Luis F. Arias-Giraldo, Marlon E. Cobos, A. Townsend Peterson
enmpa is an r package that contains a set of tools to perform Ecological Niche Modeling using presence-absence data. Some of the main functions help perform data partitioning, model calibration, model selection, variable response exploration, and model projection.
You can install the development version of enmpa from GitHub with:
The package terra is used to handle spatial data, and enmpa is used to perform ENM.
The data used in this example is included in enmpa.
# Species presence absence data associated with envrionmental variables
data("enm_data", package = "enmpa")
# Data for final model evaluation
data("test", package = "enmpa")
# Environmental data as raster layers for projections
env_vars <- rast(system.file("extdata", "vars.tif", package = "enmpa"))
# Check the example data
head(enm_data)
#> Sp bio_1 bio_12
#> 1 0 4.222687 403
#> 2 0 6.006802 738
#> 3 0 4.079385 786
#> 4 1 8.418489 453
#> 5 0 8.573750 553
#> 6 1 16.934618 319The raster layers for projections were obtained from WorldClim:
To explore the relevance of the variables to be used in niche models, we implemented in the methods developed by (Cobos and Peterson 2022). These methods help to identify signals of ecological niche considering distinct variables and the presence-absence data. By characterizing the sampling universe, this approach can determine whether presences and absences can be separated better than randomly considering distinct environmental factors.
sn_bio1 <- niche_signal(data = enm_data, variables = "bio_1",
condition = "Sp", method = "univariate")
sn_bio12 <- niche_signal(data = enm_data, variables = "bio_12",
condition = "Sp", method = "univariate")
Based on the univariate test results, the variables bio_1 and bio_12 help to detect signals of ecological niche in our data. In our example, the species tends to occur in areas with higher annual mean temperatures (bio_1); whereas, considering annual precipitation (bio_12), the species seems to be present in areas with lower values. See the function’s documentation for more information.
With enmpa you have the possibility to explore multiple model formulas derived from combinations of variables considering linear (l), quadratic (q), and product (p) responses. Product refers to pair interactions of variables.
The function includes the flag mode to determine what strategy to use to combine predictors based on the responses defined in . The options of mode are:
An example using linear + quadratic responses:
get_formulas(dependent = "Sp", independent = c("bio_1", "bio_12"),
type = "lq", mode = "intensive")
#> [1] "Sp ~ bio_1"
#> [2] "Sp ~ bio_12"
#> [3] "Sp ~ I(bio_1^2)"
#> [4] "Sp ~ I(bio_12^2)"
#> [5] "Sp ~ bio_1 + bio_12"
#> [6] "Sp ~ bio_1 + I(bio_1^2)"
#> [7] "Sp ~ bio_1 + I(bio_12^2)"
#> [8] "Sp ~ bio_12 + I(bio_1^2)"
#> [9] "Sp ~ bio_12 + I(bio_12^2)"
#> [10] "Sp ~ I(bio_1^2) + I(bio_12^2)"
#> [11] "Sp ~ bio_1 + bio_12 + I(bio_1^2)"
#> [12] "Sp ~ bio_1 + bio_12 + I(bio_12^2)"
#> [13] "Sp ~ bio_1 + I(bio_1^2) + I(bio_12^2)"
#> [14] "Sp ~ bio_12 + I(bio_1^2) + I(bio_12^2)"
#> [15] "Sp ~ bio_1 + bio_12 + I(bio_1^2) + I(bio_12^2)"The function calibration_glm() is a general function that allows to perform the following steps:
Model selection consists of three steps:
selection_criterion (“TSS”: TSS >= 0.4; or “ESS”: maximum Accuracy - tolerance).The results are returned as a list containing:
*$selected)*$summary)*$calibration_results)*$data)*$weights)*$kfold_index_partition)Now lets run an example of model calibration and selection:
# Linear + quadratic + products responses
cal_res <- calibration_glm(data = enm_data, dependent = "Sp",
independent = c("bio_1", "bio_12"),
response_type = "lpq", formula_mode = "intensive",
exclude_bimodal = TRUE, selection_criterion = "TSS",
cv_kfolds = 5, verbose = FALSE)Process results:
## Two models were selected out of 31 models evaluated
cal_res$selected[, 1] # Selected models
#> [1] "ModelID_29" "ModelID_31"
cal_res$selected # Metrics of evaluation
#> ModelID Formulas
#> 1 ModelID_29 Sp ~ bio_1 + I(bio_1^2) + I(bio_12^2) + bio_1:bio_12
#> 2 ModelID_31 Sp ~ bio_1 + bio_12 + I(bio_1^2) + I(bio_12^2) + bio_1:bio_12
#> Threshold_criteria Threshold_mean Threshold_sd ROC_AUC_mean ROC_AUC_sd
#> 1 maxTSS 0.0951 0.0166 0.9003 0.0190
#> 2 maxTSS 0.0991 0.0154 0.9002 0.0192
#> False_positive_rate_mean False_positive_rate_sd Accuracy_mean Accuracy_sd
#> 1 0.1755 0.0259 0.8274 0.0232
#> 2 0.1720 0.0216 0.8305 0.0198
#> Sensitivity_mean Sensitivity_sd Specificity_mean Specificity_sd TSS_mean
#> 1 0.858 0.0363 0.8245 0.0259 0.6825
#> 2 0.856 0.0404 0.8280 0.0216 0.6840
#> TSS_sd Parameters AIC Delta_AIC AIC_weight Concave_responses
#> 1 0.0404 4 2186.70 1.0201 0.3751818
#> 2 0.0450 5 2185.68 0.0000 0.6248182After one or more models are selected, the next steps are the fitting and projection of these models. In this case we are projecting the models to the whole area of interest.
# Fitting
f_models <- fit_selected(cal_res)
# Prediction for the two selected models and their consensus
preds <- predict_selected(f_models, newdata = env_vars, consensus = TRUE)
# Visualization
plot(preds$predictions, mar = c(1, 1, 2, 4))
An alternative to strict selection of a single model is to use a consensus model. The main idea is to avoid selecting the best model and instead rely on multiple models with similar performance.
During the prediction of selected models, we calculated model consensus using the mean, median, and a weighted average (using Akaike weights) (Akaike 1973; Wagenmakers and Farrell 2004).
An important step in understanding the ecological niches with these models is to explore variable response curves. The following lines of code help to do so:
# Response Curves for Bio_1 and Bio_2, first selected model
response_curve(f_models$ModelID_29, variable = "bio_1")
response_curve(f_models$ModelID_29, variable = "bio_12")
# Consensus Response Curves for Bio_1 and Bio_2, for both models
response_curve(f_models, variable = "bio_1")
response_curve(f_models, variable = "bio_12")
Variable importance or contribution to models can be calculated as a function of the relative deviance explained by each predictor.
Analysis of Deviance for the first selected model:
anova(f_models$ModelID_29, test = "Chi")
#> Analysis of Deviance Table
#>
#> Model: binomial, link: logit
#>
#> Response: Sp
#>
#> Terms added sequentially (first to last)
#>
#>
#> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
#> NULL 5626 3374.9
#> bio_1 1 869.35 5625 2505.6 < 2.2e-16 ***
#> I(bio_1^2) 1 46.77 5624 2458.8 7.972e-12 ***
#> I(bio_12^2) 1 239.69 5623 2219.1 < 2.2e-16 ***
#> bio_1:bio_12 1 42.40 5622 2176.7 7.450e-11 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Using a function from enmpa you can also explore variable importance in terms of contribution.
# Relative contribution of the deviance explained for the first model
var_importance(f_models$ModelID_29)
#> predictor contribution cum_contribution
#> 3 I(bio_12^2) 0.3572673 0.3572673
#> 1 bio_1 0.2919587 0.6492259
#> 2 I(bio_1^2) 0.2031750 0.8524009
#> 4 bio_1:bio_12 0.1475991 1.0000000The function also allows to plot the contributions of the variables for the two models together which can help with the interpretations:
Finally, we will evaluate the final models using the “independent_eval” functions. Ideally, the model should be evaluated with an independent data set (i.e., data that was not used during model calibration or for final model fitting).
Using independent data for which presence and absence is known can give the most robust results. Here an example:
# In this example, we will use the final model calculated as the weighted
# average of the two selected models.
wmean <- preds$consensus$Weighted_average
# check the test data
head(test, 10)
#> Sp lon lat
#> 1 0 -105.6639 35.81247
#> 2 0 -107.9354 33.37200
#> 3 0 -100.3134 48.96018
#> 4 1 -117.5543 33.62975
#> 5 0 -120.6168 36.59670
#> 6 0 -105.3379 40.08928
#> 7 0 -115.4178 40.65446
#> 8 1 -100.9363 38.68424
#> 9 0 -119.2357 37.23158
#> 10 0 -118.8269 38.62374
# independent evaluation
eval <- independent_eval01(prediction = wmean, observation = test$Sp,
lon_lat = test[, 2:3])
eval
#> Threshold_criteria Threshold ROC_AUC False_positive_rate Accuracy
#> 226 ESS 0.1500930 0.9570991 0.08988764 0.91
#> 192 maxTSS 0.1274123 0.9570991 0.10112360 0.91
#> 210 SEN90 0.1394197 0.9570991 0.10112360 0.90
#> Sensitivity Specificity TSS
#> 226 0.9090909 0.9101124 0.8192033
#> 192 1.0000000 0.8988764 0.8988764
#> 210 0.9090909 0.8988764 0.8079673When only presence data is available, the evaluation of performance is based on the omission error and the partial ROC analysis.
To do this in our example, we will need to define a threshold value. We can use any of the three threshold values obtained above: ESS, maxTSS or SEN90.
# Threshold based in criteria: Sensitivity = 90%
th <- eval[3, "Threshold"]
# Keep only presence records
test_p <- test[test$Sp == 1,]
# independent evaluation
eval2 <- independent_eval1(prediction = wmean, threshold = th,
lon_lat = test_p[, 2:3])
eval2
#> omission_error threshold Mean_AUC_ratio pval_pROC
#> 1 0.09090909 0.1394197 1.634595 0Akaike, H. 1973. “Information Theory and an Extension of Maximum Likelihood Principle.” In Proc. 2nd Int. Symp. On Information Theory, 267–81.
Cobos, Marlon E., and A. Townsend Peterson. 2022. “Detecting Signals of Species’ Ecological Niches in Results of Studies with Defined Sampling Protocols: Example Application to Pathogen Niches.” Biodiversity Informatics 17 (May): 50–58. https://doi.org/10.17161/bi.v17i.15985.
Wagenmakers, Eric-Jan, and Simon Farrell. 2004. “AIC Model Selection Using Akaike Weights.” Psychonomic Bulletin & Review 11 (1): 192–96. https://doi.org/10.3758/BF03206482.