Five amazing extinct creatures that aren't dinosaurs

Jumping the shark Dmitry Bogdanov, CC BY-SA

The release of Jurassic World has reignited our love for palaeontology. Many of us share a longing to understand the dinosaurs that roamed the Earth long before we arrived. But palaeontology is a discipline much broader than this.

Dinosaurs dominated the land for 135 million years, but what happened during the rest of the Earth’s 4.6 billion-year history? The role of palaeontologists past and present has been to unravel the mysteries of life on Earth, and in doing so they’ve found a lot more than just dinosaur bones.

1. The spiky-backed ocean dweller

Right side up? Natural Math/flickr, CC BY-SA

Its age means it falls into the geological Cambrian period, a pivotal moment for all life on Earth when complex lifeforms started to rapidly evolve. When originally described, Hallucigenia was first thought to have walked along the ocean floor on spiny legs and used tentacles on its back to catch food. Palaeontologists also argued over which end was its head.

But when a similar fossil was found in China, Hallucigenia was re-examined. Palaeontologists then discovered that its “legs” were actually protective spines on its back, and the tentacles formed two rows on its underside enabling it to “walk”. Researchers are still debating many of the features of Hallucigenia today, more than 100 years after it was discovered.

2. (Almost) the first fish out of water

Best foot forward Nobu Tamura, CC BY-SA

100 million years on from Hallucigenia, aquatic habitats were thriving, but life on land was still in its earliest stages. Tiktaalik, part fish, part four-legged animal, is believed to be the first creature to develop characteristics that would help animals move out of the water and on to land.

It had gills, fins and scales like a fish, but also evolved features such as a flexible neck and a reptile-like head and lungs, beneficial for life on the ground. Fossils also show Tiktaalik had long fins that acted as legs, meaning it could “walk” along riverbeds as well as swim.

3. The giant Scottish scorpion

Sting in the tail Nobu Tamura, CC BY

Pulmonoscorpius kirktonensis, a 70cm-long scorpion, lived in what we now know as Scotland 340 million years ago. At a length greater than that of the average pet cat, this terrifying creature used its tail to catch and kill its prey.

4. The spiral-lipped shark

Giving some lip Dmitry Bogdanov, CC BY-SA

Helicoprion, a shark-like fish alive during the Permian (290 million years ago), had a rather unique dental structure. With a face that baffled palaeontologists for years, this creature had a lower jaw made up of a spiral of teeth, known as a tooth-whorl.

Modern sharks are able to lose and replace their teeth, but Helicoprion kept them all, with older teeth hidden within the inner layers of the tooth-whorl. When it caught its prey (most likely relatives of the squid), it would close its mouth and rotate its tooth-whorl to shred its catch.

5. A tiny, drunk horse

Gone to that big horsey ring in the sky Daderot

The Messel Oil Shale, once a volcanic lake in Germany, has plenty to offer the world of palaeontology. Eurohippus messelensis, was a miniature horse (the size of a modern day fox) originally thought to have died from eating fermented berries and in a drunken stupor, fallen into the lake. It’s now believed the 47 million year old horse actually died from inhaling toxic gas occasionally released from the depths of the lake.

But the misfortune continues, as it was later discovered that the horse was pregnant. Palaeontologists used high-resolution microscopes to identify the bones of a foal within the adult Eurohippus, improving our understanding of foetal development in these animals.

Palaeontology is a career firmly seated on many childhood wish-lists alongside movie stars and astronauts, and rightly so. But it’s important to remember there’s a lot more to palaeontology than the dinosaurs. This list is just the start.

After years of conflict, mega project could help scientists decipher the brain

Even if we could simulate the brain, could we make sense of it? Betty Lee, CC BY-NC-ND

They said it was crazy – and in truth the European Commission’s billion-euro plan to build a computer model of the human brain appears to have been too ambitious. But after years of controversy and dispute, many neuroscientists believe that the Human Brain Project may no longer be doomed to failure.

Not only have the governance arrangements of the project been overhauled, the scientific programme is now refocused as an information hub for neuroscientists, providing researchers with computational tools and mathematical models for understanding brain processes.

The Human Brain Project was originally conceived by the charismatic neuroscientist Henry Markram, who famously outlined his vision to build a brain in a supercomputer back in 2009. This idea rapidly gained momentum and in 2013, Markram became director of an EU flagship project aiming to “integrate research data from neuroscience and medicine in an effort to understand the human brain by simulation”.

The project would be funded by a billion euros spread over a decade and would, it was hoped, help support diagnosis and therapy of neurodegenerative diseases such as Alzheimer’s.

Henry Markram – visionary or verbose? Playing Futures: Applied Nomadology/Flickr, CC BY-SA

But the response from many neuroscientists was less than enthusiastic. One researcher dismissed the stated aim of creating a computer model of the human brain as “crazy” while another, more bluntly, described it as “crap”.

In July 2014, several hundred scientists eligible for funding through the project criticised it in an open letter. Among them were two of last year’s Nobel laureates in physiology or medicine: Edvard and May-Britt Moser.

The letter stated that, if the signatories' concerns were not met, they would boycott the effort. It blasted the project’s “narrow” approach and accused it of “substantial failures” in openness and governance. In response, a mediation process was set up and in March this year the committee submitted its report, agreeing with much of the criticism. It concluded that the project, while “visionary” and “science-driven”, was “overly ambitious in relation to the simulation of the whole human brain and in relation to potential health outcomes”.

However, the committee fully backed the project’s continued development of neuroinformatics platforms, which provide scientists with computational tools and mathematical models for understanding brain processes. It also accepted the concerns about transparency and governance thus far and agreed to follow the recommendations by the researchers.

The project’s board of directors has approved a number of these, including the abolition of the three-person executive committee Markram was on. The report also catalysed a rewriting of the “framework partnership agreement”. This crucial agreement will outline the extent to which the project’s goals will change and is due to be released later this year.

Building blocks

One of the reasons that the project is so controversial is its “bottom-up” approach to brain simulation. This involves taking the simplest building blocks of a complicated system, simulating each part mechanistically and watching as more complex behaviour emerges. In many scientific disciplines this has had big success but when applying this to the brain it immediately runs into problems.

For example, how simple are the building blocks you should start with? Atoms are extremely fundamental but you’d need to take account of a hundred trillion of them just to simulate a single neuron. How about neurons? There are around a hundred billion of them in the human brain and simulation at that level might be possible, but in that case you may well be missing out key information.

Moreover, we lack sufficient experimental data on how neurons connect to each other and how this changes dynamically with time. And finally, even assuming the simulation is successful, it’s not clear we have the theoretical understanding to make sense of what the results mean.

Neurons in a mouse brain. mark Miller/Flickr, CC BY-SA

That’s not to say that bottom-up brain simulation isn’t worthwhile. Instead, it is accepting that for many neuroscientific problems there are better ways of understanding what’s going on, such as through top-down models and sophisticated techniques for data analysis. And so we shouldn’t be narrowing the approach, we should be broadening it, enabling neuroscientists to run simulations relevant to them.

This is where the Human Brain Project will best fit in. Christof Koch, chief scientific officer of the Allen Institute for Brain Science, has described neuroscience as a “splintered field”. Laboratories across the world are heading off in different directions with a dizzying variety of tools, animal species, and behaviours, amounting to a “sociological Big Bang”.

The Seattle-based Allen Institute has aimed to address this problem with standardised large-scale databases such as a gene expression atlas of the entire mouse brain. But neuroscience still lacks an “information hub” where data from across the world is organised and analysed in a consistent way. It is imperative that the Human Brain Project works towards becoming this hub so that new insights into this data can be uncovered.

Positive signs

That the project is adapting to address the concerns raised is undoubtedly a positive sign. But implementation is key. If the overhyped bottom-up brain simulation remains the centrepiece of the project it remains likely to be mired in controversy. But if instead, it keeps to the new track of creating necessary tools to handle vast amounts of neuroscientific data, it may well prove to be popular.

With this view, it has the potential to complement another mega project, the White House BRAIN initiative announced by the Obama administration in 2013. This aims to radically improve the technologies used to record data in the brain. If the Human Brain Project can radically improve the technologies used to analyse and model this data, the controversy could one day turn into congratulation.

Driverless cars are a catch 22: we do none of the driving, but take all of the responsibility

Keep your eyes on the road. Paleofuture

The utopian vision of the motor vehicle is an onboard autodriver much like that of the autopilot in aircraft which takes over the task of driving, freeing up the human driver to work, rest or play. This is becoming an engineering reality, with technological achievements rapidly approaching those of aircraft autopilots.

Yet while technology can certainly support some of our driving shortcomings, the hands-off vision of the autopilot for cars is marred by concerns about the situational awareness of the driver, how they would take control in case of an emergency and, while the car is still equipped with steering wheel and pedals, the extent to which the human driver will be responsible for the vehicle. And so it appears a “Catch 22”: drivers are no longer required to drive, but are still required to monitor the computer that drives for them.

It’s true that driverless cars are likely to be highly reliable in most situations, most of the time. But this reliability cannot be guaranteed all of the time and the auto-driver will encounter situations that the programmers and engineers have not anticipated. The trouble is that highly reliable automatic pilots will lead even the most observant driver’s attention to wander – once the novelty has worn off, it will be like watching paint dry and decades of research have shown that humans are extremely poor at maintaining extended periods of vigilance.

So how can working, reading, using email and the internet, the envisaged benefits of driverless cars, be reconciled with the need to keep an eye on the vehicle? The truth is nobody really knows.

I’ve researched vehicle automation for 20 years and it’s clear that, in an emergency humans, are more effective than automatic pilots. Up to a third of drivers of automated vehicles did not recover from emergencies in our simulator studies, and I have witnessed human drivers fail to intervene when automatic systems fail. The concern is that driver and driverless act at cross purposes, with the driver believing the automated vehicle is in control of the situation when in fact it has not.

My research has shown that if we design the vehicle to provide continuous feedback to the driver – analogous to a chatty co-driver – we can reduce this kind of error substantially, but not completely. Drivers of automated vehicles take, on average, five times as long to apply emergency braking than manual drivers.

On the other hand, if drivers are forced to continually monitor the vehicle’s automation this does not diminish their workload at all. In fact, we know this monitoring cannot be sustained, with driver attention falling with increasing automation.

When required suddenly, a human driver is ill-prepared to take control from the vehicle. This means we’re asking the impossible, by taking away control from the driver while leaving them with all the accountability. Lessons learned from the introduction of aircraft automation appear to be going unheeded.

It seems drivers of the future will be held responsible for something over which they have little or no control. Not that this means we should stop researching and building automated vehicles – quite the opposite. We need to learn and apply the lessons of automation as used elsewhere (such as aviation) to the problems of driverless vehicles.

This means designing vehicle automation in such a way that engages the driver and accommodates gradual hand-over and hand-back processes in order to successfully integrate human drivers into the system. We need a chatty co-pilot, not a silent auto-pilot.

In the event of robot apocalypse, just wait for a system crash

Robots! Making easy tasks look difficult. DARPA

Do you find yourself worried by the implications of Humans, Channel 4’s new drama about the exploits of near-human intelligent robots? Have you ever fretted over the apocalyptic warnings of Stephen Hawking and Elon Musk about the threat of superintelligent artificial intelligence? Have your children ever lay wide-eyed thinking about robot drone armies, such as those in Marvel’s film Avengers: Age of Ultron?

But if you find this creepy or have answered “yes” to any of these questions, you should immediately watch footage from the recent DARPA Robotics Challenge.

The DARPA Robotics Challenge is unusual in that it requires bipedal robots to do only the everyday things humans do: getting out of cars, walking into buildings, climbing stairs, negotiating uneven ground, turning valves, and picking up and using a saw to cut a hole in a wall. Hardly skills worthy of Ninja Warrior UK, but to KAIST, the winning team which walked away with the US$2m prize, and all those that failed, it was tough.

The winning robot completed only eight of the nine tasks, many of which would not trouble a seven-year-old. In fact, all but three teams failed the rather basic challenge of getting out of a stationary car, even with no door to complicate matters.

Even simple things are hard

What makes this competition footage so funny is how mercilessly it punctures the myth of the supreme power of artificial intelligence. We’ve evolved – over millions of years – to live and move in the physical world. As such we tend to discount the sophistication necessary to do the simplest of things. We falsely ascribe simplicity to acts such as walking through doors and picking up power tools because we find them simple. In the same way, we find certain things – such as multiplying 82 by 17 in our heads – difficult, even though for a computer/machine this is basic.

This creates a cognitive bias: if a machine can do something we find hard, we tend to assume it can easily do the simple stuff as well. Like all biases, this isn’t necessarily true.

We also assume a generality bias: since we can do many different things, we assume that a machine which can do one of them can do the others as well. This conflicts with the way computing research happens, which tends to focus on getting a computer to do one thing (partly because there’s no way to easily research “doing everything”). Machines have grown up in a completely different environment from us, so it shouldn’t come as a surprise they are good at doing different things.

Science fiction still … fiction

The notion that “artificial intelligence” equals “computers (or non-humans) are people” stretches back to antiquity. The poet Ovid’s character Pygmalion falls in love with a statute he has carved, Galatea, so lifelike it (she) comes alive. The idea is still a powerful one. Hollywood, and fiction in general, loves robots. From The Terminator to A.I., from Her to Humans, a “machine person” is an easy trope with which to explore complex issues of embodied identity.

In fact robots (the Czech word for “worker”) emerged not from research but from the 1920’s Czech writer Karel Čapek’s play R.U.R., which played upon universal fears of the servants – the working class – taking over. So it’s the equivalent of fearing what would happen if Orcs took over London, or how to cope with a zombie apocalypse: it’s fun, but unrelated to reality.

Capek’s rise of the robots.

Computers aren’t people

Computer scientist Jaron Lanier says the problem lies with the myth of computers as people, which survives due to a domineering subculture in the technical world. Visions of robots drive researchers on, generating new achievements that feed back into myth-making in fiction, which in turn encourages funding and further research.

In the 1960s, the film 2001: A Space Odyssey saw full artificial intelligence as only ten or 20 years away, a figure which has remained remarkably constant from all experts before and since. Our reactions are channelled by the computer as people myth, pushing us to think of it as a choice between stopping Skynet, Terminator-style, or welcoming our new mechanical overlords. At its heart, these fears expose the parallel and competing visions for what computing should be.

Early AI pioneer Alan Turing strongly articulated the computer as the beginnings of a synthetic human being: his Turing test defines artificial intelligence as one that’s indistinguishable from a human being.

On the other hand Douglas Engelbart pioneered an alternative vision: computing as a means to “augment human intellect” (Engelbart also gave us the mouse, bitmapped screens, and the graphical user interface). The closest Hollywood ever got to Engelbart’s vision was Neil Burger’s film Limitless, in which a pill allows humans to use the potential power of their entire brain. But as mere augmentation doesn’t raise the kind of philosophical questions demanded by fiction it’s unlikely to create a mythology juggernaut.

If you’re worried about AI and the rise of the machines, Lanier points out that while computer power has improved reliability has not – the time between failures hasn’t changed much in the last 40 years, so a conquered human race need wait only until the next system crash. And in any case, if DARPA’s challenge is anything to go by, shutting your door seems to be very effective at keeping robots out.

We can build remote-controlled rescue robots, but what's coming next is even more exciting

Taking the wheel DARPA

Robots could one day save your life. That’s the hope of those who involved in the DARPA Robotics Challenge, which recently came to an end in California recently.

More than 20 teams from around the world built or programmed and then, importantly, controlled a robot through a series of eight tasks in a simulated disaster zone. The challenge, created in response to the Fukushima Dai-ichi nuclear disaster, required the robots to drive a car, open a door, cut a hole in a wall, traverse some rubble and climb some stairs, all in under an hour. The aim was to spur the development of robots that could perform search-and-rescue missions in locations too dangerous for humans to enter.

A team from the Korean university KAIST won the challenge by completing all the tasks in under 45 minutes. While the competition demonstrated what robotics can now do, it also showed just how challenging it is to build a machine that performs what are relatively simple tasks in human terms.

Knock knock DARPA

In robotics, there are two types of control. First there is the “low-level” control needed to coordinate the actions of motors. For example, the speed of wheels or the movement of a joint. Then there is the “high-level” control needed to carry out specific goals using the whole system. For example, picking something up then carrying it to a target.

The ideal outcome of the DARPA challenge would have been a robot that could complete the challenge autonomously, without any human control. In fact, all of the high-level control was performed by human operators (via remote control). Some of the lower-level control was also done in this way, including, in some cases, deciding where the robot should place its feet when walking.

The reason high-level autonomy was not more prominent in the competition was the incredible difficulty of creating and operating the hardware needed to perform the tasks. Most teams chose robots with a human-like body shape – although the winner extended human capabilities with wheeled knees and rotating waist – even though the rules didn’t limit them in this way. In order for a humanoid robot to perform an action with one part of its body, the rest of its body must also be coordinated to counteract the forces involved.

For example, for a robot to push a power tool through a wall it must generate enough force to push while also altering its balance to prevent itself from falling over due to the recoil. This kind of coordination happens in a very high-dimensional space, meaning parts have to moved in many different directions. Humanoid robots may have more than 30 joints that can be moved simultaneously, a complexity that is very hard to model computationally.

This difficulty meant that the majority of effort in the DARPA challenge went towards low-level control algorithms. Although this may be disappointing to those interested in fully autonomous robots, developing low-level control was actually one of the main intentions of the competition. Robust high-level autonomy can only be created once the lower-level systems are robust and reliable.

Robot to the rescue DARPA

The difference is striking if you compare DARPA’s Robotics Challenge to its Urban Challenge, in which teams competed to deliver self-driving cars. In this competition, the physical engineering tasks were mature and well-understood – we’ve been building working cars for more than 100 years. The result was a highly impressive display of autonomy as the engineers were able to concentrate on high-level control software.

The Robotics Challenge should be seen as just the beginning. As the physical bodies and low-level control software of humanoid robots improve, scientists at the interface of artificial intelligence and robotics can start to create the first complex autonomous behaviours for large-scale humanoids. So, when the next competition happens, we may see these fantastic machines thinking for themselves a little more.

Hot weather and CO2 made the tropics a no-go zone for early plant-eating dinosaurs

The hot dry conditions at low latitudes during the Triassic meant that only small carnivorous dinosaurs (background) could survive there. Victor Leshyk, Author provided

One of the missing links in our understanding of the Triassic Period – between 252 and 201m years ago – is why there were so few dinosaurs in the tropics. Our research suggests that volatile, hot dry weather and high carbon dioxide levels are to blame. The research can even tell us something about the challenges we humans face from climate change.

The first dinosaurs emerged just over 230m years ago, during the Late Triassic Period. This was one of the most dynamic intervals in the Earth’s history, with large-scale climatic changes. The planet was also coping with the recovery from one mass extinction and the onset of another. At the same time, many of the animal groups that dominate today’s terrestrial ecosystems – including frogs, salamanders, turtles, lizards, and mammals – were emerging.

One of the major unresolved questions of dinosaurs’ rise to dominance is why large-bodied herbivorous dinosaurs were missing from the tropics, despite the fact that they lived in higher latitudes. Small, carnivorous dinosaurs, however, populated the entire planet – including in the tropics.

Of crushing importance

To tackle the issue, our international team investigated sedimentary rocks from a number of places around the Upper Triassic Chinle Formation in New Mexico, including the fabled “Ghost Ranch”, whose multicoloured cliffs inspired artist Georgia O'Keeffe’s landscapes. However, they also preserved North America’s most extensive fossil evidence of the rise of dinosaurs and their competitors.

At the time that these sediments were deposited by rivers and streams, the areas were very close to the equator at about 12°N latitude (northern New Mexico is at 36°N today). This is the same as the current latitude of the southernmost tip of India.

Chinle Badlands via wikimedia

By excavating unweathered rock in the field and then crushing samples in the lab we could separate the isotopes of carbon from fossilised organic matter in rocks using mass spectrometry. The isotopic ratios suggest that major rapid changes in ecosystem productivity and atmospheric CO₂ levels took place during the Triassic Period.

These rocks also preserve abundant fossil charcoal. Based on the amount of light reflected from fossil charcoal under a microscope, we could see that wildfires must have swept the landscape at the time. These would have continually changed the vegetation available for large plant-eating dinosaurs.

By extracting fossil pollen and spores from the sediments and by excavating and identifying vertebrate body fossils, we could also identify the plants and animals living in the area. Our data show that plant groups varied in abundance in connection with climate swings, and that the only dinosaurs present were rare, meat-eating theropods. As such, we conclude that large plant-eating dinosaurs were absent because there were not enough predictable food and water resources for them to thrive.

Our study suggests that the climatic effects of elevated CO₂, which were four to six times that of modern levels, drastically reshaped the environment and had profound consequences on the composition of ecosystems on land.

The discovery is not only important for understanding our past. Rapid climate swings and extremes of drought and intense heat driven by increasing atmospheric CO₂ levels have as much ability to alter the vegetation supporting modern human populations as they did for the large plant-eating dinosaurs in the Triassic.

These data therefore suggest there are potentially profound challenges to human sustainability in the future if we experience the high CO₂ conditions predicted to develop in the coming 100-200 years.

Jurassic World reviewed by a dinosaur expert: it isn't faithful to science, but so what?

Lunchtime at the super-croc enclosure Universal

Jurassic World is shaping up to be a monster success, to say the least. The fourth instalment of the Jurassic Park series has become the first film to take more than $500m (£330m) in its first weekend (Harry Potter and the Deathly Hallows set the previous record with $487m in 2011). And there seems a good chance this will continue: audience and critics' ratings on Rotten Tomatoes are north of 70%, good numbers at a time when ever-more-cynical moviegoers have endless summer blockbuster CGI-spectacles to choose from.

Yet one group seems resolutely determined not to catch Jurassic fever. Step forward, my fellow palaeontologists. Of those who have been asked their opinions on Jurassic World, some have been positive, others lukewarm, but the vast majority have spawned articles along the lines of “palaeontologists slam Jurassic World”.

I suppose it’s a headline that gets people clicking. But they make palaeontologists look like grouchy whiners, disparaging a film because of nitpicky inaccuracies in the dinosaurs. The raptors hold their hands wrong, the Mosasaur is too big, the T.rex moves too fast, the colours of the dinosaurs look too much like crocodiles and not enough like birds. And that’s just a taster.

The contradiction is that palaeontologists are usually some of the giddiest, happiest, most enthusiastic people I know. We study the most fantastic, stupendous creatures that ever lived in the 4.5bn-year history of our planet, so cynics need not apply for our jobs. We love our dinosaurs, love talking about them, and love it when we can share our passion with others.

The power of Jurassic Park

Personally I think Jurassic World is a great thing for my discipline. I saw the film this weekend and loved it. It was a good monster movie. I was able to suspend my paleontologist’s brain for a few hours, forget about the scientific flaws, and have fun.

I kept thinking back to 22 years earlier, when I saw the original Jurassic Park in cinemas in 1993. I was a nine-year-old kid, frittering away a humid summer in the mid-western US, spending long days playing baseball with my neighborhood friends. I didn’t care much for science. It was my least favorite class in school. But I remember being awed by the dinosaurs in Jurassic Park. I didn’t become obsessed with them right away – that came about five years later – but the film brought science to life in a way that no book, museum or classroom lesson ever did.

For youngsters of this generation, Jurassic World will be a cultural milestone – just like in 1993. It will get people talking about dinosaurs, thinking, reading, doing web searches about them, asking their teachers, going to see them in museums. In my eyes, anything that gets people thinking about the world around them, the deep history of our planet, and the relationship between man and nature is a good thing.

Jurassic Park and science

Movies like Jurassic World can also have a great influence on science and scientists. The first Jurassic Park was probably the single most important thing that happened to palaeontology over the past half century. It inspired a huge number of people to study dinosaurs. Many palaeontologists of my vintage (aged 25-35) will say that it set them on their career path. This will undoubtedly be the case with Jurassic World as well, and it may even boost the take-up of other sciences. Maybe the person who eventually cures AIDS, discovers a new type of renewable energy or solves world hunger will have been hooked into science by Jurassic World.

The first Jurassic Park also led many museums and universities to hire dinosaur experts, and catalysed a burst of funding for palaeontological research. Some of the proceeds from Jurassic Park even went to fund original science, through the Dinosaur Society and the Jurassic Foundation. The latter is still active and bankrolled two of my projects as a student: a trip to China to describe the wacky meat-eater Monolophosaurus and fieldwork in Portugal discovering and excavating the “super salamander” Metoposaurus. My career may have never gotten off the ground if it wasn’t for these grants. If any of the executives from Universal or Amblin happen to be reading, I really hope that some of the staggering box office haul from Jurassic World can be pumped into research this time around.

Movies and scientific accuracy

Yes, some of the scientific inaccuracies in Jurassic World are a little annoying. I wish the dinosaurs were feathered, for instance, as we know many would have been from spectacularly preserved fossils. But Jurassic World is not a science documentary, and we shouldn’t expect it to be (unlike the recent T.rex autopsy that I was involved in).

This is entertainment. They make it very clear that the dinosaurs they feature are movie monsters quite unlike anything that actually lived during the Jurassic period. The film’s villain, Indominus rex, is a genetic mash-up of tyrannosaur and raptor and all kinds of other stuff. To even begin talking about this creature’s scientific accuracy would be like a bat specialist discussing the fine points of Batman’s anatomy and biomechanics.

Indominus Rex says aaah. Universal

To colleagues who have been bugged by all the inaccuracies, I ask: does it really matter that many people will think dinosaurs were a little bigger or toothier or scalier than they were in real life? Does it matter that the original Jurassic Park incorrectly showed T.rex sprinting at highway speeds or Velociraptors that were larger than the real thing? To those of us who study dinosaurs for a living, these matters may seem important, even existential. In the grand scheme, they’re noise. When a film has the potential to both inspire and entertain people, to the point of changing lives, I say, bring on the sequel.