Towards global insect biomonitoring with frugal methods

Area of Science:

• Ecology
• Conservation Biology
• Biodiversity Science

Background:

• Humanity's critical dependence on biodiversity is threatened by unmet global conservation targets.
• A significant data gap exists for most biodiverse regions, hindering effective monitoring efforts.
• Insufficient funding for biomonitoring, particularly in low-income countries, necessitates frugal approaches.

Purpose of the Study:

• To explore frugal adaptations of three insect biomonitoring techniques: computer vision, lidar, and DNA sequencing.
• To advocate for evaluating biomonitoring techniques for global suitability before widespread adoption in high-income countries.
• To propose an 'innovation through simplification' phase for techniques popular in high-income nations.

Main Methods:

• Discussion of adapting computer vision, lidar, and DNA sequencing for frugal insect biomonitoring.
• Evaluation of techniques based on data acquisition cost and suitability for AI analysis.
• Consideration of the impact of patented technologies on global applicability.

Main Results:

• Techniques acquiring low-cost raw data suitable for AI analysis (e.g., images, lidar) are predicted to be most effective for global insect biomonitoring.
• Techniques heavily reliant on patented technologies (e.g., DNA sequencing) may present challenges for global implementation.
• Simplification and cost-effectiveness are key factors for global biomonitoring success.

Conclusions:

• Frugal biomonitoring methods, particularly those leveraging AI, are essential for addressing global biodiversity data gaps.
• A global strategy for computational resources and training is necessary to support widespread AI use in data analysis.
• Prioritizing global suitability and simplification is vital for developing effective insect biodiversity monitoring toolkits.