| upscaleSAR {meteR} | R Documentation |
upscale SAR
Description
Based on information at an anchor scale (A0)
calcuate predicted species area relationship at larger scales
Usage
upscaleSAR(x, A0, Aup, EAR = FALSE)
Arguments
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 |
Details
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.
Value
an object of class sar inheriting from data.frame with
columns A and S giving area and species richness, respectively
Author(s)
Andy Rominger <ajrominger@gmail.com>, Cory Merow
References
Harte, J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press.
See Also
meteESF, meteSAR, empiricalSAR, downscaleSAR
Examples
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')