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Occurrence cubes at species level for European countries
<p>This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the <a href="https://www.gbif.org/">Global Biodiversity Information Facility (GBIF)</a> by year (year), 1x1km spatial <a href="https://www.eea.europa.eu/en/datahub/datahubitem-view/3c362237-daa4-45e2-8c16-aaadfb1a003b">EEA reference grid</a> cell (eea_cell_code) and species (speciesKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see <a href="https://github.com/trias-project/occ-cube/blob/master/src/3_assign_grid.Rmd#L198-L234">this code</a>). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling.</p> <p>The occurrence cubes are built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.</li> <li>The code to process the data to cubes is publicly available on GitHub at <a href="https://github.com/trias-project/occ-cube-alien">https://github.com/trias-project/occ-cube</a> (version <a href="https://github.com/trias-project/occ-cube/tree/20240124">20240124</a>).</li> </ul> <h2>Files</h2> <ul> <li><strong>Occurrence cubes at species level per country</strong>: filename format countrycode_species_cube.csv.</li> <li><strong>Taxonomic information for species in a cube</strong>: filename format countrycode_species_info.csv.</li> </ul> <h2>Included countries</h2> <ul> <li><strong>Belgium</strong> (BE): based on <a href="https://doi.org/10.15468/dl.9qx3ba">https://doi.org/10.15468/dl.9qx3ba</a></li> <li><strong>Italy</strong> (IT): based on <a href="https://doi.org/10.15468/dl.jghpm5">https://doi.org/10.15468/dl.jghpm5</a></li> <li><strong>Lithuania</strong> (LT): based on <a href="https://doi.org/10.15468/dl.duegx2">https://doi.org/10.15468/dl.duegx2</a></li> <li><strong>Slovenia</strong> (SI): based on <a href="https://doi.org/10.15468/dl.9eky98">https://doi.org/10.15468/dl.9eky98</a></li> <li><strong>Romania</strong> (RO): based on <a href="https://doi.org/10.15468/dl.b7z5vw">https://doi.org/10.15468/dl.b7z5vw</a></li> <li><strong>Portugal</strong> (PT): based on <a href="https://doi.org/10.15468/dl.b89nr4">https://doi.org/10.15468/dl.b89nr4</a></li> </ul> <p>Occurrence cubes are added on demand. To include occurrence cubes for other European countries, <a href="https://github.com/trias-project/occ-cube/issues">leave an issue</a> or contact the main author, or generate your own cube using the code in <a href="https://github.com/trias-project/occ-cube">this repository</a>.</p>
Grasshopper species occurrence data in Mt. Kilimanjaro
<p>Species occurence data of grasshopper in 60 plots on the southern slope of Mt. Kilimanjaro.</p> <p>Orthoptera assemblages were recorded on all study sites by repeatedly walking for 1.5 h on parallel tracks (distance between transects ca. 1-1.5 m) and recording all sighted species. In forested study sites, trees and bushes in the understory vegetation were shaken for approximately 1.5 h. Insects falling from the vegetation were gathered on white canvas laid on the forest floor. Species which could not be identified during visits were collected and later identified. Study sites were also visited at night where Ensifera were registered acoustically. Additionally, two rounds of sweep net sampling were conducted on study sites to collect small species which may have remained undetected during transect walks. One round was conducted during the cool dry season (July to October) and one during the warm dry season (December to March). During each sweep netting round, 100 sweeps with a 30-cm diameter sweep were taken and all collected specimens were identified in the laboratory. Species accumulation curves for Caelifera and Ensifera on Mt. Kilimanjaro were published in , showing that more than 90% of the grasshopper, locust and bushcricket fauna for Mt. Kilimanjaro have been registered.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
ZooBase: A global synthesis of marine zooplankton species occurrences.
<p><em><strong>Description of the methods used to implement the present ZooBase dataset (extarct from Section A.2 from the Methods of Benedetti et al., 2021).</strong></em></p> <p>A new dataset of global zooplankton species occurrences was compiled in a comparable fashion to that put together for phytoplankton (Righetti et al., 2020). Prior to retrieving the occurrence data online, we first identified the phyla (Order/Class/Family) that comprise the bulk of extant oceanic zooplankton communities: Copelata (i.e. appendicularians), Ctenophora, Cubozoa (i.e. box jellyfish), Euphausiidae (i.e. krill), Foraminifera, Gymnosomata (i.e. sea angels, pteropods), Hydrozoa (i.e. jellyfish), Hyperiidea (i.e. amphipods), Myodocopina (i.e. ostracods), Mysidae (i.e. small pelagic shrimps resembling krill), Neocopepoda, Podonidae and <em>Penilia</em> <em>avirostris</em> (i.e. cladocerans), Sagittoidea (i.e. chaetognaths), Scyphozoa (i.e. jellyfish), Thaliacea (i.e. salps, doliolids and pyrosomes), Thecosomata (i.e. pteropods), and four families of pelagic Polychaeta (i.e. worms) that are often found in the zooplankton and whose species are known to display holoplanktonic lifecycles (Tomopteridae, Alciopidae, Lopadorrhynchidae, Typhloscolecidae). The presence data associated with species belonging to these groups were retrieved from OBIS and GBIF between the 12/04/2018 and the 18/04/2018 using online queries via the R packages RPostgreSQL, robis and rgbif. Since the Neocopepoda infra-class comprise several thousands of benthic and parasitic taxa (https://copepodes.obs-banyuls.fr/en/), a preliminary selection of the non-parasitic planktonic species had to be carried out prior to the online downloading using the species list of Razouls et al. (https://copepodes.obs-banyuls.fr/en/) as a reference. The spatial distributions of the groups cited above were first inspected using GBIF’s and OBIS’s online mapping tools to evaluate the potential number of overlapping observations between the two databases. As a result of their relatively low contributions to total observations/diversity, and very high overlap between databases, the occurrences of Cladocera and Polychaeta were retrieved from OBIS only (which usually harbours more occurrences). On top of the data collected from OBIS and GBIF, the copepod occurrences from Cornils et al. (2018) and the pteropod occurrences from the MAREDAT initiative (Buitenhuis et al., 2013) were added to the dataset. We discarded records that: (i) presented at least one missing spatial coordinate, (ii) were associated with an incomplete sampling date (d/m/y), (iii) were associated with a year of collection older than 1800, (iv) were not associated with any sampling depth, (v) were not identified down to the species level. Occurrences associated with grid cells shallower than 10m were removed (bathymetry data from ETOPOv1 at a 15min resolution, downloaded using the 'marmap' R package). Finally, every species name was then carefully examined and compared to the taxonomic reference list of the World Register of Marine Species (WoRMS; <a href="http://www.marinespecies.org">http://www.marinespecies.org</a>) for all taxa. The AphiaID and the Status were retreived from WoRMS based on the ScientificName. To remove the duplicate occurrences due to the highly overlapping source archives (GBIF and OBIS), a unique occurrenceID was given to each record based on rounded spatial coordinates (closest 0.1°x0.1°), rounded depth layer (10m depth layers), month and year of the occurrence and the acccpted species name (e.g., AphiaID).</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Harmonized Tree Species Occurrence Points for Europe
<p>This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.</p> <p>The <em>RDS </em>file is created from an sf-object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p><strong>The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a></strong></p> <p>Data sets were last updated in September 2021.</p> <p>Variables:</p> <ul> <li><strong>id</strong> = unique point identifier</li> <li><strong>easting</strong> = x coordinate</li> <li><strong>northing </strong>= y coordinate</li> <li><strong>country </strong>= ISO country code</li> <li><strong>species </strong>= Latin species name</li> <li><strong>genus </strong>= genus name</li> <li><strong>scientific_name </strong>= long species name</li> <li><strong>gbif_taxon_key </strong>= taxon key from GBIF</li> <li><strong>gbif_genus_key </strong>= genus key from GBIF</li> <li><strong>taxon_rank </strong>= species or genus</li> <li><strong>year </strong>= year of observation</li> <li><strong>accessed_through </strong>= database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li><strong>dataset_info </strong>= data set name (individual sub-data-set)</li> <li><strong>citation </strong>= DOI citation of the individual data set</li> <li><strong>license </strong>= distribution license</li> <li><strong>location_accuracy </strong>= spatial accuracy of observation (meters)</li> <li><strong>flag_location_issue </strong>= known location issues present</li> <li><strong>flag_date_issue </strong>= known date issues present</li> <li><strong>eoo </strong>= Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)</li> <li><strong>dbh </strong>= Diameter Breast Height (only recorded for observations from the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>))</li> <li><strong>lc1 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS</a> land cover type 1 (only recorded for observations from LUCAS data)</li> <li><strong>lc2 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS </a>land cover type 2 (only recorded for observations from LUCAS data)</li> <li><strong>landmask_country </strong>= land mask overlay 30 meters (NA = not on land)</li> <li><strong>corine </strong>= <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">CORINE 2018</a> land cover type (extracted from the 100 meter raster data set)</li> <li><strong>nightlights</strong> = <a href="https://eogdata.mines.edu/download_dnb_composites.html">light pollution</a> observed by VIIRS (proxy for remoteness / distance to human structures)</li> <li><strong>canopy_height </strong>= <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li><strong>natura_2000 </strong>= Natura 2000 site code (if a point falls inside a protected area (<a href="https://www.eea.europa.eu/data-and-maps/data/natura-11">GIS-layer</a>) this variable contains the site identification code; all sites can be explored on an <a href="https://natura2000.eea.europa.eu/">interactive map</a>)</li> <li><strong>freq_location </strong>= number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)</li> <li><strong>geometry </strong>= point geometry in ETRS89 / LAEA Europe</li> </ul> <p>See <strong><a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a></strong> for more insights into each variable and individual GBIF data set citations.</p> <p>If you would like to know more about the creation of this data set, see</p> <ol> <li>the R-Markdown documenting the process (<a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">GitLab repository</a>)</li> <li>the talk at OpenGeoHub Summer School 2020 (<a href="https://www.youtube.com/watch?v=5HhmLGcqXLs&list=PLXUoTpMa_9s0Ea--KTV1OEvgxg-AMEOGv&index=40">Youtube</a>)</li> </ol> <p>Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.</p> <p> </p> <p>This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (<a href="https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095">https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095</a>).</p>
Global taxonomic occurrence grids using GBIF data for species distribution models.
<p>To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008).</p> <p>Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs.</p> <p>To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a></p> <p>To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided.</p> <p>GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005".</p> <p><strong>Raster Attributes</strong></p> <table> <tbody> <tr> <td> <p>Attribute</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>OID</p> </td> <td> <p>numeric row ID</p> </td> </tr> <tr> <td> <p>Value</p> </td> <td> <p>the number of records contained in the grid cell</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>the number of times the value appears in the raster</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The extent of each taxonomic occurrence grid:</p> <ul> <li> <p>longitude -180.0; latitude -90.0 (southwest corner)</p> </li> <li> <p>longitude 180.0; latitude 90.0 (northeast corner)</p> </li> </ul> <p> </p> <p><strong>Files:</strong></p> <p>TrIAS taxonomic occurrence grids</p> <p>amphib_1deg_min5.tif</p> <p>birds_1deg_min5.tif</p> <p>mammals_1deg_min5.tif</p> <p>molluscs_1deg_min5.tif</p> <p>reptiles_1deg_min5.tif</p> <p> </p> <p>Raw taxonomic occurrence grids</p> <p>amphib_1deg_grid.tif</p> <p>birds_1deg_grid.tif</p> <p>mammals_1deg_grid.tif</p> <p>molluscs_1deg_grid.tif</p> <p>reptiles_1deg_grid.tif</p> <p><br> </p> <p> </p> <p> </p>
Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020
<p>High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication:</p> <ul> <li>Silver fir (<em>Abies alba </em>Mill.)</li> <li>European beech (<em>Fagus sylvatica </em>L.)</li> <li>Norway spruce (<em>Picea abies </em>L.)</li> <li>Black pine (<em>Pinus nigra </em>J. F. Arnold)</li> <li>Scots pine (<em>Pinus sylvestris </em>L.)</li> <li>Common oak (<em>Quercus robur </em>L.)</li> </ul> <p>The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all.</p> <p>By combining the regression slope coefficient (<em>β</em>) and the <em>p</em>-value from the t-test statistics we assigned each pixel to one of three classes:</p> <ul> <li><em>positive</em>: <em>β</em> > 0.25 AND <em>p</em>-value < 0.05</li> <li><em>negative</em>: <em>β</em> < −0.25 AND <em>p</em>-value < 0.05</li> <li><em>no trend / stable</em>: −0.25 ≤ <em>β</em> ≥ 0.25 OR <em>p</em>-value > 0.05</li> </ul> <p>We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.:</p> <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>variable name: e.g. <strong>slope</strong>,</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v1.0</strong>.</li> </ul> <p>For each species here we provide the following layers:</p> <ul> <li>veg_abies.alba_<strong>slope</strong>:<strong> </strong>slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_<strong>pvalue</strong>:<strong> </strong><em>p</em>-value (scaling factor: 1000)</li> <li>veg_abies.alba_<strong>pos.trends_30m</strong>: pixels classified as <em>positive </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>pos.trends_1km</strong>: proportion of pixels of the <em>positive </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>neg.trends_30m</strong>: pixels classified as <em>negative </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>neg.trends_1km</strong>: proportion of pixels of the <em>negative </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>no.trends_30m</strong>: (pixels classified as <em>no trend / stable </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>no.trends_1km</strong>:<strong> </strong>proportion of pixels of the <em>no trend / stable </em>class over a 1×1 km area (range 0–100)</li> </ul> <p>Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in <em>QML</em> format</p> <p>A publication describing, in detail, all processing steps is currently in review. See at:<br> <br> Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]</p> <p> </p>
Occurrence data used to create species distribution models and apply an evaluation method
<p>These two files containing a table with three columns: species names, longitude, latitude. Each row of the tables represents a georeferenced presence record for the corresponding species. The original presence data were downloaded from the GBIF database and after going through a cleaning process, we ended with these records that passed all the tests.</p> <p>These datasets were used to create species distribution models (SDMs) that were then used to apply a new method to evaluate the performance of different SDMs. Jiménez & Soberón (2020)</p>
Marsh plant species frequency of occurrence along marsh transects for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.
Marsh plant species frequency of occurrence along marsh transects for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70µM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2011 and at Clubhead Creek in 2005 and from 2009 till 2011.
Fig. 14 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 14. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of posterior part of skull and Weberian capsule, dorsal view. Abbreviations: FR1, First Ray 1; CC, complex centrum; WC, Weberian capsule; VC, vertebrae centrum; PR, pleural ribs.
Fig. 15 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 15. Copionodon exotatos, holotype, MZUSP 120631, CT-scan images of left palatine and associated entopterygoid: a. dorsal view, b. ventral view, c. with entopterygoid removed. Abbreviations: PAL, palatine; EN, entopterygoid.
Fig. 13 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 13. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of opercle and interopercle, right side. Abbreviations: VOD, vestigial odontodes; OP, opercle; INT, interopercle. Lateral view.
Fig. 9 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 9. Copionodon exotatos, holotype, MZUSP 120631, CT-scan image of anterior portion of skull, dorsal view. Abbreviations: PMX, premaxilla; MX, maxilla; QUA, quadrate; PO, preopercle; ME, mesethmoid; AF, anterior fontanel; FR, frontal; DEN, dentary; PAL, palatine; AA, anguloarticular; LE, lateral ethmoid; HYO, hyomandibula.
Fig. 8 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 8. Collection site of Copionodon exotatos, right-hand branch of Riacho do Mosquito (trib. to rio Santo Antônio, rio Paraguaçu drainage), immediately upstream from Cachoeira do Mosquito (12º21'59.51"S, 41 ºS 22"19.37"W) at exit of rock-enclosed sector.
Fig. 5 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 5. Copionodon exotatos, paratype, MZUSP 121656, basibranchials and hypobranchials, dorsal view. Grey areas represent cartilage. Abbreviations: BB2-4, basibranchials 2 to 4; HB1-3, hypobranchials 1 to 3. Scale bar = 1 mm.
Fig. 4 in A new species of Copionodon representing a relictual occurrence of the Copionodontinae (Siluriformes: Trichomycteridae), with a CT-scan imaging survey of key subfamilial features
Fig. 4. Copionodon exotatos, paratype, MZUSP 121656, opercle and interopercle, right side, lateral view. Abbreviations: OPOD, opercular odontodes; OP, opercle; INT, interopercle. Scale bar = 1 mm.
Fig. 12 in New sibling species and new occurrences of squat lobsters (Crustacea, Decapoda) from the western Indian Ocean
Fig. 12. Dorsal view. Colours in life. A. Munida limula Macpherson & Baba, 1993, ♂, 3.4 mm, Madagascar, ATIMO VATAE, Stn TP12. B. Munida mesembria sp. nov., paratype, ♀, 6.5 mm, Mozambique, MAINBAZA, Stn CP3130. C. Munida micra sp. nov., holotype, ♂, 3.7 mm, Mozambique, MAINBAZA, Stn CC3165. D. Munida muscae Macpherson & de Saint Laurent, 2002, ♂, 3.2 mm, Madagascar, MIRIKY, Stn DW3179.
Fig. 11 in New sibling species and new occurrences of squat lobsters (Crustacea, Decapoda) from the western Indian Ocean
Fig. 11. Dorsal view. Colours in life. A. Munida africana Balss, 1913, ♀, 10.9 mm, Mozambique, MAINBAZA, Stn CP3141. B. Munida benguela de Saint-Laurent & Macpherson, 1988, ♂, 20.0 mm, Mozambique, MAINBAZA, Stn CP3138. C. Munida benguela de Saint-Laurent & Macpherson, 1988, ♀, 13.7 mm, Mozambique, MAINBAZA, Stn CP3135. D. Munida hoda sp. nov., paratype, ov. ♀, 11.4 mm, Mozambique, MAINBAZA, Stn CC3166.
Fig. 10 in New sibling species and new occurrences of squat lobsters (Crustacea, Decapoda) from the western Indian Ocean
Fig. 10. Munidopsis columbae sp. nov., holotype, ♂, 8.7 mm (MNHN-IU-2014-13472), Madagascar. A. Carapace and abdomen, dorsal view. B. Carapace and abdomen, lateral view. C. Sternal plastron, sternites 3 and 4. D. Cephalic region, showing antennular and antennal peduncles, ventral view. E. Right Mxp3, lateral view. F. Right P1, dorsal view. G. Right P2, lateral view. H. Right P3, lateral view. I. Right P4, lateral view. Scale bar: A–B, F–I = 2.0 mm; C–E = 1.0 mm.
Fig. 14 in New sibling species and new occurrences of squat lobsters (Crustacea, Decapoda) from the western Indian Ocean
Fig. 14. Dorsal view. Colours in life. A. Paramunida mozambica Cabezas et al., 2011, ♀, 6.0 mm, Mozambique, MAINBAZA, Stn CP3161. B. Bathymunida polae Balss, 1914, ♂, 3.8 mm, Mozambique, MAINBAZA, Stn DW3133. C. Munidopsis africana Balss, 1913, ♂, 7.4 mm, Mozambique, MAINBAZA, Stn CP3142. D. Eumunida minor de Saint Laurent & Macpherson, 1990, ♂, 5.3 mm, Mozambique, MAINBAZA, Stn DW3167.
Fig. 13 in New sibling species and new occurrences of squat lobsters (Crustacea, Decapoda) from the western Indian Ocean
Fig. 13. Dorsal view. Colours in life. A. Munida nesiotes Macpherson, 1999, ov. ♀, 8.5 mm, Mozambique, MAINBAZA, Stn CP3143. B. Munida shaula Macpherson & de Saint Laurent, 2002, ov. ♀, 12.4 mm, Mozambique, MAINBAZA, Stn CC3151. C. Munida tetracantha sp. nov., paratype, ♂, 7.4 mm, Mozambique, MAINBAZA, Stn CP3131. D. Paramunida marionis Cabezas et al., 2011, ♀, 6.0 mm, Mozambique, MAINBAZA, Stn CP3143.
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.