Published on 20 December 2025
Multi-locus nuclear barcode assessment of genetic diversity in Swiss Orthoptera unveils conservation status limitations
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With accelerating biodiversity loss, tracking both inter- and intraspecific diversity is critical, as within-species variation underpins population viability and adaptive potential. Traditional single-locus barcoding, targeting short mitochondrial or plastid fragments, generally provides reliable species-level resolution but poorly captures intraspecific variation. To address this, we developed an Orthoptera-specific marker panel targeting 398 nuclear loci plus the complete mitogenome and ribosomal DNA, sequencing these regions in 645 specimens representing every Swiss Orthoptera species sampled during the nationwide Red List update. This multilocus dataset produced a well-supported phylogeny and resolved several taxonomic ambiguities with direct conservation relevance. Genetic variation was further used to infer population structure and to test whether intraspecific diversity correlates with ecological traits and Red List status. While species’ IUCN threat levels were not correlated with genetic diversity, we found a significant negative association between genetic diversity and dependence on riparian habitats: river-bank specialists showed lower diversity, likely reflecting the severe fragmentation and alteration of these ecosystems. Our results demonstrate that genetic diversity measures from multilocus markers provide valuable, complementary insights into conservation status. However, the lack of correlation with IUCN threat levels highlights a challenge for integrating genetic data into Red List assessments: while low diversity often flags advanced decline and urgent action, high diversity does not necessarily signal low extinction risk, as it can persist for some time after population decline. Genomic data should therefore be incorporated cautiously alongside traditional criteria. These findings are being integrated into the upcoming Swiss Orthoptera Red List update.
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Publication Details
Subfield
Genetics
Field
Biochemistry, Genetics and Molecular Biology
Domain
Life Sciences
Confidence Score
51%
Source
Scholar Data Model