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Interdisciplinary Seminar in Ecology

by: Mr. Antoinette Keeling

Interdisciplinary Seminar in Ecology ECOL 592

Marketplace > Colorado State University > Ecology > ECOL 592 > Interdisciplinary Seminar in Ecology
Mr. Antoinette Keeling
GPA 3.64


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Class Notes
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This 14 page Class Notes was uploaded by Mr. Antoinette Keeling on Tuesday September 22, 2015. The Class Notes belongs to ECOL 592 at Colorado State University taught by Staff in Fall. Since its upload, it has received 39 views. For similar materials see /class/210208/ecol-592-colorado-state-university in Ecology at Colorado State University.

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Date Created: 09/22/15
Incorporating Space in Ecological Models A lecture For Exploring Ecological Datasets seminar Sept 4 2007 Sunil Kumar Natural Resource Ecology Laboratory Colorado State University 1quot Knowledge to Go Places Outlines 0 What is spatial autocorrelation 0 Why it may be a problem and how to deal with it 0 How to quantify spatial autocorrelation in ecological variables 0 What are Spatial correlograms and Variograms and how to interpret them 0 How to take advantage of this important phenomenon in ecological modeling 0 What are the ways to include spatial autocorrelation in your study Examples from the literature including our own studies Spatial autocorrelation Lack of independence in observations Spatial dependence or broadscale spatial trend Physical processes create spatial structure elevation slope aspect Matrix Finescale spatial autocorrelation Biotic processes dispersal growth mortality species interactions Spatial autocorrelation Positive more similar values amp Negative more dissimilar values Fire insects grazing etc Legendre P amp Legendre L 1998 Numerical ecology Second English ed Elsevier Science Amsterdam The Netherlands Wagner HH amp Fortin MJ 2005 Spatial analysis of landscapes Concepts and statistics Ecology 86 19751987 Spatial autocorrelation Problems Violation of the assumption of independence 0f observations in case of signi cant spatial autocorrelation in model residuals Incorrect parameter estimates 39 Biased hypothesis test results 0 False identi cation of ecological relationships Inaccurate predictions How to deal with this Problem Three Options 1 Remove the spatial dependency easy but not recommended 2 Modify the statistical methods to take spatial autocorrelation into account eg Mantel test 39 5 iA ckhorvledge spati ahtgo elatial 1 I the fact of life I Fortin amp Dale 2005 I Fortin MJ and Dale MRT 2005 Spatial analysis a guide for ecologists Cambridge University Press Quantifying spatial autocorrelation NN Moran s I Moran 1948 N ZWi jZiZj I r 1jl 1 highest negative N N N 2 to 1 highest positive 2 ngZa i ljzl le Geary s c Geary 1954 N N c N 1 ggw n my Ohighest positive N I N to 2 highest negative zlgjglw39i 137 Spatial correlograms plot of Moran s I vs distance intervals Variograms or semi variograms semi variance vs distance Important transform your variable and look for outliers Fortin MJ and Dale MRT 2005 Spatial analysis a guide for ecologists Cambridge University Press Select software for spatial analyses SPlus spatial statistics module Dr Robin Reich s spatial stats codes obinwarnercnrcolostateedu FREE R statistical software httpwwwrproiectorq PASSaGE Dr Michael Rosenberg at Arizona State Univ httpwwwpassaqesoftwarenetindexphp SpaceMaker2 Dr Pierre Legendre httpwwwbioumontrealcaquendreindexEnq shhtm Simulated spatial correlograms Gradient 16 patches size and distance same a d s s 12 i5 10 21 24 27 Random 9 patches size same distan cagwe different I s 6 9 i2 395 18 2 24 7 d distanci ef ifferent 3 s s 12 t5 t8 21 24 27 Distanse Figures From Fortin MJ and Dale MRT 2005 Spatial analysis a guide for ecologists Cambridge University Press Fig 38 Page 128129 Real correlograms Plant species richness Kumar et al 2006 Ecology Appendix Spatial autocorrelation Moran s 1 Plant Species richness n79 Native I 0025 P 070 Nonnative I 0096 P lt 00001 Moran s I m Model Residuals Native I 0017 P 027 Nonnative I 0013 P 091 Open diamonds Not significant Filled diamonds Significant Species richness Model reSiduals 06 06 a o C Natlve 04 04 39 02 02 00 00 02 02 0 4 04 06 06 I I o 5 5 3 v v N N m H H N N m 0639 b Nonnative 06 d 4 04 02 02 00 00 02 02 04 04 06 o6 500 1000 1500 2500 3000 2000 0 1000 1500 2000 2500 3000 may represent missing Lag distance m variable in the model Real correlograms Butter y species richness Kumar et al In review Ecology Total Species Richness model 2100 m Raw data Model residuals Ole eXtent I 0121 I O 016 Moran s std std 39 Std P lt 00001 P 0 99 Local SA Correlograms 150 m lags 1st lag 300 m Signi cant 05 39 Raw data 05 39 Model residuals ltgt NODSigni cant 04 39 SQRT total SR 04 39 03 03 g 02 m 02 U 01 01 quotC 0 0 A f A 0 0 9 39 J l V 39 o 01 01 E 02 02 390393 39 390393 39 may represent missing 04 39 04 variable in the model 05 I I I I I I 05 I I I I I I 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 Lag distance m Species richness Model residuals HesDel39iidae More butter y 3 3 3 3 Species richness 33 MW 3 W 02 02 Correlograms o4 o4 06 I l l I I I 06 I Famlly level 06 my 04 04 0 2 02 0 0 00 02 02 g g 0 4 04 E 0 6 I I I I I I Nymf falida I I I I I o Iv A III P 83 83 0 Nonsigni cant i 7 a 83 AVAWIVM 33 w 5 32 32 2 06 I I I I 06 I I I I I I 0 6 Papilgo6nidae 04 04 6 02 02 00 JVAamp ampVampVZ 00 02 02 04 04 06 I I I I I I 06 I I I I I I Pieridae 06 Q6 04 04 02 02 00 00 W O2 g i E 02 Kumar et al In Review 8 I I I I I I 32 39 I I I I I I E 39 gy Lag distance m Variogram or semi variogram Semi variance functian h 0 Y l 2 Nugget Variability at short distances random effects or measurement errors gt 0 Sill Variance of the samples Range Distance at which variable is Spatially autocorrelated Source httpwwwentovtedusharovpopechomemodeImodelhtm Kriging and cokriging Including spatial autocorrelation i Trend surface analysis approach Borcard et al 1992 A thirdorder polynomial of geographic coordinates of the sampling locations in the form 2 2 3 2 2 3 z b0 b1X b2y b3X b4xy b5y b6X b7X y ngy by Standardize X and Y variables to mean zero and unit variance to reduce collinearity Borcard et al 1992 Partialling out the spatial component of ecological variation Ecology 73 10451055 ii Principal coordinates of neighbor matrices PCNM approach Computed Principal coordinates of a truncated matrix of Euclidean distance among sampling locations using SpaceMakerZ Borcard amp Legendre 2002 Borcard et al 2004 Borcard amp Legendre 2002 Ecological Modeling 153 5168 Borcard et al 2004 Ecology 85 18261832 Spatial autoregressive term to model nescale spatial autocorrelation Lichstein et al 2002 Lichstein et al 2002 Ecological Monograph 72 445463


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