Find research datasets worth reusing

FIGURE 3 in Collohmannia Johnstoni N. Sp. (Acari, Oribatida) From West Virginia (U.S.A.), Including Description Of Ontogeny, Setal Variation, Notes On Biology And Systematics Of Collohmanniidae

FIGURE 3: Collohmannia johnstoni n. sp. female: A – dorsal view, legs omitted; B – partial ventral view of strongly distended specimen; C – ventral view of contracted specimen, legs and gnathosoma omitted; D – lateral view, ovipositor extended, leg details omitted; E – bothridial seta; F – distal part of ovipositor, posterior view (artificially distended slightly to better show setae k). See text for abbreviations.

FIGURE 10 in Collohmannia Johnstoni N. Sp. (Acari, Oribatida) From West Virginia (U.S.A.), Including Description Of Ontogeny, Setal Variation, Notes On Biology And Systematics Of Collohmanniidae

FIGURE 10: Collohmannia johnstoni n. sp. adult, ano-genital region: A, B – male, incident light; C, D – female, transmitted brightfield (C) or DIC (D) illumination. A – ventral overview, showing positions of cross-sections; large white arrow points to notch of unsclerotized cuticle between adanal and aggenital plates; B – posterior view of hysterosoma, white arrow points to soft terminus of body; C - cross section at level of postanal apodeme; D – cross-section showing composition and musculature of the mid-anal region. Abbreviations: AG, AN, AD, GE – aggenital, anal, adanal, genital plates, gl – gland, is.1, is.2 – intercalary sclerites, mu – muscle, NG – notogaster, pa.a – postanal apodeme, pr.a – preanal apodeme. Each image layered and color- and sharpness-enhanced (see Material and Methods).

FIGURE 19 in Collohmannia Johnstoni N. Sp. (Acari, Oribatida) From West Virginia (U.S.A.), Including Description Of Ontogeny, Setal Variation, Notes On Biology And Systematics Of Collohmanniidae

FIGURE 19: Collohmannia johnstoni n. sp. biology and habitat: A – tritonymph on skeletonized red maple (Acer rubrum L.) leaf; B – egg with larva visible inside, recently deposited on maple leaf in culture; C – nymphs feeding on red maple leaf; D – adult female feeding on unidentified leaf; E – type locality and typical habitat: riparian mixed forest with overstory of red maple, yellow birch (Betula alleghaniensis Britton), white spruce (Picea glauca (Moench) Voss) and eastern hemlock (Tsuga canadensis (L.) Carr.), and dense understory of rhododendron (Rhododendron maximum L.).

FIGURE 16 in Collohmannia Johnstoni N. Sp. (Acari, Oribatida) From West Virginia (U.S.A.), Including Description Of Ontogeny, Setal Variation, Notes On Biology And Systematics Of Collohmanniidae

FIGURE 16: Collohmannia johnstoni n. sp. adult, tarsi I and II (all posterior, abaxial view) showing variation of lateral setae, which are all those setae without labels: A-F – right tarsus I. A – most common case (region of variation circumscribed with dashed line); B-F – variations; G-I – right tarsus II. G – most common case (dashed line as in A); H, I – variations. Circles with full outlines indicate alveoli of posterior setae, those with dotted lines indicate anterior setae; solid internal circles indicate normal setae, open internal circles indicate eupathidia.

FIGURE 9 in Collohmannia Johnstoni N. Sp. (Acari, Oribatida) From West Virginia (U.S.A.), Including Description Of Ontogeny, Setal Variation, Notes On Biology And Systematics Of Collohmanniidae

FIGURE 9: Collohmannia johnstoni n. sp. adult, details of coxisternum, light micrographs: incident (A), or transmitted light with DIC (B, C, E) or brightfield (D) illumination: A – lateral overview, showing positions of C-E; B – separated coxisternum, dorsal view; C – separated epimere III, triangular extension of supracoxal region, lateral view; D – detail of supracoxal area of intact epimere I and part of prodorsum, lateral view; E – same view, detail of supracoxal seta. Abbreviations: ap.1, ap.2 – apodemes 1, 2, respectively; bo.2-bo.4, – borders of epimeres 2-4, respectively; bo.sj – sejugal border; car – submarginal carina; pK – prodorsal condyle; po.1a, po.3a – porose areas around respective setae; r – ridge of epimere IV; st.s – sternal scissure; vsj.f – ventrosejugal furrow; te – tendon; w – area of weakness; tr.e – triangular extension of epimere III. Each image layered and color- and sharpness-enhanced (see Material and Methods).

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

Supplementary data to publication "Growth efficiency, intestinal biology, and nutrient utilization and requirements of black soldier fly (Hermetia illucens) larvae compared to monogastric livestock species: a review"

The datasets provided in the excel file were used to generate Table 2 and Fig. 2 in the publication entitled "Growth efficiency, intestinal biology, and nutrient utilization and requirements of black soldier fly (Hermetia illucens) larvae compared to monogastric livestock species: a review" by Seyedalmoosavi et al., 2022 published in Journal of Animal Science and Biotechnology. https://doi.org/10.1186/s40104-022-00682-7

opencc-zeroFeb 2022View details →

Comparative and predictive phylogeography in the South American diagonal of open formations: Unravelling the biological and environmental influences on multitaxon demography

Phylogeography investigates historical drivers of species' geographic distribution. Special attention has been given to ecological, climatic, and geological processes in the diversification of the Neotropical biota. Several species sampled across the dry diagonal of South America (DDSA, comprising the Caatinga, Cerrado, and Chaco biomes) experienced range shifts coincident with Quaternary climatic changes. However, studies across different spatial, temporal, and biological scales on species from South America's dry biomes are still poorly represented. Here, we combine phylogeographic model selection and machine learning predictive frameworks to investigate the influence of Pleistocene climatic changes on both plant and animal species from the DDSA. We assembled mitochondrial/chloroplastic DNA sequences in public repositories and inferred the historical demographic responses of 70 lineages. We then built a random forest model using both biotic and abiotic information to identify potential traits for predicting whether species underwent population expansion, contraction, or stasis during the Pleistocene. Finally, we estimated the temporal synchrony of species demographic responses using hierarchical approximate Bayesian computation (hABC). Biotic variables largely predicted how species responded to Pleistocene climatic changes, and demographic changes were mostly synchronous during the Middle Pleistocene. Although many DDSA species underwent demographic expansion, presumably associated with the spread of aridity during glacial Pleistocene periods, our findings suggest that some species exhibited the opposite response and that species-specific attributes might have been related to these differences.

opencc-by-4.0Feb 2022View details →

Model output for analyses conducted in de la Vega & Buchanan et al. (2022) Global Change Biology

Model data for: De La Vega & Buchanan et al. (2022) Multi-decadal environmental change in the  Barents Sea recorded by seal teeth. Global Change Biology. Each netcdf file (.nc) contains model output from simulations performed with the  NEMO-SI3-PISCES global ocean-seaice-biogeochemistry model. These simulations were  forced by the Japanese Atmospheric Reanalysis 55 (Tsujino et al. 2018), which  provides "best-guess" atmospheric conditions since 1958 to present day. Simulations  for this study were performed from Jan 1958 - Dec 2019. Variables included here are:         - zonal velocity                (u)             m s-1         - meridional velocity           (v)             m s-1         - potential temperature         (thetao)        degrees C         - salinity                      (so)            psu         - sea ice concentraton          (siconc)        0-1         - nitrate                       (NO3)           mmol m-3         - nitrate 15                    (NO3_15)        mmol m-3         - small particulate organics    (POC)           mmol m-3         - small particulate organics 15 (POC_15)        mmol m-3         - large particulate organics    (GOC)           mmol m-3         - large particulate organics 15 (GOC_15)        mmol m-3         - nanophytoplankton NPP         (PPPHYN)        mol C m-3 s-1         - diatoms NPP                   (PPPHYD)        mol C m-3 s-1         - nanophytoplankton chlorophyll (NCHL)          mmol m-3         - diatoms chlorophyll           (DCHL)          mmol m-3         - atmospheric N deposition      (NDEP)          kg N m-2 s-1 Files:         ETOPO_JRA55_pic_1y_u_1958-2019.nc         ETOPO_JRA55_pic_1y_v_1958-2019.nc         ETOPO_JRA55_pic_1y_TS_1958-2019.nc         ETOPO_JRA55_pic_1y_ice_1958-2019.nc         ETOPO_JRA55_pic_1y_isos_1958-2019.nc         ETOPO_JRA55_pic_1y_npp_1958-2019.nc         ETOPO_JRA55_pic_1y_chl_1958-2019.nc         ETOPO_JRA55_ndep_1y_isos_1958-2019.nc         ETOPO_JRA55_ndep_1y_npp_1958-2019.nc         ETOPO_JRA55_ndep_1y_chl_1958-2019.nc         ETOPO_ndep_files_1801-2100.nc           --> Atmospheric deposition Naming convention:         "ETOPO" refers to being on a regular 1x1 degree horizontal grid         "JRA55" refers to forcing by the Japanese atmospheric reanalysis         "pic"   refers to an experiment WITHOUT the historical increase in                  atmospheric nitrogne deposition                "ndep"  refers to an experiment WITH the historical increase in                  atmospheric nitrogne deposition         "1y"    refers to the timestep resolution, here all 1 year. Thus, all                  data presented here is annually averaged values.         "u"     refers to zonal velocity         "v"     refers to meridional velocity         "TS"    refers to temperature and salinity         "ice"   refers to sea ice         "isos"  refers to NO3, NO3_15, POC, POC_15, GOC and GOC_15         "npp"   refers to PPPHYN and PPPHYD         "chl"   refers to NCHL and DCHL Atmospheric deposition files are created by linear interpolation using the fields  produced by Hauglustaine et al. (2014). Contact:  Camille.De-La-Vega@liverpool.ac.uk  Pearse.Buchanan@liverpool.ac.uk   Refs:  Tsujino et al. (2018) JRA-55 based surface dataset for driving ocean–sea-ice   models (JRA55-do). Ocean Modelling. doi:10.1016/j.ocemod.2018.07.002  Hauglustaine et al. (2014) A global model simulation of present and future   nitrate aerosols and their direct radiative forcing of climate. Atmospheric   Chemistry and Physics. doi:10.5194/acp-14-11031-2014.

opencc-by-4.0Jan 2022View details →

Vasculature Segmentation Validation Dataset - Part IV - Biological Difference Dataset 2/2 (Development)

Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications   Dataset description: Development: Example data of the head vasculature of developing zebrafish. Data acquisition: Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995). Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm).  Contact: kugler.elisabeth[at]gmail.com Useful code links:  <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis  </li> </ul>

opencc-by-4.0Jan 2022View details →

Vasculature Segmentation Validation Dataset - Part III - Biological Difference Dataset 1/2 (Exsanguination)

Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications Dataset description Exsanguination: Example data of the head vasculature of 4dpf of zebrafish. Data acquisition before and after exsanguination by opening of the heart cavity with forceps in Tg(kdrl:HRAS-mCherry)s916 (4dpf; n=16 embryos from 2 experimental repeats). Data acquisition: Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995). Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm).  Contact: kugler.elisabeth[at]gmail.com Useful code links:  https://github.com/ElisabethKugler/ZFVascularQuantification https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis

opencc-zeroMar 2022View details →

An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents

Classical biological control (CBC) can be used to decrease the density of invasive species to below an acceptable ecological and economic threshold. Natural enemies specific to the invasive species are selected from its native range and released into the invaded range. This approach has drawbacks, despite the performance of specificity tests to ensure its safety, because the fundamental host range defined under controlled conditions does not represent the actual host range in natura, and these tests omit indirect interactions within community. We focus on Sonchus oleraceus (Asteraceae), a weed species originating from Western Palearctic that is invasive in many countries and notably in Australia. We explore how analyses of interaction network within its native range can be used to 1) inventory herbivores associated to the target plant, 2) characterize their ecological host ranges, and 3) guide the selection of candidate biocontrol agents considering interactions with species from higher trophic levels. Arthropods were collected from plant community sympatric to S. oleraceus, in three bioclimatic regions, and interactions were inferred by a combination of molecular and morphological approaches. The networks reconstructed were structured in several trophic levels from basal species (plant community), to intermediate and top species (herbivorous arthropods and their natural enemies). The subnetwork centered on S. oleraceus related interactions contained 116 taxa and 213 interactions. We identified 47 herbivores feeding on S. oleraceus, 15 of which were specific to the target species. Some discrepancies with respect to published findings or conventional specificity tests suggested possible insufficient sampling effort for the recording of interactions or the existence of cryptic species. Among potential candidate agents, 6 exhibited interactions with natural enemies. Synthesis and applications: Adopting a network approach as prerequisite step of the classical biological control program can provide a rapid screening of potential agents to be tested in priority. Once ecological host range defined, we suggest that priority should be given to agent used by a minimum species, and, when they exist, to agents that possess enemies from the most distant taxonomical group from those occurring in the range of introduction.

opencc-by-4.0Mar 2022View details →

Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"

Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"

Compound activity data sets for 15 biological targets compiled from the ChEMBL and PubChem databases.

Compound activity data sets for the 15 biological targets are deposited, along with structure-activity relationship matrices IDs. Active compounds were extracted from the ChEMBL database and inactive were from the PubChem database. Details of the data sets are described in the original publication. and the summary of the data sets is given in the readme.txt file.

opencc-by-4.0Mar 2022View details →

Southern Hemisphere coasts are biologically connected by frequent, long-distance rafting events

Globally, species distributions are shifting in response to environmental change, and those that cannot disperse risk extinction. Many taxa, including marine species, are showing poleward range shifts as the climate warms. In the Southern Hemisphere, however, circumpolar oceanic fronts can present formidable barriers to dispersal4. Although passive, southward movement of species across this barrier has been considered unlikely, the recent discovery of buoyant kelp rafts on beaches in Antarctica demonstrates that such journeys are possible. Rafting is a key process by which diverse taxa – including terrestrial and coastal marine species – can cross oceans. Kelp rafts can carry passengers, and thus can act as vectors for long-distance dispersal of coastal organisms. The small numbers of kelp rafts previously found in Antarctica do not, however, shed much light on the frequency of such dispersal events. We here use a combination of high-resolution phylogenomic analyses (>220,000 SNPs) and oceanographic modelling to show that long-distance biological dispersal events in the Southern Ocean are not rare. We document tens of kelp (Durvillaea antarctica) rafting events of thousands of kilometres each, over several decades (1950 – 2019), with many kelp rafts apparently still reproductively viable. Modelling of dispersal trajectories from genomically-inferred source locations shows that some distant landmasses are particularly well connected, for example South Georgia and New Zealand, and the Kerguelen Islands and Tasmania. Our findings illustrate the power of genomic approaches to track, and modelling to show frequencies, of long-distance dispersal events.

opencc-zeroMar 2023View details →

Data from: Traits across trophic levels interact to influence parasitoid establishment in biological control releases

A central goal in ecology is to predict what governs a species' ability to establish in a new environment. One mechanism driving establishment success is individual species' traits, but the role of trait combinations among interacting species across different trophic levels are less clear. Deliberate or accidental species additions to existing communities provide opportunities to study larger scale patterns of establishment success. Biological control introductions are especially valuable because they contain data on both the successfully established and unestablished species. Here, we supplemented a recent dataset of importation biological control introductions with life-history traits for the parasitoid species and the herbivorus hosts they were released to control to explore how life-history traits of 132 parasitoid species and their herbivorous hosts interact to affect parasitoid establishment. We find that of five parasitoid and herbivore traits investigated, one parasitoid trait—host range—weakly predicts parasitoid establishment; parasitoids with higher levels of phylogenetic specialization have higher establishment success, though the effect is marginal. In addition, parasitoids are more likely to establish when their herbivore host has had a shorter residence time. Interestingly, we do not corroborate earlier findings that gregarious parasitoids and endo-parasitoids are more likely to establish. Most importantly, we find that life-history traits of the parasitoid species and their hosts can interact to influence establishment. Specifically, parasitoids with broader host ranges are more likely to establish when the herbivore they have been released to control is also more of a generalist. These results provide insight into how multiple species' traits and their interactions, both within and across trophic levels, can influence establishment of species of higher trophic levels.