More stories

  • in

    Selfishness driving reductive evolution shapes interdependent patterns in spatially structured microbial communities

    The logic of the model
    Our spatially-resolved model was simulated in discrete grid boxes of a 100 × 100 array, which included four basic assumptions: (1) Initial individuals were assumed to secrete three public goods but may randomly mutate to lose any of those functions with a certain probability; (2) Secreting a public good created a corresponding metabolic burden, therefore in losing a function the individual would gain a benefit; (3) All public goods were essential for growth. The net growth rates of individuals were dependent on the local concentrations of public goods; (4) Substrate and public goods diffused between two grid boxes at rates proportional to the concentration gradient.
    For the 1st assumption, we included three functions because it is the minimal unit and tersest design to simulate complex communities, allows for the emergence of three categories of interaction patterns, and a single cooperative LOF genotype might evolve from differential evolutionary paths (Fig. 1A, B). The genotypes were described by bit strings containing 1 and 0 which indicated the genotype could produce the corresponding public good or not, respectively. Eight genotypes could emerge during the simulations, which were the initial autonomous producer [1, 1, 1], three one-function loss genotypes (OFLGs, i.e., [1, 1, 0], [1, 0, 1], and [0, 1, 1]), three two-function loss genotypes (TFLGs, i.e., [1, 0, 0], [0, 1, 0], and [0, 0, 1]), and a nonproducing cheater [0, 0, 0] (Fig. 1A).
    Fig. 1: Logic of the individual-based model.

    A Possible genotypes and evolutionary relationships among them emerging from reductive evolution when starting with an autonomous genotype that performs three essential public functions. Note that in this three-function model, some genotypes, i.e., Two-function loss genotypes and cheaters, might evolve from different mother genotypes. B Interaction patterns that could possibly be established in the spatially structured communities. C Schematic of the individual-based simulations. A 100 × 100 array initialization with all autonomous phenotypic individuals (left) was conducted with a long-term stepwise iteration to investigate if diverse interaction patterns could form (right). At each time step, calculations were done from the level of individual grids (top) to whole lattice (bottom). Within each grid box, Monod equation modified by basic assumptions of the Black Queen Hypothesis was used to calculate the microbial growth, while minimum and maximum thresholds of biomass were defined to decide the division and death of individuals (top middle). Microbial individuals were allowed to randomly mutate to lose functions (top middle). Classical discretization of the diffusion equation gave local rules for updating the concentrations of public goods and nutrients in each box (middle). State changes at the individual level lead to the evolutionary dynamics of the communities, which may give rise to the formation of diverse interaction patterns (bottom).

    Full size image

    The 2nd and 3rd assumptions were developed from the basic mathematical assumption of the BQH [19], and defined individual growth by integrating the benefit and cost of function loss (Fig. 1C). To conceptualize the cost of performing a function, we supposed a parameter (α) which is the fraction of biomass used to produce a public good per unit time of an individual. In addition, we defined a second parameter (β) as the ratio of the amount of public goods required during each step to account for the produced public goods. Therefore the redundant fraction of public goods production was 1−βj, and lower βj reflected a higher amount of redundant public goods that could be gained from the producers by the LOF genotypes, resulting in decreased risk in association with function loss (see Supporting Information S1 for more details). During the model simulation, spatiotemporal dynamic variables, i.e., positioning of genotypes and the time points at which genotypes evolved, would be collected. We initiated the simulations by randomly distributing 100 ancestor cells [1, 1, 1] into the grid boxes and iterated for at least 1,500,000 time steps. During each time step, individuals grew, decayed, reproduced, and mutated according to the previously mentioned assumptions (Fig. 1C). We paid attention to whether stable communities with various interdependent patterns could be formed after a specified number of iterations, as well as recorded the spatiotemporal dynamics of the communities.
    Diverse interdependent patterns emerged with high level of function cost and varied level of functional redundancy
    For model simulations, the function cost (parameter α) and functional redundancy (parameter β) were assigned to 0.0001, 0.0005, 0.001, and 0.4, 0.6, 0.8, respectively. A total of 2891 independent simulations with 9 parameter sets displayed different community structures (Fig. 2A). When the function cost was assigned to a low level, i.e., 0.0001, the autonomous ancestor dominated the community. When function costs were assigned to higher levels, 0.0005 and 0.001, new genotypes evolved and later interacted to form three distinct types of interdependent patterns even within the same α and β combination, i.e., asymmetric functional complementation (AFC), complete functional division pattern, and one-way dependency, with the relative amounts of 1677/2891, 143/2891, and 48/2891, respectively. In addition, higher functional redundancies favored the loss of more functions, increasing complexity of the community structures.
    Fig. 2: Reductive evolution shapes diverse interdependent patterns in microbial communities.

    A The final (steady state) community structures across gradients of function cost (α) and functional redundancy (1-β). Results were summarized from at least 300 interdependent runs for each parameter set. Community structures were assessed after simulation for 170,000 iterations, where 98.9% (2891/2923) of runs reached steady state. According to the structures, replicates were clustered into several scenarios for each parameter set, which are shown separately in the area plots. Note that the values of β is the proportion of public goods that is required for growth, and thus 1-β reflects the level of function redundancy. B, C Six representative community dynamics on the spatial lattices were selected from one interdependent simulation with the given conditions (mut = 10−5, α = 0.001, β = 0.8), showing the evolution of three types of asymmetric functional complementary pairs (AFCPs) (B), three different paths for the evolution of pairs [0, 0, 1] & [1, 1, 0] (C). Left images indicate the distribution of different genotypes at different points in evolutionary time. Curve plot in the middle describes the community dynamics of the corresponding simulation. Schematics at right briefly summarize the spatiotemporal dynamics of each simulation: the arrays in (B) indicate one type of AFCP directly dominated the communities without competition from others; the boxes in (C) indicates the composition of ancestor or AFCP in the related time points, while the windows inside indicate the spatial coexistence of multiple AFCPs and the size of the windows represents the relative fraction of different AFCPs.

    Full size image

    Among the three possible kinds of interactions, the AFC pattern was the most widespread, which was the combination of a two-function-loss genotype (TFLG) and its complementary one-function-loss genotype (OFLG). For example, [0, 0, 1], which produced a single essential public good, depended on its functional complement one-function-loss partner [1, 1, 0], for the other two public goods. Specifically, three types of the asymmetric functional complementary pairs (AFCPs), that is, [0, 0, 1] coupled with [1, 1, 0], [0, 1, 0] coupled with [1, 0, 1], and [1, 0, 0] coupled with [0, 1, 1], colonized most of the grid with a similar frequency of emergence. Interestingly, under the condition of high level of cost, the emergence of AFC patterns was accompanied by some nonproducing cheaters, whose relative abundance rose with the increase in functional redundancy (Fig. 2A top row). The addition of cheaters significantly reduced the total biomass of the communities, suggesting that high functional redundancy favors the evolution of cheaters which may decrease the community productivity. In addition, function loss happened more easily with high function cost. As the function cost parameter α increased from 0.0005 to 0.001, relative abundance of TFLGs increased approximately from 55 to 70% (Fig. 2A).
    Besides the AFC patterns, two additional types of interdependent patterns evolved at a relatively lower frequency. The complete functional division pattern, that is, coexistence of [0, 0, 1], [0, 1, 0], and [1, 0, 0], only evolved when both factors were at high levels (α = 0.001, β = 0.4) with a frequency of approximately 45% (143 of 319 simulations, Fig. 2A, top right), which described a scenario with high benefit and low cost of function loss, favoring the loss of more functions and consequently more likely to maintain the evolution of TFLPs. Another form of interactions that emerged was one-way dependency, where one partner performs all functions and other none (i.e., coexistence of [1, 1, 1] and [0, 0, 0]). This form emerged at a low frequency (48 out of all 2891 simulations shown in Fig. 2A), but evolved with a higher probability under the condition of a mid-level function cost and low level of functional redundancy (α = 0.0005, β = 0.6, Fig. 2A, middle left), where the extinction of [1, 1, 1] was ~2.5 times slower than in other scenarios (Supplementary Fig. 1), leading to a higher potential for the spatial proximity between [1, 1, 1] and [0, 0, 0] during evolution.
    Taken together, these phenomena demonstrated that the mutualistic exchange of complementary functions happened only when function cost was high. The emergence of different interdependent interaction patterns was related to the function cost and function redundancy, especially for the complete functional division and one-way dependency pattern, which only emerged within a limited parameter range. However, even for a given combination of α and β, it still remained possible for the evolution of distinct interaction patterns, suggesting that stochastic processes may play a role.
    Same interdependent patterns might evolve via different modes
    Because the evolution of three kinds of AFCPs were the most common scenarios in our simulations, we then focused on the role of stochastic processes, i.e., the key random events, in deciding the winning complementary pair among the three similar but different AFCPs. As a first step, we traced the variation in the spatiotemporal dynamics, trying to cluster the numerous evolutionary dynamics into limited modes and divide the complex evolutionary courses into several stages. These simplifications would facilitate the search for key random events.
    Therefore, we analyzed the dynamics of 296 simulations with a typical parameter set (α = 0.001, β = 0.8), because under this condition, only the three types of AFCPs evolved, with a similar frequency of emergence (Fig. 2A, Top left), in order to avoid interference from the other interaction patterns. As described above, any of the three types of AFCPs could potentially take over the final community under this condition (Fig. 2B; Supplementary video 1–3). Using the emergence of AFCP [0, 0, 1] & [1, 1, 0] as an example, three categories of dynamic modes could give rise to its final domination. (1) After pair [0, 0, 1] & [1, 1, 0] emerged and formed a spatial aggregation, it rapidly expanded and took over the entire grid (Fig. 2C, first line; Supplementary video 3). (2) In addition to the pair [0, 0, 1] & [1, 1, 0], spatial aggregations of another AFCP also emerged (e.g., pair [0, 1, 0] & [1, 0, 1] in Fig. 2C, second line and Supplementary video 4). In this scenario, a special spatial pattern was established in a short period after the evolution of both AFCPs e, where pairs of two complementary members exhibited strong spatial mixing, while the two different AFCPs were totally segregated. Community succession was then governed by spatial competition between the two AFCPs. If pair [0, 0, 1] & [1, 1, 0] won the competition, it would dominate the final community. (3) Spatial aggregations of all three AFCPs emerged, and then pair [0, 0, 1] & [1, 1, 0] dominated the community after outcompeting the other two AFCPs (Fig. 2C, third line; Supplementary video 5). The clustering of these three possible modes of AFC patterns was also shown by the temporal dynamics of the α-diversity across different parameter sets (Supplementary Fig. 2), where the evolution modes of the AFC patterns were clearly clustered into three possible categories, suggesting that this clustering is independent of the determined factors α and β.
    In sum, the succession of interdependent patterns could be divided into two stages: (1) the emergence of spatial aggregations composed of two interdependent members with strong connections; (2) spatial competition among different aggregations drive the community to evolve to the final state, composed of only one type of interdependent interactions. Of course, if only one type of AFCP emerged, the spatial competition stage would be unnecessary during succession.
    Evolutionary random events play important roles in deciding the dominant AFCP in equilibrium communities
    The presence of two evolutionary stages lead us to hypothesize that the random events affecting ecological outcomes should arise from two aspects. First, in the initial evolutionary stage, the emergence of interdependent spatial aggregations should be related to the order in which new genotypes emerge. Second, the outcome of the spatial competition should be also influenced by the initial positioning of the new genotypes.
    The fact that each TFLP had two possible evolutionary paths (e.g., [1, 0, 0] could inherit its function from [1, 1, 0] or [1, 0, 1]), suggested that the effects of the random order of emergence for different genotypes were highly correlated with the evolutionary lineage. Therefore, to investigate the effects of this, we analyzed the evolutionary lineage of emergence, colonization, and loss of every genotype within the 296 simulations with the typical parameter set (α = 0.001, β = 0.8). In total, there were 24 evolutionary branches leading to the evolution of the three forms of AFC patterns (8 for each, Fig. 3). Among all these branches, we summarized two key random events (Fig. 3, red and blue boxes).
    Fig. 3: The evolutionary trajectories of 296 independent simulations with the typical parameter set (mut = 10−5, α = 0.001, β = 0.8).

    We analyzed the evolutionary trajectories of every interdependent run and clustered them into 24 types of branches (top, see Methods). The area plot shows the final community structures and the frequencies of each branch (bottom). Blue dashed box shows the evolutionary diversification into four scenarios after the first key event occurs, while the red dashed box indicates the 24 different evolutionary trajectories that diverged after the second key event occurs. Solid boxes with colored circles represent the genotypic composition of communities at different evolutionary time points. Red arrows indicate the branches where one type of asymmetric functional complementary pair (AFCP) directly dominated the communities without competition with other AFCPs, while the blue arrows indicate the branches where one type of AFCP took over the entire space after competitions with other AFCPs. Dashed boxes at the figure labels (right) indicate different AFCPs.

    Full size image

    The first event occurred after two types of OFLGs emerged. After this evolutionary time point, all three public functions were included in OFLGs. With the benefit of the function loss, these two OFLGs would expand and gradually outcompete the autonomous genotype [1, 1, 1]. Thus, the first key event was whether all three OFLGs could emerge before the autonomous genotype entirely disappeared (Fig. 3, blue box). If not, the third type of AFCP would never evolve; if so, all three types of AFCPs would still have a chance to dominate the final community. In the 296 simulations, the frequencies of these two scenarios were nearly same, that is, 147 simulations were clustered to the former, while 149 simulations were clustered to the latter. The 147 simulations, where the third type of AFCP never evolved, could be then divided into three categories with similar frequencies, where two of the three OFLGs occupied the whole space and excluded the ancestral population.
    The second key evolutionary event was the emergence of TFLGs (Fig. 3, red box). After the two or three types of OFLGs successfully colonized, whose functional complementary TFLGs first to emerge in the next evolutionary time would lead to the prior formation of the spatial aggregation of the AFCP. It is obvious that if no other AFCP aggregations formed later, this AFCP would dominate the final community (Fig. 3, red arrow indicated branches). Alternatively, if other AFCP aggregations formed during the expansion process, the spatial competition between different AFCPs would decide the dominant AFCP in the equilibrium communities (Fig. 3, blue arrow indicated branches). In our analysis, the chance of only one AFCP evolving reached 64.7% (198 of the 296 simulations). If only two OFLGs evolved after the first event, the frequency of only one AFCP evolving reached 79.6% (121 of the 152 simulations). In contrast, if three OFLGs evolved after the first event, there could be a relative higher possibility of two or three AFCPs evolving (47.4%), meaning that spatial competition could then be an important process.
    What decided the winner of the competition? We observed that after the segregated interdependent spatial pattern was newly established, the relative region sizes occupied by different AFCPs were the key to determining the winner (Fig. 2C, the second and third lines; Supplementary video 4 and 5). We analyzed the time gaps between the emergence of the two AFCPs in the second categories of succession modes and the size of the regions they occupied (Fig. 4A). The result indicated a significantly positive correlation between the length of the time gaps and the region size the prior AFCP occupied (t-test, p  1 indicates pair [0, 0, 1] & [1, 1, 0] is more spatially associated than pair [0, 1, 0] & [1, 0, 1]. Applying these definitions, the simulation results where the advantage of prior space occupancy was not significant (left side of blue line in Fig. 4C, 33 replicates) were selected for analysis, and we found a significantly positive correlation between the relative PAD at the beginning of spatial self-organization and the ‘region size advantage’ (Fig. 5A; p  1 means the prior emerged AFCPs are more spatially associated than the second AFCPs. Red dots indicate the first to emerge AFCP won the competition in the corresponding replicate, while the green dots indicate the second to emerge AFCP won the competition. B Two typical examples of simulations initialized with premixing the two types of AFCPs, [0, 0, 1] & [1, 1, 0] and [0, 1, 0] & [1, 0, 1], which represent scenarios when initial PAI001:010 1, respectively. C The significant positive correlation between the winning frequency of pair [0, 0, 1] & [1, 1, 0] and the initial value of PAI001:010. When initial PAI001:010  > 1, final communities were more likely to be dominated by pair [0, 0, 1] & [1, 1, 0], oppositely, pair [0, 1, 0] & [1, 0, 1] were more favorable when PAI001:010  More

  • in

    Distinct ecotypes within a natural haloarchaeal population enable adaptation to changing environmental conditions without causing population sweeps

    1.
    Viver T, Orellana LH, Díaz S, Urdiain M, Ramos‐Barbero MD, González‐Pastor JE, et al. Predominance of deterministic microbial community dynamics in salterns exposed to different light intensities. Environ Microbiol. 2019;21:4300–15.
    CAS  PubMed  Article  Google Scholar 
    2.
    Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci USA. 2005;102:13950–5.
    CAS  PubMed  Article  Google Scholar 

    3.
    Amann R, Rosselló-Móra R. After all, only millions? MBio. 2016;7:e00999–16.
    PubMed  PubMed Central  Google Scholar 

    4.
    Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trnas R Soc Lond B Biol Sci. 2006;361:1929–40.
    Article  Google Scholar 

    5.
    Shapiro BJ, Polz MF. Ordering microbial diversity into ecologicaly and genetically cohesive units. Trends Microbiol. 2014;22:235–47.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    McInerney JO, McNally A, O’Connell MJ. Why prokaryotes have pangenomes. Nat Microbiol. 2017;2:17040.
    CAS  PubMed  Article  Google Scholar 

    7.
    Andreani NA, Hesse E, Vos M. Prokaryote genome fluidity is dependent on effective population size. ISMEJ. 2017;11:1719–21.
    CAS  Article  Google Scholar 

    8.
    Cohan FM. What are bacterial species? Annu Rev Microbiol. 2002;56:457–87.
    CAS  PubMed  Article  Google Scholar 

    9.
    Lan R, Reeves PR. When does a clone deserve a name? A perspective on bacterial species based on population genetics. Trends Microbiol. 2001;9:419–24.
    CAS  PubMed  Article  Google Scholar 

    10.
    Fraser C, Alm EJ, Polz MF, Spratt BG, Hanage WP. The bacterial species challenge: making sense of genetic and ecological diversity. Science. 2009;323:741–46.
    CAS  PubMed  Article  Google Scholar 

    11.
    Vázquez DP, Simberloff D. Ecological specialization and susceptibility to disturbance: conjectures and refutations. Am Nat. 2002;159:606–23.
    PubMed  Article  Google Scholar 

    12.
    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 

    13.
    Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417.
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Rodriguez-R LM, Overholt WA, Hagan C, Huettel M, Kostka JE, Konstantinidis KT. Microbial community successional patterns in beach sands impacted by the Deepwater Horizon oil spill. ISME J. 2015;9:1928–40.
    PubMed  PubMed Central  Article  Google Scholar 

    15.
    Petraitis PS, Latham RE, Niesenbaum RA. The maintenance of species diversity by disturbance. Q Rev Biol. 1989;64:393–418.
    Article  Google Scholar 

    16.
    Narasingarao P, Podell S, Ugalde JA, Brochier-Armanet C, Emerson JB, Brocks JJ, et al. De novo metagenomic assembly reveals abundant novel major lineage of Archaea in hypersaline microbial communities. ISME J. 2012;6:81–93.
    CAS  PubMed  Article  Google Scholar 

    17.
    Antón J, Rosselló-Móra R, Rodriguez-Valera F, Amann R. Extremely halophilic bacteria in crystallizer ponds from solar salterns. Appl Environ Microbiol. 2000;66:3052–57.
    PubMed  PubMed Central  Article  Google Scholar 

    18.
    Gomariz M, Martínez-García M, Santos F, Rodriguez F, Capella-Gutiérrez S, Gabaldón T, et al. From community approaches to single-cell genomics: the Discovery of ubiquitous hyperhalophilic Bacteroidetes generalists. ISME J. 2015;9:1–16.
    Article  CAS  Google Scholar 

    19.
    Mora-Ruiz MR, Font-Verdera F, Díaz-Gil C, Urdiain M, Rodríguez-Valdecantos G, González G, et al. Moderate halophilic bacteria colonizing the phylloplane of halophytes of the subfamily Salicornioideae (Amaranthaceae). Syst Appl Microbiol. 2015;38:406–16.
    CAS  Article  Google Scholar 

    20.
    Antón J, Lucio M, Peña A, Cifuentes A, Brito-Echeverría J, Moritz, F, et al. High metabolomic microdiversity within co-occurring isolates of the extremely halophilic bacterium Salinibacter ruber. PLoS ONE. 2013;8:e64701.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    21.
    Conrad EC, Viver T, Hatt JK, Rosselló-Móra R, Konstantinidis KT. Unrestricted but ecologically-important gene-content diversity within a natural sequence-discrete population as revealed by sequencing of 112 isolates. 2020. In review.

    22.
    Cuadros-Orellana S, Martin-Cuadrado AB, Legault B, D’Auria G, Zhaxybayeva O, Papke RT, et al. Genomic plasticity in prokaryotes: the case of the square haloarchaeon. ISME J. 2007;1:235–45.
    CAS  PubMed  Article  Google Scholar 

    23.
    Konopka A, Lindemann S, Fredrickson J. Dynamics in microbial communities: unraveling mechanisms to identify principles. ISME J. 2015;9:1488–95.
    PubMed  Article  Google Scholar 

    24.
    Millán MM, Estrela MJ, Miró J. Rainfall components: variability and spatial distribution in a Mediterranean Area (Valencia Region). J Clim. 2005;18:2682–705.
    Article  Google Scholar 

    25.
    Santos F, Moreno-Paz M, Meseguer I, López C, Rosselló-Móra R, Parro V, et al. Metatranscriptomic analysis of extremely halophilic viral communities. ISME J. 2011;5:1621–33.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Begon M, Townsend CR, Harper JL, editors. Ecology: from individuals to ecosystems. 4th ed. Malten, MA, USA: Blackwell Publishing Ltd; 2006.

    27.
    Rodriguez-R LM, Konstantinidis KT. Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomics datasets. Bioinform. 2014;30:629–35.
    CAS  Article  Google Scholar 

    28.
    Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    29.
    Oksanen J, Kindt R, Legendre P.O’Hara B. Vegan: community ecology package. Com Ecol Pack. 2007;10:631–37.

    30.
    Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB; a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Wu YW, Tang YH, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Móra R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 2018;43:W282–8.
    Article  CAS  Google Scholar 

    34.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
    CAS  PubMed  Article  Google Scholar 

    35.
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
    PubMed  PubMed Central  Article  Google Scholar 

    36.
    Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
    Article  CAS  Google Scholar 

    37.
    UniProt Consortium. Uniprot: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.
    Article  CAS  Google Scholar 

    38.
    Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Prepr. 2016;4:e1900v1.
    Google Scholar 

    39.
    Caro-Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.
    CAS  PubMed  Article  Google Scholar 

    40.
    Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:81–91.
    CAS  PubMed  Article  Google Scholar 

    41.
    Viver T, Orellana LH, Hatt JK, Urdiain M, Díaz S, Richter M, et al. The low diverse gastric microbiome of the jellyfish Cotylorhiza tuberculata is dominated by four novel taxa. Environ Microbiol. 2017;19:3039–58.
    PubMed  Article  Google Scholar 

    42.
    Haynes WM, Lide DR, Bruno TJ, editors. CRC handbook of chemistry and physics, 94th ed. London, UK: CRC Press; 2013. p. 4–89.

    43.
    Griebler C, Lueders T. Microbial biodiversity in groundwater ecosystems. Freshw Biol. 2009;54:649–77.
    Article  Google Scholar 

    44.
    Konstantinidis KT, Tiedje JM. Towards a genome-based taxonomy for prokaryotes. J Bacteriol. 2005;187:6258–64.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Oren A. Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Syst. 2008;4:2.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    46.
    Pedrós-Alió C. Marine microbial diversity: can it be determined? Trends Micribiol. 2006;14:257–63.
    Article  CAS  Google Scholar 

    47.
    Azua-Bustos A, Fairén AG, González-Silva C, Ascaso C, Carrizo D, Fernández-Martínez MÁ, et al. Unprecedented rains decimate surface microbial communities in the hyperarid core of the Atacama Desert. Sci Rep. 2018;8:16706.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci U S A. 2008;105:11512–19.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Uritskiy G, Getsin S, Munn A, Gomez-Silva B, Davila A, Glass B, et al. Halophilic microbial community compositional shift after a rare rainfall in the Atacama Desert. ISME J. 2019;13:2737–49.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Ghai R, Pašić L, Fernández AB, Martin-Cuadrado AB, Mizuno CM, McMahon KD, et al. New abundant microbial groups in aquatic hypersaline environments. Sci Rep Nat 2011;1:135.
    Article  CAS  Google Scholar 

    51.
    Burns DG, Janssen PH, Itoh T, Minegishi H, Usami R, Kamekura M, et al. Natronomonas moolapensis sp. nov., non-alkaliphilic isolates recovered from a solar saltern crystallizer pond, and emended description of the genus Natronomonas. Int J Syst Evol Microbiol. 2010;60:1173–76.
    CAS  PubMed  Article  Google Scholar 

    52.
    López-Pérez M, Ghai R, Leon MJ, Rodríguez-Olmos Á, Copa-Patiño JL, Soliveri J, et al. Genomes of “Spiribacter”, a streamlined, successful halophilic bacterium. BMC Genom. 2013;14:787.
    Article  CAS  Google Scholar 

    53.
    Martin-Cuadrado AB, Pašić L, Rodriguez-Valera F. Diversity of the cell-wall associated genomic island of the archaeon Haloquadratum walsbyi. BMC Genom. 2015;16:603.
    Article  CAS  Google Scholar 

    54.
    Mirete S, Mora-Ruiz MF, Lamprecht-Grandío M, de Figueras CG, Rosselló-Móra R, González-Pastor J. Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment. Front Microbiol. 2015;6:1121.
    PubMed  PubMed Central  Article  Google Scholar 

    55.
    Cray JA, Bell AN, Bhaganna P, Mswaka AY, Timson DJ, Hallsworth JE. The biology of habitat dominance; can microbes behave as weeds? Microb Biotechnol. 2013;6:453–92.
    PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Ecological responses to flow variation inform river dolphin conservation

    Study area
    This project was conducted in the downstream segment of the Karnali River basin of Nepal (Fig. 1), which is the largest of Nepal’s three major river systems and is characterized by the steep terrain of the Himalayan Mountains. The highest runoff occurs during the monsoon season (e.g., June–October), and the lowest occurs during the winter season (e.g., December–May). Below the Siwalik Mountain range (a physiographic zone, Fig. 1), a vast network of small tributaries combines to form a single narrow channel of the Karnali River with well-defined banks. Originating from the Tibetan Plateau, the Karnali River is the largest tributary to the Ganges River in India, which harbours the most significant density of GRDs in the world. The lower Karnali River basin provides the furthest upstream range for GRDs, critically endangered gharials (Gavialis gangeticus), smooth Indian otters (Lutrogale perspicillata), and 36 native fish species49. The GRD population size in the Karnali River has declined from 26 to six individuals50. Such a sharp decline in the GRD population is due to the effects of habitat degradation, mainly from water-based development projects (i.e., water diversion,51). Concurrently, several upstream development projects are proposed, under construction, or completed [e.g., planned: the Karnali Chisapani multipurpose dam, 10,800 megawatt (MW); under construction: the upper Karnali hydropower project, 900 MW, and Bhari Babai diversion project; completed: Rani Jamara Kulariya irrigation intakes] and further threaten downstream aquatic life. All projects adopt traditional preconstruction environmental impact assessments procedure to define flow proportions (generally 10–20% of natural regimes) anecdotally and unscientifically. Thus, traditional flow proportions might be inadequate to sustain native aquatic biodiversity. Our study focused on the lower catchment area of the Karnali River basin, which is downstream from all megaprojects. All measurement protocols, including dolphin observation methods, were carried out in accordance with the Department of National Parks and Wildlife Conservation, Government of Nepal, guidelines and regulations. Habitat measurement protocols, including dolphin observation methods, were approved by the Department of National Parks and Wildlife Conservation, Government of Nepal (No 1129; 12 December 2016).
    Available habitat assessment
    Reduced water levels during the low-water season (e.g., December–May) escalate threats to aquatic biota by limiting physical habitat availability. Here, habitat refers to the hydro-physical habitat, which is defined by the flow and depth interactions at a particular geomorphic condition over space and time. Therefore, habitat availability (i.e. the area accessible to species) is assumed to be the greatest bottleneck, critically limiting species reproduction and survival42,51. We measured the available habitats in the low-water season when suitable habitat is critically limited (i.e., December–May in 2018/2019), excluding the monsoon season (June–November). Further, to capture dynamic flow variation within the dry (i.e., low) water season, we selected three temporal periods—March (mid dry season), May (late dry season), and December (early dry season)—based on 39 years of flow records available from the Department of Hydrology, Government of Nepal. Assessing available habitat includes habitat mapping and bank and instream surveys. We divided the study area into three segments [upper segment (S1): length = 11 km, average width = 218 m; middle segment (S2): length = 29 km, average width = 121 m; and lower segment (S3): length = 10 km, average width = 198 m; Fig. 1] based on uniform flow and channel geomorphology mapped along the selected stretch of the river. The three segments vary hydrologically and structurally. S1 consists of river channels with natural flows without any infrastructural diversion. Because of water diversion operations (e.g., Rani Jamuna irrigation intake and several traditional agricultural irrigation channels) and distributaries, the natural flow volume in S2 was low compared to that in S1. S3 benefited slightly from distributaries and received more water than S2.
    Within each segment, the study reach (the linear segment where cross-sections are established) was established in such a way that the length of each reach was at least higher than the mean width (so the number varies among segments) of the respective segment. We also tried to maintain relatively similar flow at the top and bottom of the reach. Within each reach, random cross-sections were established to capture the hydraulic properties based on flow variation. As the flow variability of the stream increased, the number of cross-sections increased, and each section was kept at least 300 m apart from the other sections. Therefore, the number of cross-sections was based on the flow variation within a reach instead of the length of the reach. Bank and instream surveys started in an upstream direction, wherein directional readings of the cross-sections were noted. For the bank and instream measurements, pin heights were established at either side of the cross-section using GPS and a permanent reference marker for repeated flow measurements. Water surface elevations were estimated using a total station (an optical instrument for land surveying; Leica 772737 Builder 503) across the pin heights for each cross-section required for hydrological simulation. A new benchmark was established for each effort to measure the water surface elevation at each cross-section. The total number of cross-sections examined for the available habitats was 177 (March = 60, May = 47, and December = 60). The hydraulic parameters (see habitat characterization section below) at each cross-section were measured using a RiverSurveyor S5 acoustic water current profile reader [Sontek, Acoustic Doppler Profiler (ADP S5)], which records hydro-parameters continuously at a cell size between 0.02 to 0.5 m offering complete underwater available hydro-physical profile.
    Occupied habitat assessment
    We conducted a GRD population survey to capture occupied (selectivity) habitat characteristics (n = 97) at three temporal scales (previously mentioned) using the developed approach24. Within each temporal scale, we conducted three replications to capture the temporal and spatial variability in the characteristics of the occupied habitat. When we first detected dolphins, we observed surfacing behaviours for at least five minutes before establishing a cross-section. The habitats that were used for at least five minutes were considered occupied habitats, and then cross-sections were established to measure habitat characteristics using the ADP. If the dolphins disappeared after the location of the first sighting in less than five minutes, we excluded those habitats from our analysis. The Dolphin observation (only observation done) protocols were approved and permitted by the Department of National Parks and Wildlife Conservation, Government of Nepal.
    Data analysis
    Data preparation and software
    The ADP S5 hydraulic data were imported into Excel databases (Microsoft v. 2010) to format for System for Environmental Flow Analysis (SEFA, version 1.5; Aquatic Habitat Analysts Inc.) software. All the hydraulic properties [depth (m), velocity (m/s), wetted perimeter-WP (m), width (m), cross-sectional area-CSA (m2), Froude number, and discharge (m3/s)], suitability, and flow regime determination were calculated using SEFA software and analysed at the cross-section and segment levels. The average flow of each segment was used as a base flow while running the habitat simulation model for the respective segment. We found critical flows ( 417 m3/s, excess flow with a negative contribution to habitat suitability) in May. Therefore, the habitat retention hydraulic simulation model was performed only with excess flow (for May) using 39 years of 90% exceedance flow (the flow that is equaled or exceeded 90% of the time).
    Habitat characterization
    The cross-sectional hydro-physical parameters—width, flow, depth, velocity, wetted perimeter, cross-sectional area, and habitat (types)—were reported spatially and temporally. The habitat type (e.g., pool, run, and riffle) was classified based on the Froude number (Fr), where Froude is an index of hydraulic turbulence (the ratio of velocity by the acceleration of gravity). Points with Froude numbers exceeding 0.41 were considered riffles, points with Froude numbers less than 0.18 were considered pools, and intermediate values were classified as run habitats. The proportion of run, riffle, and pool habitats within each study reach was calculated from the Froude numbers. The GRD’s seasonal hydro-physical habitats were characterized using basic descriptive statistics (mean and 95% CI). The variation in these hydraulic parameters among seasons, habitat types, and segments was examined by an analysis of variance (ANOVA), and post hoc pairwise comparisons were performed using Tukey’s honestly significant difference (HSD) test. A two-way ANOVA test was used to investigate any interactive effects of season and habitat on hydraulic variations. The level of significance was set at p  one and zero to those categories for which w ≤ one. By assigning one and zero to each group, we developed an HSC to calculate the area weighted suitability (AWS) at each measured point. Hydraulic habitat suitability is expressed as AWS in terms of usable area in metres of width or square metres per metre of reach (m2/m).
    To obtain the AWS value for the reach, we multiplied the combined suitability index (CSI, which is the product of the suitability of depth and velocity at a point) and the proportion of the reach area represented by that point. Using a 39-year average base flow of 536.11 m3/s (90% exceedance flow) in May, we predicted the fluctuation (decrease by 10%) in the currently available maximum AWS (i.e., 22.718 m2/m, AWS of May) in the range of flows from 200–900 m3/s. We simulated the AWS in this particular range because this range represents the 39-year low and maximum values of the 90% exceedance flow for the low-water season (November–May). Covering this variation over a broader scale increases the applicability of our ecological thresholds across time. Using the same base flow and range, we also estimated the minimum flows that retain various standards (%) of habitat protection. Further, we also determined the minimum flow that provides the maximum AWS for the low-water season.
    Ecological thresholds using flow-ecology relationships
    As water depth and velocity are the result of instream habitat features, such as pools, riffles, and runs, we only incorporated depth and velocity when estimating the hydraulic habitat suitability. Additionally, GRD habitat selection is strongly guided by the depth and velocity of a river section24,51. Generalized linear models (GLMs) using logit functions were used to examine the relationship between GRD presence and hydraulic properties (depth and velocity). Four different GLMs (depth, velocity, depth*velocity, and depth + velocity) were developed, and the Akaike information criterion (AIC) was used to select the best models. The additive effect of depth and velocity on the GRD presence was found in the model with the best performance; therefore, we further used a generalized additive model (GAM) to capture the possible non-linear influence of depth and velocity on GRD presence. Because of the possibility of both linear and non-linear relationships11, we again used a GAM to capture the functional relationships between ecology (AWS) and flow. The degree of smoothness for all the GAMs identified by the iterative approach (up to 25 smoothing factors were checked) and the selected smoothing parameter (i.e., 20 for all the GAM models) that yielded a significant covariate (at the 0.005 level of significance) explained the maximum deviance and adjusted R2. Both the GLM and GAM models were fitted using the lm and mgcv packages in R Studio. More

  • in

    Skin microbiome correlates with bioclimate and Batrachochytrium dendrobatidis infection intensity in Brazil’s Atlantic Forest treefrogs

    1.
    Belden, L. K. et al. Panamanian frog species host unique skin bacterial communities. Front. Microbiol. 6, 1171 (2015).
    PubMed  PubMed Central  Article  Google Scholar 
    2.
    Jani, A. J. & Briggs, C. J. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front. Microbiol. 9, 487 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    3.
    McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. USA 110, 3229–3236 (2013).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Woodhams, D. C., Bletz, M., Kueneman, J. & McKenzie, V. Managing amphibian disease with skin microbiota. Trends Microbiol. 24, 161–164 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Flechas, S. V. et al. Current and predicted distribution of the pathogenic fungus Batrachochytrium dendrobatidis in Colombia, a hotspot of amphibian biodiversity. Biotropica 49, 685–694 (2017).
    Article  Google Scholar 

    6.
    Rollins-Smith, L. A. & Conlon, J. M. Antimicrobial peptide defenses against chytridiomycosis, an emerging infectious disease of amphibian populations. Dev. Comp. Immunol. 29, 589–598 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Jiménez, R. R. & Sommer, S. The amphibian microbiome: natural range of variation, pathogenic dysbiosis, and role in conservation. Biodiv. Conserv. 26, 763–786 (2017).
    Article  Google Scholar 

    8.
    Bletz, M. C. et al. Host ecology rather than host phylogeny drives amphibian skin microbial community structure in the biodiversity hotspot of Madagascar. Front. Microbiol. 8, 1530 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    9.
    Romano-Bertrand, S., Licznar-Fajardo, P., Parer, S. & Jumas-Bilak, E. Impact de l’environnement sur les microbiotes: focus sur l’hospitalisation et les microbiotes cutanés et chirurgicaux. Revue Francophone des Laboratoires 469, 75–82 (2015).
    Article  Google Scholar 

    10.
    Woodhams, D. C. et al. Host-associated microbiomes are predicted by immune system complexity and climate. Genome Biol. 21, 23 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    11.
    Cheng, Y. et al. The Tasmanian devil microbiome—implications for conservation and management. Microbiome 3, 1–11 (2015).
    Article  Google Scholar 

    12.
    Lemieux-Labonté, V., Tromas, N., Shapiro, B. J. & Lapointe, F. J. Environment and host species shape the skin microbiome of captive neotropical bats. PeerJ 4, e2430 (2016).

    13.
    Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Daskin, J. H., Bell, S. C., Schwarzkopf, L. & Alford, R. A. Cool temperatures reduce antifungal activity of symbiotic bacteria of threatened amphibians–implications for disease management and patterns of decline. PLoS ONE 9, e100378 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Duellman, W. E. & Trueb, L. Integumentary, Sensory, and Visceral Systems. Biology of Amphibians (McGraw-Hill, New York, 1986).
    Google Scholar 

    16.
    Bataille, A. et al. Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation. Proc. R. Soc. B Biol. Sci. 282, 20143127 (2015).
    Article  Google Scholar 

    17.
    Longo, A. V., Savage, A. E., Hewson, I. & Zamudio, K. R. Seasonal and ontogenetic variation of skin microbial communities and relationships to natural disease dynamics in declining amphibians. R. Soc. Open Sci. 2, 140377 (2015).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    18.
    Kueneman, J. G. et al. The amphibian skin-associated microbiome across species, space and life history stages. Mol. Ecol. 23, 1238–1250 (2014).
    PubMed  Article  Google Scholar 

    19.
    Kueneman, J. G. et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat. Ecol. Evol. 3, 381–389 (2019).
    PubMed  Article  Google Scholar 

    20.
    Rollins-Smith, L. A., Ramsey, J. P., Pask, J. D., Reinert, L. K. & Woodhams, D. C. Amphibian immune defenses against chytridiomycosis: impacts of changing environments. Integr. Comp. Biol. 51, 552–562 (2011).

    21.
    Sanchez, E. et al. Cutaneous bacterial communities of a poisonous salamander: a perspective from life stages, body parts and environmental conditions. Microb. Ecol. 73, 455–465 (2017).
    CAS  PubMed  Article  Google Scholar 

    22.
    Antwis, R. E. et al. Ex situ diet influences the bacterial community associated with the skin of red-eyed tree frogs (Agalychnis callidryas). PLoS ONE 9, e85563 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    23.
    Jani, A. J. & Briggs, C. J. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc. Natl. Acad. Sci. USA 111, E5049–E5058 (2014).
    ADS  CAS  PubMed  Article  Google Scholar 

    24.
    Medina, D. et al. Variation in metabolite profiles of amphibian skin bacterial communities across elevations in the Neotropics. Microb. Ecol. 74, 227–238 (2017).
    CAS  PubMed  Article  Google Scholar 

    25.
    Loudon, A. H. et al. Vertebrate hosts as islands: dynamics of selection, immigration, loss, persistence, and potential function of bacteria on salamander skin. Front. Microbiol. 7, 333 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Bates, K. A. et al. Amphibian chytridiomycosis outbreak dynamics are linked with host skin bacterial community structure. Nat. Commun. 9, 1–11 (2018).
    ADS  CAS  Article  Google Scholar 

    27.
    Woodhams, D. C. et al. Interacting symbionts and immunity in the amphibian skin mucosome predict disease risk and probiotic effectiveness. PLoS ONE 9, e96375 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    28.
    Costa, S., Lopes, I., Proença, D. N., Ribeiro, R. & Morais, P. V. Diversity of cutaneous microbiome of Pelophylax perezi populations inhabiting different environments. Sci. Total Environ. 572, 995–1004 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 

    29.
    Sabino-Pinto, J. et al. Composition of the cutaneous bacterial community in Japanese amphibians: effects of captivity, host species, and body region. Microb. Ecol. 72, 460–469 (2016).
    PubMed  Article  Google Scholar 

    30.
    Kueneman, J. G. et al. Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas). ISME J. 10, 934–944 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    31.
    Becker, C. G., Longo, A. V., Haddad, C. F. B. & Zamudio, K. R. Land cover and forest connectivity alter the interactions among host, pathogen and skin microbiome. Proc. R. Soc. B Biol. Sci. 284, 20170582 (2017).
    Article  Google Scholar 

    32.
    Belasen, A. M., Bletz, M. C., Leite, D. D. S., Toledo, L. F. & James, T. Y. Long-term habitat fragmentation is associated with reduced MHC IIB diversity and increased infections in amphibian hosts. Front. Ecol. Evol. 6, 236 (2019).
    Article  Google Scholar 

    33.
    Greenspan, S. E. et al. Arthropod–bacteria interactions influence assembly of aquatic host microbiome and pathogen defense. Proc. R. Soc. B 286, 20190924 (2019).
    CAS  PubMed  Article  Google Scholar 

    34.
    Becker, C. G. et al. Low-load pathogen spillover predicts shifts in skin microbiome and survival of a terrestrial-breeding amphibian. Proc. Roy. Soc. B 286, 20191114 (2019).
    Article  Google Scholar 

    35.
    Christian, K., Weitzman, C., Rose, A., Kaestli, M. & Gibb, K. Ecological patterns in the skin microbiota of frogs from tropical Australia. Ecol. Evol. 8, 10510–10519 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    36.
    McKenzie, V. J., Bowers, R. M., Fierer, N., Knight, R. & Lauber, C. L. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 6, 588–596 (2012).
    CAS  PubMed  Article  Google Scholar 

    37.
    Walke, J. B. et al. Amphibian skin may select for rare environmental microbes. ISME J. 8, 2207–2217 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Varela, B. J., Lesbarrères, D., Ibáñez, R. & Green, D. M. Environmental and host effects on skin bacterial community composition in Panamanian frogs. Front. Microbiol. 9, 298 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    39.
    Loudon, A. H. et al. Microbial community dynamics and effect of environmental microbial reservoirs on red-backed salamanders (Plethodon cinereus). ISME J. 8, 830–840 (2014).
    CAS  PubMed  Article  Google Scholar 

    40.
    Toledo, L. F. & Batista, R. F. Integrative study of Brazilian anurans: geographic distribution, size, environment, taxonomy, and conservation. Biotropica 44, 785–792 (2012).
    Article  Google Scholar 

    41.
    Haddad, C. F. B. et al. Guide to the Amphibians of the Antic Forest: Diversity and Biology (Anolisbooks, São Paulo, 2013).
    Google Scholar 

    42.
    Toledo, L. F., Becker, C. G., Haddad, C. F. & Zamudio, K. R. Rarity as an indicator of endangerment in Neotropical frogs. Biol. Conserv. 179, 54–62 (2014).
    Article  Google Scholar 

    43.
    Sasso, T. et al. Environmental DNA characterization of amphibian communities in the Brazilian Atlantic forest: potential application for conservation of a rich and threatened fauna. Biol. Conserv. 215, 225–232 (2017).
    Article  Google Scholar 

    44.
    Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: how much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 142, 1141–1153 (2009).
    Article  Google Scholar 

    45.
    Ledru, M. P., Montade, V., Blanchard, G. & Hély, C. Long-term spatial changes in the distribution of the Brazilian Atlantic Forest. Biotropica 48, 159–169 (2016).
    Article  Google Scholar 

    46.
    Joly, C. A., Metzger, J. P. & Tabarelli, M. Experiences from the Brazilian Atlantic F orest: ecological findings and conservation initiatives. New Phytol. 204, 459–473 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
    ADS  CAS  Article  Google Scholar 

    48.
    Eterovick, P. C. et al. Amphibian declines in Brazil: an overview 1. Biotropica 37, 166–179 (2005).
    Article  Google Scholar 

    49.
    Becker, C. G., Fonseca, C. R., Haddad, C. F. B., Batista, R. F. & Prado, P. I. Habitat split and the global decline of amphibians. Science 318, 1775–1777 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    50.
    Both, C. et al. Widespread occurrence of the American bullfrog, Lithobates catesbeianus (Shaw, 1802) (Anura: Ranidae), Brazil. S. Am. J. Herpetol. 6, 127–134 (2011).
    Article  Google Scholar 

    51.
    Carvalho, T., Becker, C. G. & Toledo, L. F. Historical amphibian declines and extinctions in Brazil linked to chytridiomycosis. Proc. R. Soc. B Biol. Sci. 284, 20162254 (2017).
    Article  Google Scholar 

    52.
    Haddad, C. F., Toledo, L. F. & Prado, C. P. Anfíbios da Mata Atlântica: guia dos anfíbios anuros da Mata Atlântica. Editora Neotropica (2008).

    53.
    Carnaval, A. C. O. Q., Toledo, L. F., Haddad, C. F. B. & Britto, F. B. (2005). Chytrid fungus infects high-altitude stream-dwelling Hylodes magalhaesi (Leptodactylidae) in the Brazilian Atlantic rainforest. Froglog 70, 3–4 (2005).

    54.
    Carnaval, A. C. O., Puschendorf, R., Peixoto, O. L., Verdade, V. K. & Rodrigues, M. T. Amphibian chytrid fungus broadly distributed in the Brazilian Atlantic Rain Forest. EcoHealth 3, 41–48 (2006).
    Article  Google Scholar 

    55.
    Toledo, L. F., Britto, F. B., Araújo, O. G., Giasson, L. M. & Haddad, C. F. The occurrence of Batrachochytrium dendrobatidis in Brazil and the inclusion of 17 new cases of infection. S. Am. J. Herpetol. 1, 185–191 (2006).
    Article  Google Scholar 

    56.
    Toledo, L. F., Haddad, C. F. B., Carnaval, A. C. O. Q. & Britto, F. B. A Brazilian anuran (Hylodes magalhaesi: Leptodactylidae) infected by Batrachochytrium dendrobatidis: a conservation concern. Amphib. Reptile Conserv. 4, 17–21 (2006).
    Google Scholar 

    57.
    de Oliveira Ramalho, A. C., De Paula, C. D., Catao-Dias, J. L., Vilarinho, B. & Brandao, R. A. First record of Batrachochytrium dendrobatidis in two endemic Cerrado hylids, Bokermannohyla pseudopseudis and Bokermannohyla sapiranga, with comments on chytridiomycosis spreading in Brazil. North West. J. Zool. 9, 145–150 (2013).
    Google Scholar 

    58.
    Rodriguez, D., Becker, C. G., Pupin, N. C., Haddad, C. F. B. & Zamudio, K. R. Long-term endemism of two highly divergent lineages of the amphibian-killing fungus in the Atlantic F orest of B razil. Mol. Ecol. 23, 774–787 (2014).
    CAS  PubMed  Article  Google Scholar 

    59.
    Preuss, J. F., Lambertini, C., da Silva Leite, D., Toledo, L. F. & Lucas, E. M. Batrachochytrium dendrobatidis in near threatened and endangered amphibians in the southern Brazilian Atlantic Forest. North West. J. Zool 11, 360–362 (2015).
    Google Scholar 

    60.
    Preuss, J. F., Lambertini, C., Leite, D. D. S., Toledo, L. F. & Lucas, E. M. Crossing the threshold: an amphibian assemblage highly infected with Batrachochytrium dendrobatidis in the southern Brazilian Atlantic forest. Stud. Neotrop. Fauna E 51, 68–77 (2016).
    Article  Google Scholar 

    61.
    Valencia-Aguilar, A., Toledo, L. F., Vital, M. V. & Mott, T. Seasonality, environmental factors, and host behavior linked to disease risk in stream-dwelling tadpoles. Herpetologica 72, 98–106 (2016).
    Article  Google Scholar 

    62.
    Becker, C. G., Rodriguez, D., Lambertini, C., Toledo, L. F. & Haddad, C. F. Historical dynamics of Batrachochytrium dendrobatidis in Amazonia. Ecography 39, 954–960 (2016).
    Article  Google Scholar 

    63.
    Skerratt, L. F. et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth 4, 125–134 (2007).
    Article  Google Scholar 

    64.
    Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    65.
    Bataille, A., Lee-Cruz, L., Tripathi, B. & Waldman, B. Skin bacterial community reorganization following metamorphosis of the fire-bellied toad (Bombina orientalis). Microb. Ecol. 75, 505–514 (2018).
    CAS  PubMed  Article  Google Scholar 

    66.
    Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    67.
    Scheele, B. C. et al. Response to Comment on “Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity”. Science 367, eaay2905 (2020).
    CAS  PubMed  Article  Google Scholar 

    68.
    Woodhams, D. C. et al. Antifungal isolates database of amphibian skin-associated bacteria and function against emerging fungal pathogens: ecological archives E096–059. Ecology 96, 595 (2015).
    Article  Google Scholar 

    69.
    Harris, R. N. et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 3, 818–824 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Muletz-Wolz, C. R. et al. Inhibition of fungal pathogens across genotypes and temperatures by amphibian skin bacteria. Front. Microbiol. 8, 1551 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    71.
    Niederle, M. V. et al. Skin-associated lactic acid bacteria from North American bullfrogs as potential control agents of Batrachochytrium dendrobatidis. PLoS ONE 14, e0223020 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    72.
    Becker, M. H. et al. Composition of symbiotic bacteria predicts survival in Panamanian golden frogs infected with a lethal fungus. Proc. R. Soc. B Biol. Sci. 282, 20142881 (2015).
    Article  CAS  Google Scholar 

    73.
    Rebollar, E. A. et al. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 10, 1682–1695 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Longo, A. V. & Zamudio, K. R. Environmental fluctuations and host skin bacteria shift survival advantage between frogs and their fungal pathogen. ISME 11, 349–361 (2017).
    Article  Google Scholar 

    75.
    Longo, A. V. & Zamudio, K. R. Temperature variation, bacterial diversity and fungal infection dynamics in the amphibian skin. Mol. Ecol. 26, 4787–4797 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    76.
    Lambertini, C. et al. Biotic and abiotic determinants of Batrachochytrium dendrobatidis infections in amphibians of the Brazilian Atlantic Forest. Fung. Ecol. 49, 100995 (2021).
    Article  Google Scholar 

    77.
    Becker, C. G. et al. Variation in phenotype and virulence among enzootic and panzootic amphibian chytrid lineages. Fung. Ecol. 26, 45–50 (2017).
    Article  Google Scholar 

    78.
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103, 626–631 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    79.
    Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. USA 104, 11436–11440 (2007).
    ADS  CAS  PubMed  Article  Google Scholar 

    80.
    Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
    ADS  CAS  PubMed  Article  Google Scholar 

    81.
    Boyle, A. H. D. et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis. Aquat. Organ. 73, 175–192 (2007).
    PubMed  Article  Google Scholar 

    82.
    Boyle, D. G., Boyle, D. B., Olsen, V., Morgan, J. A. T. & Hyatt, A. D. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis. Aquat. Organ. 60, 141–148 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    83.
    Lambertini, C., Rodriguez, D., Brito, F. B., Leite, D. S. & Toledo, L. F. Diagnóstico do fungo Quitrídio: Batrachochytrium dendrobatidis. Herpetol. Bras. 2, 12–17 (2013).
    Google Scholar 

    84.
    Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Envrion. Microbiol. 79, 5112–5120 (2013).
    CAS  Article  Google Scholar 

    85.
    Bletz, M. C. et al. Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat. Commun. 7, 13699 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    86.
    Kriger, K. M. & Hero, J. M. The chytrid fungus Batrachochytrium dendrobatidis is non-randomly distributed across amphibian breeding habitats. Divers. Distrib. 13, 781–788 (2007).
    Article  Google Scholar 

    87.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. J. R. Meteorol. Soc. 25, 1965–78 (2005).
    Article  Google Scholar 

    88.
    Kwon, S., Park, S., Lee, B. & Yoon, S. In-depth analysis of interrelation between quality scores and real errors in illumina reads. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 635–638 (2013).

    89.
    Rideout, J. R. et al. Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ 2, e545 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    90.
    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2, e00191–16 (2017).

    91.
    Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).
    CAS  PubMed  Article  Google Scholar 

    92.
    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
    CAS  PubMed  Article  Google Scholar 

    93.
    Team, R. C. R: A Language and Environment for Statistical Computing. (2013).

    94.
    Wickham, H. Ggplot: Using the Grammar of Graphics with R. (2009)

    95.
    Calcagno, V., Calcagno, M. V., Java, S. & Suggests, M. A. S. S. Package ‘glmulti’ (2020).

    96.
    Bates, K. A. et al. Captivity and infection by the fungal pathogen Batrachochytrium salamandrivorans perturb the amphibian skin microbiome. Front. Microbiol. 10, 1834 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    97.
    Lin, D., Foster, D. P. & Ungar, L. H. VIF regression: a fast regression algorithm for large data. J. Am. Stat. Assoc. 106, 232–247 (2011).
    MathSciNet  CAS  MATH  Article  Google Scholar 

    98.
    Kruskal, J. B. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115–129 (1964).
    MathSciNet  MATH  Article  Google Scholar 

    99.
    Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 326–349 (1957).
    Article  Google Scholar 

    100.
    Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P. & Minchin, P. R. In 2012: Vegan: Community Ecology Package. R Package Version 2.0-5 (eds. Hara, O. et al.) (2014).

    101.
    De Caceres, M., Jansen, F. & De Caceres, M. M. Indicspecies: relationship between species and groups of sites. R package Version 1, (2016).

    102.
    Longo, A. V., Burrowes, P. A. & Zamudio, K. R. Genomic studies of disease-outcome in host–pathogen dynamics. Am. Zool. 54, 427–438 (2014).
    Google Scholar 

    103.
    Assis, A. B. D., Barreto, C. C. & Navas, C. A. Skin microbiota in frogs from the Brazilian Atlantic forest: species, forest type, and potential against pathogens. PLoS ONE 12, e0179628 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    104.
    Estrada, A. et al. Skin bacterial communities of neotropical treefrogs vary with local environmental conditions at the time of sampling. PeerJ 7, e7044 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    105.
    Muletz-Wolz, C. R., Fleischer, R. C. & Lips, K. R. Fungal disease and temperature alter skin microbiome structure in an experimental salamander system. Mol. Ecol. 28, 2917–2931 (2019).
    CAS  PubMed  Google Scholar 

    106.
    Xu, L. L. et al. Changes in the community structure of the symbiotic microbes of wild amphibians from the eastern edge of the Tibetan Plateau. Microbiol. Open 9, e1004 (2020).
    Article  Google Scholar 

    107.
    Puschendorf, R. et al. Environmental refuge from disease-driven amphibian extinction. Conserv. Biol. 25, 956–964 (2011).
    PubMed  Article  Google Scholar 

    108.
    Whitfield, S. M., Kerby, J., Gentry, L. R. & Donnelly, M. A. Temporal variation in infection prevalence by the amphibian chytrid fungus in three species of frogs at La Selva, Costa Rica. Biotropica 44, 779–784 (2012).
    Article  Google Scholar 

    109.
    Ruggeri, J. et al. Seasonal variation in population abundance and chytrid infection in stream-dwelling frogs of the Brazilian Atlantic forest. PLoS ONE 10, e0130554 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    110.
    Longo, A. V., Burrowes, P. A. & Joglar, R. L. Seasonality of Batrachochytrium dendrobatidis infection in direct-developing frogs suggests a mechanism for persistence. Dis. Aquat. Organ. 92, 253–260 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    111.
    Ellison, S., Knapp, R. A., Sparagon, W., Swei, A. & Vredenburg, V. T. Reduced skin bacterial diversity correlates with increased pathogen infection intensity in an endangered amphibian host. Mol. Ecol. 28, 127–140 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    112.
    Piovia-Scott, J. et al. Greater species richness of bacterial skin symbionts better suppresses the amphibian fungal pathogen Batrachochytrium dendrobatidis. Microb. Ecol. 74, 217–226 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    113.
    Flechas, S. V. et al. Surviving chytridiomycosis: differential anti-Batrachochytrium dendrobatidis activity in bacterial isolates from three lowland species of Atelopus. PLoS ONE 7, e44832 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    114.
    Muletz, C. R., Myers, J. M., Domangue, R. J., Herrick, J. B. & Harris, R. N. Soil bioaugmentation with amphibian cutaneous bacteria protects amphibian hosts from infection by Batrachochytrium dendrobatidis. Biol. Conserv. 152, 119–126 (2012).
    Article  Google Scholar 

    115.
    Woodhams, D. C., Ramsey, J. P. & Rollins-Smith, L. A. Effects of cold temperature on antimicrobial peptide synthesis and release in northern leopard frogs, Rana pipiens. Integr. Comp. Biol. 45, 1099–1099 (2005).
    Google Scholar 

    116.
    Bovo, R. P. et al. Physiological responses of Brazilian amphibians to an enzootic infection of the chytrid fungus Batrachochytrium dendrobatidis. Dis. Aquat. Organ. 117, 245–252 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    117.
    Familiar López, M., Rebollar, E. A., Harris, R. N., Vredenburg, V. T. & Hero, J. M. Temporal variation of the skin bacterial community and Batrachochytrium dendrobatidis infection in the terrestrial cryptic frog Philoria loveridgei. Front. Microbiol. 8, 2535 (2017).
    PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Bumble bees in landscapes with abundant floral resources have lower pathogen loads

    1.
    Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 
    2.
    Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).
    PubMed  Article  Google Scholar 

    3.
    Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).
    CAS  PubMed  Article  Google Scholar 

    4.
    Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
    PubMed  Article  CAS  Google Scholar 

    5.
    Steffan-Dewenter, I., Münzenberg, U., Bürger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).
    Article  Google Scholar 

    6.
    Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
    PubMed  Article  Google Scholar 

    7.
    Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226 (2019).
    CAS  PubMed  Article  Google Scholar 

    8.
    Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. R. Soc. B Biol. Sci. 108, 662–667 (2011).
    CAS  Google Scholar 

    9.
    Tokarev, Y. S. et al. A formal redefinition of the genera Nosema and Vairimorpha (Microsporidia: Nosematidae) and reassignment of species based on molecular phylogenetics. J. Invertebr. Pathol. 169, 107279 (2020).
    CAS  PubMed  Article  Google Scholar 

    10.
    Levitt, A. L. et al. Cross-species transmission of honey bee viruses in associated arthropods. Virus Res. 176, 232–240 (2013).
    CAS  PubMed  Article  Google Scholar 

    11.
    Radzevičiūtė, R. et al. Replication of honey bee-associated RNA viruses across multiple bee species in apple orchards of Georgia, Germany and Kyrgyzstan. J. Invertebr. Pathol. 146, 14–23 (2017).
    PubMed  Article  CAS  Google Scholar 

    12.
    Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    13.
    Dolezal, A. G. et al. Honey bee viruses in wild bees: Viral prevalence, loads, and experimental inoculation. PLoS ONE 11, 11 (2016).
    Google Scholar 

    14.
    Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 1–11 (2020).
    Article  CAS  Google Scholar 

    15.
    Blacquiere, T., Smagghe, G., Van Gestel, C. A. & Mommaerts, V. Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Aliouane, Y. et al. Subchronic exposure of honeybees to sublethal doses of pesticides: effects on behavior. Environ. Toxicol. Chem. 28, 113–122 (2009).
    CAS  PubMed  Article  Google Scholar 

    17.
    Whitehorn, P. R., O’connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    18.
    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
    ADS  CAS  PubMed  Article  Google Scholar 

    19.
    Dolezal, A. G. & Toth, A. L. Feedbacks between nutrition and disease in honey bee health. Curr. Opin. Insect Sci. 26, 114–119 (2018).
    PubMed  Article  Google Scholar 

    20.
    DeGrandi-Hoffman, G. & Chen, Y. Nutrition, immunity and viral infections in honey bees. Curr. Opin. Insect Sci. 10, 170–176 (2015).
    PubMed  Article  Google Scholar 

    21.
    DeGrandi-Hoffman, G., Chen, Y., Huang, E. & Huang, M. H. The effect of diet on protein concentrcation, hypopharyngeal gland development and virus load in worker honey bees (Apis mellifera L.). J. Insect Physiol. 56, 1184–1191 (2010).
    CAS  PubMed  Article  Google Scholar 

    22.
    Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, 8 (2013).
    Google Scholar 

    23.
    Manley, R., Boots, M. & Wilfert, L. Condition-dependent virulence of slow bee paralysis virus in Bombus terrestris: Are the impacts of honeybee viruses in wild pollinators underestimated?. Oecologia 184, 305–315 (2017).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Ricigliano, V. A. et al. Honey bee colony performance and health are enhanced by apiary proximity to US Conservation Reserve Program (CRP) lands. Sci. Rep. 9, 1–11 (2019).
    CAS  Article  Google Scholar 

    25.
    O’Neal, S. T., Anderson, T. D. & Wu-Smart, J. Y. Interactions between pesticides and pathogen susceptibility in honey bees. Curr. Opin. Insect Sci. 26, 57–62 (2018).
    PubMed  Article  Google Scholar 

    26.
    Di Prisco, G. V. et al. Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees. Proc. Natl. Acad. Sci. 110, 18466–18471 (2013).
    ADS  PubMed  Article  CAS  Google Scholar 

    27.
    O’Neal, S. T., Swale, D. R. & Anderson, T. D. ATP-sensitive inwardly rectifying potassium channel regulation of viral infections in honey bees. Sci. Rep. 7, 8668 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    28.
    Fine, J. D., Cox-Foster, D. L. & Mullin, C. A. An inert pesticide adjuvant synergizes viral pathogenicity and mortality in honey bee larvae. Sci. Rep. 7, 40499 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Pettis, J. S., Johnson, J. & Dively, G. Pesticide exposure in honey bees results in increased levels of the gut pathogen Nosema. Naturwissenschaften 99, 153–158 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Pettis, J. S., Lichtenberg, E. M., Andree, M., Stitzinger, J. & Rose, R. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    McArt, S. H., Fersch, A. A., Milano, N. J., Truitt, L. L. & Böröczky, K. High pesticide risk to honey bees despite low focal crop pollen collection during pollination of a mass blooming crop. Sci. Rep. 7, 46554 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).
    PubMed  Article  Google Scholar 

    33.
    Piot, N. et al. Establishment of wildflower fields in poor quality landscapes enhances micro-parasite prevalence in wild bumble bees. Oecologia 189, 149–158 (2019).
    ADS  PubMed  Article  Google Scholar 

    34.
    Bailes, E. J. et al. Host density drives viral, but not trypanosome, transmission in a key pollinator. Proc. R. Soc. B Biol. Sci. 287, 20191969 (2020).
    Article  Google Scholar 

    35.
    Singh, R. et al. RNA viruses in hymenopteran pollinators: Evidence of inter-taxa virus transmission via pollen and potential impact on non-Apis hymenopteran species. PLoS ONE 5, e14357 (2010).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Manley, R., Boots, M. & Wilfert, L. Emerging viral disease risk to pollinating insects: Ecological, evolutionary and anthropogenic factors. J. Appl. Ecol. 52, 331–340 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Meeus, I., Pisman, M., Smagghe, G. & Piot, N. Interaction effects of different drivers of wild bee decline and their influence on host–pathogen dynamics. Curr. Opin. Insect Sci. 26, 136–141 (2018).
    PubMed  Article  Google Scholar 

    38.
    Huang, Z. Pollen nutrition affects honey bee stress resistance. Terr. Arthropod. Rev. 5, 175–189 (2012).
    Article  Google Scholar 

    39.
    Smart, M., Pettis, J., Rice, N., Browning, Z. & Spivak, M. Linking measures of colony and individual honey bee health to survival among apiaries exposed to varying agricultural land use. PLoS ONE 11, 3 (2016).
    Google Scholar 

    40.
    Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: a key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
    Article  Google Scholar 

    41.
    Meeus, I., Brown, M. J., De Graaf, D. C. & Smagghe, G. U. Y. Effects of invasive parasites on bumble bee declines. Conserv. Biol. 25, 662–671 (2011).
    PubMed  Article  Google Scholar 

    42.
    Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).
    PubMed  Article  Google Scholar 

    43.
    Sánchez-Bayo, F. et al. Are bee diseases linked to pesticides?—A brief review. Environ. Int. 89, 7–11 (2016).
    PubMed  Article  CAS  Google Scholar 

    44.
    Beck, M. A. & Levander, O. A. Host nutritional status and its effect on a viral pathogen. J. Infect. Dis. 182, 93–96 (2000).
    Article  Google Scholar 

    45.
    Hing, S., Narayan, E. J., Thompson, R. A. & Godfrey, S. S. The relationship between physiological stress and wildlife disease: Consequences for health and conservation. Wildl. Res. 43, 51–60 (2016).
    Article  Google Scholar 

    46.
    Graystock, P., Goulson, D. & Hughes, W. O. Parasites in bloom: Flowers aid dispersal and transmission of pollinator parasites within and between bee species. Proc. R. Soc. B Biol. Sci. 282, 20151371 (2015).
    Article  Google Scholar 

    47.
    Sponsler, D. B., Shump, D., Richardson, R. T. & Grozinger, C. M. Characterizing the floral resources of a North American metropolis using a honey bee foraging assay. Ecosphere 11, e03102 (2020).
    Article  Google Scholar 

    48.
    Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058 (2012).
    PubMed  Article  Google Scholar 

    49.
    Steffan-Dewenter, I. & Tscharntke, T. Resource overlap and possible competition between honey bees and wild bees in central Europe. Oecologia 122, 288–296 (2000).
    ADS  CAS  PubMed  Article  Google Scholar 

    50.
    Tehel, A., Brown, M. J. & Paxton, R. J. Impact of managed honey bee viruses on wild bees. Curr. Opin. Virol. 19, 16–22 (2016).
    PubMed  Article  Google Scholar 

    51.
    Sponsler, D. B. et al. Pesticides and pollinators: A socioecological synthesis. Sci. Total Environ. 662, 1012–1027 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    52.
    McCaskill, G. L. et al. Pennsylvania’s Forests 2009 (U.S Forest Service, Washington, DC, 2009).
    Google Scholar 

    53.
    Park, M. G., Blitzer, E. J., Gibbs, J., Losey, J. E. & Danforth, B. N. Negative effects of pesticides on wild bee communities can be buffered by landscape context. Proc. R. Soc. B Biol. Sci. 282, 20150299 (2015).
    Article  CAS  Google Scholar 

    54.
    Koh, I. et al. Modeling the status, trends, and impacts of wild bee abundance in the United States. Proc. R. Soc. B Biol. Sci. 113, 140–145 (2016).
    CAS  Google Scholar 

    55.
    Williams, P. H., Thorp, R. W., Richardson, L. L. & Colla, S. R. Bumble Bees of North America: An Identification Guide (Princeton University Press, Princeton, 2014).
    Google Scholar 

    56.
    National Research Council. Under the Weather: Climate, Ecosystems, and Infectious Disease (National Academy Press, Washington, DC, 2001).
    Google Scholar 

    57.
    Polgreen, P. M. & Polgreen, E. L. Infectious diseases, weather, and climate. Clin. Infect. Dis. 66, 815–817 (2018).
    PubMed  Article  Google Scholar 

    58.
    Retschnig, G., Williams, G. R., Schneeberger, A. & Neumann, P. Cold ambient temperature promotes Nosema spp. intensity in honey bees (Apis mellifera). Insects 8, 20 (2017).
    PubMed Central  Article  PubMed  Google Scholar 

    59.
    Dalmon, A., Peruzzi, M. L., Conte, Y., Alaux, C. & Pioz, M. Temperature-driven changes in viral loads in the honey bee Apis mellifera. J. Invertebr. Pathol. 160, 87–94 (2019).
    PubMed  Article  Google Scholar 

    60.
    Gardner, W. A., Sutton, R. M. & Noblet, R. Persistence of Beauveria bassiana, Nomuraea rileyi, and Nosema necatrix on Soyhean Foliage. Environ. Entomol. 6, 616–618 (1977).
    Article  Google Scholar 

    61.
    Neidel, V., Steyer, C. S. & C., & Hoch, G. ,. Simulation of rain enhances horizontal transmission of the microsporidium Nosema lymantriae via infective feces. J. Invertebr. Pathol. 149, 56–58 (2017).
    PubMed  Article  Google Scholar 

    62.
    Rangel, J. et al. Prevalence of Nosema species in a feral honey bee population: A 20-year survey. Apidologie 47, 561–571 (2017).
    Article  Google Scholar 

    63.
    Leather, S. R. “Ecological Armageddon”-more evidence for the drastic decline in insect numbers. Ann. Appl. Biol. 172, 1–3 (2017).
    Article  Google Scholar 

    64.
    Scheper, J. et al. Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries. J. Appl. Ecol. 52, 1165–1175 (2015).
    Article  Google Scholar 

    65.
    Rodríguez, J. P., Brotons, L., Bustamante, J. & Seoane, J. The application of predictive modelling of species distribution to biodiversity conservation. Divers. Distrib. 13, 243–251 (2017).
    Article  Google Scholar 

    66.
    Young, B. E. et al. Using citizen science data to support conservation in environmental regulatory contexts. Biol. Conserv. 237, 57–62 (2019).
    Article  Google Scholar 

    67.
    Lesley, J. P. A Summary Description of the Geology of Pennsylvania (Board of Commissioners for the Geological Survey, Pennsylvania, 1892).
    Google Scholar 

    68.
    Dyer, J. Revisiting the Deciduous Forests of Eastern North America. Bioscience 56, 341–352 (2006).
    Article  Google Scholar 

    69.
    Wherry, E. T., Fogg, Jr., J. M., & Wahl. H. A. Atlas of the Flora of Pennsylvania. (University of Pennsylvania, Pennsylvania, 1979).

    70.
    Albright, T. A. Forests of Pennsylvania, 2017. Resource Update FS-175. (U.S. Department of Agriculture, Forest Service, 2017).

    71.
    Wickham, J. et al. The multi-resolution land characteristics (MRLC) consortium—20 years of development and integration. Remote Sens. 6, 7424–7441 (2014).
    ADS  Article  Google Scholar 

    72.
    Shannon, C. E. A mathematical theory of communication. Bell Labs Tech. J. 27, 379–423 (1948).
    MathSciNet  MATH  Article  Google Scholar 

    73.
    Plischuk, S. et al. South American native bumblebees (Hymenoptera: Apidae) infected by Nosema ceranae (Microsporidia), an emerging pathogen of honeybees (Apis mellifera). Environ. Microbiol. Rep. 1, 131–135 (2009).
    PubMed  Article  Google Scholar 

    74.
    Chu, C. C. & Cameron, S. A. A scientific note on Nosema bombi infection intensity among different castes within a Bombus auricomus nest. Apidologie 48, 141–143 (2017).
    Article  Google Scholar 

    75.
    vanEngelsdorp, D. et al. Colony collapse disorder: A descriptive study. PLoS ONE 4, e6481–e6481 (2009).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    76.
    Simmons, W. R. & Angelini, D. R. Chronic exposure to a neonicotinoid increases expression of antimicrobial peptide genes in the bumblebee Bombus impatiens. Sci. Rep. 7, 44773 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    Muller, C. B. & Schmid-Hempel, P. Variation in life-history pattern in relation to worker mortality in the bumble-bee, Bombus lucorum. Funct. Ecol. 6, 48–56 (1992).
    Article  Google Scholar 

    78.
    Hijmans, R. J. & van Etten, J. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12. http://CRAN.R-project.org/package=raster (2012).

    79.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.html (2019).

    80.
    Knight, M. E. et al. Bumblebee nest density and the scale of available forage in arable landscapes. Insect Conserv. Diver. 2, 116–124 (2009).
    Article  Google Scholar 

    81.
    Darvill, B., Knight, M. E. & Goulson, D. Use of genetic markers to quantify bumblebee foraging range and nest density. Oikos 107, 471–478 (2004).
    Article  Google Scholar 

    82.
    Desjardins, È. C. & De Oliveira, D. Commercial bumble bee Bombus impatiens (Hymenoptera: Apidae) as a pollinator in lowbush blueberry (Ericale: Ericaceae) fields. J. Econ. Entomol. 99, 443–449 (2006).
    PubMed  Article  Google Scholar 

    83.
    Natural Capital Project. InVEST: Crop Pollination Model. Version 3.1.0. http://naturalcapitalproject.org/models/crop_pollination.html (2014).

    84.
    Kammerer, M. A., Biddinger, D. J., Joshi, N. K., Rajotte, E. G. & Mortensen, D. A. Modeling local spatial patterns of wild bee diversity in Pennsylvania apple orchards. Landsc. Ecol. 31, 2459–2469 (2016).
    Article  Google Scholar 

    85.
    Johnson, D. M. & Mueller, R. The 2009 cropland data layer. Photogramm. Eng. Remote. Sens. 76, 1201–1205 (2010).
    Google Scholar 

    86.
    PRISM Climate Group. PRISM Gridded Climate Data. Oregon State University, Corvallis Oregon, USA. http://prism.oregonstate.edu (2019).

    87.
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference—A Practical Information-Theoretic Approach (Springer, New York, 2002).
    Google Scholar 

    88.
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    89.
    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009).
    Google Scholar 

    90.
    Sokal, R. R. & Rohlf, F. J. The Principles and Practice of Statistics in Biological Research (W.H Freeman and Company, New York, 1969).
    Google Scholar  More

  • in

    Interacting effects of insect and ungulate herbivory on Scots pine growth

    1.
    Moreira, X. et al. Specificity of induced defenses, growth, and reproduction in lima bean (Phaseolus lunatus) in response to multispecies herbivory. Am. J. Bot. 102, 1300–1308 (2015).
    CAS  PubMed  Article  Google Scholar 
    2.
    Danell, K., Bergström, R. & Edenius, L. Effects of large mammalian browsers on architecture, biomass, and nutrients of woody plants. J. Mammal. 75, 833–844 (1994).
    Article  Google Scholar 

    3.
    Kaitaniemi, P., Neuvonen, S. & Nyyssönen, T. Effects of cumulative defoliations on growth, reproduction, and insect resistance in mountain birch. Ecology 80, 524–532 (1999).
    Article  Google Scholar 

    4.
    den Herder, M., Bergström, R., Niemelä, P., Danell, K. & Lindgren, M. Effects of natural winter browsing and simulated summer browsing by moose on growth and shoot biomass of birch and its associated invertebrate fauna. Ann. Zool. Fennici 46, 63–74 (2009).
    Article  Google Scholar 

    5.
    Wallgren, M., Bergquist, J., Bergström, R. & Eriksson, S. Effects of timing, duration, and intensity of simulated browsing on Scots pine growth and stem quality. Scand. J. For. Res. 29, 734–746 (2014).
    Article  Google Scholar 

    6.
    Schwenk, W. S. & Strong, A. M. Contrasting patterns and combined effects of moose and insect herbivory on striped maple (Acer pensylvanicum). Basic Appl. Ecol. 12, 64–71 (2011).
    Article  Google Scholar 

    7.
    Muiruri, E. W., Milligan, H. T., Morath, S. & Koricheva, J. Moose browsing alters tree diversity effects on birch growth and insect herbivory. Funct. Ecol. 29, 724–735 (2015).
    Article  Google Scholar 

    8.
    van Zandt, P. A. & Agrawal, A. A. Community-Wide impacts of herbivore-induced plant responses in milkweed (Asclepias syriaca). Ecology 85, 2616–2629 (2004).
    Article  Google Scholar 

    9.
    Erb, M., Robert, C. A. M., Hibbard, B. E. & Turlings, T. C. J. Sequence of arrival determines plant-mediated interactions between herbivores. J. Ecol. 99, 7–15 (2011).
    Article  Google Scholar 

    10.
    Kafle, D., Hänel, A., Lortzing, T., Steppuhn, A. & Wurst, S. Sequential above- and belowground herbivory modifies plant responses depending on herbivore identity. BMC Ecol. 17, 1–10 (2017).
    Article  Google Scholar 

    11.
    Stephens, A. E. A., Srivastava, D. S. & Myers, J. H. Strength in numbers? Effects of multiple natural enemy species on plant performance. Proc. R. Soc. B Biol. Sci. 280, 20122756 (2013).
    Article  Google Scholar 

    12.
    Gagic, V. et al. Interactive effects of pests increase seed yield. Ecol. Evol. 6, 2149–2157 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Strauss, S. Y. Direct, indirect, and cumulative effects of three native herbivores on a shared host plant. Ecology 72, 543–558 (1991).
    Article  Google Scholar 

    14.
    Gómez, J. M. & González-Megías, A. Asymmetrical interactions between ungulates and phytophagous insects: being different matters. Ecology 83, 203–211 (2002).
    Article  Google Scholar 

    15.
    Ohgushi, T. Indirect interaction webs: herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).
    Article  Google Scholar 

    16.
    Mauch-Mani, B., Baccelli, I., Luna, E. & Flors, V. Defense priming: an adaptive part of induced resistance. Annu. Rev. Plant Biol. 68, 485–512 (2017).
    CAS  PubMed  Article  Google Scholar 

    17.
    Hilker, M. et al. Priming and memory of stress responses in organisms lacking a nervous system. Biol. Rev. 91, 1118–1133 (2016).
    PubMed  Article  Google Scholar 

    18.
    Lyytikäinen-Saarenmaa, P. The responses of scots pine, Pinus silvestris, to natural and artificial defoliation stress. Ecol. Appl. 9, 469–474 (1999).
    Article  Google Scholar 

    19.
    Ericsson, A., Larsson, S. & Tenow, O. Effects of early and late season defoliation on growth and carbohydrate dynamics in scots pine. J. Appl. Ecol. 17, 747–769 (1980).
    Article  Google Scholar 

    20.
    Edenius, L. Browsing by moose on Scots pine in relation to plant resource availability. Ecology 74, 2261–2269 (1993).
    Article  Google Scholar 

    21.
    Nordkvist, M. et al. Trait-mediated indirect interactions: Moose browsing increases sawfly fecundity through plant-induced responses. Ecol. Evol. 9, 10615–10629 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    22.
    Edenius, L., Danell, K. & Nyquist, H. Effects of simulated moose browsing on growth, mortality, and fecundity in Scots pine: relations to plant productivity. Can. J. For. Res. 25, 529–535 (1995).
    Article  Google Scholar 

    23.
    Honkanen, T., Haukioja, E. & Kitunen, V. Responses of Pinus sylvestris branches to simulated herbivory are modified by tree sink/source dynamics and by external resources. Funct. Ecol. 13, 126–140 (1999).
    Article  Google Scholar 

    24.
    Persson, I. L., Bergström, R. & Danell, K. Browse biomass production and regrowth capacity after biomass loss in deciduous and coniferous trees: Responses to moose browsing along a productivity gradient. Oikos 116, 1639–1650 (2007).
    Article  Google Scholar 

    25.
    Belsky, A. J. Does herbivory benefit plants? A review of the evidence. Am. Nat. 127, 870–892 (1986).
    Article  Google Scholar 

    26.
    Bergman, M. Can saliva from moose, Alces alces, affect growth responses in the salow, Salix caprea?. Oikos 96, 164–168 (2002).
    Article  Google Scholar 

    27.
    Ohse, B. et al. Salivary cues: simulated roe deer browsing induces systemic changes in phytohormones and defence chemistry in wild-grown maple and beech saplings. Funct. Ecol. 31, 340–349 (2017).
    Article  Google Scholar 

    28.
    Kollberg, I. et al. Temperature affects insect outbreak risk through tritrophic interactions mediated by plant secondary compounds. Ecosphere 6, 1–17 (2015).
    Article  Google Scholar 

    29.
    Lyytikäinen-Saarenmaa, P. & Tomppo, E. Impact of sawfly defoliation on growth of Scots pine Pinus sylvestris (Pinaceae) and associated economic losses. Bull. Entomol. Res. 92, 137–140 (2002).
    PubMed  Article  Google Scholar 

    30.
    Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165–1183 (1998).
    Article  Google Scholar 

    31.
    Edenius, L., Bergman, M., Ericsson, G. & Danell, K. The role of moose as a disturbance factor in managed boreal forests. Silva Fennica 36, 57–67 (2002).
    Article  Google Scholar 

    32.
    Hódar, J. A., Zamora, R., Castro, J., Gómez, J. M. & García, D. Biomass allocation and growth responses of Scots pine saplings to simulated herbivory depend on plant age and light availability. Plant Ecol. 197, 229–238 (2008).
    Article  Google Scholar 

    33.
    Bergström, R. & Hjeljord, O. Moose and vegetation interactions in northwestern Europe and Poland. Swedish Wildl. Res. Suppl. 1, 213–228 (1987).
    Google Scholar 

    34.
    Nilsson, U., Berglund, M., Bergquist, J., Holmström, H. & Wallgren, M. Simulated effects of browsing on the production and economic values of Scots pine (Pinus sylvestris) stands. Scand. J. For. Res. 31, 279–285 (2016).
    Article  Google Scholar 

    35.
    Långsström, B. & Hellqvist, C. Effects of different pruning regimes on growth and sapwood area of Scots pine. For. Ecol. Manag. 44, 239–254 (1991).
    Article  Google Scholar 

    36.
    Mathisen, K. M., Milner, J. M. & Skarpe, C. Moose-tree interactions: rebrowsing is common across tree species. BMC Ecol. 17, 1–15 (2017).
    Article  Google Scholar 

    37.
    Bergqvist, G., Bergström, R. & Edenius, L. Effects of moose (Alces alces) rebrowsing on damage development in young stands of Scots pine (Pinus sylvestris). For. Ecol. Manag. 176, 397–403 (2003).
    Article  Google Scholar 

    38.
    Bergqvist, G., Bergström, R. & Edenius, L. Patterns of stem damage by moose (Alces alces) in young Pinus sylvestris stands in Sweden. Scand. J. For. Res. 16, 363–370 (2001).
    Article  Google Scholar 

    39.
    Riipi, M., Lempa, K., Haukioja, E., Ossipov, V. & Pihlaja, K. Effects of simulated winter browsing on mountain birch foliar chemistry and on the performance of insect herbivores. Oikos 111, 221–234 (2005).
    Article  Google Scholar 

    40.
    Kupferschmid, A. D. & Bugmann, H. Timing, light availability and vigour determine the response of Abies alba saplings to leader shoot browsing. Eur. J. For. Res. 132, 47–60 (2013).
    Article  Google Scholar 

    41.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

    42.
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. _nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1–142. https://CRAN.R-project.org/package=nlme (2019).

    43.
    Fox, J. & Weisberg, F. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019).

    44.
    Darling, E. S., Mcclanahan, T. R. & Côté, I. M. Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conserv. Lett. 3, 122–130 (2010).
    Article  Google Scholar 

    45.
    Bansal, S., Hallsby, G., Löfvenius, M. O. & Nilsson, M. C. Synergistic, additive and antagonistic impacts of drought and herbivory on Pinus sylvestris: leaf, tissue and whole-plant responses and recovery. Tree Physiol. 33, 451–463 (2013).
    CAS  PubMed  Article  Google Scholar  More

  • in

    Comparative analysis of bacterioplankton assemblages from two subtropical karst reservoirs of southwestern China with contrasting trophic status

    1.
    Neuenschwander, S. M., Pernthaler, J., Posch, T. & Salcher, M. M. Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17(3), 781–795 (2015).
    CAS  PubMed  Article  Google Scholar 
    2.
    Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313(5790), 1068–1072 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    3.
    United Nations Environment Programme. GEO Year Book 2004/5: An Overview of Our Changing Environment (2004). https://www.unep.org/resources/report/geo-year-book-20045-overview-our-changing-environment.

    4.
    Lindström, E. S. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb. Ecol. 40(2), 104–113 (2000).
    PubMed  Article  Google Scholar 

    5.
    Ávila, M. P., Staehr, P. A., Barbosa, F. A., Chartone-Souza, E. & Nascimento, A. Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93, fw218 (2017).
    Article  CAS  Google Scholar 

    6.
    Zhang, H. et al. Biogeographic distribution patterns of algal community in different urban lakes in China: insights into the dynamics and co-existence. J. Environ. Sci. 100, 216–227 (2021).
    Article  Google Scholar 

    7.
    Ji, B. et al. Bacterial communities of four adjacent fresh lakes at different trophic status. Ecotoxicol. Environ. Safe 157, 388–394 (2018).
    CAS  Article  Google Scholar 

    8.
    Iliev, I. et al. Metagenomic profiling of the microbial freshwater communities in two Bulgarian reservoirs. J. Basic Microb. 57(8), 669–679 (2017).
    CAS  Article  Google Scholar 

    9.
    Linz, A. M. et al. Bacterial community composition and dynamics spanning five years in freshwater bog lakes. mSphere 2(3), e00169 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Hartmann, A., Goldscheider, N., Wagener, T., Lange, J. & Weiler, M. Karst water resources in a changing world: review of hydrological modeling approaches. Rev. Geophys. 52(3), 218–242 (2014).
    ADS  Article  Google Scholar 

    11.
    Yu, S. et al. Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiol. Open 8(9), e00849 (2019).
    Article  CAS  Google Scholar 

    12.
    Li, Q., Sun, H., Han, J., Liu, Z. & Yu, L. High-resolution study on the hydrochemical variations caused by the dilution of precipitation in the epikarst spring: an example spring of Landiantang at Nongla, Mashan, China. Environ. Geol. 54(2), 347–354 (2008).
    ADS  CAS  Article  Google Scholar 

    13.
    Song, A., Yue, M. L. & Li, Q. Influence of precipitation on bacterial structure in a typical karst spring, SW China. J. Groundw. Sci. Eng. 6(3), 193–204 (2018).
    Google Scholar 

    14.
    Gray, C. J. & Engel, A. S. Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer. ISME J. 7(2), 325–337 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89(1), 111–126 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Li, Q. et al. Contribution of aerobic anoxygenic phototrophic bacteria to total organic carbon pool in aquatic system of subtropical karst catchments, Southwest China: evidence from hydrochemical and microbiological study. FEMS Microbiol. Ecol. 93, fix065 (2017).
    Google Scholar 

    17.
    Stevanović, Z. & Milanović, P. Engineering challenges in karst. Acta Carsol. 44(3), 381–399 (2015).
    Article  Google Scholar 

    18.
    Lu, X. X. et al. Water chemistry and characteristics of dissolved organic carbon during the wet season in Wulixia Reservoir, SW China. Huanjing Kexue 39(5), 2075–2085 (2018) (in Chinese with English abstract).
    PubMed  PubMed Central  Google Scholar 

    19.
    Xin, S. L. et al. Relationship between the bacterial abundance and production with environmental factors in a subtropical karst reservoir. Huanjing Kexue 39(12), 5647–5656 (2018) (in Chinese with English abstract).
    PubMed  PubMed Central  Google Scholar 

    20.
    National Research Council. Assessing the TMDL Approach to Water Quality Management (National Academy Press, Washington, DC, 2001).
    Google Scholar 

    21.
    Cunha, D. G. F., do Carmo Calijuri, M. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).
    Article  Google Scholar 

    22.
    Lorenzen, C. J. Determination of chlirophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12(2), 343–346 (1967).
    ADS  CAS  Article  Google Scholar 

    23.
    Tamaki, H. et al. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS ONE 6(9), e25263 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr. Protoc. Microbiol. 27(1), 1–20 (2012).
    Google Scholar 

    25.
    Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. J. Ecol. 18(1), 117–143 (1993).
    Article  Google Scholar 

    27.
    Palmer, M. W., McGlinn, D. J., Westerberg, L. & Milberg, P. Indices for detecting differences in species composition: some simplifications of RDA and CCA. Ecology 89(6), 1769–1771 (2008).
    PubMed  Article  Google Scholar 

    28.
    Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).
    PubMed  Article  CAS  Google Scholar 

    29.
    Sanchez, G. PLS Path Modeling with R (Trowchez Editions, Berkeley, 2013).
    Google Scholar 

    30.
    Lopez-Chicano, M., Bouamama, M., Vallejos, A. & Pulido-Bosch, A. Factors which determine the hydrogeochemical behaviour of karstic springs. A case study from the Betic Cordilleras, Spain. Appl. Geochem. 16(9–10), 1179–1192 (2001).
    CAS  Article  Google Scholar 

    31.
    Stumm, W. & Morgan, J. J. Aquatic chemistry: chemical equilibria and rates in natural waters. In Environmental Science and Technology (eds Stumm, W. & Morgan, J. J.) (Wiley, New York, 2012).
    Google Scholar 

    32.
    Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. R. 75, 14–49 (2011).
    CAS  Article  Google Scholar 

    33.
    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2(1), 589 (2011).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    34.
    Li, D. et al. Microbial community evolution during simulated managed aquifer recharge in response to different biodegradable dissolved organic carbon (BDOC) concentrations. Water Res. 47(7), 2421–2430 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Miranda, C. D. & Zemelman, R. Bacterial resistance to oxytetracycline in Chilean salmon farming. Aquaculture 212(1–4), 31–47 (2002).
    CAS  Article  Google Scholar 

    36.
    Dul’tseva, N. M., Chernitsina, S. M. & Zemskaya, T. I. Isolation of bacteria of the genus Variovorax from the Thioploca mats of Lake Baikal. Microbiology 81(1), 67–78 (2012).
    Article  CAS  Google Scholar 

    37.
    Mohiuddin, M. M., Salama, Y., Schellhorn, H. E. & Golding, G. B. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115, 360–369 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Fuentes, S., Méndez, V., Aguila, P. & Seeger, M. Bioremediation of petroleum hydrocarbons: catabolic genes, microbial communities, and applications. Appl. Microbiol. Biotechnol. 98(11), 4781–4794 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Gomes, B. C. et al. Analysis of a microbial community associated with polychlorinated biphenyl degradation in anaerobic batch reactors. Biodegradation 25(6), 797–810 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Cai, J. et al. Characterization of bacterial and microbial eukaryotic communities associated with an ephemeral hypoxia event in Taihu Lake, a shallow eutrophic Chinese lake. Environ. Sci. Pollut. R. 25(31), 31543–31557 (2018).
    CAS  Article  Google Scholar 

    41.
    Zhang, S. et al. Characterization of a novel bacteriophage specific to Exiguobacterium indicum isolated from a plateau eutrophic lake. J. Basic Microb. 59(2), 206–214 (2019).
    CAS  Article  Google Scholar 

    42.
    Li, S., Luo, Z. & Ji, G. Seasonal function succession and biogeographic zonation of assimilatory and dissimilatory nitrate-reducing bacterioplankton. Sci. Total Environ. 637, 1518–1525 (2018).
    ADS  PubMed  Article  CAS  Google Scholar 

    43.
    Savio, D. et al. Spring water of an alpine karst aquifer is dominated by a taxonomically stable but discharge-responsive bacterial community. Front. Microbiol. 10, 28 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Freedman, Z. & Zak, D. R. Soil bacterial communities are shaped by temporal and environmental filtering: evidence from a long-term chronosequence. Environ. Microbiol. 17(9), 3208–3218 (2015).
    PubMed  Article  Google Scholar 

    45.
    Subramani, T., Elango, L. & Damodarasamy, S. R. Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ. Geol. 47(8), 1099–1110 (2005).
    CAS  Article  Google Scholar 

    46.
    Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439 (2007).
    PubMed  Article  Google Scholar 

    47.
    Niño-García, J. P., Ruiz-González, C. & del Giorgio, P. A. Interactions between hydrology and water chemistry shape bacterioplankton biogeography across borssseal freshwater networks. ISME J. 10(7), 1755 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar  More

  • in

    Microbial community composition in the rhizosphere of Larix decidua under different light regimes with additional focus on methane cycling microorganisms

    1.
    Paul, E. A. (ed.) Soil Microbiology, Ecology and Biochemistry (Academic Press, Amsterdam, 2015).
    Google Scholar 
    2.
    Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil. Sci. 54, 655–670 (2003).
    Article  Google Scholar 

    3.
    Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil. Biol. Biochem. 83, 184–199 (2015).
    CAS  Article  Google Scholar 

    4.
    Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13 (2009).
    CAS  Article  Google Scholar 

    5.
    Bais, H. P., Park, S.-W., Weir, T. L., Callaway, R. M. & Vivanco, J. M. How plants communicate using the underground information superhighway. Trends Plant. Sci. 9, 26–32 (2004).
    CAS  Article  Google Scholar 

    6.
    Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
    CAS  Article  PubMed  Google Scholar 

    7.
    Praeg, N., Pauli, H. & Illmer, P. Microbial diversity in bulk and rhizosphere soil of Ranunculus glacialis along a high-alpine altitudinal gradient. Front. Microbiol. 10, 1429. https://doi.org/10.3389/fmicb.2019.01429 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    8.
    Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, e17000. https://doi.org/10.1371/journal.pone.0017000 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    9.
    Jackson, R. B., Solomon, E. I., Canadell, J. G., Cargnello, M. & Field, C. B. Methane removal and atmospheric restoration. Nat. Sustain. 2, 436–438. https://doi.org/10.1038/s41893-019-0299-x (2019).
    Article  Google Scholar 

    10.
    Ciais, P. et al. Climate Change 2013: The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013).
    Google Scholar 

    11.
    Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407. https://doi.org/10.1038/ismej.2017.122 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    12.
    Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).
    Article  Google Scholar 

    13.
    Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
    CAS  Article  Google Scholar 

    14.
    Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. https://doi.org/10.1111/j.1758-2229.2009.00022.x (2009).
    CAS  Article  PubMed  Google Scholar 

    15.
    Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714. https://doi.org/10.1128/AEM.69.11.6703-6714.2003 (2003).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    16.
    Kolb, S. The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346 (2009).
    CAS  Article  Google Scholar 

    17.
    Plesa, I. et al. Effects of drought and salinity on European Larch (Larix decidua Mill.) seedlings. Forests 9, 320. https://doi.org/10.3390/f9060320 (2018).
    Article  Google Scholar 

    18.
    Falk, W., Bachmann-Gigl, U. & Kölling, C. Die Europäische Lärche im Klimawandel. In Beiträge zur Europäischen Lärche (ed. Schmidt, O.) 19–27 (Bayrische Landesanstalt für Wald und Forstwirtschaft, Freising, 2012).
    Google Scholar 

    19.
    Obojes, N. et al. Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley. New Phytol. 220, 460–475 (2018).
    Article  Google Scholar 

    20.
    Wieser, G. (ed.) Trees at Their Upper Limit. Treelife Limitation at the Alpine Timberline (Springer, Dordrecht, 2007).
    Google Scholar 

    21.
    Dedysh, S. N. et al. Methylocapsa palsarum sp. nov., a methanotroph isolated from a subArctic discontinuous permafrost ecosystem. Int. J. Syst. Evol. Microbiol. 65, 3618–3624. https://doi.org/10.1099/ijsem.0.000465 (2015).
    CAS  Article  PubMed  Google Scholar 

    22.
    Praeg, N., Wagner, A. O. & Illmer, P. Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils. Plant Soil 410, 193–206 (2017).
    CAS  Article  Google Scholar 

    23.
    Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00063-16 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    24.
    Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil. Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).
    CAS  Article  Google Scholar 

    25.
    Liu, J. et al. Characteristics of bulk and rhizosphere soil microbial community in an ancient Platycladus orientalis forest. Appl. Soil Ecol. 132, 91–98. https://doi.org/10.1016/j.apsoil.2018.08.014 (2018).
    ADS  Article  Google Scholar 

    26.
    Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Štursová, M., Bárta, J., Šantrůčková, H. & Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw185 (2016).
    Article  PubMed  Google Scholar 

    28.
    Ferrari, B., Winsley, T., Ji, M. & Neilan, B. Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci. Rep. 4, 3957. https://doi.org/10.1038/srep03957 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    29.
    Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122. https://doi.org/10.1186/s40168-018-0499-z (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    30.
    Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. https://doi.org/10.1038/nmicrobiol.2016.198 (2016).
    CAS  Article  PubMed  Google Scholar 

    31.
    Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    32.
    Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
    Article  Google Scholar 

    33.
    Johnston-Monje, D., Lundberg, D. S., Lazarovits, G., Reis, V. M. & Raizada, M. N. Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405, 337–355. https://doi.org/10.1007/s11104-016-2826-0 (2016).
    CAS  Article  Google Scholar 

    34.
    Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Nat. Acad. Sci. USA 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
    ADS  Article  PubMed  Google Scholar 

    35.
    Kottke, I. & Oberwinkler, F. Comparative studies on the mycorrhization of Larix decidua and Picea abies by Suillus grevillei. Trees https://doi.org/10.1007/BF00196758 (1988).
    Article  Google Scholar 

    36.
    Uroz, S., Buée, M., Murat, C., Frey-Klett, P. & Martin, F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. https://doi.org/10.1111/j.1758-2229.2009.00117.x (2010).
    CAS  Article  PubMed  Google Scholar 

    37.
    Mapelli, F. et al. The stage of soil development modulates rhizosphere effect along a High Arctic desert chronosequence. ISME J. 12, 1188. https://doi.org/10.1038/s41396-017-0026-4 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    38.
    Mello, B. L., Alessi, A. M., McQueen-Mason, S., Bruce, N. C. & Polikarpov, I. Nutrient availability shapes the microbial community structure in sugarcane bagasse compost-derived consortia. Sci. Rep. 6, 38781. https://doi.org/10.1038/srep38781 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    39.
    Turnbull, G. A., Morgan, J. A. W., Whipps, J. M. & Saunders, J. R. The role of bacterial motility in the survival and spread of Pseudomonas fluorescens in soil and in the attachment and colonisation of wheat roots. FEMS Microbiol. Ecol. 36, 21–31. https://doi.org/10.1111/j.1574-6941.2001.tb00822.x (2001).
    CAS  Article  PubMed  Google Scholar 

    40.
    Rees, D. C., Johnson, E. & Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell. Biol. 10, 218–227. https://doi.org/10.1038/nrm2646 (2009).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    41.
    Aronson, E. L., Allison, S. D. & Helliker, B. R. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4, 225. https://doi.org/10.3389/fmicb.2013.00225 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    42.
    Dalal, R. C., Allen, D. E., Livesley, S. J. & Richards, G. Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes. A review. Plant Soil 309, 43–76 (2008).
    CAS  Article  Google Scholar 

    43.
    Martins, C. S. C., Nazaries, L., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Water availability and abundance of microbial groups are key determinants of greenhouse gas fluxes in a dryland forest ecosystem. Soil Biol. Biochem. 86, 5–16. https://doi.org/10.1016/j.soilbio.2015.03.012 (2015).
    CAS  Article  Google Scholar 

    44.
    Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnz259 (2020).
    Article  Google Scholar 

    45.
    Ström, L., Mastepanov, M. & Christensen, T. R. Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
    Article  Google Scholar 

    46.
    Borrel, G. et al. Genome sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a third thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e004523. https://doi.org/10.1128/genomeA.00453-13 (2013).
    Article  Google Scholar 

    47.
    Deng, Y., Liu, P. & Conrad, R. Effect of temperature on the microbial community responsible for methane production in alkaline NamCo wetland soil. Soil Biol. Biochem. 132, 69–79. https://doi.org/10.1016/j.soilbio.2019.01.024 (2019).
    CAS  Article  Google Scholar 

    48.
    Söllinger, A. et al. Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, 149. https://doi.org/10.1093/femsec/fiv149 (2016).
    CAS  Article  Google Scholar 

    49.
    Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl. Acad. Sci. U.S.A. 116, 5037. https://doi.org/10.1073/pnas.1815631116 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    50.
    Cai, Y., Zheng, Y., Bodelier, P. L. E., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).
    ADS  CAS  Article  Google Scholar 

    51.
    Henckel, T., Jäckel, U., Schnell, S. & Conrad, R. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl. Environ. Microbiol. 60, 1801–1808 (2000).
    Article  Google Scholar 

    52.
    Ricke, P., Kolb, S. & Braker, G. Application of a newly developed ARB software-integrated tool for in silico terminal restriction fragment length polymorphism analysis reveals the dominance of a novel pmoA cluster in a forest soil. Appl. Environ. Microbiol. 71, 1671–1673. https://doi.org/10.1128/AEM.71.3.1671-1673.2005 (2005).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    53.
    Pratscher, J., Dumont, M. G. & Conrad, R. Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USCα clade. Environ. Microbiol. 13, 2692–2701. https://doi.org/10.1111/j.1462-2920.2011.02537.x (2011).
    CAS  Article  PubMed  Google Scholar 

    54.
    Cai, Y., Zhou, X., Shi, L. & Jia, Z. Atmospheric methane oxidizers are dominated by upland soil cluster alpha in 20 forest soils of China. Microb. Ecol. 80, 859–871. https://doi.org/10.1007/s00248-020-01570-1 (2020).
    CAS  Article  PubMed  Google Scholar 

    55.
    Täumer, J. et al. Divergent drivers of the microbial methane sink in temperate forest and grassland soils. Glob. Change Biol. https://doi.org/10.1111/gcb.15430 (2020).
    Article  Google Scholar 

    56.
    Andreote, F. D. et al. Culture-independent assessment of Rhizobiales-related alphaproteobacteria and the diversity of Methylobacterium in the rhizosphere and rhizoplane of transgenic eucalyptus. Microb. Ecol. 57, 82–93. https://doi.org/10.1007/s00248-008-9405-8 (2009).
    Article  PubMed  Google Scholar 

    57.
    Iguchi, H., Yurimoto, H. & Sakai, Y. Interactions of methylotrophs with plants and other heterotrophic bacteria. Microorganisms 3, 137–151. https://doi.org/10.3390/microorganisms3020137 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    58.
    Ho, A. et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front. Microbiol. 7, 1285. https://doi.org/10.3389/fmicb.2016.01285 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    59.
    Iguchi, H., Yurimoto, H. & Sakai, Y. Stimulation of methanotrophic growth in cocultures by cobalamin excreted by rhizobia. Appl. Environ. Microbiol. 77, 8509–8515. https://doi.org/10.1128/AEM.05834-11 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    60.
    Veraart, A. J. et al. Living apart together—bacterial volatiles influence methanotrophic growth and activity. ISME J. 12, 1163–1166 (2018).
    CAS  Article  Google Scholar 

    61.
    Karlsson, A. E., Johansson, T. & Bengtson, P. Archaeal abundance in relation to root and fungal exudation rates. FEMS Microbiol. Ecol. 80, 305–311 (2012).
    CAS  Article  Google Scholar 

    62.
    Haichar, F. E. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230. https://doi.org/10.1038/ismej.2008.80 (2008).
    CAS  Article  PubMed  Google Scholar 

    63.
    Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    64.
    Schinner, F. et al. (eds) Methods in Soil Biology (Springer, Berlin, 1996).
    Google Scholar 

    65.
    Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
    CAS  Article  Google Scholar 

    66.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Nat. Acad. Sci. U.S.A. 108(Suppl 1), 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).
    ADS  Article  Google Scholar 

    67.
    Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x (2012).
    CAS  Article  PubMed  Google Scholar 

    68.
    White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
    Google Scholar 

    69.
    Schloss, P. D. et al. Introducing mothur. Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  Article  Google Scholar 

    70.
    Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 25, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
    Article  Google Scholar 

    71.
    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mah, F. VSEARCH. A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
    Article  Google Scholar 

    72.
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
    CAS  Article  PubMed  Google Scholar 

    73.
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).
    CAS  Article  PubMed  Google Scholar 

    74.
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    75.
    Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
    CAS  Google Scholar 

    76.
    Martin, A. P. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68, 3673–3682. https://doi.org/10.1128/AEM.68.8.3673-3682.2002 (2002).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    77.
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
    Article  Google Scholar 

    78.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org. Accessed 24 Sept 2018.

    79.
    Oksanen, J. et al. vegan. Community Ecology Package. R package version 2.4–4 (2017). https://CRAN.R-project.org/package=vegan. Accessed 24 Sept 2018.

    80.
    Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10. https://doi.org/10.1093/nar/gkw343 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    81.
    Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16SrRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
    CAS  Article  Google Scholar 

    82.
    White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    83.
    Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    84.
    Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
    CAS  Article  PubMed  Google Scholar 

    85.
    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
    Article  Google Scholar 

    86.
    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    87.
    Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A.-K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029. https://doi.org/10.1111/1462-2920.14036 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    88.
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    89.
    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).
    CAS  Article  PubMed  Google Scholar  More