gridMetrics {epm} | R Documentation |
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 = FALSE)
Arguments
x |
object of class |
metric |
name of metric to use, see Details. |
column |
If a univariate morphological metric is specified, and the data
in |
verbose |
print various messages to the console. Default is TRUE. |
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
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)