title: Automatic track recognition of footprints for identifying cryptic species.
abstract: The recognition of tracks plays an important role in ecological research and monitoring, and tracking tunnels are a cost-effective method for indexing species over large areas. Traditionally, tracks are collected by a tracking system, and analysis is cairied out in a manual identification procedure by experienced wildlife biologists. Unfortunately, human experts are unable to reliably distinguish tracks of morphologically similar species. We propose a new method using image analysis, which allows automatic species identification of tracks, and apply the method to identifying cryptic small-mammal species. We demonstrate the method by identifying footprints of three invasive rat species with similar morphology that co-occur in New Zealand, including detection of a recent invasion of a rat-free island. Automatic footprint recognition successfully identified the species of rat for >70% of footprints, and >83% of tracking cards. With appropriate changes to the image recognition, the method could be broadly applicable to any taxa that can be tracked. Identification of tracks to species level gives better estimates of species presence and composition in communities.
authors.affiliation: School of Biological Sciences and Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand. jrussell@berkeley.edu
authors.forename: James C
authors.initials: JC
authors.lastname: Russell
authors.forename: Nils
authors.initials: N
authors.lastname: Hasler
authors.forename: Reinhard
authors.initials: R
authors.lastname: Klette
authors.forename: Bodo
authors.initials: B
authors.lastname: Rosenhahn
mesh_terms: D000818:Animals; D003247:Conservation of Natural Resources; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D009520:New Zealand; D011157:Population Dynamics; D051381:Rats / Q000145:classification*