Title: | Models and Data Sets for the Study of Species-Area Relationships |
---|---|
Description: | Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>. |
Authors: | Peter Solymos [cre, aut] |
Maintainer: | Peter Solymos <[email protected]> |
License: | GPL-2 |
Version: | 1.0-6 |
Built: | 2024-09-18 05:09:33 UTC |
Source: | https://github.com/psolymos/sharx |
Data sets and SAR, SARX, HSAR and HSARX models as described in Solymos and Lele (2012).
Peter Solymos
Maintainer: Peter Solymos <[email protected]>
Solymos, P. and Lele, S. R., 2012. Global pattern and local variation in species-area relationships. Global Ecology and Biogeography 21, 109–120.
Fit SAR, SARX, HSAR and HSARX models to data as described in Solymos and Lele (2012).
hsarx(formula, data, n.clones, cl = NULL, ...)
hsarx(formula, data, n.clones, cl = NULL, ...)
formula |
Formula. |
data |
Data. |
n.clones |
Number of clones to be used. |
cl |
Cluster object for parallel computations. |
... |
Other arguments for MCMC. |
Fit SAR, SARX, HSAR and HSARX models to data as described in Solymos and Lele (2012).
An S4 object object of class 'hsarx'. It inherits from 'dcMle', and has additional slots for storing the data.
Peter Solymos
Solymos, P. and Lele, S. R., 2012. Global pattern and local variation in species-area relationships. Global Ecology and Biogeography 21, 109–120.
sardata
for data sets.
## Not run: ## to reproduce results from Solymos and Lele (Table 1) data(sardata) DAT <- data.frame(sardata$islands, sardata$studies[match(sardata$islands$study, rownames(sardata$studies)),]) x <- hsarx(log(S+0.5) ~ log(A) | (taxon.group + island.type + abs(latitude) + I(log(extent)))^2 | study, DAT, n.clones=5, n.adapt=2000, n.update=3000, n.iter=1000) ## SAR DATS <- DAT[1:191,] (x1 <- hsarx(log(S+0.5) ~ log(A), DATS[DATS$study=="abbott1978bird",], n.clones=2)) ## SARX DATS$rnd <- rnorm(nrow(DATS), log(DATS$extent)) (x2 <- hsarx(log(S+0.5) ~ log(A) * rnd, DATS[DATS$study=="abbott1978bird",], n.clones=2)) ## HSAR (x3 <- hsarx(log(S+0.5) ~ log(A) | 1 | study, DATS, n.clones=2, n.iter=1000)) ## HSARX (x4 <- hsarx(log(S+0.5) ~ log(A) | abs(latitude) | study, DATS, n.clones=2, n.iter=1000)) ## End(Not run)
## Not run: ## to reproduce results from Solymos and Lele (Table 1) data(sardata) DAT <- data.frame(sardata$islands, sardata$studies[match(sardata$islands$study, rownames(sardata$studies)),]) x <- hsarx(log(S+0.5) ~ log(A) | (taxon.group + island.type + abs(latitude) + I(log(extent)))^2 | study, DAT, n.clones=5, n.adapt=2000, n.update=3000, n.iter=1000) ## SAR DATS <- DAT[1:191,] (x1 <- hsarx(log(S+0.5) ~ log(A), DATS[DATS$study=="abbott1978bird",], n.clones=2)) ## SARX DATS$rnd <- rnorm(nrow(DATS), log(DATS$extent)) (x2 <- hsarx(log(S+0.5) ~ log(A) * rnd, DATS[DATS$study=="abbott1978bird",], n.clones=2)) ## HSAR (x3 <- hsarx(log(S+0.5) ~ log(A) | 1 | study, DATS, n.clones=2, n.iter=1000)) ## HSARX (x4 <- hsarx(log(S+0.5) ~ log(A) | abs(latitude) | study, DATS, n.clones=2, n.iter=1000)) ## End(Not run)
Data sets for the study of the species-area relationship
data(sardata)
data(sardata)
A list of two data frames, see Details.
The element sardata$islands
is a data frame with variables:
study
Factor, levels are the study identifiers.
id
Numeric, island identifiers according to the original references.
S
Numeric, number of species according to the original references.
A
Numeric, area of the island in square kilometres, according to the original references.
The element sardata$studies
is a data frame with variables:
study
Factor, levels are the study identifiers.
taxon.group
Factor, taxonomic group.
island.type
Factor, island type.
latitude
Numeric, middle band of latitude for the study calculated as (min + max) / 2, where min is the latitude close to the Equator, max is the latitude close to the poles.
extent
Numeric, latitudinal extent of the study.
location
Character, location of the study.
References are in the files sardata.txt
and sardata.bib
in the sharx
library of R
, labelled by study identifiers.
See examples on how to recall the files from the console.
## data structure data(sardata) str(sardata$islands) str(sardata$studies) ## references file.show(system.file(package = "sharx", "sardata.txt")) file.show(system.file(package = "sharx", "sardata.bib"))
## data structure data(sardata) str(sardata$islands) str(sardata$studies) ## references file.show(system.file(package = "sharx", "sardata.txt")) file.show(system.file(package = "sharx", "sardata.bib"))
Fit a breakpoint regression model to data to find threshold for the small island effect (SIE) as described in Lomolino...
sie(S, A, method = "Nelder-Mead", ...) sieplot(x, add = FALSE, ...)
sie(S, A, method = "Nelder-Mead", ...) sieplot(x, add = FALSE, ...)
S |
untransformed species richness, vector. |
A |
untransformed area, vector. |
x |
a fitted model object of class 'sie'. |
method |
optimization method. |
add |
logical, if lines should be added to existing plot ( |
... |
graphical arguments passed to |
sie
fits the breakpoint regression to the data,
richness is log(S+0.5)
transformed, area is log(A)
transformed before analysis.
There is a coef
, summary
, print
method for fitted objects.
sieplot
plots the observed (transformed)
data and the fitted line.
An S4 object of class 'sie' inheriting from class 'mle'.
Peter Solymos
Lomolino, M. V., and M. D. Weiser. 2001. Towards a more general species-area relationship: diversity on all islands, great and small. Journal of Biogeography, 28, 431–445.
data(sardata) DAT <- sardata$islands[sardata$islands$study=="abbott1978plant",] (x <- sie(DAT$S, DAT$A)) coef(x) summary(x) sieplot(x)
data(sardata) DAT <- sardata$islands[sardata$islands$study=="abbott1978plant",] (x <- sie(DAT$S, DAT$A)) coef(x) summary(x) sieplot(x)