Environmental Statistics and Spatial Modeling for Conservation
Statistical Biostatistics + Machine Learning for Ecological Modeling
| Audience | Researchers in Ecology, Environmental Science, Conservation Biology |
|---|---|
| Prerequisites | Introductory Statistics, Ecological Foundations, Basic R (helpful but not required) |
| Format | 5 sessions x 90 minutes (30 min theory + 60 min hands-on R practice) |
Course Overview
This advanced course integrates key concepts from spatial ecology, biostatistics, and machine learning to build statistically rigorous ecological models.
The workshop combines ecological theory with practical R-based spatial modeling workflows, enabling participants to design robust sampling strategies and build reproducible species distribution models (SDMs).
Core topics include:
- Spatial sampling theory
- Biostatistical diagnostics
- BioClim climate predictors
- Multivariate ecological analysis
- Machine learning for SDM
- Spatial cross-validation
Participants will learn to interpret models using biological reasoning and statistical rigor.
Spatial Ecology & Ecological Modeling Framework
| Topic | Tools | Focus |
|---|---|---|
| Spatial Ecology & Ecological Modeling | sf, terra, ggplot2, dplyr, spatstat, spdep, tmap, dismo, randomForest, blockCV, mlr3 |
Spatial sampling theory, spatial point-pattern analysis, BioClim climate predictors, multivariate analysis, and machine learning for species distribution modeling with spatial cross-validation |
Schedule & Sessions
Session 1 - Spatial Sampling & Ecological Inference
- Random, systematic, stratified, transect sampling designs
- Road and river sampling bias
- Dendritic network dependence
- Moran’s I and spatial autocorrelation
- Effective sample size
Session 2 - Data Exploration & Normalization
- Distribution diagnostics (histograms, QQ plots, density plots)
- Transformations: log, Box-Cox, scaling
- Outlier detection (Isolation Forest)
- Ecological interpretation of transformations
Session 3 - Collinearity & Regularization
- Correlation matrices
- Variance Inflation Factor (VIF)
- Ecological trade-offs in variable selection
- Ridge regression, LASSO, Elastic Net
Session 4 - Multivariate & Dimensionality Reduction
- PCA interpretation in ecological space
- Loadings and gradient interpretation
- UMAP / t-SNE visualization
- Climate niche space analysis
Session 5 - Habitat Suitability / Species Distribution Modeling (SDM)
- GLM and GAM foundations
- Random Forest and Boosted Trees
- Regularized MaxEnt modeling
- Variable importance and interpretation
- Spatial cross-validation
Software Requirements
Please install R >= 4.2 and RStudio before Session 1.
Install Required R Packages
# Data manipulation and visualization
install.packages(c("tidyverse","dplyr","tidyr","ggplot2"))
# Spatial data
install.packages(c("sf","terra","spdep","tmap"))
# Statistical modeling
install.packages(c("MASS","car","glmnet","mgcv"))
# Machine learning
install.packages(c("caret","randomForest","gbm","dismo"))
# Multivariate analysis
install.packages(c("vegan","FactoMineR","factoextra"))
# Outlier detection
install.packages("isolationForest")
# Spatial cross-validation
install.packages(c("blockCV","spatialsample"))
# Advanced machine learning workflow
install.packages("mlr3")Workshop Format & Materials
Each session includes:
- 30 minutes theoretical concepts
- 60 minutes hands-on R analysis
- datasets and scripts provided beforehand
- reproducible ecological modeling workflows
Sessions build progressively:
- Spatial sampling
- Data diagnostics
- Collinearity and regularization
- Multivariate ecological space
- Species distribution modeling
Final session includes model comparison and spatial cross-validation.
Session links will be activated one week before each workshop.
Learning Objectives
By the end of this workshop participants will be able to:
- Design ecologically meaningful spatial sampling strategies
- Diagnose statistical distributions and transformations
- Detect multicollinearity in environmental predictors
- Apply dimensionality reduction to ecological datasets
- Build species distribution models using multiple algorithms
- Interpret model predictions in ecological context
- Apply spatial cross-validation to avoid overfitting
Recommended Reading
Core Books
- Zuur et al. (2009) Mixed Effects Models and Extensions in Ecology with R
- James et al. (2021) An Introduction to Statistical Learning
- Franklin (2010) Mapping Species Distributions
Key Papers
- Dormann et al. (2007) Methods to account for spatial autocorrelation
- Elith et al. (2006) Novel methods for predicting species distributions
- Valavi et al. (2019) blockCV for spatial cross-validation
License
These workshop materials are shared under CC BY-SA 4.0.