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you_pick_the_species.Rmd
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---
title: "Now you pick the species : )"
output: html_notebook
---
First, we'll load up all the important R packages
```{r}
library(dismo)
library(rgbif)
```
```{r}
your_species_data <- occ_search(scientificName = "GENUS SPECIES",
country = 'MX', fields=c('name','country','countryCode','stateProvince','year','decimalLatitude','decimalLongitude'), limit = 500, return = 'data')
```
Make a simple map just to show where we have GPS (occurence) points for whatever species you choose
```{r}
data("wrld_simpl") # this loads up the map
xlim <- c(-129,-79) # this tells us to focus on mexico only
ylim <- c(15,35) # this tells us to focus on mexico only
plot(wrld_simpl,xlim=xlim,ylim=ylim) # make a zoomed-in map of mexico
points(your_species_data$decimalLongitude,your_species_data$decimalLatitude, col='purple')
```
#### Yay!! You made the map
Now, we gather our climate data. This is the same climate data we have used in the past. Again, we make sure that we are keeping the climate data to Mexico only.
```{r}
path <- file.path(system.file(package="dismo"), 'ex')
files <- list.files(path, pattern='grd$', full.names=TRUE )
files
predictors <- stack(files)
predictors
extent_of_mexico_map <- extent(-129, -79, -15, 35) # Set your extent to the area we're focused on
predictors_cropped_to_mexico <- crop(predictors, extent_of_mexico_map)
```
```{r}
set.seed(0)
group <- kfold(your_species_data, 5)
pres_train_your_species <- your_species_data[group != 1, ]
pres_train_your_species <- as.data.frame(pres_train_your_species[,1:2])
pres_test_your_species <- your_species_data[group == 1, ]
pres_test_your_species <- as.data.frame(pres_test_your_species[,1:2])
```
```{r}
pred_nf <- dropLayer(predictors_cropped_to_mexico, 'biome')
backg <- randomPoints(pred_nf, n=1000, ext=extent_of_mexico_map, extf = 1.25)
colnames(backg) = c('lon', 'lat')
group <- kfold(backg, 5)
backg_train <- backg[group != 1, ]
backg_test <- backg[group == 1, ]
```
Now start making your MaxEnt distribution model
```{r}
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
xm <- maxent(predictors_cropped_to_mexico, pres_train_your_species, factors='biome')
plot(xm)
```
Making maps
```{r}
e <- evaluate(pres_test_your_species, backg_test, xm, predictors_cropped_to_mexico)
e
px <- predict(predictors_cropped_to_mexico, xm, ext=extent_of_mexico_map, progress='')
par(mfrow=c(1,2))
plot(px, main='## YOUR SPECIES NAME ##')
```
**Whoo hooo!! Nice work on your model!!**
