Landscape mosaics: Analysing diversity for ecological and economic insight

Quantifying and valuing biodiversity remains one of the major challenges for the bioeconomy and nature-based solutions. Emerging biodiversity and ecosystem services credit schemes requires robust, scalable indicators, capable of capturing the ecological integrity and environmental value while ensuring standardization and cross-regional comparability. Conventional biodiversity assessment rely on field surveys, taxonomic expertise, and long-term monitoring, limiting their applicability for large-scale or continuous evaluation. This study proposes a methodological framework using landscape ecology metrics as proxy indicators of biodiversity potential and ecological value. Using COSc2024 Interim Land-Use/Land-Cover Map of Portugal, five indices (Shannon Diversity, Class Richness, Patch Density, Contagion, and Connectivity) were calculated across a 10 × 10 km grid covering mainland Portugal. The objective is to evaluate the applicability of landscape-level metrics as scalable proxies for the national scale classification of territorial units according to biodiversity potential and ecological integrity. Results reveal consistent spatial gradients linking compositional diversity, fragmentation and connectivity, delineating eight distinct landscape typologies. Heterogeneous northern mosaics exhibit higher biodiversity potential and resilience, whereas homogeneous southern agroforestry systems show stronger connectivity but lower diversity. Overall, the findings demonstrate that landscape metrics provide reliable and scalable proxies for ecological integrity and ecosystem functionality. These findings provide a practical basis for identifying priority areas for biodiversity credit schemes and supporting spatially explicit decision-making in nature-based investment strategies. By linking landscape structure to ecological valuation and environmental policy, this research contributes to the development of nature-positive investment frameworks integrating sustainable land management and bioeconomic planning.