vegan包varpart分析中GPS坐标变量的处理及脚本咨询

Hey there! Let's break down how to handle your GPS coordinates (lat + long) as the X2 variable in vegan::varpart(), including whether you need transformations and updated script examples.

Approach 1: Use Standardized Raw Coordinates #

If your research focuses on linear spatial gradients (e.g., how latitude/longitude directly drives fungal community composition in a linear way), you can use the raw coordinates—but you should standardize them first, just like you did with the age variable.

Why standardize? #

Raw GPS coordinates have large, variable ranges (e.g., latitude from 30 to 50, longitude from 100 to 120), which don't match the scaled age (mean = 0, SD = 1). Using unstandardized coordinates can skew the model's weight toward spatial variables, leading to misleading variance partitioning results.

Updated varpart() Script: #

# First, standardize the GPS coordinates
Cr_var$lat_scaled <- scale(Cr_var$lat)
Cr_var$long_scaled <- scale(Cr_var$long)

# Run variance partitioning with standardized variables
CR_all.fun_var_part <- varpart(all_fungi, 
                               ~ age,  # X1: Scaled stand age
                               ~ lat_scaled + long_scaled,  # X2: Scaled GPS coordinates
                               data = Cr_var)

# Inspect results and plot
CR_all.fun_var_part
plot(CR_all.fun_var_part, digits = 2)

Approach 2: Convert to Spatial Structure Variables (Recommended for Community Ecology) #

Fungal communities often exhibit spatial autocorrelation (similar communities in nearby sites), which raw linear coordinates can't fully capture. For this scenario, converting coordinates to spatial eigenvector variables (like Moran's Eigenvector Maps, MEMs) is a better choice—it captures multi-scale spatial patterns (large- and small-scale spatial variation).

Why this is better? #

MEMs extract spatial features that represent autocorrelation across different scales, making them more ecologically meaningful for community data than raw coordinates alone.

Script with MEMs: #

library(vegan)

# 1. Create a spatial distance matrix from GPS coordinates
spatial_dist <- dist(cbind(Cr_var$lat, Cr_var$long))

# 2. Generate MEMs (automatically select significant eigenvectors)
spatial_mems <- mem(spatial_dist, select = TRUE)

# 3. Combine MEMs with your original data frame
Cr_var_with_mems <- cbind(Cr_var, spatial_mems)

# 4. Run variance partitioning: X2 = all significant MEMs
CR_all.fun_var_part <- varpart(all_fungi, 
                               ~ age,  # X1: Scaled stand age
                               ~ .,  # X2: All significant spatial MEMs
                               data = Cr_var_with_mems)

# Inspect results and plot
CR_all.fun_var_part
plot(CR_all.fun_var_part, digits = 2)

Bonus: Non-Linear Spatial Trends #

If you suspect non-linear spatial gradients (e.g., fungal communities change more rapidly at certain latitudes), you can fit polynomial terms of the standardized coordinates:

# Create standardized coordinates and their polynomial/interaction terms
Cr_var$lat_scaled <- scale(Cr_var$lat)
Cr_var$long_scaled <- scale(Cr_var$long)
Cr_var$lat_sq <- Cr_var$lat_scaled^2
Cr_var$long_sq <- Cr_var$long_scaled^2
Cr_var$lat_long_interact <- Cr_var$lat_scaled * Cr_var$long_scaled

# Variance partitioning with non-linear terms
CR_all.fun_var_part <- varpart(all_fungi, 
                               ~ age, 
                               ~ lat_scaled + long_scaled + lat_sq + long_sq + lat_long_interact, 
                               data = Cr_var)

Quick Decision Guide: #

• Choose Approach 1 if you're testing linear latitude/longitude gradient effects.
• Choose Approach 2 (MEMs) if you're interested in spatial autocorrelation or multi-scale spatial patterns (most common in fungal community studies).
• Try the polynomial approach if you hypothesize non-linear spatial trends.

内容的提问来源于stack exchange，提问作者i.b