The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Species richness and community structure can also be represented in
attribute space, where axes correspond to any quantitative information
that can be attributed to a species. The letsR package
provides tools to construct and analyze presence–absence matrices (PAMs)
in attribute space, allowing researchers to examine biodiversity
patterns beyond geography and environment.
This vignette demonstrates how to:
lets.attrpam();lets.plot.attrpam();lets.attrcells();lets.summarizer.cells(); andWe begin by generating a dataset of 2,000 species with two correlated traits:
The lets.plot.attrpam() function plots the richness
surface across the bivariate trait space.
Each cell represents a unique combination of traits (binned values of
trait_a and trait_b), and the color intensity
indicates the number of species falling within that bin.
The function lets.attrcells() quantifies structural
properties of each cell in the trait space, including measures of
centrality, isolation, and border proximity.
attr_desc <- lets.attrcells(attr_obj, perc = 0.2)
head(attr_desc)
#> Cell_attr Weighted Mean Distance to midpoint Mean Distance to midpoint
#> 3 1 -2.326864 -2.353019
#> 4 2 -2.249095 -2.275839
#> 5 3 -2.174679 -2.202012
#> 6 4 -2.103971 -2.131885
#> 7 5 -2.037358 -2.065836
#> 8 6 -1.975254 -2.004267
#> Minimum Zero Distance Minimum 20% Zero Distance Distance to MCP border
#> 3 0 0.7875427 0
#> 4 0 0.7238193 0
#> 5 0 0.6742795 0
#> 6 0 0.6362405 0
#> 7 0 0.6116810 0
#> 8 0 0.5974101 0
#> Frequency Weighted Distance
#> 3 2.401182
#> 4 2.325796
#> 5 2.253747
#> 6 2.185353
#> 7 2.120959
#> 8 2.060930We can visualize these metrics using
lets.plot.attrcells():
Each panel represents a different descriptor (e.g., distance to midpoint, distance to border, weighted isolation) mapped across the trait space.
To derive species-level summaries, we can aggregate descriptor values
across all cells occupied by each species using the
lets.summarizer.cells() function.
attr_desc_by_sp <- lets.summaryze.cells(attr_obj, attr_desc, func = mean)
head(attr_desc_by_sp)
#> Species Weighted Mean Distance to midpoint Mean Distance to midpoint
#> 1 sp1 -0.50820192 -0.4896199
#> 2 sp2 -0.12253919 -0.1470435
#> 3 sp3 -0.84093748 -0.8298832
#> 4 sp4 -0.65172785 -0.6818160
#> 5 sp5 -0.07626174 -0.1050365
#> 6 sp6 -0.95462516 -0.9441664
#> Minimum Zero Distance Minimum 20% Zero Distance Distance to MCP border
#> 1 0.4618802 1.1195434 1.2701706
#> 2 0.8164966 1.2218190 1.5011107
#> 3 0.3265986 0.8310473 0.7745967
#> 4 0.2309401 0.9311691 1.0392305
#> 5 0.8082904 1.2360965 1.6041613
#> 6 0.2309401 0.7661288 0.6733003
#> Frequency Weighted Distance
#> 1 0.8225743
#> 2 0.6921972
#> 3 1.0446166
#> 4 0.9069837
#> 5 0.6857976
#> 6 1.1319803This produces a data frame in which each row corresponds to a species, and each column corresponds to the mean descriptor value across the cells where that species occurs. `
When a geographic PAM generated by lets.presab() is
supplied through the y argument, lets.attrcells() links the
species occurring in each attribute cell to the geographic cells
occupied by those species. This enables the computation of additional
descriptors in geographic space.
data("PAM")
n <- length(PAM$Species_name)
Species <- PAM$Species_name
trait_a <- rnorm(n)
trait_b <- trait_a * 0.2 + rnorm(n)
df <- data.frame(Species, trait_a, trait_b)
x <- lets.attrpam(df, n_bins = 4)
cell_desc_geo <- lets.attrcells(x, y = PAM)
head(cell_desc_geo)
#> Cell_attr Frequency Area Isolation (Min.) Isolation (1st Qu.)
#> 3 1 625 7.610774e+12 101354.56 995744.9
#> 4 2 0 0.000000e+00 0.00 0.0
#> 5 3 104 1.277753e+12 101354.56 435455.5
#> 6 4 15 1.822784e+11 110577.35 156797.2
#> 7 5 72 8.858890e+11 96965.99 322461.4
#> 8 6 497 6.048791e+12 91839.61 857643.9
#> Isolation (Median) Isolation (Mean) Isolation (3rd Qu.) Isolation (Max.)
#> 3 1642232.9 1747128.1 2384391.6 4749476
#> 4 0.0 0.0 0.0 0
#> 5 672554.2 722752.7 942685.7 2959492
#> 6 331739.8 644769.6 663456.3 2894931
#> 7 491756.2 513232.8 686374.2 1284485
#> 8 1352699.2 1517451.6 2012096.3 4548857
#> Weighted Mean Distance to midpoint Mean Distance to midpoint
#> 3 -1.5126554 -1.5627376
#> 4 -1.0102709 -1.0708381
#> 5 -1.1194498 -1.1624284
#> 6 -1.7279145 -1.7480804
#> 7 -1.1689967 -1.1913948
#> 8 -0.3147603 -0.3520894
#> Minimum Zero Distance Minimum 10% Zero Distance Distance to MCP border
#> 3 0.8660254 0.8660254 0.0000000
#> 4 0.0000000 0.0000000 0.0000000
#> 5 0.8660254 0.8660254 0.0000000
#> 6 1.7320508 1.7320508 0.0000000
#> 7 1.2247449 1.2247449 0.8660254
#> 8 0.8660254 0.8660254 0.8660254
#> Frequency Weighted Distance
#> 3 1.988764
#> 4 1.385760
#> 5 1.506749
#> 6 1.928870
#> 7 1.489442
#> 8 1.181192When y is provided, the output includes additional
variables:
Frequency: number of geographic cells associated with
each attribute cell;Area: summed area of those geographic cells; andIsolation (Min.)Isolation (1st Qu.)Isolation (Median)Isolation (Mean)Isolation (3rd Qu.)Isolation (Max.)These variables allow direct integration of position in attribute space with occupancy and isolation patterns in geographic space.
The resulting descriptors can also be plotted:
Vilela, B. & Villalobos, F. (2015). letsR: a new R package for data handling and analysis in macroecology. Methods in Ecology and Evolution, 6(10), 1229–1234.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.