betaper {betaper} | R Documentation |
Function betaper computes the uncertainty range of the estimated correlation between floristic and geographical or environmental distances in the face of taxonomic uncertainty.
betaper(data, geodist, index = NULL, nsim = 100, vegdist.method = "bray", binary = FALSE, cor.method = "pearson")
data |
Community data matrix. The three first columns are factors referring to the family, genus and species specific names. The remaining columns are numeric vectors indicating species abundances at each site. |
geodist |
Dissimilarity matrix or a dist object of geographical or environmental distances. |
index |
List of additional parameters to determine the level at which species have been identified. Default values include 'Indet', 'indet', 'sp', 'sp1' to 'sp100', 'sp 1' to 'sp 100', '', and ' '. |
nsim |
Number of simulations of species' identities in assessing bounds to the estimated correlation. |
vegdist.method |
Dissimilarity index, as in vegdist . Partial match to "manhattan", "euclidean", "canberra", "bray",
"kulczynski", "jaccard", "gower", "morisita", "horn", "mountford", "raup" , "binomial" or "chao". |
binary |
Perform presence/absence standardization before calculation of vegetation distances using vegdist
function. |
cor.method |
Correlation method, as accepted by cor.test : "pearson", "spearman" or "kendall". |
The method describes the manner to incorporate taxonomic uncertainty to the calculation of the correlation coefficient that measures the degree of correspondence between floristic and geographical or environmental distances, by estimating some credible bounds under plausible scenarios of re-assigned species identities. The approach involves the following steps:
Step 1. Codification of the species according to their degree of identification. Identified species are codified as 0, morphospecies identified only to genus (e.g. Eugenia sp1) as 1, only to family (e.g. Myrtaceae sp1) as 2, and fully unidentified morphospecies (e.g. Undetermined sp1) are codified as 3.
Step 2. Morphospecies identified only to genus are then randomly re-assigned in the group of species and morphospecies that share the same genus provided they are not found in the same sites. In the re-assignment of the species identity, the species can also get its own identity.
Step 3. Once the re-assignment of morphospecies has been accomplished, we calculate a floristic distance matrix. A distance matrix is a square, symmetric matrix in which rows and columns are sites and cells are the floristic distance between pairs of sites. A single distance matrix thus represents the floristic pattern among sites for a single set of inventories. The Bray-Curtis' or Sorensen's coefficient is used by default to calculate floristic distance between sites. Note that although dissimilarity indexes are calculated with the vegdist function, function betaper calculates the complementary similarity index (i.e. 1 - dissimilarity index).
Step 4. The Pearson's correlation coefficient is computed between the floristic distance matrix and the geographical or environmental distance matrix.
Step 5. Steps 2 to 4 are iterated n times. The process can be time-consuming if the floristic matrix is large. The mean and standard deviation, and the minimum and maximum values of the Pearson correlation coefficient, r, are then estimated. These measures provide credible bounds to the true correlation coefficient in the face of taxonomic uncertainty.
The function return a list of class betaper
with the following components
perm |
Permutated floristic distance matrices coerced to columns in a data frame. As many permutations (columns) as nsim . |
cordis |
Pearson's correlation values (r ) between simulated floristic distances and geographical or environmental distances. |
taxunc |
Summary of the number of species fully identified (0), identified to genus (1), identified to family (2), or fully undetermined (3). |
geodist |
Dissimilarity matrix of geographical or environmental distances. |
cond |
Numeric vector indicating the assignment of species to any of the taxunc categories. |
...
The method uses floristic similarities instead of floristic dissimilarities, as calculated by vegdist
function. The method simply
computes floristic similarities by substracting the value output by vegdist
to one
Luis Cayuela luis.cayuela@uah.es and Marcelino de la Cruz marcelino.delacruz@upm.es
mantel
for computation of the Mantel statistic, vegdist
for methods to calculate vegetation
distances. See also bioenv
for methods to select environmental variables.
## Not run: require(vegan) ################################################ # Tree data from 16 0.1-ha plots from the Highlands of Chiapas, southern Mexico data(HCP) data(HCP.coords) geodist.HCP <- dist(HCP.coords)/1000 # Geographical distances in kms # Define a new index that includes the terms used in the HCP dataset to define undetermined taxa at genus and family levels index.new <- c(paste("Genus", as.character(c(1:20)), sep=""), paste("Family", as.character(c(1:20)), sep="")) beta.HCP <- betaper(HCP, geodist = geodist.HCP, index = index.new) beta.HCP # This results in a relatively low uncertainty range of the estimated correlation beta.HCP$taxunc # Number of identified and unidentified species at different taxonomic levels ################################################ # Tree data from nine lowland sites in Western Amazonia data(Amazonia) data(soils) # Define a new index that includes the terms used in the Amazonia dataset to define undetermined taxa at different taxonomic levels index.Amazon <- c(paste("sp.", rep(1:20), sep=""), "Indet.", "indet.") beta.Amazonia <- betaper(Amazonia, geodist = dist(log(soils)), index = index.Amazon, nsim = 50) # This can take a few minutes beta.Amazonia # The uncertainty range of the estimated correlation is much larger than in the previous example beta.Amazonia$taxunc # Number of identified and unidentified species at different taxonomic levels ## End(Not run)