Matrix & Gribnick
Gribnick
Hey Matrix, I’ve been mapping the distribution of bracket fungi on the old‑growth stand—there’s a pattern I think could use a bit of data crunching to reveal the underlying ecological rules.
Matrix
Sure thing. What kind of data do you have—coordinates, species counts, environmental variables? And what software are you comfortable with, or would you like me to suggest something?
Gribnick
I’ve got GPS coordinates for each plot, a tally of every bracket species seen there, and a few environmental notes—soil pH, canopy cover, and a rough light index. I also have a small log of moisture readings taken in the morning and evening. I normally use R for the heavy lifting, with packages like sp, sf, and ggplot2 for mapping, plus tidyverse for cleaning the data. If you’re more comfortable in Python, I can swap it out for geopandas and matplotlib, but R keeps my workflow tight. Let me know what you’re comfortable with and we’ll set up the analysis.
Matrix
Sounds solid. Stick with R; I’ll outline a quick workflow: 1) Import the GPS data into an sf object, 2) join species tallies by plot ID, 3) calculate species richness and Shannon diversity per plot, 4) run a multivariate model—maybe a redundancy analysis (rda) or a GLMM with plot as a random effect to see how pH, canopy, light, and moisture explain community composition, 5) plot the results with ggplot2, using a heatmap or ordination plot for the species matrix. Let me know if you need help scripting the rda or setting up the GLMM.
Gribnick
Here’s a quick skeleton you can copy into your script.
```r
# Packages
library(sf)
library(tidyverse)
library(vegan)
library(lme4)
# 1. Import GPS and species data
gps <- st_read("plots.gpkg") # shapefile or GeoPackage
spp <- read_csv("species_counts.csv") # columns: plot_id, species, count
# 2. Join data
plot_data <- gps %>%
left_join(spp, by = "plot_id") %>%
group_by(plot_id) %>%
summarise(across(starts_with("species_"), sum, na.rm = TRUE),
pH = first(pH),
canopy = first(canopy),
light = first(light),
moisture = first(moisture))
# 3. Diversity metrics
diversity <- plot_data %>%
mutate(richness = rowSums(select(., starts_with("species_")), na.rm = TRUE),
shannon = diversity(select(., starts_with("species_")), index = "shannon"))
# 4a. Redundancy Analysis (RDA)
species_matrix <- select(plot_data, starts_with("species_"))
env_vars <- select(plot_data, pH, canopy, light, moisture)
rda_res <- rda(species_matrix ~ pH + canopy + light + moisture, data = env_vars)
summary(rda_res)
# 4b. GLMM (if you prefer a mixed model)
glmm <- glmer(count ~ pH + canopy + light + moisture + (1|plot_id),
data = spp, family = poisson())
# 5. Plotting
ggplot(rda_res, aes(colour = pH, size = canopy)) +
geom_point() +
labs(title = "RDA of Bracket Community",
colour = "pH",
size = "Canopy Cover")
```
Just replace the file names and column prefixes with your actual data. Feel free to tweak the family in the GLMM if overdispersion shows up. Let me know how the plots look!
Matrix
Looks solid. Just double‑check that the species columns actually start with “species_” or adjust the select calls. If you see overdispersion in the Poisson GLMM, switch to a negative binomial with glmmTMB. For the RDA, you might want to standardise the environmental variables first—centering and scaling can make the axes more interpretable. After you run it, a biplot of species and sites will show you which variables drive the pattern. Let me know if you hit any hiccups.