Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Area of Science:

• Ecology
• Remote Sensing
• Conservation Biology

Background:

• Ecosystem service management relies on understanding human impacts on ecosystem processes, best achieved through functional traits.
• Trait variation is typically studied locally, but regional assessments are needed for effective ecosystem service management.
• Remote sensing offers a scalable approach to assess trait variation across landscapes.

Purpose of the Study:

• To review concepts and methods for scaling up plant and animal functional traits from local to regional scales.
• To assess the impacts of human modification on ecosystem processes and services using trait-based approaches.
• To provide considerations for local sampling and regional upscaling of trait variation via remote sensing.

Main Methods:

• Review of existing literature on functional trait sampling and remote sensing techniques.
• Analysis of methods for local plot-level sampling of trait variation, considering land cover change and species introductions.
• Evaluation of passive and active remote sensing for mapping plant traits (phenological, chemical, structural) and inferring animal traits from landscape structure.

Main Results:

• Upscaling trait variation requires accounting for modifications due to land cover change and species introductions.
• Intraspecific variation, land cover stratification, or trait inference may be necessary sampling strategies.
• Remote sensing effectively maps plant traits, and combining methods enhances capacity; landscape structure mapping aids animal trait inference.
• Relationships between trait variation and remote sensing data exhibit high context dependency and are not directly transferable across regions.

Conclusions:

• Bridging functional trait and remote sensing methods is essential for ecosystem service management.
• Further research is needed to link functional trait metrics (e.g., community-weighted mean) with remote sensing data.
• Developing approaches that relate remotely sensed traits to non-remotely sensed traits and proxies is crucial for comprehensive ecosystem assessment.