Biodiversity Analysis in National Parks

Introduction

This project analyzes biodiversity data from the National Parks Service, focusing on species observed across various parks. Conducted in Jupyter Notebook, the analysis utilizes pandas, NumPy, matplotlib, seaborn, and chi2_contingency for data processing, statistical testing, and visualization.

The goal is to explore patterns in conservation status, endangered species, and species distribution across parks.

Key Questions Explored:

• What is the distribution of conservation status for species?
• Are certain types of species more likely to be endangered?
• Are the differences between species and their conservation status statistically significant?
• Which animal is most prevalent, and how is their distribution spread across parks?

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Data Sources

The project is based on two datasets provided by Codecademy:

• species_info.csv: Contains species classification, scientific names, and conservation status.
• observations.csv: Records species sightings across national parks in the past seven days.

This dataset is inspired by real-world data and helps in understanding biodiversity trends across different regions.

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Technologies Used

• Python
• Jupyter Notebook
• pandas (Data processing & analysis)
• NumPy (Numerical computation)
• matplotlib & seaborn (Data visualization)
• chi2_contingency (Statistical testing)

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Exploratory Data Analysis (EDA)

1. Understanding the Data

• The species_info.csv dataset contains 5,824 rows and 4 columns (Category, Scientific Name, Common Names, Conservation Status).
• The observations.csv dataset contains 23,296 rows and 3 columns (Scientific Name, Park Name, Observations).

2. Conservation Status Distribution

• The majority of species are not under conservation efforts, while a small subset falls under categories such as Endangered, Threatened, and Species of Concern.
• A pie chart was created to visualize the distribution of conservation status.

3. Species Analysis

• The dataset contains 7 unique categories: Mammals, Birds, Reptiles, Amphibians, Fish, Vascular Plants, and Nonvascular Plants.
• Mammals and Birds were found to have the highest rate of species under protection.

4. Statistical Significance Testing

• Chi-squared tests were performed to analyze differences in conservation status.
• Results show that mammals and reptiles exhibit a statistically significant difference in conservation status, while mammals and birds do not.

5. Most Observed Species

• The Deep-Root Clubmoss had the highest number of sightings, while the Golden Corydalis was the least observed species.
• Bats were identified as the most frequently occurring mammals, with Yellowstone National Park having the highest bat sightings.

6. Species Observations by National Park

• The dataset includes data from four national parks:
  • Great Smoky Mountains
  • Yosemite
  • Bryce Canyon
  • Yellowstone
• The number of observed species was artificially identical across all parks, suggesting synthetic data generation.

7. Protected vs. Non-Protected Species

• Protected species sightings were higher than non-protected species in most parks, except for the Great Smoky Mountains National Park.

8. Visualizations

• Bar charts comparing species counts across parks.
• Stacked bar chart showing species categories under conservation.
• Pie charts visualizing conservation status distribution.
• Statistical plots comparing endangered species proportions.

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Findings & Insights

✔ Most species in the dataset are not under conservation efforts.
✔ Mammals and birds have the highest percentage of protected species.
✔ There is a significant difference in conservation status between mammals and reptiles.
✔ Bats are the most commonly observed mammals, especially in Yellowstone National Park.
✔ The identical species count across parks suggests that the data is artificially generated.

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Future Research & Enhancements

🔹 Incorporate a time-based dataset to analyze trends in species conservation over the years.
🔹 Include national park area size to assess biodiversity density.
🔹 Conduct spatial analysis to identify clustered distributions of species.

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How to Run the Project

1. Clone the repository:

   git clone https://github.com/Sabdikay/Analysis-of-Biodiversity.git
   cd Biodiversity-Analysis

2. Install required dependencies:

   pip install pandas numpy matplotlib seaborn scipy

3. Run the Jupyter Notebook:

   jupyter notebook

4. Open and execute the analysis notebook.

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Conclusion

This project successfully analyzed biodiversity data, highlighting conservation trends and species distribution patterns across national parks. Through statistical testing and visualizations, I uncovered key insights about endangered species and national park ecosystems.

This analysis can serve as a foundation for more in-depth studies on biodiversity, wildlife conservation, and ecological trends in protected areas.

📊 Explore the project and contribute to further research! 🚀