Multidimensional Analyses for Testing Ecological, Ethnobiological, and Conservation Hypotheses

  • Thiago Gonçalves-SouzaEmail author
  • Michel V. Garey
  • Fernando R. da Silva
  • Ulysses Paulino Albuquerque
  • Diogo B. Provete
Part of the Springer Protocols Handbooks book series (SPH)


This chapter outlines the commonest multidimensional analysis used in ecology, ethnobiology, and conservation. After mastering how to create a scientific research workflow based on a problem-based platform (Chap.  7, in this book), readers will learn how to visualize complex datasets (e.g., species or ethnospecies lists, several social-political or environmental variables, and so on), and test multivariate hypotheses. We provide a full reproducible example of every analysis discussed in this chapter as an Online Material. We further encourage students and researchers to move from a “description-based multidimensional analysis” (such as using unconstrained ordination, like PCA) to an explicit hypothesis-testing framework that can greatly improve learning and research programs.

Key words

Data analysis Hypothesis testing Scientific research workflow Ordination 


  1. 1.
    Borcard D, Gillet F, Legendre P (2018) Numerical ecology with R, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  2. 2.
    Gower JC, Legendre P (1986) Metric and euclidean properties of dissimilarity coefficients. J Classif 3:5–48CrossRefGoogle Scholar
  3. 3.
    Dolédec S, Chessel D, ter Braak CJF, Champely S (1996) Matching species traits to environmental variables: a new three-table ordination method. Environ Ecol Stat 3:143–166CrossRefGoogle Scholar
  4. 4.
    Legendre P, Legendre L (2012) Numerical ecology, 3rd English edn. Elsevier, AmsterdamGoogle Scholar
  5. 5.
    Chase JM, Leibold MA (2003) Ecological niches: linking classical and contemporary approaches. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  6. 6.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, Solymos P, Stevens MH, Szoecs E, Wagner E (2018) Vegan: community ecology package. R package version 2.5-1. Scholar
  7. 7.
    Dray S, Blanchet G, Borcard D, Clappe S, Guenard G, Jombart T, Larocque G, Legendre P, Madi N, Wagner HH (2017) Adespatial: multivariate multiscale spatial analysis. R package version 0.0-9. Scholar
  8. 8.
    Dray S, Dufour AB (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22:1–20CrossRefGoogle Scholar
  9. 9.
    Legendre P, Gallagher ED (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280CrossRefGoogle Scholar
  10. 10.
    Legendre P, Borcard D (2018) Box-Cox-chord transformations for community composition data prior to beta diversity analysis. Ecography 41:1–5CrossRefGoogle Scholar
  11. 11.
    Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2:559–572CrossRefGoogle Scholar
  12. 12.
    Rodrigues A, Bones F, Schneiders A, Oliveira L, Vibrans A, Gasper A (2018) Plant trait dataset for tree-like growth forms species of the subtropical Atlantic Rain Forest in Brazil. Data 3:16CrossRefGoogle Scholar
  13. 13.
    Revell LJ (2012) Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol 3:217–223CrossRefGoogle Scholar
  14. 14.
    Jombart T, Dray S (2010) Adephylo: exploratory analyses for the phylogenetic comparative method. Bioinformatics 26:1907–1909CrossRefGoogle Scholar
  15. 15.
    Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338CrossRefGoogle Scholar
  16. 16.
    Pavoine S, Dufour AB, Chessel D (2004) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. J Theor Biol 228:523–537CrossRefGoogle Scholar
  17. 17.
    Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  18. 18.
    Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems: data exploration. Methods Ecol Evol 1:3–14CrossRefGoogle Scholar
  19. 19.
    Hadi AS, Ling RF (1998) Some cautionary notes on the use of principal components regression. Am Stat 52:15–19Google Scholar
  20. 20.
    Boaratti AZ, Silva FR (2015) Relationships between environmental gradients and geographic variation in the intraspecific body size of three species of frogs (Anura). Austral Ecol 40:869–9765CrossRefGoogle Scholar
  21. 21.
    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  22. 22.
    Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46Google Scholar
  23. 23.
    McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82:290–297CrossRefGoogle Scholar
  24. 24.
    Anderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecol Lett 9:683–693CrossRefGoogle Scholar
  25. 25.
    Legendre P, Galzin R, Harmelin-Vivien ML (1997) Relating behavior to habitat: solutions to the fourth-corner problem. Ecology 78:547–562Google Scholar
  26. 26.
    Garnier E, Cortez J, Billès G, Navas ML, Roumet C, Debussche M, Laurent G, Blanchard A, Aubry D, Bellmann A, Neill C, Toussaint JP (2004) Plant functional markers capture ecosystem properties during secondary succession. Ecology 85:2630–2637CrossRefGoogle Scholar
  27. 27.
    Lavorel S, Grigulis K, McIntyre S, Williams NSG, Garden D, Dorrough J, Berman S, Quétier F, Thébault A, Bonis A (2007) Assessing functional diversity in the field – methodology matters! Funct Ecol 22:134–147Google Scholar
  28. 28.
    Peres-Neto PR, Dray S, ter Braak CJF (2017) Linking trait variation to the environment: critical issues with community-weighted mean correlation resolved by the fourth-corner approach. Ecography 40:806–816CrossRefGoogle Scholar
  29. 29.
    ter Braak CJF, Peres-Neto P, Dray S (2017) A critical issue in model-based inference for studying trait-based community assembly and a solution. PeerJ 5:e2885CrossRefGoogle Scholar
  30. 30.
    Rigal F, Cardoso P, Lobo JM, Triantis KA, Whittaker RJ, Amorim IR, Borges PAV (2018) Functional traits of indigenous and exotic ground-dwelling arthropods show contrasting responses to land-use change in an oceanic island, Terceira, Azores. Divers Distrib 24:36–47CrossRefGoogle Scholar
  31. 31.
    Kleyer M, Dray S, Bello F, Lepš J, Pakeman RJ, Strauss B, Thuiller W, Lavorel S (2012) Assessing species and community functional responses to environmental gradients: which multivariate methods? J Veg Sci 23:805–821CrossRefGoogle Scholar
  32. 32.
    Solé-Senan XO, Juárez-Escario A, Conesa JA, Recasens J (2018) Plant species, functional assemblages and partitioning of diversity in a Mediterranean agricultural mosaic landscape. Agric Ecosyst Environ 256:163–172CrossRefGoogle Scholar
  33. 33.
    Laliberté E, Legendre P, Shipley B (2014) FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1, pp 0–12Google Scholar
  34. 34.
    ter Braak CJF (1987) Ordination. In: RHG J, CJF T, OFR V (eds) Data analysis in community and landscape ecology. Cambridge University Press, Cambridge, pp 91–173 Pudoc, Wageningen, The Netherlands. Reissued in 1995Google Scholar
  35. 35.
    Dray S, Pélissier R, Couteron P, Fortin M, Legendre P, Peres-Neto PR, Bellier E, Bivand R, Blanchet FG, De Cáceres M, Dufour A, Heegaard E, Jombart T, Munoz F, Oksanen J, Thioulouse J, Wagner HH (2012) Community ecology in the age of multivariate multiscale spatial analysis. Ecol Monogr 82:257–275CrossRefGoogle Scholar
  36. 36.
    Vasconcelos T, Santos T, Haddad C, Rossa-Feres DC (2010) Climatic variables and altitude as predictors of anuran species richness and number of reproductive modes in Brazil. J Trop Ecol 26:423–432CrossRefGoogle Scholar
  37. 37.
    Hecnar SJ, M’Closke RT (1996) Regional dynamics and the status of amphibians. Ecology 77:2091–2097CrossRefGoogle Scholar
  38. 38.
    de Cáceres MD, Legendre P (2009) Associations between species and groups of sites: indices and statistical inference. Ecology 90:3566–3574CrossRefGoogle Scholar
  39. 39.
    Dufrêne M, Legendre P (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67:345–366Google Scholar
  40. 40.
    McGeoch MA, Van Rensburg BJ, Botes A (2002) The verification and application of bioindicators: a case study of dung beetles in savanna ecosystem. J Appl Ecol 39:661–672CrossRefGoogle Scholar
  41. 41.
    Tejeda-Cruz C, Mehltreter K, Sosa VJ (2008) Indicadores ecológicos multi-taxonómicos. In: Manson RH, Hernández-Ortiz V, Gallina S, Mehltreter K (eds) Agroecosistemas cafetaleros de Veracruz: Biodiversidad, Manejo y Conservación. INECOL – INE-SEMARNAT, México, pp 271–278Google Scholar
  42. 42.
    Roberts DW (2016) labdsv: ordination and multivariate analysis for ecology. R package version 1.8-0.
  43. 43.
    Mayo DG (2018) Statistical inference as severe testing: how to get beyond the statistics wars. Cambridge Univ. Press, CambridgeCrossRefGoogle Scholar
  44. 44.
    Taper ML, Lele S (2004) The nature of scientific evidence: statistical, philosophical, and empirical considerations. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  45. 45.
    Salsburg D (2002) The lady tasting tea: how statistics revolutionized science in the twentieth century. Holt, New York, NYGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Thiago Gonçalves-Souza
    • 1
    Email author
  • Michel V. Garey
    • 2
  • Fernando R. da Silva
    • 3
  • Ulysses Paulino Albuquerque
    • 4
  • Diogo B. Provete
    • 5
  1. 1.Laboratório de Ecologia Filogenética e Funcional, Departamento de BiologiaUniversidade Federal Rural de PernambucoRecifeBrazil
  2. 2.Laboratório de Ecologia de Metacomunidades, Instituto Latino-Americano de Ciências da Vida e da NaturezaUniversidade Federal da Integração Latino-AmericanaFoz do IguaçuBrazil
  3. 3.Laboratório de Ecologia Teórica: Integrando Tempo, Biologia e Espaço (LET.IT.BE), Departamento de Ciências AmbientaisUniversidade Federal de São CarlosSão PauloBrazil
  4. 4.Laboratório de Ecologia e Evolução de Sistemas Socioecológicos (LEA), Departamento de Botânica, Centro de BiociênciasUniversidade Federal de PernambucoRecifeBrazil
  5. 5.Laboratório de Síntese em Biodiversidade, Setor de Ecologia, Instituto de BiociênciasUniversidade Federal de Mato Grosso do SulMato Grosso do SulBrazil

Personalised recommendations