Biodiversity Prediction Models

Introduction to Ecosystem Dynamics

Ecosystem Dynamics: refer to the alterations and processes that occur within ecosystems, including the influence of:

• Natural processes: Such as weather systems and seasonal variations.
• Climate change: Characterised by temperature fluctuations and changed precipitation patterns.
• Human activities: Including industrial growth and pollution.

Components of Ecosystems:

• Biotic components: The living entities, such as animals, plants, and microbes.
• Abiotic components: The non-living elements, such as soil, water, and climate.

Diagram Caption: This diagram illustrates the various interactions within ecosystems, labelling producers, consumers, and decomposers.

Significance of Biodiversity

Biodiversity:

• Resilience and Stability: crucial for maintaining balance in ecosystems.
• Resistance to Disturbances: Regulates pests and supports the provision of clean air, water, and soil fertility.

Impact on Ecosystem Services

• Supports plant diversity in controlling pests.
• Facilitates the provision of vital services such as clean air and water.

Historical Changes and Human Activity

Historical Ecosystem Changes

• Neolithic Agricultural Revolution:
  • Characterised by deforestation for agricultural purposes.
  • Altered habitats and species domestication, impacting biodiversity.
• Post-Ice Age Climate Shifts:
  • Modified the conditions for species survival.

These historical events have laid the foundation for contemporary biodiversity challenges.

Human Activities Impact

Land-Use Changes

• Deforestation: Causes habitat loss and influences carbon balances.
• Urban Expansion: Transforms natural areas, reducing biodiversity.
• Agricultural Practices: Modify native habitats, impacting biodiversity.

Pollution and Invasive Species

• Chemical/Plastic Pollution: Adversely affects ecosystems and organism health.

• Case Study - Cane Toads in Australia: Compete with native species, causing ecological disruption.

• Ecosystem Services:

  • Critical functions such as pollination and purification are impacted by habitat loss and pollution.

Technological Advancements in Prediction

Technological Tools:

• Remote Sensing and GIS: Facilitate real-time data acquisition for understanding ecosystem dynamics.
• Machine Learning: Enhances data analysis, improving the accuracy of predictive models.

Biodiversity Prediction Models

Purpose: To forecast impacts such as species extinction and habitat changes.

Types of Models

• Climate Models:
  • Simulate ecological changes; predict the consequences for habitats.
• Species Distribution Models (SDMs):
  • Predict shifts in species' ranges.
• Population Viability Analysis (PVA):
  • Assess extinction risks.

Model Type	Strengths	Limitations
Climate Models	Accurate predictions, scalable	Data dependency, complexity
SDMs	Useful for conservation planning	May oversimplify dynamics
PVA	Risk assessments, guides recovery	Requires comprehensive data

Emerging Trends

• AI and Big Data: Enhance models with more accurate, real-time data.

Community Involvement in Model Development

Citizen Science: Public participation in data collection enriches models.

Challenges and Solutions:

• Data Reliability: Training ensures consistency.
• Communication: Engages stakeholders; digital tools enhance transparency.

Predicting Future Impacts and Adaptive Management

Adaptive Management

• Proactive Strategy: Models inform action plans that adapt to environmental changes.
• Case Study - Bird Species Restoration in the UK:
  • Models identified critical habitats; policy improvements were informed by monitoring.

Challenges in Policy Implementation

• Data Limitations & Stakeholder Engagement: Open access to data and inclusive consultations help address these challenges.
• Framework Development: Encourages collaboration among scientists, policymakers, and the community.

Flowchart illustrating adaptive management based on biodiversity predictions.