Wainwright Lab

University of California, Davis

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Bigger eyes at high latitudes

There is a

growing body of evidence that light levels have a profound effect on the evolution of eyes. Most of these studies deal with comparisons between different species, but now there is a new intriguing twist to the story. In a paper recently published in Biology Letters, Eiluned Pearce and her PhD supervisor Robin Dunbar present data indicating that light levels drive intraspecific variation in visual system size among human populations.

Pearce and Dunbar found a positive correlation between absolute geographic latitude and the size of the eye socket (orbita) and brain cavity in humans. Museum collections house a large number of human skulls with known geographic origin [assuming modern migration can be excluded; the study does not provide the historic age of the skulls], and this came in quite handy for the purpose of this study. Pearce and Dunbar quantified eye size by filling the eye socket with small glass pearls and measuring volume in graduated cylinders, which should be a pretty good proxy of eye volume. Brain cavity was filled with wax beads, instead. To account for scaling effects, they measured the size of the foramen magnum (that’s the large, round skull opening at the base of the skull), a well-supported proxy for body mass in humans. All in all, Pearce and Dunbar measured skulls from 12 different populations (55 skulls total), with a good range of geographic latitudes (1-64deg).

One might think that the correlation between latitude and visual system size may be partially driven by shared ancestry of populations, because many of the high latitude populations have small genetic distances. However, this is apparently not the case: a phylogenetically informed linear model yielded equivalent results.

So, why do human populations at high absolute latitudes have larger eyes? Well, it may indeed be related to light levels. Illumination and day length decrease with an increase in absolute latitude, which means that populations in the far North and South are exposed to lower light levels. And large eye size may improve light sensitivity. Let’s focus on this in more detail.

A large eye can have a larger optical aperture (pupil), i.e., more light can enter the eye chamber. However, the higher number of photons entering the eye does not necessarily result in better light sensitivity, somewhat in contrast to what Pearce and Dunbar say. The light-gathering capacity of an eye also depends on the size of the retinal image, or, the size of the area over which the photons are spread out or distributed. As a larger eye can also have a longer focal length, the retinal image will be larger, as well.

Physiological optics provides simple equations that help to predict how to optimize sensitivity. For example, light sensitivity to extended sources is approximated by means of the f-number (focal length/aperture diameter) or the optical ratio (aperture/[retinal diameter x focal length]). Both proxies are ratios and hence they are independent of eye size. It’s the relative size of the aperture that matters.

However, there is another possible mechanism to improve light sensitivity. The optical system is obviously only one part of the visual system. Another pathway is to increase summation, or the convergence of photoreceptors on ganglion cells. Summation increases light gathering capacity at the cost of visual acuity. So, how does visual acuity compare among different populations? Visual acuity can be approximated by the ratio of focal length/ganglion cell density. If the degree of summation, i.e., density of ganglion cell does not vary among populations then populations with large eyes should have better acuity. Intriguingly, this is not the case as Pearce and Dunbar show, which strongly suggests that populations at high latitudes have higher summation and, accordingly, better light sensitivity.

It is possible that the requirement to maintain good visual acuity at lower light levels drives the evolution of larger eyes in high-latitude populations. Thanks for a great article, and I hope that many of you will now get the calipers (glass pearls, laser-scanners, you name it) out and measure eye size in non-human subjects.

Where do they get all those wonderful videos?

This blog is also posted on my personal blog.

I joined the Wainwright lab in October of last year. While I had experience with swimming fish, including high-speed video analyses, I had not done any filming of fish feeding. At the beginning of this year I got my first taste of obtaining high-speed videos of fish suction feeding. Since that time I have been amazed at the diversity of fish the lab studies (for example, check out the Inimicus didactylus video), the speed of the strikes, and kinematics during the strike; some of the little fish have quite a big gape to capture their prey. The data we are gathering is allowing us to get a glimpse of the patterns of diversity in the kinematics during suction feeding among various species of marine fish, as well as the potential morphological and mechanical correlates of that kinematic data.

Many of the videos that we obtain as a result of this research we upload to our Youtube channel to share with the public, usually the best videos, in focus and lateral. When we film we always try to get focused and lateral sequences for subsequent digitizations. These clear lateral videos allow us to digitize several landmarks on the fish during the strike sequence to get several kinematic variables such as maximum gape, time to pre capture, and ram speed to name a few. But we don’t just need clear lateral videos to showcase on Youtube; we mainly need clear videos to be able to track the landmarks throughout the sequence, and we need lateral videos to obtain accurate kinematics. For example, if the sequences are not clear, it may be harder to track a landmark and there may be more error because the points may drift. If the fish isn’t completely lateral, we may not be able to see all the points, or if the fish as at an angle (going into the third dimension, such as toward the back of the aquarium, which we don’t capture in the 2-dimensional video) the kinematic variables may not be accurate. So, there is a reason for us obtaining these clear lateral videos. However, we also recognize that some of these strike sequences are pretty amazing, so we share them on Youtube.

Lately, our videos (especially the Inermia vittata video you can see in a previous post) have attracted the attention of several science, news and tech blogs. Thank you to all that have posted our videos. However, obtaining these videos is not always easy work, something else that I have learned since being a part of the Wainwright lab. Obtaining these sequences can sometimes (and often) take lots of hours of filming, patience, and hard work. Much of this depends on the fish or the species. Some fish are very good performers, and obtaining several good sequences does not take long (for example the Histrio histrio you can in another previous post). Others require some training to get the fish use to the lights required to capture the sequences at 1000 frames per second. Furthermore, not every fish feeds perfectly lateral every time, or we have multiple individuals in the aquarium that all want food, and the fish themselves are not always perfect. In fact, there are plenty of instances when the predator will miss the prey. This itself is interesting; a former Wainwright Lab member Tim Higham has done some work on the accuracy of strikes, what makes a predator accurate and what can make them miss? Perhaps having a farther strike distance and faster strike velocity decreases accuracy, but to compensate, species have larger gapes to ingest a greater amount of water to increase chances of prey capture (e.g., Higham et al. 2007). We recently posted a video on our Youtube channel of some of these ‘outtakes.’ Again, it is not always easy to capture the clear lateral videos and it takes a lot of work, so this video highlights a ‘bad day at the office’.

Patrick Fuller on feeding duty, Tomomi Takada on camera duty during a typical filming session.

So how do we get all these wonderful videos? First, it is almost always a two person job (although Matt has filmed sticklebacks alone). One person feeds the fish, trying to get them in view of the camera, and striking laterally. This job is almost an art form in itself. You have to learn the behavior of the fish; are they sit and wait predators like the frogfish, fast strikers like the white-streaked grouper, or more active swimmers like Inermia vittata? Therefore, the person feeding has to be aware of the fish’s behavior to try and get good sequences. Challenges may also arise is there are multiple individuals.

Another view of a filming session

We want to ensure all fish eat and we want to get sequences from all individuals, so the person feeding has to keep track of the fish or target the various individuals. The other person involved in the process is the person responsible for tracking the prey and predator, focusing the camera and triggering the high-speed camera. This job is also not easy. It takes some skill to track and focus and quickly trigger the camera. We film at 1000 frames per second and many of the videos on Youtube are played back at 10 frames per second. So what do these strikes actually look like in real time, how much time does the person manning the camera have to respond? To demonstrate this we made a video of a full sequence captured during filming,  in real time and about 200ms of that sequence played back at 10 frames per second for comparison. The person on camera duty has 3 seconds to trigger. You can see from the video, the person responsible for this part of filming either has, or hopefully obtains quick reflexes!

Although our Youtube channel features some of the best sequences we capture, keep in my mind we always strive to get the best videos. And the next time you see one of our videos on Youtube or elsewhere remember that one video is probably the product of hours of work. I want to also note that many of these videos are the work of undergraduate assistants we have in the lab. Many of our Youtube ‘stars’ were captured by our undergraduates, their assistance has been greatly appreciated and many of these videos would not have been captured without them.

Size, Scales and Sceloporus

This weeks blog (also posted on my blog) is a departure from fish, but is about a recent paper of mine that uses phylogenetic comparative methods to test hypotheses for body size and scale evolution among Sceloporus lizards.

Oufiero, C.E.$, G.E.A. Gartner$, S.C. Adolph,  and T. Garland Jr. 2011. Latitudinal and climatic variation in scale counts and body size in Sceloporus lizards:  a phylogenetic perspective. In press  Evolution. DOI: 10.1111/j.1558-5646.2011.01405.x
$ These authors contributed equally

This summer the lab has a reading group on phylogenetic comparative methods, where we are reading through some of the classic phylogenetic papers discussing the various methods. This past week we focused our attention on phylogenetic generalized least squares methods or PGLS. This method was introduced by Grafen in 1989, and although it wasn’t initially a common phylogenetic comparative approach, has seen more use in recent years. For those not familiar with this method, it utilizes a regression approach to account for phylogenetic relationships. In this method the phylogeny is converted to a variance-covariance matrix, where the diagonals in the matrix represent the “summed length of the path from the root of the tree to the species node in question (Grafen 1992).” That is, how far each tip is from the root; in an ultrametric tree the diagonals in the variance-covariance matrix will all be the same. The off diagonals represent the “shared path length in the paths from the root to the two species (Grafen 1992)”. In other words, the off diagonals are the distance from the root to the last common ancestor for the two species. Similar to independent contrasts, this method assumes Brownian motion evolution; however, unlike independent contrasts PGLS assumes the residual traits are undergoing Brownian motion evolution, whereas independent contrasts assumes the characters themselves are undergoing Brownian motion evolution. The other main difference  in PGLS is the use of raw data instead of computing independent contrasts. In short, the PGLS approach is similar to a weighted regression, where the weighted matrix is the variance-covairnace matrix based on the phylogeny of the group, and assuming the same phylogeny will produce the same results as independent contrasts.

So what does this have to do with size, scales and Sceloporus? Well, in a recent study we used a PGLS approach to examine patterns of body size and scale evolution in relation to latitude and climate among Sceloporus lizards. Sceloporus (fence and spiny lizards) are a group of more than 90 species of lizards found from Central America up to Washington State in the U.S. Throughout their range they experience a diversity of habitats, from deserts to tropical forests to temperate forests; and have been used in many studies examining physiological ecology, life history evolution and thermal biology. In our study we used Sceloporus to test two hypotheses for the evolution of morphology. 1) Lizards  exhibit an inverse Bergmann’s Rule, with larger individuals found at lower latitudes and/or warmer climates. 2) Lizards from hotter environments will exhibit fewer and thus larger scales to aid in heat dissipation; whereas lizards from colder environments will exhibit more/smaller scales to aid in heat retention. There has been conflicting results for these hypotheses in the literature, and latitude has often been used as a proxy for climate. However, one of the unique things about our study is the incorporation of multivariate techniques to describe habitat. We use latitude as a predictor as well as climatic variables (temperatures, precipitation and a composite aridity index Q), and also utilize principal component analysis to characterize habitat. We therefore can test for specific climate predictors of these traits without assuming that higher latitudes necessarily equate to colder environments.

To test our hypotheses we gathered data on 106 species and populations of Sceloporus from the literature and museum specimens. We obtained latitude from the literature and source maps, and climate date from the International Water Management Institute’s World Water and Climate Atlas (http://www.iwmi.cgiar.org/WAtlas/Default.aspx). Using a recent phylogenetic hypothesis for Sceloporus (Wiens et al. 2010) we examined the relationship between maximum snout-vent length with latitude and 5 climatic predictors under three models of evolution (no phylogenetic relationships (OLS), Brownian motion (PGLS) and a model in which the branch lengths are transformed in an Ornstein-Uhlenbeck process (RegOU). To examine hypothesis 2 we examined a multiple regression with dorsal scale rows as the dependent, body size as a covariate and latitude or one of the 5 climatic predictors as independents. We also compared results with principal components 1-3 as predictors of dorsal scale counts.

So what did we find? First, we found that phylogenetic models (PGLS or RegOU) were always better fit than non-phylogenetic (OLS) based on likelihood ratio tests and AICc scores. We also found that as latitude increases mean and minimum temperatures decrease, as well as precipitation and aridity, but maximum temperature tends to increase. Thus, lizards from this group found at higher latitudes may be experiencing more desert like environments. 

For hypothesis 1, we found support for the inverse of Bergmann’s Rule when viewed from a climatic perspective; larger lizards were found in areas with higher maximum temperatures, but not at lower latitudes. We also found that larger lizards were found in more arid environments.

Photo copyright Mark Chappell

Our results for hypothesis 2 were a little more complex. We did not find support for the first part of hypothesis 2, lizards with fewer scales were not found in hotter environments. We did find support for the second part of hypothesis 2, lizards with more scales are found in environments with lower minimum temperatures. We also found a positive effect of latitude, and a significant negative effect of aridity (with lizards with more scales inhabiting more arid environments). Results with principal components were also consistent, with PC1  (a latitude/temperature axis) having a significant negative effect on scale count; and PC2 (a maximum temperature/precipitation axis) having a significant positive effect.

Our results suggest several things. First, latitude alone may not be an accurate description of the environment organisms face, particularly at the finer spatial scales over which an individual species may exist. Second, we found support for the inverse of Bergmann’s Rule at the inter-specific level, which has also been found to be a consistent trend intra-specifically in some ectotherms (see Ashton and Feldman 2003). Finally, our analyses suggest that both temperature and precipitation (hence aridity) are important to the evolution of scale counts in this group. These findings also suggest that scale size may be important for other physiological processes, such as evaporative water loss (lizards in more arid environments may have more/smaller scales to reduce rates of evaporation through the skin as has been suggested by Soulé and Kerfoot 1972 ). Examining the relationship of morphological traits that may function in physiological processes may provide insight into how these organisms may respond to global of climate change.

Stickleback attack (part 1)

Since our last video posting, many of the videos on our lab’s Youtube channel have gone viral. As of this blog post, the video of Inermia vitatta has accrued over 120,000 hits and has been featured on TV programs and newspaper articles around the globe. Not bad for a small fish!

[youtube=http://www.youtube.com/watch?v=psdLbN7skg4]

Today’s video features the threespine stickleback, Gasterosteus aculeatus, feeding on a cladoceran (Daphnia pulex). If you have a short attention span like me, one of the first things you’ll notice from the video is how shiny the fish is. The reflective armor plates and large spines are a clue that this is a threespine stickleback from an anadromous population. Anadromous stickleback have a life history similar to a miniature salmon – they are born in freshwater, travel to the ocean, then return to freshwater to breed. Unlike salmon, anadromous stickleback do not necessarily return to their home stream to breed. Anadromous stickleback also look very similar to each other – an Alaska anadromous fish looks very similar to a California anadromous fish.

Sometimes, these anadromous stickleback will travel to a newly-formed lake or river, and instead of returning to the ocean, some fish will stay in freshwater, founding a new population of freshwater stickleback. Over time, this freshwater population will evolve to better match its new freshwater habitat.

These anadromous and freshwater populations are one of the reasons stickleback are such a good system for studying evolutionary biology. We can study the result of rapid evolution in the freshwater populations, and then turn around and study the anadromous fish that resemble the fish that founded the freshwater population. Studying ancestral and derived populations is one of the few ways – short of a time machine – that we can learn the dynamics of adaptation in natural populations.

If we study how this anadromous stickleback captures prey, and then study how freshwater stickleback catch prey, we can learn a lot about the process of adaptation. I’ve devoted much of my PhD work to studying this system, and I’ll be talking more about it in future posts.

Showcasing the latest phylogenetic methods: AUTEUR

While high-speed fish feeding videos may be the signature of the lab, dig a bit deeper and you’ll find a wealth of comparative phylogenetic methods sneaking in.  It’s a natural union — expert functional morphology is the key to good comparative methods, just as phylogenies hold the key to untangling the evolutionary origins of that morphology.  The lab’s own former graduate, Brian O’Meara, made a revolutionary step forward in the land of phylogenetic methods when he unveiled Brownie in 2006, allowing researchers to identify major shifts in trait diversification rates across the tree.  This work spurred not only a flood of empirical applications but also methodological innovations, such as Liam’s brownie-lite, and today’s focus: Jon Eastman et al.‘s auteur package.

Auteur, short for “Accommodating uncertainty in trait evolution using R,” is the grown-up Bayesian RJMCMC version of that original idea in Brownie.  Diversification rates can change along the phylogenetic tree — only this time, you don’t have to specify where those changes could have occurred, or how many there may have been — auteur simply tries them all.

If you want the details, definitely go read the paper — it’s all there, clear and thorough.  Meanwhile, what we really want to do, is take it out for a test drive.

The package isn’t up on CRAN yet, so you can grab the development version from Jon’s github page, or click here.  Put that package in a working directory and fire up R in that directory.  Let’s go for a spin.

[sourcecode language=”R”] install.packages("auteur_0.11.0612.tar.gz", repos=NULL)
library(auteur)

[/sourcecode]

Great, the package installed and loaded successfully. Looks like Jon’s put all 73 functions into the NAMESPACE, but it’s not hard to guess which one looks like the right one to start with.  rjmcmc.bm.  Yeah, that looks good.  It has a nice help file, with — praise the fish — example code.  Looks like we’re gonna run a simulation, where we know the answer, and see how it does:

[sourcecode language=”R”]

#############
## generate tree
n=24
while(1) {
phy=prunelastsplit(birthdeath.tree(b=1,d=0,taxa.stop=n+1))
phy$tip.label=paste("sp",1:n,sep="")
rphy=reorder(phy,"pruningwise")

# find an internal edge
anc=get.desc.of.node(Ntip(phy)+1,phy)
branches=phy$edge[,2] branches=branches[branches>Ntip(phy) & branches!=anc] branch=branches[sample(1:length(branches),1)] desc=get.descendants.of.node(branch,phy)
if(length(desc)>=4) break()
}
rphy=phy
rphy$edge.length[match(desc,phy$edge[,2])]=phy$edge.length[match(desc,phy$edge[,2])]*64

e=numeric(nrow(phy$edge))
e[match(c(branch,desc),phy$edge[,2])]=1
cols=c("red","gray")
dev.new()
plot(phy,edge.col=ifelse(e==1,cols[1],cols[2]), edge.width=2)
mtext("expected pattern of rates")

#############
## simulate data on the ‘rate-shifted’ tree
dat=rTraitCont(phy=rphy, model="BM", sigma=sqrt(0.1))

[/sourcecode]

That creates this beautiful example (sorry, no random generator seed, you’re results may vary but that’s ok) tree:


Okay, so that’s the target, showing where the shift occurred.  Note the last line got us some data on this tree.  We’re ready to run the software.  It looks super easy:

[sourcecode language=”R”] ## run two short reversible-jump Markov chains
r=paste(sample(letters,9,replace=TRUE),collapse="")
lapply(1:2, function(x) rjmcmc.bm(phy=phy, dat=dat, ngen=10000, sample.freq=10, prob.mergesplit=0.1, simplestart=TRUE, prop.width=1, fileBase=paste(r,x,sep=".")))
[/sourcecode]

The data is going in as “phy” and “dat”, just as expected.  We won’t worry about the optional parameters that follow for the moment.  Note that because we use lapply to run multiple chains, it would be super easy to run this on multiple processors.

Note that Jon’s creating a bunch of directories to store parameters, etc.  This can be important for MCMC methods where chains get too cumbersome to handle in memory.  Enough technical rambling, let’s merge and load those files in now, and plot what we got:

[sourcecode language=”R”] # collect directories
dirs=dir("./",pattern=paste("BM",r,sep="."))
pool.rjmcmcsamples(base.dirs=dirs, lab=r)

## view contents of .rda
load(paste(paste(r,"combined.rjmcmc",sep="."),paste(r,"posteriorsamples.rda",sep="."),sep="/"))
print(head(posteriorsamples$rates))
print(head(posteriorsamples$rate.shifts))

## plot Markov sampled rates
dev.new()
shifts.plot(phy=phy, base.dir=paste(r,"combined.rjmcmc",sep="."), burnin=0.5, legend=TRUE, edge.width=2)

# clean-up: unlink those directories
unlink(dir(pattern=paste(r)),recursive=TRUE)
[/sourcecode]

Not only is that a beautiful plot, but it’s nailed the shift in species 12-16.  How’d your example do?

Auteur comes with three beautiful large data sets described in the paper.  Check them out, but expect longer run times than our simple example!

[sourcecode language=”R”]

data(chelonia)
# take a look at this data
> chelonia
$phy
Phylogenetic tree with 226 tips and 225 internal nodes.

Tip labels:
Elseya_latisternum, Chelodina_longicollis, Phrynops_gibbus, Acanthochelys_radiolata, Acanthochelys_macrocephala, Acanthochelys_pallidipectoris, …

Rooted; includes branch lengths.

$dat
Pelomedusa_subrufa                   Pelusios_williamsi
2.995732                             3.218876

dat <- chelonia$dat
phy <- chelonia$phy
## ready to run as above

[/sourcecode]

Thanks Jon and the rest of the Harmon Lab for a fantastic package. This is really just a tip of the iceberg, but should help get you started. See the paper for a good example of posterior analyses requisite after running any kind of MCMC, or stay tuned for a later post.

Inermia vittata: Camera Debut

Below is one of the first ever recorded high-speed video sequences of Inermia vittata, a zooplanktivore from the tropical western Atlantic.  We are using its first live appearance in the lab to see how the feeding kinematics of Inermia compare with that of other reef fishes.  Watch how far that upper jaw projects forward!

[youtube=http://www.youtube.com/watch?v=WOQ3US92Tt0]

One common name for this fish is the bonnetmouth, named after the appearance of the protruded mouth.  Like other reef zooplanktivores, Inermia appears qualitatively to be specialized at picking prey from the water column.  As you can see in the video, the mouth reaches forward, closing the distance to the prey while preparing to pull the prey closer with suction.

The evolutionary relationship of Inermia to other species has been tricky to resolve because it is very similar in appearance and behavior to other zooplanktivores such as fusiliers (Lutjanidae).  However, molecular analysis shows Inermia to be nested within the grunts (Haemulidae), which typically feed on benthic invertebrates.  A look at the pictures below will show how much different Inermia appears from a typical grunt and how similar it looks to the distantly-related fusilier.

boga boga bonnetmouth boga

Our new star, Inermia vittata

Doubleline fusilier

A fusilier, nested within the snappers (Lutjanidae)

French grunt

A close relative of Inermia

Why does Inermia look so different from a typical grunt, and why does it look so similar to a distantly related species?  Perhaps the feeding mechanisms captured in these videos can help to resolve this evolutionary anomaly.

Stickleback camouflage

This week, the Wainwright blog returns to a topic of perennial interest, the threespine stickleback. I will discuss a recent paper from the Schluter lab at UBC on color plasticity and background matching in stickleback.

To set the stage, it’s important to realize that from a stickleback’s perspective, “bird” is a four-letter word. Predation by diving birds like grebes and coots is commonplace in many freshwater stickleback populations. Unlike predatory dragonfly larva, which detect prey by vision and by water movement, diving birds generally detect their prey by sight alone. In other words, if you’re a freshwater stickleback, it’s very important that the top of your body blends in with your surroundings.

This stickleback didn't get the memo. (http://www.lifeontheslea.co.uk )

In this paper, Jason Clarke and Dolph Schluter tried to assay background matching capability between limnetic and benthic sticklebacks in Paxton Lake, British Columbia. First, they used a spectrometer to record the background color in the limnetic and benthic habitats. The open-water limnetic habitat was a bluish color, but the benthic habitat, which has more aquatic vegetation, tended to be more greenish. Additionally, the benthic habitat showed much more variation in color than the limnetic habitat.

After checking the background color, the authors painted two sets of cups, one designed to look like the limnetic background, and one designed to look like the benthic background. Then they put benthic and limnetic sticklebacks on each background, let them adjust their color for 15 minutes, photographed each fish, then measured how well each fish matched its background. They also did the same experiment again, but this time taking pictures every 20 seconds.

What did they find? Limnetic fish and benthic fish were equally good at matching the blue limnetic background, but limnetic fish were not as good at matching the green benthic background as benthics were. The time trial experiment helped to clear up what was going on: benthics rapidly adapted their colors to match the background, but limnetics were doing something different. Limnetic fish were cycling through different colors instead of fixing a particular color. Limnetics were more variable in color when viewed with a benthic background, but even on their “home turf” in the limnetic background, they still showed variation in color, but to a lesser degree.

The authors suggest that the patterns of color chance exhibited by benthics and limnetics are probably adaptive. Their spectrometer data indicates that the benthic habitat is more variable in color, and their background experiments show that benthics are better at rapidly changing their colors to match the background. The limnetic habitat, on the other hand, is much more uniform, so there would be little incentive for limnetics to evolve rapid color matching. However, limnetics may be adapting to their light environment in an entirely different way:  the  “flickering” exhibited by limnetics could be an adaptation to fluctuating light intensity in open water.

After reading this paper, I’m particularly curious what the color-matching abilities of the ancestral marine sticklebacks are like. If they resemble the limnetic, then this color matching ability will be another interesting benthic stickleback adaptation. It will be cool to see if it is possible to discern the genetic basis for this shift in plasticity.

Clark JM, Schluter D. Colour plasticity and background matching in a threespine stickleback species pair. Biological Journal of the Linnean Society. DOI: 10.1111/j.1095-8312.2011.01623.x

An optical illusion?

Zooplanktivory is one of the most distinct feeding niches in coral reef fish and many morphological traits have been interpreted as adaptations to feeding on plankton in the water column above the reef. One of these traditional hypotheses is that zooplanktivorous fish have larger eyes for sharper visual acuity. A larger eye usually has a longer focal length and thus is expected to produce a better-resolved image.

Peter and I tested this hypothesis with a data set on eye morphology of labrids (wrasses and parrot fishes):

Schmitz, L. & P.C. Wainwright (2011). Ecomorphology of the eyes and skull in zooplanktivorous labrid fishes. Coral Reefs, 30: 415-428. reprint.

Labrids are a species-rich clade of reef fish with enormous morphological and ecological diversity. We sampled a total of 21 species, with three independent origins of zooplanktivory: Clepticus parrae, the Creole Wrasse (photo: fishbase.org), Halichoeres pictus, the Rainbow Wrasse (photo: wetwebmedia.com), and Cirrhilabrus solorensis, the Red-eyed Fairy Wrasse (photo: fishbase.org).

To our surprise we failed to find any indication of larger eyes in zooplanktivores. We tried several methods, including phylogenetic residuals of eye diameter on body mass and evolutionary changes in eye size along branches leading to zooplanktivores, but zooplanktivorous labrids did not show any signs of having larger eyes than other trophic specialists. Instead, we suspect that the notion of large eyes in zooplanktivorous labrids is an optical illusion evoked by a size reduction of the anterior facial region, which makes the eye look bigger.

However, we did find other features interpreted as adaptations to zooplanktivory in labrids. Both Clepticus parrae and Halichoeres pictus have a large lens for given axial length of the eye, related to better visual acuity, a round pupil, possibly an adaptation to search a three-dimensional body of water for food, and longer gill rakers to help retain captured prey.

Our results are quite interesting in that they highlight the importance of many-to-one-mapping in form-function relations. There often is more than one possible pathway to perform a function. In labrids, increase in eye size to improve visual acuity apparently is not part of the evolutionary response. But, let’s see what we can find in other groups!

Evolution Meeting 2011, Norman, OK

A majority of the lab is presenting at the Evolution Meetings in Norman, OK this weekend.  Almost all of our talks use a group of fish as a model system (Labrids, Haemulids, pupfish, sticklebacks, Xiphophorus, and reef fish), but our topics are very broad;  including sexual selection, morphological diversity, ecological novelty, nocturnality, phylogenetic comparative methods and ecological speciation. Below is a list of our talks at the meeting. Bold indicates presenting author. Hope to see you there.

Chris Martin and Peter Wainwright
Beyond ecological opportunity: adaptive radiation and the origins of novel ecological niches.
Sunday, June 19th, 1:45 pm, in room Oklahoma D, Ecological Speciation session.

Matt McGee
Functional morphology and kinematics of feeding in stickleback: implications for ecological
speciation.
Sunday, June 19th, 2:00 pm, in room Oklahoma D, Ecological Speciation session.

Lars Schmitz and Peter Wainwright
The effect of diel activity pattern on eye shape

in reef fishes.
Monday, June 20th, 11:00 am, in room University C, Morphological Evolution II session.

Samantha Price, Peter Wainwright, Roi Holzman, Jose Tavera, Thomas Near
Reef habitats promote the evolution of morphological diversity in fishes.
Monday, June 20th, 11:15 am, in room University C, Morphological Evolution II session.

Carl Boettiger
A new phylogenetic comparative method to estimate key evolutionary transitions involving a release of constraint. (slides)
Monday, June 20th,  4:00 pm, in room Oklahoma A, Phylogenetic Methods V session

Chris Oufiero, Kristine Jugo, Mark Chappell, Theodore Garland, Jr.

Does the evolution of a sexually selected trait compromise sprint and endurance performance in Xiphophorus swordtails and their close relatives?
Tuesday, June 21st, 9:15 am, in room University A, Sexual Selection & Behavior session.

Is this fish crazy?

This post is cross-posted with my personal website’s Blog.

We recently got some new fish in the lab, Butis butis, commonly called the crazy fish or Duckbill Sleeper. This is a fresh water fish, originating from East Africa to Fiji and belongs to the Eliotridae. These fish get to a maximum size of about 15 cm total length, live in brackish mangrove swamps and estuaries, feeding on small fish and crustacean, and is commonly found in the hobby industry.

The question is, are these fish in fact crazy? These fish tend to be unique because they can be seen swimming, floating, and even eating upside down. This behavior has been noted in nature and in aquariums, where they will also be seen pressed up the glass. They tend to be ambush predators and are often found floating among plants, in any position. Having them in the lab, we have begun filming them and have been able to capture their feeding right-side up and upside down. What will be interesting to see is if the kinematics of their feeding differs between the orientations, as well as if one orientation is better than the other at eliciting successful strikes. In the meantime, enjoy the videos of these crazy fish feeding in the two orientations.

Upside down filmed at 1000 frames per second, played back at 10 frames per second.

Right-side up filmed at 1000 frames per second, played back at 10 frames per second.

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