Based on information at an anchor scale (
calcuate predicted species area relationship at larger scales
an object of class meteESF
the anchor scale at which community data are availible.
the larges area to which to upscale
logical. TRUE computes the endemics area relatinship; currently not supported
Currently only doublings of area are supported and only the SAR (not EAR) is supported. Upscaling works by iteratively solving for the constraints (S and N at larger scales) that would lead to the observed data at the anchor scale. See references for more details on this approach.
an object of class
sar inheriting from
S giving area and species richness, respectively
Andy Rominger <firstname.lastname@example.org>, Cory Merow
Harte, J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press.
meteESF, meteSAR, empiricalSAR, downscaleSAR
1 2 3 4 5 6 7 8 9
data(anbo) anbo.sar <- meteSAR(anbo$spp, anbo$count, anbo$row, anbo$col, Amin=1, A0=16) anbo.sar plot(anbo.sar, xlim=c(1, 2^10), ylim=c(3, 50), log='xy') ## get upscaled SAR and add to plot anbo.esf <- meteESF(spp=anbo$spp, abund=anbo$count) # need ESF for upscaling anbo.sarUP <- upscaleSAR(anbo.esf, 16, 2^10) plot(anbo.sarUP, add=TRUE, col='blue')
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