Ecology

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More

1.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to conservation genetics (Cambridge university press, 2002).2.Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Martínez-Freiría, F., Velo-Antón, G. & Brito, J. C. Trapped by climate: Interglacial refuge and recent population expansion in the endemic Iberian adder Vipera seoanei. Divers. Distrib. 21, 331–344 (2015).Article 

Google Scholar 
4.Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).Article 

Google Scholar 
5.Veríssimo, J. et al. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biol. J. Linn. Soc. 119, 943–959 (2016).Article 

Google Scholar 
6.Chattopadhyay, B., Garg, K. M., Gwee, C. Y., Edwards, S. V. & Rheindt, F. E. Gene flow during glacial habitat shifts facilitates character displacement in a Neotropical flycatcher radiation. BMC Evol. Biol. 17, 1–15 (2017).Article 

Google Scholar 
7.Garg, K. M., Chattopadhyay, B., Koane, B., Sam, K. & Rheindt, F. E. Last Glacial Maximum led to community-wide population expansion in a montane songbird radiation in highland Papua New Guinea. BMC Evol. Biol. 20, 82 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Goodman, S. M., Raherilalao, M. J. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description and Biota (Association Vahatra in Antananarivo, The University of Chicago Press, 2018).10.Douglass, K. The diversity of late holocene shellfish exploitation in Velondriake, Southwest Madagascar. J. Island Coast. Archaeol. 12, 333–359 (2016).11.Yoder, A. D., Campbell, C. R., Blanco, M. B., Ganzhorn, J. U. & Goodman, S. M. Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar’s forests past. PNAS 113, 8049–8056 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L., Climate change. and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Townsend, T. M., Vieites, D. R., Glaw, F. & Vences, M. Testing species-level diversification hypotheses in Madagascar: the case of microendemic Brookesia leaf Chameleons. Syst. Biol. 58, 641–656 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Brown, J. L., Cameron, A., Yoder, A. D. & Vences, M. A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar. Nat. Commun. 5, 5046 (2014).15.Schüßler, D. et al. Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in northeastern Madagascar, with the description of a new species. Am. J. Primatol. 82, e23180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chikhi, L. & Bruford, M. Mammalian population genetics and genomics. Mamm. Genome https://doi.org/10.1079/9780851999104.0539 (2005).17.Olivieri, G. L., Sousa, V., Chikhi, L. & Radespiel, U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol. Conserv. 141, 1257–1271 (2008).Article 

Google Scholar 
18.Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).19.Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).21.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 47, 555–559 (2015).22.Salmona, J., Heller, R., Lascoux, M. & Shafer, A. Inferring demographic history using genomic data. in Population Genomics 511–537 (Springer, 2017).23.Beichman, A. C., Huerta-Sanchez, E. & Lohmueller, K. E. Using genomic data to infer historic population dynamics of nonmodel organisms. Annu. Rev. Ecol. Evol. Syst. 49, 433–456 (2018).Article 

Google Scholar 
24.Sgarlata, G. M. et al. Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern Madagascar: The discovery of a putative new species? Am. J. Primatol. 81, e23070 (2019).25.Demenocal, P. et al. Abrupt onset and termination of the African humid period:: rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361 (2000).Article 

Google Scholar 
26.Tierney, J. E. & DeMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Los, S. O. et al. Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya. Quat. Sci. Rev. 218, 34–48 (2019).Article 

Google Scholar 
28.Ivory, S. J. & Russell, J. Climate, herbivory, and fire controls on tropical African forest for the last 60ka. Quat. Sci. Rev. 148, 101–114 (2016).Article 

Google Scholar 
29.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, M. Holocene changes in eastern tropical Pacific climate inferred from a Galápagos lake sediment record. Quat. Sci. Rev. 27, 1166–1180 (2008).Article 

Google Scholar 
30.Martin-Puertas, C., Tjallingii, R., Bloemsma, M. & Brauer, A. Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data. J. Quat. Sci. 32, 427–436 (2017).Article 

Google Scholar 
31.Flenley, J. R. Tropical forests under the climates of the last 30,000 years. in Potential Impacts of Climate Change on Tropical Forest Ecosystems, 37–57 (Springer, 1998).32.Burrough, S. L. & Thomas, D. S. G. Central southern Africa at the time of the African humid period: a new analysis of Holocene palaeoenvironmental and palaeoclimate data. Quat. Sci. Rev. 80, 29–46 (2013).Article 

Google Scholar 
33.Ivory, S. J. & Russell, J. Lowland forest collapse and early human impacts at the end of the African humid period at Lake Edward, equatorial East. Afr. Quat. Res. 89, 7–20 (2018).Article 

Google Scholar 
34.Anderson, A. et al. New evidence of megafaunal bone damage indicates late colonization of Madagascar. PLoS ONE 13, 1–14 (2018).
Google Scholar 
35.Hansford, J. et al. Early Holocene human presence in Madagascar evidenced by exploitation of avian megafauna. Sci. Adv. 4, eaat6925 (2018).36.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late holocene extinctions in Madagascar. Proc. Natl Acad. Sci. USA 100, 10800–10805 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Railsback, L. B. et al. Relationships between climate change, human environmental impact, and megafaunal extinction inferred from a 4000-year multi-proxy record from a stalagmite from northwestern Madagascar. Quat. Sci. Rev. 234, 106244 (2020).Article 

Google Scholar 
38.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. PNAS 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Radimilahy, C. Mahilaka: an Archaeological Investigation of an Early Town in Northwestern Madagascar. Acta Universitatis Upsaliensis (University of Uppsala, 1998).40.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet 47, 555–559 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Lapierre, M., Lambert, A. & Achaz, G. Accuracy of demographic inferences from the site frequency spectrum: the case of the yoruba population. Genetics 206, 139–449 (2017).Article 

Google Scholar 
42.Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
Article 

Google Scholar 
43.Patton, A. H. et al. Contemporary demographic reconstruction methods are robust to genome assembly quality: a case study in Tasmanian devils. Mol. Biol. Evol. 36, 2906–2921 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mazet, O., Rodríguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity 116, 362–371 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Orozco-terWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).46.Mazet, O., Rodríguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Chikhi, L. et al. The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: Insights into demographic inference and model choice. Heredity 120, 13–24 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Simons, E. L., Godfrey, L. R., Vuillaume-Randriamanantena, M., Chatrath, P. S. & Gagnon, M. Discovery of new giant subfossil lemurs in the Ankarana Mountains of Northern Madagascar. J. Hum. Evol. 19, 311–319 (1990).Article 

Google Scholar 
49.Jungers, W. L., Godfrey, L. R., Simons, E. L. & Chatrath, P. S. Subfossil Indri indri from the Ankarana Massif of northern Madagascar. Am. J. Phys. Anthropol. 97, 357–366 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Wilson, J. M., Stewart, P. D. & Fowler, S. V. Ankarana — a rediscovered nature reserve in northern Madagascar. Oryx 22, 163–171 (1988).Article 

Google Scholar 
51.Everson, K. M., Jansa, S. A., Goodman, S. M. & Olson, L. E. Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest. J. Biogeogr. 47, 2059–2072 (2020).Article 

Google Scholar 
52.Douglass, K., Hixon, S., Wright, H. T., Godfrey, L. R. & Crowley, B. E. A critical review of radiocarbon dates clarifies the human settlement of Madagascar. Quat. Sci. Rev. 221, 105878 (2019).53.Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus. Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Johnson, J. A. et al. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol. Ecol. 18, 54–63 (2009).PubMed 
PubMed Central 

Google Scholar 
55.Sommer, S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol. Ecol. 12, 2845–2851 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Craul, M. et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol. Conserv. 142, 2862–2871 (2009).Article 

Google Scholar 
57.Parga, J. A., Sauther, M. L., Cuozzo, F. P., Jacky, I. A. Y. & Lawler, R. R. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am. J. Phys. Anthropol. 147, 21–29 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. Proc. Natl Acad. Sci. USA 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Schüler, L. & Hemp, A. Atlas of pollen and spores and their parent taxa of Mt. Kilimanjaro and tropical East Africa. Quat. Int. 425, 301–386 (2016).Article 

Google Scholar 
60.Du Puy, D. J. & Moat, J. Vegetation mapping and classification in Madagascar (using GIS): implications and recommendations for the conservation of biodiversity. in Chorology, Taxonomy and Ecology of the floras of Africa and Madagascar, 97–117 (1998, in press).61.Guillaumet, J.-L., Betsch, J.-M. & Callmander, M. W. Renaud Paulian et le programme du CNRS sur les hautes montagnes à Madagascar: étage vs domaine. Zoosystema 30, 723 (2008).PubMed 
PubMed Central 

Google Scholar 
62.Weisrock, D. W. et al. Delimiting species without nuclear monophyly in Madagascar’s mouse lemurs. PLoS ONE 5, e9883 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Publ. 267, 51–63 (2006).CAS 
Article 

Google Scholar 
64.Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landforms 26, 1237–1248 (2001).Article 

Google Scholar 
65.Hogg, A. G. et al. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55, 1889–1903 (2013).CAS 
Article 

Google Scholar 
66.Rina Evasoa, M. et al. Sources of variation in social tolerance in mouse lemurs (Microcebus spp.). BMC Ecol. 19, 1–16 (2019).CAS 
Article 

Google Scholar 
67.Aleixo-Pais, I. et al. The genetic structure of a mouse lemur living in a fragmented habitat in Northern Madagascar. Conserv. Genet. 20, 229–243 (2019).Article 

Google Scholar 
68.Radespiel, U., Jurić, M. & Zimmermann, E. Sociogenetic structures, dispersal and the risk of inbreeding in a small nocturnal lemur, the golden-brown mouse lemur (Microcebus ravelobensis). Behaviour 146, 607–628 (2009).Article 

Google Scholar 
69.Radespiel, U., Ehresmann, P. & Zimmermann, E. Species-specific usage of sleeping sites in two sympatric mouse lemur species (Microcebus murinus and M. ravelobensis) in northwestern Madagascar. Am. J. Primatol. 59, 139–151 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Larsen, P. A. et al. Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus). BMC Biol. 15, 1–17 (2017).Article 
CAS 

Google Scholar 
71.Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing. Data 195, 693–702 (2013).CAS 

Google Scholar 
73.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 1–13 (2014).Article 

Google Scholar 
74.Korneliussen, T. S. & Moltke, I. Sequence analysis NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-Statistic on low-coverage whole-genome data. G3 8, 551–566 (2017).PubMed Central 
Article 

Google Scholar 
76.Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L. Climate change and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Schneider, N., Chikhi, L., Currat, M. & Radespiel, U. Signals of recent spatial expansions in the grey mouse lemur (Microcebus murinus). BMC Evol. Biol. 10, 105 (2010).81.Radespiel, U., Lutermann, H., Schmelting, B. & Zimmermann, E. An empirical estimate of the generation time of mouse lemurs. Am. J. Primatol. 81, 1–8 (2019).Article 

Google Scholar 
82.Hawkins, M. T. R. et al. Genome sequence and population declines in the critically endangered greater bamboo lemur (Prolemur simus) and implications for conservation. BMC Genomics 19, 1–15 (2018).Article 
CAS 

Google Scholar 
83.Poelstra, J. et al. Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent. Syst. Biol. https://doi.org/10.1093/sysbio/syaa053 (2020).84.Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127.2, 233–244 (2021).Article 

Google Scholar 
85.Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Fredsted, T., Pertoldi, C., Schierup, M. H. & Kappeler, P. M. Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Mol. Ecol. 14, 2363–2372 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Radespiel, U., Schulte, J., Burke, R. J. & Lehman, S. M. Molecular edge effects in the endangered golden-brown mouse lemur Microcebus ravelobensis. Oryx 53, 716–726 (2019).Article 

Google Scholar 
88.Radespiel, U., Lutermann, H., Schmelting, B., Bruford, M. W. & Zimmermann, E. Patterns and dynamics of sex-biased dispersal in a nocturnal primate, the grey mouse lemur, Microcebus murinus. Anim. Behav. 65, 709–719 (2003).Article 

Google Scholar 
89.Radespiel, U., Rakotondravony, R. & Chikhi, L. Natural and anthropogenic determinants of genetic structure in the largest remaining population of the endangered golden-brown mouse lemur, Microcebus ravelobensis. Am. J. Primatol. 70, 860–870 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Schliehe-Diecks, S., Eberle, M. & Kappeler, P. M. Walk the line-dispersal movements of gray mouse lemurs (Microcebus murinus). Behav. Ecol. Sociobiol. 66, 1175–1185 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).92.Beerli, P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13, 827–836 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).Article 

Google Scholar 
94.Bagley, R. K., Sousa, V. C., Niemiller, M. L. & Linnen, C. R. History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei). Mol. Ecol. 26, 1022–1044 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Milner AM, Khamis K, Battin TJ, Brittain JE, Barrand NE, Füreder L, et al. Glacier shrinkage driving global changes in downstream systems. Proc Nat Acad Sci USA. 2017;114:9770.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Battin TJ, Wille A, Sattler B, Psenner R. Phylogenetic and functional heterogeneity of sediment biofilms along environmental gradients in a glacial stream. Appl Environ Microbiol. 2001;67:799–807.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Wilhelm L, Singer GA, Fasching C, Battin TJ, Besemer K. Microbial biodiversity in glacier-fed streams. ISME J. 2013;7:1651.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Ren Z, Gao H, Elser JJ, Zhao Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci Rep. 2017;7:12668.PubMed 
PubMed Central 

Google Scholar 
5.Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Nat Acad Sci USA. 2015;112:1326.
Google Scholar 
6.Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
PubMed Central 

Google Scholar 
7.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.8.Allen R, Hoffmann LJ, Larcombe MJ, Louisson Z, Summerfield TC. Homogeneous environmental selection dominates microbial community assembly in the oligotrophic South Pacific Gyre. Mol Ecol. 2020;29:4680–91.CAS 
PubMed 

Google Scholar 
9.Li Y, Gao Y, Zhang W, Wang C, Wang P, Niu L, et al. Homogeneous selection dominates the microbial community assembly in the sediment of the Three Gorges Reservoir. Sci Tot Environ. 2019;690:50–60.CAS 

Google Scholar 
10.Zhang K, Shi Y, Cui X, Yue P, Li K, Liu X, et al. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems. 2019;4:e00225–18.11.Thrash CJ, Temperton B, Swan BK, Landry ZC, Woyke T, DeLong EF, et al. Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. 2014;8:1440–51.PubMed 

Google Scholar 
12.Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science. 2008;320:1081.CAS 
PubMed 

Google Scholar 
13.Kent AG, Baer SE, Mouginot C, Huang JS, Larkin AA, Lomas MW, et al. Parallel phylogeography of Prochlorococcus and Synechococcus. ISME J. 2019;13:430–41.PubMed 

Google Scholar 
14.Brown MV, Furham JA. Marine bacterial microdiversity as revealed by internal transcribed spacer analysis. Aquat Microb Ecol. 2005;41:15–23.
Google Scholar 
15.Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Yung C-M, Vereen MK, Herbert A, Davis KM, Yang J, Kantorowska A, et al. Thermally adaptive tradeoffs in closely related marine bacterial strains. Environ Microbiol. 2015;17:2421–9.PubMed 

Google Scholar 
17.Props R, Denef VJ. Temperature and nutrient levels correspond with lineage-specific microdiversification in the ubiquitous and abundant freshwater genus. Limnohabitans Appl Environ Microbiol. 2020;86:e00140–00120.CAS 
PubMed 

Google Scholar 
18.Chase AB, Karaoz U, Brodie EL, Gomez-Lunar Z, Martiny AC, Martiny JBH. Microdiversity of an abundant terrestrial bacterium encompasses extensive variation in ecologically relevant traits. mBio. 2017;8:e01809–17.19.Choudoir MJ, Buckley DH. Phylogenetic conservatism of thermal traits explains dispersal limitation and genomic differentiation of Streptomyces sister-taxa. ISME J. 2018;12:2176–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.CAS 
PubMed 

Google Scholar 
21.Cohan FM, Koeppel AF. The origins of ecological diversity in prokaryotes. Curr Biol. 2008;18:R1024–34.CAS 
PubMed 

Google Scholar 
22.Larkin AA, Martiny AC. Microdiversity shapes the traits, niche space, and biogeography of microbial taxa. Environ Microbiol Rep. 2017;9:55–70.CAS 
PubMed 

Google Scholar 
23.Fodelianakis S, Lorz A, Valenzuela-Cuevas A, Barozzi A, Booth JM, Daffonchio D. Dispersal homogenizes communities via immigration even at low rates in a simplified synthetic bacterial metacommunity. Nat Commun. 2019;10:1314.PubMed 
PubMed Central 

Google Scholar 
24.Duarte CM, Røstad A, Michoud G, Barozzi A, Merlino G, Delgado-Huertas A, et al. Discovery of Afifi, the shallowest and southernmost brine pool reported in the Red Sea. Sci Rep. 2020;10:910.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Kohler TJ, Peter H, Fodelianakis S, Pramateftaki P, Styllas M, Tolosano M, et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front Microbiol. 2020;11:2922.
Google Scholar 
26.Amalfitano S, Fazi S. Recovery and quantification of bacterial cells associated with streambed sediments. J Microbiol Methods. 2008;75:237–43.CAS 
PubMed 

Google Scholar 
27.Hammes F, Berney M, Wang Y, Vital M, Köster O, Egli T. Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 2008;42:269–77.CAS 
PubMed 

Google Scholar 
28.Busi SB, Pramateftaki P, Brandani J, Fodelianakis S, Peter H, Halder R, et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ. 2020;8:e9973.PubMed 
PubMed Central 

Google Scholar 
29.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

Google Scholar 
30.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotech. 2019;37:852–7.CAS 

Google Scholar 
32.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Meth. 2016;13:581–3.CAS 

Google Scholar 
33.Props R, Kerckhof F-M, Rubbens P, De Vrieze J, Hernandez-Sanabria E, Waegeman W, et al. Absolute quantification of microbial taxon abundances. ISME J. 2017;11:584–7.PubMed 

Google Scholar 
34.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 

Google Scholar 
35.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Singer E, Bushnell B, Coleman-Derr D, Bowman B, Bowers RM, Levy A, et al. High-resolution phylogenetic microbial community profiling. ISME J. 2016;10:2020–32.PubMed 
PubMed Central 

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

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

Google Scholar 
39.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539–9.PubMed 
PubMed Central 

Google Scholar 
40.Foster ZSL, Sharpton TJ, Grünwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLOS Comput Biol. 2017;13:e1005404.PubMed 
PubMed Central 

Google Scholar 
41.R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.42.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan.43.Fodelianakis S, Moustakas A, Papageorgiou N, Manoli O, Tsikopoulou I, Michoud G, et al. Modified niche optima and breadths explain the historical contingency of bacterial community responses to eutrophication in coastal sediments. Mol Ecol. 2017;26:2006–18.CAS 
PubMed 

Google Scholar 
44.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 

Google Scholar 
45.Washburne AD, Silverman JD, Leff JW, Bennett DJ, Darcy JL, Mukherjee S, et al. Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets. PeerJ. 2017;5:e2969.PubMed 
PubMed Central 

Google Scholar 
46.Washburne AD, Silverman JD, Morton JT, Becker DJ, Crowley D, Mukherjee S, et al. Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data. Ecol Monogr. 2019;89:e01353.
Google Scholar 
47.Gawor J, Grzesiak J, Sasin-Kurowska J, Borsuk P, Gromadka R, Górniak D, et al. Evidence of adaptation, niche separation and microevolution within the genus Polaromonas on Arctic and Antarctic glacial surfaces. Extremophiles. 2016;20:403–13.PubMed 
PubMed Central 

Google Scholar 
48.Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10:333–45.CAS 
PubMed 

Google Scholar 
49.Tromas N, Taranu ZE, Castelli M, Pimentel JSM, Pereira DA, Marcoz R, et al. The evolution of realized niches within freshwater. Synechococcus Environ Microbiol. 2020;22:1238–50.PubMed 

Google Scholar 
50.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 

Google Scholar 
51.Cerqueira T, Barroso C, Froufe H, Egas C, Bettencourt R. Metagenomic signatures of microbial communities in deep-sea hydrothermal sediments of Azores Vent Fields. Microb Ecol. 2018;76:387–403.CAS 
PubMed 

Google Scholar 
52.Osburn MR, LaRowe DE, Momper LM, Amend JP. Chemolithotrophy in the continental deep subsurface: Sanford underground research facility (SURF), USA. Front Microbiol. 2014;5:610.53.Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice-covered Lakes. Environ Microbiol. 2018;20:2568–84.CAS 
PubMed 

Google Scholar 
54.Vick-Majors TJ, Priscu JC, Amaral-Zettler LA. Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered Antarctic lakes. ISME J. 2014;8:778–89.CAS 
PubMed 

Google Scholar 
55.Darcy JL, Lynch RC, King AJ, Robeson MS, Schmidt SK. Global distribution of Polaromonas phylotypes – evidence for a highly successful dispersal capacity. PloS ONE. 2011;6:e23742.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Smith HJ, Foreman CM, Ramaraj T. Draft genome sequence of a metabolically diverse Antarctic supraglacial stream organism, Polaromonas sp. strain CG9_12, determined using Pacific Biosciences single-molecule real-time sequencing technology. Genome Announc. 2014;2:e01242–01214.PubMed 
PubMed Central 

Google Scholar 
57.Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Liu Q, Zhou Y-G, Xin Y-H. High diversity and distinctive community structure of bacteria on glaciers in China revealed by 454 pyrosequencing. Syst Appl Microbiol. 2015;38:578–85.PubMed 

Google Scholar 
59.Kalyuzhnaya MG, Bowerman S, Lara JC, Lidstrom ME, Chistoserdova L. Methylotenera mobilis gen. nov., sp. nov., an obligately methylamine-utilizing bacterium within the family Methylophilaceae. Int J Syst Evol Microbiol. 2006;56:2819–23.CAS 
PubMed 

Google Scholar 
60.Kane SR, Chakicherla AY, Chain PSG, Schmidt R, Shin MW, Legler TC, et al. Whole-genome analysis of the methyl tert-butyl ether-degrading Beta-Proteobacterium Methylibium petroleiphilum PM1. J Bacteriol. 2007;189:1931.CAS 
PubMed 

Google Scholar 
61.Martineau C, Mauffrey F, Villemur R, Müller V. Comparative analysis of denitrifying activities of Hyphomicrobium nitrativorans, Hyphomicrobium denitrificans, and Hyphomicrobium zavarzinii. Appl Environ Microbiol. 2015;81:5003–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Dieser M, Broemsen ELJE, Cameron KA, King GM, Achberger A, Choquette K, et al. Molecular and biogeochemical evidence for methane cycling beneath the western margin of the Greenland Ice Sheet. ISME J. 2014;8:2305–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Michaud AB, Dore JE, Achberger AM, Christner BC, Mitchell AC, Skidmore ML, et al. Microbial oxidation as a methane sink beneath the West Antarctic Ice Sheet. Nat Geosci. 2017;10:582–6.CAS 

Google Scholar 
64.Bendall ML, Stevens SLR, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.PubMed 
PubMed Central 

Google Scholar 
65.Baker JM, Riester CJ, Skinner BM, Newell AW, Swingley WD, Madigan MT, et al. Genome sequence of Rhodoferax antarcticus ANT.BRT; a psychrophilic purple nonsulfur bacterium from an Antarctic microbial mat. Microorganisms. 2017;5:8.66.Crisafi F, Giuliano L, Yakimov MM, Azzaro M, Denaro R. Isolation and degradation potential of a cold-adapted oil/PAH-degrading marine bacterial consortium from Kongsfjorden (Arctic region). Rendiconti Lincei. 2016;27:261–70.
Google Scholar 
67.Zhong Z-P, Solonenko NE, Gazitúa MC, Kenny DV, Mosley-Thompson E, Rich VI, et al. Clean low-biomass procedures and their application to ancient ice core microorganisms. Front Microbiol. 2018;9:1094.68.Bai Y, Huang X, Zhou X, Xiang Q, Zhao K, Yu X, et al. Variation in denitrifying bacterial communities along a primary succession in the Hailuogou Glacier retreat area, China. PeerJ. 2019;7:e7356.PubMed 
PubMed Central 

Google Scholar 
69.Garcia-Lopez E, Rodriguez-Lorente I, Alcazar P, Cid C. Microbial communities in coastal glaciers and tidewater tongues of Svalbard archipelago, Norway. Front Mar Sci. 2019;5:512.70.Liu S, Wang H, Chen L, Wang J, Zheng M, Liu S, et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 2020;14:2488–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Harrold ZR, Skidmore ML, Hamilton TL, Desch L, Amada K, van Gelder W, et al. Aerobic and anaerobic thiosulfate oxidation by a cold-adapted, subglacial chemoautotroph. Appl Environ Microbiol. 2016;82:1486–95.CAS 
PubMed Central 

Google Scholar 
72.Franzetti A, Pittino F, Gandolfi I, Azzoni RS, Diolaiuti G, Smiraglia C, et al. Early ecological succession patterns of bacterial, fungal and plant communities along a chronosequence in a recently deglaciated area of the Italian Alps. FEMS Microbiol Ecol. 2020;96:10.73.Kohler TJ, Van Horn DJ, Darling JP, Takacs-Vesbach CD, McKnight DM. Nutrient treatments alter microbial mat colonization in two glacial meltwater streams from the McMurdo Dry Valleys, Antarctica. FEMS Microbiol Ecol. 2016;92:4.
Google Scholar 
74.Sawayama M, Suzuki T, Hashimoto H, Kasai T, Furutani M, Miyata N, et al. Isolation of a Leptothrix strain, OUMS1, from ocherous deposits in groundwater. Cur Microbiol. 2011;63:173–80.CAS 

Google Scholar 
75.Li Y, Cha Q-Q, Dang Y-R, Chen X-L, Wang M, McMinn A, et al. Reconstruction of the functional ecosystem in the high light, low temperature union glacier region, Antarctica. Front Microbiol. 2019;10.76.Cauvy-Fraunié S, Dangles O. A global synthesis of biodiversity responses to glacier retreat. Nat Ecol Evol. 2019;3:1675–85.PubMed 

Google Scholar 
77.Jorquera MA, Graether SP, Maruyama F. Editorial: bioprospecting and biotechnology of extremophiles. Front Bioeng Biotech. 2019;7:204.
Google Scholar 
78.Thompson JR, Pacocha S, Pharino C, Klepac-Ceraj V, Hunt DE, Benoit J, et al. Genotypic diversity within a natural coastal bacterioplankton population. Science. 2005;307:1311.CAS 
PubMed 

Google Scholar 
79.Chase AB, Gomez-Lunar Z, Lopez AE, Li J, Allison SD, Martiny AC, et al. Emergence of soil bacterial ecotypes along a climate gradient. Environ Microbiol. 2018;11:4112–26.
Google Scholar 
80.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 

Google Scholar 
81.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
PubMed Central 

Google Scholar 
82.Garcia-Garcia N, Tamames J, Linz AM, Pedros-Alio C, Puente-Sanchez F. Microdiversity ensures the maintenance of functional microbial communities under changing environmental conditions. ISME J. 2019;13:2969–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Becraft ED, Wood JM, Rusch DB, Kühl M, Jensen SI, Bryant DA, et al. The molecular dimension of microbial species: 1. Ecological distinctions among, and homogeneity within, putative ecotypes of Synechococcus inhabiting the cyanobacterial mat of Mushroom Spring, Yellowstone National Park. Front Microbiol. 2015;6:590.PubMed 
PubMed Central 

Google Scholar 
84.Becraft ED, Cohan FM, Kühl M, Jensen SI, Ward DM. Fine-scale distribution patterns of Synechococcus ecological diversity in microbial mats of Mushroom Spring, Yellowstone National Park. Appl Environ Microbiol. 2011;77:7689–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Koeppel A, Perry EB, Sikorski J, Krizanc D, Warner A, Ward DM, et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc Nat Acad Sci USA. 2008;105:2504.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.88.Ning D, Deng Y, Tiedje JM, Zhou J. A general framework for quantitatively assessing ecological stochasticity. Proc Nat Acad Sci USA. 2019;116:16892–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Zhou J, Deng Y, Zhang P, Xue K, Liang Y, Van Nostrand JD, et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc Nat Acad Sci USA. 2014;111:E836–45.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 

Google Scholar 
91.Ning D, Yuan M, Wu L, Zhang Y, Guo X, Zhou X, et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat Commun. 2020;11:4717.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Cohan FM. Systematics: the cohesive nature of bacterial species taxa. Curr Biol. 2019;29:169–72.
Google Scholar 
93.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

Google Scholar 
94.Callahan BJ, Grinevich D, Thakur S, Balamotis MA, Yehezkel TB. Ultra-accurate microbial amplicon sequencing with synthetic long reads. Microbiome. 2021;9:130.PubMed 
PubMed Central 

Google Scholar 
95.Matsuo Y, Komiya S, Yasumizu Y, Yasuoka Y, Mizushima K, Takagi T, et al. Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION™ nanopore sequencing confers species-level resolution. BMC Microbiol. 2021;21:35.CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Sci Rep. 2020;10:3209.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 

Google Scholar 
2.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 

Google Scholar 
3.Huang, Y., Wu, S. & Kaplan, J. O. Sensitivity of global wildfire occurrences to various factors in the context of global change. Atmos. Environ. 121, 86–92 (2015).ADS 
CAS 

Google Scholar 
4.van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).ADS 

Google Scholar 
5.Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4, 1321–1326 (2020).
Google Scholar 
6.Kablick III, G. P., Allen, D. R., Fromm, M. D. & Nedoluha, G. E. Australian PyroCb smoke generates synoptic-scale stratospheric anticyclones. Geophys. Res. Lett. 47, e2020GL088101 (2020).ADS 

Google Scholar 
7.Hirsch, E. & Koren, I. Record-breaking aerosol levels explained by smoke injection into the stratosphere. Science 371, 1269–1274 (2021).ADS 
CAS 

Google Scholar 
8.Schlosser, J. S. et al. Analysis of aerosol composition data for western United States wildfires between 2005 and 2015: Dust emissions, chloride depletion, and most enhanced aerosol constituents. J. Geophys. Res. Atmos. 122, 8951–8966 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, Tropical Atlantic Ocean, and Southern Ocean. Proc. Natl Acad. Sci. USA 116, 16216–16221 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Guieu, C., Bonnet, S., Wagener, T. & Loÿe-Pilot, M.-D. Biomass burning as a source of dissolved iron to the open ocean? Geophys. Res. Lett. 32, L19608 (2005).ADS 

Google Scholar 
11.Ito, A. Mega fire emissions in Siberia: potential supply of bioavailable iron from forests to the ocean. Biogeosciences 8, 1679–1697 (2011).ADS 
CAS 

Google Scholar 
12.Abram, N. J., Gagan, M. K., McCulloch, M. T., Chappell, J. & Hantoro, W. S. Coral reef death during the 1997 Indian Ocean Dipole linked to Indonesian wildfires. Science 301, 952–955 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Ito, A. et al. Pyrogenic iron: the missing link to high iron solubility in aerosols. Sci. Adv. 5, eaau7671 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Jia, G. et al. in Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems Ch. 2 (IPCC, in the press).15.Jiang, Y. et al. Impacts of wildfire aerosols on global energy budget and climate: the role of climate feedbacks. J. Clim. 33, 3351–3366 (2020).ADS 

Google Scholar 
16.Bowman, D. et al. Wildfires: Australia needs national monitoring agency. Nature 584, 188–191 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.New WWF report: 3 billion animals impacted by Australia’s bushfire crisis. WWF https://www.wwf.org.au/news/news/2020/3-billion-animals-impacted-by-australia-bushfire-crisis#gs.ebzve2 (2020).18.van der Velde, I. R. et al. Vast CO2 release from Australian fires in 2019–2020 constrained by satellite. Nature https://doi.org/10.1038/s41586-021-03712-y (2021).19.National Greenhouse Gas Inventory Report: 2018 (Australian Government, 2020); https://www.industry.gov.au/data-and-publications/national-greenhouse-gas-inventory-report-2018.20.Mahowald, N. M. et al. Aerosol impacts on climate and biogeochemistry. Annu. Rev. Environ. Res. 36, 45–74 (2011).
Google Scholar 
21.Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. Science 315, 612–617 (2007).ADS 
CAS 

Google Scholar 
22.Jickells, T. et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science 308, 67–71 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Martin, J. H. Glacial‐interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).ADS 

Google Scholar 
24.Tagliabue, A. et al. Surface-water iron supplies in the Southern Ocean sustained by deep winter mixing. Nat. Geosci. 7, 314–320 (2014).ADS 
CAS 

Google Scholar 
25.Cassar, N. et al. The Southern Ocean biological response to aeolian iron deposition. Science 317, 1067–1070 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Gabric, A. J., Cropp, R., Ayers, G. P., McTainsh, G. & Braddock, R. Coupling between cycles of phytoplankton biomass and aerosol optical depth as derived from SeaWiFS time series in the Subantarctic Southern Ocean. Geophys. Res. Lett. 29, 16-11–16-14 (2002).
Google Scholar 
27.Ardyna, M. et al. Hydrothermal vents trigger massive phytoplankton blooms in the Southern Ocean. Nat. Commun. 10, 2451 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
28.Duprat, L. P. A. M., Bigg, G. R. & Wilton, D. J. Enhanced Southern Ocean marine productivity due to fertilization by giant icebergs. Nat. Geosci. 9, 219–221 (2016).ADS 
CAS 

Google Scholar 
29.Bixby, R. J. et al. Fire effects on aquatic ecosystems: an assessment of the current state of the science. Freshwater Sci. 34, 1340–1350 (2015).
Google Scholar 
30.Inness, A. et al. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. 19, 3515–3556 (2019).ADS 
CAS 

Google Scholar 
31.Shafeeque, M., Sathyendranath, S., George, G., Balchand, A. N. & Platt, T. Comparison of seasonal cycles of phytoplankton chlorophyll, aerosols, winds and sea-surface temperature off Somalia. Front. Marine Sci. 4, 384 (2017).
Google Scholar 
32.Cassar, N. et al. The influence of iron and light on net community production in the Subantarctic and Polar Frontal zones. Biogeosciences 8, 227–237 (2011).ADS 
CAS 

Google Scholar 
33.Mitchell, B. G. & Holm-Hansen, O. Observations of modeling of the Antartic phytoplankton crop in relation to mixing depth. Deep Sea Res. Part A 38, 981–1007 (1991).ADS 
CAS 

Google Scholar 
34.Longo, A. F. et al. Influence of atmospheric processes on the solubility and composition of iron in Saharan dust. Environ. Sci. Technol. 50, 6912–6920 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Meskhidze, N., Nenes, A., Chameides, W. L., Luo, C. & Mahowald, N. Atlantic Southern Ocean productivity: fertilization from above or below? Global Biogeochem. Cycles 21, GB2006 (2007).ADS 

Google Scholar 
36.Sarmiento, J. L., Slater, R. D., Dunne, J., Gnanadesikan, A. & Hiscock, M. R. Efficiency of small scale carbon mitigation by patch iron fertilization. Biogeosciences 7, 3593–3624 (2010).ADS 
CAS 

Google Scholar 
37.Brzezinski, M. A., Jones, J. L. & Demarest, M. S. Control of silica production by iron and silicic acid during the Southern Ocean Iron Experiment (SOFeX). Limnol. Oceanogr. 50, 810–824 (2005).ADS 
CAS 

Google Scholar 
38.Lovenduski, N. S. & Gruber, N. Impact of the Southern Annular Mode on Southern Ocean circulation and biology. Geophys. Res. Lett. 32, L11603 (2005).ADS 

Google Scholar 
39.Cai, W., Cowan, T. & Raupach, M. Positive Indian Ocean Dipole events precondition southeast Australia bushfires. Geophys. Res. Lett. 36, L19710 (2009).ADS 

Google Scholar 
40.Chen, Y. et al. A pan-tropical cascade of fire driven by El Niño/Southern Oscillation. Nat. Climate Change 7, 906–911 (2017).ADS 
CAS 

Google Scholar 
41.Lim, E.-P. et al. Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci. 12, 896–901 (2019).ADS 
CAS 

Google Scholar 
42.Cai, W. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254–258 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Cropp, R. A. et al. The likelihood of observing dust-stimulated phytoplankton growth in waters proximal to the Australian continent. J. Mar. Syst. 117–118, 43–52 (2013).
Google Scholar 
44.Hamilton, D. S. et al. Impact of changes to the atmospheric soluble iron deposition flux on ocean biogeochemical cycles in the anthropocene. Global Biogeochem. Cycles 34, e2019GB006448 (2020).ADS 
CAS 

Google Scholar 
45.Duce, R. et al. Impacts of atmospheric anthropogenic nitrogen on the open ocean. Science 320, 893–897 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Han, Y. et al. Asian inland wildfires driven by glacial-interglacial climate change. Proc. Natl Acad. Sci. USA 117, 5184–5189 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Sys. Sci. Data 9, 697–720 (2017).ADS 

Google Scholar 
48.Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I 42, 641–673 (1995).
Google Scholar 
49.Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).ADS 
CAS 

Google Scholar 
50.Morcrette, J.-J. et al. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: forward modeling. J. Geophys. Res. Atmospheres 114, D06206 (2009).ADS 

Google Scholar 
51.Levy, R. C. et al. Exploring systematic offsets between aerosol products from the two MODIS sensors. Atmos. Meas. Tech. 11, 4073–4092 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Benedetti, A. et al. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J. Geophys. Res. 114, D13 (2009).
Google Scholar 
53.Kaiser, J. W. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012).ADS 
CAS 

Google Scholar 
54.Y. Bennouna et al. Validation Report of the CAMS Global Reanalysis of Aerosols and Reactive Gases, Years 2003–2019 (Copernicus Atmosphere Monitoring Service, 2020).55.Ito, A. et al. Evaluation of aerosol iron solubility over Australian coastal regions based on inverse modeling: implications of bushfires on bioaccessible iron concentrations in the Southern Hemisphere. Prog. Earth Planet. Sci. 7, 42 (2020).ADS 

Google Scholar 
56.Khaykin, S. et al. The 2019/20 Australian wildfires generated a persistent smoke-charged vortex rising up to 35 km altitude. Commun. Earth Environ. 1, 22 (2020).57.Haëntjens, N., Boss, E. & Talley, L. D. Revisiting Ocean Color algorithms for chlorophyll a and particulate organic carbon in the Southern Ocean using biogeochemical floats. J. Geophys. Res. Oceans 122, 6583–6593 (2017).ADS 

Google Scholar 
58.Boss, E. et al. The characteristics of particulate absorption, scattering and attenuation coefficients in the surface ocean; contribution of the Tara Oceans expedition. Methods Oceanogr. 7, 52–62 (2013).
Google Scholar 
59.de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: an examination of profile data and a profile‐based climatology. J. Geophys. Res. Oceans 109, C12003 (2004).ADS 

Google Scholar 
60.Dong, S., Sprintall, J., Gille, S. T. & Talley, L. Southern Ocean mixed-layer depth from Argo float profiles. J. Geophys. Res. Oceans 113, C06013 (2008).ADS 

Google Scholar 
61.Cutter, G. A. et al. Sampling and Sample-handling Protocols for GEOTRACES Cruises, version 3.0 (2017).
Google Scholar 
62.Morton, P. L. et al. Methods for the sampling and analysis of marine aerosols: results from the 2008 GEOTRACES aerosol intercalibration experiment. Limnol. Oceanogr. Methods 11, 62–78 (2013).CAS 

Google Scholar 
63.Perron, M. M. G. et al. Assessment of leaching protocols to determine the solubility of trace metals in aerosols. Talanta 208, 120377 (2020).CAS 

Google Scholar 
64.Shelley, R. U., Landing, W. M., Ussher, S. J., Planquette, H. & Sarthou, G. Regional trends in the fractional solubility of Fe and other metals from North Atlantic aerosols (GEOTRACES cruises GA01 and GA03) following a two-stage leach. Biogeosciences 15, 2271–2288 (2018).ADS 
CAS 

Google Scholar 
65.Sanz Rodriguez, E. et al. Analysis of levoglucosan and its isomers in atmospheric samples by ion chromatography with electrospray lithium cationisation—triple quadrupole tandem mass spectrometry. J. Chromatogr. A 1610, 460557 (2020).CAS 

Google Scholar 
66.McLennan, S. M. Relationships between the trace element composition of sedimentary rocks and upper continental crust. Geochem. Geophys. Geosyst. 2, 1201 (2001).
Google Scholar 
67.Shelley, R. U. et al. Quantification of trace element atmospheric deposition fluxes to the Atlantic Ocean ( >40°N; GEOVIDE, GEOTRACES GA01) during spring 2014. Deep Sea Res. Part I 119, 34–49 (2017).CAS 

Google Scholar 
68.Sholkovitz, E. R., Sedwick, P. N., Church, T. M., Baker, A. R. & Powell, C. F. Fractional solubility of aerosol iron: synthesis of a global-scale data set. Geochim. Cosmochim. Acta 89, 173–189 (2012).ADS 
CAS 

Google Scholar 
69.Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2016).ADS 

Google Scholar 
70.Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).ADS 

Google Scholar 
71.Tatlhego, M., Bhattachan, A., Okin, G. S. & D’Odorico, P. Mapping areas of the Southern Ocean where productivity likely depends on dust‐delivered Iron. J. Geophys. Res. Atmospheres 125, e2019JD030926 (2020).ADS 
CAS 

Google Scholar 
72.Stein, A. F., Rolph, G. D., Draxler, R. R., Stunder, B. & Ruminski, M. Verification of the NOAA smoke forecasting system: model sensitivity to the injection height. Weather Forecast. 24, 379–394 (2009).ADS 

Google Scholar 
73.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite‐based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
CAS 

Google Scholar 
74.Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochem. Cycles 19, GB1006 (2005).ADS 

Google Scholar 
75.Westberry, T., Behrenfeld, M. J., Siegel, D. A. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochem. Cycles 22, GB2024 (2008).ADS 

Google Scholar 
76.Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Global Biogeochem. Cycles 30, 1756–1777 (2016).ADS 
CAS 

Google Scholar 
77.Laws, E. A., D’Sa, E. & Naik, P. Simple equations to estimate ratios of new or export production to total production from satellite‐derived estimates of sea surface temperature and primary production. Limnol. Oceanogr. Methods 9, 593–601 (2011).
Google Scholar 
78.Dunne, J. P., Armstrong, R. A., Gnanadesikan, A. & Sarmiento, J. L. Empirical and mechanistic models for the particle export ratio. Global Biogeochem. Cycles 19, GB4026 (2005).ADS 

Google Scholar 
79.Li, Z. & Cassar, N. Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochem. Cycles 30, 735–752 (2016).ADS 
CAS 

Google Scholar 
80.Siegel, D. A. et al. Global assessment of ocean carbon export by combining satellite observations and food‐web models. Global Biogeochem. Cycles 28, 181–196 (2014).ADS 
CAS 

Google Scholar 
81.Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Climate 16, 4134–4143 (2003).ADS 

Google Scholar 
82.Saji, N. H. & Yamagata, T. Possible impacts of Indian Ocean Dipole mode events on global climate. Climate Res. 25, 151–169 (2003).ADS 

Google Scholar  More

The Thau lagoon dataThe measurement campaign concerned four wastewater treatment plants (WWTP) in the Thau lagoon area in France, serving the cities of Sète, Pradel-Marseillan, Frontignan and Mèze. The measurements were obtained by using digital PCR20 (dPCR) to estimate the concentration of SARS-CoV-2 virus in samples taken weekly from 2020-05-12 to 2021-01-12. We provide further details about the measurement method in the “Methods” section.Figure 1Concentrations of SARS-CoV-2 (genome units per litre in logarithmic scale) from four WWTPs in Thau lagoon, measured weekly with dPCR technology from May 12th 2020 to January 12th, 2021. Note that there are some missing points.Full size imageFigure 1 shows the outcomes in a logarithmic scale over a nine months period. We summarise now their main features.

1.

An exponential phase starts simultaneously in Mèze and Frontignan WWTPs in early June.

2.

The genome units concentration curves in these two places reach, again simultaneously, a plateau. It has stayed essentially stable or slightly decreasing since then.

3.

The evolution at Sète and Pradel-Marseillan remarkably followed the previous two zones in a parallel way, with a two weeks lag. The measurements at Sète and Pradel-Marseillan continued to grow linearly (recall that this is in log scale, thus exponentially in linear scale), while the Mèze and Frontignan figures have stabilised ; then, after two weeks, they too stabilised at a plateau with roughly the same value as for the other two towns.

4.

The measurements seem to show a tendency to increase over the very last period.

The epidemiological model with heterogeneity and natural variability of population behaviourThe appearance of such plateaus and shoulders need not be explained either by psychological reactions or by public health policy effects. Indeed, the regulations were roughly constant during the measurement campaign and awareness or fatigue effects do not seem to have altered the dynamics over this long period of time. Witness to this is the fact that two groups of towns saw the same evolution, but two weeks apart one from the other. To understand this phenomena we propose a new model.Given the complexity and multiplicity of behavioural factors favouring the spread of the epidemic, we assume that the transmission rate involves a normalised variable (a in (0,1)) that defines an aggregated indicator of risky behaviour within the susceptible population. Thus, we represent the heterogeneity of individual behaviours with this variable. We take a as an implicit parameter that we do not seek to calculate. The classical SIR model is macroscopic and the type of model we discuss here can be viewed as intermediate between macroscopic and microscopic.The initial distribution of susceptible individuals (S_0(a)) in the framework of a SIR-type compartmental description of the epidemic can be reasonably taken as a decreasing function of a. We take the infection transmission rate (a mapsto beta (a)) to be an increasing function of a. In the Supplementary Information (SI) Appendix, the reader will find a more general version of this model involving a probability kernel of transition from one state to another. The model here can be derived as a limiting case of that more general version.Likewise, the behaviour of individuals usually changes from one day to another21. Many factors are at work in this variability: social imitation, public health campaigns, opportunities, outings, the normal variations of activity (e.g. work from home certain days and use of public transportation and work in office on others) etc. Therefore, the second key feature of our model is variability of such behaviours: variations of the population density for a given a do not only come from individuals becoming infected and leaving that compartment but also results from individuals moving from one state a to another21. In the simplest version of the model, variability is introduced as a diffusion term in the dynamics of susceptible individuals.The modelWe denote by S(t, a) the density of individuals at time t associated with risk parameter a, by I(t) the total number of infected, and by R(t) the number of removed individuals. We are then led to the following system:$$begin{aligned} frac{{partial S(t,a)}}{{partial t}} & = d{mkern 1mu} frac{{partial ^{2} S(t,a)}}{{partial a^{2} }} – beta (a)S(t,a)frac{{I(t)}}{N} \ frac{{{text{d}}I(t)}}{{{text{d}}t}} & = frac{{I(t)}}{N}{mkern 1mu} intlimits_{0}^{1} beta (a)S(t,a);da – gamma I(t), \ frac{{{text{d}}R(t)}}{{{text{d}}t}} = & gamma I(t), \ end{aligned}$$
(1)
where (gamma) denotes the inverse of typical duration (in days) of the disease and d a positive diffusion coefficient. System (1) is supplemented with initial conditions$$begin{aligned} S(0,a) = S_0(a), quad I(0) = I_0, quad hbox {and} quad R(0) = 0, end{aligned}$$
(2)
and with zero flux condition in a at (a=0, 1). In the “Methods” section below, we discuss the relation of this model with other current works.A more general modelIn a more general version of our model, we can consider the population of infected as also structured by the parameter a. The equations are as before but now we keep track of the class a in the infected population. The mechanism here is that a susceptible individual from class a can be infected by infectious from any class I(t, b) but then gives rise to an individual I(t, a) of the same parent class. We also assume that there is a diffusion of the infected behaviours. We denote by ({mathfrak {B}}(a,b)) the transmission rate of S(t, a) by I(t, b). For simplicity and because it is natural, we will assume that it is of the form$$begin{aligned} {mathfrak {B}}(a,b)= beta (a) beta (b) end{aligned}$$where (beta) is as before. For full generality, we can also envision multi-dimensional parameters (ain {mathbb {R}}^d), with (a_iin (0,1)). We are then led to the system:$$begin{aligned} frac{{partial S(t,a)}}{{partial t}} & = d;Delta _{a} S(t,a) – S(t,a)frac{{beta (a)}}{N}intlimits_{0}^{1} beta (b)I(t,b);db \ frac{{partial I(t,a)}}{{partial t}} & = d^{prime}Delta _{a} I(t,a) + S(t,a)frac{{beta (a)}}{N}intlimits_{0}^{1} beta (b)I(t,b)db – gamma I(t,a), \ frac{{{text{d}}R(t)}}{{{text{d}}t}} & = gamma intlimits_{0}^{1} I (t,b){mkern 1mu} db, \ end{aligned}$$
(3)
In the SI we write further, more general, forms of this model, with ({mathfrak {B}}(a,b)) and more general diffusion of behaviours, that can include jumps or non-local variations. The type of models we discuss here may also shed light on the initial phase of the epidemic. We plan to investigate these questions in future work.Patterns generated by the modelIn the next section, we will discuss how the model fits the data observed in the Thau lagoon measurements. But before that, we start by showing that the above model (1) can generate the different patterns we mentioned. For this we rely on numerical simulations without fitting real data. And indeed we obtain plateaus, shoulders, and oscillations. The latter can be interpreted as epidemic rebounds.The key parameter here is the diffusion coefficient d, which controls the amplitude of behavioural variability (see Fig. 2). Large values of d rapidly yield homogenised behaviours, leading to classical SIR-like dynamics of infectious individuals. For very small values of d, the system also has a simple dynamics, in the sense that I(t) has a unique maximum, and therefore has no rebounds. We derive this in the limit (d=0) for which we show in the SI that there are neither plateaus nor rebounds.For some intermediate range of the parameter d, plateaus may appear after an exponential growth, like in the initial phase of the SIR model. A small amplitude oscillation, called “shoulder”, precedes a temporary stabilisation on a plateau, followed by a large time convergence to zero of infectious population. We also show that for small enough d, time oscillations of the infectious population curve, i.e. epidemic rebounds, may be generated by Model (1). Such oscillations also appear after a plateau, in a similar way to what one can see in observations.Simulations in Fig. 2 illustrate the various patterns obtained on the dynamics of infected population as a function of the diffusion parameter. For small enough d, in the top left graph of Fig. 2, one can see oscillations of the fraction of infectious individuals. These oscillations cannot be achieved in the classical SIR model. In fact, the two lower graphs of that figure, for somewhat larger values of d, exhibit the SIR model outcomes. Indeed, for sufficiently large d, the system becomes rapidly homogeneous (i.e. constant with respect to a). Yet, such oscillations are standard in the dynamics of actual epidemics, like the current Covid-19 pandemic. The intermediate value of d, represented in the upper right corner of Fig. 2 shows the typical onset of a plateau at a rather high value of I. Note that this plateau is preceded by a first small dip and then a characteristic “shoulder-like” oscillation.Figure 2Model behaviour depending on diffusion parameter values: infected rate dynamics in logarithmic scale. From left to right and then top to bottom, graphs are associated with (d=10^{-5}), (d=5times 10^{-5}), (d=10^{-3}) and (d=5times 10^{-3}) (in (day^{-1}) unit).Full size imageSecondary epidemic peaks are of lower amplitude than the first one, as shown in the top graphs of Fig. 2. This empirical observation leads us to conjecture that, at least in many cases, it is a general property of this model (with (beta) independent of time). This property would then reflect a kind of dissipative nature of Model (1). It is natural to surmise that a change of behaviours in time may generate oscillations with higher secondary peaks. Such changes result for instance from lifting social distancing measures or from fatigue effects in the population.We illustrate this with numerical simulations in Fig. 3. We assume a collective time modulation of the (beta (a)) transmission profile. That is, we replace (beta (a)) by (beta (a)varphi (t)) for some time dependent function (varphi), the other parameters are the same as in the simulations shown in Fig. 2. We look at the effect of a “lockdown exit” type effect. Then, (varphi (t)) takes two constant values, 1 from (t=0) to (t={1000}) and 1.2 after (t={1100}). In between, that is, for (tin ({1000}, {1100})), (varphi (t)) changes linearly from the value 1 to 1.2.Figure 3Multiple epidemic rebounds: susceptible individuals are divided into 50 discrete groups in the case where relaxation of social distancing measures starts on Day (t=1000) and ends up on Day (t=1100). The fraction of infected individuals in the population is represented in the left graph in logarithmic scale and in linear scale in the right graph.Full size imageOne can clearly see a secondary peak with higher amplitude than the first one. Note that after this peak, a third one occurs, with a lower amplitude than the second one. This third peak happens in the regime when (beta) is again constant in time.The effect of variantsAnother important factor that yields secondary peaks with higher amplitudes is the appearance of variants. Consider the situation with two variants. We denote by (I_1(t)) and (I_2(t)) the corresponding infected individuals. The first variant, which we call the historical strain, is associated with (beta _1) and (I_1(0)) and starts at (t=0). The variant strain corresponds to (beta _2) and (I_2) and starts at Day (t=1000). In this situation, the system (1) is extended by the following system:$$begin{aligned} frac{{partial S(t,a)}}{{partial t}} & = d{mkern 1mu} frac{{partial ^{2} S(t,a)}}{{partial a^{2} }} – left( {beta _{1} (a)I_{1} (t) + beta _{2} (a)I_{2} (t)} right)frac{{S(t,a)}}{N}, \ frac{{{text{d}}I_{2} (t)}}{{{text{d}}t}} & = frac{{I_{2} (t)}}{N}{mkern 1mu} intlimits_{0}^{1} {beta _{2} } (a)S(t,a){mkern 1mu} da – gamma _{2} I_{2} (t), \ frac{{{text{d}}I_{1} (t)}}{{{text{d}}t}} & = frac{{I_{1} (t)}}{N}{mkern 1mu} intlimits_{0}^{1} {beta _{1} } (a)S(t,a){mkern 1mu} da – gamma _{1} I_{1} (t) \ frac{{{text{d}}R(t)}}{{{text{d}}t}} & = gamma _{1} I_{2} (t) + gamma _{1} I_{2} (t), \ end{aligned}$$
(4)
The total infected population is (I(t)=I_1(t)+I_2(t)). Figure 4 shows a simulation of this system. Before the onset of the second variant, i.e. for (t< 1000), we observe a peak, followed by a small shoulder and a downward tilted plateau. The second variant corresponds to a higher transmission coefficient: namely, we take here (beta _2(a) equiv frac{3}{2} beta _1(a)). When it appears at time (t=1000), initially there is no effect, because the initial number of infectious with variant 2 is very small. Then, there is an exponential growth caused by this second variant gaining strength. The secondary peak is then higher than the first one. A very small shoulder precedes another stabilisation on a downward plateau.Figure 4 also shows the dynamics of fractions of infected with each one of the variants. Note that the infectious with variant 1 very rapidly all but disappear at the onset of the second exponential growth phase. One might have expected that the historical strain would be gradually replaced by the new strain, merely tilting further downward the plateau. But that does not happen. Thus, it is remarkable that the historical strain gets nearly wiped out at the very beginning of the second exponential growth.Figure 4Multiple epidemic rebounds due to a variant virus: susceptible individuals are divided into 50 discrete groups in the case where a new variant appears at Day (t=1000). The transmission rate (beta _2) is taken as (beta _2(a) = 1.5 , beta _1(a)), (d=0.0002), (gamma _1=0.1) and (gamma _2= 0.05). The fraction of infected individuals in the population is represented in the left graph in logarithmic scale. The total infected population is represented in linear scale in the right graph (black curve), variant 1 in red and variant 2 in green.Full size imageApplication to the Thau lagoon measurementsModel (1) describes the dynamics of the fraction of infectious in the population, that is (t mapsto I(t)/N). Therefore, we need to derive this fraction from the wastewater measurements. To this end, we use an “effective proportionality coefficient” between the two quantities. This coefficient itself is derived from the measurements (compare Section “SARS-CoV-2 concentration measurement from wastewater with digital PCR” in the “Methods” part below). Calibration of model (1) also requires fitting the values of (gamma), the profiles (a mapsto beta (a)) and the initial distribution of susceptible individuals in terms of a.We carried this procedure and the resulting fitted curve is displayed in Fig. 5. Note that the outcome correctly captures the shoulder and plateau patterns.Figure 5Calibrated model on Sète area: blue dots are measures of SARS-CoV-2 genome units and black curve represents the total infected individuals as an output of the model discretized into (n_g=20) groups in a. Initial distribution of susceptible individuals and (beta) function are taken as described in supplementary information. Parameters d and (gamma) are taken as follows: (d=2.5 times 10^{-4}) (day^{-1}), and (gamma =0.1) (day^{-1}).Full size imageThe underlying dynamics of the rate of susceptible individuals is given in Fig. 6 below for (n_g=20) groups. The lower curve illustrates the competition phenomenon between diffusion and sink term due to new infections, depending on the level of risk a of each state.Figure 6Calibrated model on Sète WWTP: density of susceptible individuals of each group for (n_g=20). The densities of susceptible of each group is represented in colour curves as functions of time. The curves are ordered from top to bottom according to increasing a. The resulting average total susceptible population is represented in black. Susceptible individuals of highest a trait, which are represented in the bottom light blue curve exhibit a non monotonic behaviour.Full size image More

AffiliationsPhysics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USAMartina Dal Bello, Hyunseok Lee, Akshit Goyal & Jeff GoreAuthorsMartina Dal BelloHyunseok LeeAkshit GoyalJeff GoreCorresponding authorsCorrespondence to
Martina Dal Bello or Jeff Gore. More

1.Diaz, S. et al. Pervasive human-driven decline of life on earth points to the need for transformative change. Science 366, eaax3100 (2020).Article 
CAS 

Google Scholar 
2.Jones, K. R. et al. Area requirements to safeguard Earth’s marine species. One Earth 2, 188–196 (2020).Article 

Google Scholar 
3.Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, e00590 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
4.IUCN. International Union for Conservation of Nature Annual Report 2018. (Gland, Switzerland, 2018).5.Walker, T. I., Hudson, R. J. & Gason, A. S. Catch evaluation of target, by-product and by-catch species taken by gillnets and longlines in the shark fishery of south-eastern Australia. J. Northwest Atlantic Fishery Sci. 35, 505–530 (2005).Article 

Google Scholar 
6.Braccini, M., Van Rijn, J. & Frick, L. High post-capture survival for sharks, rays and chimaeras discarded in the main shark fishery of Australia?. PLoS ONE 7(1–9), e32547 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Sumpton, W. D., Taylor, S. M., Gribble, N. A., McPherson, G. & Ham, T. Gear selectivity of large-mesh nets and drumlines used to catch sharks in the Queensland shark control program. Afr. J. Mar. Sci. 33, 37–43 (2011).Article 

Google Scholar 
8.Broadhurst, M. K. & Cullis, B. R. Mitigating the discard mortality of non-target, threatened elasmobranchs in bather-protection gillnets. Fisheries Res. 222, 105435 (2020).Article 

Google Scholar 
9.Stevens, J. D. & Wayte, S. E. Case study: The bycatch of pelagic sharks in Australia’s tuna longline fisheries. In Sharks of the Open Ocean; Biology, Fisheries and Conservation (eds Camhi, M. D. et al.) 260–267 (Blackwell Publishing, 2009).
Google Scholar 
10.Roff, G. et al. The ecological role of sharks on coral reefs. Trends Ecol. Evol. 31(5), 395–407 (2016).PubMed 
Article 

Google Scholar 
11.Roff, G., Brown, C. J., Priest, M. A. & Mumby, P. J. Decline of coastal apex shark populations over the past half century. Commun. Biol. 1, 223 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Raoult, V., Broadhurst, M. K., Peddemors, V. M., Williamson, J. E. & Gaston, T. F. Resource use of great hammerhead sharks (Sphyrna mokarran) off eastern Australia. J. Fish Biol. 95, 1430–1440 (2019).PubMed 
Article 

Google Scholar 
13.Raoult, V. et al. Predicting geographic ranges of marine animal populations using stable isotopes: A case study of great hammerhead sharks in eastern Australia. Front. Mar. Sci. 7, 594636 (2020).Article 

Google Scholar 
14.Chapman, D. D. & Gruber, S. H. A further observation of the prey-handling behavior of the great hammerhead shark, Sphyrna mokarran: Predation upon the spotted eagle ray, Aetobatus narinari. Bull. Mar. Sci. 70, 947–952 (2002).
Google Scholar 
15.Cliff, G. Sharks caught in the protective gill nets off KwaZulu-Natal, South Africa. 8. The great hammerhead shark Sphyrna mokarran (Rüppell). S. Afr. J. Mar. Sci. 15, 105–114 (1995).Article 

Google Scholar 
16.Strong, W. R., Snelson, F. F. & Gruber, S. H. Hammerhead shark predation on stingrays: An observation of prey handling on Sphyrna mokarran. Copeia 3, 836–840 (1990).Article 

Google Scholar 
17.Mourier, J., Planes, S. & Buray, N. Trophic interactions at the top of the coral reef food chain. Coral Reefs 32, 285–285 (2013).ADS 
Article 

Google Scholar 
18.Roemer, R. P., Gallagher, A. J. & Hammerschlag, N. Shallow water tidal flat use and associated specialized foraging behavior of the great hammerhead shark (Sphyrna mokarran). Mar. Freshw. Behav. Physiol. 49, 235–249 (2016).Article 

Google Scholar 
19.Gallagher, A. J. & Klimley, A. P. The biology and conservation status of the large hammerhead shark complex: The great, scalloped and smooth hammerheads. Rev. Fish Biol. Fisheries 28, 777–794 (2018).Article 

Google Scholar 
20.Barry, K. P., Condrey, R. E., Driggers, W. B. & Jones, C. M. Feeding ecology and growth of neonate and juvenile blacktip sharks Carcharhinus limbatus in the Timbalier-Terrebone Bay complex, LA, U.S.A. J. Fish Biol. 73, 650–662 (2008).Article 

Google Scholar 
21.Tavares, R. Occurrence, diet and growth of juvenile blacktip sharks, Carcharhinus limbatus, from Los Roques Archipelago National Park, Venezuela. Carib. J. Sci. 44, 291–302 (2008).Article 

Google Scholar 
22.Plumlee, J. D. & Wells, R. J. D. Feeding ecology of three coastal shark species in the northwest Gulf of Mexico. Mar. Ecol. Prog. Ser. 550, 163–174 (2016).ADS 
CAS 
Article 

Google Scholar 
23.Young, J. W. et al. The trophodynamics of marine top predators: Current knowledge, recent advances and challenges. Deep Sea Res. Part II 113, 170–187 (2015).Article 

Google Scholar 
24.Leigh, S. C., Papastamatiou, Y. & German, D. P. The nutritional physiology of sharks. Rev. Fish Biol. Fisheries 27, 561–585 (2017).Article 

Google Scholar 
25.Amundsen, P.-A. & Sánchez-Hernández, J. Feeding studies take guts—critical review and recommendations of methods for stomach contents analysis in fish. J. Fish Biol. 95, 1364–1373 (2019).PubMed 
Article 

Google Scholar 
26.Alberdi, A. et al. Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol. Ecol. Resour. 19, 327–348 (2019).PubMed 
Article 

Google Scholar 
27.Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

Google Scholar 
28.Pompanon, F. et al. Who is eating what: diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).CAS 
PubMed 
Article 

Google Scholar 
29.Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).PubMed 
Article 

Google Scholar 
30.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA for Biodiversity Research and Monitoring (Oxford University Press, 2018).
Google Scholar 
31.Barbato, M., Kovacs, T., Coleman, M., Broadhurst, M. & de Bruyn, M. Metabarcoding of stomach-content analyses: Comparing tissue and ethanol preservative-derived DNA. Ecol. Evol. 9(5), 2678–2687 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Berry, O. et al. Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar. Ecol. Prog. Ser. 540, 167–181 (2015).ADS 
CAS 
Article 

Google Scholar 
33.Bessey, C. et al. DNA metabarcoding assays reveal a diverse prey assemblage for Mobula rays in the Bohol Sea, Philippines. Ecol. Evol. 9(5), 2459–2474 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Clarke, L. J., Trebilco, R., Walters, A., Polanowski, A. M. & Deagle, B. E. DNA-based diet analysis of mesopelagic fish from the southern Kerguelen Axis. Deep Sea Res. Part II Top. Stud. Oceanogr. 174, 104494 (2020).CAS 

Google Scholar 
35.Sousa, L. L. et al. DNA barcoding identifies a cosmopolitan diet in the ocean sunfish. Sci. Rep. 6, 28762 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Takahashi, M. et al. Partitioning of diet between species and life history stages of sympatric and cryptic snappers (Lutjanidae) based on DNA metabarcoding. Sci. Rep. 10(1), 1–13 (2020).Article 
CAS 

Google Scholar 
37.Yoon, T.-H. et al. Metabarcoding analysis of the stomach contents of the Antarctic Toothfish (Dissostichus mawsoni) collected in the Antarctic Ocean. PeerJ 5, e3977 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Clare, E. L. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7, 1144–1157 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Varennes, Y.-D., Boyer, S. & Wratten, S. D. Un-nesting DNA Russian dolls: The potential for constructing food webs using residual DNA in empty aphid mummies. Mol. Ecol. 23, 3925–3933 (2014).CAS 
PubMed 
Article 

Google Scholar 
40.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2(7), 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Berry, T. E. et al. DNA metabarcoding for diet analysis and biodiversity: A case study using the endangered Australian sea lion (Neophoca cinerea). Ecol. Evol. 7(14), 5435–5453 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4, 558–565 (2013).Article 

Google Scholar 
43.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8(1), 1–11 (2017).Article 
CAS 

Google Scholar 
45.Mousavi-Derazmahalleh, M., Stott, A., Lines, R., Peverley, G., Nester, G., Simpson, T., Zawierta, M., De La Pierre, M., Bunce, M., & Christophersen, C. eDNAFlow, an automated, reproducible and scalable workflow for analysis of environmental DNA (eDNA) sequences exploiting Nextflow and Singularity. Mol. Ecol. Resour. 21, 1697–1704 (2020).Article 
CAS 

Google Scholar 
46.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).MATH 
Book 

Google Scholar 
47.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2017).48.Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).49.Compagno, L. J. V. Sharks of the Order Carcharhiniformes (Princeton University Press, 1988).
Google Scholar 
50.Johnsen, P. B. & Teeter, J. H. Behavioral responses of the bonnethead shark (Sphyrna tiburo) to controlled olfactory stimulation. Mar. Behav. Phys. 11, 283–291 (1985).Article 

Google Scholar 
51.Nakaya, K. Hydrodynamic function of the head in the hammerhead sharks (Elasmobranchii: Sphyrinidae). Copeia 2, 330–336 (1995).Article 

Google Scholar 
52.Leray, M., Agudelo, N., Mills, S. C. & Meyer, C. P. Effectiveness of annealing blocking primers versus restriction enzymes for characterization of generalist diets: unexpected prey revealed in the gut contents of two coral reef fish species. PLoS ONE 8(4), e58076 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Leray, M., Meyer, C. P. & Mills, S. C. Metabarcoding dietary analysis of coral dwelling predatory fish demonstrates the minor contribution of coral mutualists to their highly partitioned, generalist diet. PeerJ 3, e1047 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Van Zinnicq Bergmann, M. P. M. et al. Elucidating shark diets with DNA metabarcoding from cloacal swabs. Mol. Ecol. Resour. 21, 1056–1067 (2021).PubMed 
Article 
CAS 

Google Scholar  More

1.Kalnay E, Cai M. Impact of urbanization and land-use change on climate. Nature. 2003;423:528–31.CAS 
PubMed 
Article 

Google Scholar 
2.Archer SDJ, Pointing SB. Anthropogenic impact on the atmospheric microbiome. Nat Microbiol. 2020;5:229–31.CAS 
PubMed 
Article 

Google Scholar 
3.Powers RP, Jetz W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat Clim Change. 2019;9:323–9.Article 

Google Scholar 
4.Sandifer PA, Sutton-Grier AE, Ward BP. Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation. Ecosyst Serv. 2015;12:1–15.Article 

Google Scholar 
5.Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol. 2020;18:35–46.CAS 
PubMed 
Article 

Google Scholar 
6.Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.CAS 
Article 

Google Scholar 
7.Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
PubMed 
Article 

Google Scholar 
8.Sapp M, Ploch S, Fiore-Donno AM, Bonkowski M, Rose LE. Protists are an integral part of the Arabidopsis thaliana microbiome. Environ Microbiol. 2018;20:30–43.CAS 
PubMed 
Article 

Google Scholar 
9.Vorholt JA. Microbial life in the phyllosphere. Nat Rev Microbiol. 2012;10:828–40.CAS 
PubMed 
Article 

Google Scholar 
10.Laforest-Lapointe I, Messier C, Kembel SW. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome. 2016;4:27.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Andrews JH, Harris RF. The ecology and biogeography of microorganisms on plant surfaces. Annu Rev Phytopathol. 2000;38:145–80.Article 

Google Scholar 
12.Lugtenberg B, Kamilova F. Plant-growth-promoting Rhizobacteria. Annu Rev Microbiol. 2009;63:541–56.CAS 
PubMed 
Article 

Google Scholar 
13.Philippot L, Raaijmakers JM, Lemanceau P, van der Putten WH. Going back to the roots: the microbial ecology of the rhizosphere. Nat Rev Microbiol. 2013;11:789–99.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Davison J. Plant beneficial bacteria. Bio/Technol. 1988;6:282–6.CAS 

Google Scholar 
15.Schauer S, Kutschera U. A novel growth-promoting microbe, Methylobacterium funariae sp. nov., isolated from the leaf surface of a common moss. Plant Signal Behav. 2011;6:510–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Innerebner G, Knief C, Vorholt JA. Protection of arabidopsis thaliana against leaf-pathogenic pseudomonas syringae by sphingomonas strains in a controlled model system. Appl Environ Microbiol. 2011;77:3202–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Laforest-Lapointe I, Paquette A, Messier C, Kembel SW. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature. 2017;546:145–7.CAS 
PubMed 
Article 

Google Scholar 
18.Koskella B, Meaden S, Crowther WJ, Leimu R, Metcalf CJE. A signature of tree health? Shifts in the microbiome and the ecological drivers of horse chestnut bleeding canker disease. N Phytol. 2017;215:737–46.CAS 
Article 

Google Scholar 
19.Isbell F, Tilman D, Polasky S, Loreau M. The biodiversity-dependent ecosystem service debt. Ecol Lett. 2015;18:119–34.PubMed 
Article 

Google Scholar 
20.Barnosky A, Matzke N, Tomiya S, Wogan G, Swartz B, Quental T, et al. Has the earth’s sixth mass extinction already arrived? Nat Nat. 2011;471:51–7.CAS 
Article 

Google Scholar 
21.Pascual U, Balvanera P, Díaz S, Pataki G, Roth E, Stenseke M, et al. Valuing nature’s contributions to people: the IPBES approach. Curr Opin Environ Sustain. 2017;26–27:7–16.Article 

Google Scholar 
22.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 
23.Annamalai J, Namasivayam V. Endocrine disrupting chemicals in the atmosphere: Their effects on humans and wildlife. Environ Int. 2015;76:78–97.CAS 
PubMed 
Article 

Google Scholar 
24.Jumpponen A, Jones KL. Seasonally dynamic fungal communities in the Quercus macrocarpa phyllosphere differ between urban and nonurban environments. N Phytol. 2010;186:496–513.CAS 
Article 

Google Scholar 
25.Imperato V, Kowalkowski L, Portillo-Estrada M, Gawronski SW, Vangronsveld J, Thijs S. Characterisation of the Carpinus betulus L. Phyllomicrobiome in urban and forest areas. Front Microbiol. 2019;10:1110.PubMed 
PubMed Central 
Article 

Google Scholar 
26.Bowers RM, McLetchie S, Knight R, Fierer N. Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments. ISME J. 2011;5:601–12.CAS 
PubMed 
Article 

Google Scholar 
27.Lymperopoulou DS, Adams RI, Lindow SE. Contribution of vegetation to the microbial composition of nearby outdoor air. Appl Environ Microbiol. 2016;82:3822–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.De Kempeneer L, Sercu B, Vanbrabant W, Van Langenhove H, Verstraete W. Bioaugmentation of the phyllosphere for the removal of toluene from indoor air. Appl Microbiol Biotechnol. 2004;64:284–8.PubMed 
Article 
CAS 

Google Scholar 
29.Hanski I, Hertzen Lvon, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci. 2012;109:8334–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Smets W, Wuyts K, Oerlemans E, Wuyts S, Denys S, Samson R, et al. Impact of urban land use on the bacterial phyllosphere of ivy (Hedera sp.). Atmos Environ. 2016;147:376–83.CAS 
Article 

Google Scholar 
31.Laforest-Lapointe I, Messier C, Kembel SW. Tree Leaf Bacterial Community Structure and Diversity Differ along a Gradient of Urban Intensity. mSystems. 2017;2:e00087–17.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Espenshade J, Thijs S, Gawronski S, Bové H, Weyens N, Vangronsveld J. Influence of urbanization on epiphytic bacterial communities of the platanus × hispanica tree leaves in a Biennial Study. Front Microbiol. 2019;10:675.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Wuyts K, Smets W, Lebeer S, Samson R. Green infrastructure and atmospheric pollution shape diversity and composition of phyllosphere bacterial communities in an urban landscape. FEMS Microbiol Ecol 2020;96:fiz173.CAS 
PubMed 
Article 

Google Scholar 
34.Zhao D, Liu G, Wang X, Daraz U, Sun Q. Abundance of human pathogen genes in the phyllosphere of four landscape plants. J Environ Manag. 2020;255:109933.CAS 
Article 

Google Scholar 
35.Gandolfi I, Canedoli C, Imperato V, Tagliaferri I, Gkorezis P, Vangronsveld J, et al. Diversity and hydrocarbon-degrading potential of epiphytic microbial communities on Platanus x acerifolia leaves in an urban area. Environ Pollut. 2017;220:650–8.CAS 
PubMed 
Article 

Google Scholar 
36.Weyens N, van der Lelie D, Taghavi S, Vangronsveld J. Phytoremediation: plant–endophyte partnerships take the challenge. Curr Opin Biotechnol. 2009;20:248–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Afzal M, Khan QM, Sessitsch A. Endophytic bacteria: prospects and applications for the phytoremediation of organic pollutants. Chemosphere. 2014;117:232–42.CAS 
PubMed 
Article 

Google Scholar 
38.Siciliano SD, Fortin N, Mihoc A, Wisse G, Labelle S, Beaumier D, et al. Selection of specific endophytic bacterial genotypes by plants in response to soil contamination. Appl Environ Microbiol. 2001;67:2469–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Barac T, Taghavi S, Borremans B, Provoost A, Oeyen L, Colpaert JV, et al. Engineered endophytic bacteria improve phytoremediation of water-soluble, volatile, organic pollutants. Nat Biotechnol. 2004;22:583–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Sandhu A, Halverson LJ, Beattie GA. Bacterial degradation of airborne phenol in the phyllosphere. Environ Microbiol. 2007;9:383–92.CAS 
PubMed 
Article 

Google Scholar 
41.Weyens N, Thijs S, Popek R, Witters N, Przybysz A, Espenshade J, et al. The role of plant–microbe interactions and their exploitation for phytoremediation of air pollutants. Int J Mol Sci. 2015;16:25576–604.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Essl F, Dullinger S, Rabitsch W, Hulme PE, Hülber K, Jarošík V, et al. Socioeconomic legacy yields an invasion debt. Proc Natl Acad Sci. 2011;108:203–7.CAS 
PubMed 
Article 

Google Scholar 
43.Walther G-R, Roques A, Hulme PE, Sykes MT, Pyšek P, Kühn I, et al. Alien species in a warmer world: risks and opportunities. Trends Ecol Evol. 2009;24:686–93.PubMed 
Article 

Google Scholar 
44.Blüthgen N, Menzel F, Blüthgen N. Measuring specialization in species interaction networks. BMC Ecol. 2006;6:9.PubMed 
PubMed Central 
Article 

Google Scholar 
45.Cobian GM, Egan CP, Amend AS. Plant–microbe specificity varies as a function of elevation. ISME J. 2019;13:2778–88.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Bálint M, Bartha L, O’Hara RB, Olson MS, Otte J, Pfenninger M, et al. Relocation, high-latitude warming and host genetic identity shape the foliar fungal microbiome of poplars. Mol Ecol. 2015;24:235–48.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Vacher C, Cordier T, Vallance J. Phyllosphere fungal communities differentiate more thoroughly than bacterial communities along an elevation gradient. Micro Ecol. 2016;72:1–3.Article 

Google Scholar 
48.Callaway RM, Brooker RW, Choler P, Kikvidze Z, Lortie CJ, Michalet R, et al. Positive interactions among alpine plants increase with stress. Nature. 2002;417:844–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Bever JD. Feeback between plants and their soil communities in an old field. Community Ecol. 1994;75:1965–77.Article 

Google Scholar 
50.Bever JD. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. N Phytol. 2003;157:465–73.Article 

Google Scholar 
51.Klironomos JN. Feedback with soil biota contributes to plant rarity and invasiveness in communities. Nature. 2002;417:67–70.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Reinhart KO, Callaway RM. Soil biota and invasive plants. N Phytol. 2006;170:445–57.Article 

Google Scholar 
53.Callaway RM, Thelen GC, Rodriguez A, Holben WE. Soil biota and exotic plant invasion. Nature. 2004;427:731–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Brown CD, Vellend M. Non-climatic constraints on upper elevational plant range expansion under climate change. Proc R Soc B Biol Sci. 2014;281:20141779.Article 

Google Scholar 
55.Carteron A, Parasquive V, Blanchard F, Guilbeault‐Mayers X, Turner BL, Vellend M, et al. Soil abiotic and biotic properties constrain the establishment of a dominant temperate tree into boreal forests. J Ecol. 2020;108:931–44.Article 

Google Scholar 
56.Williamson M. Biological invasions. 1996. Springer Netherlands.57.Mitchell CE, Power AG. Release of invasive plants from fungal and viral pathogens. Nature. 2003;421:625–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Ramirez KS, Snoek LB, Koorem K, Geisen S, Bloem LJ, ten Hooven F, et al. Range-expansion effects on the belowground plant microbiome. Nat Ecol Evol. 2019;3:604–11.PubMed 
PubMed Central 
Article 

Google Scholar 
59.Diez JM, Dickie I, Edwards G, Hulme PE, Sullivan JJ, Duncan RP. Negative soil feedbacks accumulate over time for non-native plant species. Ecol Lett. 2010;13:803–9.PubMed 
Article 

Google Scholar 
60.Lenssen NJL, Schmidt GA, Hansen JE, Menne MJ, Persin A, Ruedy R, et al. Improvements in the GISTEMP uncertainty model. J Geophys Res Atmos. 2019;124:6307–26.Article 

Google Scholar 
61.O’brien RD, Lindow SE. Effect of plant species and environmental conditions on ice nucleation activity of pseudomonas syringae on leaves. Appl Environ Microbiol. 1988;54:2281–6.PubMed 
PubMed Central 
Article 

Google Scholar 
62.Klinkert B, Narberhaus F. Microbial thermosensors. Cell Mol Life Sci. 2009;66:2661–76.CAS 
PubMed 
Article 

Google Scholar 
63.Velásquez AC, Castroverde CDM, He SY. Plant-pathogen warfare under changing climate conditions. Curr Biol CB. 2018;28:R619–R634.PubMed 
Article 
CAS 

Google Scholar 
64.Compant S, van der Heijden MGA, Sessitsch A. Climate change effects on beneficial plant-microorganism interactions. FEMS Microbiol Ecol. 2010;73:197–214.CAS 
PubMed 

Google Scholar 
65.Cheng YT, Zhang L, He SY. Plant-microbe interactions facing environmental challenge. Cell Host Microbe. 2019;26:183–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Guerra CA, Delgado‐Baquerizo M, Duarte E, Marigliano O, Görgen C, Maestre FT, et al. Global projections of the soil microbiome in the Anthropocene. Glob Ecol Biogeogr. 2021;30:987–99.PubMed 
Article 

Google Scholar 
67.Frindte K, Pape R, Werner K, Löffler J, Knief C. Temperature and soil moisture control microbial community composition in an arctic–alpine ecosystem along elevational and micro-topographic gradients. ISME J. 2019;13:2031–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Cordier T, Robin C, Capdevielle X, Fabreguettes O, Desprez-Loustau M-L, Vacher C. The composition of phyllosphere fungal assemblages of European beech (Fagus sylvatica) varies significantly along an elevation gradient. N Phytol. 2012;196:510–9.Article 

Google Scholar 
69.Tedersoo L, Bahram M, Toots M, Diédhiou AG, Henkel TW, Kjøller R, et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol Ecol. 2012;21:4160–70.PubMed 
Article 

Google Scholar 
70.Gomes T, Pereira JA, Benhadi J, Lino-Neto T, Baptista P. Endophytic and epiphytic phyllosphere fungal communities are shaped by different environmental factors in a Mediterranean ecosystem. Micro Ecol. 2018;76:668–79.Article 

Google Scholar 
71.Peñuelas J, Rico L, Ogaya R, Jump AS, Terradas J. Summer season and long-term drought increase the richness of bacteria and fungi in the foliar phyllosphere of Quercus ilex in a mixed Mediterranean forest. Plant Biol Stuttg Ger. 2012;14:565–75.Article 

Google Scholar 
72.Rico L, Ogaya R, Terradas J, Peñuelas J. Community structures of N2 -fixing bacteria associated with the phyllosphere of a Holm oak forest and their response to drought. Plant Biol Stuttg Ger. 2014;16:586–93.CAS 
Article 

Google Scholar 
73.Grady KL, Sorensen JW, Stopnisek N, Guittar J, Shade A. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nat Commun. 2019;10:1–10.Article 
CAS 

Google Scholar 
74.Redford AJ, Fierer N. Bacterial Succession on the Leaf Surface: A Novel System for Studying Successional Dynamics. Micro Ecol. 2009;58:189–98.Article 

Google Scholar 
75.Edwards JA, Santos-Medellín CM, Liechty ZS, Nguyen B, Lurie E, Eason S, et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLOS Biol. 2018;16:e2003862.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421:37–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci. 2017;114:9326–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLOS ONE. 2013;8:e66428.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Angel R, Soares MIM, Ungar ED, Gillor O. Biogeography of soil archaea and bacteria along a steep precipitation gradient. ISME J. 2010;4:553–63.PubMed 
Article 

Google Scholar 
80.Kaisermann A, Vries FTde, Griffiths RI, Bardgett RD. Legacy effects of drought on plant–soil feedbacks and plant–plant interactions. N Phytol. 2017;215:1413–24.CAS 
Article 

Google Scholar 
81.Hawkes CV, Kivlin SN, Rocca JD, Huguet V, Thomsen MA, Suttle KB. Fungal community responses to precipitation. Glob Change Biol. 2011;17:1637–45.Article 

Google Scholar 
82.Lau JA, Lennon JT. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc Natl Acad Sci. 2012;109:14058–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Sheik CS, Beasley WH, Elshahed MS, Zhou X, Luo Y, Krumholz LR. Effect of warming and drought on grassland microbial communities. ISME J. 2011;5:1692–700.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
85.Li F, Deng J, Nzabanita C, Li Y, Duan T. Growth and physiological responses of perennial ryegrass to an AMF and an Epichloë endophyte under different soil water contents. Symbiosis. 2019;79:151–61.CAS 
Article 

Google Scholar 
86.Ibekwe AM, Ors S, Ferreira JFS, Liu X, Suarez DL, Ma J, et al. Functional relationships between aboveground and belowground spinach (Spinacia oleracea L., cv. Racoon) microbiomes impacted by salinity and drought. Sci Total Environ. 2020;717:137207.CAS 
PubMed 
Article 

Google Scholar 
87.Prosser JI, Bohannan BJM, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, et al. The role of ecological theory in microbial ecology. Nat Rev Microbiol. 2007;5:384–92.CAS 
PubMed 
Article 

Google Scholar 
88.Shoemaker WR, Locey KJ, Lennon JT. A macroecological theory of microbial biodiversity. Nat Ecol Evol. 2017;1:0107.Article 

Google Scholar 
89.Ratzke C, Denk J, Gore J. Ecological suicide in microbes. Nat Ecol Evol. 2018;2:867–72.PubMed 
PubMed Central 
Article 

Google Scholar 
90.Shade A, Dunn RR, Blowes SA, Keil P, Bohannan BJM, Herrmann M, et al. Macroecology to unite all life, large and small. Trends Ecol Evol. 2018;33:731–44.PubMed 
Article 

Google Scholar 
91.Grilli J. Macroecological laws describe variation and diversity in microbial communities. Nat Commun. 2020;11:4743.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Knief C, Ramette A, Frances L, Alonso-Blanco C, Vorholt JA. Site and plant species are important determinants of the Methylobacterium community composition in the plant phyllosphere. ISME J. 2010;4:719–28.CAS 
PubMed 
Article 

Google Scholar 
93.Redford AJ, Bowers RM, Knight R, Linhart Y, Fierer N. The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves: Biogeography of phyllosphere bacterial communities. Environ Microbiol. 2010;12:2885–93.PubMed 
PubMed Central 
Article 

Google Scholar 
94.Remus-Emsermann MNP, Tecon R, Kowalchuk GA, Leveau JHJ. Variation in local carrying capacity and the individual fate of bacterial colonizers in the phyllosphere. ISME J. 2012;6:756–65.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Kembel SW, O’Connor TK, Arnold HK, Hubbell SP, Wright SJ, Green JL. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc Natl Acad Sci. 2014;111:13715–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Maignien L, DeForce EA, Chafee ME, Eren AM, Simmons SL. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio. 2014;5:e00682–13.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
97.Wagner MR, Lundberg DS, del Rio TG, Tringe SG, Dangl JL, Mitchell-Olds T. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun. 2016;7:12151.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Carlström CI, Field CM, Bortfeld-Miller M, Müller B, Sunagawa S, Vorholt JA. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol Evol. 2019;3:1445–54.
Google Scholar 
99.Lajoie G, Maglione R, Kembel SW. Adaptive matching between phyllosphere bacteria and their tree hosts in a neotropical forest. Microbiome. 2020;8:70.PubMed 
PubMed Central 
Article 

Google Scholar 
100.Massoni J, Bortfeld-Miller M, Jardillier L, Salazar G, Sunagawa S, Vorholt JA. Consistent host and organ occupancy of phyllosphere bacteria in a community of wild herbaceous plant species. ISME J. 2020;14:245–58.CAS 
PubMed 
Article 

Google Scholar 
101.Lajoie G, Kembel SW. Host neighborhood shapes bacterial community assembly and specialization on tree species across a latitudinal gradient. Ecol Monogr. 2021;91:e01443.Article 

Google Scholar 
102.Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.Article 
PubMed 

Google Scholar 
103.Bernhardt ES, Rosi EJ, Gessner MO. Synthetic chemicals as agents of global change. Front Ecol Environ. 2017;15:84–90.Article 

Google Scholar  More