Merge pull request GEO-BON#110 from GEO-BON/sdm

Maxent pipeline: some docs, bug fix, defaults and simplified create background function
PEM-Humboldt · Nov 14, 2023 · 70ba4bd · 70ba4bd
2 parents 1a12a53 + b98ca25
commit 70ba4bd
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Showing 5 changed files with 139 additions and 184 deletions.
diff --git a/pipelines/SDM/SDM_maxEnt.json b/pipelines/SDM/SDM_maxEnt.json
@@ -366,7 +366,6 @@
       "data": {
         "type": "text[]",
         "value": [
-          "L",
           "LQ",
           "LQHP"
         ]
@@ -384,8 +383,7 @@
         "type": "float[]",
         "value": [
           0.5,
-          1,
-          2
+          1
         ]
       }
     },
@@ -763,7 +761,8 @@
         "random",
         "inclusion_buffer",
         "weighted_raster",
-        "unweighted_raster"
+        "unweighted_raster",
+        "thickening"
       ],
       "example": "random"
     },
@@ -866,14 +865,18 @@
       "type": "text[]",
       "example": [
         "chelsa-clim|bio1",
-        "chelsa-clim|bio2"
+        "chelsa-clim|bio7",
+        "chelsa-clim|bio12",
+        "earthenv_topography|elevation_median",
+        "earthenv_landcover|class_3",
+        "earthenv_landcover|class_12"
       ]
     },
     "data>loadFromStac.yml@119|spatial_res": {
       "description": "Integer, spatial resolution of the rasters",
       "label": "spatial resolution",
       "type": "float",
-      "example": 1000.0
+      "example": 5000.0
     },
     "data>loadFromStac.yml@119|mask": {
       "description": "Shapefile, used to mask the output rasters",
@@ -896,5 +899,17 @@
         1
       ]
     }
+  },
+  "metadata": {
+    "name": "Maxent SDM pipeline",
+    "description": "A pipeline to produce species distribution models using Maxent",
+    "author": [
+      {
+        "name": "Sarah Valentin",
+        "identifier": "https://orcid.org/0000-0002-9028-681X"
+      }
+    ],
+    "license": null,
+    "external_link": null
   }
 }
diff --git a/scripts/SDM/runMaxent.R b/scripts/SDM/runMaxent.R
@@ -31,7 +31,7 @@ print("Inputs : ")
 print(input)
 
 
-presence_background <- read.table(file = input$presence_background, sep = '\t', header = TRUE) 
+presence_background <- read.table(file = input$presence_background, sep = '\t', header = TRUE, check.names = FALSE) 
 predictors <- terra::rast(unlist(input$predictors))
 mod_tuning <- run_maxent(presence_background, 
                          with_raster = F, # can be set to F to speed up
@@ -52,7 +52,7 @@ mod_tuning <- run_maxent(presence_background,
 res_tuning <- mod_tuning@results
 tuned_param <- select_param(res_tuning, method = input$method_select_params, list = T)
 
-predictors <- raster::stack(predictors)
+#predictors <- raster::stack(predictors)
 
 sdms <- predict_maxent(presence_background,
   algorithm = "maxent.jar", 
@@ -73,17 +73,17 @@ names(sdm_pred) <- "prediction"
 sdm_runs <- sdms[["pred_runs"]]
 
 pred.output <- file.path(outputFolder, "sdm_pred.tif")
-runs.output <- paste0(outputFolder,"/sdm_runs_", 1:nlayers(sdm_runs), ".tif")
+runs.output <- paste0(outputFolder,"/sdm_runs_", 1:nlyr(sdm_runs), ".tif")
 #runs.output <- file.path(outputFolder, "sdm_runs.tif")
 
-sdm_pred<-project(rast(sdm_pred),crs(input$proj)) ##Temporary fix while maxent transitions to terra
+sdm_pred<-project(sdm_pred,crs(input$proj)) ##Temporary fix while maxent transitions to terra
 terra::writeRaster(x = sdm_pred,
                           filename = pred.output,
                           filetype = "COG",
                           wopt= list(gdal=c("COMPRESS=DEFLATE")),
                           overwrite = TRUE)
-for (i in 1:nlayers(sdm_runs)){
-    thisrun<-project(rast(sdm_runs[[i]]),crs(input$proj))  ##Temporary fix while maxent transitions to terra
+for (i in 1:nlyr(sdm_runs)){
+    thisrun<-project(sdm_runs[[i]],crs(input$proj))  ##Temporary fix while maxent transitions to terra
     terra::writeRaster(x = thisrun,
     filename = file.path(outputFolder, paste0("/sdm_runs_", i, ".tif")),
     filetype = "COG",

diff --git a/scripts/SDM/runMaxentFunc.R b/scripts/SDM/runMaxentFunc.R
@@ -22,8 +22,8 @@ run_maxent <- function(presence.bg, with_raster = F,
                         parallelType = "doParallel"
 ) {
 
-  presence <- presence.bg |> dplyr::filter(pa == 1) |> data.frame()
-  background <- presence.bg |> dplyr::filter(pa == 0) |> data.frame()
+  presence <- presence.bg |> dplyr::filter(pa == 1) |> as.data.frame()
+  background <- presence.bg |> dplyr::filter(pa == 0) |> as.data.frame()
 
   if (with_raster) {
     ENMmodel <- ENMeval::ENMevaluate(occs = presence[, c("lon", "lat")],
@@ -259,8 +259,8 @@ predict_maxent <- function(presence_background,
 
   # We calculate the prediction with the whole dataset
 
-      presence <- presence_background |> dplyr::filter(pa == 1) |> data.frame()
-      background <- presence_background |> dplyr::filter(pa == 0) |> data.frame()
+      presence <- presence_background |> dplyr::filter(pa == 1) |> as.data.frame()
+      background <- presence_background |> dplyr::filter(pa == 0) |> as.data.frame()
 
 
       mod_tuning <- ENMeval::ENMevaluate(occs = presence[, c("lon", "lat", layers)], 
@@ -304,8 +304,8 @@ runs <- c("run_1")
       backg_test <- backgr[bg.grp == g, ]
       presence_bg_train <- group.all[which(group.all[,1] == g),]
 
-      presence <- presence_bg_train |> dplyr::filter(pa == 1) |> data.frame()
-      background <- presence_bg_train |> dplyr::filter(pa == 0) |> data.frame()
+      presence <- presence_bg_train |> dplyr::filter(pa == 1) |> as.data.frame()
+      background <- presence_bg_train |> dplyr::filter(pa == 0) |> as.data.frame()
 
 
       mod_tuning <- ENMeval::ENMevaluate(occs = presence[, c("lon", "lat", layers)], 
@@ -322,12 +322,18 @@ runs <- c("run_1")
       pred_pres <- dismo::predict(mod_tuning@models[[tuned_param]], predictors,
                                   args = sprintf("outputformat=%s", type))
 
-        if (inherits(pred_runs, "RasterLayer") || inherits(pred_runs, "RasterStack")) {
-              pred_runs <- raster::stack(pred_runs, pred_pres)
-
-        } else {
-           pred_runs <-  pred_pres 
-           }
+        #if (inherits(pred_runs, "RasterLayer") || inherits(pred_runs, "RasterStack")) {
+        #    pred_runs <- raster::stack(pred_runs, pred_pres)
+        #} else {
+        #   pred_runs <-  pred_pres 
+        #}
+      if (is.null(pred_runs)) {
+        pred_runs <- pred_pres
+      } else {
+        pred_runs <-  c(pred_runs, pred_pres) 
+      }
+
+
 
  }
 

diff --git a/scripts/SDM/selectBackground.yml b/scripts/SDM/selectBackground.yml
@@ -26,6 +26,7 @@ inputs:
       - inclusion_buffer
       - weighted_raster
       - unweighted_raster
+      - thickening
     example: random
   n_background: 
     label: number of background points