Skip to main content
SpringerLink
Log in

Persisting challenges in multiple models: a note on commonly unnoticed issues regarding collinearity and spatial structure of ecological data

  • Point of View
  • Published:
Brazilian Journal of Botany Aims and scope Submit manuscript

Abstract

There is a growing need to heed some caveats in numerical data analysis. In 2013, I set out some issues regarding multiple regression frameworks. Here, I used both hypothetical and real data sets collected in Brazil to discuss the implications of, and provide suggestions for, some statistical issues regarding to collinearity and spatial structure of ecological data. For example, a weak treatment of collinearities might lead to discarding important variables for the model, and this can be avoided by a correct approach to collinearities before the model selection. Moreover, studies have demonstrated that the spatial structure in both predictor and response variables is an important point to be addressed, rather than the presence of this structure only in the residuals. Aiming to facilitate the controlling of such bias, I provide two fully explained scripts for R language. Considering the seriousness of spatial structure, my opinion is that no article that presents confirmatory analysis should be considered for publication if their authors do not heed that caveat; facing this issue, I strongly suggest that one performs a variance partitioning scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

References

  • Austin MP, Heyligers PC (1989) Vegetation survey design for conservation: gradsect sampling of forests in North-East New South Wales. Biol Conserv 50:13–32

    Article  Google Scholar 

  • Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer, New York

    Book  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretical approach, 2nd edn. Springer-Verlag, New York

    Google Scholar 

  • Chakrabarty KC (2012) Uses and misuses of Statistics. Available at <http://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/02SPBUL090412.pdf>. Acessed on 26 Feb, 2014

  • Chase JM, Myers JA (2011) Disentangling the importance of ecological niches from stochastic processes across scales. Philos T R Soc B 366:2351–2363

    Article  Google Scholar 

  • Diniz Filho JAF, Bini LM (2005) Modelling geographical patterns in species richness using eigenvector-based spatial filters. Glob Ecol Biogeogr 14:177–185

    Article  Google Scholar 

  • Diniz Filho JAF, Bini LM, Hawkins BA (2003) Spatial autocorrelation and red herrings in geographical ecology. Glob Ecol Biogeogr 12:53–64

    Article  Google Scholar 

  • Diniz Filho JAF, Rangel TFLVB, Bini LM (2008) Model selection and information theory in geographical ecology. Glob Ecol Biogeogr 17:479–488

    Article  Google Scholar 

  • Dormann CF et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46

    Article  Google Scholar 

  • Dray S, Legendre P, Peres-Neto P (2006) Spatial modeling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecol Model 196:483–493

    Article  Google Scholar 

  • Dunning JB, Danielson BJ, Pulliam HR (1992) Ecological processes that affect populations in complex landscapes. Oikos 65:169–175

    Article  Google Scholar 

  • Eisenlohr PV (2013) Challenges in data analysis: pitfalls and suggestions for a statistical routine in Vegetation Ecology. Braz J Bot 36:83–87

    Article  Google Scholar 

  • Gotelli NJ, Ellison AM (2004) A primer of ecological statistics. Sinauer Associates, Sunderland

    Google Scholar 

  • Götzenberger et al (2012) Ecological assembly rules in plant communities–approaches, patterns and prospects. Biol Rev 87:111–127

    Article  PubMed  Google Scholar 

  • Guisan A, Zimmerman NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  • Heffernan JB et al (2014) Macrosystems ecology: understanding ecological patterns and processes at continental scales. Front Ecol Environ 12:5–14

    Article  Google Scholar 

  • Landeiro VL, Magnusson WE (2011) The geometry of spatial analyses: implications for conservation biologists. Nat Cons 9:7–20

    Article  Google Scholar 

  • Legendre P, Gallagher E (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280

    Article  Google Scholar 

  • Legendre P, Legendre L (2012) Numerical ecology, 3rd edn. Elsevier, Amsterdam

    Google Scholar 

  • Legendre P, Dale MRT, Fortin M-J, Gurevitch J, Hohn M, Myers D (2002) The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 25:601–615

    Article  Google Scholar 

  • McCune B, Grace JB (2002) Analysis of ecological communities. MjM Software Design, Gleneden Beach

    Google Scholar 

  • Meloun M, Militký J, Brereton RG (2002) Crucial problems in regression modelling and their solutions. Analyst 127:433–450

    Article  PubMed  CAS  Google Scholar 

  • Öpik M, Bello F, Price JN, Fraser LH (2014) New insights into vegetation patterns and processes. New Phytol 201:383–387

    Article  Google Scholar 

  • Peres-Neto PR, Legendre P (2010) Estimating and controlling for spatial structure in the study of ecological communities. Glob Ecol Biogeogr 19:174–184

    Article  Google Scholar 

  • Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-plus. Springer, New York

    Book  Google Scholar 

  • Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, Melbourne

    Book  Google Scholar 

  • Ramage BS et al (2013) Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation. Conserv Biol 27:364–372

    Article  PubMed  Google Scholar 

  • Richards SA (2005) Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86:2805–2814

    Article  Google Scholar 

  • Ricklefs RE, Schluter D (1993) Species diversity in ecological communities: historical and geographical perspectives. University of Chicago Press, Chicago

    Google Scholar 

  • Scheiner S, Gurevitch J (2001) Design and analysis of ecological experiments. Oxford University Press, Oxford

    Google Scholar 

  • The R Foundation for Statistical Computing (2013) http://www.r-project.org/. Accessed 30 Dec 2013

  • Zar JH (2010) Biostatistical analysis. Prentice-Hall, New Jersey

    Google Scholar 

  • Zuur AF, Ieno EN, Smith GM (2007) Analysing ecological data. Springer, New York

    Book  Google Scholar 

  • Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Method Ecol Evol 1:3–14

    Article  Google Scholar 

Download references

Acknowledgment

I am grateful to CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for the financial support. I am also indebted to the reviewers, for their insightful comments on an earlier version of this article; MSc. Pamela Moser (Universidade de Brasília), who allowed me to use her dissertation data; Dr. Danilo R.M. Neves (University of Leeds), who prepared earlier versions of the scripts presented in the online supplementary material; MSc. Mário J.M. Azevedo (Universidade Estadual de Campinas), who provided important suggestions for these scripts; Professor Pierre Legendre (Université de Montréal), who has solved essential questions; Professor Ary T. de Oliveira Filho (Universidade Federal de Minas Gerais), other colleagues and my graduate students, who helped me in fruitful discussions and gave me important feedbacks regarding the first manuscript (from 2013).

Author information

Authors and Affiliations

  1. Universidade do Estado de Mato Grosso, Campus Universitário de Alta Floresta, P.O. Box 324, Alta Floresta, MT, 78580-000, Brazil

    Pedro V. Eisenlohr

Authors
  1. Pedro V. Eisenlohr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro V. Eisenlohr.

Electronic supplementary material

Below is the link to the electronic supplementary material.

S1 Additional details on key concepts used in this article. (DOCX 15 kb)

40415_2014_64_MOESM2_ESM.docx

S2 Variance partitioning as a tool for controlling the type I error rate in regression/canonical routines: a suggested R code. (DOCX 17 kb)

40415_2014_64_MOESM3_ESM.docx

S3 Variance partitioning as a tool for controlling the type I error rate in ANOVA/MANOVA routines: a suggested R code. (DOCX 17 kb)

S4 Artificial data that can be used to process the R codes provided in S2 and S3. (XLSX 101 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eisenlohr, P.V. Persisting challenges in multiple models: a note on commonly unnoticed issues regarding collinearity and spatial structure of ecological data. Braz. J. Bot 37, 365–371 (2014). https://doi.org/10.1007/s40415-014-0064-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40415-014-0064-3

Keywords

Search

Navigation