Modeling Food Web and Forecasting Population for Endangered Wildlife Species

Background

With the alarming rise in mass extinctions and climate change, understanding the financial and ecological value of biodiversity has become essential. Endangered Wildlife OÜ, a tech startup, aims to quantify the value of species through a data-driven approach, fostering investments in environmental conservation. This process involves building biodiversity food web models to demonstrate species’ roles and justify their protection in monetary terms.

Objective

The project aimed to create automated processes for identifying, collecting, and organizing biodiversity food web and population data. These processes would enable stakeholders to understand species’ financial and ecological importance, facilitating scalable valuation and conservation efforts.

Approach

In a two-month collaboration, 50 AI changemakers worked on developing methods to build biodiversity food web and population datasets.

1. Food Web Creation:
   • Identified target species’ predators, prey, and cohabitating species using automated data extraction from scientific literature and online resources.
   • Created comprehensive species interaction maps down to the lowest trophic levels.
2. Population Data Compilation:
   • Automated the extraction of time-series data for population sizes from scientific reports and environmental databases.
   • Organized data by species, year, population size, and location, ensuring location-specific accuracy.

Tools and techniques included AI-based natural language processing for data extraction, scalable database management, and machine learning for missing data estimation.

Results and Impact

• Data Deliverables: Developed a scalable, location-specific biodiversity food web and population dataset, including:
  • Species interactions within food webs.
  • Time-series population data with gap estimations.
• Efficiency Gains: Automated processes significantly reduced the time and effort needed to build datasets.
• Broader Impact: Provided stakeholders with a clear monetary and ecological valuation of species, enhancing conservation strategies and resource allocation.

This project equips conservationists and policymakers with actionable insights to promote biodiversity protection and secure investments for environmental sustainability.

Wildlife OU about the AI Challenge results

Future Implications

The tools and datasets developed in this project can transform biodiversity valuation by scaling up data collection and analysis efforts globally. This work lays the groundwork for innovative policies, resource prioritization, and further research into the interdependence of species in complex ecosystems, ultimately reinforcing global conservation efforts.