An immense frustration ecologists encounter is prompted by the attempt to keep track of individual animals in a study. This task only becomes more difficult when trying to pinpoint small, mobile animals like songbirds. While intelligent computer algorithms can help scientists better complete this task, training these systems to recognize different species -- let alone individuals in a species -- can take thousands of data points, time, and money. However, French and Portuguese researchers recently devised a way to streamline this process. They designed a deep-learning network that can identify individual birds with up to 92 percent accuracy in three different species. This tech can not only save scientists resources but can help them collect important data about the lives of birds -- and better understand what may be leading to their decline in North America.

Distinguishing between individual animals is important for long-term monitoring of populations and protecting species from pressures such as climate change. However, it is also one of the most expensive, troublesome, and time-consuming aspects of animal behaviour research. While some creatures such as leopards have unique markings which allow humans to recognise individuals by eye, most species require additional visual identifiers such as coloured bands to be distinguished. Attaching bands to birds' legs can be stressful and disruptive to the animals, limiting the scope of research. Seeking an alternative method for distinguishing between individual birds, researchers from institutes in France, Germany, Portugal, and South Africa developed the first AI bird identification tool of its kind.

Trying to identify a wild bird while frantically leafing through a bird-spotters' guide is no easy task. But modern technology has come to the rescue, with artificial intelligence trained to help out amateur twitchers. Where people may be confused by two similar looking birds, or a juvenile which does not yet have its adult plumage, AI has been found to identify birds with up to around 90 per cent accuracy. The technology was trained using pictures of wild great tits and sociable weavers, as well as captive zebra finches. It works in a similar way to the face-recognition programmes used to identify people in crowds.

For even the most sharp-eyed of ornithologists, one great tit can look much like another. But now researchers have built the first artificial intelligence tool capable of identifying individual small birds. Computers have been trained to learn to recognise dozens of individual birds – which could potentially save scientists arduous hours in the field with binoculars, as well as the catching of birds to fit coloured rings to their legs. "We show that computers can consistently recognise dozens of individual birds, even though we cannot ourselves tell these individuals apart," said André Ferreira, a PhD student at the Centre for Functional and Evolutionary Ecology (CEFE-CNRS), in France. "In doing so, our study provides the means of overcoming one of the greatest limitations in the study of wild birds – reliably recognising individuals."