Collect model output

Make sure your models finished and produced data:

ls -lh data
ls -lh figures

Git add, commit, push

git add figures
git add data

git commit -m 'collect data and figures'

*Be sure to sync your RStudio Git Repo now with a ‘Pull’

Visualizing model output

The analysis script generated image files (*.png) for the GBIF data points and the Maxent model output for the current distribution (continuous and range) as well as the 2.6 and 8.5 emissions scenarios for 2070.

Here is the continuous Maxent model ouptut for Pinus strobus (White Pine):

And, here is the model prediction for 2070 under the 8.5 emissions scenario:

Distribution Change

The maps above show there is clear potential for distribution shifts under extreme climate change in the next 50 years. However, the pattern is not easily visualized and no quantitative values can be determined just by looking at the figures.

Using R we will create visualizations showing the modeled potential change in the distributions.

Read in model data

library(raster)
## Loading required package: sp
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:raster':
## 
##     intersect, select, union
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
taxon = "Pinus strobus"
files = list.files('data', pattern = taxon, full.names=T)
print(files)
## [1] "data/ Pinus strobus _model.grd"   "data/ Pinus strobus _model.gri"  
## [3] "data/ Pinus strobus _model26.grd" "data/ Pinus strobus _model26.gri"
## [5] "data/ Pinus strobus _model85.grd" "data/ Pinus strobus _model85.gri"
gri.files = files[grep('.gri', files)] #Only need one of the two files to read. R 'raster::stack()' finds the other.

mods = stack(gri.files)
names(mods) = c('current', '2070_2.6', '2070_8.5') ## Verify this is the same order as in gri.files object
plot(mods)

Shifts

2070 2.6 emissions scenario (best case)

library(ggplot2)

shift26 = mods[[2]] - mods[[1]] # future 2.6 scenario minus current prediction

shift26_df = as.data.frame(shift26, xy=T)

(base26 = ggplot() +
  geom_raster(data = shift26_df, 
              aes(x = x, y = y, fill = layer)) + 
  coord_quickmap() +
  theme_bw() + 
  scale_fill_gradientn(colours=c('navy', 'white', 'darkred'), na.value = "black"))

#if you want to save the image as a file:
# ggsave(base26, filename=paste('figures/', taxon, '26_shift.png', sep=''), width=6, height=6, dpi=400, units='in')

2070 8.5 emissions scenario (worst case)

library(ggplot2)

shift85 = mods[[3]] - mods[[1]] # future 2.6 scenario minus current prediction

shift85_df = as.data.frame(shift85, xy=T)

(base85 = ggplot() +
  geom_raster(data = shift85_df, 
              aes(x = x, y = y, fill = layer)) + 
  coord_quickmap() +
  theme_bw() + 
  scale_fill_gradientn(colours=c('navy', 'white', 'darkred'), na.value = "black"))

#if you want to save the image as a file:
# ggsave(base85, filename=paste('figures/', taxon, '85_shift.png', sep=''), width=6, height=6, dpi=400, units='in')

Zoom In

To get a better idea of how this may result in local biotic changes on campus zoom in on New England to see the relative change in suitability on a local scale:

ext = extent(-74, -69, 40, 45)
shift26.NE = crop(shift26, ext)
shift26.NE_df = as.data.frame(shift26.NE, xy=T)

(base26.NE = ggplot() +
  geom_raster(data = shift26.NE_df, 
              aes(x = x, y = y, fill = layer)) + 
  coord_quickmap() +
  theme_bw() + 
  scale_fill_gradientn(colours=c('navy', 'white', 'darkred'), na.value = "black"))

Challenge/Homework

Generate the distribution shift graphics for both climate scenarios and for all 10 of your species (repeat models if necessary for failed analyses).

How many of the 10 species will find the climate of the campus more suitable in the future? (HINT: P. strobus in the example code here will NOT – look closely at the map and crop if needed). NOTE: Less suitable does not mean that a species is likely to disappear from an area, just that the climate will be less optimal than it is now.

Post some of your maps and the count of more suited species to #general

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