Beijing: A study says that footprints left by giant pandas in the wild can help conservationists identify the individual panda that made them and determine its gender as well.
The new approach uses an interactive software tool called the Footprint Identification Technique (FIT) to “read” and analyse digital images of footprints, which are submitted electronically to a global database for matching.
Field tests showed that the technique accurately identified individual animals and their sex more than 90 per cent of the time, said the study published in the journal Biological Conservation.
This accuracy, combined with the system’s ease of use in the field — a smartphone and a ruler are all you need to collect and submit images — makes it particularly well suited for studying a species as elusive as the giant panda, said lead researcher Binbin Li, Assistant Professor at Duke Kunshan University in Jiangsu, China.
“Giant pandas live in remote and hard-to-reach areas and their population density is so low that actual sightings of pandas themselves are not common. What we do see a lot of are footprints and fecal droppings,” said Li, who holds a secondary faculty appointment at Duke University’s Nicholas School of the Environment in the US.
Identifying individual animals based on a DNA analysis of their fecal droppings provides accurate results, she said, but is costly and requires very fresh samples and sophisticated laboratory equipment.
Trying to identify a panda using estimates of its bite size — based on the average length of bamboo fragments found in its droppings — is less technical but not very precise since many pandas in the same geographic area may have similar bites.
Footprints, on the other hand, are unique to each individual animal, somewhat like fingerprints in humans.
“Each species has a unique characteristic foot structure and the panda, in particular, has a beautifully complex foot that makes it a perfect candidate for monitoring with FIT,” said Zoe Jewell from Duke’s Nicholas School.
The software is based on a customised statistical model that uses cross-validated discriminant analysis and clustering methodology to “read” a panda’s footprint and identify its distinguishing features.
Based on these data, the programme can identify the animal’s sex and pinpoint if its prints are already in the FIT dataset or new to it, said Sky Alibhai, an adjunct faculty member at Duke’s Nicholas School.
The new technique may prove especially useful for monitoring the reintroduction of captive pandas back into the wild, said Zhang Hemin, Director of the China Conservation and Research Centre for the Giant Panda, where the field tests were conducted.