Retrieving models
Due to the total size of the 6177 ENMs currently included in
elements (1.8GB when compressed, 7.5GB in memory) the ENMs
are not exported in a .rda object. Instead they are made available
through a filehash (Peng, 2005) database, which provides
access to the ENMs without loading all models into memory. To access the
ENMs a connection to this database must be initialised using
elements::startup. As mentioned above the Github repository
does not include the “./inst/extdata/Models” object containing all the
ENMs, the elements::startup will check whether the
“./inst/extdata/Models” is present and if it is not found will load the
“./inst/testdata/TestModels” models instead. The models to load can also
be accessed by passing “all” or “test” to the ‘models’ argument of
elements::startup.
elements::startup()
model <- Models[["stellaria_graminea"]]#>
#> Call:
#> svm(formula = Presence ~ L + M + N + R + S + SD + GP + bio05 + bio06 +
#> bio16 + bio17, data = data, type = "C-classification", probability = TRUE)
#>
#>
#> Parameters:
#> SVM-Type: C-classification
#> SVM-Kernel: radial
#> cost: 0.4
#>
#> Number of Support Vectors: 13481Using the models
The raw ENMs retrieved using the method above can be used as regular
e1071 SVM model objects. Alternatively, the helper function
elements::predict_occ_taxon retrieves a model using the
method above, generates predictions, and formats the results as a data
frame.
results <- elements::predict_occ_taxon(taxon = "stellaria_graminea", predictors = elements::ExampleData1, pa = "Present", limit = NULL, dp = 2, append_predictors = FALSE)#> Present
#> 1 0.89
#> 2 0.02
#> 3 0.28
#> 4 0.55
#> 5 0.04
#> 6 0.40An additional helper function elements::predict_occ can
generate predictions for multiple taxa, by either specifing the taxa to
model in the ‘taxa_codes’ argument, or by setting ‘taxa_codes’ to NULL
and including an additional column in the predictors data frame named
‘taxon_code’.
results <- elements::predict_occ(taxa_codes = NULL, predictors = elements::ExampleData2, pa = "Present", limit = NULL, dp = 2, append_predictors = FALSE)#> Present taxon_code
#> 201 0.00 silene_flos-cuculi
#> 202 0.00 silene_flos-cuculi
#> 203 0.00 silene_flos-cuculi
#> 204 0.46 silene_flos-cuculi
#> 205 0.00 silene_flos-cuculi
#> 206 0.01 silene_flos-cuculiTwo helper arguments provide additional functionality in controlling
model use. First, is the ‘limit’ argument, which assigns probability
values of zero if one or more predictors are outside a specified range
e.g. the 10% and 90% quantiles (see elements::NicheWidths).
Second, is the ‘holdopt’ argument, which holds specified variable values
at their optima (as defined by the mean value present in
elements::NicheWidths).
As a simple demonstration, below two sets of predictions for Stellaria graminea are generated, holding all variables apart from N at their optima: 1) with no limit set, and 2) with a limit set to the 1% and 99% quantiles.
n_gradient <- data.frame("N" = seq(0, 10, 0.01))
vary_N_no_limit <- elements::predict_occ_taxon(taxon = "stellaria_graminea", predictors = n_gradient,
pa = "Present", limit = NULL, holdopt = c("bio05", "bio06", "bio16", "bio17", "GP", "L", "M", "R", "S", "SD"),
dp = 2, append_predictors = TRUE)
vary_N_limit <- elements::predict_occ_taxon(taxon = "stellaria_graminea", predictors = n_gradient,
pa = "Present", limit = "q01_q99", holdopt = c("bio05", "bio06", "bio16", "bio17", "GP", "L", "M", "R", "S", "SD"),
dp = 2, append_predictors = TRUE)
Please note that as ten out of the eleven variables are held at their
optima the predicted probabilities will be high as the influence of
unsuitable N values will be partially offset. Consequently, the shape of
the response curves above will be wider than the corresponding PDP plot
produced with the elements::plot_me function (see the
Model Inspection section below).
Scenarios
elements is designed to be used to model scenarios of
environmental change considering multiple interacting drivers. The
object elements::ExampleScenarios provides a basic set of
example scenarios: (A) Climate Change - RCP4.5, (A) Climate Change -
RCP8.5, (B) Grazing Intensification (+0.025GP per year), (B) Grazing
Reduction (-0.025GP per year), and (C) Nutrient Enrichment (+0.25N per
year) along with a Baseline scenario. Below the predicted probabilities
for the taxa in elements::ExamplePlot for Scenario C are
displayed.
scenario_c_results <- elements::predict_occ(taxa_codes = elements::ExamplePlot$taxon_code,
predictors = subset(x = elements::ExampleScenarios, scenario_code == "c"),
append_predictors = TRUE)
Environmental filtering
elements can also be used to filter species pools based
on a given set of predictor values using the function
elements::env_filter. Two sets of methods are available: 1)
“svm” which generates predictions using
elements::predict_occ and uses the resultant probability
values; and 2) “mean” and “median” which calculates the normalised
euclidean distance between the values supplied in the ‘predictors’
argument and the mean or median niche positions as present in
elements::NicheWidths.
For example, below elements::env_filter is applied to
all taxa in elements::TaxonomicBackbone using the svm
method, with the predictors derived from the baseline environmental
variable data from elements::ExamplePlot as present in
elements::ExampleScenarios[1,].
filter_results_svm <- elements::env_filter(predictors = elements::ExampleScenarios[1,], taxa = elements::TaxonomicBackbone$taxon_code, method = "svm")#> taxon_code Present rank
#> 1 comarum_palustre 0.991 1
#> 2 hydrocotyle_vulgaris 0.982 2
#> 3 eriophorum_angustifolium 0.967 3
#> 4 menyanthes_trifoliata 0.965 4
#> 5 carex_nigra 0.962 5
#> 6 ranunculus_flammula 0.958 6
#> 7 equisetum_fluviatile 0.952 7
#> 8 epilobium_palustre 0.950 8
#> 9 lysimachia_thyrsiflora 0.949 9
#> 10 carex_rostrata 0.944 10
#> 11 carex_canescens 0.933 11
#> 12 salix_repens_subsp._repens 0.933 12
#> 13 stellaria_palustris 0.933 13
#> 14 galium_palustre_subsp._palustre 0.928 14
#> 15 viola_palustris 0.925 15
#> 16 calliergon_cordifolium 0.914 16
#> 17 juncus_effusus 0.910 17
#> 18 potamogeton_polygonifolius 0.908 18
#> 19 carex_echinata 0.898 19
#> 20 calamagrostis_canescens 0.893 20NOTE: The mean and median methods are only included for demonstrative purposes only and should not be used in practice as they do not consider the joint distribution of variables as expressed through the SVM model hypervolumes.
Shutting down
At the end of the analyis run elements::shutdown to
close the connection to the filehash database.
elements::shutdown()