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opencc-by-4.0Jan 2020View details →

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

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 Chollett, D. Ross Robertson     Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá   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 (6-330 N, 57-1000 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.   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 et al. 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).   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.   Global Biodiversity Information Facility (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).   Ocean Biogeographic Information System (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).   Integrated Digitized Biocollections (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).   FishNet2 (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).   FishBase (<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).   Smithsonian Tropical Research Institute (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.   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 Abudefduf saxatilis 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.   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 et al., 2013; Last et al., 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%.   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.   Acknowledgements   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.   References   Allen, G.R. (1991) Damselfishes of the World. Mergus, Melle, 271 p. Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. Scientific Reports, 8, 4920. Carpenter, K.E. (Ed) (2002) The living marine resources of the Western Central Atlantic. Vols 1-3, FAO, Rome, 2127 p. Ebert, D.A., Fowler, S., Compagno, L. (2013) Sharks of the World: a fully illustrated guide. Wild Nature Press, Plymouth. 528 p. GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e). Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). Marine Geodesy, 5, 73–80. Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. Atoll Research Bulletin, 496, 611–624. Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). Rays of the World. CSIRO, Clayton. 790 p. Pattengill-Semmens, C.V. & Semmens, B.X. (2003) Conservation and management applications of the reef volunteer fish monitoring program. 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 (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht. Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. Diversity and Distributions, 14, 891-892 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. Zookeys 843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a> Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. Smithsonian Tropical Research Institute, Balboa, Panamá. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>. Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research: Solid Earth, 101, 8741–8743.

Figure 4 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 4. Joint field research in Yemen: Project participants sample fish in Socotra Island for studies of connectivity among populations. Th e results are important for fisheries management (photo U. Zajonz).

Figure 3 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 3. Collection management and museum curatorship training workshop: Participants sampling biological specimens aboard the RV "Senckenberg" (photo N. Manasfi).

Figure 2 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 2. Red Sea coral reef: Th e Red Sea is the enclosed sea with the highest biodiversity on Earth (photo F. Krupp).

Figure 5 in The biodiversity of the terrestrial malacofauna of Turkey – status and perspectives

Figure 5. The edge of a house wall where the freshwater and the terrestrial gastropods were collected from the excavation area in Catalhöyük archaeological site (Bar-Yosef Mayer and Gümüş 2008).

Figure 6 in The biodiversity of the terrestrial malacofauna of Turkey – status and perspectives

Figure 6. An assemblage of native pulmonate species found at Çatalhüyük (ca. 10.000 y bp). Note the holes in the shells of a small terrestrial snail species on the bottom. They probably were used as a necklace (Bar-Yosef Mayer and Gümüş 2008; Catalhoyuk Research Project Archives, Photographed by Burçin AşkIm Gümüş).

Figure 1 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 1. Terrestrial biodiversity in the Middle East is strongly influenced by seasonality: Desert area in northern Saudi Arabia after the winter rainfall (photo F. Krupp).

Fig. 6 in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Fig. 6. Teratonotum stauromatum (Child, 1982) gen. et comb. nov. (MNHN-IU-2013-17964). A. Dorsal view. B. Propodus of third leg. C. Dorsal view of body. D. Ventral view of body. Abbreviations: ab = abdomen; ac = auxiliary claw; ch = chelifore; ct = chelifore tubercle on the anterior tip of the first scape; dt = dorsal tubercle; mc = main claw; ot = ocular tubercle; ov = oviger; pa = palp; pb = bulbous tubercle bearing the palp; pp = propodus; pr = proboscis; s = strigilis; t = tarsus. Scale bars: A = 0.5 mm; B = 0.1 mm; C–D = 0.2 mm.

Fig. 3 in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Fig. 3. Bayesian tree of Pycnogonida based on 179 sequences of the mitochondrial CO1 gene (partitioned analysis). Coloured rectangles show non-ammotheid families, and coloured branches discriminate ammotheid genera. The numbers at the nodes indicate posterior probabilities greater than 0.5. Symbols associated with each taxon name indicate the bias in base composition, as expressed by AT (circles) and CG (squares) skews (see main text for details): blue symbols represent a significant positive bias; red symbols indicate a significant negative bias; uncoloured symbols show insignificant values of skews. Asterisks after taxon names indicate holotype specimens. The arrow at the top of the tree shows the connection with Part 2 of the tree (see next page).

Appendix 2A in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Appendix 2A. Bayesian tree of Pycnogonida based on 135 CO1 sequences (un-partitioned analysis). Coloured rectangles show non-ammotheid families, and coloured branches discriminate ammotheid genera. The numbers at the nodes indicate the posterior probabilities superior to 0.5. Asterisks associated to taxon names indicate holotype specimens. Outgroups were removed for better readability.

Fig. 5 in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Fig. 5. Bayesian tree of Pycnogonida obtained from the concatenation of CO1 and 18S genes (135 taxa). Coloured rectangles show non- ammotheid families and coloured branches discriminate ammotheid genera. The numbers at the nodes indicate posterior probabilities greater than 0.5. Bold branches indicate CO1 (yellow), 18S (blue), or both (red) support in the independent analyses of CO1 and 18S genes provided in Appendix 1. Asterisks after taxon names indicate holotype specimens. Outgroups were removed for better readability.

Fig. 4 in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Fig. 4. Bayesian tree of Pycnogonida based on 159 sequences of the nuclear 18S rRNA gene. Coloured rectangles show non-ammotheid families and coloured branches discriminate ammotheid genera. The numbers at the nodes indicate posterior probabilities greater than 0.5. Asterisks after taxon names indicate holotype specimens. Outgroups were removed for better readability. The arrow at the top of the tree shows the connection with Part 2 of the tree (see next page).

Fig. 1 in Biodiversity and phylogeny of Ammotheidae (Arthropoda: Pycnogonida)

Fig. 1. Barcode richness in GenBank before and after this study. A–B. Number of CO1 haplotypes before (A) and after (B) this study as a function of the origins of the specimens sequenced. C. Number of species (grey) and genera (black) represented by a CO1 sequence found in GenBank databases before (left) and after (right) this study, classified by ecoregions. Littoral ecoregions defined according to Spalding et al. (2007) and abyssal ecoregions following a simplification of Bachraty et al. (2009).

opencc-by-4.0Mar 2020View details →

Dataset of biodiversity of the Seine nursery over 20 years in a highly disturbed environment

Estuaries are crucial ecosystems where human activities deeply affect numerous ecological functions. The Seine estuary, located on the French coast of the eastern English Channel, is a very dynamic environment where this conflict between the two contrasting backgrounds exists. The Seine watershed is highly disturbed by human activities due to significant industrial development and high population density. The estuary is a historic fishing ground for brown shrimp (Crangon crangon) and various species of flatfish, among which sole (Solea solea) and plaice (Pleutronectes platessa). However, it is a nursery area for fish and plays a crucial role in the life cycle of many demersal and benthic fish and invertebrates. Here we present a survey dataset of biodiversity in the nursery of the Seine estuary and eastern bay of Seine collected using a beam trawl throughout three periods from 1995 to 2019. IFREMER (the French Institute for the Exploitation of the Sea) implemented scientific cruises on coastal nursery grounds aimed at describing the fish population and give an insight into the ecosystem functioning in these areas. The NOURSEINE survey presented here came to existence in this context. The surveys happen at the start of autumn to maximize the catchability of juvenile fish.  The beam trawl targets mainly benthic and demersal species over a more than 600 square kilometers study area. The dataset includes abundance and densities of 161 species for 634 hauls performed at around 40 stations each year. These data can be used by fishery scientists and ecologists motivated by earlier stage life of commercial species or by the impact of human disturbances, such as harbor developments, on estuarine communities. They can help in understanding how the nursery functions may change through time and potential human disturbances.   Dataset.csv: The data represents the density for the different species encounters in the trawl stations across the 14 years where the NOURSEINE campaign took place.  The table contains 22435 rows and 22 columns. Each row corresponds to the density of a species or individuals of the same size in a given haul, and this separation comes from the sorting operation. After each haul, the content of the trawl is emptied on deck, and a total or partial sorting is carried out depending on the volume and homogeneity of the capture. All species, both fish and benthic, are sorted, identified, counted and weighted. Fishes of commercial value and all others flatfish are measured. Fish’s otoliths are collected on the main commercial fish species (sole, plaice, flounder, dab, pouting, large whiting and European bass) and their age group determined later on in the laboratory. In 1999, the sampling was incomplete for technical reasons, and only commercial invertebrates were sampled (King scallop and lobster). Sorting the capture can be separated into three different steps (See pdf figure attached):                1. Total capture weighting: when the hauls are emptied on the deck, the whole capture is distributed in several baskets/box in order to weight it.                2. Fish and large taxa sorting: All fish and large taxa of invertebrates easily identified (edible crab, common spider crab, large cephalopods) are sorted, identified, numbered, measured (for fish) and weighted (total weight per taxa). Depending on the size of the capture, a subsample might be necessary, and the operation is only performed on it. In case visual identification is too difficult (for instance due to a large mud proportion), the capture may be washed using a 5mm sieve. The weight ratio between the total capture and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a taxon has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.                3. Benthic fauna sorting: What is left from the second step is weighted before the sorting operation. All taxa constituting benthic fauna are sorted, identified, numbered and weighted (total weight per taxa). Some taxa may be measured (whelk, scallop). Just like step 2, according to the quantity of benthic fauna, a subsample might be necessary before sorting. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.     Description of the columns found in the dataset <ul> <li>Year</li> </ul> The year when the sampling event took place. Ranges between 1995 and 2019. <ul> <li>Month</li> </ul> The month when the sampling event took place. Either 08 (August) or 09 (September). <ul> <li>Day</li> </ul> The day of the month when the sampling event took place. Ranges from 01 to 30. <ul> <li>Boat_Engine_kw</li> </ul> Engine power reflecting the boat used for sampling, given in kilowatt. Ranges between 81 and 552. <ul> <li>Gear_Code</li> </ul> Code describing the fishing gear used during the sampling event. “BT2” corresponds to beam trawl 2 meters and “BT3” is beam trawl 3 meters. <ul> <li>Haul_duration</li> </ul> The number of minutes of the haul operation. For 1250 rows, NA values indicate that this information was not available. <ul> <li>Station_Code</li> </ul> A unique number identifying the sampling event. Ranges between 1 and 1299. <ul> <li>Starting_Longitude_decimal</li> </ul> The longitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Ending_Longitude_decimal</li> </ul> The longitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Starting_Latitude_decimal</li> </ul> The latitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Ending_Latitude_decimal</li> </ul> The latitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Sector</li> </ul> The sector of the sampling area where the sampling event took place. The study area is divided in 12 sectors roughly based on bathymetry and distance to the mouth of the estuary and identified by letters from “A” to “M”. <ul> <li>Starting_Depth</li> </ul> The depth registered when the sampling event started. Given in meters, ranges from 2 to 26. For 13 rows, NA values indicate that this information was not available. <ul> <li>Ending_Depth</li> </ul> The depth registered when the sampling event ended. Given in meters, ranges from 2 to 26. For 313 rows, NA values indicate that this information was not available. <ul> <li>Trawled_Distance_m</li> </ul> The distance covered between the starting position and the ending position. Given in meters, ranges from 332 to 3128. <ul> <li>Trawled_Surface_m2</li> </ul> The surface trawled between the starting position and the ending position. Given in square meters, ranges from 963 to 9070. <ul> <li>Scientific_Name</li> </ul> Scientific name of the individuals identified in the haul. All names have been checked on WORMS (last accessed: 11/12/2019). <ul> <li>Number_Measured</li> </ul> The number of individuals measured during the sampling event. Ranges between 1 and 340. <ul> <li>Size</li> </ul> Size recorded during the measure of the individuals’ size. Given in centimeters for all species, except benthic fauna where millimeters are used. Ranges between 1 and 185. <ul> <li>Age_Group</li> </ul> Code giving the age category of a particular individual belongs to. “G0” means the individuals are less than 1 year old and born the year the sampling event took place. “G1” means the individuals are 1 year old and born the year before the sampling event took place. “G2+” means they are 2 years old <ul> <li>Number_in_Haul</li> </ul> The number of individuals counted or estimated in the entire haul (if individuals have not been numbered). Depending on the size of the capture, a subsample might be necessary. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. <ul> <li>Weight_in_Haul</li> </ul> The weight of individuals counted or estimated in the entire haul (if individuals have not been numbered). Given in gram. Depending on the size of the capture, a subsample might be necessary. The weight ratio between the total catch and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. For 7 rows, NA values indicated that this information was not available. <ul> <li>Subsample</li> </ul> Depending on the size of the capture, a subsample might be performed to estimate abundance and weight. This column indicates “Yes” if the capture has been subsample to identified the taxa and “No” if the taxa as identified on the whole catch. <ul> <li>Division</li> </ul> A number used to calculate the density from the “Number_in_Haul” column. It represents the weight ratio between the total capture and the subsample. It ranges between 1 and 512. When it is equal to one, the species was sorted in the whole capture. <ul> <li>Species_Density</li> </ul> The density calculated in individuals per square meters. Species densities are calculated based on the trawled surface but also taking into account if the haul has been partially sorted or not. The formula to calculate the density of individual per surface unit is: Density = (Number_in_Haul * Division) / Trawled_Surface_m2 where Division is a factor used to elevate the abundance if the whole haul was not sorted.

opencc-by-4.0Jun 2019View details →

A biodiversity dataset graph: Biodiverity Heritage Library (BHL)

A biodiversity dataset graph: BHL The intended use of this archive is to facilitate (meta-)analysis of the Biodiversity Heritage Library (BHL). The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community. This dataset provides versioned snapshots of the BHL network as tracked by Preston [2] between 2019-05-19 and 2020-05-09 using "preston update -u https://biodiversitylibrary.org". The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the BHL content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 .   To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using: $ java -jar preston.jar clone --remote https://zenodo.org/record/3849560/files After that, verify the index of the archive by reproducing the following provenance log history: $ java -jar preston.jar history <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> . <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> . <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> <http://purl.org/pav/previousVersion> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> . <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> <http://purl.org/pav/previousVersion> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> . <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> <http://purl.org/pav/previousVersion> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> . <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> <http://purl.org/pav/previousVersion> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> . <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> . <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> <http://purl.org/pav/previousVersion> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> . <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> <http://purl.org/pav/previousVersion> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> . <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> <http://purl.org/pav/previousVersion> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> . <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> <http://purl.org/pav/previousVersion> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> . <hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1> <http://purl.org/pav/previousVersion> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> . To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while. $ java -jar preston.jar verify hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    OK    CONTENT_PRESENT_VALID_HASH    49458087    hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    OK    CONTENT_PRESENT_VALID_HASH    25745    hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    OK    CONTENT_PRESENT_VALID_HASH    519892    hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    OK    CONTENT_PRESENT_VALID_HASH    787414    hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". Files in this data publication: --- start of file descriptions --- -- description of archive and its contents (this file) -- README -- executable java jar containing preston[2] v0.1.15. -- preston.jar -- preston archives containing BHL data files, associated provenance logs and a provenance index -- preston-[00-ff].tar.gz -- individual provenance index files -- 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a 2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800 4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f 47a2816f8b5600b24487093adcddfea12434cc4f270f3ab09d9215fbdd546cd2 6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c 7ae46d7cd9b5a0f5889ba38bac53c82e591b0bdf8b605f5e48c0dce8fb7b717f 82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3 9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70 a8308fbf4530e287927c471d881ce0fc852f16543d46e1ee26f1caba48815f3a bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2 cfe47c25ec0210ac73c06b407beb20d9c58355cb15bae427fdc7541870ca2e4e f73fc9e70bce8f21f0c96b8ef0903749d8f223f71343ab5a8910968f99c9b8b6 --- end of file descriptions --- References [1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-19 to 2020-05-09 with provenance hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1. [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

opencc-zeroNov 2018View details →

A biodiversity dataset graph: DataONE

A biodiversity dataset graph: DataONE The intended use of this archive is to facilitate (meta-)analysis of the Data Observation Network for Earth (DataONE). DataONE is a distributed infrastructure that provides information about earth observation data. This dataset provides versioned snapshots of the DataONE network as tracked by Preston [2] between 2018-11-06 and 2020-05-07 using "preston update -u https://dataone.org". The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the DataONE content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 .   To retrieve and verify the downloaded DataONE biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using: $ java -jar preston.jar clone --remote https://zenodo.org/record/3849494/files After that, verify the index of the archive by reproducing the following provenance log history: $ java -jar preston.jar history <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> . <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> <http://purl.org/pav/previousVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> . <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> <http://purl.org/pav/previousVersion> <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> . <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> <http://purl.org/pav/previousVersion> <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> . <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> <http://purl.org/pav/previousVersion> <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> . <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> <http://purl.org/pav/previousVersion> <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> . <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> <http://purl.org/pav/previousVersion> <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> . <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> <http://purl.org/pav/previousVersion> <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> . <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> <http://purl.org/pav/previousVersion> <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> . <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> <http://purl.org/pav/previousVersion> <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> . <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> <http://purl.org/pav/previousVersion> <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> . <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> <http://purl.org/pav/previousVersion> <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> . <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> <http://purl.org/pav/previousVersion> <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> . <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> <http://purl.org/pav/previousVersion> <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> . <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> <http://purl.org/pav/previousVersion> <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> . <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> <http://purl.org/pav/previousVersion> <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> . <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> <http://purl.org/pav/previousVersion> <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> . <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> <http://purl.org/pav/previousVersion> <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> . <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> <http://purl.org/pav/previousVersion> <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> . <hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3> <http://purl.org/pav/previousVersion> <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> . To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while. $ java -jar preston.jar verify hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945    file:/home/preston/preston-dataone/data/e5/5c/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945    OK    CONTENT_PRESENT_VALID_HASH    21580    hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945 hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f    file:/home/preston/preston-dataone/data/d0/dd/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f    OK    CONTENT_PRESENT_VALID_HASH    2035    hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53    file:/home/preston/preston-dataone/data/47/2d/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53    OK    CONTENT_PRESENT_VALID_HASH    1935    hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53 hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687    file:/home/preston/preston-dataone/data/b2/98/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687    OK    CONTENT_PRESENT_VALID_HASH    1553    hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687 Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". Files in this data publication: --- start of file descriptions --- -- description of archive and its contents (this file) -- README -- executable java jar containing preston[2] v0.1.15. -- preston.jar -- preston archives containing DataONE data files, associated provenance logs and a provenance index -- preston-[00-ff].tar.gz -- individual provenance index files -- 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a 2aecaf289def0e23a27058bf7715f226ef9189905f0be13228174825633125cf 2f65ae542401d4c2daf1bca70de640211da6749188f67d28ea71acd7d8ba070b 35eb1e17e2bf3e71212cde35bdb03e8a6545a57483ea3c1633929257b70cf637 3d38b70198e448674be6a63d14b9817f3a956f48bba7418fa7baa086a56c05b7 66ad3e5e904740f1e835ac6718dda4279e0c24b204ea0d1113cda1352a5072ba 7466a35e42dea7e2be068060ec0c926f9a8686388ed504ef5c6c990c1ba4e8d0 81161d9746c2a5823641c436e773fb4508516b055da85f4494b38c545349da39 8bf062872ce958545d361e9d53a552ffb025ac29ab875caad1157c0995d34f66 a90eed8d70c54c8e554f2dfde4fceb434eda162d9615d62de96ded2344f88a78 c33ef5e29100b323412f1f3bc66908c8e01e4f0d1db4ea3685d2fffc47981dd6 c84dffef20fec958255e759db6445fc469d73695674a33ae6f7e567a088c9fe0 d362d599d72000c4feb464db5a669b12e15fc3ca1a49b1e7d4d6f7d6d5d15411 d9378616636be3686bbabd5bf29d50f0ef0e5ceb5ddd7dfce47f7e755b596b7d da26fa6e7371385ed3f61af9a766221c833060d59dfd4869bbd7110f95f288db e4103a75627857de3ee2e317429108611c244fc448c01d1d7bf652115c3b8a55 eb368fedb8f100210dd968edcf80f4d13cab3dd64135a6ab744102cf15e68c94 f13ab4bca04f894ae8eabb51fa01b4dfbc69f717eabc9896c728e2ba39c4db27 f493baf276892a199a0b0d078359f64a38fe8ad3f807921f8d41ef73f7343b1f ff92b6c06ae5286bd2f1db679e0fcc4da294acb9bc01b2e9522378d99218c2e3 --- end of file descriptions --- References [1] Data Observation Network for Earth (DataONE, https://dataone.org) accessed from 2018-11-06 to 2020-05-07 with provenance hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3. [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

opencc-by-4.0Jun 2020View details →

List and date of establishment of Marine Protected Areas and Key Biodiversity Areas of the Alboran Sea

List of Marine Protected Areas and Key Biodiversity Areas for the Alboran Sea (Abbreviation in Spanish, French and English with lenguage among brackets- Fr: French; S: Spanish), indicating its figure of conservation, year of establishment for each figure of protection and national or regional management body (in brackets). IBA: Importante Bird Area; IMMA: Important Marine Mammals Area; MR: Marine Reserve; MR/FR: Marine  and Fishing Reserve; NA: Natural Area; NM: Natural monument; NP: Natural Park; SPAMI: Specially Protected Areas of Mediterranean Importance; RAMSAR: Wetlands of International Importance (Ramsar Sites); SCI: Site of Community Importance of Natura 2000; SAC: Special Area of Conservation of Natura 2000; SPA: Special Protection Area of Natura 2000; ZEPA: Zona de Especial Protección para las Aves; ZEPIM: Zonas Especialmente Protegidas de Importancia para el Mediterráneo; LIC: Lugar de Importancia Comunitaria de Natura 2000; ZEC: Zona de Especial Conservación de Natura 2000; ASPIM: Aire Spécialement Protégée d'Importance Méditerranéenne; SIC: Site d'Importance Communautaire; ZPS: Zones de Protection Spéciale.

dryad40/100

Data from: Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation

Amidst the global biodiversity crisis, identifying general principles for variation in biodiversity remains a key challenge. Scientific consensus is limited to a few macroecological rules, such as species richness increasing with area, which provide limited guidance for conservation. In fact, few agreed ecological principles apply at the scale of sites or reserve management, partly because most community-level studies are restricted to single habitat types and species groups. We used the recently proposed ecospace framework and a comprehensive data set for aggregating environmental variation to predict multi-taxon diversity. We studied richness of plants, fungi, and arthropods in 130 sites representing the major terrestrial habitat types in Denmark. We found the abiotic environment (ecospace position) to be pivotal for the richness of primary producers (vascular plants, mosses, and lichens) and, more surprisingly, little support for ecospace continuity as a driver. A peak in richness at intermediate productivity adds new empirical evidence to a long-standing debate over biodiversity responses to productivity. Finally, we discovered a dominant and positive response of fungi and insect richness to organic matter accumulation and diversification (ecospace expansion). Two simple models of producer and consumer richness accounted for 77 % of the variation in multi-taxon species richness suggesting a significant potential for generalization beyond individual species responses. Our study widens the traditional conservation focus on vegetation and vertebrate populations unravelling the importance of diversification of carbon resources for diverse heterotrophs, such as fungi and insects.

opencc-zeroJun 2020View details →

dryad40/100

Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species

<ol> <li>DNA metabarcoding was utilized for a large-scale, multi-year assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). </li> <li>Principal Component Analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park.</li> <li>Jaccard distance matrices of the presences of BINs at collection sites in the two survey years (2016 and 2018) were significantly correlated.</li> <li>Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens.</li> <li>A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol.</li> <li> A single 98.6% match to the invasive bark beetle Ips duplicatus was detected in an ethanol sample. This species has not previously been detected in the National Park. </li> </ol>

opencc-zeroJul 2020View details →