Fixed color scaling between current and future SDM

maxent_outputs/maxent.html

@@ -1,6 +1,6 @@
 <title>Maxent model</title>
 <CENTER><H1>Maxent model</H1></CENTER>
-<br> This page contains some analysis of the Maxent model result, created Tue Apr 09 19:52:32 UTC 2024 using 'dismo' version  1.3-14 & Maxent version 3.4.3.  If you would like to do further analyses, the raw data used here is linked to at the end of this page.<br>
+<br> This page contains some analysis of the Maxent model result, created Tue Apr 16 16:37:58 UTC 2024 using 'dismo' version  1.3-14 & Maxent version 3.4.3.  If you would like to do further analyses, the raw data used here is linked to at the end of this page.<br>
 <br><HR><H2>Analysis of omission/commission</H2>
 The following picture shows the omission rate and predicted area as a function of the cumulative threshold.  The omission rate is is calculated both on the training presence records, and (if test data are used) on the test records.  The omission rate should be close to the predicted omission, because of the definition of the cumulative threshold.
 <br><img src="plots/species_omission.png"><br>
@@ -23,7 +23,7 @@
 <a href = "maxentResults.csv">Results for all species modeled in the same Maxent run, with summary statistics and (optionally) jackknife results</a><br>
 <br><br>
 Regularized training gain is 0.230, training AUC is 0.696, unregularized training gain is 0.282.<br>
-Algorithm terminated after 500 iterations (1 seconds).<br>
+Algorithm terminated after 500 iterations (0 seconds).<br>
 <br>
 The follow settings were used during the run:<br>
 234 presence records used for training.<br>

maxent_outputs/maxent.log

@@ -1,9 +1,9 @@
-Tue Apr 09 19:52:31 UTC 2024
+Tue Apr 16 16:37:58 UTC 2024
 MaxEnt version 3.4.3
 Reading file absence
-readGrid: max memory 536870912, total allocated 270532608, free 267385128, used 3147480, increment 3147480
+readGrid: max memory 536870912, total allocated 270532608, free 104312496, used 166220112, increment 38187728
 Reading samples from presence
-Read samples: max memory 536870912, total allocated 270532608, free 261093672, used 9438936, increment 6291456
+Read samples: max memory 536870912, total allocated 270532608, free 259935680, used 10596928, increment -155623184
 Adding samples to background in feature space
 Command line used:  autorun -e maxent_outputs/absence -o maxent_outputs -s maxent_outputs/presence -z
 Command line to repeat: java density.MaxEnt nowarnings noprefixes outputdirectory=maxent_outputs samplesfile=maxent_outputs/presence environmentallayers=maxent_outputs/absence autorun novisible
@@ -16,20 +16,19 @@ environmentallayers: maxent_outputs/absence
 autorun: true
 visible: false
 
-getSamples: max memory 536870912, total allocated 270532608, free 253753640, used 16778968, increment 7340032
+getSamples: max memory 536870912, total allocated 270532608, free 255741376, used 14791232, increment 4194304
 Making features
-makeFeatures: max memory 536870912, total allocated 270532608, free 252703608, used 17829000, increment 1050032
+makeFeatures: max memory 536870912, total allocated 270532608, free 254165008, used 16367600, increment 1576368
 species: 
 Regularization values: linear/quadratic/product: 0.050, categorical: 0.250, threshold: 1.000, hinge: 0.500
 234 samples
-Density: max memory 536870912, total allocated 270532608, free 249559336, used 20973272, increment 3144272
-linearPredictor: max memory 536870912, total allocated 270532608, free 249559336, used 20973272, increment 0
-FeaturedSpace: max memory 536870912, total allocated 270532608, free 249559336, used 20973272, increment 0
-Sequential: max memory 536870912, total allocated 270532608, free 249559336, used 20973272, increment 0
+Density: max memory 536870912, total allocated 270532608, free 251547072, used 18985536, increment 2617936
+linearPredictor: max memory 536870912, total allocated 270532608, free 251547072, used 18985536, increment 0
+FeaturedSpace: max memory 536870912, total allocated 270532608, free 251547072, used 18985536, increment 0
+Sequential: max memory 536870912, total allocated 270532608, free 251547072, used 18985536, increment 0
 Initial loss: 6.077642243349034
-500: time = 0.550000 loss = 5.847453
+500: time = 0.447000 loss = 5.847453
 Resulting gain: 0.2301887690288824
-Time since start: 1.201
 Projecting...
 Writing file maxent_outputs/species.csv
 Ending

maxent_outputs/species.html

@@ -1,6 +1,6 @@
 <title>Maxent model for species</title>
 <CENTER><H1>Maxent model for species</H1></CENTER>
-<br> This page contains some analysis of the Maxent model for species, created Tue Apr 09 19:52:32 UTC 2024 using Maxent version 3.4.3.  If you would like to do further analyses, the raw data used here is linked to at the end of this page.<br>
+<br> This page contains some analysis of the Maxent model for species, created Tue Apr 16 16:37:58 UTC 2024 using Maxent version 3.4.3.  If you would like to do further analyses, the raw data used here is linked to at the end of this page.<br>
 <br><HR><H2>Analysis of omission/commission</H2>
 The following picture shows the omission rate and predicted area as a function of the cumulative threshold.  The omission rate is is calculated both on the training presence records, and (if test data are used) on the test records.  The omission rate should be close to the predicted omission, because of the definition of the cumulative threshold.
 <br><img src="plots/species_omission.png"><br>
@@ -23,7 +23,7 @@
 <a href = "maxentResults.csv">Results for all species modeled in the same Maxent run, with summary statistics and (optionally) jackknife results</a><br>
 <br><br>
 Regularized training gain is 0.230, training AUC is 0.696, unregularized training gain is 0.282.<br>
-Algorithm terminated after 500 iterations (1 seconds).<br>
+Algorithm terminated after 500 iterations (0 seconds).<br>
 <br>
 The follow settings were used during the run:<br>
 234 presence records used for training.<br>

src/CurrentSdm.R

@@ -158,8 +158,8 @@ ggplot() +
   geom_polygon(data = wrld, mapping = aes(x = long, y = lat, group = group),
                fill = "grey75") +
   geom_raster(data = rhyacotritonPredictDf, aes(x = x, y = y, fill = layer)) + 
-  scale_fill_gradientn(colors = terrain.colors(10, rev = T)) +
-  coord_fixed(xlim = c(xmin, xmax), ylim = c(ymin, ymax), expand = F) +#expand=F fixes margin
+  scale_fill_gradientn(colors = c("red", "orange", "yellow", "green"), limits = c(0, 0.85)) +
+  coord_fixed(xlim = c(xmin, xmax), ylim = c(ymin, ymax), expand = FALSE) +
   scale_size_area() +
   borders("state") +
   borders("world", colour = "black", fill = NA) + 

src/FutureSdm.R

@@ -7,14 +7,14 @@
 # More info: https://pcmdi.llnl.gov/CMIP6/
 
 futureClimateRaster <- cmip6_world("CNRM-CM6-1", "585", "2061-2080", var = "bioc", res=2.5, path="data/cmip6")
-futureClimateRaster <- rast(futureClimateRaster)
+
 # 7. Prep for the model
 
 
 names(futureClimateRaster)=names(currentClimRasterStack)
 
 
-geographicAreaFutureC6 <- crop(futureClimateRaster, extent(currentClimRasterStack))
+geographicAreaFutureC6 <- crop(futureClimateRaster, predictExtent)
 
 
 # 8. Run the future SDM
@@ -33,13 +33,12 @@ xmin <- min(rhyacotritonFutureSDMDf$x)
 ymax <- max(rhyacotritonFutureSDMDf$y)
 ymin <- min(rhyacotritonFutureSDMDf$y)
 
-
 ggplot() +
   geom_polygon(data = wrld, mapping = aes(x = long, y = lat, group = group),
                fill = "grey75") +
   geom_raster(data = rhyacotritonFutureSDMDf, aes(x = x, y = y, fill = maxent)) + 
-  scale_fill_gradientn(colors = terrain.colors(10, rev = T)) +
-  coord_fixed(xlim = c(xmin, xmax), ylim = c(ymin, ymax), expand = F) +
+  scale_fill_gradientn(colors = c("red", "orange", "yellow", "green"), limits = c(0, 0.8)) +
+  coord_fixed(xlim = c(xmin, xmax), ylim = c(ymin, ymax), expand = FALSE) +
   scale_size_area() +
   borders("state") +
   borders("world", colour = "black", fill = NA) +