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Taxonomy and Biogeography of Nematode Communities at Harvard Forest 2014
Our overall goal is to describe and map nematode biodiversity in North America. Specifically we will concentrate on Criconematina, a suborder of plant parasitic, soil-dwelling nematodes. Criconematina, commonly referred to as ring-nematodes, are distributed globally associated with a wide range of hosts and habitats. In native grasslands and forests, they may constitute as much as 30% of the below-ground nematode community. Their abundance often approaches 500 individuals per 100cc of soil with as many as a dozen species recorded from a single habitat. Host associations may be broad, covering entire plant families, or they may specialize in feeding on a few closely related plant species. Several are known agronomic pest species, but the vast majority is known only from native habitats and responds negatively to soil disturbance. Due to their sensitivity to disturbance and associations with a range of plant species, some ecologists have suggested that ring nematodes could serve as a below-ground biological indicator of habitat quality. Before this application is possible taxonomic boundaries need to be evaluated and a reference database needs to be established.
Ecology and Biogeography of a Northern Caddisfly in Cape Cod MA 1996-2002
We have documented a large population of a rarely collected northern caddisfly, Phanocelia canadensis (Banks) (Trichoptera: Limnephilidae), on Cape Cod, Massachusetts. This species' range is generally considered to be centered in northern Canada, although there are individual records of adults from eastern Maine and New Hampshire, and a single adult was collected from Sherborn, Massachusetts, in the 1920s. The scarcity of this species in collections may reflect true rarity, with populations sparsely but widely distributed across the northern part of the continent. Alternately, it may reflect a sampling bias, in that the larvae of this species were unknown until the late 1980s, adults are diurnal and fly late in the fall, and larvae occur in wetlands and are closely associated with Sphagnum, from which they make their cases. We have carried out an in-depth habitat comparison of sites in which we found or did not find larvae on Cape Cod in seven years of intensive sampling. Habitat characteristics distinguishing wetlands with and without Phanocelia include dominance by Sphagnum sp., shrub cover, and low pH. We are currently searching in similar habitats across the state for additional populations.
Ancient mitogenomes reveal the evolutionary history and biogeography of sloths
<p><strong>Supplementary Material for:</strong></p> <p>Delsuc F., Kuch M., Gibb G.C., Karpinski E., Hackenberger D., Szpak P., Martínez J.G., Mead J.I., McDonald H.G., MacPhee R.D.E., Billet G., Hautier L., and Poinar H.N. (2019). Ancient mitogenomes reveal the evolutionary history and biogeography of sloths. Current Biology. doi:10.1016/j.cub.2019.05.043.</p> <p> </p> <p><strong>Delsuc-CurrBiol-2019_capture_baits.fasta: </strong>Sequence baits designed from living xenarthran mitogenomes and reconstructed ancestral sequences used to capture ancient sloth mitogenomes. </p> <p><strong>Delsuc-CurrBiol-2019_dataset.fasta:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in fasta format.</p> <p><strong>Delsuc-CurrBiol-2019_dataset.phylip:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in phylip format.</p> <p><strong>Delsuc-CurrBiol-2019_dataset_partitions.nex:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in nexus format with partitions.</p> <p><strong>Delsuc-CurrBiol-2019_FigS2_RAxML_MLtree_100BP_nexus_for_FigTree.tree: </strong>Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using RAxML. Related to Figure 1.<strong> </strong>Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS3_IQ-TREE_MLtree_100BP_nexus_for_FigTree.tree</strong><strong>:</strong> Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using IQ-TREE. Related to Figure 1. Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS4_MrBayes_consensus_nexus_for_FigTree.tree: </strong>Bayesian consensus mitogenomic tree inferred under the best-fitting partitioned model using MrBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree. </p> <p><strong>Delsuc-CurrBiol-2019_FigS5_PhyloBayes_consensus_nexus_for_FigTree.tree: </strong>Bayesian consensus mitogenomic tree inferred under the CAT-GTR+G<sub>4</sub> mixture model using PhyloBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS6_PhyloBayes_chronogram_nexus_for_FigTree.tree</strong><strong>: </strong>Bayesian mitogenomic chronogram. Related to Figure 2. This chronogram was inferred under the CAT-GTR+G<sub>4</sub> mixture model and an autocorrelated lognormal model of clock relaxation using PhyloBayes. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_Megatherium_bone_extraction_protocol.pdf: </strong>Detailed protocol for <em>Megatherium americanum</em> MAPB4R 3965 bone sample preparation.</p> <p><strong>Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MOL_constraint.pdf: </strong>Maximum likelihood ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the molecular topology as a backbone constraint. </p> <p><strong>Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MORPH_constraint.pdf: </strong>Maximum likelihood ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions. </p> <p><strong>Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MOL_constraint.pdf: </strong>Maximum parsimony ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. obtained using the molecular topology as a backbone constraint.</p> <p><strong>Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MORPHO_constraint.pdf: </strong>Maximum parsimony ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. (2019) on the maximum parsimony topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions. </p> <p><strong>Delsuc-CurrBiol-2019_TableS1_PartitionFinder_RAxML_best_partition_scheme.txt: </strong>Detailed results of the PartitionFinder analysis for RAxML.</p> <p><strong>Delsuc-CurrBiol-2019_TableS2_ModelFinder_IQ-TREE_best_partition_scheme.txt: </strong>Detailed results of the ModelFinder analysis for IQ-TREE.</p> <p><strong>Delsuc-CurrBiol-2019_TableS3_PartitionFinder_MrBayes_best_partition_scheme.txt: </strong>Detailed results of the PartitionFinder analysis for MrBayes.</p> <p> </p>
Biogeography of root fungi in grasslands
Aim: Roots and rhizospheres host diverse microbial communities that can influence the fitness, phenotypes, and environmental tolerances of host plants. Documenting the biogeography of microbiomes can detect the potential for a changing environment to disrupt host-microbe interactions, particularly in cases where microbes, such as root-associated Ascomycota, buffer hosts against abiotic stressors. We evaluated whether root-associated fungi had poleward declines in diversity as occur for many animals and plants, tested whether microbial communities shifted near host plant range edges, and determined the relative importance of latitude, climate, edaphic factors, and host plant traits as predictors of fungal community structure. Location: North American plains grasslands Taxon: Foundation North American grass species ⎯ Andropogon gerardii, Bouteloua eriopoda, B. gracilis, B. dactyloides, and Schizachyrium scoparium and their root-associated fungi Methods: At each of 24 sites representing three replicate latitudinal gradients spanning 17° latitude, we collected roots from 12 individual plants per species along five transects spaced 10 m apart (40 m × 40 m grid). We used next-generation sequencing of the fungal ITS2 region, direct fungal culturing from roots, and microscopy to survey fungi associated with grass roots. Results: Root-associated fungi did not follow the poleward declines in diversity documented for many animals and plants. Instead, host plant identity had the largest influence on fungal community structure. Edaphic factors outranked climate or host plant traits as correlates of fungal community structure; however, the relative importance of these environmental predictors differed among plant species. As sampling approached host species range edges, fungal composition converged among individual plants of each grass species. Main conclusions: Environmental predictors of root-associated fungi depended strongly on host plant species identity. Biogeographic patte
Data for: Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods (Journal of Biogeography, 2024)
<p>Original research article:</p> <p>Knüsel, M., Alther, R., Locher, N., Ozgul, A., Fišer, C. & Altermatt, F. (2024). Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods. <em>Journal of Biogeography</em>, https://doi.org/10.1111/jbi.14975.</p>
Appendix S3 from: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190
<p>This dataset corresponds to GIS file that were generated in the study published by Droissart, Dauby et al. in <em>Journal of Biogeography</em>:</p> <p>Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. <em>Journal of Biogeography. </em>DOI:10.1111/jbi.13190</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p> <p> </p> <p>The GIS file is referred in the paper as <strong>Appendix S3</strong> and correspond to the map presented in Figure 1. Each polygons of the shapefile correspond to the main floristic bioregions and transition zones of tropical Africa delimited using bipartite network clustering analysis of 24,719 plant species.</p> <p>The coordinate system of the ESRI shapefile is GCS_WGS_1984. Field descriptions for the associate table are:</p> <ul> <li><strong>bionames</strong>: name of the bioregions as given in Table S1.1.</li> <li><strong>bioreg_ID</strong>: identifier of the bioregions as given in Table S1.1 and Fig. 1. T= Transition zones</li> <li><strong>cluster_ID</strong>: identifier of clusters delimited using bipartite network clustering on the 24,719 plant species of the RAINBIO database, as given in Table S1.1 and Fig. S2.1.</li> </ul>
Kelp Metapopulations: Macrocystis pyrifera microsatellite marker biogeography study
Dataset contains microsatellite genotypes specific for Macrocystis pyrifera (giant kelp). Table 1 describes seven loci from blades collected from 62 sites from Alaska, USA, to Baja California, Mexico, and Table 2 blades collected at 38 sites (subpopulations) in central California (Monterey Bay). Each row is the multilocus genotype for a single specimen (individual). These data were described in <ulink url="http://dx.doi.org/10.1111/mec.13371">Johansson ML, Alberto F, Reed DC, Raimondi PT, Coelho NC, Young MA, Drake PT, Edwards CA, Cavanaugh K, Assis J, Ladah LB, Bell TW, Coyer JA, Siegel DA, Serrão EA (2015) Seascape drivers of Macrocystis pyrifera population genetic structure in the northeast Pacific. Molecular Ecology. 24, 4866–4885.</ulink>
A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled: Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study Iliana Chollett1, D. Ross Robertson2 1 Sea Cottage, Louisburgh, Co. Mayo, Ireland 2 Smithsonian Tropical Research Institute, Balboa, Panamá
<p><strong>A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:</strong></p> <p><strong><em> </em></strong></p> <p><strong><em>Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study</em></strong></p> <p> </p> <p> Iliana Chollett, D. Ross Robertson</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong>Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá</strong></p> <p><strong> </strong></p> <p>This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-33<sup>0</sup> N, 57-100<sup>0</sup> W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.</p> <p> </p> <p>Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin <em>et al.</em> 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).</p> <p> </p> <p>Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.</p> <p><strong> </strong></p> <p><strong>Global Biodiversity Information Facility </strong>(GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Ocean Biogeographic Information System</strong> (OBIS, <a href="https://obis.org/">https://obis.org/</a>): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Integrated Digitized Biocollections</strong> (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).</p> <p> </p> <p><strong>FishNet2 </strong>(http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).</p> <p> </p> <p><strong>FishBase</strong> (<a href="http://www.fishbase.org/">http://www.fishbase.org</a>): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).</p> <p> </p> <p><strong>Smithsonian Tropical Research Institute</strong> (STRI; <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources: data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO; http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.</p> <p> </p> <p>Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a perpetual existence. For example the damselfish <em>Abudefduf saxatilis </em>is a common and widespread inhabitant of tropical reefs on both sides of the Atlantic, but does not naturally occur outside that ocean. Despite the fact that its taxonomic status and range were resolved ~30 y ago (e.g. see Allen, 1991) museum data presented by the all five aggregators that contributed to the multi-source database used in this study currently (December 10, 2019) show large numbers of records of this species throughout the entire tropical Indo-Pacific, as well as across its native range in the Atlantic. Since many of the databases accumulating on aggregators are derivative (lists derived from records and from other derivative lists) it will become increasingly difficult to eliminate such errors as corrections to data in primary sources do not automatically propagate through the chain of usage by different databases. Due to increasing limitations on resources for taxonomic work, museums themselves have difficulty dealing with errors in specimen identity and location, and old specimens become unidentifiable, specimens never get returned when loaned out, or simply vanish, and entire collections can get destroyed by hurricanes or fires, or get dumped when museums close or experience a major change in mission. Georeferenced location data on fish distributions in the neotropics (and presumably most other areas) hosted by aggregators, particularly GBIF and OBIS, which take data from a broad range of source types, might best be described as messy, and the significant potential for errors in location records and an inability to verify records always needs to be taken into account when incorporating data from aggregators, primary museum sources, and analog sources.</p> <p> </p> <p>Data considered for inclusion in the STRI database were screened as follows to exclude questionable records. Data from two databases hosted by OBIS and GBIF were excluded entirely due to lack of reliability: BioGoMx (https://www.gulfbase.org/project/biodiversity-gulf-mexico-biogomx-database) and Diveboard (http://www.diveboard.com). The only REEF data used were from “expert” REEF recorders on readily identifiable species that are unlikely to be confused with similar species (e.g. data for some genera of sparids, gerreids, labrisomids and gobies that include various sympatric species with very similar appearances, were not used). After data from aggregators and museum sources were combined into a single database duplicate records were filtered out by rounding all records to three decimal places and eliminating duplicates, a process that inevitably deleted some valid records as well as duplicates. The sizes of the databases and abundance of such duplicates precluded individual manual exclusion. Finally, all location data for each species were revised by DRR by examining the distribution of its georeferenced coordinates overlayed on a digital map of the current known distribution range of that species (for such range information see Carpenter & De Angelis, 2002; Ebert <em>et al.</em>, 2013; Last <em>et al.</em>, 2016; Robertson & Van Tassell, 2019; IUCN Redlist species accounts for most species considered here: https://www.iucnredlist.org/search). Such revision took into account any recent modifications to taxonomy and distributions due to new data and new publications, or as a result of discussions between DRR and experts in the taxonomy of particular species or genera. Source information of many individual questionable records provided by aggregators with the hosted data was inspected to try and assess their validity. Records thought likely to be erroneous were deleted. Those included inexplicable records lacking adequate documentation located well outside the known distribution range, and records in unlikely habitats (e.g. on land for marine species; in deep water for shallow-water species). This revision process reduced the number of records by about 30%.</p> <p> </p> <p>Data from the five individual aggregator databases that are used in the comparisons described here were all downloaded from their online portals during May, 2019. However, data from those five aggregators that were incorporated in the STRI database were downloaded in March 2017, with data from other sources described above added to the STRI database intermittently between then and May 2019, when the entire dataset was curated as described above. Hence the five individual aggregator databases analyzed in this study undoubtedly contain data not included in the version of the STRI database used in the present analyses.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>Data acquisition and construction of the STRI database was supported by funds from STRI, the Smithsonian Marine Science Network, the Smithsonian Publications Fund, the Smithsonian’s Deep Reef Observation Project, the National Geographic Society, the IUCN Red List program, the Harte Research Institute, and CONABIO. We thank REEF and AGRRA for supplying species-location records, various people for taxonomic and location-record information used to construct that database (principal among them C Baldwin, S Brandl, K Conway B Frable, T Menut, T Munroe, R Robins, L Tornabene, J Van Tassell and B Victor), and hundreds of citizen-scientist submarine photographers whose images (see <a href="https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists">https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists</a>) acted as vouchers for location records.</p> <p> </p> <p><strong>References</strong></p> <p><strong> </strong></p> <p>Allen, G.R. (1991) <em>Damselfishes of the World</em>. Mergus, Melle, 271 p.</p> <p>Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. <em>Scientific Reports</em>, 8, 4920.</p> <p>Carpenter, K.E. (Ed) (2002) <em>The living marine resources of the Western Central Atlantic.</em> Vols 1-3, FAO, Rome, 2127 p.</p> <p>Ebert, D.A., Fowler, S., Compagno, L. (2013) <em>Sharks of the World: a fully illustrated guide</em>. Wild Nature Press, Plymouth. 528 p.</p> <p>GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e).</p> <p>Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). <em>Marine Geodesy</em>, 5, 73–80.</p> <p>Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. <em>Atoll Research Bulletin</em>, 496, 611–624.</p> <p>Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). <em>Rays of the World</em>. CSIRO, Clayton. 790 p.</p> <p>Pattengill-Semmens, C.V. & Semmens, B.X. (2003) <em>Conservation and management applications of the reef volunteer fish monitoring program</em>. <em>Coastal Monitoring through Partnerships: Proceedings of the Fifth Symposium on the Environmental Monitoring and Assessment Program (EMAP) Pensacola Beach, FL, U.S.A., April 24–27, 2001</em> (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht.</p> <p>Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. <em>Diversity and Distributions, 14<strong>,</strong> 891-892</em></p> <p>Robertson, D.R,, Dominguez-Dominguez, O., Lopez Arollo, Y.M., Moreno Mendoza. R., Simoes, N. (2019) Reef-associated fishes from the offshore reefs of western Campeche Bank, Mexico, with a discussion of mangroves and seagrass beds as nursery habitats. <em>Zookeys </em>843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a></p> <p>Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. <em>Smithsonian Tropical Research Institute, Balboa, Panamá</em>. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>.</p> <p>Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. <em>Journal of Geophysical Research: Solid Earth</em>, 101, 8741–8743.</p>
Fig. 3 in Revision of Bairdiella (Sciaenidae: Perciformes) from the western South Atlantic, with insights into its diversity and biogeography
Fig. 3. The Atlantic coast of North, Central, and South America showing the geographic distribution of Bairdiella goeldi sp. nov. (yellow = molecular north–northeastern lineage, light blue = molecular southeastern lineage, white = no molecular data), B. ronchus (red), and B. veraecrucis (green). Some symbols represent more than one locality or a large number of specimens.
Fig. 3 in Systematics, Morphology and Biogeography Phylogeny of the Augochlora clade with the description of four new species (Hymenoptera, Apoidea)
Fig. 3. Paratypemaleof Augochlorellakelliae sp. nov.(A) Dorsalview; (B) frontalview of head; (C) ventral view of genitalia. Scale bar: Aand Bat 0.5 mm, Cat 0.25 mm.
Fig. 5 in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 5. Median-joining haplotype network for Chrotogale owstoni Thomas, 1912 Cytb haplotypes (top: 837 bp, bottom: 235 bp). The size of each circle is proportional to the haplotype frequency. White = central Vietnam clade; grey = northern Vietnam clade, black = China.
Fig. 4 in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 4. Median-joining haplotype network for Hemigalus derbyanus (Gray, 1837) Cytb haplotypes (top: 837 bp, bottom: 253bp). The size of each circle is proportional to the haplotype frequency. The colours of the haplotypes correspond to those on the map: black = Borneo; dark grey = Siberut Island (Mentawai Islands); light grey = Penang Island (Peninsular Malaysia); white = Sumatra; hatched = Zoo samples (on top network: Singapore Zoo (H2), Negara Zoo, Kuala Lumpur (H7) and Batu Secret Zoo & Maharani Zoo, Java (H8)).
Fig. 1 in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 1. Map showing the distribution of the samples of Hemigalinae used in this study. The gray shadings show the range of Chrotogale owstoni Thomas, 1912, Cynogale bennettii Gray, 1837 and Hemigalus derbyanus (Gray, 1837); Diplogale hosei (Thomas, 1892) is only found on Borneo and Macrogalidia musschenbroekii (Schlegel, 1879) only occurs on Sulawesi. The size of the sample symbols corresponds to the number of samples from each area (the smallest equals 1, and the largest equals 12–14).
Fig. 2. Bayesian tree reconstructed from a in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 2. Bayesian tree reconstructed from a combined dataset of Cytb + ND2 + FGB + IRBP (3342 bp). The values on the branches are bayesian posterior probabilities for the partitioned analysis (see text for models) and bootstrap proportions obtained from ML analysis (model: GTR + I + G).
Fig. 3 in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 3. Phylogenetic tree obtained with NJ for a fragment of Cytb (893 bp). The values over the branches are the bootstrap proportions for NJ, and below the branches are those for ML. Co = Chrotogale owstoni Thomas, 1912; Hd = Hemigalus derbyanus (Gray, 1837); Dh = Diplogale hosei (Thomas, 1892); Cb = Cynogale bennettii Gray, 1837.
FIGURE 136 in The marine ichthyofauna of Lebanon: an annotated checklist, history, biogeography, and conservation status
FIGURE 136. Tylerius spinosissimus (Regan 1908), Okaibeh, 8 December 2006, AUBM (OS3803), Crocetta & Bariche in Dailianis et al. (2016).
FIGURE 134 in The marine ichthyofauna of Lebanon: an annotated checklist, history, biogeography, and conservation status
FIGURE 134. Sphoeroides pachygaster (Müller & Troschel 1848), Aabdeh, April 2017, AUBM (OS3927-29), Crocetta & Bariche in Gerovasileiou et al. (2017).
FIGURE 131. Ostracion cubicus Linnaeus 1758 in The marine ichthyofauna of Lebanon: an annotated checklist, history, biogeography, and conservation status
FIGURE 131. Ostracion cubicus Linnaeus 1758, Beirut, 2 November 2015, AUBM (OS3913), Crocetta & Bariche in Dailianis et al. (2016).
FIGURE 118 in The marine ichthyofauna of Lebanon: an annotated checklist, history, biogeography, and conservation status
FIGURE 118. Cephalopholis taeniops (Valenciennes 1828), Tripoli, 14 June 2016, AUBM (OS3914), Crocetta & Bariche in Gerovasileiou et al. (2017).
FIGURE 108 in The marine ichthyofauna of Lebanon: an annotated checklist, history, biogeography, and conservation status
FIGURE 108. Parupeneus forsskali (Fourmanoir & Guézé 1976), Beirut, 28 December 2012, AUBM (OS3889), Bariche et al. (2013b).
ScienceDex guides
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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
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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
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