Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

Area of Science:

• Ecology and Conservation Biology
• Remote Sensing and Geospatial Analysis
• Biodiversity Informatics

Background:

• Global environmental changes necessitate continuous updates of biodiversity status for effective conservation.
• Traditional methods using climate and land-cover data have limitations in rapidly assessing ecosystem responses.
• Satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and timely evaluation of ecosystem changes.

Purpose of the Study:

• To develop and test a modeling framework assessing the predictive ability of EFAs as Essential Biodiversity Variables (EBVs).
• To compare EFA-based predictions with traditional climate and land-cover datasets in Species Distribution Models (SDMs).
• To evaluate the multi-scale performance of EFAs for two protected plant species with differing distributions.

Main Methods:

• Developed a modeling framework to evaluate EFAs as EBVs against climate and land-cover data at multiple scales.
• Fitted four sets of SDMs for Iris boissieri and Taxus baccata using interpolated climate, landscape variables, EFAs, and combined datasets.
• Assessed model performance using Area Under the Curve (AUC) metrics across different spatial scales.

Main Results:

• EFA-based SDMs demonstrated high predictive performance across scales, comparable to traditional climate and land-cover models.
• EFA models identified additional suitable habitats and provided functional insights into habitat suitability for both species.
• The predictive ability of satellite-derived EFAs showed minimal scale-dependence, supporting their use from regional to local scales.

Conclusions:

• Satellite-derived EFAs are effective EBVs for SDMs, offering valuable, scalable information for biodiversity monitoring.
• The proposed framework aids conservation managers in decision-making for biodiversity reporting and management.
• EFAs provide a robust and integrative approach to assess habitat suitability and species conservation status in a changing environment.