Geospatial analysis reveals significant benthic-pelagic habitat mismatch across global marine networks, indicating that vast high-seas biodiversity hotspots remain unprotected.
Under the “30 × 30” target for ocean conservation and the BBNJ Agreement, a quantitative and science-based identification scheme for conservation priorities is required. The Ecologically or Biologically Significant Marine Areas (EBSAs) provide a crucial foundation but rely heavily on empirical biological data and expert consensus. This makes it susceptible to data deficiencies and spatial biases in deep-sea and high seas, while also lacking cross-regional comparability. Habitat heterogeneity is recognized as a robust surrogate indicator for biodiversity. Utilizing multi-source, high-resolution marine environmental data, this study constructed a Benthic Habitat Heterogeneity Index (BHHI) and a Pelagic Habitat Heterogeneity Index (PHHI) using the Shannon index and the Entropy Weight Method. We analyzed global spatial patterns and evaluated existing EBSAs based on single- and dual-dimensional heterogeneity hotspots. Results demonstrate that the global EBSA network's BHHI (0.38 ± 0.23) and PHHI (0.18 ± 0.12) significantly exceed global ocean averages (Mann-Whitney U test, p < 0.001), indicating overall prioritization of high-heterogeneity areas. However, 8.06 × 10 6 km 2 (8.4% of existing EBSAs) are dual-dimensional inefficient zones. Conversely, 17.45 × 10 6 km 2 of new high-efficiency zones were identified outside the network, with 54.33% in the high seas. Significant spatial mismatches between benthic and pelagic heterogeneity cause extensive single-dimensional inefficiencies within EBSAs (29.2% and 28.8%, respectively). Furthermore, 73.16 × 10 6 km 2 of benthic and 65.83 × 10 6 km 2 of pelagic high-efficiency zones exist outside the network, with 56.9% and 54.0% in the high seas. The quantitative habitat heterogeneity framework provides a novel tool for evaluating global conservation priorities and offers actionable insights for optimizing the EBSA network.
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Zhang et al. (2026) studied this question.
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