Month: July 2017

Catfish and the Lazarus Taxon Problem

Some time ago I mentioned the phenomenon of Lazarus taxa. For those who don’t remember, Lazarus taxa are taxa that are known from fossils that appear to disappear from the fossil record and then re-emerge (“from the dead”) significantly later. The concept gets tossed around a lot when discussing the possibility that groups thought to be extinct might still persist. One of the central questions in evaluating the odds of Lazarus taxa occurring is the completeness of the fossil record. Now, nothing I’m going to say is going to be surprising to a paleontologist, but the fossil record is, as far as completeness goes, pretty terrible. I’ve sometimes heard the fossil record compared to a TV show that is missing chunks. It’s probably more like five non-consecutive frames from an hour long show.

One of the clades that demonstrates this well, and that I happen to know about, is catfish. By catfish I mean the Siluriformes, a quite large group of mostly freshwater fish, some of whom don’t look very much like the “classic” catfish. Catfishes often have a heavy-duty first fin ray in their pectoral fins which happens to preserve well and is identifiable to major group. (This fin spine is meant to stab potential predators, as more than a few recreational anglers have found out.) Because of this catfish fossils are sometimes identifiable even in a severely fragmented state. This is good because fish bones are pretty lightweight compared to tetrapods and fish skeletons are easily scattered and the bones broken.

Diogo (2004) discusses a fossil from the catfish genus Corydoras dated to the Paleocene. Corydoras is a common modern genus, found not only as many species in South America but also as the “cory catfish” in pet stores across the world. The Paleocene period is just past the massive extinction event that brought the Mesozoic1 to a cataclysmic end. To give an idea how unlike our modern world the Paleocene was consider that mice, bears, and cats had not yet evolved, and that the ancestors of whales were walking on land at this time. And yet here’s a modern genus, Corydoras. Moreover, Corydoras is endowed with bony, armored plates that wrap around its body, making it potentially much more fossilizable than other fish (although it is a small fish). Given the success of the modern genus we might expect to find that the world is littered with Corydoras fossils. Instead, Diogo (and I have seen nothing more recent to suggest that new discoveries have changed the picture) notes that there are no other Corydoras fossils until, perhaps, recent sub-fossils. There’s a 56 million year fossil lacuna for this genus.

However, the story gets more interesting. Diogo was arguing for what now seems to be a seriously minority position about the age of the Siluriformes but what makes this an argument is that everyone is sure that we do not have the earliest Siluriformes in the fossil record. A short digression into geologic timelines (really just what I needed to teach myself to follow the arguments I was reading) is needed here. We live in a large era called the Cenozoic. The prior large era is the Mesozoic, known for dinosaurs. The Mesozoic is coarsely divided into three pieces: the Triassic (oldest), Jurassic (middle), and Cretaceous (most recent2). The Cretaceous is divided more finely into a number of periods which I can’t remember because only two have catfish fossils, the last two, the Campanian and the Maastrichtian. The Maastrichtian is the very last sliver of time before the disaster that closes out the whole Mesozoic.

The very earliest catfish fossil comes from the Campanian in Argentina but skip forward just briefly in time to the Maastrichtian and catfish fossils are found in Bolivia (de Muizon et al., 1983; Gayet et al., 2001), India (Cione & Prasad, 2002), Niger (possibly, I cannot find the original description, just a mention in Cione & Prasad [2002]), the western United States (possibly, the species Vorhisia is not unanimously agreed to be a siluriform, Frizzel & Koenig, 1973), and Spain (Pena & Soler-Gijon, 1996, possibly not actually Maastrichtian but just beyond, the authors place the fossil on the border between the Maastrichtian and the following era). Also importantly, even within just the undisputed (as far as I know) Bolivian finds at least two major catfish clades are present. de Muizon et al. identify these two clades as the families Ictaluridae and Ariidae. Even if these exact assignments were to be disputed it seems unlikely that the total diversity of catfish at this site will be reduced. What this means is that the snapshot we have of early catfish is a diverse, widespread clade. Clades don’t start this way. Clades start as small, localized groups with low diversity. They then spread and diversify. It’s a bit like looking through a photo album and finding that the earliest photo you have of someone who you are researching is a photo of them holding their first child. You know they are not a child themselves but you also don’t know quite how old this makes them. All you know is that the beginning is further back.

I won’t get into the (long, complex) debate about when the first catfish swam the rivers of Earth (and the first catfish probably did swim in rivers, not lakes) but what we know is that the Siluriformes have a significant ghost lineage, the name for a lineage that is unrepresented in the fossil record. When we discuss things like Lazarus taxa we should be aware just how many creatures we know existed during times in which they did not leave fossil records.

Perhaps fittingly, since the last Lazarus taxa article discussed the coelacanth, there are particular biases against bony fish in the fossil record. Becker et al. (2009) discuss our friend Vorhisia and the other osteichthyan fish found in that time and region. They also discuss why the fossil record for Cretaceous fish is so poor. They list four things that bias the fossil record against bony fish.

  1. Fish skeletons are lightly built and fall apart easily. Many are from small animals. The bones can be destroyed easily and what is collected are frequently only a few more durable parts of the skeleton (like teeth).
  2. Fish bones are often hard to identify because a lot of basic work remains to be done. Perhaps someone has already found a Jurassic catfish, which would be a major find, but can’t identify it as such because the work comparing catfish skeletons to other fish skeletons hasn’t been done and so this person can’t determine what they have.
  3. Bias in collection and research. Basically, nobody cares about fish. People grab the big stuff and prioritize that when they do research. Fish bones may get left in the ground or in a file drawer instead of being described.
  4. In the specific area Becker et al. described the rock type was also not a good one for preserving fish.

Now, there are examples where it would be hard to claim that the fossil record is simply too patchy to show the continued existence of a taxon. For instance, the (unfortunately frequent) claims that pterosaurs or plesiosaurs have made it into the modern era require that entire lineages of large creatures with very distinctive bones have made it millions of years without leaving fossils. However, individual species seem quite capable of dodging fossilization for extensive periods of time.

 

Becker, M. A., Chamberlain Jr., J. A., Robb, A. J., Terry Jr., D. O., & Garb, M. P. (2009). Osteichthyans from the Fairpoint Member of the Fox Hills Formation (Maastrichtian), Meade County, South Dakota, USA. Cretaceous Research, 30(4), 1031–1040. http://doi.org/http://dx.doi.org/10.1016/j.cretres.2009.03.006

Cione, A. L., & Prasad, G. V. R. (2002). The Oldest Known Catfish (Teleostei:Siluriformes) from Asia (India, Late Cretaceous). Journal of Paleontology, 76(1), 190–193. http://doi.org/10.2307/1307189

Diogo, R. (2004). Phylogeny, origin and biogeography of catfishes: support for a Pangean origin of “modern teleosts” and reexamination of some Mesozoic Pangean connections between the Gondwanan and Laurasian supercontinents. Animal Biology, 54(4), 331–351.

Frizzell, D. L., & Koenig, J. W. (1973). Upper Cretaceous Ostariophysine (Vorhisia) Redescribed from Unique Association of Utricular and Lagenar Otoliths (Lapillus and Asteriscus). Copeia, 1973(4), 692–698. http://doi.org/10.2307/1443069

Gayet, Mireille; Marshall, Larry G.; Sempere, Thierry; Meunier, François J.; Cappetta, Henri; Rage, J.-C. (2001). Middle Maastrichtian vertebrates (fishes, amphibians, dinosaurs and other reptiles, mammals) from Pajcha Pata (Bolivia). Biostratigraphic, palaeoecologic and palaeobiogeographic implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 169(1–2), 39–68.

de Muizon, C., Gayet, M., Lavenu, A., Marshall, L. G., Sigé, B., & Villaroel, C. (1983). Late Cretaceous vertebrates, including mammals,from Tiupampa, Southcentral Bolivia. Geobios, 16(6), 747–753. http://doi.org/http://dx.doi.org/10.1016/S0016-6995(83)80091-6

Pena, A. D. E. L. A., & Soler-Gijon, R. (1996). The first siluriform fish from the Cretaceous-Tertiary interval of Eurasia. Lethaia, 29(1), 85–86. http://doi.org/10.1111/j.1502-3931.1996.tb01841.x

CamoEvolve 1.1 is Here

CamoEvolve 1.1 has just been uploaded to the main site. Since I haven’t actually discussed the purpose of CamoEvolve here before I’ll cover both the changes in 1.1 and also the basic idea of the program.

CamoEvolve is an evolutionary simulator designed to run in a small window, making it suitable for tablets and phones (although it continues not to work on iOS touch-enabled devices for reasons that are unclear to me). In it there are objects that have a color, a shape, and a size. Some of these objects are background objects and some are critters hiding from you. The background objects are all generated from the same template and so they tend to be similar in color, shape, and size. The critters maintain there own gene lines and inherit their color, shape, and size (with mutations) from the critters who survive.

Your task is to identify the critters and kill them by clicking on them. For a real-world biological parallel imagine being a bird trying to find camouflaged insects amongst rocks and twigs. Like this hypothetical bird who grabs a stick and has to spit it out you will suffer a penalty for clicking on the wrong thing and will be temporarily unable to kill critters.

A full help file is available and so I want to talk here a little bit about why I wrote CamoEvolve, what I’ve added in Version 1.1, and what you might do with the program.

The aim of CamoEvolve is to model natural selection in a manner that is very basic (no frills to confuse anyone), game-like, easy to interpret, and usable on almost any device. It’s meant to be an easy-to-use teaching tool where students can see natural selection first-hand. I attempted to accomplish each of these goals as follows:

  1. Basic: All objects in the game world have the same properties, of which there are only three, and the only possible actions are to click on things (killing them if they are critters). Critters can’t do anything at all. They live or die based entirely on the player’s actions and the inherent time limit the player is working against.
  2. Game-like: This is the part I think works best. It can be genuinely hard to find the critters (although one gets much, much better with practice). Instead of telling students to try and pretend to select for certain traits you are actually trying to do something that is genuinely challenging, and there’s a fairly harsh scoring system to keep you on your toes.
  3. Easy to interpret: You know when you start having trouble finding critters. You can also see all the elements in the game.
  4. Usable on almost any device: While many of my simulators have a graphics area and a control area CamoEvolve is deliberately built around a single area that is graphics and controls and is made so simple that it can be played on pretty much any reasonable screen size. I was specifically targeting phones here since all my students seem to have them and it’s much easier to say, “Take out your phone,” than to march everyone to a computer lab. However, as noted, there are some crippling issues on iPhones and iPads.

Changes

So what did I change? Well, I ran across two issues in CamoEvolve 1.0. The first was that rendering all of the objects and moving them steadily down the screen took more processing power than many phones seemed to be able to spare. Given my phone-compatibility goal that wasn’t very good and so the first change is that the scrolling-background mode is only one option, and not the default one. Instead, the default mode presents a screen of objects for a set time and then switches to a new scene. To accommodate this change I’ve tweaked the “lockout time” used in this mode, so you may notice that on the same screen the amount of time you are “jammed up” after a mis-click varies between modes. Also, you cannot get locked-out right after the scene changes. In my early testing I kept clicking on a critter as the scene changed meaning that I mis-clicked, got locked-out, and then couldn’t click anything. Now there is a small grace period as the scene changes. Also, of course, critters have a win condition where they are counted as surviving if they live to the scene change, rather than if they make it past the bottom of the screen.

This seems to have reduced the processor burden sufficiently, and this mode seems to work well on phones.

The second change is a simple one. In early usage students were sometimes unclear as to what was going on because it was easy to lose sight of the critters very rapidly as selection pressure made them better camouflaged. There is now a no-background mode to allow you to see how killing off some critters changes the gene pool.

To implement these mode options there is a new button on the main screen, Settings, that allows you to access these options.

What would you do with CamoEvolve?

Simply playing CamoEvolve demonstrates natural selection. A short explanation of how it works (critters that live have children like, but not identical to, them) seems enough to get students off on the right foot and then being able to play around with the game makes the concept clearer.

CamoEvolve also demonstrates genetic drift. If a player stops playing but lets the game run the critters become bizarre and mismatched. Since the critters are asexual each gene-line develops in its own way, and this way is random without a selective agent. I’ve had students switch between playing and not several times to see how a selective pressure steers a random process.

Artificial selection and natural selection are closely tied conceptually. The no-background mode makes it much easier to practice artificial selection. I generally set a random target for the students (red triangles is my go-to, unless someone actually starts with anything like that) and have them compete to get there first.

Continent-wide analysis of how urbanization affects bird-window collision mortality in North America

I’m late on this, but I figure late is better than never. Recently a paper which I collaborated on (Continent-wide analysis of how urbanization affects bird-window collision mortality in North America) was published in Biological Conservation. The first and second authors (and organizers of the entire project) put together a press release and asked the other collaborators to disseminate it.

The short version is that we were looking at the factors that lead to birds killing themselves hitting windows. It’s not always appreciated just how many birds kill themselves hitting windows because birds tend to be small and their carcasses are both easily overlooked and easily removed or eaten whole by scavengers.

What we found was that building size is linked to bird mortality. This isn’t surprising since a larger building has more killing surfaces on it. What is more interesting is that a building of a given size was more dangerous to birds if it was in a region of low urbanization. (It also helped if the building itself was in a more “natural” area with grass or trees – again, not surprising, since these areas attract birds.) This may suggest that birds in urbanized areas behave differently and are perhaps more cautious around buildings. It may also suggest that certain sorts of birds may be more at risk and avoid urban areas. For instance, if migratory birds are more likely to kill themselves on windows (which seems reasonable, since they haven’t learned the area) and are also more likely to avoid cities then rural areas should see higher numbers of fatal bird strikes on buildings. Another hypothesis (and none of these are mutually exclusive) is more macabre. It posits that urban areas are not areas in which birds are less likely to die but that they are areas where birds have so many places to kill themselves that no one building becomes Bird Murder Central. Meanwhile, in rural areas all the birds that are going to try to fly through windows survive until they hit the one set of available windows, concentrating bird-murder around single buildings.

Anyway, all of that and more is available through the press release package and the paper itself. (Although the paper is paywalled, and some of you may not be able to access it1.) What I think I can add in this post is what it was like for me to be part of this project.

I came into this project because a friend of mine mentioned to me that they knew of someone at Duke who was doing it. I’m going to guess that this person was Nicolette Cagle, who is listed right after me (because of alphabetization and not geography). This friend then passed on some information and I contacted Steve Hager and offered to participate. At the time I assumed that I would be able to find several undergraduate students who would be interested in participating in a study this large and that I would be able to mostly supervise the project.

I began the project by selecting sites to survey (which included our tallest dormitory building, the tallest building on campus, and also two houses used as office buildings). I had to alter the sites twice. First, I selected a site that I was told I would have access to but, when the time came, I was told I couldn’t access the back of the building anymore. This building was actually just off campus and so I relocated to a building controlled by the university. Second, the buildings all needed to be 100 meters apart. Initially I selected a building that (unusually for our campus) had a large set of plate glass windows. I hoped by selecting this building that we could get enough variation in window size to try to get some real sense for how important this variable is. However, when I checked the measurements one last time before we started I realized that I needed 100 meters between the outer walls of each building, not the center of each building, and so I had to switch to a different building with smaller windows. As a result, we didn’t survey any buildings with particularly large windows on our campus.

Once the semester started I started recruiting students. I had thought I would get several volunteers, but I didn’t. Instead, one student (who I will not name without specific permission) volunteered. This particular student was an exceptionally dependable person and I sometimes wonder if they initially volunteered simply to be helpful.

At this point the student and I taught ourselves the sampling protocols and did some dry-runs. There was a protocol for one worker and one for two workers. We practiced both, and ended up needing both, since on several days we weren’t both available. The first thing we needed to do was clean up any dead birds already present around the buildings so they wouldn’t appear as fresh kills in the surveys. Most of the carcasses we found in clean-up were thoroughly rotten anyway. We then spent 21 consecutive days checking each of our six buildings at around 2-3 pm. When you come on to campus on the weekends looking for dead birds this stirs up a lot of excitement. One day I was stopped by another faculty member who didn’t recognize me and was sure I was some crazy person up to no good. Another Saturday an enthusiastic student took me on the random dead animal tour of campus (“And there’s a dead cat that got hit by a car and crawled under the dumpster over here….”).

All in all we found no freshly-dead birds. At some point a half-rotten starling showed up in our sample zone but I am pretty sure it blew off a roof because nothing rots that fast. This is in marked contrast to some other sites where dead birds showed up regularly. It was also somewhat disappointing in that while “no fatalities” is good data and good for birds it feels a lot like we found nothing.

Having turned in the data we got a request to collect data at the same time the following year. I didn’t do this. I feel sort of bad about this, but the reality was that I had assumed I would be directing a relatively large group of students, none of whom would need to take more than half the shifts. Instead, I ended up taking a lot of shifts myself. I just didn’t (and still don’t) have that sort of time in the afternoons when a lot of the classes I teach run. I should point out that I’ve been nothing but happy about the way Steve Hager, who has been the primary contact person for the study, has handled everything. From that angle my work on this project was really good. However, if I were to re-do this experience I would change two things.

First, I would line up student workers in advance. This would have been an easier study, and would have been done better (two surveyors per site every day), with more students. We could have managed two years, and I could have done more of the data analysis on my end instead of passing it off to Steve, with more workers.

Second, I would have put more effort in the summer into making sure I had my sites right early on and done some of the satellite image work early on.

It’s worth thinking about the fact that a lot of these issues were rookie issues. I wasn’t running a lab with seven undergraduates back then and I didn’t have a good handle on how to recruit undergraduate researchers, how to vet undergraduate researchers (this is linked to recruiting, because some recruitment techniques get you lots of volunteers at the expense of quality), and what you could realistically expect undergraduates to handle without your direct supervision. I lucked out on this last one because I had a really solid undergraduate assistant but I have now supervised plenty of really enthusiastic, hard working students who would not have been able to take that task on and get it done correctly until they had more research experience. I also hadn’t done some of the analysis types before and assumed they would be straightforward. These days I generally make what I think is a reasonable estimate and then assume that some unforeseen issue will triple the time required.

However, I think that ultimately this sort of collaborative venture can be an excellent way to do some research at a primarily-teaching institute. By splitting up the data collection load no one person is stuck holding up the project forever (unlike some of my projects, which are held up until I can personally find time to go to a field site that is not now very close to where I live). This one worked best when at least one undergraduate was involved (since the best sampling protocol involved two people) which also gives the undergraduate student some real research experience.