Foray NL Fungal Diversity Analysis

This repository contains the code and documentation for analyzing fungal diversity data collected by Foray NL, a non-profit organization conducting amateur mushroom forays in Newfoundland and Labrador. The analysis aims to address the following objectives:

1. Identifying hotspots of fungal diversity in the province.
2. Understanding fungal diversity patterns in space and time.
3. Comparing diversity at the same locations across different years.

Dataset Exploration & Cleaning

• Load and assess the dataset structure and quality.
• Filter out irrelevant columns and retain those related to fungal diversity.
• Handle missing values appropriately.

Data Visualization

• Create scatter plots of latitude and longitude to visualize geographical distribution.
• Utilize matplotlib and seaborn libraries for plotting.

Identifying Hotspots or Diversity Patterns

• Analyze visualization plots to identify clusters or concentrations of data points.
• Infer hotspots based on areas with dense concentrations of data points.

Applying Machine Learning

• Apply supervised machine learning classifier algorithms for prediction.
• Perform cross-validation and evaluate model predictions.

Methodology for Objective 3 (Machine Learning Integration)

Objective 3 involves comparing diversity at the same locations across different years, making it suitable for applying supervised machine learning techniques. Here's a summary of the approach:

1. Clear Objective: The goal is to compare diversity levels at the same locations but in different years.
2. Structured Data: The dataset likely has a structured format, making it suitable for supervised learning.
3. Predictive Modeling: Various predictive modeling techniques will be applied to learn patterns and make predictions.
4. Evaluation Metrics: Metrics like mean squared error (MSE) or correlation coefficient will assess prediction accuracy.
5. Validation Techniques: Cross-validation will validate model performance.

Summary of Results

• Geographical Hotspots: Identified using scatter plots and heatmaps.
• Habitat Influence: Primary habitats supporting fungal diversity highlighted.
• Temporal Patterns: Analysis of event date distribution and dominant fungal families.
• Spatial-Temporal Dynamics: Dynamic changes in species diversity observed.
• Machine Learning Integration: Foundation for future predictive modeling.

For more detailed analysis and results, refer to the Jupyter notebooks and documentation provided in this repository.

Dataset Source

• Foray NL Website.