Description Usage Arguments Details Value Author(s) References See Also Examples
Based on information at an anchor scale (A0
)
calcuate predicted species area relationship at larger scales
1 | upscaleSAR(x, A0, Aup, EAR = FALSE)
|
x |
an object of class meteESF |
A0 |
the anchor scale at which community data are availible. |
Aup |
the larges area to which to upscale |
EAR |
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 data.frame
with
columns A
and S
giving area and species richness, respectively
Andy Rominger <ajrominger@gmail.com>, 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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