SPATIAL DISTRIBUTION MODELS-1
Terra 9A
Naimul Islam
November 19, 2024
Install Packages
call terra and rspat package
library(terra) # Spatial data analysis
library(rspat) # Spatial distribution modeling
bf <- spat_data("bigfoot") # Load dataset for analysisObservation
dim(bf) # Get data dimensions## [1] 3092 3head(bf) # Preview first few rows of data## lon lat Class
## 1 -142.9000 61.50000 A
## 2 -132.7982 55.18720 A
## 3 -132.8202 55.20350 A
## 4 -141.5667 62.93750 A
## 5 -149.7853 61.05950 A
## 6 -141.3165 62.77335 A- Purpose: Checks data dimensions and previews data.
- Use: Understand the structure of your dataset.
Plot: Species Occurrence Points
plot(bf[,1:2], cex=0.5, col="red") # Plot species occurrence points
library(geodata) # Load geodata
wrld <- geodata::world(path=".") # Load world boundaries
bnds <- wrld[wrld$NAME_0 %in%
c("Canada", "Mexico", "United States"), ]
lines(bnds) # Overlay boundaries
- Purpose: Visualize species occurrence in specific regions.
- Interpretation: Map with red points showing occurrences and boundaries for selected countries.
- Use: Explore spatial distribution and regional context of data.
This image shows a map of North America with red dots representing the occurrence points of a specific species. The red dots are mainly concentrated in the United States and Mexico, illustrating the species’ geographical distribution.
Visualization: Climate Data
wc <- geodata::worldclim_global("bio", res=10, ".") # Load climate data
plot(wc[[c(1, 12)]], nr=2) # Plot temperature and precipitation
- Purpose: Visualize global climate data.
- Interpretation: Displays two climate variables (temperature and precipitation) globally.
– Map 1 : This map shows global temperature distribution, with colors ranging from blue (cold) to yellow (warm).
– Map 2 : This map illustrates global precipitation levels, with colors ranging from purple (low precipitation) to green (high precipitation).
- Use: Helps understand environmental variables relevant to species distribution.
Climate Data
bfc <- extract(wc, bf[,1:2]) # Extract climate data for species occurrence points
head(bfc, 3) # Preview the first 3 rows## ID wc2.1_10m_bio_1 wc2.1_10m_bio_2 wc2.1_10m_bio_3 wc2.1_10m_bio_4
## 1 1 -1.832979 12.504708 28.95899 1152.4308
## 2 2 6.360650 5.865935 32.27475 462.5731
## 3 3 6.360650 5.865935 32.27475 462.5731
## wc2.1_10m_bio_5 wc2.1_10m_bio_6 wc2.1_10m_bio_7 wc2.1_10m_bio_8
## 1 20.34075 -22.840000 43.18075 5.327750
## 2 16.65505 -1.519947 18.17500 3.964495
## 3 16.65505 -1.519947 18.17500 3.964495
## wc2.1_10m_bio_9 wc2.1_10m_bio_10 wc2.1_10m_bio_11 wc2.1_10m_bio_12
## 1 -0.6887083 11.80792 -16.038542 991
## 2 10.4428196 12.28183 1.467686 3079
## 3 10.4428196 12.28183 1.467686 3079
## wc2.1_10m_bio_13 wc2.1_10m_bio_14 wc2.1_10m_bio_15 wc2.1_10m_bio_16
## 1 120 42 31.32536 337
## 2 448 141 35.27518 1127
## 3 448 141 35.27518 1127
## wc2.1_10m_bio_17 wc2.1_10m_bio_18 wc2.1_10m_bio_19
## 1 157 288 216
## 2 468 630 873
## 3 468 630 873- Extract climate data (e.g., temperature, precipitation) at the locations of species occurrences.
Remove the First Column with the ID
bfc <- bfc[,-1] # Remove the ID columnPlot the Species’ Distribution
plot(bfc[ ,"wc2.1_10m_bio_1"], bfc[, "wc2.1_10m_bio_12"], col="red", xlab="Annual mean temperature (.C)", ylab="Annual precipitation (mm)") 
- Purpose: Visualize the relationship between two key climate variables.
- Interpretation: A scatter plot of annual mean temperature vs. precipitation for species occurrences.
- Use: Explore the climate preferences or niches of the species.
Background data
ext_bf <- ext(vect(bf[, 1:2])) + 1 # Create an extended spatial extent around occurrence points
ext_bf # Display the extent ## SpatExtent : -157.75, -63.4627, 24.141, 70.5 (xmin, xmax, ymin, ymax)Take 5000 Random Samples
set.seed(0)
window(wc) <- ext_bf
bg <- spatSample(wc, 5000, "random", na.rm=TRUE, xy=TRUE)
head(bg) ## x y wc2.1_10m_bio_1 wc2.1_10m_bio_2 wc2.1_10m_bio_3
## 1 -99.2500 66.75000 -13.2934895 7.870646 14.96619
## 2 -106.0833 42.08333 5.6722708 14.530958 36.82943
## 3 -111.9167 46.58333 6.7605939 14.135854 35.23372
## 4 -106.9167 54.75000 0.4086979 11.528605 24.43290
## 5 -118.2500 67.08333 -9.1363859 8.185354 16.34505
## 6 -111.2500 38.91667 8.4194584 15.997125 38.84047
## wc2.1_10m_bio_4 wc2.1_10m_bio_5 wc2.1_10m_bio_6 wc2.1_10m_bio_7
## 1 1638.6833 15.42850 -37.16100 52.58950
## 2 894.3715 27.86600 -11.58875 39.45475
## 3 927.7927 28.14375 -11.97650 40.12025
## 4 1290.1088 22.55225 -24.63250 47.18475
## 5 1567.0846 17.46575 -32.61275 50.07850
## 6 904.0610 30.49050 -10.69625 41.18675
## wc2.1_10m_bio_8 wc2.1_10m_bio_9 wc2.1_10m_bio_10 wc2.1_10m_bio_11
## 1 6.484917 -31.617332 7.518209 -31.76942
## 2 9.226916 -4.839750 17.168291 -4.83975
## 3 15.638333 -4.921750 18.186209 -4.92175
## 4 15.417084 -13.864500 15.417084 -16.31392
## 5 8.609292 -21.353209 10.573625 -27.49783
## 6 19.076958 3.179209 19.812834 -2.50475
## wc2.1_10m_bio_12 wc2.1_10m_bio_13 wc2.1_10m_bio_14 wc2.1_10m_bio_15
## 1 171 33 4 70.29919
## 2 288 42 13 38.78144
## 3 293 48 9 53.40759
## 4 471 86 16 58.32499
## 5 223 43 7 61.21693
## 6 228 28 11 32.40370
## wc2.1_10m_bio_16 wc2.1_10m_bio_17 wc2.1_10m_bio_18 wc2.1_10m_bio_19
## 1 90 13 78 13
## 2 112 41 90 41
## 3 129 35 115 35
## 4 220 53 220 56
## 5 108 27 93 29
## 6 83 40 72 44- Purpose: Generate random background points from the climate data within the extent.
- Use: Serves as control data for comparison with species occurrences in modeling.
Plot spatSample
plot(bg[, c("x", "y")]) 
- Purpose: Visualize the distribution of randomly sampled points.
- Interpretation: A scatter plot showing the locations of sampled points.
- Use: Confirms spatial coverage and distribution of background data.