A non-invasive footprint technique for accurate identification of cryptic small mammal species : a sengi case study

Abstract

The acceleration of biodiversity loss highlights the need for practical, affordable species monitoring tools. A key requirement of monitoring is the accurate identification of species, a particular challenge with cryptic species. This study introduces a non-invasive footprint identification technology to classify two cryptic sengi species (Elephantulus myurus and Elephantulus intufi) - key bioindicators in the rapidly changing Southern African biomes. Front footprints were collected, using a custom Small Mammal Reference Track box, from live-captured individuals that were identified by experts in small mammal taxonomy and verified through genetic analyses. Morphometric features of the footprints (lengths, angles and areas) were extracted using JMP software. Linear Discriminant Analysis, based on nine key variables, achieved a mean classification accuracy of 94–96% across training, validation, and test datasets, robustly distinguishing the two species using a single footprint image. By integrating our field capture locations with data from the IUCN expert-defined ranges and the Global Biodiversity Information Facility, we demonstrate that FIT empowers non-experts to contribute reliable, high-resolution occurrence data. This scalable approach has the potential to transform community-science efforts, improving the accuracy of species distribution maps and ultimately strengthening conservation outcomes. Planned advancements include open-ended track tunnels and expanded machine learning models to monitor more small mammals in at-risk ecosystems. This approach offers a scalable, low-impact alternative to traditional trapping and genetic methods, reduces animal stress, morbidity and mortality, and empowers local communities to enhance data quality and monitoring through integration with traditional ecological knowledge.