gridMetrics: Grid Metrics

In epm: EcoPhyloMapper

Grid Metrics

Description

Calculate various morphological and phylogenetic community metrics for every cell in a epmGrid object. To implement other metrics not available here, see customGridMetric.

Usage

gridMetrics(
  x,
  metric,
  column = NULL,
  verbose = TRUE,
  dataType = c("auto", "univariate", "multivariate", "pairwise")
)

Arguments

x	object of class epmGrid
metric	name of metric to use, see Details.
column	If a univariate morphological metric is specified, and the data in x are multivariate, which trait should be used? This can also specify which subset of columns a multivariate metric should be applied to.
verbose	Print various messages to the console. Default is TRUE.
dataType	Specify the type of input data that the metric will be calculated from. Defaults to 'auto' in which case this is determined based on the data structure.

Details

Univariate trait metrics

• mean

• median

• range

• variance

• mean_NN_dist: mean nearest neighbor distance

• min_NN_dist: minimum nearest neighbor distance

• evenness: variance of nearest neighbor distances, larger values imply decreasing evenness

• arithmeticWeightedMean (see below)

• geometricWeightedMean (see below)

Multivariate trait metrics

• mean: mean of pairwise distance matrix derived from multivariate data

• median: median of pairwise distance matrix derived from multivariate data

• disparity

• partialDisparity: contribution of species in each gridcell to overall disparity, returned as the ratio of summed partial disparities to total disparity.

• range

• mean_NN_dist: mean nearest neighbor distance

• min_NN_dist: minimum nearest neighbor distance

• evenness: variance of nearest neighbor distances, larger values imply decreasing evenness.

Phylogenetic metrics

• pd: Faith's phylogenetic diversity, including the root

• meanPatristic

• meanPatristicNN: mean nearest neighbor in patristic distance

• minPatristicNN: minimum nearest neighbor in patristic distance

• phyloEvenness: variance of nearest neighbor patristic distances, larger values imply decreasing evenness

• phyloDisparity: sum of squared deviations in patristic distance

• PSV: Phylogenetic Species Variability

• PSR: Phylogenetic Species Richness

• DR: non-parametric estimate of speciation rates

Range-weighted metrics

• weightedEndemism: Species richness inversely weighted by range size.

• correctedWeightedEndemism: Weighted endemism standardized by species richness

• phyloWeightedEndemism: Phylogenetic diversity inversely weighted by range size associated with each phylogenetic branch.

If data slot contains a pairwise matrix, column is ignored. Weighted mean options are available where, for each cell, a weighting scheme (inverse of species range sizes) is applied such that small-ranged species are up-weighted, and broadly distributed species are down-weighted. This can be a useful way to lessen the influence of broadly distributed species in the geographic mapping of trait data.

It may be desirable to have metrics calculated for a dataset where only taxa shared across geography, traits and phylogeny are included. The function reduceToCommonTaxa does exactly that.

If a set of trees are associated with the input epmGrid object x, then the metric is calculated for each tree, and a list of epmGrid objects is returned. This resulting list can be summarized with the function summarizeEpmGridList. For instance the mean and variance can be calculated, to show the central tendency of the metric across grid cells, and to quantify where across geography variability in phylogenetic topography manifests itself.

To implement other metrics not available here, see customGridMetric.

Value

object of class epmGrid where the grid represents calculations of the metric at every cell. The species identities per grid cell are those that had data for the calculation of the metric. If taxa were dropped from the initial epmGrid object, then they have been removed from this epmGrid. If a set of trees was involved, then returns a list of epmGrid objects.

References

partial disparity
Foote, M. (1993). Contributions of individual taxa to overall morphological disparity. Paleobiology, 19(4), 403–419. https://doi.org/10.1017/s0094837300014056

PSV, RSV
Helmus, M. R., Bland, T. J., Williams, C. K., & Ives, A. R. (2007). Phylogenetic Measures of Biodiversity. The American Naturalist, 169(3), E68–E83. https://doi.org/10.1086/511334

DR
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444–448. https://doi.org/10.1038/nature11631

weighted endemism
Crisp, M. D., Laffan, S., Linder, H. P., & Monro, A. (2001). Endemism in the Australian flora. Journal of Biogeography, 28(2), 183–198. https://doi.org/10.1046/j.1365-2699.2001.00524.x

phylo weighted endemism
Rosauer, D., Laffan, S. W., Crisp, M. D., Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular Ecology, 18(19), 4061–4072. https://doi.org/10.1111/j.1365-294x.2009.04311.x

Examples

tamiasEPM <- addPhylo(tamiasEPM, tamiasTree)
tamiasEPM <- addTraits(tamiasEPM, tamiasTraits)

# univariate morphological example
x <- gridMetrics(tamiasEPM, metric='mean', column='V2')
plot(x, use_tmap = FALSE)

# multivariate morphological
x <- gridMetrics(tamiasEPM, metric='disparity')
plot(x, use_tmap = FALSE)

# phylogenetic metrics
x <- gridMetrics(tamiasEPM, metric='meanPatristic')
plot(x, use_tmap = FALSE)

epm documentation built on April 4, 2025, 1:42 a.m.