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'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

Released Friday, 12th March 2021
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'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

'Autocorrelation' as another Dealbreaker in Science-based Conservation Management Worldwide

Friday, 12th March 2021
Good episode? Give it some love!
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Autocorrelation -the serial correlation - as a correlated signal with a delayed copy of itself is inherent in Nature. It's not just a problem to get rid of, to be modeled away or a Red Herring in time and space, but it should be used to your advantage. In reality though, it's either ignored or disturbing the classic frequentist analysis of data and inference for a science-based analysis. There is virtually no conservation policy and legal text addressing it. Here I elaborate on the meaning and relevance of autocorrelation and how to tackle it and I promote to put it more on the forefront of science-based conservation.

Citations and related references:

Borcard D., F. Gillet and P. Legendre (2011) Numertial Ecology with R. Springer Textbook. New York.

Dale M.R.T. and M.J. Fortin (2001) Spatial autocorrelation and statistical tests in ecology.  Ecoscience 9:162-167. DOI: 10.1080/11956860. 2002.11682702

Diggle, P. J. (2005) Spatio-temporal Point Processes: Methods and Applications. Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 78. http://biostats.bepress.com/jhubiostat/paper78

Fortin M.J. and M. R. T. Dale (2009) Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths Geographical Analysis 41:392 - 397. DOI: 10.1111/j.1538-4632.2009.00766.x

Georgescu-Roegen N. (1971) The Entropy Law and the Economic Process. Cambridge, Massachusetts: Harvard University Press. 

Greig-Smith, P. (1982). Quantitative plant ecology. 3d ed. Studies in Ecology 9. Berkeley: Univ. of California Press.

Hawkins, B.A., J.A.F. Diniz‐Filho, B. Mauricio Bini, L., De Marco and T.M. Blackburn (2007) Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology. Ecography, 30: 375-384. https://doi.org/10.1111/j.0906-7590.2007.05117.x

Huettmann F. and A.W. Diamond (2006) Large-Scale Effects on the Spatial Distribution of Seabirds in the Northwest Atlantic. Landscape Ecology 21: 1089-1108.

Papritz A. (2020) Tutorial and Manual for Geostatistical Analyses with the R package georob. https://cran.r-project.org/web/packages/georob/vignettes/georob_vignette.pdf

Schneider D. (1990) Spatial autocorrelation in marine birds. Polar Research 8: 89-97. https://doi.org/10.3402/polar.v8i1.6807

Schneider D. (1994) Quantitative Ecology: Spatial and Temporal Scaling. Academic Press

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