How well is Italian biodiversity represented in red lists and conservation legislation? Taxonomic biases, coverage gaps and the assessment-to-legislation bottleneck

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Abstract

Italy, one of Europe’s most biodiverse countries, is expected to undergo landscape-wide nature recovery thanks to the EU Nature Restoration Regulation. National red lists represent critical instruments for informing conservation strategies at finer geographic resolutions and prioritizing taxa of national importance; yet the taxonomic completeness of Italian red lists remains unquantified in relation to national biodiversity. Equally, no systematic evaluation has been conducted on the representation of threatened and endemic Italian taxa within European and international conservation directives and treaties.

We present the first comprehensive review of threat status and policy inclusion of Italian biodiversity, encompassing animals, plants, fungi, lichens, and algae. We cross-referenced national species checklists with Italian, European, Mediterranean, and global IUCN red lists, alongside policy annexes from the Birds and Habitats Directives, the Bern and Barcelona Conventions, and CITES. Our dataset comprised 76,845 taxa, of which 8,389 are endemic; yet red list assessments exist for only 10% of this total (7,349 taxa, including 1,700 endemics).

Conservation policy coverage is even more restrictive: only 1,346 taxa are listed under at least one legislative instrument, with only half of these classified as threatened. This constitutes a compounding double bottleneck with most of the Italian biodiversity remaining both unassessed and unprotected and systematically biasing conservation policy toward an unrepresentative fraction of national biodiversity. We recommend accelerated national assessments and urgent establishment of a national biodiversity priority list founded on transparent prioritization protocols. This would complement ecosystem-based interventions mandated by the Nature Restoration Regulation while correcting for vertebrate-centred biases.

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