Population genetics and geometric morphometrics of the freshwater snail Segmentina nitida reveal cryptic sympatric species of conservation value in Europe

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

A qPCR assay for the rapid and specific detection of Shining ram's-horn snail (Segmentina nitida) eDNA from Stodmarsh National Nature Reserve, UK

Segmentina nitida Müller 1774 is a freshwater snail which was formerly widespread throughout England and south Wales. Since the 1840s it has seen a rapid decline in its range which has been attributed to deteriorating water quality due to nutrient enrichment, lowering of water tables and over-management of the ditches in which it resides. S. nitida has therefore been identified as a UK Biodiversity Action Plan (UKBAP) priority species which recommends further research for its conservation. Here we have developed a Taqman based qPCR eDNA assay for the detection of S. nitida at the Stodmarsh National Nature Reserve and compared the results with a manual survey of the ditches at this location. 32 ditches were surveyed in November 2020 (22 at Stodmarsh) and February 2021 (10 outside the known range of S.nitida). Our eDNA analysis exhibited an observed percentage agreement of 84% with a kappa coefficient of agreement between manual and eDNA surveys of 0.56 (95% CI 0.22 to 0.92). Three ditches determined to be negative for S. nitida by eDNA analysis were manual survey positive, and a further two ditches that were negative by manual survey were positive by eDNA analysis revealing the potential for improved overall detection rates using a combination of manual and eDNA methodologies. eDNA analysis could therefore augment manual survey techniques for S. nitida as a relatively quick and inexpensive tool for collecting presence and distribution data that could be used to inform manual surveys and management of ditches.