Why wandering albatrosses get divorced – new research

Monogamy is widespread among birds and it is well known that many seabird species mate for life. Famous examples include charismatic penguins and albatrosses which are often portrayed in the media as the perfect couples. But this romantic trope doesn’t tell the whole story.

Research has shown that up to 24% of wandering albatross chicks are reared by a male who is not their genetic father and this can be up to 31% in some penguin species. Not quite the tender idyll we are led to believe.

Seabirds are long-lived, with wandering albatrosses known to live for over 60 years. Their long pair bonding phase, accompanied by complex courtship dances, would make you think break ups should be rare. However, there is growing evidence seabirds do “divorce”.

In our new study we showed 13% of wandering albatrosses born on the remote Indian Ocean Crozet Archipelago divorce during their lifetime because of low numbers of females – and the actions of certain agressive males.

Sometimes birds split up to find a better mate. How birds assess mate quality is not fully understood. But it may be about age and behavioural traits the birds can observe. This is called adaptive divorce and has been noted in some seabird species before such as penguins.

But in our study something different was happening. Unlike some other birds divorce did not tend to increase the number of young a wandering albatross reared and didn’t seem to give them evolutionary advantages.

In the population we studied, couples were driven apart by what’s called forced divorce. This is where one bird, likely the males in our study, breaks up a bonded pair.

Show some personality

This was the first study in the wild which looked at behavioural characteristics such as aggression of these kind of “homewrecker” males.

Personality is well studied across the animal kingdom, from anemones to elephants. We know that seabirds have different personalities, linked to their foraging behaviour and reproductive success. Personality is defined as a consistent individual difference in behaviour and is measured using traits such as boldness, neophobia (fear of new things) and aggression.

In wandering albatrosses, we measured personality in two different ways. The first is what is called a human approach, where a person walks towards a bird incubating an egg on the nest and records its response. Males and females share incubation duties and both sexes can be tested in this way. The birds nest on marshy land so the tester had to wear snow shoes to avoid sinking.

We also tested how the birds responded to a novel object, in this case Betsy the space hopper cow. Unfortunately for Betsy, while the personality differences persisted among different birds, the albatrosses were all much more aggressive towards her. Betsy did not survive an attack late in the season when a particularly bold bird ripped her open.

The albatrosses ranged from shy ones, who showed no response, to dauntless types that stood up and called out. Bold females were no more likely to divorce than timid ones. However, shyer males had higher divorce rates suggesting that bolder males may be forcing timid males out of pair bonds.

Unpaired females are rare. The Crozet albatross population is male skewed because more females die trapped in fishing equipment. Males and females have different hunting strategies that change throughout their life. For instance, as males mature, they move increasingly further south to Antarctic waters. Females stay in subtropical waters throughout their lives, and forage areas further north.

In a previous study we showed divorced wandering albatrosses do not have more chicks so females are unlikely to benefit from seeking new mates. This supports the theory that wandering albatross pairs are not choosing to divorce. Instead, it seems to be a few rogue males who are ending the partnerships of these otherwise monogamous birds.

Samantha Patrick, Reader in Marine Biology, University of Liverpool

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Deepfake audio has a tell – researchers use fluid dynamics to spot artificial imposter voices

Imagine the following scenario. A phone rings. An office worker answers it and hears his boss, in a panic, tell him that she forgot to transfer money to the new contractor before she left for the day and needs him to do it. She gives him the wire transfer information, and with the money transferred, the crisis has been averted.

The worker sits back in his chair, takes a deep breath, and watches as his boss walks in the door. The voice on the other end of the call was not his boss. In fact, it wasn’t even a human. The voice he heard was that of an audio deepfake, a machine-generated audio sample designed to sound exactly like his boss.

Attacks like this using recorded audio have already occurred, and conversational audio deepfakes might not be far off.

Deepfakes, both audio and video, have been possible only with the development of sophisticated machine learning technologies in recent years. Deepfakes have brought with them a new level of uncertainty around digital media. To detect deepfakes, many researchers have turned to analyzing visual artifacts – minute glitches and inconsistencies – found in video deepfakes.

Audio deepfakes potentially pose an even greater threat, because people often communicate verbally without video – for example, via phone calls, radio and voice recordings. These voice-only communications greatly expand the possibilities for attackers to use deepfakes.

To detect audio deepfakes, we and our research colleagues at the University of Florida have developed a technique that measures the acoustic and fluid dynamic differences between voice samples created organically by human speakers and those generated synthetically by computers.

Organic vs. synthetic voices

Humans vocalize by forcing air over the various structures of the vocal tract, including vocal folds, tongue and lips. By rearranging these structures, you alter the acoustical properties of your vocal tract, allowing you to create over 200 distinct sounds, or phonemes. However, human anatomy fundamentally limits the acoustic behavior of these different phonemes, resulting in a relatively small range of correct sounds for each.

In contrast, audio deepfakes are created by first allowing a computer to listen to audio recordings of a targeted victim speaker. Depending on the exact techniques used, the computer might need to listen to as little as 10 to 20 seconds of audio. This audio is used to extract key information about the unique aspects of the victim’s voice.

The attacker selects a phrase for the deepfake to speak and then, using a modified text-to-speech algorithm, generates an audio sample that sounds like the victim saying the selected phrase. This process of creating a single deepfaked audio sample can be accomplished in a matter of seconds, potentially allowing attackers enough flexibility to use the deepfake voice in a conversation.

Detecting audio deepfakes

The first step in differentiating speech produced by humans from speech generated by deepfakes is understanding how to acoustically model the vocal tract. Luckily scientists have techniques to estimate what someone – or some being such as a dinosaur – would sound like based on anatomical measurements of its vocal tract.

We did the reverse. By inverting many of these same techniques, we were able to extract an approximation of a speaker’s vocal tract during a segment of speech. This allowed us to effectively peer into the anatomy of the speaker who created the audio sample.

From here, we hypothesized that deepfake audio samples would fail to be constrained by the same anatomical limitations humans have. In other words, the analysis of deepfaked audio samples simulated vocal tract shapes that do not exist in people.

Our testing results not only confirmed our hypothesis but revealed something interesting. When extracting vocal tract estimations from deepfake audio, we found that the estimations were often comically incorrect. For instance, it was common for deepfake audio to result in vocal tracts with the same relative diameter and consistency as a drinking straw, in contrast to human vocal tracts, which are much wider and more variable in shape.

This realization demonstrates that deepfake audio, even when convincing to human listeners, is far from indistinguishable from human-generated speech. By estimating the anatomy responsible for creating the observed speech, it’s possible to identify the whether the audio was generated by a person or a computer.

Why this matters

Today’s world is defined by the digital exchange of media and information. Everything from news to entertainment to conversations with loved ones typically happens via digital exchanges. Even in their infancy, deepfake video and audio undermine the confidence people have in these exchanges, effectively limiting their usefulness.

If the digital world is to remain a critical resource for information in people’s lives, effective and secure techniques for determining the source of an audio sample are crucial.

Logan Blue, PhD student in Computer & Information Science & Engineering, University of Florida and Patrick Traynor, Professor of Computer and Information Science and Engineering, University of Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.