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A fine-tuned global distribution dataset of marine forests

  • 1.

    Assis, J., Araújo, M. B. & Serrão, E. A. Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Glob. Chang. Biol. 24, 1365–2486 (2017).

    • Google Scholar
  • 2.

    Thuiller, W. Patterns and uncertainties of species’ range shifts under climate change. Glob. Chang. Biol. 10, 2020–2027 (2004).

  • 3.

    Verbruggen, H. et al. Macroecology meets macroevolution: Evolutionary niche dynamics in the seaweed Halimeda. Glob. Ecol. Biogeogr. 18, 393–405 (2009).

    • Article
    • Google Scholar
  • 4.

    Waltari, E. & Hickerson, M. J. Late Pleistocene species distribution modelling of North Atlantic intertidal invertebrates. J. Biogeogr. 40, 249–260 (2013).

    • Article
    • Google Scholar
  • 5.

    Azzurro, E., Broglio, E., Maynou, F. & Bariche, M. Citizen science detects the undetected: the case of Abudefduf saxatilis from the Mediterranean Sea. Manag. Biol. Invasions 4, 167–170 (2013).

    • Article
    • Google Scholar
  • 6.

    Cox, J. et al. Defining and Measuring Success in Online Citizen Science: A Case Study of Zooniverse Projects. Comput. Sci. Eng. 17, 28–41 (2015).

    • Article
    • Google Scholar
  • 7.

    Assis, J. et al. Findkelp, a GIS-based community participation project to assess Portuguese kelp conservation status. J. Coast. Res. 3, 1469–1473 (2009).

    • Google Scholar
  • 8.

    Assis, J., Lucas, A. V., Bárbara, I. & Serrão, E. Á. Future climate change is predicted to shift long-term persistence zones in the cold-temperate kelp Laminaria hyperborea. Mar. Environ. Res. 113, 174–182 (2016).

  • 9.

    Neiva, J. et al. Genes Left Behind: Climate Change Threatens Cryptic Genetic Diversity in the Canopy-Forming Seaweed Bifurcaria bifurcata. PLoS One 10, e0131530 (2015).

  • 10.

    Boavida, J., Assis, J., Silva, I. & Serrão, E. A. Overlooked habitat of a vulnerable gorgonian revealed in the Mediterranean and Eastern Atlantic by ecological niche modelling. Sci. Rep. 6, 36460 (2016).

  • 11.

    Assis, J. et al. Deep reefs are climatic refugia for genetic diversity of marine forests. J. Biogeogr. 43, 833–844 (2016).

    • Article
    • Google Scholar
  • 12.

    Chefaoui, R. M., Assis, J., Duarte, C. M. & Serrão, E. A. Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic). Estuaries and Coasts 39, 123–137 (2015).

  • 13.

    Shanmughavel, P. An overview on biodiversity information in databases. Bioinformation 1, 367–369 (2007).

  • 14.

    Duputié, A., Zimmermann, N. E. & Chuine, I. Where are the wild things? Why we need better data on species distribution. Glob. Ecol. Biogeogr. 23, 457–467 (2014).

    • Article
    • Google Scholar
  • 15.

    Yesson, C. et al. How global is the global biodiversity information facility? PLoS One 2, e1124 (2007).

  • 16.

    Morris, R. A. Encyclopedia of Biodiversity: Second Edition. Academic Press (Princeton University, 2013).

  • 17.

    Aubry, K. B., Raley, C. M. & McKelvey, K. S. The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species. PLoS One 12, e0179152 (2017).

  • 18.

    Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Inform. 19, 1–10 (2014).

    • Article
    • Google Scholar
  • 19.

    Ceccarelli, S. et al. Data Descriptor: DataTri, a database of American triatomine species occurrence. Sci. Data 24, 180071 (2018).

    • Article
    • Google Scholar
  • 20.

    Dyer, E. E., Redding, D. W. & Blackburn, T. M. The global avian invasions atlas, a database of alien bird distributions worldwide. Sci. Data 4, 170041 (2017).

  • 21.

    Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1998).

  • 22.

    Araújo, R. M. et al. Status, trends and drivers of kelp forests in Europe: an expert assessment. Biodivers. Conserv. 25, 1319–1348 (2016).

    • Article
    • Google Scholar
  • 23.

    Green, E. P. & Short, F. T. World Atlas Seagrass. (University of California Press, Berkeley, USA, 2003).

  • 24.

    Hemminga, M. A. & Duarte, C. M. Seagrass Ecology. (Cambridge University Press, 2000).

  • 25.

    Christie, H., Norderhaug, K. M. & Fredriksen, S. Macrophytes as habitat for fauna. Mar. Ecol. Prog. Ser. 396, 221–233 (2009).

  • 26.

    Borg, J. A., Rowden, A. A., Attrill, M. J., Schembri, P. J. & Jones, M. B. Wanted dead or alive: High diversity of macroinvertebrates associated with living and ‘dead’ Posidonia oceanica matte. Mar. Biol. 149, 667–677 (2006).

    • Article
    • Google Scholar
  • 27.

    Reaka-Kudla, M. L. The Global Biodiversity of Coral Reefs: A Comparison with Rain Forests. In Biodiversity II: Understanding and Protecting Our Biological Resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 83–108 (Joseph Henry Press, 1997).

  • 28.

    Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509 (2012).

  • 29.

    Chung, I. K. et al. adaptation against global warming: Korean Project Overview. ICES J. Mar. Sci. 68, 66–74 (2012).

    • Google Scholar
  • 30.

    Neiva, J. et al. Climate Oscillations, Range Shifts and Phylogeographic Patterns of North Atlantic Fucaceae. In Seaweed Phylogeography (eds. Hu, Z.-M. & Fraser, C.) 279–308 (Springer Netherlands, 2016).

  • 31.

    Assis, J. et al. Major shifts at the range edge of marine forests: the combined effects of climate changes and limited dispersal. Sci. Rep. 7(44348), 1–10 (2017).

    • CAS
    • Google Scholar
  • 32.

    Wieczorek, J. et al. Darwin core: An evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).

  • 33.

    Haklay, M. & Weber, P. OpenStreet map: User-generated street maps. IEEE Pervasive Comput. 1, 12–18 (2008).

    • Article
    • Google Scholar
  • 34.

    Contributors, O. Openstreetmap. Retrieved from, https://planet.openstreetmap.org (2015).

  • 35.

    Graham, M. H., Kinlan, B. P., Druehl, L. D., Garske, L. E. & Banks, S. Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity. Proc. Natl. Acad. Sci. USA 104, 16576–16580 (2007).

  • 36.

    Assis, J. et al. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2017).

    • Article
    • Google Scholar
  • 37.

    Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl. Acad. Sci. 105, 15452–15457 (2008).

  • 38.

    Amaral-Zettler, L. A. et al. Comparative mitochondrial and chloroplast genomics of a genetically distinct form of Sargassum contributing to recent “Golden Tides” in the Western Atlantic. Ecol. Evol. 7, 516–525 (2017).

  • 39.

    Taylor, W. R. A pelagic Sargassum from the Western Atlantic. Contr. Univ. Mich, Herb. 27, 73–75 (1975).

    • Google Scholar
  • 40.

    Spalding, M. D. et al. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. Bioscience 57, 573–583 (2007).

    • Article
    • Google Scholar
  • 41.

    Assis, J. et al. A fine-tuned global distribution dataset of marine forests. figshare https://doi.org/10.6084/m9.figshare.7854767 (2019).

  • 42.

    Costello, M. J. et al. Global Coordination and Standardisation in Marine Biodiversity through the World Register of Marine Species (WoRMS) and Related Databases. 8 (2013).

  • 43.

    Waters, J. M., King, T. M., Fraser, C. I. & Craw, D. Crossing the front: Contrasting stormforced dispersal dynamics revealed by biological, geological and genetic analysis of beach-cast kelp. J. R. Soc. Interface 15 (2018).

  • 44.

    Assis, J. et al. Past climate changes and strong oceanographic barriers structured low – latitude genetic relics for the golden kelp Laminaria ochroleuca. 45, 2326–2336 (2018).

  • 45.

    Thiel, M. & Haye, P. A. The ecology of rafting in the marine environment. iii. Biogeographical and evolutionary consequences. Oceanogr. Mar. Biol. An Annu. Rev. 44, 323–429 (2006).

    • Google Scholar
  • 46.

    Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

  • 47.

    Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography (Cop.). 39, 368–375 (2016).

    • Article
    • Google Scholar
  • 48.

    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD – A platform for ensemble forecasting of species distributions. Ecography (Cop.). 32, 369–373 (2009).

    • Article
    • Google Scholar
  • 49.

    Chaudhary, C., Saeedi, H. & Costello, M. J. Bimodality of Latitudinal Gradients in Marine Species Richness. Trends Ecol. Evol. 31, 670–676 (2017).

    • Article
    • Google Scholar
  • 50.

    Assis, J. et al. Oceanographic Conditions Limit the Spread of a Marine Invader along Southern African Shores. PLoS One 10, e0128124 (2015).

  • 51.

    Lee-Yaw, J. A. et al. A synthesis of transplant experiments and ecological niche models suggests that range limits are often niche limits. Ecol. Lett. 19, 710–722 (2016).

  • 52.

    Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    • Article
    • Google Scholar
  • 53.

    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).

  • 54.

    Scherner, F. et al. Coastal urbanization leads to remarkable seaweed species loss and community shifts along the SW Atlantic. Mar. Pollut. Bull. 76, 106–115 (2013).

  • 55.

    Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

  • 56.

    Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–5 (2014).

  • 57.

    Martínez, B. et al. Distribution models predict large contractions of habitat-forming seaweeds in response to ocean warming. Divers. Distrib. 24, 1350–1366 (2018).

    • Article
    • Google Scholar
  • 58.

    Waltari, E. et al. Locating pleistocene refugia: Comparing phylogeographic and ecological niche model predictions. PLoS One 2, e563 (2007).

  • 59.

    Assis, J., Serrão, E. A., Claro, B., Perrin, C. & Pearson, G. A. Climate-driven range shifts explain the distribution of extant gene pools and predict future loss of unique lineages in a marine brown alga. Mol. Ecol. 23, 2797–2810 (2014).

  • 60.

    Hannah, L., Midgley, G. F. & Millar, D. Climate change-integrated conservation strategies. Glob. Ecol. Biogeogr. 11, 485–495 (2002).

    • Article
    • Google Scholar
  • 61.

    R Development Core Team. R: A Language and Environment for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  • 62.

    GBIF.org, Global Biodiversity Information Facility Home Page, https://www.gbif.org (2019).

  • 63.

    OBIS: Ocean Biogeographic Information System Home Page, https://www.obis.org (2019).

  • 64.

    Core, D. Darwin Core maintenance group, Biodiversity Information Standards (TDWG). Zenodo 1 (2014).


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