ausplotsR: quickstart guide to basic analysis of TERN AusPlots vegetation data

Greg Guerin & Bernardo Blanco-Martin

2023-05-17

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 and rmarkdown are required to build this package vignette (i.e., if ‘build_vignettes’ is set to TRUE above).

library(ausplotsR)

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 <- try(get_ausplots(bounding_box = c(125, 140, -40, -10)))
#> calling
#> search
#>  status code
#> 200
#> calling
#> site
#>  status code
#> 200
#> calling
#> veg_voucher
#>  status code
#> 200
#> calling
#> veg_pi
#>  status code
#> 200

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 
#> 184  48 214  33

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
#> 1   SAASTP0005-53723         SAASTP0005                  53723    E2-W2
#> 19  SAASTP0005-53723         SAASTP0005                  53723    E2-W2
#> 115 SAASTP0005-53723         SAASTP0005                  53723    N2-S2
#> 140 SAASTP0005-53723         SAASTP0005                  53723    N2-S2
#> 218 SAASTP0005-53723         SAASTP0005                  53723    N4-S4
#> 228 SAASTP0005-53723         SAASTP0005                  53723    S1-N1
#>     point_number herbarium_determination substrate in_canopy_sky  dead
#> 1             61 Pterocaulon sphacelatum    Litter         FALSE FALSE
#> 19            88     Stemodia florulenta    Litter         FALSE FALSE
#> 115           33 Pterocaulon sphacelatum      Bare         FALSE FALSE
#> 140           73     Stemodia florulenta    Litter         FALSE FALSE
#> 218           90    Erodium carolinianum      Bare         FALSE FALSE
#> 228            3     Eucalyptus coolabah      Bare         FALSE FALSE
#>     growth_form height veg_barcode       standardised_name
#> 1          Forb   0.05  SAA 000357 Pterocaulon sphacelatum
#> 19         Forb   0.02  SAA 000351     Stemodia florulenta
#> 115        Forb   0.03  SAA 000357 Pterocaulon sphacelatum
#> 140        Forb   0.40  SAA 000351     Stemodia florulenta
#> 218        Forb   0.05  SAA 000377                    aedo
#> 228   Tree/Palm   0.10  SAA 000425     Eucalyptus coolabah
#>                             standardised_scientific_name             kingdom
#> 1   Pterocaulon sphacelatum (Labill.) Benth. ex F.Muell.             Plantae
#> 19                        Stemodia florulenta W.R.Barker             Plantae
#> 115 Pterocaulon sphacelatum (Labill.) Benth. ex F.Muell.             Plantae
#> 140                       Stemodia florulenta W.R.Barker             Plantae
#> 218                        Erodium carolinianum Aldasoro  C.Navarro & L.Sáez
#> 228                 Eucalyptus coolabah Blakely & Jacobs             Plantae
#>                                             taxa_id
#> 1   https://id.biodiversity.org.au/name/apni/227263
#> 19  https://id.biodiversity.org.au/name/apni/227263
#> 115 https://id.biodiversity.org.au/name/apni/227263
#> 140 https://id.biodiversity.org.au/name/apni/227263
#> 218                                        Aldasoro
#> 228 https://id.biodiversity.org.au/name/apni/227407
#>                                             family       genus specific_epithet
#> 1                                       Asteraceae Pterocaulon      sphacelatum
#> 19                                  Plantaginaceae    Stemodia       florulenta
#> 115                                     Asteraceae Pterocaulon      sphacelatum
#> 140                                 Plantaginaceae    Stemodia       florulenta
#> 218 https://id.biodiversity.org.au/name/apni/90608     Plantae    Equisetopsida
#> 228                                      Myrtaceae  Eucalyptus         coolabah
#>     infraspecific_rank infraspecific_epithet          taxa_status    taxa_group
#> 1                   NA                    NA             accepted Equisetopsida
#> 19                  NA                    NA             accepted Equisetopsida
#> 115                 NA                    NA             accepted Equisetopsida
#> 140                 NA                    NA             accepted Equisetopsida
#> 218        Geraniaceae  Erodium carolinianum Erodium carolinianum      accepted
#> 228                 NA                    NA             accepted Equisetopsida
#>               genus_species                   authorship
#> 1   Pterocaulon sphacelatum (Labill.) Benth. ex F.Muell.
#> 19      Stemodia florulenta                   W.R.Barker
#> 115 Pterocaulon sphacelatum (Labill.) Benth. ex F.Muell.
#> 140     Stemodia florulenta                   W.R.Barker
#> 218   Plantae Equisetopsida           C.Navarro & L.Sáez
#> 228     Eucalyptus coolabah             Blakely & Jacobs
#>                                               published_in    rank hits_unique
#> 1      https://id.biodiversity.org.au/reference/apni/49840 Species    E2-W2 61
#> 19     https://id.biodiversity.org.au/reference/apni/49840 Species    E2-W2 88
#> 115    https://id.biodiversity.org.au/reference/apni/49840 Species    N2-S2 33
#> 140    https://id.biodiversity.org.au/reference/apni/49840 Species    N2-S2 73
#> 218                                                Erodium    Aedo    N4-S4 90
#> 228 https://id.biodiversity.org.au/reference/apni/51428473 Species     S1-N1 3

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
#> NTABRT0005-58863 NTABRT0005-58863 0.0 57.82 15.15 27.03
#> NTADAC0001-53518 NTADAC0001-53518 0.0  0.40 41.29 58.32
#> NTADAC0002-58039 NTADAC0002-58039 0.2  9.21 24.55 66.04
#> NTADMR0001-58868 NTADMR0001-58868 0.1 45.25 30.69 23.96
#> NTAFIN0001-53519 NTAFIN0001-53519 0.0 39.31 53.27  7.43
#> NTAFIN0003-53623 NTAFIN0003-53623 0.0 68.61 10.10 21.29

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 NTABRT0005-58863 57.82 15.15 27.03 0.0  133.6121 -22.29108
#> 2 NTADAC0001-53518  0.40 41.29 58.32 0.0  130.7779 -13.15835
#> 3 NTADAC0002-58039  9.21 24.55 66.04 0.2  132.3403 -12.73922
#> 4 NTADMR0001-58868 45.25 30.69 23.96 0.1  134.5674 -19.35310
#> 5 NTAFIN0001-53519 39.31 53.27  7.43 0.0  133.4679 -24.12430
#> 6 NTAFIN0003-53623 68.61 10.10 21.29 0.0  133.4770 -24.13220

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 
#> -35.230 -12.067  -3.814  10.811  66.688 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -148.13350   25.59925  -5.787 8.99e-08 ***
#> latitude     -16.07236    2.14155  -7.505 3.12e-11 ***
#> quadratic     -0.32951    0.04254  -7.745 9.79e-12 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 19.55 on 96 degrees of freedom
#> Multiple R-squared:  0.3908, Adjusted R-squared:  0.3781 
#> F-statistic: 30.79 on 2 and 96 DF,  p-value: 4.658e-11

#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 `$veg.voucher` module as input, but `m_kind="PA"` must be specified to get a sensible presence/absence output.
#The 'species_name' argument below specifies use of the "standardised_name" field to identify species, which is based on herbarium_determination names (i.e., "HD" option in species_name) matched to accepted scientific name according to a standard (APC: https://www.anbg.gov.au/cpbr/program/hc/hc-APC.html).
my.sppBYsites <- species_table(my.ausplots.data$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]  469 2181

#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
#> NTAARP0004-58918        0                0                   0               0
#> NTAARP0005-58919        0                0                   0               0
#>                  Abutilon.hannii.subsp..prostrate..p.k.latz.427.
#> NTAARP0001-58422                                               0
#> NTAARP0002-58423                                               0
#> NTAARP0003-58424                                               0
#> NTAARP0004-58918                                               0
#> NTAARP0005-58919                                               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]
#>                      Na       Triodia.basedowii         Triodia.pungens 
#>                666.1903                634.2583                412.4752 
#>                 Poaceae   Eucalyptus.tetrodonta       Triodia.bitextura 
#>                394.5625                392.4752                389.0123 
#> Eucalyptus.diversifolia      Eucalyptus.baxteri      Eucalyptus.obliqua 
#>                386.0396                381.6362                370.1086 
#>       Cenchrus.ciliaris 
#>                346.5918

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 with alternative relative abundance models fitted to the plant community data:
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")
#> $NTAGFU0011
#>  [1] Abutilon leucopetalum      Acacia adoxa              
#>  [3] Acacia ancistrocarpa       Acacia phlebocarpa        
#>  [5] Acacia wickhamii           Afrohybanthus aurantiacus 
#>  [7] Alectryon oleifolius       Aristida holathera        
#>  [9] Aristida perniciosa        Aristida pruinosa         
#> [11] Atalaya hemiglauca         Bauhinia cunninghamii     
#> [13] Blumea saxatilis           Boerhavia coccinea        
#> [15] Bonamia media              Capparis lasiantha        
#> [17] Chrysopogon fallax         Corchorus sidoides        
#> [19] Crotalaria medicaginea     Cymbopogon bombycinus     
#> [21] Dichanthium annulatum      Dichanthium fecundum      
#> [23] Digitaria brownii          Dodonaea physocarpa       
#> [25] Dolichandrone heterophylla Enneapogon avenaceus      
#> [27] Enneapogon robustissimus   Eriachne ciliata          
#> [29] Eriachne melicacea         Eriachne obtusa           
#> [31] Eucalyptus leucophloia     Eucalyptus pruinosa       
#> [33] Evolvulus alsinoides       Flueggea virosa           
#> [35] Glycine canescens          Grevillea parallela       
#> [37] Grevillea striata          Grewia retusifolia        
#> [39] Hakea arborescens          Heliotropium parviantrum  
#> [41] Heliotropium ventricosum   Heteropogon contortus     
#> [43] Hibiscus sturtii           Indigofera linifolia      
#> [45] Indigofera linnaei         Indigofera trita          
#> [47] Jacquemontia browniana     Marsdenia viridiflora     
#> [49] Melhania oblongifolia      Murdannia graminea        
#> [51] NA NA                      Oldenlandia mitrasacmoides
#> [53] Phyllanthus exilis         Polycarpaea breviflora    
#> [55] Polygala longifolia        Polygala pterocarpa       
#> [57] Pterocaulon niveum         Pterocaulon serrulatum    
#> [59] Pterocaulon sphacelatum    Ptilotus fusiformis       
#> [61] Rhynchosia minima          Scaevola ovalifolia       
#> [63] Schizachyrium fragile      Sehima nervosum           
#> [65] Spermacoce brachystema     Stylosanthes hamata       
#> [67] Tephrosia leptoclada       Terminalia canescens      
#> [69] Themeda triandra           Tinospora smilacina       
#> [71] Triodia bitextura          Tripogonella loliiformis  
#> [73] Vigna lanceolata           Waltheria indica          
#> [75] Zornia muriculata         
#> 
#> $QDAMII0002
#>  [1] Abutilon leucopetalum                                          
#>  [2] Acacia cowleana                                                
#>  [3] Acacia lysiphloia                                              
#>  [4] Acacia sericophylla                                            
#>  [5] Alternanthera sp. Mt Isa (R.L.Specht+ 49) Qld Herbarium        
#>  [6] Aristida latifolia                                             
#>  [7] Atalaya hemiglauca                                             
#>  [8] Boerhavia coccinea                                             
#>  [9] Bonamia media                                                  
#> [10] Bothriochloa ewartiana                                         
#> [11] Bulbostylis barbata                                            
#> [12] Chrysopogon fallax                                             
#> [13] Convolvulus clementii                                          
#> [14] Corymbia aparrerinja                                           
#> [15] Corymbia terminalis                                            
#> [16] Crotalaria medicaginea                                         
#> [17] Cucumis melo                                                   
#> [18] Dichanthium sericeum                                           
#> [19] Enneapogon polyphyllus                                         
#> [20] Enneapogon purpurascens                                        
#> [21] Enneapogon robustissimus                                       
#> [22] Eucalyptus leucophloia                                         
#> [23] Eucalyptus pruinosa                                            
#> [24] Eulalia aurea                                                  
#> [25] Euphorbia tannensis                                            
#> [26] Gossypium australe                                             
#> [27] Indigofera colutea                                             
#> [28] Indigofera linifolia                                           
#> [29] Ipomoea coptica                                                
#> [30] Ipomoea polymorpha                                             
#> [31] Iseilema macratherum                                           
#> [32] Mnesithea formosa                                              
#> [33] NA NA                                                          
#> [34] Paspalidium rarum                                              
#> [35] Portulaca oleracea                                             
#> [36] Rhynchosia minima                                              
#> [37] Salsola australis                                              
#> [38] Senna notabilis                                                
#> [39] Sida cleisocalyx                                               
#> [40] Solanum quadriloculatum                                        
#> [41] Sporobolus australasicus                                       
#> [42] Tephrosia NA                                                   
#> [43] Themeda triandra                                               
#> [44] Triodia pungens                                                
#> [45] Urochloa subquadripara                                         
#> [46] Ventilago viminalis                                            
#> [47] Vigna sp. McDonald Downs Station (R.A.Perry 3416) Qld Herbarium

#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--Brunoniella australis                     
#>  [2] Acanthaceae--Brunoniella linearifolia                  
#>  [3] Acanthaceae--Dicliptera armata                         
#>  [4] Acanthaceae--Dipteracanthus australasicus              
#>  [5] Acanthaceae--Hygrophila angustifolia                   
#>  [6] Acanthaceae--Nelsonia campestris                       
#>  [7] Acanthaceae--Pseuderanthemum variabile                 
#>  [8] Acanthaceae--Rostellularia adscendens                  
#>  [9] Acanthaceae--Rostellularia adscendens var. pogonanthera
#> [10] Aizoaceae--Carpobrotus                                 
#> [11] Aizoaceae--Carpobrotus rossii                          
#> [12] Aizoaceae--Carpobrotus virescens                       
#> [13] Aizoaceae--Disphyma crassifolium subsp. clavellatum    
#> [14] Aizoaceae--Gunniopsis                                  
#> [15] Aizoaceae--Gunniopsis calcarea                         
#> [16] Aizoaceae--Gunniopsis kochii                           
#> [17] Aizoaceae--Gunniopsis quadrifida                       
#> [18] Aizoaceae--Gunniopsis septifraga                       
#> [19] Aizoaceae--Gunniopsis zygophylloides                   
#> [20] Aizoaceae--Mesembryanthemum crystallinum

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.