Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user-friendly, high-resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive,uniform,high-resolutionandreadilyusablepackageof globalenvironmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite-based and in situ measured data, representing various aspects of the marine environment relevant for species distributions.Rasters were assembled at a resolution of 5 arcmin (c.9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio-ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user-friendly data package for marine species distribution modelling is available for download at http://www.bio-oracle.ugent.be. The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow-water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM.The high predictive success of the presenceonly model of a notorious invasive seaweed shows that the information contained in Bio-ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.

[1]  A. Townsend Peterson,et al.  Inferring distributions of chirodropid box-jellyfishes (Cnidaria: Cubozoa) in geographic and ecological space using ecological niche modeling , 2009 .

[2]  D. Booth,et al.  Tracking biological invasions in space and time: elucidating the invasive history of the green alga Codium fragile using old DNA , 2007 .

[3]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[4]  J. Leathwick,et al.  Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas) , 2010 .

[5]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[6]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[7]  C. Graham,et al.  Habitat history improves prediction of biodiversity in rainforest fauna. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[9]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[10]  D. Karentz,et al.  Influence of Ozone-Related Increases in Ultraviolet Radiation on Antarctic Marine Organisms1 , 2001 .

[11]  O. Phillips,et al.  Extinction risk from climate change , 2004, Nature.

[12]  Michel Rixen,et al.  Space and time distributions of phosphate in the Mediterranean Sea , 2002 .

[13]  A. Peterson,et al.  Evolution of seasonal ecological niches in the Passerina buntings (Aves: Cardinalidae) , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[14]  C. Graham,et al.  INTEGRATING PHYLOGENETICS AND ENVIRONMENTAL NICHE MODELS TO EXPLORE SPECIATION MECHANISMS IN DENDROBATID FROGS , 2004, Evolution; international journal of organic evolution.

[15]  Carrie V. Kappel,et al.  A Global Map of Human Impact on Marine Ecosystems , 2008, Science.

[16]  Hidetoshi Shimodaira,et al.  Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling , 2004, math/0508602.

[17]  Charles Troupin,et al.  High‐resolution climatology of the northeast Atlantic using Data‐Interpolating Variational Analysis (Diva) , 2009 .

[18]  M. Zettler,et al.  Diversity and distribution of benthic macrofauna in the Baltic Sea Data inventory and its use for species distribution modelling and prediction , 2010 .

[19]  W. Gregg,et al.  Sampling biases in MODIS and SeaWiFS ocean chlorophyll data , 2007 .

[20]  A. Peterson,et al.  Niche Modeling Perspective on Geographic Range Predictions in the Marine Environment Using a Machine-learning Algorithm , 2003 .

[21]  Rainer Froese,et al.  Predicting the distributions of marine organisms at the global scale , 2010 .

[22]  Kaishan Song,et al.  Assessment of Chlorophyll-a Concentration and Trophic State for Lake Chagan Using Landsat TM and Field Spectral Data , 2007, Environmental monitoring and assessment.

[23]  L. Tyberghein,et al.  Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda , 2009 .

[24]  A. Iqbal,et al.  Modeling habitat distribution from organism occurrences and environmental data: case study using anemonefishes and their sea anemone hosts , 2006 .

[25]  Roberto Danovaro,et al.  A Census of Marine Biodiversity Knowledge, Resources, and Future Challenges , 2010, PloS one.

[26]  David G. Reid,et al.  Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalized additive models , 1997 .

[27]  K. Lüning,et al.  Seaweeds: Their Environment, Biogeography, and Ecophysiology , 1990 .

[28]  D. Hanelt,et al.  Sensitivity of Laminariales zoospores from Helgoland (North Sea) to ultraviolet and photosynthetically active radiation: implications for depth distribution and seasonal reproduction , 2005 .

[29]  Jane Elith,et al.  Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities , 2011 .

[30]  K. Gaston,et al.  Hemispheric Asymmetries in Biodiversity—A Serious Matter for Ecology , 2004, PLoS biology.

[31]  Dan L Warren,et al.  Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. , 2011, Ecological applications : a publication of the Ecological Society of America.

[32]  A. Peterson.  Uses and requirements of ecological niche models and related distributional models , 2006 .

[33]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[34]  J. Roberts,et al.  Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia) , 2008 .

[35]  Jean-Marie Beckers,et al.  Seasonal temperature and salinity fields in the Mediterranean Sea: Climatological analyses of a historical data set , 1996 .

[36]  D. Pauly,et al.  Mapping world-wide distributions of marine mammal species using a relative environmental suitability (RES) model , 2006 .

[37]  Knut Schmidt-Nielsen,et al.  Animal Physiology: Adaptation and Environment , 1985 .

[38]  Stuart Banks,et al.  Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity , 2007, Proceedings of the National Academy of Sciences.

[39]  M. Mangel,et al.  Modelling the effects of UV radiation on the survival of Antarctic krill (Euphausia superba Dana) in the face of limited data , 2010 .

[40]  Christopher Daly,et al.  Guidelines for assessing the suitability of spatial climate data sets , 2006 .

[41]  A. Peterson,et al.  Niche Modeling and Geographic Range Predictions in the Marine Environment Using a Machine-learning Algorithm , 2003 .

[42]  K. Lüning,et al.  Sensitivity of intertidal and subtidal red algae to UVA and UVB radiation, as monitored by Chlorophyll fluorescence measurements: Influence of collection depth and season, and length of irradiation , 1996 .

[43]  Hidetoshi Shimodaira,et al.  Pvclust: an R package for assessing the uncertainty in hierarchical clustering , 2006, Bioinform..

[44]  D. Tittensor,et al.  Predicting global habitat suitability for stony corals on seamounts , 2009 .

[45]  G. Pierce,et al.  Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS , 2008, Hydrobiologia.

[46]  M. Austin.  Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[47]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[48]  Pierre Brasseur,et al.  Application of a 3-D variational inverse model to the analysis of ecohydrodynamic data in the Northern Bering and Southern Chukchi Seas , 1991 .

[49]  C. D. Trowbridge.  ECOLOGY OF THE GREEN MACROALGA CODIUM FRAGILE (SURINGAR) HARIOT 1889: INVASIVE AND NON-INVASIVE SUBSPECIES , 1998 .

[50]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[51]  R. Feely,et al.  Ocean acidification: the other CO2 problem. , 2009, Annual review of marine science.

[52]  J. Forester,et al.  Dynamic patterns and ecological impacts of declining ocean pH in a high-resolution multi-year dataset , 2008, Proceedings of the National Academy of Sciences.

[53]  D. Richardson,et al.  Niche‐based modelling as a tool for predicting the risk of alien plant invasions at a global scale , 2005, Global change biology.