h3sdm workflow for a single model

Introduction

This vignette demonstrates a complete workflow for species distribution modeling (SDM) for a single species using h3sdm. We cover data preparation, model fitting, spatial cross-validation, prediction, and feature importance.

Load packages

library(h3sdm)
library(sf)
library(terra)
library(dplyr)
library(ggplot2)
library(tidymodels)
library(spatialsample)
library(DALEX)
library(themis)

1. Define the Area of Interest

cr <- cr_outline_c

2. Load Environmental Predictors

bio <- terra::rast(system.file("extdata", "bioclim_current.tif", package = "h3sdm"))
names(bio) <- gsub(".*bio_", "bio", names(bio))

3. Species Occurrence Data

The package includes a precomputed presence/pseudo-absence dataset for Silverstoneia flotator at H3 resolution 7.

data(records)
head(records)
#> Simple feature collection with 6 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -84.06344 ymin: 8.486587 xmax: -82.77295 ymax: 9.643344
#> Geodetic CRS:  WGS 84
#>          h3_address presence                       geometry
#> 43  8766b4415ffffff        1 MULTIPOLYGON (((-84.05549 9...
#> 165 87679b636ffffff        1 MULTIPOLYGON (((-82.79149 9...
#> 198 8766b54d3ffffff        1 MULTIPOLYGON (((-83.18895 8...
#> 427 87679b78effffff        1 MULTIPOLYGON (((-82.85228 9...
#> 796 8766b0135ffffff        1 MULTIPOLYGON (((-83.71366 8...
#> 893 8766b014cffffff        1 MULTIPOLYGON (((-83.53362 8...
table(records$presence)
#> 
#>   0   1 
#> 300 123
ggplot() +
  theme_minimal() +
  geom_sf(data = records, aes(fill = presence)) +
  scale_fill_manual(
    name = "Presence/Absence",
    values = c("red", "blue"),
    labels = c("Absence", "Presence")
  )

4. Prepare Predictors

h7 <- h3sdm_get_grid(cr, res = 7)
bio_predictors <- h3sdm_extract_num(bio, h7)
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predictors <- h3sdm_predictors(bio_predictors) |>
  dplyr::select(h3_address, bio1, bio12, bio15, geometry)
ggplot() +
  theme_minimal() +
  geom_sf(data = predictors, aes(fill = bio1)) +
  scale_fill_viridis_c(option = "B")

5. Combine Records and Predictors

dat <- h3sdm_data(records, predictors)

6. Spatial Cross-Validation

scv <- h3sdm_spatial_cv(dat, v = 5, repeats = 1)
autoplot(scv) + theme_minimal()

7. Define Recipe and Model

receta <- h3sdm_recipe(dat) |>
  themis::step_downsample(presence)

modelo <- parsnip::logistic_reg() |>
  parsnip::set_engine("glm") |>
  parsnip::set_mode("classification")

8. Create Workflow

wf <- h3sdm_workflow(modelo, receta)

9. Fit the Model

presence_data <- dat |>
  dplyr::filter(presence == 1)

f <- h3sdm_fit_model(wf, scv, presence_data)

10. Evaluate Model Performance

evaluation_metrics <- h3sdm_eval_metrics(
  fitted_model  = f$cv_model,
  presence_data = presence_data
)
evaluation_metrics
#> # A tibble: 8 × 6
#>   .metric  .estimator  mean std_err conf_low conf_high
#>   <chr>    <chr>      <dbl>   <dbl>    <dbl>     <dbl>
#> 1 accuracy binary     0.673  0.0391  0.596       0.749
#> 2 f_meas   binary     0.700  0.0651  0.572       0.827
#> 3 kap      binary     0.220  0.111   0.00213     0.437
#> 4 roc_auc  binary     0.663  0.0348  0.595       0.732
#> 5 sens     binary     0.602  0.0823  0.440       0.763
#> 6 spec     binary     0.686  0.168   0.356       1.02 
#> 7 tss      binary     0.434 NA      NA          NA    
#> 8 boyce    binary     0.621 NA      NA          NA
ggplot(evaluation_metrics, aes(.metric, mean)) +
  theme_minimal() +
  geom_col(width = 0.03, color = "dodgerblue3", fill = "dodgerblue3") +
  geom_point(size = 3, color = "orange") +
  ylim(0, 1)

11. Make Predictions

p <- h3sdm_predict(f, predictors)
ggplot() +
  theme_minimal() +
  geom_sf(data = p, aes(fill = prediction)) +
  scale_fill_viridis_c(name = "Habitat\nsuitability", option = "B", direction = -1)

12. Model Interpretation

e <- h3sdm_explain(f$final_model, data = dat)
#> Preparation of a new explainer is initiated
#>   -> model label       :  h3sdm workflow 
#>   -> data              :  423  rows  6  cols 
#>   -> target variable   :  423  values 
#>   -> predict function  :  custom_predict 
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package Model of class: workflow package unrecognized , ver. Unknown , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  0.005488075 , mean =  0.4332904 , max =  0.8491438  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8491438 , mean =  -0.1425102 , max =  0.8620933  
#>   A new explainer has been created!

predictors_to_evaluate <- setdiff(names(e$data), c("h3_address", "x", "y", "presence"))

var_imp <- DALEX::model_parts(
  explainer = e,
  variables = predictors_to_evaluate
)
plot(var_imp)

pdp_glm <- ingredients::partial_dependence(e, variables = c("bio12", "bio1", "bio15"))
plot(pdp_glm)

13. Future Scenario Predictions

bio_future <- terra::rast(system.file("extdata", "bioclim_future.tif", package = "h3sdm"))
names(bio_future) <- c("bio1", "bio12", "bio15")

bio_future_predictors <- h3sdm_extract_num(bio_future, h7)
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predictors_future <- h3sdm_predictors(bio_future_predictors)

p_future <- h3sdm_predict(f, predictors_future)
ggplot() +
  theme_minimal() +
  geom_sf(data = p_future, aes(fill = prediction)) +
  scale_fill_viridis_c(name = "Habitat\nsuitability", option = "B", direction = -1)

Conclusions

This vignette demonstrated a complete SDM workflow using h3sdm, including data preparation, model fitting with spatial cross-validation, performance evaluation, predictions, and variable importance analysis for both current and future climate scenarios.