Defines functions ecospat.climan

Documented in ecospat.climan

#### Coded by Blaise Petitpierre (bpetitpierre@gmail.com), 9th December 2015 after Mesgaran et al 2014
# it allows to assess climate analogy between a projection extent (p) and a reference extent (ref, usend in general as the background to calibrate SDMS)
# ref : a dataframe with the value of the variables (i.e columns) for each point of the reference exent
# p : a dataframe with the value of the variables (i.e columns) for each point of the projection exent
# return a vector. Values below 0 are novel conditions at the univariate level (similar to the MESS), values between 0 and 1 are analog and values above 1 are novel covariate condtions. For more information, see the reference
# References: Mesgaran, M.B., Cousens, R.D. & Webber, B.L. (2014) Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity & Distributions, 20: 1147-1159, DOI: 10.1111/ddi.12209

minref<-matrix(a,nrow=nrow(p),ncol=ncol(p),byrow = TRUE)
maxref<-matrix(b,nrow=nrow(p),ncol=ncol(p),byrow = TRUE)

nt1<-rowSums(apply(array(data=c(p-minref, maxref-p, rep(0,nrow(p)*ncol(p))),dim=c(dim(p),3)), c(1,2),min)/(maxref-minref))


mah.ref <- mahalanobis(x=ref, center=a, cov=b)
mah.pro   <- mahalanobis(x=p, center=a, cov=b)
mah.max <- max(mah.ref[is.finite(mah.ref)])
nt2 <- mah.pro/mah.max

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ecospat documentation built on Nov. 10, 2022, 5:55 p.m.