How a dam in Volgograd has almost killed off the caviar fish

Volgograd. Image: Getty.

Yesterday, the floodlights were turned on at the newly built Volgograd Arena for the first World Cup match to be held there, between England and Tunisia.

But, as an expert in the illegal caviar trade, I know Volgograd because the energy powering those same floodlights will be generated by the nearby Volgograd HydroElectric Station. This is the largest hydro power plant in Europe, and a dam which has played a pivotal role in driving sturgeon – the source of the iconic Russian delicacy, black caviar – to the brink of extinction.

The 725m long and 44m high concrete giant sits about 20km outside the city centre and dissects Europe’s longest and most powerful river, the Volga. Construction began in the 1950s, as part of post-war industrialisation initiatives known as the “Great Construction Projects of Communism”. This in a city which during the World War II – when it was known as Stalingrad – was the site of one of the bloodiest battles in history. The dam was completed in 1961 and today produces around 12 billion KW-hours of energy a year.

The Volga flows 3,500km northwards from the Caspian Sea. One of its tributaries even reaches Moscow. Image: Kmusser/Wikimedia Commons.

The station was groundbreaking in both scale and energetic output. For a few years, it may have been the single largest power plant in the world. But despite the benefits to the climate of “clean” hydro-powered energy, the Volgograd station has been particularly damaging for the sturgeon species that attempt to migrate from the Caspian Sea to reproduce in the upper reaches of the Volga.

Russia’s pride

Sturgeon, affectionately referred to as “Tsar Fish” are perhaps more critically endangered than any other group of species on the planet. There are 27 species in all, of which four are found in the Volga: Russian sturgeon, sterlet, stellate, and the beluga which is famous for producing the world’s finest caviar.

These fish are often described as “living fossils”. They’ve been around since dinosaurs walked the earth 150m years ago, and individual fish can live for more than a century. Sturgeon have attained a cultural and historical significance in Russia and are a source of national pride.

But socioeconomic change in Russia has been disastrous for these fish. Their rivers have been polluted, fragmented and dammed and this – along with overfishing and poaching for caviar – has caused populations in the Volga to plummet by 90 per cent since 1970.

A slow reproductive cycle means numbers cannot recover quickly. Females do not carry eggs annually, they take many years to reach sexual maturity and, of the 250,000 - 400,000 eggs they can release at one time, only two or three fish will survive.

Damming and decline

As the last of eight hydroelectric works in the Volga-Kama cascade of dams, the Volgograd Hydroelectric station is the first barrier sturgeon migrating upstream from the Caspian Sea will encounter. In theory sturgeon can pass the dam thanks to a hydraulic fish-lift in the original design. However, it is not clear whether the lift is still operational and, even if it is, its benefits have been counteracted by further dams built upstream. Even if fish do manage to cross the dam, the return journey can prove fatal, as it often requires passing through turbines that can weigh as much as a 747 aeroplane.

The Volgograd Hydroelectric station not only blocks sturgeon migration, but alters the natural flow and temperature of the river. Sturgeon are very sensitive and rely upon signals such as flow speed and temperature to determine when and where to reproduce. Therefore, the dam is said to have directly reduced the spawning grounds of sturgeon from 3,600 hectares to only 430 hectares. For beluga sturgeon in particular, 90 per cent of their natural spawning grounds have disappeared as a result of the Volgograd dam.


An illegal caviar trade is flourishing

It is undeniable that the Volgograd station has played a part in the demise of the Russian caviar industry. Due to rapidly declining wild sturgeon populations, Russia banned commercial sturgeon fishing and black caviar exports in 2002. Now, Russia allows just 9 tonnes of the delicacy to be sold on the domestic market annually, produced by a few government-regulated fish farms. These farms cannot come close to producing enough caviar to meet Russian, let alone worldwide, demand. As a result an illegal trade meets the shortfall, with reports suggesting that 250 tonnes of illegal caviar are produced each year.

Unsurprisingly then, almost all migrating spawners are poached below the Volgograd dam, and a particular hotspot is Russia’s so-called “Caviar Capital”, Astrakhan, around 400km downstream from Volgograd. There, illegal poaching of sturgeon and trade in caviar is said to be rampant – and beluga caviar fetches up to $10,000/kg. This has devastating ecological impacts – when sturgeon are removed at this point in the river the fish have not had the chance to reproduce.

Save the sturgeon

The situation looks bleak. Despite Russia releasing 50m or more sturgeon raised in hatcheries, there is sparse evidence that restocking is successful. In fact, despite such releases there has been an overall decline over the past decade. And it seems counter-intuitive to release millions of juvenile sturgeon when the Volgograd dam still prevents their migration and spawning – and given that downstream poaching is rife. Greater enforcement against poaching would be a good start, along with assertive efforts to help fish move along their natural rivers. (Something similar has helped shortnose sturgeon in the US.)

The ConversationSo, for football fans visiting Volgograd for the World Cup the best way to help sturgeon is to avoid the lure of purchasing any black caviar as souvenirs. But, if you are that way inclined, make sure to stick to customs regulations and try your utmost to ensure the caviar is from reputable farmed sources.

Hannah Dickinson, PhD Researcher in Wildlife Trafficking, University of Sheffield.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.