ORIGINAL RESEARCH article
Front. Ecol. Evol.
Sec. Conservation and Restoration Ecology
A Non-Invasive Footprint Technique for Accurate Identification of Cryptic Small Mammal Species: A Sengi Case Study
Provisionally accepted
- 1Duke University Nicholas School of the Environment, Durham, United States
- 2WildTrack Inc., Durham, United States
- 3National Museum of South Africa, Bloemfontein, South Africa
- 4University of the Free State, Bloemfontein, South Africa
- 5University of Pretoria, Pretoria, South Africa
- 6University of South Africa, Pretoria, South Africa
- 7Compass Cartographic, Providence, United States
- 8WildTrack Inc, Durham, United States
- 9Oppenheimer Generations Research and Conservation, Pretoria, South Africa
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.
Keywords: sengi, cryptic species, Footprint Identification Technology, Species monitoring, Footprints, Track plates, ecological integrity
Received: 06 Oct 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Alibhai, Avenant, Oosthuizen, Carlson, MacFadyen and Jewell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sky Alibhai
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.