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opencc-by-4.0Dec 2019View details →

Influence of Paleolithic Range Contraction, Admixture and Long-Distance Dispersal on Genetic Gradients of Modern Humans in Asia

Each folder is identified according to the scenario, and contains another folder with the input files (files *.txt, *.par, *.sam, *.asc) to simulate it, the corresponding simulated genetic data (files *.arp) and the derived PC maps (files *.png). A file with the locations of the samples is also included (coord.txt). * Pure Paleolithic expansion *  The folder “Paleo” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion, ignoring the range contraction induced by the LGM and LDD events. * Pure Paleolithic expansion considering the range contraction induced by the LGM *  The folder “Paleo_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion suffering the range contraction induced by the LGM.  * Pure Paleolithic expansion considering long-distance dispersal (LDD) events *  The folder “Paleo_LDD” contains the input files (INFILES), the genetic data  and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion considering LDD events.  * Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering the range contraction induced by the LGM *  The folder “Paleo2NeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and later from East Asia suffering the range contraction induced by the LGM.  * Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering LDD events *  The folder “Paleo2NeoIR0_LDD” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions from Middle East and later from East Asia considering LDD events.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering the range contraction induced by the LGM *  The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East suffering the range contraction induced by the LGM.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering the range contraction induced by the LGM *  The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia suffering the range contraction induced by the LGM.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering LDD events *  The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files  (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East considering LDD events.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events *  The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering the range contraction induced by the LGM *  The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia suffering the range contraction induced by the LGM.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering the range contraction induced by the LGM *  The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES), the genetic data  and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia suffering the range contraction induced by the LGM.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering LDD events *  The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events.  * Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events *  The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files  (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia considering LDD events.

opencc-zeroNov 2023View details →

Synergistic polyploidization and long-distance dispersal enable the global diversification of yellowcresses (Rorippa, Brassicaceae)

<div> Aim: Long-distance dispersal (LDD) plays an important role in shaping the distribution of global biodiversity. Polyploidy could favor invasion and thereby facilitate LDD. However, how and to what extent polyploidy interacts with LDD is unclear. Here, we test the putative role of polyploidy in the global dispersal of Rorippa species.</div> <div> </div> <div> Location: Global.</div> <div> </div> <div> Time period: Late Miocene to present.</div> <div> </div> <div> Major taxa studied: Rorippa.</div> <div> </div> <div> Methods: We traced the biogeographic and speciation history for 17 diploids and 41 polyploids of Rorippa using variation from plastid genomes and multiple nuclear loci. The ploidy role in dispersal rate difference was demonstrated using trait-dependent biogeographic modeling.</div> <div> </div> <div> Results: LDD shaped the amphitropical disjunction of Rorippa, during which polyploids showed higher dispersal rates than those of diploids, with 5.6× increase under the best-fitted model. Five diploids and 21 polyploids were identified as products of transoceanic speciation events. Polyploidy-involved LDD was more common in terms of polyploidization following LDD than those preceding LDD.</div> <div> </div> <div> Main conclusions: We demonstrate that polyploidy would be not only a driver but also a responder of LDD in Rorippa, highlighting a synergistic relationship between them. Our results provide a framework to uncover the biogeographic consequences of polyploidization and the joint roles of polyploidy and LDD in shaping the distribution of biodiversity.</div>

opencc-zeroFeb 2024View details →

Rare, long-distance dispersal underpins genetic connectivity in the pink sea fan, Eunicella verrucosa

Characterising patterns of genetic connectivity in marine species is of critical importance given the anthropogenic pressures placed on the marine environment. For sessile species, population connectivity can be shaped by many processes, such as pelagic larval duration, oceanographic boundaries, and currents. This study combines restriction-site associated DNA sequencing (RADseq) and passive particle dispersal modelling to delineate patterns of population connectivity in the pink sea fan, Eunicella verrucosa, a temperate octocoral. Individuals were sampled from 20 sites covering most of the species' northeast Atlantic range, and a site in the northwest Mediterranean Sea to inform on connectivity across the Atlantic-Mediterranean transition. Using 7,510 neutral SNPs, a geographic cline of genetic clusters was detected, partitioning into: Ireland, Britain, France, Spain (Atlantic), and Portugal and Spain (Mediterranean). Evidence of significant inbreeding was detected at all sites, a finding not detected in a previous study of this species based on microsatellite loci. Genetic connectivity was characterised by an isolation by distance pattern (IBD) (r2 = 0.78, p<0.001), which persisted across the Mediterranean-Atlantic boundary. In contrast, exploration of ancestral population assignment using the program ADMIXTURE indicated genetic partitioning across the Bay of Biscay, which we suggest represents a natural break in the species' range, possibly linked to a lack of suitable habitat. As the pelagic larval duration (PLD) is unknown, passive particle dispersal simulations were run for 14 and 21 days. For both modelled PLDs, inter-annual variations in particle trajectories suggested that in a long-lived, sessile species, range-wide IBD is driven by rare, longer dispersal events which act to maintain gene flow. These results suggest that oceanographic patterns may facilitate range-wide stepping-stone genetic connectivity in E. verrucosa, and highlight that both oceanography and natural breaks in a species' range should be considered in the designation of ecologically coherent MPA networks.

opencc-by-4.0Jul 2022View details →

Supplementary material for: Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift

Raw data and input files for phylogenetic and biogeographic analysis of the article "Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift". Supplementary material S1. Matrix of phenotypic characters in .ss format. Supplementary material S2. Alignment of COI sequences in fasta format.. Supplementary material S3. Alignment of H3 sequences in fasta format. Supplementary material S4. Alignment of 16S sequences in fasta format before trimming with gblocks. Supplementary material S5. Alignment of 28S sequences in fasta format before trimming with gblocks. Supplementary material S6. Input for running parsimony analysis using TNT (phenotypic data only). Supplementary material S7. Input for running Bayesian inference using MrBayes (phenotypic data only). Supplementary material S8. Input for running parsimony analysis using TNT (sequence data only). Supplementary material S9. Input for running Bayesian inference using MrBayes (sequence data only). Supplementary material S10. Input for running parsimony analysis using TNT (total evidence). Supplementary material S11. Input for running Bayesian inference using MrBayes (total evidence). Supplementary material S12. Input for running parsimony analysis using TNT (total evidence, dataset with reduced number of terminals). Supplementary material S13. Input for running Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals). Supplementary material S14. Input for running Bayesian inference using MrBayes (total evidence) and estimating node ages using tip-dating. Supplementary material S15. Input for running Bayesian inference using Beast (sequence data only) and estimating node ages using node-dating. Supplementary material S16. Raw geographic distances among areas in each time slice and dispersal probability matrices for each biogeographic model. Supplementary material S17. Inputs for estimating ancestral ranges and performing biogeographic stochastic maps for our dataset. Supplementary material S18. Consensus tree found with parsimony analysis using TNT (phenotypic data only). Supplementary material S19. Consensus tree found with Bayesian inference using MrBayes (phenotypic data only). Supplementary material S20. Consensus tree found with parsimony analysis using TNT (sequence data only). Supplementary material S21. Consensus tree found with Bayesian inference using MrBayes (sequence data only). Supplementary material S22. Consensus tree found with parsimony analysis using TNT (total evidence). Supplementary material S23. Consensus tree found with Bayesian inference using MrBayes (total evidence). Supplementary material S24. Consensus tree found with parsimony analysis using TNT (total evidence, dataset with reduced number of terminals). Supplementary material S25. Consensus tree found with Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals). Supplementary material S26. Consensus tree found with Bayesian inference using MrBayes (total evidence) and with node ages estimated using tip-dating. Supplementary material S27. Maximum clade credibility tree found with Bayesian inference using Beast (sequence data only) and with node ages estimated using node-dating.

opencc-zeroApr 2022View details →

Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds

Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species. In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear. Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines. We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle. Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.

opencc-zeroApr 2022View details →

Path-finding algorithm as a dispersal assessment method for invasive species with human-vectored long-distance dispersal event

Aim: An assessment method that can precisely represent human-vectored long-distance dispersals (HVLDD) is currently in need for effective management of invasive species. Here, we focused on HVLDD happening along roads and proposed a path-finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study. Location: Busan Metropolitan City, Republic of Korea Methods: A path-finding algorithm, which calculates distances by considering spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019 and their dispersal distances were calculated using the path-finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017, and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm. Results: The potential dispersal ranges of the path-finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length. Main Conclusions: The path-finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path-finding algorithm for dispersal assessment of invasive species that move along road networks, as well as for the development of more powerful HVLDD prediction models.a

opencc-by-4.0Mar 2022View details →

Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"

All the data needed to reproduce the results and Figures of our article: Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. Biology, 11(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a> Please, find the R scripts and guidelines to reproduce our results on the article's Github repository : <a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a>

opencc-zeroSep 2022View details →

Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions

Understanding how niche-based and neutral processes contribute to the spatial variation in plant-pollinator interactions is central to designing effective pollination conservation schemes. Such schemes are needed to reverse declines of wild bees and other pollinating insects and to promote pollination services to wild and cultivated plants. We used data on wild bee interactions with plants belonging to the four tribes Loteae, Trifolieae, Anthemideae, and either spring- or summer-flowering Cichorieae, sampled systematically along a 682km latitudinal gradient to build models that allowed us to (a) predict occurrences of pairwise bee-flower interactions across 115 sampling locations, and (b) estimate the contribution of variables hypothesized to be related to niche-based assembly structuring processes (viz. annual mean temperature, landscape diversity, bee sociality, bee phenology, and flower preferences of bees) and neutral processes (viz. regional commonness and dispersal distance to conspecifics). While neutral processes were important predictors of plant-pollinator distributions, niche-based processes were reflected in the contrasting distributions of solitary bee and bumble bees along the temperature gradient, and in the influence of bee flower preferences on the distribution of bee species across plant types. In particular, bee flower preferences separated bees into three main groups, albeit with some overlap: visitors to spring-flowering Cichorieae; visitors to Anthemideae and summer-flowering Cichorieae; and visitors to Trifolieae and Loteae. Our findings suggest that both neutral and niche-based processes are significant contributors to the spatial distribution of plant-pollinator interactions so that conservation actions in our region should be directed towards areas: near high concentrations of known occurrences of regionally rare bees; in mild climatic conditions; and that are surrounded by heterogeneous landscapes. Given the observed niche-based differences, the proportion of functionally distinct plants in flower-mixes could be chosen to target bee species, or guilds, of conservation concern.

opencc-by-4.0Mar 2023View details →

SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.

Fig. 8 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 8. Scanning electron microscopy. Polymerurus insularis sp. nov. A–B, D. Paratype (ZUEC GCH 59). C, E. Paratype (ZUEC GCH 60). A–B. Posterior dorsal view. C. Detail of the furca base in posterior dorsolateral view. D–E. Details of the furcal rami. Abbreviations: fb = furca base; fr = furcal rami; sc-4 = Type 4 scales; sc-5 = Type 5 scales. Scale bars = 10 µm.

Fig. 4 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 4. Light microscopy – DIC. Polymerurus insularis sp. nov. Posterior region of the body. A. Paratype (ZUEC GCH 56). B, D. Holotype (ZUEC GCH 55). C. Paratype (ZUEC GCH 57). A–B. Posterior dorsal view. C. Posterior dorsolateral view. D. Posterior ventral view. Abbreviations: fr = furcal rami; sc-1 = Type 1 scales; sc-2 = Type 2 scale; sc-3 = Type 3 scales; sc-4 = Type 4 scales. Scale bars = 40 µm.

Fig. 5 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 5. Close-up of the different types of scales described forPolymerurus insularis sp. nov.A–C. Paratype (ZUEC GCH 59). D, F. Holotype (ZUEC GCH 55). E. Paratype (ZUEC GCH 56). A–C. Scanning electron microscopy. D–F. Light microscopy – DIC. A. Detail of a section of the dorsal middle trunk, showing the most common type of scale, Type 1, with emphasis on its characteristic shape. B. Detail of a section of the dorsal posterior trunk, showing Type 2 and Type 3 scales. C–E. Detail of the transition between the dorsal posterior trunk and the dorsal furca base, showing the particular scale covering of this region. F. Detail of the transition between the ventral posterior trunk and the ventral furca base, showing the particular scale covering this region. Abbreviations: sc-1 = Type 1 scales; sc-2 = Type 2 scale; sc-3 = Type 3 scales; sc-4 = Type 4 scales; sc-5 = Type 5 scales; sc-6 = Type 6 scales; sc-7 = Type 7 scales; sc-8 = Type 8 scales; svs = small ventral pair of spines. Scale bars: A–C = 5 µm; D = 10 µm; E = 15 µm; F = 10 µm.

Fig. 3 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 3. Light microscopy – DIC. Polymerurus insularis sp. nov., holotype (ZUEC GCH 55). A–C. Correspond to the most anterior third of the specimen. A. Anterior dorsal region. B. Anterior internal region. C. anterior ventral region. D–F. Correspond to the trunk. D. Dorsal trunk. E. Internal trunk. F. Ventral trunk. G–I. Correspond to the posterior third of the specimen. G. Dorsal posterior third. H. Internal posterior third. I. Ventral posterior third. Abbreviations: ce = cephalion; ct = cephalic bristles; eg = egg; hy = hypostomium; i = intestine; is = interciliary spines; lc = locomotory cilia; lce = lateral cephalic expansions; mo = mouth; ne = nephridia; pl = pleurae; ph = pharynx; PhIJ = pharyngealintestinal junction; sc-1 = Type 1 scales; sc-2 = Type 2 scale; sc-3 = Type 3 scales; sc-4 = Type 4 scales; vs = Type 1 ventral scale. Scale bars = 20 µm.

Fig. 1 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 1. Sampling location, at the state of Pernambuco, Fernando de Noronha archipelago, Brazil A. Brazil. B. Fernando de Noronha Archipelago. C. Xaréu açude. Images provided by Google Earth (A–B) and Prof Dr Felipe Toledo, University of Campinas (C).

Fig. 6 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 6. Schematic illustration of dorsal and ventral posterior regions and type scales of Polymerurus insularis sp. nov. A–B. Paratype (ZUEC GCH 56). A. Dorsal view of the posterior end. Some Type 1 scales are faded for a better visualization of Types 2, 3 and 5 scales. B. Ventral view of the posterior end. C. Each type of scale, individually depicted (not to scale). Abbreviations: ff = furcal furrow; fr = furcal rami; lc = locomotory cilia; is = interciliary spines; sc-1 = Type 1 scales; sc-2 = Type 2 scale; sc-3 = Type 3 scales; sc-4 = Type 4 scales; sc-5 = Type 5 scales; sc-6 = Type 6 scales; sc-7 = Type 7 scales; sc-8 = Type 8 scales. Scale bars = 40 µm.

Fig. 9 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 9. Maximum Likelihood tree based on multigene approach with 18S and 28S sequences. Highlighted branches correspond to the Polymerurus Remane, 1927 species sequences. Values on the branches correspond, respectively, to: SH-aLRT support (%) / aBayes support / ultrafast bootstrap support (%).

Fig. 2 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks

Fig. 2. Light microscopy – DIC. Polymerurus insularis sp. nov., holotype (ZUEC GCH 55). Full body view. A. Dorsal view. B. Internal view. C. Ventral view. Scale bars = 30 µm.