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Effects of Prevailing Wind Direction on Spatial Statistics of Plant Disease Epidemics
Author(s) -
Bin Xu,
Martin S. Ridout
Publication year - 2001
Publication title -
journal of phytopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.53
H-Index - 60
eISSN - 1439-0434
pISSN - 0931-1785
DOI - 10.1046/j.1439-0434.2001.00591.x
Subject(s) - quadrat , statistics , power law , biology , biological dispersal , mathematics , common spatial pattern , sample size determination , spatial distribution , ecology , demography , population , shrub , sociology
A stochastic simulation model was used to study the effects of the strength of prevailing wind ( W ), the size/shape ( Q ) of sampling quadrats and their orientation in relation to the prevailing wind direction ( D ) on spatial statistics describing plant diseases. Spore dispersal followed a half‐Cauchy distribution with median distance μ , which depended on simulated wind speed. The relationship of spatial autocorrelation at distance k ( ρ k ) to disease incidence ( p ) and distance was well described by a four‐parameter ( α , β 1 , β 2 , β 3 ) power‐law model; at a given p , ρ k declined exponentially with distance. A total of 35 different quadrat sizes, ranging from 4 to 432 plants, were used to sample the simulated epidemics for estimating intraclass correlation ( κ ). The κ ‐values decreased exponentially with increasing quadrat size; a binary power law model with three parameters ( α 1 , β 4 , β 5 ) successfully related κ to p . In general, the effect of W and D was greatest on the parameters α , β 1 , β 2 and β 3 . The effect of W on α , β 1 , β 2 and β 3 depended critically on the spatial pattern of initial infected plants ( Y ); W had greatest effect for the random pattern. In contrast, the main effect of D and its interaction with W on the parameters α , β 1 , β 2 and β 3 were large and consistent over different initial conditions. Variations in α 1 , β 4 and β 5 were predominantly due to Y and Q . Only for β 5 under the clumped pattern was the effect of W very large. For the parameters α 1 , β 4 and β 5 there was a large interaction among W , Q and D for the clumped and regular patterns. As expected, in general, the effect of D increased with increasing prevailing wind strength, quadrat size and quadrat length : width ratio. Using square quadrats reduced significantly the effect of W on the parameters α 1 , β 4 and β 5 ; however, the effect of W on β 5 was still very large for the clumped pattern. Sampling perpendicular to the prevailing wind direction generally resulted in larger differences in the nine estimated parameters.