Ecological Informatics

Overview

• Ecological Informatics applies computational and data science methods to ecological data.
• Purpose is to enhance understanding and management of ecosystems.

Big Data in Ecological Informatics

• Facilitates analysis of large and complex datasets.
• Improves ecological modeling and decision-making.

Key Concepts

Ecological Modeling

• Mathematical representations of ecological systems.
• Used to simulate and predict ecological dynamics.

Tools

• R: Common for statistical analysis and visualization.
• GIS: Analyzes spatial data, aiding in habitat mapping and conservation planning.
• Ecological Modeling Software: Tools like NetLogo or Ecopath for simulations.

Machine Learning

• Algorithms identify patterns and make predictions based on ecological data.
• Improves species conservation efforts by analyzing large datasets to identify at-risk species.

Biodiversity and Databases

• Biodiversity Database: Structured data on species distributions, characteristics, and interactions.
• Ecological Databases: Store and organize data for research and policy.

Remote Sensing

• Provides data on land cover, vegetation health, and environmental changes.

Data Integration

• Combines data from various sources for a comprehensive view of ecological systems.
• Data Interoperability: Ensures different data systems work together seamlessly.

Spatial and Temporal Analysis

• Spatial Analysis: Understands species distribution and ecological phenomena geographically.
• Temporal Data: Offers insights into changes over time.

Ecological Concepts and Terms

Ecological Footprint

• Measures environmental impact in terms of land and resource use.

Species and Habitat

• Species Distribution Model: Predicts species’ geographic distribution.
• Habitat Fragmentation: Division of habitats into smaller, isolated patches.

Ecological Networks

• Represent interactions between species and their environment.

Ecological Indicators

• Metrics to assess health and integrity of ecosystems.

Data Mining

• Extracts useful patterns and knowledge from large datasets.

Management and Policy

Adaptive Management

• Systematic approach to improve practices by learning from outcomes.

Ecosystem Services

• Benefits humans derive from ecosystems (provisioning, regulating, cultural, supporting).

Policy-making

• Ecological Models: Provide evidence-based predictions for conservation and resource management.

Challenges and Strategies

Biodiversity Loss

• Ecological Informatics identifies threats and develops conservation strategies.

Climate Change and Adaptation

• Ecological Modeling: Predicts climate impacts on ecosystems, guiding adaptation strategies.

Invasive Species

• Non-native species that cause harm in new environments.

Ecosystem Health and Resilience

• Ability to absorb disturbances and maintain structure and function.

Citizen Science

• Engages the public in data collection and monitoring.

Social Media

• Used for citizen engagement, data collection, awareness, and conservation efforts.

Conclusion

• Ecological Informatics is pivotal for sustainability, providing data-driven insights for managing natural resources and conserving biodiversity.