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727 results for “phylogenetic diversity”
FIGURE 9 in Morphological diversity and phylogenetic relationships within a South-American clade of iguanian lizards (Liolaemidae: Phymaturus)
FIGURE 9. Cladogram obtained applying implied weights' method (concavity constant K value=3). The patagonicus and palluma groups indicated with blue and red branches respectively. Nodes indicated with letters are for apomorphies and topology discussion purposes only.
Data from: Functional and phylogenetic diversity promotes litter decomposition across terrestrial ecosystems
Aim: Litter decomposition is a vital process of carbon and nutrient cycling in terrestrial ecosystems. Despite rapid declines in plant diversity worldwide, the plant diversity effects on litter decomposition, along with the factors driving their directions and magnitudes, remain uncertain. Location: Globe. Time period: 1985-2018. Major taxa studied: Plants. Methods: By synthesizing 492 paired observations of leaf litter mixtures and monocultures from 110 studies, we conducted a global meta-analysis of the effects of litter mixtures on litter decomposition rates, which were calculated as <i>k</i> coefficients from <i>m</i><sub><i>t</i></sub>/<i>m</i><sub>0</sub> =<i>e</i><sup>-<i>kt</i></sup>, where <i>m</i><sub><i>t</i></sub>/<i>m</i><sub>0</sub> was litter mass remaining proportion corresponding to time <i>t</i>. Results: Litter mixtures on average increased litter decomposition rates by 5.6% (95% confident intervals, 3.0%-8.1%), and the effects of litter mixtures increased with litter species richness, the functional diversity of chemical traits (leaf C, N, P contents and C:N ratio) and phylogenetic diversity consistently across terrestrial ecosystems. The decomposer abundance and function, including soil fauna abundance, microbial biomass, and extracellular enzyme activities were positively associated with litter mixture effects on decomposition rates. The structural equation models accounted for 48.6% of the global variation in litter decomposition rates and revealed that the positive effects of litter functional diversity on decomposer abundance and function led to increased litter decomposition rates, while litter phylogenetic diversity had a direct effect on litter decomposition rates. Main conclusions: The functional diversity of the chemical traits and phylogenetic diversity, both as indicators for complementarity effects, are important drivers for increasing litter mixture effects on decomposition. The positive litter diversity effects on decomposition rates are mechanistically linked with soil fauna abundance, microbial biomass, and extracellular enzyme activities. Our results suggest that plant diversity, especially functional and phylogenetic diversity, increases decomposer abundance and function, and thus plays a key role in the carbon and nutrient cycling across terrestrial ecosystems.
Figure 2. Phylogenetic tree resulting from a in Host specialization and species diversity in the genus Stylops (Strepsiptera: Stylopidae), revealed by molecular phylogenetic analysis
Figure 2. Phylogenetic tree resulting from a Bayesian analysis of the partial sequence from the mitochondrial NADH gene. The names of the host Andrena bees are indicated with every Stylops voucher number. The posterior probabilities are given before the slash; the bootstrap values from the maximum-likelihood (ML) analysis are given after the slash. Posterior probability values lower than 0.9, and bootstrap values lower than 50, are considered as unsupported and are thus replaced by an asterisk (*); incongruent nodes between the two analyses are indicated by a dash (-). Branch support is omitted at the nodes that were unsupported in both the Bayesian and the ML analyses.
Figure 3. Phylogenetic tree resulting from a in Host specialization and species diversity in the genus Stylops (Strepsiptera: Stylopidae), revealed by molecular phylogenetic analysis
Figure 3. Phylogenetic tree resulting from a Bayesian analysis of the partial sequence from the nuclear EF1 gene. Names of host Andrena bees are indicated at every Stylops voucher number. The names of the host Andrena bees are indicated with every Stylops voucher number. The posterior probabilities are given before the slash; the bootstrap values from the maximum-likelihood (ML) analysis are given after the slash. Posterior probability values lower than 0.9, and bootstrap values lower than 50, are considered as unsupported and thus replaced by an asterisk (*); incongruent nodes between the two analyses are indicated by a dash (-). Branch support is omitted at the nodes that were unsupported in both the Bayesian and the ML analyses.
Figure 1 in Molecular data extend Australian Cricotopus midge (Chironomidae) species diversity, and provide a phylogenetic hypothesis for biogeography and freshwater monitoring
Figure 1. Schematic phylogeny based on a majority rule consensus maximum-likelihood (ML) topology for the reduced data set. Relevant nodal support values are shown, which correspond to Bayesian posterior probabilities and ML bootstrap support, respectively; –, nodes unresolved by ML; *, posterior probabilities of 1.00 or bootstrap support of 100. Thick branches denote Australian clades; thin branches are non-Australian taxa. Geographical distributions and tolerances to ecosystem impact for Australian Cricotopus species are shown according to the inset legend, with the forms of divergent species coded based on assumptions made from the current sampled localities.
Figure 8. A summary tree showing 52 in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 8. A summary tree showing 52 New World nightjar species using the taxonomy recommended in this study. Also shown are 15 Old World species. Maximum-likelihood (ML) and maximum-parsimony (MP) bootstrap scores are displayed above and below the nodes.
Figure 3. A phylogeny from a in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 3. A phylogeny from a maximum-likelihood (ML) analysis of a concatenated 5298-bp molecular data set. The figure shows the Old World taxa in the basal genera Eurostopodus, Gactornis, and Lyncornis, as well as the members of the Old World crown clade. ML bootstrap values higher than 60 and Bayesian posterior probability values higher than 0.95 are displayed on the nodes. Names on the tree represent current taxonomy; names on the right represent those resulting from this revision (see text).
Figure 2 in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 2. Phylogenetic overview of relationships among the main clades of the Caprimulgidae based on a maximum-likelihood (ML) analysis of a concatenated 5298-bp molecular data set. ML bootstrap values higher than 60 and Bayesian posterior probability values higher than 0.85 are displayed on the nodes.
Figure 7. A in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 7. A phylogeny of the Hydropsalis group of the South American clade based on a maximum-likelihood (ML) analysis of a concatenated 5298-bp molecular data set. ML bootstrap values higher than 60 and Bayesian posterior probability values higher than 0.95 are displayed on the nodes. Names on the tree represent current taxonomy; names on the right represent those resulting from this revision (see text).
Figure 1. A in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 1. A summary figure, showing the individual gene trees of the four markers: ND2 (1041 bp); CYTB (1143 bp); RAG-1 (2873 bp); and ACO1 I9 (871 bp). The trees show the relationships among the three basal nightjar genera and the four core radiations. Bootstrap values from maximum-likelihood (ML) analyses are displayed on the nodes (only values> 60 are displayed).
Figure 4. A in Deciphering the diversity and history of New World nightjars (Aves: Caprimulgidae) using molecular phylogenetics
Figure 4. A phylogeny of the nighthawk clade based on a maximum-likelihood (ML) analysis of a concatenated 5298-bp molecular data set. ML bootstrap values higher than 60 and Bayesian posterior probability values higher than 0.95 are displayed on the nodes. Names on the tree represent current taxonomy; names on the right represent those resulting from this revision (see text).
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from partial cytochrome c oxidase subunit I (coxI) sequence alignment under a transversional of invariable sites and gamma-shaped distribution model TVM + I + G model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study in this study are in bold. Scale bar = expected changes per site.
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from internal transcribed spacer 1 (ITS1) rRNA sequence alignment under the general timereversible and gamma-shaped distribution model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study are in bold. Scale bar = expected changes per site.
Data: Use of environmental DNA in assessment of fish functional and phylogenetic diversity
<p><strong>Abstract</strong></p> <p>Assessing the impact of global changes and protection effectiveness is a key step in monitoring marine fishes. Most traditional census methods are demanding or destructive. Nondisturbing and nonlethal approaches based on video and environmental DNA are alternatives to underwater visual census or fishing. However, their ability to detect multiple biodiversity factors beyond traditional taxonomic diversity is still unknown. For bony fishes and elasmobranchs, we compared the performance of eDNA metabarcoding and long-term remote video to assess species’ phylogenetic and functional diversity. We used 10 eDNA samples from 30 L of water each and 25 hours of underwater videos over 4 days on Malpelo Island (pacific coast of Colombia), a remote marine protected area. Metabarcoding of eDNA detected 66% more molecular operational taxonomic units (MOTUs) than video. We found 66 and 43 functional entities with a single eDNA marker and videos, respectively, and higher functional richness for eDNA than videos. Despite gaps in genetic reference databases, eDNA also detected a higher fish phylogenetic diversity than videos; accumulation curves showed how 1 eDNA transect detected as much phylogenetic diversity as 25 hours of video. Environmental DNA metabarcoding can be used to affordably, efficiently, and accurately census biodiversity factors in marine systems. Although taxonomic assignments are still limited by species coverage in genetic reference databases, use of MOTUs highlights the potential of eDNA metabarcoding once reference databases have expanded.</p> <p><strong>Methods</strong></p> <p>We sampled around the Sanctuary of Fauna and Flora in Malpelo, a remote oceanic island 490 km off the Colombia in the eastern tropical Pacific (Fig. 1), for 4 days (25-28 March 2018) at 1 site (El Arrecife). Malpelo is surrounded by deep water and fishing activities are prohibited in the surrounding 8,757 km2 (Edgar et al. 2011). The reef ecosystem around the island is influenced by major oceanic currents (Rodríguez-Rubio et al. 2003) and local upwelling, and the benthos is bare rock with low coral cover (Quimbayo et al. 2017). Malpelo Island is one of the most pristine and vulnerable reef ecosystems in the tropical eastern Pacific. Fish biomass and biodiversity are high (>250 vertebrates species) and provide a baseline for undisturbed assemblages in this marine province (Quimbayo et al. 2017).</p> <p>We deployed 1 long-duration remote underwater video system (RUV) (Extrem-Vision, Rivesaltes, France) that films up to 12 hours (screenshots in Appendix S1). The camera was 40 cm above the seafloor (13 m deep, 04.00600°, -81.60433°) and had a 90° field of view in which benthic and pelagic areas were recorded over 10 m2. Resolution was 1920 x 1080 pixels, and 30 frames/second were shot. Recording occurred on 25 (day and night) and 28 March (day) (Fig 1c). Cameras filmed 24 hours and 50 minutes of video. At night 2 dive lights illuminated the camera’s view. A Hero 5 (GoPro, San Mateo, California) was mounted on top the RUVs to film in the opposite direction for the first 2 hours of deployment of each daylight recording. Three hours and 30 minutes were recorded with the GoPros. During video recordings, we sampled eDNA above the camera in round surface transects. We did 5 identical transects at different times, corresponding to 10 samples (i.e., eDNA filters) (Fig. 1) because we collected 2 samples/transect. Transects sampled from a boat, and we pumped 30 L of water/sampled. We used an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) (nominal flow 1.0 L/minute) on each side of the boat to filter water through a VigiDNA 0.20 μm cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France). To avoid contamination, we used only disposable sterile tubing and gloves for each filtration capsule. Immediately after filtration, the filter units were filled with CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature (20-25° C) for 5.5 months until DNA extraction.</p> <p>For video processing, two frames/second were extracted from all videos. Fishes were identified at the lowest taxonomic level possible, following Fishbase taxonomy (Froese & Pauly 2000), by trained personnel, who recorded the first occurrence of each species in each of the videos (i.e., number of individuals per species was not recorded).</p> <p><strong>Usage Notes</strong></p> <p>* eDNA</p> <p>Pre and post-processed eDNA metabarcoding data for the three markers used in the study (teleo, chon, vert01) alongside metadata. </p> <p>* Videos </p> <p>Recording of each fish species' first occurence. </p> <p>Please see the README.md file for more information on the files.</p>
Figure 69 in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 69. Tree produced by implied weighting analysis with k = 10. Monophyletic superfamilies coloured. Abbreviations as in Figure 67.
Figure 68 in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 68. Tree produced by implied weighting analysis with k = 1. Monophyletic superfamilies coloured. Abbreviations as in Figure 67.
Figure 66 in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 66. Petiole, ventral view, anterior to the top. A, Megalyra fasciipennis (Megalyridae); B, Doryctes erythromelas (Braconidae); C, Evaniella semaeoda (Evaniidae); D, Megastigmus transvaalensis (Torymidae); E, Gonatocerus morrilli (Mymaridae); F, Asaphes vulgaris (Pteromalidae). Abbreviations in Appendix 2.
Figure 63. Propodeal foramen. A in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 63. Propodeal foramen. A, Lagynodes sp. (Megaspilidae); B, Spilomicrus stigmaticalis (Diapriidae); C, Sparasion formosum (Scelionidae); D, Sapyga pumila (Sapygidae); E, Nasonia vitripennis (Pteromalidae); F, Brachygaster minuta (Evaniidae). A, ventral view; B–F, posterior view. Abbreviations in Appendix 2.
Figure 59. Hind leg, lateral view. A in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 59. Hind leg, lateral view. A, Phaenoserphus sp. (Proctotrupidae); B, Eurytoma gigantea (Eurytomidae); C, Megischus sp. (Stephanidae); D, Acanthochalcis nigricans (Chalcididae). Abbreviations in Appendix 2.
Figure 61 in Beyond the wasp-waist: structural diversity and phylogenetic significance of the mesosoma in apocritan wasps (Insecta: Hymenoptera)
Figure 61. Propodeum, lateral view, anterior to the left. A, B, lateral view: A, Megischus sp. (Stephanidae); B, Doryctes erythromelas (Braconidae); C, D, dorsal view: C, Sapyga pumila (Sapygidae); D, Pantolytomyia ferruginea (Diapriidae). Abbreviations in Appendix 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.