnitude (CCM) or pdfs of abundance (OIF) shown in (A). (B) normalized CCM correlation coefficients ((rho)) between all possible pairs of species. (C) normalized transfer entropies (TEs) between all pairs of species from the OIF model. Both CCM and OIF predict that the most interacting species (in terms of magnitude rather than frequency) are 7, 8 and 9 on average. Thus, interaction matrices are more proportional to the asynchronicity than the divergence of species in terms of abundance pdf, although abundance value range defines the uncertainty (and diversity) for each species that ultimately affects entropy and interactions (e.g., if one species have many zero abundance instances or many equivalent values, such as species 2, TEs of that species are expected to be low due to lower uncertainty despite the asynchrony and divergence).Full size imageFigure S2 shows that the strongest linear correlation is for the most divergent and asynchronous species (from species 4–9) for which both (rho) and TE are the highest (Fig. 7B,C). This confirms the results of Fig. 3 and the fact that competition (or dynamical diversity more generally) increases predictability. This also highlights the fact that linear correlation among state variables does not imply synchronicity or dynamic similarity as commonly assumed. The interaction matrices in Fig. 7B, C confirm that TE has the ability to infer a larger gradient of interactions than (rho) and the total entropy of the TE matrix is lower than (rho). Pairwise the inferred interaction values by CCM and OIF are different but (rho) and TE patterns appear clearly similar and yet proportional to each other.CCM and OIF models are applied to calculate the potential interactions between all pairs of species. Figure 7B,C show interaction matrices describing the normalized (rho) from CCM and TE from OIF model of all pairwise species, respectively. The greater the strength of likely interaction, the warmer the color. These results demonstrate that CCM and OIF model present similar patterns for the interaction matrices in terms of interaction distribution, gradient and magnitude in order of similarity. This indicates that both CCM and OIF are able to infer the potentially causal relationships between species. Compared to the CCM interaction heatmap the OIF heatmap presents larger gradients of inferred interactions that highlight the divergence and asynchrony in fish populations of species 4–9 from other species. This difference can be observed in Figure S2 that shows the strongest linear correlations for the most divergent and asynchronous species (4–9). It is worth noting that species 4-8 are all native species (See Table SS11). Therefore, despite the patterns of interactions of CCM and TE are similar, CCM allows one a better identification of clusters of species with similar or distinct interaction ranges. Additionally TE estimates some weak observed interactions such as of species 2 (E. japonicus) with others, while CCM essentially considers null interactions for these species.Precisely, the most interacting species (4–9) are the most divergent and asynchronous species (with respect to the whole community) as well as diverse in terms of values of abundance; these species form the ”collective core” that is likely determining the stability of the ecosystem. Interestingly the number of these species is relatively small and it confirms results of other studies (see39, 54,55,56,57) showing that the number of species with weak interactions is much larger. Theory suggests that this pattern promotes stability as weak interactors dampen the destabilizing potential of strong interactors54. Mediated cooperation (e.g. by many “weakly” interacting competitors) as shown by Tu et al.52 promotes biodiversity and diversity increases stability. When considering abundance values (at same time steps) of collective core species (Fig. S2) these species are linearly related and this increases their mutual predictability by either using linear or non-linear models based on correlation coefficient and TE. This proves that non-linearity increases predictability.The choice of the optimal u that maximizes MI leads to the optimal TE model and resultant interaction network. The observed u over time is really small (Fig. S9) and this signifies how likely the ecosystem has small memory and responds quickly to rapid changes, or the information of change is carried over time by ecosystem’s interactions which lead to accurate short-term forecasting. In other words, temperature-induced changes may take long time but the information of change is replicated at short time periods. The chosen time delay (u =1) corresponds to the species sampling of two weeks. Note that values of u are also dependent on the data resolution and they are strongly related to fluctuations rather than absolute (alpha)-diversity value. Thus, while biodiversity may fluctuate rapidly in time, value of (alpha)-diversity for seasons or longer time periods can be more stable and manifesting higher memory (representative of u for the whole ecosystem) than the one between species pairs (related to pair’s u). Short-term catastrophic dynamics (for instance related to dramatic habitat change, sudden invasions, extinctions or rapid adaptations) may lead to irreversible shifts in interactions (strength and sign); this, in turn can affect biodiversity patterns that are completely uninformed by past dynamics. Thus, there is certainly a limit to predictability and to the validity of time delays which can change very rapidly. However, we insist in emphasizing that models are predictive tools and predictions are not necessarily causality reflecting the many and highly complex underlying processes. Yet, interpretation of results must be done with care.We also study temporally dynamical networks for the fish ecosystem community (see “Real-world sardine–anchovy-temperature ecosystem” section). CCM and OIF model are applied to quantify the causality between all possible pairs of species at each time period by calculating (rho) and TE, respectively. Estimated effective (alpha)-diversity (Eq. S1.2) from CCM- and TE-based inferred networks at each time point can be obtained and then compared to the taxonomic (or ”real”) (alpha)-diversity. Results are shown in Fig. 8 and Fig. S6. In the whole time period, the estimated (alpha)-diversity from CCM is constant, whereas the global trend of the estimated (alpha)-diversity from OIF model slightly decreases over time that is consistent with the global trend of real (alpha)-diversity. CCM always predicts a non-zero interaction for all species (including negative values) whereas OIF predicts zero interactions for some species that are then not making part of the estimated effective (alpha)-diversity.Figure 8Predicted (alpha) -diversity via optimal interaction threshold for CCM’s (rho) and OIF’s TE versus taxonomic diversity. Effective (alpha)-diversity from CCM and OIF are shown (blue and red) for an optimal threshold of (rho) and TE (i.e., 0.2 and 0.3) that maximizes the correlation coefficient and mutual information (MI) between (alpha _{CCM}) or (alpha _{TE}) and the taxonomic (alpha), respectively. The maximization of the correlation coefficient and MI guarantees that the estimated effective (alpha) are the closest to the taxonomic (alpha). g is the resolution of the network inference determined by the minimum number of points required to construct pdfs and infer TE robustly (see Supplementary Information section S1.3).Full size imageFigure 8 shows the effective (alpha) diversity from CCM and OIF for an optimal threshold of (rho) and TE (i.e., 0.2 and 0.3) that maximizes the correlation coefficient and Mutual Information (MI) between (alpha _{CCM}) or (alpha _{TE}) and the taxonomic (alpha), respectively. The maximization of the correlation coefficient and MI guarantees that the estimated effective (alpha) are the closest to the taxonomic (alpha). Figure S6 shows effective (alpha) for unthresholded interactions and other thresholds. Note that the threshold on TE does not coincide with the value of TE that maximizes the total network entropy (Fig. 8) and then some of the reported species may not be part of the ecosystem strongly. Thus, this threshold method is also useful to identify species that are truly forming local diversity versus transient species. Considering the pattern of fluctuations of effective (alpha)-diversity from CCM, they are poorly unrelated to the real (alpha)-diversity, while those from OIF are much more synchronous with seasonal fluctuations of real (alpha)-diversity. However, (alpha _{TE}) is a bit higher than the average taxonomic (alpha). Both CCM and TE predict a decrease in (alpha) in time that corresponds to an increase in SST. As shown in Fig S6, OIF is attributing higher sensitivity to SST for small interaction species because (alpha) fluctuations show seasonality that happens when species follow environmental dynamics closely. Vice versa, CCM predicts a broader sensitivity for all positively interacting species. These results reveal that OIF gives an effective tool to measure meaningful interdependence relationships between species for constructing temporally dynamical networks where the number of nodes over time [estimated (alpha (t))] can reflect closely the taxonomic (alpha)-diversity. This allows us to find more reliably how changes of environmental factors (e.g. SST) affect biodiversity in ecosystems. The establishment of thresholds on interactions is also useful for exploring ranges of interdependencies and associated effective (alpha)-diversity with respect to the average taxonomic diversity. (beta) effective diversity is another very important macroecological indicator informing about ecosystem changes; for instance in Li and Convertino39 (beta)-diversity identified distinct ecosystem health states. However, (alpha) and (beta) (effective diversity) variability are highly linked to each other and yet looking into one or another would provide equivalent results. The difference between taxonomic and effective (beta)-diversity may provide some information about invasive or rare species have weak influence on the ecosystem since they are characterized by low TEs. Supplementary Information contains further elaborations on results. More

  • in

    The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization

    1.Bradford MA, Wieder WR, Bonan GB, Fierer N, Raymond PA, Crowther TW. Managing uncertainty in soil carbon feedbacks to climate change. Nat Clim Chang. 2016;6:751–8.Article 
    CAS 

    Google Scholar 
    2.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Crowther TW, Hoogen JVD, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Heimann M, Reichstein M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature. 2008;451:289–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Wieder WR, Bonan GB, Allison SD. Global soil carbon projections are improved by modelling microbial processes. Nat Clim Chang. 2013;3:909–12.CAS 
    Article 

    Google Scholar 
    6.Walker TWN, Kaiser C, Strasser F, Herbold CW, Leblans NIW, Woebken D, et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat Clim Chang. 2018;8:885–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, et al. Quantifying global soil carbon losses in response to warming. Nature. 2016;540:104–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Li JQ, Pei JM, Pendall E, Fang CM, Nie M. Spatial heterogeneity of temperature sensitivity of soil respiration: a global analysis of field observations. Soil Biol Biochem. 2020;141:107675.CAS 
    Article 

    Google Scholar 
    9.Wang QK, Zhao XC, Chen LC, Yang QP, Chen S, Zhang WD, et al. Global synthesis of temperature sensitivity of soil organic carbon decomposition: latitudinal patterns and mechanisms. Funct Ecol. 2019;33:514–23.Article 

    Google Scholar 
    10.Nottingham AT, Baath E, Reischke S, Salinas N, Meir P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob Chang Biol. 2019;25:827–38.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Ye JS, Bradford MA, Dacal M, Maestre FT, García-Palacios P. Increasing microbial carbon use efficiency with warming predicts soil heterotrophic respiration globally. Glob Chang Biol. 2019;25:3354–64.PubMed 
    Article 

    Google Scholar 
    12.Smith TP, Thomas TJH, Garcia-Carreras B, Sal S, Yvon-Durocher G, Bell T, et al. Community-level respiration of prokaryotic microbes may rise with global warming. Nat Commun. 2019;10:5124.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    13.Schipper LA, Hobbs JK, Rutledge S, Arcus VL. Thermodynamic theory explains the temperature optima of soil microbial processes and high Q10 values at low temperatures. Glob Chang Biol 2014;20:3578–86.PubMed 
    Article 

    Google Scholar 
    14.Pietikainen J, Pettersson M, Baath E. Comparison of temperature effects on soil respiration and bacterial and fungal growth rates. FEMS Microbiol Ecol. 2005;52:49–58.PubMed 
    Article 
    CAS 

    Google Scholar 
    15.Bárcenas-Moreno G, Gómez-Brandón M, Rousk J, Bååth E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob Chang Biol. 2009;15:2950–7.Article 

    Google Scholar 
    16.Engqvist MKM. Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures. BMC Microbiol. 2018;18:177.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Oliverio AM, Bradford MA, Fierer N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob Chang Biol. 2017;23:2117–29.PubMed 
    Article 

    Google Scholar 
    18.Bier RL, Bernhardt ES, Boot CM, Graham EB, Hall EK, Lennon JT, et al. Linking microbial community structure and microbial processes: an empirical and conceptual overview. FEMS Microbiol Ecol. 2015;91:fiv113.PubMed 
    Article 
    CAS 

    Google Scholar 
    19.Dubey A, Malla MA, Khan F, Chowdhary K, Yadav S, Kumar A, et al. Soil microbiome: a key player for conservation of soil health under changing climate. Biodivers Conserv. 2019;28:2405–29.Article 

    Google Scholar 
    20.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microbiol. 2015;81:7570–81.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Koch BJ, McHugh TA, Hayer M, Schwartz E, Blazewicz SJ, Dijkstra P, et al. Estimating taxon-specific population dynamics in diverse microbial communities. Ecosphere. 2018;9:e02090.Article 

    Google Scholar 
    22.Hamdi S, Moyano F, Sall S, Bernoux M, Chevallier T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol Biochem. 2013;58:115–26.CAS 
    Article 

    Google Scholar 
    23.Martiny AC, Treseder K, Pusch G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 2013;7:830–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    24.DeAngelis KM, Pold G, Topçuoğlu BD, van Diepen LTA, Varney RM, Blanchard JL, et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front Microbiol. 2015;6:104.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Euskirchen ES, Bret-Harte MS, Shaver GR, Edgar CW, Romanovsky VE. Long-term release of carbon dioxide from Arctic Tundra ecosystems in Alaska. Ecosystems. 2017;20:960–74.CAS 
    Article 

    Google Scholar 
    26.Reed SC, Reibold R, Cavaleri MA, Alonso-Rodríguez AM, Berberich ME, Wood TE. Chapter six—soil biogeochemical responses of a tropical forest to warming and hurricane disturbance. In: Dumbrell AJ, Turner EC, Fayle TM, editors. Advances in ecological research. (Academic Press, Cambridge MA, 2020) pp 225–52.27.Witt C, Gaunt JL, Galicia CC, Ottow JCG, Neue HU. A rapid chloroform-fumigation extraction method for measuring soil microbial biomass carbon and nitrogen in flooded rice soils. Biol Fertil Soils. 2000;30:510–9.CAS 
    Article 

    Google Scholar 
    28.Berry D, Ben Mahfoudh K, Wagner M, Loy A. Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Appl Environ Microbiol. 2012;78:612.CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    29.Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

    Google Scholar 
    30.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939–46.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Aronesty E. ea-utils: “Command-line tools for processing biological sequencing data”. 2011. https://github.com/ExpressionAnalysis/ea-utils.33.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Caporaso JG, Bittinger K, Bushman FD, Desantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10:57–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Morrissey EM, Mau RL, Schwartz E, McHugh TA, Dijkstra P, Koch BJ, et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 2017;11:1890–9.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Li J, Mau RL, Dijkstra P, Koch BJ, Schwartz E, Liu X-JA, et al. Predictive genomic traits for bacterial growth in culture versus actual growth in soil. ISME J. 2019;13:2162–72.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Gross N, Bagousse-Pinguet YL, Liancourt P, Berdugo M, Gotelli NJ, Maestre FT. Functional trait diversity maximizes ecosystem multifunctionality. Nat Ecol Evol. 2017;1:0132.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Laliberté E, Norton DA, Scott D. Contrasting effects of productivity and disturbance on plant functional diversity at local and metacommunity scales. J Veg Sci. 2013;24:834–42.Article 

    Google Scholar 
    42.Plass-Johnson JG, Taylor MH, Husain AAA, Teichberg MC, Ferse SCA. Non-random variability in functional composition of coral reef fish communities along an environmental gradient. PLOS ONE. 2016;11:e0154014.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.Götzenberger L, Botta-Dukát Z, Lepš J, Pärtel M, Zobel M, de Bello F. Which randomizations detect convergence and divergence in trait-based community assembly? A test of commonly used null models. J Veg Sci. 2016;27:1275–87.Article 

    Google Scholar 
    44.Delgado-Baquerizo M, Trivedi P, Trivedi C, Eldridge DJ, Reich PB, Jeffries TC, et al. Microbial richness and composition independently drive soil multifunctionality. Funct Ecol. 2017;31:2330–43.Article 

    Google Scholar 
    45.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. https://www.R-project.org/.46.Bruelheide H, Dengler J, Purschke O, Lenoir J, Jiménez-Alfaro B, Hennekens SM, et al. Global trait–environment relationships of plant communities. Nat Ecol Evol. 2018;2:1906–17.PubMed 
    Article 

    Google Scholar 
    47.Piton G, Legay N, Arnoldi C, Lavorel S, Clément J-C, Foulquier A. Using proxies of microbial community-weighted means traits to explain the cascading effect of management intensity, soil and plant traits on ecosystem resilience in mountain grasslands. J Ecol. 2020;108:876–93.CAS 
    Article 

    Google Scholar 
    48.Alster CJ, von Fischer JC, Allison SD, Treseder KK. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob Chang Biol. 2020;26:3221–9.PubMed 
    Article 

    Google Scholar 
    49.Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Li J, Nie M, Pendall E, Reich PB, Pei J, Noh NJ, et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob Chang Biol. 2020;26:1873–85.PubMed 
    Article 

    Google Scholar 
    51.Lipson DA. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front Microbiol. 2015;6:615.PubMed 
    PubMed Central 

    Google Scholar 
    52.Buckeridge KM, Mason KE, McNamara NP, Ostle N, Puissant J, Goodall T, et al. Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization. Commun Earth Environ. 2020;1:36.Article 

    Google Scholar 
    53.Ali A, Yan E-R, Chang SX, Cheng J-Y, Liu X-Y. Community-weighted mean of leaf traits and divergence of wood traits predict aboveground biomass in secondary subtropical forests. Sci Total Environ. 2017;574:654–62.CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Buzzard V, Michaletz ST, Deng Y, He Z, Ning D, Shen L, et al. Continental scale structuring of forest and soil diversity via functional traits. Nat Ecol Evol. 2019;3:1298–308.PubMed 
    Article 

    Google Scholar 
    55.Luo Y-H, Cadotte MW, Burgess KS, Liu J, Tan S-L, Zou J-Y, et al. Greater than the sum of the parts: how the species composition in different forest strata influence ecosystem function. Ecol Lett. 2019;22:1449–61.PubMed 
    Article 

    Google Scholar 
    56.Díaz S, Lavorel S, de Bello F, Quétier F, Grigulis K, Robson TM. Incorporating plant functional diversity effects in ecosystem service assessments. Proc Natl Acad Sci USA. 2007;104:20684–9.PubMed 
    Article 

    Google Scholar 
    57.Bradford MA. Thermal adaptation of decomposer communities in warming soils. Front Microbiol. 2013;4:333.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Morrissey EM, Mau RL, Schwartz E, Koch BJ, Hayer M, Hungate BA. Taxonomic patterns in the nitrogen assimilation of soil prokaryotes. Environ Microbiol. 2018;20:1112–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Coskun OK, Ozen V, Wankel SD, Orsi WD. Quantifying population-specific growth in benthic bacterial communities under low oxygen using H218O. ISME J. 2019;13:1546–59.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Zhou G, Zhou X, Liu R, Du Z, Zhou L, Li S, et al. Soil fungi and fine root biomass mediate drought-induced reductions in soil respiration. Funct Ecol. 2020;34:2634–43.Article 

    Google Scholar 
    61.Melillo JM, Frey SD, Deangelis KM, Werner WJ, Bernard MJ, Bowles FP, et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science. 2017;358:101–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Johnston ASA, Sibly RM. The influence of soil communities on the temperature sensitivity of soil respiration. Nat Ecol Evol. 2018;2:1597–602.PubMed 
    Article 

    Google Scholar  More