Insects Develop Pesticide Resistance Through Symbiosis With Gut Flora

First time accepted submitter blinkin247 writes "The indiscriminate spraying of pesticides has probably caused as many problems as it has solved, but here's one that was not expected: some bacteria have decided that insecticide is a very tasty meal. Unfortunately for us, one of the strains of bacteria that has evolved the ability to digest the toxin happens to be able to find a home in an insect's gut. When it does so, it provides the insect with resistance."

Re:Evelution in action.

[Theovon]

I didn't RTFM, but on the surface, although this looks like evolution and symbiosis, it doesn't look like symbiotic evolution. The insect didn't change. The bacteria did, and the bacteria is living in the insect. The bacteria didn't cause the insect to develop a resistance. The bacteria is PROVIDING the resistance. If you were to remove the bacteria from the insect, the insect would be vulnerable again.

Re:Evelution in action.

[datsa]

It's not that simple. Being able to harbor the new bacteria is now a measure of fitness in these insects. Insects that reject the bacteria will die off (if they haven't already), and insects that do a better job accommodating the bacteria are more likely to survive to the next generation. We happen to be seeing the end product of that process.

Now put bacteria in farmers

[ignavus]

Yay! So now we can put those bacteria in farmers, and they won't get sick or die when they spray their farms.

Re:Organic farming is not for hippies

[Mashiki]

So, will you be the first to sign your own death and the death of 4 billion other people? Organic farming is unsustainable for our population levels.

Re:Curses!

[Chris Burke]

Few creationists deny natural selection. Few creationists deny genetic mutations occur.

Great! Then few creationists would deny evolutionary theory! Because natural selection + mutations explains the diversity of species very, very well!

Effectively, what we do deny is that these mechanisms can violate the second law of themodynamics

Creationists always try to use the second law,
to disprove evolution, but their theory has a flaw.
The second law is quite precise about where it applies,
only in a closed system must the entropy count rise.
The earth's not a closed system' it's powered by the sun,
so fuck the damn creationists, Doomsday get my gun!
- MC Hawking, "Entropy"

Re:Curses!

[Chris Burke]

If I have a billion self replicating programs, and randomly change the object code in all of them every second, they all won't suddenly die, but I will see the entire population gradully LOSE information and thus FUNCTION.

You should actually try this. I have. So have many others. What we've learned by doing it is that if you just randomly modify your billion programs with an external program and use this same program to do the copying (so none of the population of programs you're "evolving" can ever fail to reproduce), and nothing else then yeah you'll just get a big mess of programs that mostly don't work.

However if you constrain those that are allowed to be copied in some way, for example by running them through some tests to see if they have the desired functionality and only copying the best-working programs then randomly modify them, you prevent regression and select for enhancement. Iterating on this process, you'll find that you can achieve order and you can increase function. Dramatically so, and faster than you would think, too.

There's a whole field of computer science on the subject: genetic algorithms. They're only like biological evolution in principle, but it's the principle of random changes resulting in increased order that you have an issue with. Well, genetic algorithms provide a mathematical description of how that is not only perfectly possible, but a common, expected outcome.

We call the criterion we use to decide what solutions will be allowed to propagate the "fitness function", and it is the main thing that guides what the solution looks like, so defining it well is the major issue when you're a human trying to solve a specific problem. Even if you do a good job, you can still get solutions that are wildly outside what you assumed the solution should look like -- which is one of the strengths of genetic algorithms.

In nature, the "fitness function" is the same as the problem to be solved: Survive to reproduce. And what we see is the incredible number of ways that problem can be solved.