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How does discontinuous distribution provide evidence for evolution?

How does discontinuous distribution provide evidence for evolution?


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One of the evidences for evolution is bio-geographical evidence.

In it,'discontinuous distribution' is mostly cited as an evidence.

For example,

Alligators are found only in south-eastern US & eastern China; elephants are found only in Africa & in & around India; lung fishes are found only in South America, Australia, Africa.

Their distribution is discontinuous due to possible changes like continental drift etcetera.

But, I am not understanding how these discontinuous distributions prove evolution.

Can anyone help me explain how they provide evidence for evolution?


There are probably several relevant perspectives. Since you mentioned continental drift, consider a taxon (i.e. species, family, etc.) that evolved when all the continents were joined together to form the supercontinent Pangaea.

If it was a successful species that continued evolving and expanding its range, then we might expect to find its fossils - or maybe even living descendants - on multiple modern continents. In fact, dinosaurs evolved on Pangaea, and their fossils are found on every modern continent.

On the other hand, imagine a taxon that evolved after Pangaea broke up into Laurasia and Gondwanaland. If this taxon evolved in Gondwanaland, then its fossils and descendants will likely occur only on the southern continents. (Gondwanaland broke up into Africa, South America, Australia and Antarctica.)

In fact, you mentioned a good example - lungfish. On closer inspection, lungfish fossils have been discovered in North America, however. So we might guess that lungfish evolved in Pangaea but died out in the northern portion (Laurasia) for some reason.

Elephants belong to the order Proboscidea, and proboscideans were once widespread in Eurasia and North America, and they even ranged into South America for a time. The familiar mammoths and mastodons were apparently exterminated by changing climate and/or human predation. This tells us more about extinction than evolution.

But the fact that proboscidean fossils are found only in the Northern Hemisphere and South America tell us that they evolved in the Northern Hemisphere, spreading into South America only after it was connected to North America.

I'm not sure when proboscideans first evolved, but it was probably long after Pangaea broke up. However, North America and Eurasia have connected and reconnected at various times, making it possible for species to migrate between the two continents. The most famous link is the Bering Land Bridge, which existed during the last "ice age" (Pleistocene).

At any rate, elephants today survive only in the tropics, which, like the sea, are a sort of refuge for "living fossils."

There are a number of taxonomic groups - living and extinct - that are found only in the Northern Hemisphere, only in the Southern Hemisphere, or only on particular continents that are evidence of both evolution and changing environments (including climate change and continental drift).


How does discontinuous distribution provide evidence for evolution? - Biology

Many people are under the false impression that evolution is just a guess or a belief, when in reality, it is one of the most well-supported concepts in all of science. The evidence for it is overwhelming and comes from many different disciplines such as paleontology, comparative anatomy, biogeography, and perhaps most significantly, genetics. Indeed, modern genetic tools have allowed us to repeatedly test evolution’s predictions, and those predictions have consistently come true. Therefore, I am going to explain in simple terms what the genetic evidence is and why it is so compelling. As I will show, the evidence perfectly matches the predictions that the theory of evolution made decades before we could test those predictions. Further, the patterns do not make sense if our modern organisms were specially created, because there is no reason why a creator would have had to make life with these patterns. In other words, if you want to say that God created our modern organisms, then you are left in the awkward position of arguing that out of an infinite range of possibilities available to him, God chose to create life in the one and only way that would be consistent with the predictions of evolution.

Note: Throughout this post I will use the term “creationist” to refer to people who deny evolution. There are many sub-categories within that, and there are also Christians who accept both evolution and the Bible (theistic evolutionists). I am not attacking Christianity or religion here. Rather, I am simply explaining why the evidence overwhelmingly supports evolution and refutes creationism.

Note: I am going to talk about relationships based on genetic similarities and shared genetic traits throughout this post, but please realize that I am doing this for simplicity. Actual phylogenetic studies employ rigorous statistical analyses to look not just at the proportion of shared DNA, but also at parsimony and various other factors. So I am being simplistic to avoid losing anyone, but the actual science is more complex, and the more that you understand it, the clearer it becomes that evolution is correct.

That basics that everyone agrees on

To start this post, I need to explain the most basic concepts of how we use genetics to assign evolutionary relationships, and the easiest way to do that is with human families. Imagine that you gave me blood samples from yourself and five relatives, all of whom were in your generation. I then extracted and sequenced the DNA from those samples, and I found that there was one person whose genetic code was very similar to yours but more different from the other four samples. Thus, you two share a substantial portion of the variable regions of your DNA. From that, I would conclude that you two share a more recent common ancestor with each other than with your other relatives. In this case, that ancestor would probably be your parents (i.e., you’re probably siblings). This should make good sense. You obviously got your DNA from your parents, as did your sibling. Thus, since you both got your DNA from the same source, we naturally expect your genetic code to be more similar to your sibling’s than to the codes of people who have different parents.

As I look at the data further, I also find another pair of two individuals who are more similar to each other than they are to either you or your sibling, thus suggesting that they share a recent common ancestor (their parents) that you do not have. However, both of them are more similar to you and your sibling than they are to the final two relatives. This would suggest that you and they share a recent common ancestor that is not shared by the final two relatives (e.g., you’re cousins who share grandparents). Finally, the last two individuals are again closely related to each other, but they are more distant to the rest of you. This would suggest that the six of you have a more distant common ancestor (perhaps you share a great grandparent).

This is an example of a cladogram (aka phylogenetic tree) showing the relationships between you and your five relatives in my example.

As you can see, we can use those genetic data to reconstruct your family tree (what we like to call your phylogeny), and we can illustrate it using a phylogenetic tree or cladogram like the one on the right. On these diagrams, vertical lines represent common ancestors. Thus, you can see that you and your sibling share a recent ancestor (your parents), and you, your sibling, and your cousins share an ancestor slightly further back (your grandparents), and all six of you share an ancestor even further back (your great grandparents). Again, this should all make good sense when you think about how DNA is passed. All six of you share a certain amount of DNA because you all inherited it from your great grandparents. After that, however, things began to diverge. One of your great grandparents’ children went on to become your grandparent, while another one went on to produce your more distant relatives. Thus, you, your sibling, and your cousins are more alike because you all received DNA from the same source (your grandparent). Then, one of your grandparents’ children went on to become your parents, while another became your aunt/uncle and produced your cousins. Does that make sense?

I want to pause here for a moment to make a crucially important point. In my example, we did not need actual DNA from your ancestors. Rather, we were able to infer their existence from the patterns that we saw in the DNA from the current generation. This is a very important strength of genetic analyses: we can use data from the current generation to infer the existence of past ancestors.

Broadening the scope

Everything that I have said thus far is universally accepted. No one disagrees that these genetic tools can determine family relationships like this, and even the most die-hard creationist would have no problem with what I have said. However, the power of these tools doesn’t stop there. We can also use them for an entire species. For example, we can trace the ancestry of all humans back to a common source. Here again, creationists have no problems. They agree that these methods are reliably showing true relationships, and it’s not simply a case of some people happening to have similar DNA. They agree that the similarities are similar by descent and indicate common ancestry (i.e., they accept that these methods can reliably identify ancestors that we do not have DNA samples from). In other words, they agree that these are actually showing real, evolutionary relationships within people (they would argue that the tree goes back to Noah and his family as the common ancestor).

We can, however, go even further than just a species, we can also use it for complex species with many breeds (such as dogs) or even for entire families of animals (in the scientific classification of organisms, family is the third most specific classification, followed by genus and species). We can, for example, show that all species of ducks (family Anatidae) descended from a common ancestor. We can also show that all tree frogs (family Hylidae) share a common ancestor, all pythons (family Pythonidae) share a common ancestor, all kangaroos (family Macropodidae) share a common ancestor, etc. Again, creationists are OK with this. At the family level, they agree that these methods are showing true relationships. You see, young-earth creationists argue that on Noah’s ark, Noah did not take two of each species, but rather took two of each “kind,” which they arbitrarily define as being roughly equivalent to scientists’ term “family.” Thus, they agree with these data, because they think that all modern ducks descended from a single pair of ducks on the ark, all modern tree frogs descended from a pair of tree frogs on the ark, etc. I have even seen some of them go as far as saying that the genetic evidence within families is evidence of creationism/Noah’s Ark (that is a logical fallacy known as affirming the consequent).

Cladogram of dog breeds. Figure 1a from vonHoldt et al. 2010.

Creationist’s disagree, however, the instant that we start extending beyond the family level. Take marsupials (pouched mammals) for example. Using these genetic techniques, we can tell that many carnivorous marsupials, like Tasmanian devils and quolls, are all in a single family (Dasyuridae) and share a common ancestor. Creationists are fine with that, and agree that the methods are showing true relationships. However, we can use exactly the same methods to broaden the scope just a little bit further and show that members of Dasyuridae are more closely related to the Myrmecobiidae family than they are to any other living marsupials. Thus, we can tell that Dasyuridae and Myrmecobiidae evolved from a common ancestor, and we group them together into the order Dasyuromorphia (order is one step broader than family). At that point, creationists suddenly disagree. Suddenly they insist that these methods are just showing similarities, not true relationships. They are even more upset when we use exactly the same techniques to show that the order Dasyuromorphia evolved from the same common ancestors as the orders Notoryctemorphia and Peramelemorphia (Gallus et al. 2015). Further, we can keep going with this until eventually we have a cladogram for all marsupials that shows that all of them share a common ancestor and are more related to each other than they are to other mammals (just like you are more related to your sibling than to your cousins Cardillo et al. 2004).

A phylogenetic tree of several marsupial families. Figure 7 from Cardillo et al. 2004.

We don’t have to stop there, however. We can continue to use the same methods to show that all mammals share a common ancestor, all animals share a common ancestor, and ultimately that all life on planet earth evolved from a common ancestor. Creationists, of course, object to this in the strongest possible terms. They insist that these genetic similarities aren’t actually showing real relationships, and they are adamant that the fact that two groups share more DNA with each other than with some other group doesn’t indicate that those two groups evolved from a common ancestor. As you can hopefully now see, however, that argument is logically inconsistent because it is completely and totally arbitrary to say that these methods work within families, but don’t work for taxonomic levels higher than that. That reasoning is logically invalid and completely ignores the evidence. Look at the cladogram above, for example. It shows some of the relationships that I described in marsupials, and I have colored the parts that creationists agree with green and the parts that the disagree with red. As you can see, within each family, they accept quite a few common ancestors. They agree that these methods can reliably show ancestry, yet as soon as we move beyond the family level, they say that the methods don’t actually show common ancestry. They agree, for example, that all members of the genera Dasyurus, Neophascogale, and Phascolosorex descended from a common ancestor, yet they disagree that the families Dasyuridae and Myrmecobiidae descended from a common ancestor. That belief is completely arbitrary and has no scientific basis or logical credibility. To put this another way, look at the clodagram that I showed earlier for dog breeds (which creationists have no problems with), then look at the cladogram below for all life on planet earth, and tell me what the difference is. Explain to me why we should accept that these methods work for dogs but arbitrarily believe that they don’t work for higher taxonomic levels.

Phylogenetic tree of life on planet earth. Via the University of Texas.

Extraordinary predictions

I want to take a minute here to try to impress on you just how extraordinary these genetic results are. Scientific theories are often judged by their predictive power. In other words, good theories are ones that can accurately predict the results of future experiments, and the more extreme the predictions, the better. In this case, the theory of evolution made the astounding prediction that we should see these genetic patterns decades before we actually had the ability to test them.

When Darwin first proposed the theory of evolution, genetics were unknown. No one knew what DNA was or how traits where inherited (see note). In fact, Darwin himself was totally wrong about how inheritance worked (he subscribed to the “blending” hypothesis wherein the traits of two parents blended together). Nevertheless, despite being wrong about the mechanism, it was clear that there had to be some way that the information for traits was passed from parents to offspring, and if evolution was true, then scientists realized that the information should record evolutionary history. In other words, if evolution was true, it should be possible to use that information in exactly the way that I described to show that all life traces back to a single common ancestor.

That was already an extreme prediction, but it didn’t stop there. You see, it wasn’t enough for there to be a pattern. Rather, the pattern had to match overarching morphological patterns. In other words, it had to show that all of the parrot families share a common ancestor, all frogs share a common ancestor, all marsupials share a common ancestor, etc., and that is exactly what we find. Further, this pattern had to match the fossil record, which is where things get even more extraordinary. You see, it may make intuitive sense to you to expect that all frogs would be genetically similar, even if they were specially created (more on that later), but why would genetics show that modern amphibians and modern reptiles share a common ancestor? That’s not something that you would expect under creationism, but it is what evolution predicted, because the fossil record clearly showed that both modern amphibians and reptiles evolved from ancient amphibians. Thus, evolution predicted that modern amphibians and reptiles should share a common ancestor.

Similarly, the fossil record showed that amphibians evolved from fish, and that both mammals and birds evolved from reptiles. Therefore, if those fossilized patterns are correct, we should see the same patterns in DNA, and we do! Think about how amazing that is. Evolution predicted the existence of an extremely precise pattern long before we could test that prediction. If evolution isn’t actually true, then you have to say that the patterns that we see in morphology, the fossil record, and genetics just happen to perfectly match up. That’s insane! Further, let’s be clear that I am only naming a handful of the predictions here. They also extend to all plants, bacteria, archaea, invertebrates, and other chordates. We are talking about thousands of predictions that evolution nailed! That is extremely strong evidence that evolution is correct. To put that another way, what are the odds that evolution would have gotten all of those predictions right if evolution wasn’t actually true?

Crocodiles are more closely related to birds than other reptiles. Image via Green et al. 2014.

To really drive this home, let’s talk more about birds for a minute, because their story is incredible. As I explained in a previous post, there is a ton of fossil evidence showing that birds evolved from dinosaurs. We have lots of transitional fossils showing that this occurred. Further, the fossil record shows the existence of a large phylogenetic group known as archosaurs, which included both ancient crocodilians and the group of dinosaurs that evolved into birds (more details at the University of California). This tells us that crocodiles and birds should actually be each other’s closest living relatives, and it leads to an absolutely incredible prediction. Genetically, not only should birds fall out as reptiles, but crocodiles should actually be more closely related to birds than they are to other reptiles. That is an amazing prediction that makes no sense under creationism. Why would God give crocodiles a genetic code that shares more in common with birds than other reptiles?

As you might have guessed, however, this prediction totally came true! Genetically, birds are actually reptiles, and crocodiles share more DNA with birds than with other reptiles (Green et al. 2014)! Again, this is because birds and crocs share a common ancestor (just as you and your sibling are genetically similar because of a common ancestor). If you stop and think about this for a second, it is mind-blowing. Genetically, crocodiles are more similar to birds than they are to other reptiles. If that doesn’t make you question everything, then I don’t know what will.

Note: Technically, Gregor Mendel (who discovered how genetic inheritance works) was Darwin’s contemporary, but Mendel’s work was largely unknown until well after his death.

Functionally arbitrary similarities

At this point, you might be tempted to think that these genetic patterns are there by necessity. For example, you might think that all frogs have similar genetic codes simply because they all have to have similar codes in order to have the characteristics of a frog. Thus, you might think that these genetic patterns are functionally necessary and would have to exist even if modern organisms were specially created. There are, however, numerous problems with that line of reasoning.

First, that argument would only have the potential to apply to the patterns within fairly narrow taxonomic units, and it would not explain the overarching patterns. In other words, the fossil record tells us that modern amphibians evolved from ancient fish, modern reptiles evolved from ancient amphibians, modern mammals evolved from ancient reptiles, birds and crocodiles both evolved from an ancient archosaur (reptile), etc. As I have already explained, genetics show us exactly the same progression, and there is no reason why that pattern had to exist. An all-powerful being could easily have created birds, reptiles, amphibians, mammals, etc. without making this pattern. Indeed, he could have created life such that each “kind” was unique and did not show any patterns of relatedness to the other “kinds.” To put this another way, why did God make crocodiles more similar to birds than to turtles?

Second, even within more narrow taxonomic groups (defined by morphology in this case), there is still actually no need for the level of genetic similarities that we observe. As I will explain, the genetic code is remarkably redundant and pliable, and you can have two very similar organisms with very different genetic codes and evolutionary histories (conversely, you can also have two very different organisms with comparatively similar genetic codes, think about crocodiles and birds again). I will explain more details about how that works in a moment, but let me give you the big picture first. There is a process known as convergent evolution wherein similar habitats and life histories cause two distantly related species to evolve to have similar morphological or physiological traits, but because they evolved independently, their genetics are quite different.

A sugar glider (left) and flying squirrel (right). Despite appearing similar, they are actually very distantly related, and each species evolved to be similar via convergent evolution.

Sugar gliders (Petaurus breviceps) and northern flying squirrels (Glaucomys sabrinus) provide a really nice example of convergent evolution. As you can see in the image, they look extremely similar, and they both possess remarkable adaptations such as a large flap of skin that they can use to glide, a large bushy tail to steer with, large forward-set eyes for good night vision, etc. If you didn’t know any better, you would probably think that they are close relatives, but you’d be very wrong. You see, sugar gliders are marsupials, whereas flying squirrels are placental mammals. So genetically, flying squirrels are far closer to you and me than to a sugar glider, and sugar gliders are far more related to kangaroos than to flying squirrels. Nevertheless, despite having very different genetic codes, they have very similar morphology (with regards to the adaptations for gliding) because they both adapted to similar habitats/life styles. There are tons of other examples like this that I could give, but hopefully you see my point: there are often multiple ways to achieve the same basic outcome, and you don’t need to have similar genetics to be morphologically similar.

Note: Lest anyone try to say that this example actually discredits evolution because it shows that morphology and genetics don’t always match up, there are other traits that distinguished them long before genetics (e.g., the pouch), so this was not a case of morphology and genetics disagreeing. Nevertheless, my point stands that both species evolved many of the same traits in different ways, and different genetic codes can achieve the same outcome.

So why is it that the genetic code is so malleable? Why can organisms with different genes evolve the same basic structures? To answer that, you need to understand how DNA works. It consists of four base pairs (represented as A, T, C, and G), and those bases are arranged in groups of three, with each group coding for an amino acid. The arrangement of those amino acids then determines what proteins are formed. Thus, a string of DNA codes for a series of amino acids which in turn forms a protein. That code is, however, highly redundant, and several different groups of bases can form the same amino acid (and therefore same protein). For example, the amino acid proline can be formed by the codes CCT, CCC, CCA, or CCG. They all form the same amino acid, and therefore, the same subsequent proteins. Indeed, most amino acids can be formed by at least two different sets of bases. Therefore, because proteins are formed from strings of numerous amino acids, you can have tons of organism all producing the same protein, but doing so via different genetic codes (there is also redundancy in the proteins themselves in that you can swap some amino acids and still get the same basic protein, this does have an effect on the function of the protein, but not a significant enough one to really make creationists’ argument persuasive).

Additionally, large portions of the genomes of most organisms are what are referred to as “junk DNA” (Rands et al. 2014 ENCODE Project Consortium). Exactly what these are and what they do is still the subject of much debate, but it does appear that they are not actively coding nearly as much as regular DNA (if at all), and mutations in those regions are unlikely to have large impacts on organisms. Indeed, when you combine the presence of junk DNA and the redundancy in the genetic code, it turns out that for many species, most mutations are actually “neutral” and have no effect on the organism (Eyre-Walker et al. 2007).

The consequence of all of this is really important. It means that there can be a lot of variation in genetic codes without it affecting functional traits (or in some cases, with it only have minor affects). In other words, an omnipotent, all powerful being could easily have designed two organisms that were nearly identical in morphology and physiology, but had extremely different genetic codes. To put that another way, as it turns out, it is not at all necessary for two species that look and behave like frogs to have similar DNA. To be clear, there certainly are conserved sections of DNA, and some sections of the genetic code are similar for functional reasons, but there is no reason why the similarities should consistently extend across the entire genome. Because of the redundancies in the genetic code, you could easily have two “frogs” with radically different genetics. Indeed, it would be entirely possible for an all-powerful all-knowing God to make four identical “frogs” one of which had protein sequences that matched those of birds, one of which had protein sequences that matched those of fish, one of which had protein sequences that matched those of reptiles, and one of which had protein sequences that matched those of mammals! Lest you think that I am pulling your leg, think about birds and crocodiles again. They are extremely different organisms with similar genetic codes.

“God did it”
I want to conclude this post by talking about the most common response that I get to all of this. More often than not, when I present this evidence to a creationist, I get the following reply, “well, those patterns are just the way that God created everything, and the common patterns exist because of a common creator, not because of a common ancestor.” There are, however, numerous problems with this response, so let me lay them out for you.

First, as I explained at length earlier, this response is logically inconsistent. If you agree that genetics show true relationships at the family level (as all creationists seem to), then you cannot arbitrarily say that they don’t work at higher levels. That is not valid reasoning.

Second, this response is what is known as an ad hoc fallacy. It is a logically invalid cop-out that is not falsifiable (thereby violating a key requirement for science) and would never be accepted by anyone who wasn’t already convinced that creationism is true. You might as well propose that Barney the dinosaur is actually a real magical dinosaur who created these patterns just to screw with us. Just like the “God did it” response, I technically can’t disprove that claim, but it is clearly not a rational argument.

Third, this response has serious logical problems because of the nature of the genetic code. There are several parts to this, but first I want to address the one that I haven’t talked about yet, and it is easiest to do that by way of example. Like most modern scientists, I have been forced to learn some computer coding, which I use to write codes for organizing and sorting data, running statistical models, simulating data, and even making fictional examples for this blog. I am, however, a pretty horrible programmer. My codes always work in the end, but they tend to be clunky, inelegant, and redundant. Further, frequently when I need to code something, I simply take an existing code and modify it. That saves me time, but it generally produces codes with irrelevant lines that are left-overs from the codes’ original functions, as well as unnecessarily complicated processes that would have been far simpler if I had started from scratch. In contrast, someone who knew what they were doing and built each code from scratch, would be able to make codes that do exactly what mine do, but theirs would be very elegant and free of redundancies and irrelevant lines of code.

It may seem like I am off topic here, but computer codes are actually remarkably analogous to genetic codes. Zeros and ones tell computers what to do in much the same way that As, Ts, Cs, and Gs tell organisms what to do. Now, ask yourself this question, if all life was created by an omnipotent, omniscient God, would you expect elegant, well-written codes that were free of redundancies, or would you expect clunky, bulky codes, that were hodgepodged together from existing codes and are full of redundancies and lines that no longer do anything? I would certainly expect the former, but what we find is the latter. The more that we examine organisms’ genetic codes, the clearer it becomes that they were made by randomly modifying existing codes, rather than writing new codes from scratch. That is why we end up with large non-functional (or barely functional) regions and codes that carry over from one group to the next. To put it simply, if God specially created modern organisms, then he is a terrible programmer.

This brings me to my final point, which is probably the most important one. As I have tried to make clear throughout this post, the genetic patterns that we see among organism are exactly what evolution predicted at every level. The relationships and patterns within groups are exactly what evolution predicted, and the overarching patterns of relationships among groups are exactly what evolution predicted. We are talking about thousands of predictions that evolution consistently got right. Further, as explained earlier, these patterns don’t have to exist for us to have organisms that look and function like our modern organisms. An all-powerful, all-knowing being could easily have created modern organisms such that there was no pattern at all. He could have scrambled protein sequences such that, for example, some bird proteins matched frogs, others matched fish, others matched reptiles, others matched trees, etc. Alternatively, he could have made extremely inconsistent patterns. He could have made some birds appear to be related to reptiles, others to fish, others to amphibians, etc. He even could have made a consistent pattern, but one that didn’t match evolution’s predictions. For example, he could have given all birds protein sequences that most closely match fishes. Any of those patterns would have been absolutely devastating for evolution. Anything other than exactly the pattern that we see would have falsified our understanding of life on this planet.

My point here is simple, if you want to say that God created all life on planet earth, then what you have to say is this: God (who according to the Bible is a God of truth, not deception) had a nearly infinite number of options for how to create life, yet out of all of those options, he chose the one and only pattern that would confirm the theory of evolution. To put that another way, life looks like it evolved. You absolutely cannot say that the evidence doesn’t support evolution, because evolution’s predictions have consistently come true. You can choose to ignore the evidence, but you cannot deny that it perfectly matches evolution’s predictions. So, you are left with saying that life on planet earth looks exactly the way it would if it evolved, but it didn’t actually evolve, God just created it in the one and only way that would make it look like it evolved.

In closing, I would like to ask you a simple question. If you are going to write off these genetic patterns as “just similarities,” if you are going to ignore this overwhelming evidence and these astounding predictions, then what would convince you that evolution was true? If the fact that it accurately predicted the genetic patterns of all living things isn’t enough for you, then what would be? What would it take to convince you that you were wrong?

Note: Some creationists try to contest arguments like this by pointing to cases where scientists have disagreed about how two groups of animals are related. There are, however numerous problems with that counterargument. First, in the modern genetic era, those debates usually only occur for pretty narrow taxonomic boundaries, while the overarching patterns remain undisputed. Second, those debates arise from one of two things: unclear morphology or unclear genetics. To put that another way, often what happens is that scientists are working with incomplete fossils and it is difficult to use them to determine how things are related. Thus, disagreements arise not because evolution was wrong but simply because scientists are working with incomplete data sets that have been preserved for millions of years (convergent evolution can also sometimes confound things even for living organisms). Other times, this arises from using a limited number of genetic markers. Sometimes, genetic patterns are hard to decipher, particular if you are only using a tiny portion of the genome, and these tools aren’t perfect, but as more and more studies are done using more and more markers, the picture becomes increasingly clear, and it overwhelming matches what we expect to see from evolution.


Abstract

Developing a thorough understanding of how ectotherm physiology adapts to different thermal environments is of crucial importance, especially in the face of global climate change. A key aspect of an organism’s thermal performance curve (TPC)—the relationship between fitness-related trait performance and temperature—is its thermal sensitivity, i.e., the rate at which trait values increase with temperature within its typically experienced thermal range. For a given trait, the distribution of thermal sensitivities across species, often quantified as “activation energy” values, is typically right-skewed. Currently, the mechanisms that generate this distribution are unclear, with considerable debate about the role of thermodynamic constraints versus adaptive evolution. Here, using a phylogenetic comparative approach, we study the evolution of the thermal sensitivity of population growth rate across phytoplankton (Cyanobacteria and eukaryotic microalgae) and prokaryotes (bacteria and archaea), 2 microbial groups that play a major role in the global carbon cycle. We find that thermal sensitivity across these groups is moderately phylogenetically heritable, and that its distribution is shaped by repeated evolutionary convergence throughout its parameter space. More precisely, we detect bursts of adaptive evolution in thermal sensitivity, increasing the amount of overlap among its distributions in different clades. We obtain qualitatively similar results from evolutionary analyses of the thermal sensitivities of 2 physiological rates underlying growth rate: net photosynthesis and respiration of plants. Furthermore, we find that these episodes of evolutionary convergence are consistent with 2 opposing forces: decrease in thermal sensitivity due to environmental fluctuations and increase due to adaptation to stable environments. Overall, our results indicate that adaptation can lead to large and relatively rapid shifts in thermal sensitivity, especially in microbes for which rapid evolution can occur at short timescales. Thus, more attention needs to be paid to elucidating the implications of rapid evolution in organismal thermal sensitivity for ecosystem functioning.

Citation: Kontopoulos D, Smith TP, Barraclough TG, Pawar S (2020) Adaptive evolution shapes the present-day distribution of the thermal sensitivity of population growth rate. PLoS Biol 18(10): e3000894. https://doi.org/10.1371/journal.pbio.3000894

Academic Editor: Simon A. Levin, Princeton University, UNITED STATES

Received: December 30, 2019 Accepted: September 14, 2020 Published: October 16, 2020

Copyright: © 2020 Kontopoulos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and source code for the analyses of the present study are available at https://doi.org/10.6084/m9.figshare.12816140.v1 and https://github.com/dgkontopoulos/Kontopoulos_et_al_thermal_sensitivity_2020, respectively.

Funding: DGK was supported by a Natural Environment Research Council (NERC) Doctoral Training Partnership (DTP) scholarship (NE/L002515/1 http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FL002515%2F1). TPS was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) DTP scholarship (BB/J014575/1 https://bbsrc.ukri.org/research/grants-search/AwardDetails/?FundingReference=BB%2FJ014575%2F1). SP was supported by NERC grants NE/M004740/1 (http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FM004740%2F1) and NE/M020843/1 (http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FM020843%2F1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: DIC, deviance information criterion HPD, highest posterior density MTE, Metabolic Theory of Ecology TPC, thermal performance curve UTD, universal temperature dependence


Oceanic Islands

Since the beginnings of the theory of evolution, Charles Darwin used remote oceanic islands to show how isolated environments seemed to give rise to new species. For example, Darwin asked the question of why the Galapagos and the Cape Verde Islands, which are off the coast of northwestern Africa, have such different species, despite having nearly identical climates.

Darwin observed that the species on both islands appeared to be closely related to the species on the nearest continent. He concluded that the animals on these isolated islands must have been originally from the nearby continent, but because they were separated from the other species on the continent, they gradually evolved into something different over thousands of years.


Analogous / Convergent Structures

Some biological characteristics are analogous (also called "convergent"), which means that they serve the same function in different species but they evolved independently rather than from the same embryological material or from the same structures in a common ancestor. An example of an analogous structure would be the wings on butterflies, bats, and birds.

Another important example would be the development of a camera-type eye in both mollusks and vertebrates. This example of analogous structures is especially useful because one of the most common claims made by religious creationists is that something as complex as an eye couldn't possibly have evolved naturally - they insist that the only viable explanation is a supernatural designer (which is always their god, though they rarely admit this outright).

The fact that eyes in different species are analogous structures proves not only that the eye could evolve naturally, but that it, in fact, evolved several times, independently, and in slightly different ways. The same is true of other analogous structures as well, and this is because certain functions (like being able to see) are just so useful that it's inevitable they will evolve eventually. No supernatural beings, whether gods or not, are necessary to explain or understand how eyes evolved multiple times.


A: VARIETY OF ORGANISMS

1. Living organisms:

a. Characteristics
b. Cell structure and functions of cell Components
c. Level of organization
i. Cell e.g. euglena and paramecium,
ii. Tissue, e.g. epithelial tissues and hydra
iii. Organ, e.g. onion bulb
iv. Systems, e.g. reproductive, digestive and excretory
v. Organisms e.g. Chlamydomonas

Candidates should be able to:
i. differentiate between the characteristics of living and non-living things.
ii. identify the structures of plants and animal cells.
iii. analyse the functions of the components of plants and animal cells.
iv. compare and contrast the structure of plant and animal cells.
v. trace the levels of organization among organisms in their logical sequence in relation to the five level of organization of living organisms.

2. Evolution among the following:

a. Monera (prokaryotes), e.g. bacteria and blue green algae.
b. Protista (protozoans and protophyta),
e.g. Amoeba, Euglena and Paramecium
c. Fungi, e.g. mushroom and Rhizopus.
d. Plantae (plants)
i. Thallophyta (e.g. Spirogyra)
ii. Bryophyta (mosses and liveworts) e.g. Brachmenium and Merchantia.
iii. Pteridophyta (ferns) e.g. Dryopteris.
iv. Spermatophyta (Gymnospermae and Angiospermae)
– Gymnosperms e.g. Cycads and conifers.
– Angiosperms (monocots, e.g. maize dicots, e.g. water leaf)
e. Animalia (animals)
i. Invertebrates
– coelenterate (e.g. Hydra)
– Platyhelminthes (flatworms) e.g. Taenia
– Nematoda (roundworms)
– Annelida (e.g. earthworm)
– Arthropoda e.g. mosquito, cockroach, housefly, bee, butterfly
– Mollusca (e.g. snails)
ii. Multicellular animals (vertebrates)
– pisces (cartilaginous and bony fish)
– Amphibia (e.g. toads and frogs)
– Reptilia (e.g. lizards, snakes and turtles)
– Aves (birds)
– Mammalia (mammals)

Candidates should be able to:
i. analyse external features and characteristics of the listed organisms:
ii. apply the knowledge from (i) above to demonstrate increase in structural complexity .
iii. trace the stages in the life histories of the listed organisms.
iv. apply the knowledge of the life histories to demonstrate gradual transition from life in water to life on land.
v. trace the evolution of the listed plants.
Candidates should be able to:
i. trace the advancement of the invertebrate animals.
ii. determine the economic importance of the insects studied.
iii. asses their values to the environment.
i. trace the advancement of multi-cellular animals.
ii. determine their economic importance.

3.a Structural/functional and behavioural adaptations of organisms.

b. adaptive colouration and its functions
c. Behavioural adaptations in social animals
d. Structural adaptations in organisms.

Candidates should be able to:
i. describe how the various structures, functions and behaviour adapt these organisms to their environment, and way of lifeCandidates should be able to:
i. Categorize countershading in fish, toads and snakes and warning colouration in mushrooms.Candidates should be able to:
i. Differentiate various castes in social insects like termites and thei functions in their colony hive.
ii. Account for basking in lizards, territorial behavour of other animals under unfavourable conditions (hibernation and aestivation). Candidates should be able to account for adaptation in organisms with respect to the following:
i. Obtaining food (beaks and legs of birds, mouthparts of insects especially mosquito, butterfly and moth.)
ii. Protection and defence (stick insects, praying mantis and toad).
iii. Securing mates (redhead male and female Agama lizards, display of fathers by birds).
iv. Regulating body temperature (skin, feathers and hairs)
v. Conserving water (spines in plants and scales in mammals).


4 DISCUSSION

More than half of all global raptor species have declining populations, and there is a significant knowledge gap on the extent of their distribution and ecological requirements (McClure et al., 2018 ). In particular, accurate distribution estimates are lacking for many tropical forest raptors (Buechley et al., 2019 Sarasola et al., 2018 ). We provide an analytical framework for applying predictive spatial models to address these fundamental issues to a tropical forest raptor. More broadly, we propose this analytical framework as an efficient and cost-effective approach to tackling this problem across all taxa. Using a PPM regression framework is now viewed as one of the most effective methods to determine species distributions and relative abundance (Aarts et al., 2012 Isaac et al., 2019 Renner et al., 2015 ), as supported by our results. Using climatic and topographical predictors resulted in high model predictive performance, defining in more detail the spatial and environmental requirements for the harpy eagle across its geographic range. However, we recognize that including predictors such as landcover and human impact, which are changing rapidly, would improve predictions. These, however, will be analyzed and presented elsewhere.

4.1 Spatial requirements

How species are distributed in geographic and environmental space is fundamental to conservation planning (Loiselle et al., 2003 Pearce & Boyce, 2006 ). Yet accurate and reliable spatial information, such as geographic range size and environmental constraints, is often lacking in many tropical biodiversity assessments (Cayuela et al., 2009 Tobias et al., 2013 ), and specifically for Neotropical raptors (Sarasola et al., 2018 ). Using a PPM framework enables the predictions given here to be interpreted as areas of relative abundance (Phillips et al., 2017 Renner et al., 2015 ) under the assumption that historical habitat is still intact. Building on a previous SDM (Miranda et al., 2019 ), our continuous prediction adds further spatial detail showing a discontinuous distribution. This is likely a consequence of patchy environments, resulting in spatial heterogeneity in harpy eagle distribution. Miranda et al. ( 2019 ) used both climatic and vegetation predictors, and there is a close visual correspondence between their predictions and both our continuous and binary models. This suggests that at the continental scale, biologically relevant climatic and topographical predictors alone can accurately predict the distribution for the harpy eagle.

Our models refine previous coarse estimates of harpy eagle distribution (Birdlife International, 2017 Ferguson-Lees & Christie, 2005 ), providing an empirically derived range size to complement the species’ current IUCN status. Our binary threshold polygon estimate of geographic range size (Figure 2 9,844,399 km 2 ) was 11% smaller than the current IUCN polygon (11,064,295 km 2 ), and our estimated EOO (13,050,940 km 2 ) was 25.9% less than the current IUCN EOO (17,600,000 km 2 ). Based on these figures, we recommend reviewing the IUCN distributional area for the harpy eagle, which can overestimate avian geographic range sizes (Jetz et al., 2007 Peterson et al., 2016 Ramesh et al., 2017 ). Specifically, the removal of semiarid areas (such as the Caatinga in eastern Brazil) from across the IUCN range would show a more realistic geographic distribution. The Caatinga area had low predicted suitability, no current or historical occurrence records, and was not predicted suitable for the harpy eagle including during the last glacial maximum (LGM). Similarly, the Cerrado (in central Brazil) was not predicted as suitable for the harpy eagle either during the LGM, and all recent records for the species show no evidence of breeding in the area. Although early naturalists reported breeding harpy eagles in this region (Sick & Barruel, 1984 ), there is no evidence of a functional population and the area should be removed from the IUCN range polygon (and any present range projections) following IUCN guidelines for not including areas where the species does not exist (IUCN, 2019 ).

4.2 Species–environment relationships

The continuous model highlighted distinct areas of high environmental suitability (Figure 1), with the binary model closely matching the primary vegetation types for recognized harpy eagle habitat (lowland tropical broadleaf forest, Beck et al., 2018 ). Thus, in the Chocó biogeographic region of north-west Ecuador and south-west Colombia west of the Andes, the current model defined areas of high environmental suitability, which correlate with new records of harpy eagles in the Pacific slope region (Muñiz-López, 2005 Muñiz-López et al., 2007 Zuluaga et al., 2018 ). However, due to continued habitat loss in this area and across the species range, climatically suitable areas predicted for some regions may over-represent suitability where there is no longer harpy eagle forest habitat. Our models also defined previously unrecognized areas of high environmental suitability in south-east Colombia, northern Guyana, and along the east Andean slope of Peru and Bolivia. All these regions may hold viable populations of harpy eagles, with further research and continued surveys in these areas recommended where possible.

Environmental suitability predicted for the harpy eagle largely correlates with habitat selection studies from Amazonian Peru (Robinson, 1994 ). Here, highest frequency of harpy eagle sightings was recorded in mature flood plain forest, with high nesting densities below 300 m elevation in lowland humid forest in Darien, Panama (Vargas González & Vargas, 2011 ), analogous to the environmental suitability predictions here. Due to the rarity and large home range sizes of harpy eagles, Thiollay ( 1989 ) was not able to provide population density estimates from French Guiana, but suggested harpy eagles are rare but widespread throughout the largely tropical lowland forest in the region, consistent with our results. Although largely thought to be extirpated from much of Central America, our models identify areas of high suitability for harpy eagles along the Caribbean slopes of Costa Rica, Honduras, Nicaragua, and Panama (Figure S3), which should be prioritized for continued surveys and habitat protection.

Using the combined analytical approach enabled a further development of the spatial modeling process by unraveling the preferred environmental space and ecological conditions where harpy eagle abundance should be at its highest (Osorio-Olvera et al., 2019 VanDerWal et al., 2009 ). Climatic Moisture Index (CMI) was the most important environmental variable defining harpy eagle distribution, with a preferred CMI =

0.4 (Figure 3), along with the highest model gain when used solely in a jackknife test, demonstrating its importance to account for harpy eagle distribution. This indicates a preference for wet, moist environments, correlating with lowland tropical forest across Central and South America (Beck et al., 2018 Willmott & Feddema, 1992 ), and suggests that CMI may be a useful surrogate predictor for habitat in tropical areas. Aligned with CMI and lowland tropical forest distribution was the positive response to higher minimum temperatures in the warmest month (Figure 3). Harpy eagle environmental suitability was highest in areas with a minimum temperature of

24°C, reflected in the stable temperature conditions found across lowland tropical forests.

Assessing harpy eagle distribution in environmental space revealed similar patterns of environmental tolerances to the geographic models (Figures 4 and 5), with CMI having the highest positive correlation with harpy eagle occurrence. However, precipitation in the wettest month was also highly correlated with harpy eagle occurrence (Table 4), following the general observation for tropical regions that seasonal rainfall patterns are the main limiting factor for primary productivity and therefore species distributions (Schloss et al., 1999 Williams & Middleton, 2008 ). The ENFA confirmed the specialized environmental requirements for the harpy eagle, strongly linked to CMI and precipitation, which are likely operating as useful surrogate predictors of lowland tropical forest habitat. Importantly, minimum temperature of the warmest month (MTWM) had a high negative coefficient value on the specialization axis (Table 4). This indicates that MTWM is a key climatic predictor restricting harpy eagle distribution, linked to harpy eagle preference for lower elevations (Muñiz-López, 2008 Piana, 2007 Vargas González & Vargas, 2011 ). Harpy eagle nests are rarely found above an altitude of 300 m (Vargas González & Vargas, 2011 ), and as temperature and elevation are closely correlated it seems likely the harpy eagle is negatively responding to lower temperatures at higher elevations restricting breeding distribution.

4.3 Paleo-distributions

The two paleoclimate predictions given here place current harpy eagle distribution in context. During the LGM, highest suitability was centered on northern and western Amazonia and present-day Panama. This follows current evidence that suggests during the LGM much of Amazonia was forested (Mayle et al., 2004 ), contrary to the rainforest refugia hypothesis (Haffer, 1969 ). However, forest structure was likely quite different from the present-day, due to lower temperatures, rainfall, and atmospheric CO2 (Mayle et al., 2004 ), resulting in mixed-forest communities. Climate reconstructions from Amazonia during the LGM show that temperatures were 5°C cooler than today (Guilderson et al., 1994 Stute et al., 1995 ) and that rainfall was spatially highly variable, as it is in the present-day. Thus, dry forest-savannahs may have dominated the region of central and southern Amazonia during the LGM, which may explain the low environmental suitability for the harpy eagle in this region from the LGM paleoclimate model.

During the Mid-Holocene, the continuous prediction was similar to the current model with expansion of high suitability across Amazonia and north into Central America (Figure S7, top right, Appendix 3). This may be explained by the correlation of these areas with expansion of deciduous broadleaf forest in the region during the Mid-Holocene, ultimately related to changing precipitation levels (Mayle et al., 2004 ). The increase in distributional area size during this period correlates with a population expansion identified from genetics from 60,000 cal yr BP, well before the LGM, and subsequently through the Mid-Holocene (Lerner et al., 2009 ). The population expansion prior to the LGM occurred with climatic changes in Amazonia, leading to a reduction of tropical forest (Mayle et al., 2004 ), followed by expansion of forest through the LGM and Mid-Holocene up to pre-Industrial times. Thus, harpy eagle distribution area is strongly associated with changing climatic conditions (and therefore vegetation), which suggests a potential reduction in range size under future drier climate change conditions predicted across much of Central and South America (da Costa et al., 2010 ). However, our stable refugia prediction identified key areas of stable conditions since the LGM where a suitable climatic envelope for the harpy eagle is likely to persist into the future (Figure 6). We recommend these areas be prioritized for conservation and research, holding some encouragement for the future survival of the species as long as habitat can be maintained.

Explaining the observed distribution and ecological constraints of an organism by reference to its environmental requirements is one of the central goals in ecology (Krebs, 2009 ). Species at high trophic levels with slow life histories are often at increased risk of extinction (Purvis et al., 2000 ). Therefore, understanding the environmental processes regulating distribution of apex predators is an especially pressing conservation need. By refining previous range estimates using relevant abiotic variables (including those that may act as vegetation surrogates), our models define the ecological processes shaping both current and past harpy eagle distribution. However, future distribution models should include variables such as biotic interactions, landcover and human impacts at broad and fine scales to improve current predictions, and project into future climate change scenarios. With recent work demonstrating strong relationships between suitability predictions from SDMs and species abundance (Osorio-Olvera et al., 2020 Weber et al., 2017 ), we confirmed the suitability of spatial point process models to deliver cost-effective and reliable first estimates of relative abundance for species conservation management. Having accurate distributional data on the current ranges of tropical birds and raptors has long been a priority in the Neotropics (Bierregaard, 1998 Snow, 1985 ). Using a range of spatial modeling methods, we were able to establish a baseline of ecological constraints for the harpy eagle that may help to better plan its conservation across its vast continental distribution.


A REPERTORY OF BASIC DEVELOPMENTAL MECHANISMS

Cell autonomous mechanisms

Cell autonomous developmental mechanisms all involve one cellular behavior:mitosis. Thus, cells do not interact mechanically or by signaling. See Fig. 1.

Schematic examples of the basic developmental mechanisms. Division of an heterogeneous egg: different parts of the egg bind different molecules(indicated by different shading) resulting in different blastomere cells. Asymmetric mitosis: molecules are differentially transported into different parts of a cell resulting in different daughter cells. Internal temporal dynamics coupled to mitosis: cells that have oscillating levels of molecules before their division can produce spatial patterns. Hierarchic induction: inducing cell (gray) affects neighboring cells but the induced cells (white) do not affect the production of the inducing signal. Emergent induction: inducing cell affects neighboring cells,which in turn signal back affecting the production of the inducing signal. Directed mitosis: consistently oriented mitotic spindles may direct tissue growth. Differential growth: cells dividing at a higher rate(gray) can alter tissue shape. Apoptosis: transformation of an established pattern into another can result from apoptosis affecting specific cells (gray). Migration: cells can migrate to a new location. Adhesion: a change in pattern can result if a set of cells have differential adhesion properties (strong adhesion among gray cells). Contraction: differential contraction of cells can cause buckling of a tissue. Matrix swelling, deposition, and loss: matrix swelling can cause budding.

Schematic examples of the basic developmental mechanisms. Division of an heterogeneous egg: different parts of the egg bind different molecules(indicated by different shading) resulting in different blastomere cells. Asymmetric mitosis: molecules are differentially transported into different parts of a cell resulting in different daughter cells. Internal temporal dynamics coupled to mitosis: cells that have oscillating levels of molecules before their division can produce spatial patterns. Hierarchic induction: inducing cell (gray) affects neighboring cells but the induced cells (white) do not affect the production of the inducing signal. Emergent induction: inducing cell affects neighboring cells,which in turn signal back affecting the production of the inducing signal. Directed mitosis: consistently oriented mitotic spindles may direct tissue growth. Differential growth: cells dividing at a higher rate(gray) can alter tissue shape. Apoptosis: transformation of an established pattern into another can result from apoptosis affecting specific cells (gray). Migration: cells can migrate to a new location. Adhesion: a change in pattern can result if a set of cells have differential adhesion properties (strong adhesion among gray cells). Contraction: differential contraction of cells can cause buckling of a tissue. Matrix swelling, deposition, and loss: matrix swelling can cause budding.

Division of a heterogeneous egg

With few exceptions (mammalian and some turbellarian clades) different parts of the egg contain different protein or mRNA gene products. Non-uniformities in the egg may result from asymmetric assembly of materials from follicle or nurse cells during the course of oogenesis, or non-uniformities inherent to all cells(Gilbert, 2000 Muller, 2001). In some cases,as in Drosophila (Riechman and Ephrussi, 2001), the oocyte is patterned by inductive interactions with the cells in the gonads.

Asymmetric mitosis

Nearly all cells exhibit some kind of internal polarity causing gene products or mRNAs to be distributed into different parts of a cell and become incorporated into different daughter cells. The difference with the previous mechanism is that here gene products or mRNAs are asymmetrically transported to the future daughter cells while the mother cell is dividing, whereas in the previous case no transport occurs during cleavage. A non-random pattern results from asymmetric mitosis if cells take invariable positions after division. Asymmetric mitosis is found in the early cleavage divisions of many groups such as nematodes (Bowerman and Shelton, 1999), mollusks(Collier, 1997), ctenophores(Freeman, 1976) and annelids(Bissen, 1999), but also in later processes such as the formation of the central nervous system of Drosophila (Doe and Bowerman,2001). In some cases, cell signaling may also determine which daughter cell will receive which set of factors(Doe and Bowerman, 2001).

Internal temporal dynamics coupled to mitosis

Temporally cyclical expression of genes can produce a pattern if oscillation becomes decoupled from cell division. Cyclical gene expression can result from closed chains of molecular events that trigger each other in a sequential fashion (`dominoes') or by genetic networks with inherent oscillatory dynamics (`clocks') (Murray and Kirschner, 1989). If, when cells divide, one of the daughter cell stops or resets its temporal dynamics, then cells can acquire different states depending on the time of their mitosis. As in the case of asymmetric mitosis, an invariable positioning of cells is required in order to generate non-random patterns. This mechanism has been proposed for the segmentation of hirudean leeches, oligochaetes (Weisblat et al., 1994), short germ-band insects(Newman, 1993 Salazar-Ciudad et al., 2001b),the somitogenesis of vertebrates (Newman,1993) and in the formation of morphological structures, such as the limb and the tail, involving `progress zone' growth(Duboule, 1995). Experimental evidence for this mechanism is still limited, but in vertebrates it has been shown that expression of genes involved in somitogenesis exhibit oscillatory behavior (Maroto and Pourquié,2001).

Inductive mechanisms

Cells can affect each other by secreting diffusible molecules, by means of membrane-bound molecules or by chemical coupling through gap junctions. A large number of mechanisms which use only these developmental functions are capable of pattern formation. In inductive mechanisms tissue pattern changes as a direct consequence of changes in cell state. This, in turn, is due to the processing or interpretation of signals sent by other cells. In certain cases,inductive pattern formation assumes a simple form, that is, one cell or tissue type will change the state of another cell or tissue type from what it would have been without the interaction, with no morphological consequence following directly from this. In other cases a morphological consequence accompanies, or follows closely upon, the change in state of the induced target cells. Since our aim here is to show how such composite inductive-morphogenetic mechanisms comprise highly divergent categories of developmental mechanisms,we will focus initially on the simple case without immediate morphological consequences.

Examples of simple inductive mechanism are mesendoderm induction in amphibians by maternal factors produced by the Nieuwkoop center(Harland and Gerhart, 1997),and the short-range signaling hierarchy in the echinoid blastula, in which the oral-aboral axis is established by signaling from the micromere tier to the macromeres, which, in turn, signal the mesomeres(Davidson et al., 2002). Other examples include generation of the gradient patterns of gap gene products in the Drosophila syncytial blastula induced by the patterns of maternal gene products, and the subsequent induction of striped patterns of pair-rule gene products, based on these gap patterns(Rivera-Pomar and Jackle,1996).

Many basic inductive mechanisms appear to be based on hierarchicgenetic networks (Salazar-Ciudad et al.,2000). In such networks a territory (or a single cell) may signal another, and this second may respond to such signaling by sending a signal back. This back-signal, however, does not affect the signaling rate or capacity of the first territory. Inductive mechanisms can also be based on emergent genetic networks in which cells or territories send signals in a way that is affected by neighboring cells' responses to such signals(Salazar-Ciudad et al., 2000). Emergent genetic networks, which comprise reaction-diffusion mechanisms(Turing, 1952 Meinhardt and Gierer, 2000 Salazar-Ciudad et al., 2001a)but also include other mechanisms in which cells affect one another in reciprocal ways, such as those used in the Notch-Delta signaling system (for details, see Salazar-Ciudad et al.,2000), have been suggested to underlie limb skeletal patterning(Newman and Frisch, 1979 Miura and Shiota, 2000a Miura and Shiota, 2000b),pigment patterning in the butterfly wing(Nijhout, 2001), feather bud spacing in avian skin (Jiang et al.,1999 Prum and Williamson,2002) and fish colour patterns(Kondo and Asai, 1995). Theoretical studies have indicated that hierarchical and emergent mechanisms together exhaust the possibilities for simple inductive mechanisms(Salazar-Ciudad et al., 2000),and have explored their variational properties(Salazar-Ciudad et al.,2001a).

Morphogenetic mechanisms

A number of patterning mechanisms use cellular behaviors other than signaling (although signaling may have been active at a prior stage). These mechanisms alter pattern by affecting form. This can be defined as a mechanism that changes the relative arrangement of cells over space without affecting their states.

Directed mitosis

Intracellular or extracellular signals can affect the direction of the mitotic spindle. Once the mitotic spindle assumes a set direction, new cells are forced to be positioned at specific places. The central nervous system of Drosophila, for example, forms by the dorsally directed budding of presumptive neuroblasts from the ectoderm (Broadus and Spana, 1999). This produces two cordons of neuroblasts that extend longitudinally in the ventral part of the embryo. Asymmetric mitosis and inductive signals are involved in determining which cells will become neuroblasts, but their localization is ultimately determined by the control of mitotic spindle orientation. External inductive signals have been shown to direct the mitotic spindle in the first divisions of C. elegans(Goldstein, 2000) and in the leech (Bissen, 1999). In ctenophores the form of the whole blastula is attained through precise regulation of the orientation of the mitotic spindle(Freeman, 1976).

Differential growth

A change in a pattern can be produced if, in a previously existing pattern,cells with different states divide at different rates. The new pattern depends on the previous pattern, the relative rates and directions of mitosis and on other epigenetic factors such as the adhesion between cells and the influences of surrounding matrices. One such example is the establishment, maintenance,and waning of the growth plate during the formation of long bones in vertebrates (Sandell and Adler,1999).

Apoptosis

A pattern can be transformed into another if some of the cells undergo apoptosis. Apoptosis can be strictly dependent on a cell's lineage, or triggered by interaction, or abrogation of interaction, with surrounding cells(Meier et al., 2000). Although apoptosis, in the first instance, is a cell autonomous function, the patterning consequences depend on the existence and arrangement of surrounding cells. The associated developmental mechanism is thus morphogenetic rather than cell autonomous. A wide range of developmental processes are dependent on apoptosis, including the outflow tract and valves of the heart(Poelman et al., 2000), development of neural circuitry in the brain(Kuan et al., 2000), and freeing up of the digits during vertebrate limb development(Chen and Zhao, 1998). In particular, it has been shown that the final shape of the interdigital membranes depends on the amount of apoptosis in such membranes(Gañan at al.,1998).

Migration

Cells can rearrange their relative positions without changing their states simply by migrating. Migration can be directionally random, random but speeded up by an ambient chemical signal (`chemokinesis'), or have a preferred direction in relation to a chemical gradient (`chemotaxis') or an insoluble substrate gradient (`haptotaxis'). While mesencephalic neural crest cell migration in the mouse appears to be controlled in part by a chemotactic response to members of the FGF family of growth factors(Kubota and Ito, 2000),migration of trunk neural crest cells in the chicken appears to depend on more random dispersal mechanisms (Erickson,1988). The migration of premuscle cells into the developing vertebrate limb is regulated by both chemokinetic and chemotactic responses to hepatocyte growth factor (Lee et al.,1999). Regardless of the migratory mechanism, specificity of outcome will also, in general, be controlled by the adhesive environment of the destination sites (Lallier et al.,1994).

Differential adhesion

Cell adhesion is the defining property of multicellular organisms. It is an indispensable requirement for cell shape, differentiation and migration. A large, but limited number of pattern changes can be produced in tissues by constituent cells expressing different adhesion molecules or the same molecules at different levels. Hence, differential adhesion can cause subpopulations of cells to sort out into distinct groups. In a solid epithelioid tissue compartments may have straight or curved boundaries, or engulf or be engulfed by each other, depending on the magnitude of the adhesive differences (Steinberg,1996). If adhesion is expressed nonuniformly on the surfaces of individual polarized cells, interior spaces or lumens can form in solid tissues (Newman and Tomasek,1996). In planar epithelia, polar expression of adhesion along with differential adhesion of subpopulations can produce invaginations,evagination, placodes and the formation of cysts(Newman, 1998). Convergent extension, a reshaping of tissue masses during gastrulation which involves cell intercalation (Keller et al.,2000) can also be accounted for by energy minimization in populations of anisotropic cells (Zajac et al., 2000), particularly those that exhibit `planar cell polarity' (Mlodzik, 2002). In well-studied cases some of these processes also involve mitosis or cell contraction, but this is not strictly required. Differential adhesion and cell polarity or anisotropy are in principle sufficient to achieve these morphological outcomes. Altered adhesion is also the final step in the set of transformations known as epithelial-mesenchymal and mesenchymal-epithelial conversions. An example of the first occurs during development of the neural crest (Le Douarin and Kalcheim,1999) and the second occurs during the formation of the kidney tubules (Davies and Bard,1998).

Contraction

Individual cell contraction mediated by actin-myosin complexes can have morphogenetic effects on neighboring cells and the tissue as a whole. Contraction of tissues during development is thought to trigger shape change and determine the character of the morphological outcomes(Beloussov, 1998). Contraction is propagated in epithelial tissues by direct physical attachment and in mesenchymal tissues by the extracellular matrix. In a planar epithelium contraction can also lead to buckling, and thus invagination or evagination(Newman, 1998). A recent study considered the role of myocardial contraction in trabeculation in the developing heart (Taber and Zahalak,2001).

Matrix swelling, deposition and loss

The cells of mesenchymal and connective tissues are surrounded and separated by semi-solid or solid extracellular matrices. Changes in pattern may be accomplished by increased hydration or swelling of a preexisting matrix, increase in the amount of matrix separating the cells, or matrix degradation. During development of the avian eye, the primary corneal stroma swells in anticipation of its invasion by mesenchymal cells from the periphery(Hay, 1980). This swelling has been found to be controlled by tissue-specific, developmentally regulated proteolysis of collagen IX (Fitch et al.,1998). Vertebrate limb chondrogenesis is an example of a developmental process in which cellular rearrangement occurs as a result of matrix deposition. Here there is dispersal of newly differentiated chondrocytes within compact precartilage mesenchymal condensations and consequent flattening of more peripheral mesenchyme into a perichondrion(Hall and Miyake, 2000). Developmentally regulated matrix degradation, particularly of basement membrane components, has the capacity to alter cell positional relationships. Such changes are important in triggering new developmental events, for example during sea urchin gastrulation (Vafa et al., 1996) and mammary gland morphogenesis(Werb at al., 1996).


Results

In the case of total water loss rate, treatment temperature(χ 2 (1)=153.5, P<0.0001) and species identity (χ 2 (4)=285.4, P<0.0001)contributed significantly to the model, verifying that there is indeed considerable variation amongst species in the total rate of water loss(Fig. 1). Mean rates of water loss for each species covaried significantly with the mean annual rainfall of the area in which each species was collected (rs=0.9, P=0.037). Summary statistics for the DGC and water loss characteristics also revealed substantial variation amongst species in these parameters (Table 2). The best-fit model for total water loss rate included only cuticular water loss rate and spiracular water loss rate (full model AIC=441.4, with just these terms AIC=438.1) (Table 3). Therefore, both cuticular water loss and spiracular water loss contribute to variation in total water loss rate (each of the latter are affected by temperature, which has an effect on cuticular transpiration and DGC frequency,but temperature tends not to enter the models significantly when forced in initially using a Type I approach - data not shown). When wing status was incorporated, the best-fit model included wing status, cuticular water loss rate and spiracular water loss rate, with a lower AIC (424.12) than the model excluding wing status. However, Quinn and Keough(2002) have pointed out that the AIC should be interpreted with caution when both categorical and continuous predictors are included in the model. In this context the reduction in the deviance/d.f. value (Table 3) compared with the model not including wing status suggests that there is little difference between the two models.

(A) Water loss rate (mg h -1 ) adjusted for body mass, and (B)proportional contribution of spiracular to total water loss (%), adjusted for body mass, at 20°C in the five Scarabaeus species investigated in this study. Variation in rate of water loss is in the direction expected from the mean annual rainfall of the area from which the species was collected. Values are means ± s.e.m. Gari, S. gariepinusStri, S. striatus Gale, S. galenus Rust, S. rusticus West, S. westwoodi.

(A) Water loss rate (mg h -1 ) adjusted for body mass, and (B)proportional contribution of spiracular to total water loss (%), adjusted for body mass, at 20°C in the five Scarabaeus species investigated in this study. Variation in rate of water loss is in the direction expected from the mean annual rainfall of the area from which the species was collected. Values are means ± s.e.m. Gari, S. gariepinusStri, S. striatus Gale, S. galenus Rust, S. rusticus West, S. westwoodi.

Generalized linear model of the explanatory variables on total water loss rate

Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . χ 2 (deviance/d.f.) . P .
Excluding wing status
log10(Cuticular H2O8.621 −325.7 219.3 0.0001
log10(Spiracular H2O) 2.277 (151) −225.5 (149.15) 18.83 (0.987) 0.0001
Including wing status
log10(Cuticular H2O) 9.240 −323.0 229.9 0.0001
log10(Spiracular H2O) 2.427 −219.7 23.2 0.0001
Wing status (150) −216.1 (134.44) 15.9 (0.896) 0.0001
Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . χ 2 (deviance/d.f.) . P .
Excluding wing status
log10(Cuticular H2O8.621 −325.7 219.3 0.0001
log10(Spiracular H2O) 2.277 (151) −225.5 (149.15) 18.83 (0.987) 0.0001
Including wing status
log10(Cuticular H2O) 9.240 −323.0 229.9 0.0001
log10(Spiracular H2O) 2.427 −219.7 23.2 0.0001
Wing status (150) −216.1 (134.44) 15.9 (0.896) 0.0001

The best-fit model for spiracular water loss rate included CF-period duration, O-period duration, and CO2 (AIC=-187.3,compared to -184.8 for the full model)(Table 4). When wing status was considered, the best-fit model included CF-period duration, O-period duration, CO2 and wing status (AIC=-189.2). However, CF-period duration was not significant(χ 2 =2.995, P=0.08). Therefore, the best fit model included wing status, O-period duration and CO2(Table 4). One explanation for the exclusion of CF-period duration from the model including wing status is the particularly prolonged duration of the CF period in S. gariepinus, and to a lesser extent in S. striatus (see Table 2).

Generalized linear model of the explanatory variables on spiracular water loss rate

Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . ## 2 (deviance/d.f.) . P .
Excluding wing status
log10(CF duration) −0.138 92.5 10.3 0.0014
log10(O duration) 0.387 85.0 25.2 0.0001
log10(CO2) 0.496 (150) 47.0 (2.537) 101.2 (0.017) 0.0001
Including wing status
log10(O duration) 0.263 89.1 18.0 0.0001
log10(CO2) 0.654 31.3 133.6 0.0001
Wing status (150) 92.5 (2.522) 11.1 (0.017) 0.0009
Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . ## 2 (deviance/d.f.) . P .
Excluding wing status
log10(CF duration) −0.138 92.5 10.3 0.0014
log10(O duration) 0.387 85.0 25.2 0.0001
log10(CO2) 0.496 (150) 47.0 (2.537) 101.2 (0.017) 0.0001
Including wing status
log10(O duration) 0.263 89.1 18.0 0.0001
log10(CO2) 0.654 31.3 133.6 0.0001
Wing status (150) 92.5 (2.522) 11.1 (0.017) 0.0009

In the case of cuticular water loss, mass and treatment temperature contributed significantly to the model, as did wing status(Table 5). To obtain an indication of the scaling of cuticular and respiratory water loss rate, the relationship between log10(mass) and log10(cuticular water loss), and log10(mass) and log10(spiracular water loss) was investigated for measurements made at 20°C (for which most data were available) using generalized linear models. Cuticular water loss scaled significantly (P=0.002) as mass 0.721±0.234 , which is not significantly different from a value expected from geometric considerations alone (mass 0.667 , t(37)=0.231, P>0.5). Spiracular water loss scaled significantly(P=0.0009) as mass 0.531±0.160 , which is also not significantly different from a value expected from geometric considerations alone (mass 0.667 , t(37)=-0.85, P>0.4). By contrast, CO2 scaled significantly (P=0.0001) as mass 1.284±0.160 .

Generalized linear model of the explanatory variables on cuticular water loss rate

Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . χ 2 (deviance/d.f.) . P .
Temperature 0.216 −347.4 46.3 0.0001
Log10(mass) 9.184 −345.8 43.0 0.0001
Wing status −327.5 6.14 0.013
(150) (608.3) (4.05)
Explanatory variables . Parameter estimate (d.f.) . Type III log-likelihood (deviance) . χ 2 (deviance/d.f.) . P .
Temperature 0.216 −347.4 46.3 0.0001
Log10(mass) 9.184 −345.8 43.0 0.0001
Wing status −327.5 6.14 0.013
(150) (608.3) (4.05)

Materials and methods

We set up a local basic local alignment search tool (BLAST) database of the proteins encoded in 26 completely sequenced fungal genomes (A. niger, A. nidulans, A. terreus, A. flavus, A. oryzae, A. clavatus, N. fischeri, A. fumigatus Af293, A. fumigatus CEA10, C. immitis, C. posadasii, P. chrysogenum, U. reesii, S. sclerotiorum, F. graminearum, F. oxysporum, F. verticillioides, M. grisea, N. crassa, C. globosum, H. jecorina (T. reesei), N. haematococca (F. solani), P. chrysosporium, S. nodorum (P. nodorum), C.neoformans, U. maydis). To find candidate ACE1-like clusters in other fungi, we used a two-step process outlined below.

In the first step, each protein encoded by the M. grisea ACE1 cluster was used as a query in protein-protein BLAST (BLASTP) searches against this database, and for each query the top 25 hits were retained provided that their E-values were less than 1e-4. Each set of proteins was aligned using ClustalW [49] and poorly aligned regions were removed using Gblocks [50]. Sequence alignments are available as Additional data file 2. Maximum likelihood trees were constructed using PHYML [51] with the JTT amino acid substitution matrix and four categories of substitution rates. Bootstrapping was done using the default options in PHYML with 100 replicates per run. To avoid long branch attraction problems we withdrew highly divergent sequences and repeated the alignment and tree reconstruction steps on the new sets. We also verified at each step that the alignment obtained after running Gblocks represented at least 30% of the initial protein sequence. Genes were considered as orthologs of an M. grisea ACE1 cluster gene if they grouped in a monophyletic clade with a bootstrap support of ≥70%.

Many of the genes identified in this first step were located in gene clusters. For each cluster so identified (defined as the presence of at least two homologs of M. grisea ACE1 cluster genes adjacent to one another) we then made a second step of analysis, examining any other genes that are physically located within these clusters but which were not picked up at the first step (either because their BLASTP E-values were too weak, or because they were not in the top 25 hits when the database was searched). This process added genes CHG05286.1, CHG05287.1, SNU00307.1 and FVEG_12610 to the analyses.