Standardised quantitative censuses of reef fishes and echinoderms (holothurians, echinoids, asteroids, crinoids), molluscs (gastropods, cephalopods), and crustaceans (decapods) were undertaken by trained recreational SCUBA divers along 7,040 transects at 2,447 sites worldwide through the Reef Life Survey (RLS) program. Full details of fish census methods are provided in refs 20, 21, and an online methods manual (http://www.reeflifesurvey.com) describes all data collection methods, including for invertebrates. Data quality and training of divers are detailed in ref. 20 and supplementary material in ref. 24. Data used in this study are densities of all species recorded per 500 m2 transect area for fishes (2 × 250 m2 blocks), and per 100 m2 for invertebrates (2 × 50 m2 blocks). Four per cent of all records were not identified to species level (mostly invertebrates) and were omitted from analyses for this study. Data from fish and invertebrate surveys were analysed separately for thermal biogeography analyses, but combined for the vulnerability predictions shown in Fig. 3. Although collected on the same transect lines, these survey components cover different areal extents, and so were combined to represent densities per 50 m2 (block size for invertebrate surveys). Raw invertebrate data were therefore used, but one in five individual fishes were randomly subsampled from those surveyed in each 250 m2 block to provide equivalent densities and richness of fishes per 50 m2. A realized thermal distribution was constructed for all species recorded on RLS transects, based on occurrences rather than species distribution models. All individual records within the RLS database were combined with all records of these species in the Global Biodiversity Information Facility (GBIF: http://www.gbif.org/), after applying filters to limit records to depths shallower than 26 m and time of collection since 2004. This resulted in a data set of 399,927 geo-referenced occurrences of 3,920 species. Remotely sensed local SST data were then matched to each occurrence location. Long-term mean annual SST values from 2002–2009 from the Bio-ORACLE data set22 were used to provide a time-integrated picture of temperatures species were typically associated with for the thermal biogeographic analysis. The fifth and 95th percentiles of the temperature distribution occupied by each species were then calculated, and the midpoint between these used as a measure of central tendency of their realized thermal distribution. Midpoints were considered a reasonable proxy for the temperature associated with species’ maximum ecological success, confirmed by a close alignment of midpoints with the temperatures at which species occurred in maximum abundance in the global RLS data set (slope of midpoint versus temperature of sites at which species were at maximum abundance = 1.003, Pearson correlation = 0.93, P < 0.001). Thus, although interspecific variation is expected, deviation in temperatures either side of the midpoint results in reduced abundance for the average species. We also calculated and explored other metrics from the thermal range, including the median and mode, but these were more sensitive to the distribution and intensity of sampling effort across the temperature range of species, and therefore less robust than the midpoints. Fifth and 95th percentiles were deliberately chosen as endpoints rather than the maximum and minimum because marine species range boundaries are not static, with dynamic tails in distributions44. Sightings of individual vagrants are common, sometimes at large distances from the nearest viable populations. Furthermore, any misidentification errors would have greatest influence if at the edge of species ranges. CTI was calculated separately for fishes and invertebrates for each transect in the RLS database as the average of thermal midpoint values for each species recorded, weighted by their log(x + 1) abundance. Multiple transects were usually surveyed at each site (2.8 transects global mean across sites used in this study). CTI values were averaged across these to create a site-level mean that was used for analyses. In some cases this averaged out seasonal effects, where sites were surveyed across multiple seasons. Thermal bias was calculated as the difference between the CTI and mean annual SST at each site. Mean thermal bias values across sites surveyed in each ecoregion are shown in Extended Data Fig. 3, with sample sizes for ecoregions shown in Extended Data Table 1. The number of occurrence records for each species ranged from a single record (numerous species) to 1,009 (the Indo-Pacific cleaner wrasse, Labroides dimidiatus), with an overall mean of 36 records (47 for fishes, 16 for invertebrates). In order to consider how variation in the comprehensiveness of data on the thermal distribution for each species affected the calculation of CTI and provide an objective measure of confidence in site-level CTI values, we used a semiquantitative confidence scoring system. A confidence value ranging from one (very little confidence) to three (high confidence) was allocated to each species through a four-step process: (1) The number of records (sites) for each species was used as a first pass for classification, with species observed at 30 or more sites given a value of three, 10–29 sites a value of two, and less than 10 sites, a value of one. (2) The thermal range for each species (the difference between 95th and fifth percentiles) was used in a second pass for all species that were initially given a value of two. For this, those species with a thermal range of less than 3 °C were reduced to a value of one, as it is possible these species have not been surveyed across their full potential thermal range. (3) Species with a value of three and a thermal range of less than 1 °C were reduced to a two, given these likely represent well-sampled, but range-restricted species, and their potential thermal range is likely greater than their realized range (which is probably limited by other factors such as dispersal or historical biogeography). (4) The frequency of occurrences across temperatures was also plotted separately for each species. Frequency histograms were visually inspected as a last pass, and confidence scores reduced by one if the thermal distribution appeared to be unduly influenced by widely separated records. We then recalculated CTI for using confidence scores for each species, weighted by their abundance (also log(x + 1) transformed), creating a CTI confidence score for each transect and each site. A mean site confidence score of >2.5 was used as a cut-off for many analyses and figures, as indicated in figure captions. Although a score of 2.5 can be achieved in many ways, this effectively represents at least 75% of the individuals present belonging to species with the maximum confidence score of three. Given few truly subtropical species were identified in this study, and this outcome could potentially result from bias in the distribution of sampling effort towards areas outside of subtropical locations (see Supplementary Information for more detail), we replicated Fig. 2 along a comprehensively sampled latitudinal gradient in Australia. The majority of Australian species are well-sampled across their geographic distributions and numerous sites have been surveyed in subtropical locations in Australia. We divided the RLS data from 968 sites into 10° latitudinal bands along the east coast of Australia (and Papua New Guinea and Solomon Islands) from the equator to 43.7° S, and plotted histograms of thermal distribution midpoints of 1,105 species with a confidence of two or three (Extended Data Fig. 6). These clearly show very few species with midpoints of 23–24 °C, even in the band from 20° S to 30° S where the mean annual SST of sites was 23.97 °C. They also show the intrusion of numerous tropical species in temperate latitudes, particularly for fishes. Vulnerability predictions required characterization of the warmest temperatures experienced by species across their range. We re-constructed the thermal distributions for each species using the maximum of the weekly mean SST from all occurrence sites over the 12 weeks before the sampling date, obtaining the 95th percentile of these. We then calculated the difference between this value and the mean of summer temperatures (the mean of the warmest 8 weeks was taken for each year between 2008 and 2014, with the mean of these used). This is analogous to a form of thermal safety margin, although in this case it does not mean a species cannot survive if the summer SST exceeds the 95th percentile, but rather that it has been recorded at very few sites in the combined RLS and GBIF databases at times in which the temperatures exceeded this value. We re-calculated this value for 10 years and 100 years from present, using rates of SST warming projected by coupled climate models’ CMIP5 PCP8.5 scenario, calculated and freely provided by the NOAA Ocean Climate Change Web Portal (http://www.esrl.noaa.gov/psd/ipcc/ocn/). Sea surface temperature anomaly (difference in the mean climate in the future time period, 2050–2099, compared to the historical reference period, 1956–2005) was selected as the statistic representing the average of 25 models, interpolated to a 1° latitude by 1° longitude grid and matched to each RLS site. Summer SST was predicted for each RLS site for 10 and 100 year time periods using these values. Vulnerability was then estimated as the proportion of all species (fishes and invertebrates) recorded on each RLS survey that is expected to exceed the 95th percentile, based on the predicted SST at that site. This component of analyses did not incorporate abundance data, as the goal was to assess local species loss, rather than loss of individuals. Weighting by abundance had little influence on conclusions, however. Confidence scores were also recalculated without abundance (and thus represent the mean confidence of species present), and sites with confidence scores <2.5 were excluded from calculation of ecoregion means for all ecoregions with three or more sites with confidence >2.5. Twenty-one of 81 ecoregions had fewer than three sites with confidence >2.5 with which to calculate means, so low confidence sites were included in means for these ecoregions. The effect of this is conservative, theoretically reducing thermal bias (see Supplementary Information), but the rationale was that ecoregion means would be more accurate through their inclusion than if heavily weighted by few sites. To provide an additional cut-off for ecoregions in which the overall mean confidence was still low, we excluded ecoregions with mean confidence <1.75. This resulted in the exclusion of six ecoregions (North and East Barents Sea, Oyashio Current, Agulhas Bank, Sea of Japan/East Sea, Gulf of Maine/Bay of Fundy, Malvinas/Falklands). To explore the contributions of warming rates and thermal bias to vulnerability predictions, we also recalculated CTI as the mean 95th percentiles of fish and invertebrate species recorded on transects (CTI ) and thermal bias (TBias ) as the difference between site-level CTI and mean summer SST. TBias can therefore be considered the sensitivity component of the vulnerability predictions, based on recent mean summer SST and not accounting for warming rates (exposure). We applied GAMMs to assess vulnerability scores as a function of TBias and warming rates, with ecoregion as a random factor (Extended Data Table 2). Conclusions are robust to the warming data used, with qualitatively similar results using historical warming data from another source8, instead of future predictions (site warming rates in °C per decade taken from http://www.coastalwarming.com/data.html), and ecoregion mean vulnerability scores changing very little when the 99th percentile of species’ thermal distributions were used instead of the 95th percentile, even for 2115 predictions (Pearson correlation = 0.97, P < 0.01). No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment.
News Article | October 29, 2015
Substantial amount of documented occurrence records is awaiting publication stored in repositories and data indexing platforms, such as the Global Biodiversity Information Facility (GBIF), Barcode of Life Data Systems (BOLD Systems), or Integrated Digitized Biocollections (iDigBio). In order to streamline the authoring process, save taxonomists time, and provide a workflow for peer-review and quality checks, Pensoft has introduced an innovative feature that makes it possible to easily import occurrence records into a taxonomic manuscript.
Robertson T.,Global Biodiversity Information Facility |
Doring M.,Global Biodiversity Information Facility |
Guralnick R.,University of Colorado at Boulder |
Bloom D.,University of California at Berkeley |
And 5 more authors.
The planet is experiencing an ongoing global biodiversity crisis. Measuring the magnitude and rate of change more effectively requires access to organized, easily discoverable, and digitally-formatted biodiversity data, both legacy and new, from across the globe. Assembling this coherent digital representation of biodiversity requires the integration of data that have historically been analog, dispersed, and heterogeneous. The Integrated Publishing Toolkit (IPT) is a software package developed to support biodiversity dataset publication in a common format. The IPT's two primary functions are to 1) encode existing species occurrence datasets and checklists, such as records from natural history collections or observations, in the Darwin Core standard to enhance interoperability of data, and 2) publish and archive data and metadata for broad use in a Darwin Core Archive, a set of files following a standard format. Here we discuss the key need for the IPT, how it has developed in response to community input, and how it continues to evolve to streamline and enhance the interoperability, discoverability, and mobilization of new data types beyond basic Darwin Core records. We close with a discussion how IPT has impacted the biodiversity research community, how it enhances data publishing in more traditional journal venues, along with new features implemented in the latest version of the IPT, and future plans for more enhancements. © 2014 Roberston et al. Source
Agency: Cordis | Branch: FP7 | Program: CPCSA | Phase: INFRA-2010-1.2.3 | Award Amount: 6.26M | Year: 2010
Biodiversity science brings information science and technologies to bear on the data and information generated by the study of organisms, their genes, and their interactions. ViBRANT will help focus the collective output of biodiversity science, making it more transparent, accountable, and accessible. Mobilising these data will address global environmental challenges, contribute to sustainable development, and promote the conservation of biological diversity. Through a platform of web based informatics tools and services we have built a successful data-publishing framework (Scratchpads) that allows distributed groups of scientists to create their own virtual research communities supporting biodiversity science. The infrastructure is highly user-oriented, focusing on the needs of research networks through a flexible and scalable system architecture, offering adaptable user interfaces for the development of various services. In just 28 months the Scratchpads have been adopted by over 120 communities in more than 60 countries, embracing over 1,500 users. ViBRANT will distribute the management, hardware infrastructure and software development of this system and connect with the broader landscape of biodiversity initiatives including PESI, Biodiversity Heritage Library (Europe), GBIF and EoL. The system will also inform the design of the LifeWatch Service Centre and is aligned with the ELIXIR and EMBRC objectives, all part of the ESFRI roadmap. ViBRANT will extend the userbase, reaching out to new multidisciplinary communities including citizen scientists by offering an enhanced suite of services and functionality.
Agency: Cordis | Branch: FP7 | Program: CPCSA | Phase: INFRA-2010-1.2.3 | Award Amount: 3.09M | Year: 2010
Partners to this proposal include the six major global programmes exploring the full extent of species diversity, a core dimension in human knowledge of global biodiversity.\nThey are: GBIF and distribution modelling, the EBI/INDSC, and Barcode of Life initiatives and molecular diversity, IUCN Red Lists and the species conservation movement, and the Species 2000 Catalogue of Life taxonomic framework. These will work closely with ELIXIR and LifeWatch, the ESFRI Infrastructures covering biodiversity, and build on the 4D4Life Project that develops the internal e-infrastructure of the Catalogue of Life.\nThe i4Life project is to establish a Virtual Research Community that will enable each of these global projects to engage in a common programme enumerating the extent of life on earth. It builds on the common need of each organisation to specify the entire set of organisms, their growing use of the Catalogue of Life as a common taxonomic resource alongside their own catalogues, and the different expertise that each programme brings to the task.\nThese key players present particular hurdles to Catalogue integration because they a) have established their own architectures, standards and protocols, b) have special requirements, and c) have their own partial catalogues that need to be integrated with the Catalogue of Life in a two way flow.\nIn each case i4Life will design, implement and test the necessary special pipelines, as well as contributing significantly to enhancement of the Catalogue of Life for all to use through the inflows from the partners. By providing access to a common species catalogue within each of the organisations, we expect to contribute a much needed level of knowledge integrity across the various scientific and community studies of the global biota. To make sense of global biodiversity it is vital that these organisations can communicate through a unified view of the extent of life.