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Biodiversity Literature Repository
Published March 28, 2022 | Version v1
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Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Creators

  • 1. Museum of Biological Diversity, The Ohio State University, Columbus, OH 43212 & Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212
  • 2. Department of Biology, Radford University, Radford, VA 24142
  • 3. Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212 & Department of Evolution, Ecology, and Organismal Biology, School of Environment and Natural Resources, Ohio Biodiversity Conservation Partnership, The Ohio State University, Columbus, OH 43210

Description

Parsons, Danielle J., Pelletier, Tara A., Wieringa, Jamin G., Duckett, Drew J., Carstens, Bryan C. (2022): Analysis of biodiversity data suggests that mammal species are hidden in predictable places. Proceedings of the National Academy of Sciences 119 (14): 1-7, DOI: 10.1073/pnas.2103400119, URL: http://dx.doi.org/10.1073/pnas.2103400119

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References

  • 1. F.E.Zachos,Species Concepts in Biology: Historical Development,Theoretical Foundations and Practical Relevance (Springer US,2016).
  • 2. V.H.Heywood,R.T.Watson,U.N.E.Programme,Global Biodiversity Assessment (Cambridge University Press,Cambridge;New York,1995).
  • 3. M.J.Costello,R.M.May,N.E.Stork,Can we name Earth 's species before they go extinct? Science 339, 413-416 (2013).
  • 4. D.Bickford et al., Cryptic species as a window on diversity and conservation.Trends Ecol.Evol.22, 148-155 (2007).
  • 5. B.C.Carstens,T.A.Pelletier,N.M.Reid,J.D.Satler,How to fail at species delimitation.Mol.Ecol. 22, 4369-4383 (2013).
  • 6. M.Pfenninger,K.Schwenk,Cryptic animal species are homogeneously distributed among taxa and biogeographical regions.BMC Evol.Biol.7, 121 (2007).
  • 7. G.Ceballos,P.R.Ehrlich,Discoveries of new mammal species and their implications for conservation and ecosystem services.Proc.Natl.Acad.Sci.U.S.A. 106, 3841-3846 (2009).
  • 8. M.A.Titley,J.L.Snaddon,E.C.Turner,Scientific research on animal biodiversity is systematically biased towards vertebrates and temperate regions.PLoS One 12, e0189577 (2017).
  • 9. D.M.Reeder,K.M.Helgen,D.E.Wilson,Global trends and biases in new mammal species discoveries.Occas.Pap.Mus.Texas Tech Univ.269, 1-36 (2007).
  • 10. C.J.Burgin,J.P.Colella,P.L.Kahn,N.S.Upham,How many species of mammals are there? J. Mammal. 99, 1-14 (2018).
  • 11. R.J.Baker,R.D.Bradley,Speciation in mammals and the genetic species concept.J.Mammal.87, 643-662 (2006).
  • 12. R.Poulin,G.P´erez-Ponce de Le´on,Global analysis reveals that cryptic diversity is linked with habitat but not mode of life.J.Evol.Biol. 30, 641-649 (2017).
  • 13. B.Sidlauskas et al., Linking big:The continuing promise of evolutionary synthesis.Evolution 64, 871-880 (2010).
  • 14. N.Puillandre,A.Lambert,S.Brouillet,G.Achaz,ABGD,Automatic Barcode Gap Discovery for primary species delimitation.Mol.Ecol.21, 1864-1877 (2012).
  • 15. J.Pons et al., Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst.Biol.55, 595-609 (2006).
  • 16. L.Breiman,Random forests.Mach.Learn.45, 5-32 (2001).
  • 17. American Society of Mammalogists,Mammal Diversity Database (2021).https://www. mammaldiversity.org.Accessed 5 February 2021.
  • 18. G.Biau,Analysis of a random forests model.J.Mach.Learn.Res.13, 1063-1095 (2012).
  • 19. K.E.Jones et al., PanTHERIA:A species-level database of life history,ecology,and geography of extant and recently extinct mammals.Ecology 90, 2648 (2009).
  • 20. D.P.Bebber et al., Herbaria are a major frontier for species discovery.Proc.Natl.Acad.Sci.U.S.A. 107, 22169-22171 (2010).
  • 21. H.C.J.Godfray,Challenges for taxonomy.Nature 417, 17-19 (2002).
  • 22. J.D.S.Witt,D.L.Threloff,P.D.N.Hebert,DNA barcoding reveals extraordinary cryptic diversity in an amphipod genus:Implications for desert spring conservation.Mol.Ecol.15, 3073-3082 (2006).
  • 23. L.L.Knowles,B.C.Carstens,Delimiting species without monophyletic gene trees.Syst.Biol. 56, 887-895 (2007).
  • 24. J.A.Esselstyn,B.J.Evans,J.L.Sedlock,F.A.Anwarali Khan,L.R.Heaney,Single-locus species delimitation:A test of the mixed Yule-coalescent model,with an empirical application to Philippine round-leaf bats.Proc.Biol.Sci.279, 3678-3686 (2012).
  • 25. F.M.Cohan,Systematics:The cohesive nature of bacterial species taxa.Curr.Biol.29, R169-R172 (2019).
  • 26. M.J.Hickerson,C.P.Meyer,C.Moritz,DNA barcoding will often fail to discover new animal species over broad parameter space.Syst.Biol. 55, 729-739 (2006).
  • 27. T.Fujisawa,T.G.Barraclough,Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach:A revised method and evaluation on simulated data sets.Syst. Biol. 62, 707-724 (2013).
  • 28. J.Gurevitch,J.Koricheva,S.Nakagawa,G.Stewart,Meta-analysis and the science of research synthesis.Nature 555, 175-182 (2018).
  • 29. T.A.Pelletier,B.C.Carstens,D.C.Tank,J.Sullivan,A.Esp´ indola,Predicting plant conservation priorities on a global scale.Proc.Natl.Acad.Sci.U.S.A. 115, 13027-13032 (2018).
  • 30. A.V.Suarez,N.D.Tsutsui,The value of museum collections for research and society.Bioscience 54, 66-74 (2004).
  • 31. N.Eisenhauer,A.Bonn,C.A Guerra,Recognizing the quiet extinction of invertebrates.Nat. Commun. 10, 50 (2019).
  • 32. N.S.Upham,J.A.Esselstyn,W.Jetz,Inferring the mammal tree:Species-level sets of phylogenies for questions in ecology,evolution,and conservation.PLoS Biol.17, e3000494 (2019).
  • 33. R.C.Edgar,MUSCLE:Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792-1797 (2004).
  • 34. D.Darriba,G.L.Taboada,R.Doallo,D.Posada,jModelTest 2:More models,new heuristics and parallel computing.Nat.Methods 9, 772 (2012).
  • 35. R.Bouckaert et al., BEAST 2.5:An advanced software platform for Bayesian evolutionary analysis. PLOS Comput.Biol. 15, e1006650 (2019).
  • 36. T.Ezard,T.Fujisawa,T.G.Barraclough,SPLITS:Species ' limits by threshold statistics (2009). https://r-forge.r-project.org/projects/splits/.Accessed 1 December 2020.
  • 37. R.C.Team,R:A language and environment for statistical computing (2020).https://www.r-project.org/. Accessed 1 December 2020.
  • 38. A.J.Barley,R.C.Thomson,Assessing the performance of DNA barcoding using posterior predictive simulations.Mol.Ecol.25, 1944-1957 (2016).
  • 39. D.L.Swofford,"PAUP (phylogenetic analysis using parsimony)," in Encyclopedia of Genetics, Genomics,Proteomics,and Informatics, G.P.R´edei,Ed.(Springer Press,2008),pp.1455-1455.
  • 40. ASTER GDEM Validation Team,Aster global digital elevation model version 2 (2011). https://asterweb.jpl.nasa.gov/gdem.asp.Accessed 1 December 2019.
  • 41. R.J.Hijmans,S.E.Cameron,J.L.Parra,P.G.Jones,A.Jarvis,Very high resolution interpolated climate surfaces for global land areas.Int.J.Climatol. 25, 1965-1978 (2005).
  • 42. CIESIN,Socioeconomic Data And Applications Center (SEDAC)Gridded Populations of the World (GPW) (2016).https://sedac.ciesin.columbia.edu/data/collection/gpw-v4.Accessed 1 December 2019.
  • 43. DECRG,World Bank Development Economics Research Group (DECRG) Gross Domestic Product (2010).https://datacatalog.worldbank.org/dataset/gross-domestic-product-2010/resource/ addfd173-a15f-4cee-8f07-0ad76ae389b0.Accessed 1 December 2019.
  • 44. NOAA,NOAA Night Light Development Index (NLDI) (2006).https://www.ngdc.noaa.gov/eog/ dmsp/download_nldi.html.Accessed 1 December 2019.
  • 45. European Environment Agency,World database on orotected areas (2012).https://www.eea. europa.eu/data-and-maps/figures/overview-of-protected-areas-as.Accessed 1 December 2019.
  • 46. E.C.Ellis,K.Klein Goldewijk,S.Siebert,D.Lightman,N.Ramankutty,Anthropogenic transformation of the biomes,1700 to 2000.Glob.Ecol.Biogeogr.19, 589-606 (2010).
  • 47. European Space Agency,ESA GlobCover project (2009).due.esrin.esa.int/page_globcover.php. Accessed 1 December 2019.
  • 48. R.J.Hijmans,Geosphere:Spherical trigonometry (2016).https://cran.r-project.org/ package=geosphere.Accessed 1 December 2020.
  • 49. R.J.Hijmans,raster:Geographic data analysis and modeling (2016).https://cran.r-project.org/ package=raster.Accessed 1 December 2020.
  • 50. R.Bivand,T.Keitt,B.Rowlingson,rgdal:Bindings for the Geospatial Data Abstraction Library (2017).https://cran.r-project.org/package=rgdal.Accessed 1 December 2020.
  • 51. H.Wickham,The split-apply-combine strategy for data analysis.J.Stat.Softw. 40, 1-29 (2011).
  • 52. C.Baker,wosr:Clients to the "Web of Science" and "InCites" APIs (2018). https://cran.r-project. org/package=wosr.Accessed 1 December 2020.
  • 53. M.Kuhn,Caret package.J.Stat.Softw.28, 1-26 (2008).