Overview based on the following RSTUDIO code script

• Fire_Diversity_MSR_Mantel_Results.R

This R script performs comprehensive spatial correlation analysis using Moran Spectral Randomization (MSR) Mantel tests to examine the relationships between plant diversity patterns and spatial/environmental factors in a mesquite-oak savanna fire study. The analysis evaluates how plant diversity responds to fire disturbance across different soil types and spatial scales.

Study Background

• Study Area: Mesquite-oak savanna ecosystem with stratified sampling design
• Sampling Design: 288 quadrats (1-m²) randomly located within the study area
• Sampling Period:
  • Prefire: September-December 2018
  • Postfire: September-December 2019
• Stratification: Sampling stratified by soil type and vegetation cover class

Soil Series Distribution

• TA (Dominant): 120 quadrats
• VaB (Dominant): 120 quadrats
• KaB (Secondary): 48 quadrats

Data Structure

Input Files Required

The script expects the following CSV files in the working directory:

1. MainDataset.csv - Main dataset containing all sites
2. Data_TA.csv - TA soil series subset
3. Data_VaB.csv - VaB soil series subset
4. Data_KaB.csv - KaB soil series subset

Data Variables

Spatial Variables

• Sampling_ID - Unique identifier for each quadrat
• X_Coordinate - Spatial X coordinate
• Y_Coordinate - Spatial Y coordinate

Treatment Variable

• Treatment - Binary variable (0/1) indicating burn status
  • 0 = Unburned
  • 1 = Burned

Alpha Diversity Metrics

Calculated for both prefire and postfire periods:

• Shannon Diversity
  • Prefire_Shannon - Shannon diversity index before fire
  • Postfire_Shannon - Shannon diversity index after fire
• Species Richness
  • Prefire_Richness - Number of species before fire
  • Postfire_Richness - Number of species after fire
• Evenness
  • Prefire_Evenness - Species evenness before fire
  • Postfire_Evenness - Species evenness after fire

Beta Diversity Metrics

Six β-diversity measures calculated for all pairs of sampling points:

Incidence-based measures:

• Prefire_BSIM / Postfire_BSIM - Sørensen turnover component (β.SIM)
• Prefire_BSNE / Postfire_BSNE - Sørensen nestedness component (β.SNE)
• Prefire_BSOR / Postfire_BSOR - Sørensen dissimilarity index (β.SOR)

Abundance-based measures:

• Prefire_BBAL / Postfire_BBAL - Balanced variation component (β.BAL)
• Prefire_BGRA / Postfire_BGRA - Abundance gradient component (β.GRA)
• Prefire_BRAY / Postfire_BRAY - Bray-Curtis dissimilarity (β.BRAY)

Analysis Types

1. Simple Mantel Tests

• Diversity ~ Geography: Tests spatial autocorrelation in diversity patterns
• Diversity ~ Treatment: Tests treatment effects on diversity patterns

2. Cross Mantel Tests

• Prefire ~ Postfire: Examines temporal consistency in diversity patterns

3. Partial Mantel Tests

• Diversity ~ Geography | Treatment: Tests spatial patterns while controlling for treatment effects

Required R Packages

install.packages(c("vegan", "adespatial", "ade4", "spdep", "dplyr", "ggplot2"))

• vegan - For Partial Mantel tests
• adespatial - For MSR Mantel tests
• ade4 - For basic Mantel tests
• spdep - For spatial neighborhood structures
• dplyr - For data manipulation
• ggplot2 - For visualization

Output Files

Alpha Diversity Results

• MSR_Mantel_Prairie_Alpha_Main_Site_Results.csv - Detailed results for main dataset
• MSR_Mantel_Prairie_Alpha_TA_Soil_Results.csv - Results for TA soil series
• MSR_Mantel_Prairie_Alpha_VaB_Soil_Results.csv - Results for VaB soil series
• MSR_Mantel_Prairie_Alpha_KaB_Soil_Results.csv - Results for KaB soil series
• MSR_Mantel_Prairie_Alpha_All_Combined.csv - Combined results from all datasets

Beta Diversity Results

• MSR_Mantel_Prairie_Beta_Results.csv - Beta diversity analysis (Main Site only)
• MSR_Mantel_Prairie_Beta_Summary.csv - Beta diversity summary statistics

Summary Statistics

• MSR_Mantel_Prairie_Alpha_[Dataset]_Summary.csv - Summary statistics for each dataset

Key Features

Data Safety

• Comprehensive error handling for file loading
• Safe data preparation with NA replacement
• Validation checks for required variables

Spatial Analysis

• Quantile-based distance thresholds for spatial weights
• Automatic handling of isolated spatial points
• K-nearest neighbors fallback for connectivity

Statistical Rigor

• 999 permutations for robust significance testing
• Seed setting for reproducible results
• Multiple comparison corrections available

##Explanation of the Warnings after running MSR##

• These warnings indicate that the distance matrix contains zero distances,
  meaning that at least some pairs of observations have identical coordinates or values.
• This can cause problems for analyses that require strictly positive distances.
• However, we are dealing with sampling locations that may have similar values,
  so some small distances are expected

Flexible Design

• Handles missing datasets gracefully
• Processes multiple soil series independently
• Modular functions for easy modification

Usage Instructions

1. Set Working Directory: Update the setwd() path to your data location
2. Prepare Data: Ensure all CSV files are in the correct format with required columns
3. Run Script: Execute the entire script or run sections stepwise
4. Check Results: Review output CSV files and console summaries

Memory Management

The script includes memory management features:

• Initial memory clearing (rm(list=ls()))
• Memory limit setting (100GB)
• Garbage collection (gc())

Interpretation Guidelines

Statistical Significance

• *** p ≤ 0.001
• ** p ≤ 0.01
• * p ≤ 0.05
• . p ≤ 0.1

MSR Mantel R-statistic

• Positive values indicate positive spatial correlation
• Negative values indicate negative spatial correlation
• Magnitude indicates strength of relationship

Additional detail for abstract

The dataset structure enables multiple analytical approaches including Moran Spectral Randomization (MSR) Mantel tests for spatial autocorrelation analysis, partial Mantel tests for controlling confounding variables, and temporal comparison studies examining fire effects on plant community structure. Data values include continuous spatial coordinates, binary treatment indicators (0=unburned, 1=burned), and standardized diversity indices calculated from species percent cover data. The dataset's reuse potentially extends to spatial ecological research, fire ecology studies, community ecology investigations, and methodological development in spatial statistical analysis, particularly for researchers examining disturbance effects on biodiversity patterns across heterogeneous landscapes.

The accompanying R script provides a comprehensive analytical framework implementing MSR Mantel tests with robust error handling, automated spatial weight matrix generation, and production of results tables, making this dataset applicable for spatial ecological research and educational uses in landscape ecology and disturbance ecology.

References

Research Methods

• Baselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19(1), 134-143.
• Baselga, A. (2013). Separating the two components of abundance‐based dissimilarity: balanced changes in abundance vs. abundance gradients. Methods in Ecology and Evolution, 4(6), 552-557.
• Baselga, A., & Orme, C. D. L. (2012). betapart: an R package for the study of beta diversity. Methods in Ecology and Evolution, 3(5), 808-812.

R Packages

• Bivand, R., & Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association. TEST, 27(3), 716-748. [spdep package]
• Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N., & Wagner, H. H. (2018). adespatial: Multivariate multiscale spatial analysis. R package version 0.3-2. https://CRAN.R-project.org/package=adespatial
• Dray, S., & Dufour, A. B. (2007). The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software, 22(4), 1-20. https://doi.org/10.18637/jss.v022.i04
• Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2020). vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan
• Wagner, H. H., & Dray, S. (2015). Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods. Methods in Ecology and Evolution, 6(10), 1169-1178. https://doi.org/10.1111/2041-210X.12407 [MSR methodology]
• Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr
• Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Date README initially composed

2025-06-30