They have a reflective silver metallic appearance which is achieved through thin film interference within layers of chitin. These layers of the chitin coating are chirped (in layers of differing thicknesses), forming a complex multilayer as each layer decreases in depth; as the thickness changes, so too does the optical path-length. Each chirped layer is tuned to a different wavelength of light. The multilayer found on C. limbata reflects close to 97% of light across the visible wavelength range.
The rain forest of Costa Rica where C. limbata lives has water suspended from leaves at ground level. Light is refracted in different directions, and it allows metallic beetles to fool predators.
In case anyone else was wondering how this could be effective camouflage. I wonder what happens if the predators are thirsty though.
it’s called dielectric mirror, this type of mirror can be made to be extremely effective over narrow range of wavelengths and it’s used in lasers for this reason. this is also the only way to manipulate extreme UV (13nm tin source, used in EUV semiconductor manufacturing)
similar effect is responsible for blue color of some butterflies and feathers that don’t contain blue pigment
Like, how does nature know to manipulate quantum states and electromagnetism to achieve this result. Trial and error / random mutation /survival etc just doesn’t explain why it happens.
That’s a staggering amount of non trivial science/math stacked layer by layer. On a beetle.
For those giving me replies:
I’m asking more of a philosophical question.
Why are these things there in the first place.
I’m not asking his evolution works.
I know how evolution works. I state that in my comment. Random mutations, survival, etc. Etc etc. Is how.
Why.
To say it is because it is isn’t answering my question.
How and why does/could a random mutation play on the laws of physics in a meticulously optimized way to benefit itself. What is the method that would cause something to randomly say, today I think I’m going to make cells that act in such a way as to make me appear reflective, or transparent, or mimic the environment.
The result is because of evolution. OK. Good.
How do these processes which use quantum mechanics and wild optical physics become an innate part of nature to begin with.
It’s always funny seeing arguments like this as someone with a computer science education. A lot of people act like you can’t have anything complex unless some intelligent being deterministically writes a lot of if-else statements to implement it, which requires them to know and understand in detail what they are implementing at every step.
But what people don’t realize is that this is not how it works at all, there are many problems that are just impractical to actually “know” how to solve yet we solve them all the time, such as voice recognition. Nobody in human history has ever written a bunch of if-else statements to be able to accurately translate someone’s voice to text, because it’s too complicated of a problem, no one on earth knows how it works.
Yet, of course, your phone can do voice recognition just fine. That is because you can put together a generic class of algorithms which find solutions to problems on their own, without you even understanding how to solve problem. These algorithms are known as metaheuristics. Metaheuristics fundamentally cannot be deterministic, they require random noise to work properly, because something that is deterministic will always greedily go in the direction of a more correct solution, and will never explore more incorrect solutions, whereby an even better solution may be beyond the horizon of many incorrect ones. They also do have to be somewhat deterministic as well, because you need some greed or else the random exploration would be aimless.
A simple example of a metaheuristic is that of annealing. If you want to strengthen a sword, you can heat up the metal really hot and let it slowly cool. While it’s really hot, the atoms in the sword will randomly explore different configurations, and as it cools, they will explore less and less, and the overall process leads them to finding rather optimal configurations that strengthen the crystaline structure of the metal.
This simple process can actually be applied generally to solve pretty much any problem. For example, if you are trying to figure out the optimal route to deliver packages, you can simulate this annealing process but rather than atoms searching for an optimal crystaline structure, you have different orders of stops on a graph searching for the shortest path. The “temperature” would be a variable that represents how much random exploration you are willing to accept, i.e. if you alter the configuration and it’s worse, how much worse does it have to be for you to not accept it. A higher temperature would accept worse solutions, at very low temperatures you would only accept solutions that improve upon the route.
I once implemented this algorithm to solve sudoku puzzles and it was very quick at doing so, and the funny thing is, I’ve never even played sudoku before! I do not know how to efficiently solve a sudoku puzzle, I’ve honestly never even solved one by hand, but with sudoku it is very easy to verify whether or not a solution is correct even if you have no idea how to find the solution and even if finding it is very difficult, verifying it is trivially easy. So all I had to do is right the annealing algorithm so that the greedy aspect is based on verifying how many rows/columns are correct, and the exploration part is just randomly moving numbers around.
There are tons of metaheuristic algorithms, and much of them we learn from nature, like annealing, however, there’s also genetic algorithms. The random exploration is done through random mutations through each generation, but the deterministic and greedy aspect of it is the fact that only the most optimal generations are chosen to produce the next generation. This is also a generic algorithm that can be applied to solve any problem. You can see a person here who uses a genetic algorithm to teach a computer how to fly a plane in a simulation.
Modern AI is based on neural networks, which the greedy aspect of them is something called backpropagation, although this on its own is not a metaheuristic, but modern AI tech arguably qualifies because it does not actually work until you introduce random exploration like a method known as drop out whereby you randomly remove neurons during training to encourage the neural network to not overfit. Backpropagation+dropout forms a kind of metaheuristic with both a greedy and exploratory aspect to it, and can be used to solve just about any generic problem. (Technically, ANNs are just function-approximators, so if you want to think of it as a metaheuristic, the full metaheuristic would have to include all the steps of creating, training, and then applying the ANN in practice, as a metaheuristic is a list of steps to solve any generic problem, whereas an ANN on its own is just a function-approximator.)
Indeed, that’s how we get phones to recognize speech and convert it to text. Nobody sat down and wrote a bunch of if-else statements to translate speech into text. Rather, we took a generic nature-inspired algorithm that can produce solutions for any problem, and just applied it to speech recognition, and kept increasing the amount of compute until it could solve the problem on its own. Once it solves it, the solution it spits out is kind of a black box. You can put in speech as an input, and it gives you text as an output, but nobody really even knows fully what is going on in between.
People often act like somehow computers could not solve problems unless humans could also solve them, but computers already have solved millions of problems which not only has no human ever solved but no human can even possibly understand the solution the computer spits out. All we know from studying nature is that there are clever ways to combine random exploration and deterministic greed to form processes which can solve any arbitrary problem given enough time and resources, so we just implement those processes into computers and then keep throwing more time and resources at it until it spits out an answer.
We already understand how nature can produce complex things without anyone “knowing” how it works, because we do that all the time already! You do not need a sentient being to tell the beetle how to evolve to fit into its environment. There is random exploration caused by genetic mutations, but also a deterministic greedy aspect caused by “survival of the fittest.” This causes living organisms to gradually develop over many generations to something fit for its environment. And life has had plenty of time and resources to become more suited to its environment, life has been evolving for billions of years, with the whole resources of the planet earth and the sun.
There doesn’t need to be any knowledge involved. It happens, because it works. Neither the beetle nor evolution itself “know” anything about quantum physics. The beetle is just a beetle and evolution is not even an entity that has any agency, it’s just a process that’s happening and that leads to remarkable results over time.
This is just one more example for the old discussion how complexity can develop through evolution. The classic example is the eye of vertebrates. Read up on that, if you’re interested in that discussion.
There’s nothing “special” in the way you imagine about quantum phenomena. They are complicated to describe mathematically because we are limited to a fundamentally imperfect set of symbols, but they are not complicated to obtain the requirements for.
All chemical and light-interaction processes use quantum phenomena if you dig enough into how they work, and it’s especially clear on a smaller scale. If you just make something thin enough, it will start displaying quantum effects, but there is nothing that complicated about “thin”.
They’re not manipulating wavelengths with great complexity, they’re just growing a really thin layer on their shell.
It doesn’t. Step by tiny step random mutations that grant a fraction of a percentage of survivability, multiplied across millions of generations.
random mutation /survival etc just doesn’t explain why it happens.
Yes it does, assuming there’s a benefit to be had along the way. It doesn’t have to be as effective as it is today to confer some tiny amount of benefit. It just has to be better than those without the mutation.
The biggest element you are not accounting for is time. It takes an unfathomably long amount of time for the benefits of random mutation to shape a population.
That’s just assumed. (But it isn’t always. Check out the birds of Fukushima - another great example of evolution manipulating physics by using a mechanism that made their feathers bright to instead use that chemistry to prevent damage to DNA from radioactivity.)
What I’m stumped by is why evolution chose that particularly bizarre and complex method that is, as explained by OP, insanely involved with manipulation of wavelengths of light vs just getting bigger or growing more claws or something similarly simple, biologically speaking.
insanely involved with manipulation of wavelengths of light vs just getting bigger or growing more claws or something similarly simple, biologically speaking.
Nature did. That’s how speciation works. We’re just focusing on the shiny beetles because they grab our attention and the big dung beetles with big horns don’t.
Also, as far as evolution is concerned? There’s nothing insane about it, they’re all equally simple. You’re thinking of it from the perspective of industrialisation, and how tough it would be for us to manufacture such materials. That’s not the viewpoint evolution cares about, if it can be grown it obviously isn’t difficult to do.
How do these processes which use quantum mechanics and wild optical physics become an innate part of nature to begin with.
There are no quantum mechanics involved. And the physics are not wild, they’re the basic laws of physics. It’s only humans that assign difficulty and exoticness to these mechanisms because our technology base is incapable of reproducing it easily.
For every random genetic change that did something that turned out to be useful, there were countless ones that did nothing useful at all or were even counter-productive (to get a sense of how many “tries” there were, consider every time every beetle in the World tries to reproduce times how many eggs they lay times several random genetic changes per egg times millions or billions of years - we’re talking grains of sand in a beach level or even more, and this is just for one kind of creature that doesn’t even reproduce all that frequently - in things like bacteria there are so many reproducing so many times that we actually see evolution in action in a short time frame, for example with the growth of antibiotic resistance).
Then for all those random genetic changes that did something that turned out useful, there are only going to be some were that make enough of a difference in terms of increasing the survival of a beetle till reproduction and way more that didn’t make a difference.
You know what happen to all those quadrillions or whatever of tries that went nowhere? We’ll never know about them because the creatures in question are long dead (if their eggs were viable to begin with). We’ll only ever know about the random genetic changes which did work well enough to give reproductive advantages.
[There are actually a lot of cognitive falacies around how we perceive success because we only really get to know about what worked, not about the countless things that didn’t work. A good example is how most people pretty much only hear about Startups that made it big, yet for every Startup that does succeed enough to become widelly known there are tends or even hundreds of thousands that fail and we never hear about, so it might seem that Startups are generally successful when the reality is, in average, the very opposite]
Continuing on the Evolution story, if the previous part of the process worked based on the Maths of “trully insane large numbers”, at this point we add an effect akin to compounding interest: even if a genetic change adds a very small increase in reproduction for an animal - say, a beetle with a given random genetic change that did do something useful and gives it a 1% higher chance of successfully reproduce - as long as that trait gets passed down to the next generation, it means (rought) that all else be the same there will be 101 beetles born with that change for every 100 beetles born without it, for every reproductive cycle. This might seems little but as I said it compounds, so for example after 71 generations that will have grown to 200 for every 100 and it will keep growing.
This is how even a random genetic change that gives even just a tiny increase in success of living till reproduction and reproduction itself will, given enough time, come to dominate a population.
And then all those slightly different beetles keep on having the random genetic changes happen (the first part of the process) and those additional changes that did work and gave a tiny bit more success over that ones with just the original change will get the compounding part of the process, so those are the ones for whom there are more and more individuals, to which in turn the same process applies.
TL;DR (but you should)
A beetle with a random genetic change that affected its shell that makes it every so slightly harder to spot for predators in a place that has lots of water droplets on leaves will have more descendants than the rest. Some of those will randomly get additional changes that make that effect even more successful at making the beetle harder to detect for predators thus having even more descendants than the rest. Amongst those, the ones with random changes that make it even better will have more descendants and so on: changes towards looking more and more like a water-dropplet make the beetles with them more successful at reproducing that those without the changes.
Given enough time and enough beetles this is how you go from beetles with a “normal” carapace to beetles with a mirror-like carapace.
Evolution doens’t chose anything, it’s just one big statistical N-dimensional field of probabilites with local stable minima (points of maximum success at reproducing) and then some random genetic changes might just happen to matematically nudge a subset of the beetle population towards a specific stable minima on some characteristic (i.e. on one of dimension of those N dimensions) but it could’ve just as easilly and by chance have been a different one, but that didn’t happen so we’ll never hear about it (it’s a bit like the answer to the “Why has evolution made humans that think?” question - "Because if it didn’t made us think we wouldn’t be thinking about it, and if it made humans look different that different being would be what we think is “human”).
Maybe I don’t understand the question right but that’s just how evolution works. Nature doesn’t choose anything, the beetle doesn’t choose anything, it just happened to be a successful evolution trait that boosted survivability and you don’t see the failed evolutions.
Always remember nature never chooses anything, you just see the successful ones and the failed ones simply die off compared to others with better traits. The small traits add up over years and you have a new species. I am no expert but that’s how I understand it.
Evolution can only build off what came before, and in this case, all the parts were already there, they just needed to be fine tuned. Beetles across the world manipulate wavelengths of light to iridesce using variable reflective layers of chitin.
Nature as a whole is way beyond our present understanding of math and science. In fact, math and science only exist as concepts for humans to try to explain elements of nature
It didn’t. It just does random shit in different offspring until they manage to survive and pass on the genes to the next generation and the next round of random shit, ad infinitum.
Easy, mature killed all the other Beatles of the same species which were not shiny. Then probably the shiny females only liked shiny males for mating. Finally the male penis got some weird curly twist and that eventually locked the mutation to just one species. I don’t known, just making stuff up.
Nature is insane
In case anyone else was wondering how this could be effective camouflage. I wonder what happens if the predators are thirsty though.
it’s called dielectric mirror, this type of mirror can be made to be extremely effective over narrow range of wavelengths and it’s used in lasers for this reason. this is also the only way to manipulate extreme UV (13nm tin source, used in EUV semiconductor manufacturing)
similar effect is responsible for blue color of some butterflies and feathers that don’t contain blue pigment
Like, how does nature know to manipulate quantum states and electromagnetism to achieve this result. Trial and error / random mutation /survival etc just doesn’t explain why it happens.
That’s a staggering amount of non trivial science/math stacked layer by layer. On a beetle.
For those giving me replies:
I’m asking more of a philosophical question.
Why are these things there in the first place.
I’m not asking his evolution works. I know how evolution works. I state that in my comment. Random mutations, survival, etc. Etc etc. Is how.
Why.
To say it is because it is isn’t answering my question.
How and why does/could a random mutation play on the laws of physics in a meticulously optimized way to benefit itself. What is the method that would cause something to randomly say, today I think I’m going to make cells that act in such a way as to make me appear reflective, or transparent, or mimic the environment.
The result is because of evolution. OK. Good.
How do these processes which use quantum mechanics and wild optical physics become an innate part of nature to begin with.
It’s always funny seeing arguments like this as someone with a computer science education. A lot of people act like you can’t have anything complex unless some intelligent being deterministically writes a lot of if-else statements to implement it, which requires them to know and understand in detail what they are implementing at every step.
But what people don’t realize is that this is not how it works at all, there are many problems that are just impractical to actually “know” how to solve yet we solve them all the time, such as voice recognition. Nobody in human history has ever written a bunch of if-else statements to be able to accurately translate someone’s voice to text, because it’s too complicated of a problem, no one on earth knows how it works.
Yet, of course, your phone can do voice recognition just fine. That is because you can put together a generic class of algorithms which find solutions to problems on their own, without you even understanding how to solve problem. These algorithms are known as metaheuristics. Metaheuristics fundamentally cannot be deterministic, they require random noise to work properly, because something that is deterministic will always greedily go in the direction of a more correct solution, and will never explore more incorrect solutions, whereby an even better solution may be beyond the horizon of many incorrect ones. They also do have to be somewhat deterministic as well, because you need some greed or else the random exploration would be aimless.
A simple example of a metaheuristic is that of annealing. If you want to strengthen a sword, you can heat up the metal really hot and let it slowly cool. While it’s really hot, the atoms in the sword will randomly explore different configurations, and as it cools, they will explore less and less, and the overall process leads them to finding rather optimal configurations that strengthen the crystaline structure of the metal.
This simple process can actually be applied generally to solve pretty much any problem. For example, if you are trying to figure out the optimal route to deliver packages, you can simulate this annealing process but rather than atoms searching for an optimal crystaline structure, you have different orders of stops on a graph searching for the shortest path. The “temperature” would be a variable that represents how much random exploration you are willing to accept, i.e. if you alter the configuration and it’s worse, how much worse does it have to be for you to not accept it. A higher temperature would accept worse solutions, at very low temperatures you would only accept solutions that improve upon the route.
I once implemented this algorithm to solve sudoku puzzles and it was very quick at doing so, and the funny thing is, I’ve never even played sudoku before! I do not know how to efficiently solve a sudoku puzzle, I’ve honestly never even solved one by hand, but with sudoku it is very easy to verify whether or not a solution is correct even if you have no idea how to find the solution and even if finding it is very difficult, verifying it is trivially easy. So all I had to do is right the annealing algorithm so that the greedy aspect is based on verifying how many rows/columns are correct, and the exploration part is just randomly moving numbers around.
There are tons of metaheuristic algorithms, and much of them we learn from nature, like annealing, however, there’s also genetic algorithms. The random exploration is done through random mutations through each generation, but the deterministic and greedy aspect of it is the fact that only the most optimal generations are chosen to produce the next generation. This is also a generic algorithm that can be applied to solve any problem. You can see a person here who uses a genetic algorithm to teach a computer how to fly a plane in a simulation.
Modern AI is based on neural networks, which the greedy aspect of them is something called backpropagation, although this on its own is not a metaheuristic, but modern AI tech arguably qualifies because it does not actually work until you introduce random exploration like a method known as drop out whereby you randomly remove neurons during training to encourage the neural network to not overfit. Backpropagation+dropout forms a kind of metaheuristic with both a greedy and exploratory aspect to it, and can be used to solve just about any generic problem. (Technically, ANNs are just function-approximators, so if you want to think of it as a metaheuristic, the full metaheuristic would have to include all the steps of creating, training, and then applying the ANN in practice, as a metaheuristic is a list of steps to solve any generic problem, whereas an ANN on its own is just a function-approximator.)
Indeed, that’s how we get phones to recognize speech and convert it to text. Nobody sat down and wrote a bunch of if-else statements to translate speech into text. Rather, we took a generic nature-inspired algorithm that can produce solutions for any problem, and just applied it to speech recognition, and kept increasing the amount of compute until it could solve the problem on its own. Once it solves it, the solution it spits out is kind of a black box. You can put in speech as an input, and it gives you text as an output, but nobody really even knows fully what is going on in between.
People often act like somehow computers could not solve problems unless humans could also solve them, but computers already have solved millions of problems which not only has no human ever solved but no human can even possibly understand the solution the computer spits out. All we know from studying nature is that there are clever ways to combine random exploration and deterministic greed to form processes which can solve any arbitrary problem given enough time and resources, so we just implement those processes into computers and then keep throwing more time and resources at it until it spits out an answer.
We already understand how nature can produce complex things without anyone “knowing” how it works, because we do that all the time already! You do not need a sentient being to tell the beetle how to evolve to fit into its environment. There is random exploration caused by genetic mutations, but also a deterministic greedy aspect caused by “survival of the fittest.” This causes living organisms to gradually develop over many generations to something fit for its environment. And life has had plenty of time and resources to become more suited to its environment, life has been evolving for billions of years, with the whole resources of the planet earth and the sun.
This is the kind of answer I can appreciate.
Thanks.
You’re trying to assign agency to a natural process. It doesn’t work like that no matter how many times you ask why.
It’s kinda how LLMs are, in that there is no agency involved and yet people can’t help but anthropomorphise the process.
The flowers that look like various insects especially remind me of AI generated images.
There doesn’t need to be any knowledge involved. It happens, because it works. Neither the beetle nor evolution itself “know” anything about quantum physics. The beetle is just a beetle and evolution is not even an entity that has any agency, it’s just a process that’s happening and that leads to remarkable results over time.
This is just one more example for the old discussion how complexity can develop through evolution. The classic example is the eye of vertebrates. Read up on that, if you’re interested in that discussion.
There’s nothing “special” in the way you imagine about quantum phenomena. They are complicated to describe mathematically because we are limited to a fundamentally imperfect set of symbols, but they are not complicated to obtain the requirements for.
All chemical and light-interaction processes use quantum phenomena if you dig enough into how they work, and it’s especially clear on a smaller scale. If you just make something thin enough, it will start displaying quantum effects, but there is nothing that complicated about “thin”.
They’re not manipulating wavelengths with great complexity, they’re just growing a really thin layer on their shell.
It doesn’t. Step by tiny step random mutations that grant a fraction of a percentage of survivability, multiplied across millions of generations.
Yes it does, assuming there’s a benefit to be had along the way. It doesn’t have to be as effective as it is today to confer some tiny amount of benefit. It just has to be better than those without the mutation.
The biggest element you are not accounting for is time. It takes an unfathomably long amount of time for the benefits of random mutation to shape a population.
That’s just assumed. (But it isn’t always. Check out the birds of Fukushima - another great example of evolution manipulating physics by using a mechanism that made their feathers bright to instead use that chemistry to prevent damage to DNA from radioactivity.)
What I’m stumped by is why evolution chose that particularly bizarre and complex method that is, as explained by OP, insanely involved with manipulation of wavelengths of light vs just getting bigger or growing more claws or something similarly simple, biologically speaking.
It does it. But why. (Nobody knows)
Nature did. That’s how speciation works. We’re just focusing on the shiny beetles because they grab our attention and the big dung beetles with big horns don’t.
Also, as far as evolution is concerned? There’s nothing insane about it, they’re all equally simple. You’re thinking of it from the perspective of industrialisation, and how tough it would be for us to manufacture such materials. That’s not the viewpoint evolution cares about, if it can be grown it obviously isn’t difficult to do.
There are no quantum mechanics involved. And the physics are not wild, they’re the basic laws of physics. It’s only humans that assign difficulty and exoticness to these mechanisms because our technology base is incapable of reproducing it easily.
For every random genetic change that did something that turned out to be useful, there were countless ones that did nothing useful at all or were even counter-productive (to get a sense of how many “tries” there were, consider every time every beetle in the World tries to reproduce times how many eggs they lay times several random genetic changes per egg times millions or billions of years - we’re talking grains of sand in a beach level or even more, and this is just for one kind of creature that doesn’t even reproduce all that frequently - in things like bacteria there are so many reproducing so many times that we actually see evolution in action in a short time frame, for example with the growth of antibiotic resistance).
Then for all those random genetic changes that did something that turned out useful, there are only going to be some were that make enough of a difference in terms of increasing the survival of a beetle till reproduction and way more that didn’t make a difference.
You know what happen to all those quadrillions or whatever of tries that went nowhere? We’ll never know about them because the creatures in question are long dead (if their eggs were viable to begin with). We’ll only ever know about the random genetic changes which did work well enough to give reproductive advantages.
[There are actually a lot of cognitive falacies around how we perceive success because we only really get to know about what worked, not about the countless things that didn’t work. A good example is how most people pretty much only hear about Startups that made it big, yet for every Startup that does succeed enough to become widelly known there are tends or even hundreds of thousands that fail and we never hear about, so it might seem that Startups are generally successful when the reality is, in average, the very opposite]
Continuing on the Evolution story, if the previous part of the process worked based on the Maths of “trully insane large numbers”, at this point we add an effect akin to compounding interest: even if a genetic change adds a very small increase in reproduction for an animal - say, a beetle with a given random genetic change that did do something useful and gives it a 1% higher chance of successfully reproduce - as long as that trait gets passed down to the next generation, it means (rought) that all else be the same there will be 101 beetles born with that change for every 100 beetles born without it, for every reproductive cycle. This might seems little but as I said it compounds, so for example after 71 generations that will have grown to 200 for every 100 and it will keep growing.
This is how even a random genetic change that gives even just a tiny increase in success of living till reproduction and reproduction itself will, given enough time, come to dominate a population.
And then all those slightly different beetles keep on having the random genetic changes happen (the first part of the process) and those additional changes that did work and gave a tiny bit more success over that ones with just the original change will get the compounding part of the process, so those are the ones for whom there are more and more individuals, to which in turn the same process applies.
TL;DR (but you should)
A beetle with a random genetic change that affected its shell that makes it every so slightly harder to spot for predators in a place that has lots of water droplets on leaves will have more descendants than the rest. Some of those will randomly get additional changes that make that effect even more successful at making the beetle harder to detect for predators thus having even more descendants than the rest. Amongst those, the ones with random changes that make it even better will have more descendants and so on: changes towards looking more and more like a water-dropplet make the beetles with them more successful at reproducing that those without the changes.
Given enough time and enough beetles this is how you go from beetles with a “normal” carapace to beetles with a mirror-like carapace.
Evolution doens’t chose anything, it’s just one big statistical N-dimensional field of probabilites with local stable minima (points of maximum success at reproducing) and then some random genetic changes might just happen to matematically nudge a subset of the beetle population towards a specific stable minima on some characteristic (i.e. on one of dimension of those N dimensions) but it could’ve just as easilly and by chance have been a different one, but that didn’t happen so we’ll never hear about it (it’s a bit like the answer to the “Why has evolution made humans that think?” question - "Because if it didn’t made us think we wouldn’t be thinking about it, and if it made humans look different that different being would be what we think is “human”).
Maybe I don’t understand the question right but that’s just how evolution works. Nature doesn’t choose anything, the beetle doesn’t choose anything, it just happened to be a successful evolution trait that boosted survivability and you don’t see the failed evolutions.
Always remember nature never chooses anything, you just see the successful ones and the failed ones simply die off compared to others with better traits. The small traits add up over years and you have a new species. I am no expert but that’s how I understand it.
Evolution can only build off what came before, and in this case, all the parts were already there, they just needed to be fine tuned. Beetles across the world manipulate wavelengths of light to iridesce using variable reflective layers of chitin.
Nature as a whole is way beyond our present understanding of math and science. In fact, math and science only exist as concepts for humans to try to explain elements of nature
It didn’t. It just does random shit in different offspring until they manage to survive and pass on the genes to the next generation and the next round of random shit, ad infinitum.
So what factors affect this then
Easy, mature killed all the other Beatles of the same species which were not shiny. Then probably the shiny females only liked shiny males for mating. Finally the male penis got some weird curly twist and that eventually locked the mutation to just one species. I don’t known, just making stuff up.
So sad all the Beatles were killed