ausplotsR: quickstart guide to basic analysis of TERN AusPlots vegetation data
Greg Guerin & Bernardo Blanco-Martin
2020-10-28
Introduction
TERN AusPlots is a national plot-based terrestrial ecosystem surveillance monitoring method and dataset for Australia (Sparrow et al. 2020). Through ausplotsR, users can directly access AusPlots data collected by on-ground observers on vegetation and soils, including physical sample/voucher details and barcode numbers. The dataset can be downloaded in its entirety or as individual modules, and can be subsetted by geographic bounding box or species name search. The package also includes a series of bespoke functions for working with AusPlots data, including visualisation, creating tables of species composition, and calculation of tree basal area, fractional cover or vegetation cover by growth form/structure/strata and so on.
This is a short guide for getting started with analysis of AusPlots data through the ausplotsR R package. More information on making use of AusPlots data in ausplotsR is available through the package help files and manual. Below, we demonstrate installing the package, accessing some AusPlots data, generating matrices and running simple example analyses.
More comprehensive tutorials on accessing and analysing AusPlots data (Blanco-Martin 2019) are available at: https://github.com/ternaustralia/TERN-Data-Skills/tree/master/EcosystemSurveillance_PlotData
Installing the package and accessing raw data
The latest version of ausplotsR can be installed directly from github using the devtools package, which must be installed first.
library(devtools)
install_github("ternaustralia/ausplotsR", build_vignettes = TRUE, dependencies = TRUE)
Once installed, load the package as follows. Note, packages vegan, maps and mapdata are required for ausplotsR to load, and functions are also imported from packages: plyr, R.utils, simba, httr, jsonlite, sp, maptools, ggplot2, gtools, jose, curl and betapart, while knitr, rmarkdown and goeveg are required to build this package vignette (i.e., if ‘build_vignettes’ is set to TRUE above).
We can now access live data, starting here with basic site information and vegetation point-intercept modules and using a bounding box to spatially filter the dataset to central Australia. All data modules are extracted via a single function, get_ausplots:
# See ?get_ausplots to explore all data modules available
my.ausplots.data <- get_ausplots(bounding_box = c(125, 140, -40, -10))
The output of the above call is a list with the following $elements:
names(my.ausplots.data)
#> [1] "site.info" "veg.vouch" "veg.PI" "citation"
The ‘site.info’ table contains basic site and visit details. Here are a selected few of the many fields:
head(my.ausplots.data$site.info[, c("site_location_name", "site_unique", "longitude",
"latitude", "bioregion_name")])
#> site_location_name site_unique longitude latitude bioregion_name
#> 1 NTADAC0001 NTADAC0001-53518 130.7779 -13.15835 DAC
#> 2 NTASSD0015 NTASSD0015-53565 135.6168 -25.12393 SSD
#> 3 QDAMII0002 QDAMII0002-53546 138.1606 -20.00789 MII
#> 4 SATSTP0005 SATSTP0005-53513 138.8488 -29.45660 STP
#> 5 SATSTP0005 SATSTP0005-58639 138.8488 -29.45660 STP
#> 6 NTTDAB0001 NTTDAB0001-53580 131.6740 -13.96288 DAB
Each survey is identified by the ‘site_unique’ field, which is unique combination of site ID (‘site_location_name’) and visit ID (‘site_location_visit_id’). The ‘site_unique’ field therefore links all tables returned from the get_ausplots function.
The ‘site.info’ table and can be used to identify, subset or group surveys in space and time, for example:
# count plot visits per Australian States:
summary(as.factor(my.ausplots.data$site.info$state))
#> NT QLD SA WA
#> 151 48 172 25
Map AusPlots sites and visualise data
The package has an in-built function - see ?ausplots_visual - to rapidly map AusPlots over Australia and to visualise the relative cover/abundance of green vegetation, plant growth forms and species. Maps can also be generated manually using the longitude and latitude fields in the $site.info table.
# Sites are coded by IBRA bioregion by default.
map_ausplots(my.ausplots.data)
Alternatively, the following call generates a pdf with a map of all sites and attribute graphics for selected AusPlots: ausplotsR::ausplots_visual()
Here is a snippet of the raw point-intercept data that will be used in the following examples to derive vegetation attributes:
head(subset(my.ausplots.data$veg.PI, !is.na(herbarium_determination)))
#> site_unique site_location_name site_location_visit_id transect
#> 506 SATSTP0001-58652 SATSTP0001 58652 S5-N5
#> 722 SATSTP0001-58652 SATSTP0001 58652 S3-N3
#> 1444 SATFLB0006-58655 SATFLB0006 58655 W5-E5
#> 1445 SATFLB0006-58655 SATFLB0006 58655 W5-E5
#> 1448 SATFLB0006-58655 SATFLB0006 58655 W5-E5
#> 1503 SATFLB0006-58655 SATFLB0006 58655 N2-S2
#> point_number herbarium_determination substrate in_canopy_sky dead
#> 506 27 Eragrostis sp. Crypto FALSE FALSE
#> 722 68 Eragrostis sp. Crypto FALSE FALSE
#> 1444 1 Eutaxia microphylla Litter FALSE FALSE
#> 1445 8 Eutaxia microphylla Litter FALSE FALSE
#> 1448 12 Eutaxia microphylla Litter FALSE FALSE
#> 1503 5 Eutaxia microphylla Gravel FALSE FALSE
#> growth_form height veg_barcode standardised_name family genus
#> 506 Tussock grass 0.10 SAT006797 Eragrostis Poaceae Eragrostis
#> 722 Tussock grass 0.05 SAT006797 Eragrostis Poaceae Eragrostis
#> 1444 Heath-shrub 0.20 SAT006533 Eutaxia microphylla Fabaceae Eutaxia
#> 1445 Heath-shrub 0.01 SAT006533 Eutaxia microphylla Fabaceae Eutaxia
#> 1448 Heath-shrub 0.10 SAT006533 Eutaxia microphylla Fabaceae Eutaxia
#> 1503 Heath-shrub 0.25 SAT006533 Eutaxia microphylla Fabaceae Eutaxia
#> specific_epithet infraspecific_rank infraspecific_epithet taxa_status
#> 506 <NA> <NA> <NA> Accepted
#> 722 <NA> <NA> <NA> Accepted
#> 1444 microphylla <NA> <NA> Accepted
#> 1445 microphylla <NA> <NA> Accepted
#> 1448 microphylla <NA> <NA> Accepted
#> 1503 microphylla <NA> <NA> Accepted
#> taxa_group genus_species authorship published_in
#> 506 angiosperms Eragrostis Wolf Gen. Pl. 23. 1776.
#> 722 angiosperms Eragrostis Wolf Gen. Pl. 23. 1776.
#> 1444 angiosperms Eutaxia microphylla (R.Br.) J.M.Black <NA>
#> 1445 angiosperms Eutaxia microphylla (R.Br.) J.M.Black <NA>
#> 1448 angiosperms Eutaxia microphylla (R.Br.) J.M.Black <NA>
#> 1503 angiosperms Eutaxia microphylla (R.Br.) J.M.Black <NA>
#> rank hits_unique
#> 506 genus S5-N5 27
#> 722 genus S3-N3 68
#> 1444 SPECIES W5-E5 1
#> 1445 SPECIES W5-E5 8
#> 1448 SPECIES W5-E5 12
#> 1503 SPECIES N2-S2 5
Note that ‘veg_barcode’ links species hits to the vegetation vouchers module, while the ‘hits_unique’ field identifies the individual point-intercept by transect and point number (see help(ausplotsR) and references for more details on the plot layout and survey method). At each point, plant species (if any), growth form and height are recorded along with substrate type.
Example 1: latitudinal pattern in proportional vegetation cover
Let’s visualise basic vegetation cover as a function of latitude. First, we call the fractional_cover function on the extracted point-intercept data ($veg.PI). The function converts the raw data to proportional cover of green/brown vegetation and bare substrate. Note the calculation may take a few minutes for many AusPlots, so for this example we will pull out a subset of 100 randomly drawn sites to work with.
sites100 <- my.ausplots.data$veg.PI[which(my.ausplots.data$veg.PI$site_unique %in%
sample(my.ausplots.data$site.info$site_unique, 100)), ]
my.fractional <- fractional_cover(sites100)
head(my.fractional)
#> site_unique NA. bare brown green
#> NTAARP0003-58424 NTAARP0003-58424 0.0 25.45 40.50 34.06
#> NTABRT0001-53616 NTABRT0001-53616 0.0 18.22 33.27 48.51
#> NTABRT0003-53618 NTABRT0003-53618 0.0 14.65 30.59 54.75
#> NTABRT0004-53619 NTABRT0004-53619 0.0 22.10 31.42 46.48
#> NTAFIN0001-53519 NTAFIN0001-53519 0.0 39.31 53.27 7.43
#> NTAFIN0001-58009 NTAFIN0001-58009 0.1 44.36 43.37 12.18
Next, we need to merge the fractional cover scores with longlat coordinates from the site information table. We use the ‘site_unique’ field (unique combination of site and visit IDs) to link tables returned from the get_ausplots function:
my.fractional <- merge(my.fractional, my.ausplots.data$site.info, by = "site_unique")[,
c("site_unique", "bare", "brown", "green", "NA.", "longitude", "latitude")]
my.fractional <- na.omit(my.fractional)
head(my.fractional)
#> site_unique bare brown green NA. longitude latitude
#> 1 NTAARP0003-58424 25.45 40.50 34.06 0.0 132.4571 -13.51697
#> 2 NTABRT0001-53616 18.22 33.27 48.51 0.0 133.2473 -22.28360
#> 3 NTABRT0003-53618 14.65 30.59 54.75 0.0 133.2699 -22.28358
#> 4 NTABRT0004-53619 22.10 31.42 46.48 0.0 133.6164 -22.28981
#> 5 NTAFIN0001-53519 39.31 53.27 7.43 0.0 133.4679 -24.12430
#> 6 NTAFIN0001-58009 44.36 43.37 12.18 0.1 133.4679 -24.12430
Now we can plot out the continental relationship, e.g., between the proportion of bare ground with no kind of vegetation cover above and latitude.
plot(bare ~ latitude, data = my.fractional, pch = 20, bty = "l")
There appears to be a hump-backed relationship, with a higher proportion of bare ground in the arid inland at mid-latitudes. We can add a simple quadratic model to test/approximate this:
my.fractional$quadratic <- my.fractional$latitude^2
LM <- lm(bare ~ latitude + quadratic, data = my.fractional)
summary(LM)
#>
#> Call:
#> lm(formula = bare ~ latitude + quadratic, data = my.fractional)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -31.518 -13.863 -4.278 15.051 45.286
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -185.87863 27.14777 -6.847 6.88e-10 ***
#> latitude -18.31268 2.24245 -8.166 1.19e-12 ***
#> quadratic -0.36137 0.04434 -8.150 1.29e-12 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 19.09 on 97 degrees of freedom
#> Multiple R-squared: 0.408, Adjusted R-squared: 0.3958
#> F-statistic: 33.43 on 2 and 97 DF, p-value: 9.036e-12
# generate predicted values for plotting:
MinMax <- c(min(my.fractional$latitude), max(my.fractional$latitude))
ND <- data.frame(latitude = seq(from = MinMax[1], to = MinMax[2], length.out = 50),
quadratic = seq(from = MinMax[1], to = MinMax[2], length.out = 50)^2)
ND$predict <- predict(LM, newdata = ND)
#
plot(bare ~ latitude, data = my.fractional, pch = 20, bty = "n")
points(ND$latitude, ND$predict, type = "l", lwd = 2, col = "darkblue")
Example 2: Species by sites table
Aside from ‘gross’ values from plots such as fractional cover, many analyses in community ecology begin with species abundance information. With ausplotsR you can generate this easily from the more complex vegetation point-intercept data. The first step to work with species-level AusPlots data is to create a species occurrence matrix. The species_table function in the ausplotsR package can be used to create this type of matrix. This function takes a data frame of individual raw point-intercept hits (i.e. a $veg.PI data frame) generated using the get_ausplots function and returns a ‘species against sites’ matrix:
# The species_table function below can also take the `$veg.voucher` module as
# input, but `m_kind='PA'` must be specified to get a sensible presence/absence
# output.
# The 'species_name' argument below specifies use of the 'standardised_name'
# field to identify species, which is based on herbarium_determination names
# (i.e., 'HD' option in species_name) matched to accepted scientific name
# according to a standard (http://www.worldfloraonline.org/).
my.sppBYsites <- species_table(my.ausplots.data$veg.PI, m_kind = "percent_cover",
cover_type = "PFC", species_name = "SN")
# check the number of rows (plots) and columns (species) in the matrix
dim(my.sppBYsites)
#> [1] 391 1752
# look at the top left corner (as the matrix is large)
my.sppBYsites[1:5, 1:5]
#> Abutilon Abutilon.fraseri Abutilon.halophilum Abutilon.hannii
#> NTAARP0001-58422 0 0 0 0
#> NTAARP0002-58423 0 0 0 0
#> NTAARP0003-58424 0 0 0 0
#> NTABRT0001-53616 0 0 0 0
#> NTABRT0002-53617 0 0 0 0
#> Abutilon.leucopetalum
#> NTAARP0001-58422 0
#> NTAARP0002-58423 0
#> NTAARP0003-58424 0
#> NTABRT0001-53616 0
#> NTABRT0002-53617 0
We can crudely pull out the 10 highest ranking species in terms of their percent cover cumulative across all plots they occur in:
rev(sort(colSums(my.sppBYsites)))[1:10]
#> Triodia.basedowii Aristida.holathera Eucalyptus.obliqua
#> 651.4038 456.6403 359.5146
#> Eulalia.aurea Eucalyptus.baxteri Triodia.bitextura
#> 340.4958 335.0025 325.2499
#> Cenchrus.ciliaris Triodia.pungens Acacia.shirleyi
#> 306.7625 291.3861 282.0792
#> Schizachyrium.fragile
#> 264.3876
A simple example of downstream visualisation and analysis of species-level AusPlots data is Rank-Abundance Curves (also known as Whittaker Plots). Rank-Abundance Curves provide further information on species diversity. They provide a more complete picture than a single diversity index. Their x-axis represents the abundance rank (from most to least abundant) and in the y-axis the species relative abundance. Thus, they depict both Species Richness and Species Evenness (slope of the line that fits the rank; steep gradient indicates low evenness and a shallow gradient high evenness).
# Whittaker plots for some selected AusPlots:
goeveg::racurves(my.sppBYsites[10:15, ], bw = F)
# And then with alternative relative abundance models fitted for communities
# individually:
par(mfrow = c(2, 2), mar = c(4, 4, 1, 1))
for (i in c(1:4)) {
plot(vegan::radfit(round(my.sppBYsites[9 + i, ], digits = 0), log = "xy"), pch = 20,
legend = FALSE, bty = "l")
legend("topright", legend = c("Null", "Preemption", "Lognormal", "Zipf", "Mandelbrot"),
lwd = rep(1, 5), col = c("black", "red", "green", "blue", "cyan"), cex = 0.7,
bty = "n")
}
Example 3: Quick species lists
Perhaps you simply want to browse which plant species have been recorded in AusPlots, without all the associated raw data? Here, the species_list function is your friend:
# The species_list function is designed to take $veg.voucher as input but can
# also take $veg.PI
# print a list of genus_species-only records from selected plots:
species_list(subset(my.ausplots.data$veg.vouch, site_unique %in% unique(site_unique)[1:2]),
grouping = "by_site", species_name = "GS")
#> $QDAMII0002
#> [1] Abutilon leucopetalum Acacia coriacea Acacia cowleana
#> [4] Acacia lysiphloia Aristida latifolia Atalaya hemiglauca
#> [7] Boerhavia repens Bonamia media Bothriochloa ewartiana
#> [10] Brachiaria subquadripara Bulbostylis barbata Carissa spinarum
#> [13] Chrysopogon benthamianus Cleome viscosa Convolvulus clementii
#> [16] Corymbia aparrerinja Corymbia terminalis Crotalaria medicaginea
#> [19] Cucumis melo Dichanthium sericeum Enneapogon polyphyllus
#> [22] Enneapogon purpurascens Enneapogon robustissimus Eucalyptus leucophloia
#> [25] Eucalyptus pruinosa Eulalia aurea Euphorbia tannensis
#> [28] Gossypium australe Indigofera colutea Indigofera linifolia
#> [31] Ipomoea coptica Ipomoea polymorpha Iseilema macratherum
#> [34] Mnesithea formosa Paspalidium rarum Portulaca oleracea
#> [37] Rhynchosia minima Salsola kali Senna notabilis
#> [40] Sida cleisocalyx Solanum quadriloculatum Sporobolus australasicus
#> [43] Tephrosia Themeda triandra Triodia pungens
#> [46] Ventilago viminalis Vigna
#>
#> $SAASTP0016
#> [1] Abutilon leucopetalum Acacia salicina
#> [3] Astrebla pectinata Atriplex angulata
#> [5] Atriplex incrassata Atriplex vesicaria
#> [7] Calocephalus platycephalus Calotis hispidula
#> [9] Centipeda thespidioides Cullen australasicum
#> [11] Cyperus alterniflorus Digitaria
#> [13] Dissocarpus biflorus Enchylaena tomentosa
#> [15] Eragrostis leptocarpa Eragrostis setifolia
#> [17] Eremophila longifolia Eriachne ovata
#> [19] Eulalia aurea Euphorbia drummondii
#> [21] Frankenia Goodenia lunata
#> [23] Iseilema vaginiflorum Leiocarpa leptolepis
#> [25] Malvastrum americanum Marsilea
#> [27] Minuria rigida Nitraria billardierei
#> [29] Osteocarpum dipterocarpum Panicum decompositum
#> [31] Plantago drummondii Podolepis
#> [33] Rhagodia spinescens Schenkia australis
#> [35] Sclerolaena divaricata Sclerolaena ventricosa
#> [37] Sida fibulifera Stemodia florulenta
#> [39] Streptoglossa adscendens Swainsona campylantha
#> [41] Tecticornia tenuis
# overall species list ordered by family (for demonstration we print only part):
species_list(my.ausplots.data$veg.vouch, grouping = "collapse", species_name = "SN",
append_family = TRUE)[1:20]
#> [1] Acanthaceae--Dicliptera armata
#> [2] Acanthaceae--Dipteracanthus australasicus
#> [3] Acanthaceae--Hygrophila ringens var. ringens
#> [4] Acanthaceae--Nelsonia canescens
#> [5] Acanthaceae--Rostellularia adscendens
#> [6] Aizoaceae--Carpobrotus rossii
#> [7] Aizoaceae--Carpobrotus virescens
#> [8] Aizoaceae--Disphyma clavellatum
#> [9] Aizoaceae--Gunniopsis
#> [10] Aizoaceae--Gunniopsis calcarea
#> [11] Aizoaceae--Gunniopsis kochii
#> [12] Aizoaceae--Gunniopsis quadrifida
#> [13] Aizoaceae--Gunniopsis septifraga
#> [14] Aizoaceae--Gunniopsis zygophylloides
#> [15] Aizoaceae--Mesembryanthemum crystallinum
#> [16] Aizoaceae--Mesembryanthemum nodiflorum
#> [17] Aizoaceae--Sarcozona praecox
#> [18] Aizoaceae--Tetragonia
#> [19] Aizoaceae--Tetragonia eremaea
#> [20] Aizoaceae--Tetragonia implexicoma
Explore TERN AusPlots
In addition to the key site info and vegetation point-intercept modules introduced above, get_ausplots is your gateway to raw data modules for vegetation structural summaries, vegetation vouchers (covers the full species diversity observed at the plot and includes tissue sample details), basal wedge, and soils subsites, bulk density and pit/characterisation (including bulk and metagenomics soil samples).
References
Blanco-Martin, B. (2019) Tutorial: Understanding and using the ‘ausplotsR’ package and AusPlots data. Terrestrial Ecology Research Network. Version 2019.04.0, April 2019. https://github.com/ternaustralia/TERN-Data-Skills/
Sparrow, B., Foulkes, J., Wardle, G., Leitch, E., Caddy-Retalic, S., van Leeuwen, S., Tokmakoff, A., Thurgate, N., Guerin, G.R. and Lowe, A.J. (2020) A vegetation and soil survey method for surveillance monitoring of rangeland environments. Frontiers in Ecology and Evolution, 8:157.