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462 results for “Mutualism”
Phylogenomics illuminates the phylogeny of flower weevils (Curculioninae) and reveals ten independent origins of brood-site pollination mutualism in true weevils
<p><strong>Phylogenomics illuminates the phylogeny of flower weevils (Curculioninae) and reveals ten independent origins of brood-site pollination mutualism in true weevils (142 /150 characters)</strong></p> <p>Haran J.<sup>1*</sup>, Li X.<sup>2,3,4*</sup>, Allio R.<sup>5*</sup>, Shin S.<sup>3,4,6</sup>, Benoit L.<sup>1</sup>, Oberprieler R.G.<sup>7</sup>, Farrell B.D.<sup>8</sup>, Brown S.D.J.<sup>9</sup>, Leschen R.A.B.<sup>10</sup>, Kergoat G.J.<sup>5</sup> & McKenna D.D.<sup>3,4</sup></p> <p>* Equal contribution</p> <p> </p> <p><strong>Affiliations</strong></p> <p><sup>1</sup> CBGP, CIRAD, INRAE, IRD, Institut Agro, Univ. Montpellier, Montpellier, France. ORCID: 0000-0001-9458-3785 (JH); 0000-0003-3740-5346 (LB)</p> <p><sup>2</sup> Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China. ORCID: 0000-0002-0622-2064 (XL)</p> <p><sup>3</sup> Department of Biological Sciences, University of Memphis, Memphis, TN 38152 ORCID: 0000-0002-7823-8727 (DDM)</p> <p><sup>4</sup> Center for Biodiversity Research, University of Memphis, Memphis, TN 38152</p> <p><sup>5</sup> CBGP, INRAE, IRD, CIRAD, Institut Agro, Univ. Montpellier, Montpellier, France. ORCID: 000-0003-3885-5410 (RA); 0000-0002-8284-6215 (GJK)</p> <p><sup>6</sup> School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.</p> <p>ORCID: 0000-0002-4258-8661 (SS)</p> <p><sup>7</sup> CSIRO, Australian National Insect Collection, GPO Box 1700, Canberra, ACT 2601, Australia. ORCID: 0000-0002-1837-580X (RGO)</p> <p><sup>8</sup> Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA. ORCID: 0000-0002-6843-0539 (BDF)</p> <p><sup>9</sup> Bio-Protection Research Centre, P.O. Box 85084, Lincoln University, Lincoln 7647, New Zealand. Current address: The New Zealand Institute for Plant and Food Research, Mount Albert Research Centre, Private Bag 92169, Auckland 1142, New Zealand. ORCID: 0000-0001-7112-421X (SDJB)</p> <p><sup>10</sup> Manaaki Whenua - Landcare Research, PB 92170, Auckland, New Zealand. ORCID: 0000-0001-8549-8933 (RABL)</p> <p> </p> <p><strong>Abstract</strong></p> <p>Weevils are an unusually species-rich group of phytophagous insects, for which there is increasing evidence of frequent involvement in brood-site pollination. This study examines phylogenetic patterns in the emergence of brood-site pollination mutualism among one of the most speciose beetle groups, the flower weevils (subfamily Curculioninae). We analyzed a novel phylogenomic dataset consisting of 214 nuclear loci for 202 weevil species, with a sampling that mainly includes flower weevils as well as representatives of all major lineages of true weevils (Curculionidae). Our phylogenomic analyses establish a uniquely comprehensive phylogenetic framework for Curculioninae and provide new insights into the relationships among lineages of true weevils. Based on this phylogeny, statistical reconstruction of ancestral character states revealed at least ten independent origins of brood-site pollination in higher weevils through transitions from ancestral associations with reproductive structures in the larval stage. Broadly, our results illuminate the unexpected frequency with which true weevils — typically specialized phytophages and hence antagonists of plants — have evolved mutualistic interactions of ecological significance that are key to both weevil and plant evolutionary fitness and thus a component of their deeply intertwined macroevolutionary success.</p> <p> </p> <p><strong><em>Figures </em></strong></p> <p><strong>Figure 1 (part I).</strong> Maximum-likelihood tree resulting from analyses of 214 nuclear protein-coding genes (focus on the CEGH clade and outgroups). Support at node refers to SH-aLRT values ≥ 80% and uBV ≥ 95% (**). Single * refer to SH-aLRT values ≥ 80% only. Clades with black branches and highlighted in blue are classified in Curculioninae sensu Caldara et al. (2014). Taxa displayed on the left: 1 - Hypsomus sp. (Styphlini); 2 - Myllorhinus sp. (Storeini s. lat.); 3 - Encosmia sp. (Storeini s. lat.).</p> <p><strong>Figure 1 (part II).</strong> Maximum-likelihood tree resulting from analyses of 214 nuclear protein-coding genes (focus on the CCCMS clade). Node support values refer to SH-aLRT values ≥ 80% and uBV ≥ 95% (**). Single * refer to SH-aLRT values ≥ 80% only. Clades with black branches and highlighted in blue are classified in Curculioninae sensu Caldara et al., (2014). Clades highlighted in darker blue contain genera engaged in brood-site pollination mutualism and the corresponding genera are highlighted in orange (higher taxonomic rank when specific genera are not included in the tree). Other lineages of the CCCMS clade are in bold font. Taxa displayed on the right: 1 - Tychius sp. (Tychiini); 2 - Anthonomus sp. (Athonomini); 3 - Tachyerges sp. (Rhamphini); 4 - Derelomus sp. (Derelomini); 5 - Cionus sp. (Cionini); 6 - Daeneus sp. (Ochyromerini); 7 - Meriphus sp. (Eugnomini); 8 - Archarius sp. (Curculionini); 9 - Dorytomus sp. (Ellescini); 10 - Cleopomiarus sp. (Mecinini).</p> <p><strong>Figure 2.</strong> Results of the ASE analysis of larval tissue specialization carried out on the CCCMS clade, with an ER model and using a continuous-time reversible Markov model with 1000 simulations. In addition, red arrows are used to underline the independent origins of brood-site mutualism inferred in another ASE analysis (see Fig. S4). Two clades including brood-site pollinator genera that were not sampled in our study are also highlighted using red rectangles.</p> <p> </p> <p><strong><em>Additional files</em></strong></p> <p><strong>Figure S1</strong>. Full ML tree with support values.</p> <p><strong>Figure S2</strong>. Support for ML analyses.</p> <p><strong>Figure S3</strong>. Results of the ASE analysis of the evolution of the tissue specialization by weevil larvae in the CCCMS clade, with an ER model and using a continuous time-reversible Markov model with 1000 simulations. </p> <p><strong>Figure S4</strong>. Results of the ASE analysis on the evolution of brood-site pollination in the CCCMS clade, with an ER model and using a continuous time-reversible Markov model with 1000 simulations.</p> <p> </p> <p><strong><em>Zenodo supplementary files</em></strong></p> <p><strong>AHE_pipeline.txt </strong>shows the detailed step-by-step script used to generate the phylogeny obtained in this study from raw sequencing data.</p> <p><strong>ASE Analyses.zip</strong> contains the script and the associated raw results of the ASE analyses.</p> <p><strong>Cole_tcas_probes.fasta</strong> contains the Coleopteran probes used.</p> <p><strong>IBA results.zip</strong> contains IBA results.</p> <p><strong>IQ-TREE files.zip</strong> contains input and output files of the IQ-TREE analysis.</p> <p><strong>Scripts.zip</strong> contains the scripts associated with the file AHE_pipeline.txt.</p> <p> </p>
Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
<p><strong>Data and file overview:</strong></p> <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> <p><strong>Layers used to build Zebra RSF:</strong></p> <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others)</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong></p> <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020)</p> <p>(B) Kill_ID: kill identifier.</p> <p>(C) Lion_ID: individual lion pride identifier.</p> <p>(D) Date (Day, Month, Year) when a specific kill occurred.</p> <p>(E) Zebra_kill (1 = kill site, 0 = paired random location).</p> <p>(F). Species: Zebra.</p> <p>(G) Visibility: openness measurement using a rangefinder in (m).</p> <p>(H) Lion_activity: Utilization distributions (UD) of lions.</p> <p>(I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants).</p> <p>(J) zeb.rsf: resource selection function value.</p> <p>(K) zeb.density: zebra density estimated from resource selection functions.</p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong></p> <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) Species: Zebra.</p> <p>(B) Date (Day, Month, Year) for that sighting.</p> <p>(C) Survey: count identifier (Survey 2 to 21).</p> <p>(D) GPS location (X and Y), longitude and latitude of that sighting location.</p> <p>(E) Transect: Transect number.</p> <p>(F) Used: (1= zebra sighting, 0 = paired point).</p> <p>(G) zebra.ct: total number of zebras in each sighting.</p> <p> </p> <p><strong>R CODE</strong></p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>
Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen
<p>The data files found here contain the data as obtained and displayed in : "Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen" published in Physical Review A (2023). Each file contains an explanatory header. Header lines start with #.</p> <p> </p>
Data and code from: Reciprocity and interaction effectiveness in generalised mutualisms among free-living species
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Data from: The acacia ants revisited: convergent evolution and biogeographic context in an iconic ant/plant mutualism
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Data from: Which traits optimize plant benefits? Meta-analysis on the effect of partner traits on the outcome of an ant-plant protective mutualism
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Evidence for a by-product mutualism in a group hunter depends on prey movement state
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Cheating in mutualisms
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Negotiating mutualism: a locus for exploitation by rhizobia has a broad effect size distribution and context-dependent effects on legume hosts
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When wax wanes: competitors for beeswax stabilize rather than jeopardize the honeyguide-human mutualism
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Mutualism mediates legume response to microbial climate legacies
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Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
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Host-symbiont stress response to lack-of-sulfide in the giant ciliate mutualism
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Data from: Asynchronous life histories generate uneven arms races and impact the maintenance of mutualisms
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Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community
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Ant-scale mutualism increases scale infestation, decreases folivory, and disrupts biological control in restored tropical forests
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Logical model for mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis
<p>Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.</p> <p> </p> <p>GINsim archive (zginml) with the model, its annotations and simulation parameters; the SBML file can be imported using any tool supporting the SBML qual format</p> <p>Warning: the zginml archive should be open using a recent GINsim version (>2.8)</p>
Data from: Genomic evidence of prevalent hybridization throughout the evolutionary history of the fig-wasp pollination mutualism
<p><i>Ficus</i> (figs) and their agaonid wasp pollinators present an ecologically important mutualism that also provides a rich comparative system for studying functional co-diversification throughout its coevolutionary history (~75 million years). We obtained entire nuclear, mitochondrial, and chloroplast genomes for 15 species representing all major clades of <i>Ficus</i>. Multiple analyses of these genomic data suggest that hybridization events have occurred throughout <i>Ficus</i> evolutionary history. Furthermore, cophylogenetic reconciliation analyses detect significant incongruence among all nuclear, chloroplast, and mitochondrial-based phylogenies, none of which correspond with any published phylogenies of the associated pollinator wasps. These findings are most consistent with frequent host-switching by the pollinators, leading to fig hybridization, even between distantly related clades. Here, we suggest that these pollinator host-switches and fig hybridization events are a dominant feature of fig/wasp coevolutionary history, and by generating novel genomic combinations in the figs have likely contributed to the remarkable diversity exhibited by this mutualism.</p>
Data used in 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript
<p>The files in this repository correspond to the raw and processed data described in the 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript.</p> <p>Once expanded, the zip files contains two directories and one documentation file, named data_documentation. Please refer to this file for a thorough description of the organization and contents of raw and processed data files.</p>
Data from: Facilitated exploitation of pollination mutualisms: fitness consequences for plants
Mutualisms are only rarely one-to-one interactions: each species generally interacts with multiple mutualists. Exploitation is ubiquitous in mutualisms, and we would therefore expect that each mutualist interacts with multiple exploiters as well. Exploiter species may also interact with one another. For example, the action of one exploiter species might open the opportunity for exploitation by a second species. Exploitation is common in many plant–pollinator mutualisms: 'primary' nectar robbers feed through holes they make in flowers, which can be subsequently used by 'secondary' nectar robbers unable to create holes themselves. The overall effect of nectar robbing on plant fitness is often (although not always) negative. No study has separated the effects of interacting with primary vs. secondary robbers. Here, we examine the effects of primary vs. secondary nectar robbing on pollinator visitation rate and female fitness in Ipomopsis aggregata. Manipulating the type of nectar robbing that flowers experienced, we found that secondary nectar robbing inflicted fitness costs to plants beyond that inflicted by primary robbing alone. Secondary nectar robbing significantly reduced pollen receipt to flowers, as well as fruit and seed production. Although the causes are elusive, the effect may be attributed to changes in pollinator behaviour at these plants. Synthesis. Our findings provide evidence that interacting with multiple exploiters can lead to increased negative effects for mutualists, and highlight the importance of incorporating multiple exploiters into the conceptual framework of mutualism.
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