0:00:04.620,0:00:06.120 Hey there! For this video we're going to 0:00:06.120,0:00:09.180 be talking about phenotypic plasticity; 0:00:09.180,0:00:11.700 which is how individual phenotypes can 0:00:11.700,0:00:13.280 vary in response to the environment. 0:00:13.280,0:00:15.090 We're gonna go through a number of terms 0:00:15.090,0:00:17.391 associated with plasticity. Some of the 0:00:17.391,0:00:19.430 key ones among those, are reaction norms 0:00:19.430,0:00:21.329 and polyphenisms. And we're also going to 0:00:21.329,0:00:22.710 be talking about a special form of 0:00:22.710,0:00:27.579 plasticity known as performance curves. 0:00:27.579,0:00:28.890 So, if you'll remember back to some of 0:00:28.890,0:00:31.430 our earlier videos, there are three main 0:00:31.430,0:00:33.989 sources of phenotypic variation among 0:00:33.989,0:00:36.510 individuals. The first are genetic 0:00:36.510,0:00:38.440 differences which we talked about quite 0:00:38.440,0:00:40.390 a bit in our last couple of videos. But the 0:00:40.390,0:00:42.570 second which is just as important, 0:00:42.570,0:00:44.260 sometimes more important, are 0:00:44.260,0:00:46.649 environmental effects and this includes 0:00:46.649,0:00:48.859 things like phenotypic plasticity. This 0:00:48.859,0:00:51.149 is any situation in which the 0:00:51.149,0:00:53.239 environment is inducing differences in 0:00:53.239,0:00:56.059 phenotype rather than the genes. You can 0:00:56.059,0:00:58.839 kind of think of this as twins or clones 0:00:58.839,0:01:00.890 having the exact same genetic background 0:01:00.890,0:01:03.670 but showing different phenotypes. And 0:01:03.670,0:01:05.990 this may be adaptive meaning it has a 0:01:05.990,0:01:07.460 positive effect on fitness or 0:01:07.460,0:01:10.190 maladaptive. And the final of these is 0:01:10.190,0:01:12.300 random chance. Today, because we're 0:01:12.300,0:01:13.800 talking about plasticity, we're really 0:01:13.800,0:01:15.750 going be focusing on number two: The 0:01:15.750,0:01:18.000 effects of the environment on the 0:01:18.000,0:01:21.500 phenotype. And so let's take a moment to 0:01:21.500,0:01:22.510 talk about what I like to call the 0:01:22.510,0:01:24.750 beastie area of plasticity jargon there 0:01:24.750,0:01:26.520 are a lot of words that gets thrown 0:01:26.520,0:01:29.650 around and they all mean almost the same 0:01:29.650,0:01:31.600 thing. They're highly synonymous but not 0:01:31.600,0:01:34.300 perfectly synonymous. So, the first of 0:01:34.300,0:01:36.480 these is the more overarching umbrella 0:01:36.480,0:01:39.640 term and that is phenotypic plasticity. 0:01:39.640,0:01:41.420 And we use the word plasticity in a 0:01:41.420,0:01:43.440 sort of older sense. What plastic was 0:01:43.440,0:01:46.150 mean it was named for. And it's the idea 0:01:46.150,0:01:47.710 of something be able to being able to 0:01:47.710,0:01:51.300 take any form. And so plastics we call 0:01:51.300,0:01:53.960 plastic because you can melt them down 0:01:53.960,0:01:55.720 and mold them and make them into any 0:01:55.720,0:01:57.670 form you want. That's why we call them 0:01:57.670,0:01:59.490 plastic. And so when we talk about a 0:01:59.490,0:02:01.730 phenotype being able to take multiple 0:02:01.730,0:02:03.570 forms even though an individual has the 0:02:03.570,0:02:06.340 same genotype, we call this phenotypic 0:02:06.340,0:02:08.590 plasticity. So that's sort of the 0:02:08.590,0:02:10.780 broadest overarching term for any time 0:02:10.780,0:02:12.370 the environment can be affecting 0:02:12.370,0:02:15.159 organisms. Again it can be adaptive or 0:02:15.159,0:02:16.810 maladaptive. We 0:02:16.810,0:02:18.420 usually think of plasticity though as 0:02:18.420,0:02:20.480 being active responses to the 0:02:20.480,0:02:23.000 environment. So if an organism you know 0:02:23.000,0:02:26.379 freezes to death it being so cold has 0:02:26.379,0:02:28.469 caused its body to turn into a solid 0:02:28.469,0:02:29.760 which you could say is a change in its 0:02:29.760,0:02:31.769 phenotype but that's not in any way an 0:02:31.769,0:02:33.420 active response. That's just the wreck 0:02:33.420,0:02:35.730 damage induced by the environment. That's 0:02:35.730,0:02:37.381 not really what we're talking about when 0:02:37.381,0:02:39.760 we talk about plasticity. We're instead 0:02:39.760,0:02:42.909 talking about the kind of things that 0:02:42.909,0:02:44.400 have been actively in response to the 0:02:44.400,0:02:46.780 environment. A nice example that most of 0:02:46.780,0:02:49.079 you have probably experienced is if you 0:02:49.079,0:02:53.200 go outside and put your skin in the Sun 0:02:53.200,0:02:56.180 it's likely to change color. For me, I'm 0:02:56.180,0:02:59.499 quite pale, I have the the the problem if 0:02:59.499,0:03:01.989 I do this too long my skin turns bright 0:03:01.989,0:03:04.290 red and hurts. That would not be 0:03:04.290,0:03:05.409 plasticity. 0:03:05.409,0:03:07.799 That's just damage. But if I go out in 0:03:07.799,0:03:10.359 the Sun and in response to being in the 0:03:10.359,0:03:12.989 Sun my skin produces more melanin, 0:03:12.989,0:03:17.299 becomes more brown, that actually helps 0:03:17.299,0:03:21.279 protect my skin and my DNA and my cells 0:03:21.279,0:03:24.389 from the damage caused by ultraviolet 0:03:24.389,0:03:26.680 radiation. That would be an example of 0:03:26.680,0:03:32.590 adaptive plasticity. Really anytime the 0:03:32.590,0:03:35.450 organisms are having an active response 0:03:35.450,0:03:37.750 to environmental variation, we can turn 0:03:37.750,0:03:40.670 phenotypic plasticity. Now another word 0:03:40.670,0:03:42.900 that's often used very synonymously with 0:03:42.900,0:03:46.510 phenotypic plasticity is acclamation. 0:03:46.510,0:03:47.739 Acclamations a little different than 0:03:47.739,0:03:49.430 just plasticity writ large. 0:03:49.430,0:03:51.120 Because plasticity writ large can 0:03:51.120,0:03:54.400 include variation that is reversible or 0:03:54.400,0:03:56.650 irreversible. An example of an 0:03:56.650,0:03:58.120 irreversible form of phenotypic 0:03:58.120,0:04:00.430 plasticity is temperature dependence sex 0:04:00.430,0:04:03.169 determination in many reptiles. These 0:04:03.169,0:04:06.340 animals, their genes do not determine 0:04:06.340,0:04:08.209 whether they're male or female instead, 0:04:08.209,0:04:09.529 it's the temperatures they experience 0:04:09.529,0:04:11.450 when they're in the egg. But once they 0:04:11.450,0:04:14.139 develop into a male or a female, that 0:04:14.139,0:04:16.169 can't go backwards. So, that still 0:04:16.169,0:04:18.900 phenotypic plasticity but it is not a 0:04:18.900,0:04:22.199 form of acclamation. Acclamation is 0:04:22.199,0:04:24.889 something that is reversible and tends 0:04:24.889,0:04:26.389 to be in response to extend it or 0:04:26.389,0:04:28.919 chronic exposure. Tanning is actually a 0:04:28.919,0:04:31.410 pretty good example of acclimation. 0:04:31.410,0:04:33.550 Another term that gets thrown 0:04:33.550,0:04:37.389 around is phenotypic flexibility. This is 0:04:37.389,0:04:39.509 somewhat similar to acclimation, but in a 0:04:39.509,0:04:43.460 way is an even broader term. This really 0:04:43.460,0:04:46.650 is talking about any form of response to 0:04:46.650,0:04:49.610 the environment that can reverse itself. 0:04:49.610,0:04:51.580 This is just having a generally flexible 0:04:51.580,0:04:54.930 phenotype. Tanning would be an example of 0:04:54.930,0:04:57.310 having a generally flexible phenotype as 0:04:57.310,0:04:59.330 would bodybuilders. The fact that you can 0:04:59.330,0:05:02.120 put stresses on your muscles cause them 0:05:02.120,0:05:04.050 to grow and get bigger. If that can 0:05:04.050,0:05:06.680 rapidly reverse, if you stop working out 0:05:06.680,0:05:08.229 but then if you start working out you 0:05:08.229,0:05:10.009 can build up your muscles again would be 0:05:10.009,0:05:13.729 a good example of phenotypic flexibility. 0:05:13.729,0:05:16.819 And finally, developmental plasticity are 0:05:16.819,0:05:18.221 consequences of the environment that 0:05:18.221,0:05:19.970 tend to be irreversible because they 0:05:19.970,0:05:23.520 influence how the organism develops from 0:05:23.520,0:05:27.430 a zygote to a full adult organism. So, 0:05:27.430,0:05:29.159 temperature dependent sex determination 0:05:29.159,0:05:30.990 is an example of developmental 0:05:30.990,0:05:33.259 plasticity. Other examples have to do 0:05:33.259,0:05:34.750 with things like I'm sure you've heard 0:05:34.750,0:05:38.629 that parents/when pregnant women 0:05:38.629,0:05:40.300 can't adjust their diets and things in a 0:05:40.300,0:05:41.590 way that might be beneficial to their 0:05:41.590,0:05:43.509 baby's development, brain development, 0:05:43.509,0:05:45.580 learning. Those would all be examples of 0:05:45.580,0:05:47.990 developmental plasticity, the kind of 0:05:47.990,0:05:50.009 influence of the maternal environment on 0:05:50.009,0:05:53.030 the developmental trajectory of the 0:05:53.030,0:05:55.560 babies. So these are sort of four big 0:05:55.560,0:05:57.400 terms we're going to use to describe. 0:05:57.400,0:05:59.229 Very similar things, this is all the 0:05:59.229,0:06:02.419 environment affecting the phenotype of 0:06:02.419,0:06:06.409 individuals, but with slightly different 0:06:06.409,0:06:10.060 meanings as I've just explained. Another 0:06:10.060,0:06:13.230 set of important plasticity jargon, are 0:06:13.230,0:06:15.080 the difference between a reaction norm 0:06:15.080,0:06:18.580 or a polyphenism. This totally depends 0:06:18.580,0:06:20.790 on whether the phenotype changes 0:06:20.790,0:06:22.800 continuously in response to the 0:06:22.800,0:06:25.020 environment or if there's a threshold 0:06:25.020,0:06:26.689 response so the phenotype is in one 0:06:26.689,0:06:28.880 state in one environment and a different 0:06:28.880,0:06:31.530 state in a different environment. So, 0:06:31.530,0:06:33.550 a reaction norm, sometimes also called a 0:06:33.550,0:06:35.439 norm of reaction sort of flipping the 0:06:35.439,0:06:37.280 word around, is any time we have a 0:06:37.280,0:06:40.409 continuous response to the environment, 0:06:40.409,0:06:42.849 An example is shown by the growth 0:06:42.849,0:06:43.849 heights 0:06:43.849,0:06:45.830 these plants and responds to temperature 0:06:45.830,0:06:47.040 and this shows that when it's really 0:06:47.040,0:06:49.610 really cold the plants don't grow it all, 0:06:49.610,0:06:51.370 when it's moderately cold the plants are 0:06:51.370,0:06:53.729 kind of short, when it as it warms up the 0:06:53.729,0:06:55.560 plants get bigger and bigger and bigger 0:06:55.560,0:06:57.819 until it gets too hot and the plants 0:06:57.819,0:06:59.560 start to get shorter again. This is a 0:06:59.560,0:07:02.300 continuous response of these plants to 0:07:02.300,0:07:04.830 the environment. Totally driven by the 0:07:04.830,0:07:06.750 environment you could have totally 0:07:06.750,0:07:09.460 clonal plants with the exact same genes 0:07:09.460,0:07:10.849 and their phenotype would vary in this 0:07:10.849,0:07:14.189 way. Polyphenism, an example of a 0:07:14.189,0:07:15.819 polyphenism includes temperature 0:07:15.819,0:07:17.479 dependent sex determination, as I 0:07:17.479,0:07:19.169 mentioned before. If you have one set of 0:07:19.169,0:07:20.990 temperatures, the baby develops into a 0:07:20.990,0:07:22.219 male. If you have another set of 0:07:22.219,0:07:24.330 temperatures it develops into a female. 0:07:24.330,0:07:25.860 Another really good example is in 0:07:25.860,0:07:28.819 honeybees, whether or not the individuals 0:07:28.819,0:07:31.069 develop into a regular worker bee or a 0:07:31.069,0:07:33.360 queen bee. And this is totally dependent 0:07:33.360,0:07:36.889 on the diet that the larvae are fed by 0:07:36.889,0:07:38.699 the other worker bees. If they're fed a 0:07:38.699,0:07:40.449 standard diet they grow up and develop 0:07:40.449,0:07:42.939 into a worker bee. However, if they're fed 0:07:42.939,0:07:44.530 something called royal jelly, which is a 0:07:44.530,0:07:48.830 really nutrient-rich form of the diet, 0:07:48.830,0:07:52.630 that diet induces the larvae to develop 0:07:52.630,0:07:55.669 into a much larger, very fertile honeybee. 0:07:55.669,0:07:58.080 Very different phenotype. That's the 0:07:58.080,0:07:59.760 queen bee, but the queen bee is 0:07:59.760,0:08:02.569 genetically identical to her worker bees. 0:08:02.569,0:08:04.560 This difference, this polyphenism is 0:08:04.560,0:08:09.389 completely driven by the environment. 0:08:09.389,0:08:10.580 We're generally going to be thinking 0:08:10.580,0:08:14.259 more about reaction norms than polyphenism. So I want to dive into it just a 0:08:14.259,0:08:17.740 little bit more. This graph shows sort of 0:08:17.740,0:08:20.490 the stylized version of a reaction norm. 0:08:20.490,0:08:23.060 Our x-axis is the environment, with two 0:08:23.060,0:08:25.259 extremes on the left and the right, and 0:08:25.259,0:08:28.449 the y-axis is the value for our trait. 0:08:28.449,0:08:30.439 And in this case, what we see is we have 0:08:30.439,0:08:33.500 two genotype. So, these two individuals 0:08:33.500,0:08:36.130 with different genes or these could be 0:08:36.130,0:08:38.010 two populations of clonal individuals 0:08:38.010,0:08:40.050 where they all have the exact same 0:08:40.050,0:08:43.080 genetic makeup. Either the red group 0:08:43.080,0:08:44.529 all has the exact same genetic makeup 0:08:44.529,0:08:45.810 and the blue group all has the exact 0:08:45.810,0:08:49.550 same genetic makeup. And the idea is that, 0:08:49.550,0:08:51.820 what phenotype they show is largely 0:08:51.820,0:08:53.780 driven by their environment. So in one 0:08:53.780,0:08:56.440 extreme, we see that genotype A 0:08:56.440,0:08:58.829 has a smaller value for its phenotype 0:08:58.829,0:09:01.680 than genotype B. But at the other extreme 0:09:01.680,0:09:03.110 maybe this is a hot environment, this 0:09:03.110,0:09:05.860 flips and so we can see market 0:09:05.860,0:09:08.320 phenotypic variation on the landscape 0:09:08.320,0:09:11.690 that's driven more by the the phenotype 0:09:11.690,0:09:13.630 than just the pure genotype. In this case 0:09:13.630,0:09:15.240 you actually have both sort of driving 0:09:15.240,0:09:17.270 things because the genotypes differ and 0:09:17.270,0:09:18.600 how they plastically respond to the 0:09:18.600,0:09:20.280 environment. Which we'll be talking about 0:09:20.280,0:09:23.450 a bit more in our next video. And it also 0:09:23.450,0:09:26.370 highlights that patterns of phenotypic 0:09:26.370,0:09:29.930 plasticity can vary among genotypes. 0:09:29.930,0:09:34.240 Sometimes, you have them all responding 0:09:34.240,0:09:36.500 to the environment in the exact same way, 0:09:36.500,0:09:38.370 sometimes you have the molix responding 0:09:38.370,0:09:39.682 in about the same way, but sort of with 0:09:39.682,0:09:41.029 different intercepts so it's like they 0:09:41.029,0:09:42.550 have different starting points. And 0:09:42.550,0:09:45.360 sometimes you have 0:09:45.360,0:09:48.630 genotypes whose reaction norms cross 0:09:48.630,0:09:51.250 that's when you have both plasticity and 0:09:51.250,0:09:57.190 genetic effects. And this suite of 0:09:57.190,0:09:59.899 options is really interesting and in his 0:09:59.899,0:10:03.970 important mode for evolution, but we're 0:10:03.970,0:10:05.690 going to talk about this again more in 0:10:05.690,0:10:08.160 our next video. For today and for this 0:10:08.160,0:10:09.260 video I really just want you to 0:10:09.260,0:10:13.529 understand that phenotypic plasticity is 0:10:13.529,0:10:16.800 variation and phenotype caused by the 0:10:16.800,0:10:20.220 environment. And different genotypes can 0:10:20.220,0:10:25.449 have different reaction norms. Another 0:10:25.449,0:10:27.750 example of a reaction norm that we are 0:10:27.750,0:10:29.150 going to talk about a lot in this class 0:10:29.150,0:10:32.889 and you should have already seen SimUText is the performance curve. An 0:10:32.889,0:10:35.270 idealized version is shown here in this 0:10:35.270,0:10:39.310 case we have the pressure or difficulty 0:10:39.310,0:10:42.610 of your class and the y-axis is how well 0:10:42.610,0:10:45.670 you perform. And the curve is just sort 0:10:45.670,0:10:47.310 of this hump shaped curve and the idea 0:10:47.310,0:10:49.459 is that if your class is too easy, 0:10:49.459,0:10:50.459 you're not going to perform all that 0:10:50.459,0:10:52.500 well because you're just switched off. If 0:10:52.500,0:10:55.110 the class is just challenging enough, 0:10:55.110,0:10:57.050 your performance will be optimized it's 0:10:57.050,0:10:59.410 gonna be the highest. And if the class is 0:10:59.410,0:11:01.570 way too hard ,you're gonna stress out and 0:11:01.570,0:11:04.430 your performance is going to drop. So, 0:11:04.430,0:11:05.980 that's totally dependent on how hard 0:11:05.980,0:11:07.970 your class is and has nothing to do with 0:11:07.970,0:11:09.860 your genetic propensity for class. 0:11:09.860,0:11:11.470 This would be an example of a 0:11:11.470,0:11:13.860 performance curve and thus an example of 0:11:13.860,0:11:18.690 phenotypic plasticity of a sort. We touched 0:11:18.690,0:11:20.610 on thermal performance a little bit 0:11:20.610,0:11:22.130 earlier in our very first example of a 0:11:22.130,0:11:24.120 reaction norm. But it's a nice sort of 0:11:24.120,0:11:26.510 classic example of a performance curve 0:11:26.510,0:11:27.760 so we're gonna dive into it just a 0:11:27.760,0:11:30.269 little bit more. So in this case, we see a 0:11:30.269,0:11:32.070 thermal performance curve which again is 0:11:32.070,0:11:34.970 an example of a reaction norm. Our x-axis 0:11:34.970,0:11:37.490 is body temperature and our y-axis is 0:11:37.490,0:11:39.709 relative performance. This could be how 0:11:39.709,0:11:42.140 many babies and individual makes, it 0:11:42.140,0:11:43.910 could also be something like how fast 0:11:43.910,0:11:47.160 they can run. And thermal performance 0:11:47.160,0:11:49.300 curves tend to be this hump shaped curve. 0:11:49.300,0:11:51.520 They're left skewed. The skew is the 0:11:51.520,0:11:53.720 direction the tail of the distribution 0:11:53.720,0:11:55.570 points. So, that means the humps more to 0:11:55.570,0:11:58.060 the right, a little confusing but is the 0:11:58.060,0:12:01.730 left skewed curve. And this curve like 0:12:01.730,0:12:03.089 all performance curves can be 0:12:03.089,0:12:05.990 characterized by a handful of parameters 0:12:05.990,0:12:08.370 including an optimum. So, in this case 0:12:08.370,0:12:09.970 it's the body temperature at which 0:12:09.970,0:12:13.860 performance is its highest. Two critical 0:12:13.860,0:12:16.519 limits, in this case the body 0:12:16.519,0:12:17.800 temperatures were performance is the 0:12:17.800,0:12:22.940 very lowest for ectothermic animals when 0:12:22.940,0:12:24.399 we measure these critical thermal limits 0:12:24.399,0:12:26.421 is often is when they really when they can 0:12:26.421,0:12:29.339 stand up. If you get some too hot or too 0:12:29.339,0:12:31.910 cold a bit before they could die, you 0:12:31.910,0:12:33.899 actually have them sort of slip into a 0:12:33.899,0:12:35.899 coma-type state and they can't work 0:12:35.899,0:12:37.360 their muscles anymore. So those are the 0:12:37.360,0:12:39.850 critical thermal limits and then some 0:12:39.850,0:12:42.420 parameter that describes how wide the 0:12:42.420,0:12:44.089 performance breath is because you could 0:12:44.089,0:12:47.089 have a situation where you have your 0:12:47.089,0:12:49.040 critical limits and basically a plateau 0:12:49.040,0:12:50.459 where everything in between you have 0:12:50.459,0:12:51.959 high temperatures or this could be 0:12:51.959,0:12:54.029 extremely narrow where basically they 0:12:54.029,0:12:57.000 have high performance at the optimum and 0:12:57.000,0:12:59.709 then it rapidly drops off on both sides. 0:12:59.709,0:13:01.150 So, this parameter is telling you sort of 0:13:01.150,0:13:04.889 how wide that high performance area is. 0:13:04.889,0:13:07.980 In this case it's a "B", which means 0:13:07.980,0:13:10.029 that it's the range of temperatures at 0:13:10.029,0:13:13.540 which the organism is able to have at 0:13:13.540,0:13:17.980 least 80% of its peak performance. So, 0:13:17.980,0:13:19.300 that's a performance curve, in this case 0:13:19.300,0:13:21.690 a temperature or a thermal performance 0:13:21.690,0:13:23.470 curve. 0:13:23.470,0:13:27.290 And we think a major reason for these 0:13:27.290,0:13:28.519 performance curves, particularly in 0:13:28.519,0:13:30.019 response to temperature has to do with 0:13:30.019,0:13:32.660 how biochemical reactions work. 0:13:32.660,0:13:34.300 Biochemical reactions are quite 0:13:34.300,0:13:37.350 temperature sensitive. As temperature 0:13:37.350,0:13:40.740 goes up, proteins become a bit more 0:13:40.740,0:13:43.510 flexible. Enzymes and other proteins are 0:13:43.510,0:13:44.829 pretty much always sort of wiggling 0:13:44.829,0:13:46.769 around and changing shapes. And that's 0:13:46.769,0:13:48.790 sort of how an enzyme works, is it'll 0:13:48.790,0:13:50.660 wiggle into one shape that allows it to 0:13:50.660,0:13:52.690 grab a substrate it'll then wiggle into 0:13:52.690,0:13:54.550 another shape with that substrate and 0:13:54.550,0:13:55.860 that's what it does to catalyze a 0:13:55.860,0:13:58.880 reaction. So it sort of snapping between 0:13:58.880,0:14:01.990 states between shapes. As you heat them 0:14:01.990,0:14:03.690 up, they're able to wiggle a bit, 0:14:03.690,0:14:08.529 a bit more rapidly. All compounds, 0:14:08.529,0:14:10.350 all molecules move around a little more 0:14:10.350,0:14:12.339 when they're at a higher temperature. And 0:14:12.339,0:14:13.780 so then they're able to have the 0:14:13.780,0:14:16.100 reactions go much more quickly. But at 0:14:16.100,0:14:17.800 some point they get so hot, that they're 0:14:17.800,0:14:19.519 wiggling around so rapidly that they 0:14:19.519,0:14:21.279 stop doing a very good job of catalyzing 0:14:21.279,0:14:23.530 reactions. And that is then when your 0:14:23.530,0:14:25.680 performance would drop back off. So, this 0:14:25.680,0:14:29.120 is just showing how the phenotype of the 0:14:29.120,0:14:30.980 organisms can vary in response to the 0:14:30.980,0:14:34.120 environment just because of how 0:14:34.120,0:14:35.899 biochemical reactions are sensitive to 0:14:35.899,0:14:37.639 the environment. In this case, sensitive 0:14:37.639,0:14:41.560 to the temperature environment. However, 0:14:41.560,0:14:43.190 lots of other factors can influence 0:14:43.190,0:14:45.949 enzymes too. And this is only one route 0:14:45.949,0:14:48.090 that can cause phenotypic plasticity but 0:14:48.090,0:14:50.180 it is one of the important routes, 0:14:50.180,0:14:51.870 especially for flexible phenotypic 0:14:51.870,0:14:53.630 plasticity, the kind of thing that can be 0:14:53.630,0:14:56.509 reversible. So in this case we see an 0:14:56.509,0:14:58.699 environmental variable on the x-axis and 0:14:58.699,0:15:00.980 we have enzymatic activity on the y-axis 0:15:00.980,0:15:03.100 This is really how fast it can catalyze 0:15:03.100,0:15:05.069 a reaction. We already talked about the 0:15:05.069,0:15:06.560 thermal performance curve which is just shown 0:15:06.560,0:15:08.480 in the far left. The middle one shows you 0:15:08.480,0:15:10.769 an example for pH. But this basically is 0:15:10.769,0:15:11.889 showing you is that when you have 0:15:11.889,0:15:14.730 relatively neutral pH's around 7, 0:15:14.730,0:15:16.870 the enzymes function well but if pH gets 0:15:16.870,0:15:19.569 either too acidic or too alkaline, you 0:15:19.569,0:15:23.750 get a very reduced enzymatic activity. 0:15:23.750,0:15:25.680 Showing how a phenotype can respond to a 0:15:25.680,0:15:29.620 pH environment. Finally, on the right is 0:15:29.620,0:15:31.370 just showing the response of enzymes to 0:15:31.370,0:15:34.149 substrate concentration. As the amount of 0:15:34.149,0:15:36.089 stuff for them to work on goes up, 0:15:36.089,0:15:37.709 their activity goes up. And it goes up 0:15:37.709,0:15:39.970 quite rapidly, but at some point there's 0:15:39.970,0:15:41.970 enough substrate in the environment that 0:15:41.970,0:15:45.380 the enzymes are are being able to use 0:15:45.380,0:15:46.810 substrate as fast they can, they can't 0:15:46.810,0:15:48.350 use it any faster, so you have this 0:15:48.350,0:15:53.540 asymptote. And this and it stops changing 0:15:53.540,0:15:54.540 in response to the environment at that 0:15:54.540,0:15:56.400 point. In this case, it's almost more like 0:15:56.400,0:15:59.310 a threshold response. But these are three 0:15:59.310,0:16:02.029 different forms of reaction norm. Again, 0:16:02.029,0:16:04.910 continuous phenotype variation in 0:16:04.910,0:16:09.310 response to an environmental variant. So, 0:16:09.310,0:16:13.089 quick wrap-up. The environment can pretty 0:16:13.089,0:16:15.970 dramatically influence the phenotype of 0:16:15.970,0:16:20.129 individuals. This is a really important 0:16:20.129,0:16:21.690 and it's something we often skip over in 0:16:21.690,0:16:23.660 biology classes. Although, it's something 0:16:23.660,0:16:25.399 that I'm sure you intuit, many of you 0:16:25.399,0:16:27.170 know about you all know about tanning, 0:16:27.170,0:16:29.010 you all know about working out, so you 0:16:29.010,0:16:31.480 have some notion that phenotypes 0:16:31.480,0:16:33.569 can vary in response to an environment. 0:16:33.569,0:16:36.370 But we tend to talk about how genes are 0:16:36.370,0:16:38.920 everything and a gene will code for a 0:16:38.920,0:16:40.769 trait, then that individual has that 0:16:40.769,0:16:42.680 trait as like a fixed thing. However, it 0:16:42.680,0:16:44.920 turns out there's a very large fraction 0:16:44.920,0:16:46.790 of the variation among individuals that 0:16:46.790,0:16:49.379 can be explained by the environment. As a 0:16:49.379,0:16:51.840 whole, we call this phenotypic plasticity, 0:16:51.840,0:16:53.230 although there's a number of sort of 0:16:53.230,0:16:55.230 other terms with similar meanings, 0:16:55.230,0:16:57.230 such as acclimation phenotypic 0:16:57.230,0:17:01.920 flexibility, developmental plasticity, so 0:17:01.920,0:17:04.790 on. An important thing about phenotypic 0:17:04.790,0:17:06.589 plasticity that's quite different from 0:17:06.589,0:17:09.790 evolutionary change, is plastic changes 0:17:09.790,0:17:11.589 can occur within the lifespan of an 0:17:11.589,0:17:13.360 individual, I mean they can respond to 0:17:13.360,0:17:16.059 the environment very rapidly and more 0:17:16.059,0:17:19.440 rapidly than evolutionary change can. But 0:17:19.440,0:17:20.740 there is a bit of a trade-off 0:17:20.740,0:17:23.839 in this, because if a phenotype can be 0:17:23.839,0:17:26.900 fully changed just by the environment, 0:17:26.900,0:17:28.459 that means natural selection cannot 0:17:28.459,0:17:30.880 effectively act on it because there's no 0:17:30.880,0:17:33.320 heritable trait variation. In this case, 0:17:33.320,0:17:34.740 if we think of our breeder's 0:17:34.740,0:17:37.770 equation, we would have 'h' squared a 0:17:37.770,0:17:40.090 heritability of zero, if it's a fully 0:17:40.090,0:17:41.700 plastic trait. Meaning it could not 0:17:41.700,0:17:44.480 evolve generation after generation. Now 0:17:44.480,0:17:45.780 that's not to say that phenotypic 0:17:45.780,0:17:47.150 plasticity is completely 0:17:47.150,0:17:48.720 removed from evolution by natural 0:17:48.720,0:17:51.440 selection. Because although a specific 0:17:51.440,0:17:55.309 phenotype cannot evolve, reaction norms 0:17:55.309,0:17:57.700 can evolve. So, how each individual 0:17:57.700,0:18:00.340 genotype responds to the environment is 0:18:00.340,0:18:01.780 something that natural selection can act 0:18:01.780,0:18:04.039 on. And we're talking about that as well 0:18:04.039,0:18:06.230 as how we can distinguish the fraction 0:18:06.230,0:18:07.809 of variation caused by the environment, 0:18:07.809,0:18:09.450 and the fraction of variation that's 0:18:09.450,0:18:12.290 caused by genes in our next video. Thank you!