dsi: Distance-based specialisation index

In leorjorge/dizzy: Tools to measure distance based specialisation of species and communities

Distance-based specialisation index

Description

dsi measures the distance-based specialization index (DSI*) of a set of consumers. Relies on resource use information from an interaction matrix or array, resource similarities from a distance matrix or phylogeny, and local resource availability data for creating diet null models.

Usage

dsi(Int, Dist, Abund, Rep = 999)

Arguments

Int	Is an interaction array for which Specialization is to be measured. Consumers are in rows, resources in the columns and different locations (or other "local" partition - e.g. time) are slices. When the matrix has two dimensions no local variation is assumed.
Dist	Object of class phylo with the $tip.label information matching Abundance data. The phylogeny should have all resource classes present in Abund and will be pruned to remove resource classes absent from Abund. Alternatively, a distance matrix, with dissimilarities between resource classes. Dimension names must match Abund.
Abund	Matrix of local abundances/sampling effort of the resources in the interaction array at different locations. Resource items are in rows and locations in columns, with labels as row and column names. Abundances can be inputted as relative or absolute values.
Rep	Number of iterations for the null model, defaults to 999

Value

Returns an object of class dsi, with the following elements:

data	A list with the three objects used to calculate DSI - The interaction array, distance matrix and resource availability matrix
consumers	A character vector with the names of consumer species
resources	A character vector with the names of resource classes (often species)
richness	A numeric vector with the number of resource classes used by each consumer species
samp	A numeric vector with the number of individuals or interactions recorded for each consumer species
MPD	A numeric vector with the abundance averaged mean pairwise distance between resource items used by each consumer species
null	A matrix with Rep rows and consumers in columns, with all MPD values obtained by the null model
DSI	A numeric vector with unstandardized DSI values. These are calculated as Z-scores of the MPD values, compared to the null distribution of MPDs obtained from the null model. It can be used to classify consumers as specialists, non-selective or generalists, but is not appropriate for comparisons among species with different sampling effort
class	A character vector with the specialization class of consumers. Singletons will return NA
lim	Theoretical maximum (for species with positive DSI) and minimum (for species with negative DSI) DSI values attainable for the sample size and resource similarity of each consumer species. Used to standardize DSI and generate comparable DSI* values. Maximum is obtained by assuming the species is a monophage, and minimum is calculated by using a Simulated annealing algorithm to distribute the recorded number of interactions among resource classes in such a manner that maximizes MPD
DSIstar	A numeric vector with standardized DSI values (DSI*). This is the index as proposed in Jorge et al. (2017). DSI* values vary between -1 (extreme generalization) to 1 (extreme specialization). This should be the preferred value to be used when comparing species, as it has a very straightforward interpretation and is controlled for differences both in sampling intensity, co-occurrence with resources and resource similarity differences.

leorjorge/dizzy documentation built on Jan. 18, 2025, 6:51 a.m.